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  • Articles  (3,522)
  • Oxford University Press  (2,108)
  • Frontiers Media  (1,414)
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  • 2020-2022  (3,522)
  • Computer Science  (2,943)
  • Nature of Science, Research, Systems of Higher Education, Museum Science  (579)
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  • Books
  • Articles  (3,522)
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  • 1
    Publication Date: 2021-08-20
    Description: Continuous electronic fetal monitoring and the access to databases of fetal heart rate (FHR) data have sparked the application of machine learning classifiers to identify fetal pathologies. However, most fetal heart rate data are acquired using Doppler ultrasound (DUS). DUS signals use autocorrelation (AC) to estimate the average heartbeat period within a window. In consequence, DUS FHR signals loses high frequency information to an extent that depends on the length of the AC window. We examined the effect of this on the estimation bias and discriminability of frequency domain features: low frequency power (LF: 0.03–0.15 Hz), movement frequency power (MF: 0.15–0.5 Hz), high frequency power (HF: 0.5–1 Hz), the LF/(MF + HF) ratio, and the nonlinear approximate entropy (ApEn) as a function of AC window length and signal to noise ratio. We found that the average discriminability loss across all evaluated AC window lengths and SNRs was 10.99% for LF 14.23% for MF, 13.33% for the HF, 10.39% for the LF/(MF + HF) ratio, and 24.17% for ApEn. This indicates that the frequency domain features are more robust to the AC method and additive noise than the ApEn. This is likely because additive noise increases the irregularity of the signals, which results in an overestimation of ApEn. In conclusion, our study found that the LF features are the most robust to the effects of the AC method and noise. Future studies should investigate the effect of other variables such as signal drop, gestational age, and the length of the analysis window on the estimation of fHRV features and their discriminability.
    Electronic ISSN: 2624-8212
    Topics: Computer Science
    Published by Frontiers Media
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  • 2
    Publication Date: 2021-08-20
    Description: Motivation Accurate automatic annotation of protein function relies on both innovative models and robust data sets. Due to their importance in biological processes, the identification of DNA-binding proteins directly from protein sequence has been the focus of many studies. However, the data sets used to train and evaluate these methods have suffered from substantial flaws. We describe some of the weaknesses of the data sets used in previous DNA-binding protein literature and provide several new data sets addressing these problems. We suggest new evaluative benchmark tasks that more realistically assess real-world performance for protein annotation models. We propose a simple new model for the prediction of DNA-binding proteins and compare its performance on the improved data sets to two previously published models. Additionally, we provide extensive tests showing how the best models predict across taxonomies. Results Our new gradient boosting model, which uses features derived from a published protein language model, outperforms the earlier models. Perhaps surprisingly, so does a baseline nearest neighbor model using BLAST percent identity. We evaluate the sensitivity of these models to perturbations of DNA-binding regions and control regions of protein sequences. The successful data-driven models learn to focus on DNA-binding regions. When predicting across taxonomies, the best models are highly accurate across species in the same kingdom and can provide some information when predicting across kingdoms. Code and Data Availability The data and results for this paper can be found at https://doi.org/10.5281/zenodo.5153906. The code for this paper can be found at https://doi.org/10.5281/zenodo.5153683. The code, data and results can also be found at https://github.com/AZaitzeff/tools_for_dna_binding_proteins.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 3
    Publication Date: 2021-08-20
    Description: A key challenge for the secondary prevention of Alzheimer’s dementia is the need to identify individuals early on in the disease process through sensitive cognitive tests and biomarkers. The European Prevention of Alzheimer’s Dementia (EPAD) consortium recruited participants into a longitudinal cohort study with the aim of building a readiness cohort for a proof-of-concept clinical trial and also to generate a rich longitudinal data-set for disease modelling. Data have been collected on a wide range of measurements including cognitive outcomes, neuroimaging, cerebrospinal fluid biomarkers, genetics and other clinical and environmental risk factors, and are available for 1,828 eligible participants at baseline, 1,567 at 6 months, 1,188 at one-year follow-up, 383 at 2 years, and 89 participants at three-year follow-up visit. We novelly apply state-of-the-art longitudinal modelling and risk stratification approaches to these data in order to characterise disease progression and biological heterogeneity within the cohort. Specifically, we use longitudinal class-specific mixed effects models to characterise the different clinical disease trajectories and a semi-supervised Bayesian clustering approach to explore whether participants can be stratified into homogeneous subgroups that have different patterns of cognitive functioning evolution, while also having subgroup-specific profiles in terms of baseline biomarkers and longitudinal rate of change in biomarkers.
    Electronic ISSN: 2624-909X
    Topics: Computer Science
    Published by Frontiers Media
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  • 4
    Publication Date: 2021-08-20
    Description: Tether is a stablecoin, namely a cryptocurrency associated with an underlying security. Tether provides one of the most relevant ways to buy bitcoins and has been the centre of many controversies. In fact, it has been hypothesized that new tethers are issued without the underlying reserves, and that new massive Tether emissions are the basis of strong speculative movements on the Bitcoin, with consequent bubble effects. In the course of this article, we conduct a Social Network Analysis focused on the Tether transaction graph to identify the main actors that play a leading role on the network and characterize the transaction flow between them. From our analysis, we conclude that 1) the Tether transaction network does not enjoy the Smalltalk property, with the robustness and reliability it carries with it; 2) cryptopcurrency exchanges are the nodes with the greatest centrality; 3) even Assortativity is not found, as the subjects who move Tether on a large scale do not give continuity to their presence and operations, therefore do not get a chance to consolidate stable links between them; and 4) among the exchanges, Bitfinex, which has co-ownership and co-administration relationships with the Tether issuer, can be mostly associated with the Rich-gets-Richer property.
    Electronic ISSN: 2624-7852
    Topics: Computer Science
    Published by Frontiers Media
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  • 5
    Publication Date: 2021-08-20
    Description: Earthworm-like robots have received great attention due to their prominent locomotion abilities in various environments. In this research, by exploiting the extraordinary three-dimensional (3D) deformability of the Yoshimura-origami structure, the state of the art of earthworm-like robots is significantly advanced by enhancing the locomotion capability from 2D to 3D. Specifically, by introducing into the virtual creases, kinematics of the non-rigid-foldable Yoshimura-ori structure is systematically analyzed. In addition to exhibiting large axial deformation, the Yoshimura-ori structure could also bend toward different directions, which, therefore, significantly expands the reachable workspace and makes it possible for the robot to perform turning and rising motions. Based on prototypes made of PETE film, mechanical properties of the Yoshimura-ori structure are also evaluated experimentally, which provides useful guidelines for robot design. With the Yoshimura-ori structure as the skeleton of the robot, a hybrid actuation mechanism consisting of SMA springs, pneumatic balloons, and electromagnets is then proposed and embedded into the robot: the SMA springs are used to bend the origami segments for turning and rising motion, the pneumatic balloons are employed for extending and contracting the origami segments, and the electromagnets serve as anchoring devices. Learning from the earthworm’s locomotion mechanism--retrograde peristalsis wave, locomotion gaits are designed for controlling the robot. Experimental tests indicate that the robot could achieve effective rectilinear, turning, and rising locomotion, thus demonstrating the unique 3D locomotion capability.
    Electronic ISSN: 2296-9144
    Topics: Computer Science
    Published by Frontiers Media
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  • 6
    Publication Date: 2021-08-20
    Description: AntAlate is a software framework for Unmanned Aerial Vehicle (UAV) autonomy, designed to streamline and facilitate the work of application developers, particularly in deployment of Multi-Agent Robotic Systems (MARS). We created AntAlate in order to bring our research in the field of multi-agent systems from theoretical results to both advanced simulations and to real-life demonstrations. Creating a framework capable of catering to MARS applications requires support for distributed, decentralized, control using local sensing, performed autonomously by groups of identical anonymous agents. Though mainly interested in the emergent behavior of the system as a whole, we focused on the single agent and created a framework suitable for a system of systems approach, while minimizing the hardware requirements of the single agent. Global observers or even a centralized control can be added on top of AntAlate, but the framework does not require a global actor to finalize an application. The same applies to a human in the loop, and fully autonomous UAV applications can be written in as straightforward a way as can semi-autonomous applications. In this paper we describe the AntAlate framework and demonstrate its utility and versatility.
    Electronic ISSN: 2296-9144
    Topics: Computer Science
    Published by Frontiers Media
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  • 7
    Publication Date: 2021-08-20
    Description: Currently there are 4.9 million English Language Learners (ELLs) in the United States, however, only 2% of educators are trained to support these vulnerable students. Educational robots show promise for language acquisition and may provide valuable support for ELLs, yet, little is known about social robots for this population. Inviting participants as cultural informants can ensure that the robot is appropriately designed, situated and adopted into that educational community. Therefore, we conducted an exploratory study using interactive group interviews with 95 ELLs (kindergarten through fifth grade) from 18 different home language backgrounds. We also interviewed 39 ELL parents and eight elementary school educators to understand their views of educational robots. Responses to robot images suggested a preference for a popular educational robot. Parents expressed a strong desire for educational robots to support their children at school. While children embraced the idea of a robot at school, some expressed concerns about the potential for robots to be disruptive. School educators saw the potential for educational robots to support teachers in meeting instructional needs but also raised salient concerns. Exploring social robots with ELLs as cultural informants was a valuable exploration to determine important factors in social robot design and implementation for a diverse educational setting.
    Electronic ISSN: 2504-284X
    Topics: Education , Nature of Science, Research, Systems of Higher Education, Museum Science
    Published by Frontiers Media
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  • 8
    Publication Date: 2021-08-18
    Description: Background: There is growing interest in the connection between the gut microbiome and human health and disease. Conventional approaches to analyse microbiome data typically entail dimensionality reduction and assume linearity of the observed relationships, however, the microbiome is a highly complex ecosystem marked by non-linear relationships. In this study, we use topological data analysis (TDA) to explore differences and similarities between the gut microbiome across several countries.Methods: We used curated adult microbiome data at the genus level from the GMrepo database. The dataset contains OTU and demographical data of over 4,400 samples from 19 studies, spanning 12 countries. We analysed the data with tmap, an integrative framework for TDA specifically designed for stratification and enrichment analysis of population-based gut microbiome datasets.Results: We find associations between specific microbial genera and groups of countries. Specifically, both the USA and UK were significantly co-enriched with the proinflammatory genera Lachnoclostridium and Ruminiclostridium, while France and New Zealand were co-enriched with other, butyrate-producing, taxa of the order Clostridiales.Conclusion: The TDA approach demonstrates the overlap and distinctions of microbiome composition between and within countries. This yields unique insights into complex associations in the dataset, a finding not possible with conventional approaches. It highlights the potential utility of TDA as a complementary tool in microbiome research, particularly for large population-scale datasets, and suggests further analysis on the effects of diet and other regionally varying factors.
    Electronic ISSN: 2624-8212
    Topics: Computer Science
    Published by Frontiers Media
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  • 9
    Publication Date: 2021-08-17
    Description: Application of machine and deep learning methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel computational solutions in relation to established techniques. To this end, we compare rule-based and data-driven molecular representations in prediction of drug combination sensitivity and drug synergy scores using standardized results of 14 high-throughput screening studies, comprising 64 200 unique combinations of 4153 molecules tested in 112 cancer cell lines. We evaluate the clustering performance of molecular representations and quantify their similarity by adapting the Centered Kernel Alignment metric. Our work demonstrates that to identify an optimal molecular representation type, it is necessary to supplement quantitative benchmark results with qualitative considerations, such as model interpretability and robustness, which may vary between and throughout preclinical drug development projects.
    Print ISSN: 1467-5463
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    Topics: Biology , Computer Science
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  • 10
    Publication Date: 2021-08-06
    Description: Motivation The investigation of quantitative trait loci (QTL) is an essential component in our understanding of how organisms vary phenotypically. However, many important crop species are polyploid (carrying more than two copies of each chromosome), requiring specialized tools for such analyses. Moreover, deciphering meiotic processes at higher ploidy levels is not straightforward, but is necessary to understand the reproductive dynamics of these species, or uncover potential barriers to their genetic improvement. Results Here, we present polyqtlR, a novel software tool to facilitate such analyses in (auto)polyploid crops. It performs QTL interval mapping in F1 populations of outcrossing polyploids of any ploidy level using identity-by-descent probabilities. The allelic composition of discovered QTL can be explored, enabling favourable alleles to be identified and tracked in the population. Visualization tools within the package facilitate this process, and options to include genetic co-factors and experimental factors are included. Detailed information on polyploid meiosis including prediction of multivalent pairing structures, detection of preferential chromosomal pairing and location of double reduction events can be performed. Availabilityand implementation polyqtlR is freely available from the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/package=polyqtlR. Supplementary information Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
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  • 11
    Publication Date: 2021-08-20
    Description: Circular RNAs (circRNAs) are widely expressed in highly diverged eukaryotes. Although circRNAs have been known for many years, their function remains unclear. Interaction with RNA-binding protein (RBP) to influence post-transcriptional regulation is considered to be an important pathway for circRNA function, such as acting as an oncogenic RBP sponge to inhibit cancer. In this study, we design a deep learning framework, CRPBsites, to predict the binding sites of RBPs on circRNAs. In this model, the sequences of variable-length binding sites are transformed into embedding vectors by word2vec model. Bidirectional LSTM is used to encode the embedding vectors of binding sites, and then they are fed into another LSTM decoder for decoding and classification tasks. To train and test the model, we construct four datasets that contain sequences of variable-length binding sites on circRNAs, and each set corresponds to an RBP, which is overexpressed in bladder cancer tissues. Experimental results on four datasets and comparison with other existing models show that CRPBsites has superior performance. Afterwards, we found that there were highly similar binding motifs in the four binding site datasets. Finally, we applied well-trained CRPBsites to identify the binding sites of IGF2BP1 on circCDYL, and the results proved the effectiveness of this method. In conclusion, CRPBsites is an effective prediction model for circRNA-RBP interaction site identification. We hope that CRPBsites can provide valuable guidance for experimental studies on the influence of circRNA on post-transcriptional regulation.
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  • 12
    Publication Date: 2021-08-20
    Description: Intratumoral heterogeneity is a well-documented feature of human cancers and is associated with outcome and treatment resistance. However, a heterogeneous tumor transcriptome contributes an unknown level of variability to analyses of differentially expressed genes (DEGs) that may contribute to phenotypes of interest, including treatment response. Although current clinical practice and the vast majority of research studies use a single sample from each patient, decreasing costs of sequencing technologies and computing power have made repeated-measures analyses increasingly economical. Repeatedly sampling the same tumor increases the statistical power of DEG analysis, which is indispensable toward downstream analysis and also increases one’s understanding of within-tumor variance, which may affect conclusions. Here, we compared five different methods for analyzing gene expression profiles derived from repeated sampling of human prostate tumors in two separate cohorts of patients. We also benchmarked the sensitivity of generalized linear models to linear mixed models for identifying DEGs contributing to relevant prostate cancer pathways based on a ground-truth model.
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  • 13
    Publication Date: 2021-08-20
    Description: Academic human capital (AHC) is a key element in the explanation of scientific productivity. However, few studies have analysed this topic in the academic context, and their conclusions about composition and measurement remain ambiguous. This study proposes a measurement scale to assess AHC, following a systemic procedure composed of two steps: qualitative and quantitative phases. First, the Delphi technique was applied to reach a consensus on the AHC factors, resulting in a scale of 22 items. Second, exploratory and confirmatory factor analyses were conducted to determine the underlying factorial structure of the scale, using a sample of 2,223 researchers in Spanish universities. The results provided a five-dimensional structure of AHC, measuring the knowledge and abilities required to perform research activities, as well as skills related to the organisation of scientific processes, alertness to research opportunities, and the openness to provide and receive criticism. This study poses interesting challenges for knowledge management in universities.
    Print ISSN: 0302-3427
    Electronic ISSN: 1471-5430
    Topics: Nature of Science, Research, Systems of Higher Education, Museum Science
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  • 14
    Publication Date: 2021-08-20
    Description: Efforts to elucidate protein–DNA interactions at the molecular level rely in part on accurate predictions of DNA-binding residues in protein sequences. While there are over a dozen computational predictors of the DNA-binding residues, they are DNA-type agnostic and significantly cross-predict residues that interact with other ligands as DNA binding. We leverage a custom-designed machine learning architecture to introduce DNAgenie, first-of-its-kind predictor of residues that interact with A-DNA, B-DNA and single-stranded DNA. DNAgenie uses a comprehensive physiochemical profile extracted from an input protein sequence and implements a two-step refinement process to provide accurate predictions and to minimize the cross-predictions. Comparative tests on an independent test dataset demonstrate that DNAgenie outperforms the current methods that we adapt to predict residue-level interactions with the three DNA types. Further analysis finds that the use of the second (refinement) step leads to a substantial reduction in the cross predictions. Empirical tests show that DNAgenie’s outputs that are converted to coarse-grained protein-level predictions compare favorably against recent tools that predict which DNA-binding proteins interact with double-stranded versus single-stranded DNAs. Moreover, predictions from the sequences of the whole human proteome reveal that the results produced by DNAgenie substantially overlap with the known DNA-binding proteins while also including promising leads for several hundred previously unknown putative DNA binders. These results suggest that DNAgenie is a valuable tool for the sequence-based characterization of protein functions. The DNAgenie’s webserver is available at http://biomine.cs.vcu.edu/servers/DNAgenie/.
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  • 15
    Publication Date: 2021-08-20
    Description: Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http://jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https://github.com/jiangBiolab/DLpTCR.
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  • 16
    Publication Date: 2021-08-21
    Description: Unlike other developed countries, the Fourth Industrial Revolution (4IR) discourse has become the central element within technology governance in Korea. This paper examines the reasons for the discourse’s success and its political and social implications. Based on the analysis of policy documents and the media coverage, I argue that political and economic elites have actively introduced the 4IR discourse to create novel momentum for promoting Information and Communications Technology (ICT) and to justify deregulatory measures while re-enacting the developmentalist imaginary. I also highlight that the 4IR discourse’s promoters have drawn upon the dialectics between the desirable future and the nation’s shared fear to urge the Korean society to accept the measures privileging the industry as the means of making the nation a developed country and avoiding being colonized again.
    Print ISSN: 0302-3427
    Electronic ISSN: 1471-5430
    Topics: Nature of Science, Research, Systems of Higher Education, Museum Science
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  • 17
    Publication Date: 2021-07-11
    Description: Motivation The investigation of the structure of biological systems at the molecular level gives insights about their functions and dynamics. Shape and surface of biomolecules are fundamental to molecular recognition events. Characterizing their geometry can lead to more adequate predictions of their interactions. In the present work, we assess the performance of reference shape retrieval methods from the computer vision community on protein shapes. Results Shape retrieval methods are efficient in identifying orthologous proteins and tracking large conformational changes. This work illustrates the interest for the protein surface shape as a higher-level representation of the protein structure that (i) abstracts the underlying protein sequence, structure or fold, (ii) allows the use of shape retrieval methods to screen large databases of protein structures to identify surficial homologs and possible interacting partners and (iii) opens an extension of the protein structure–function paradigm toward a protein structure-surface(s)-function paradigm. Availabilityand implementation All data are available online at http://datasetmachat.drugdesign.fr. Supplementary information Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
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  • 18
    Publication Date: 2021-08-18
    Description: Over the past decade, genome-wide assays for chromatin interactions in single cells have enabled the study of individual nuclei at unprecedented resolution and throughput. Current chromosome conformation capture techniques survey contacts for up to tens of thousands of individual cells, improving our understanding of genome function in 3D. However, these methods recover a small fraction of all contacts in single cells, requiring specialised processing of sparse interactome data. In this review, we highlight recent advances in methods for the interpretation of single-cell genomic contacts. After discussing the strengths and limitations of these methods, we outline frontiers for future development in this rapidly moving field.
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  • 19
    Publication Date: 2021-08-14
    Description: Good knowledge of a peptide’s tertiary structure is important for understanding its function and its interactions with its biological targets. APPTEST is a novel computational protocol that employs a neural network architecture and simulated annealing methods for the prediction of peptide tertiary structure from the primary sequence. APPTEST works for both linear and cyclic peptides of 5–40 natural amino acids. APPTEST is computationally efficient, returning predicted structures within a number of minutes. APPTEST performance was evaluated on a set of 356 test peptides; the best structure predicted for each peptide deviated by an average of 1.9Å from its experimentally determined backbone conformation, and a native or near-native structure was predicted for 97% of the target sequences. A comparison of APPTEST performance with PEP-FOLD, PEPstrMOD and PepLook across benchmark datasets of short, long and cyclic peptides shows that on average APPTEST produces structures more native than the existing methods in all three categories. This innovative, cutting-edge peptide structure prediction method is available as an online web server at https://research.timmons.eu/apptest, facilitating in silico study and design of peptides by the wider research community.
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  • 20
    Publication Date: 2021-08-20
    Description: Deep generative models have been an upsurge in the deep learning community since they were proposed. These models are designed for generating new synthetic data including images, videos and texts by fitting the data approximate distributions. In the last few years, deep generative models have shown superior performance in drug discovery especially de novo molecular design. In this study, deep generative models are reviewed to witness the recent advances of de novo molecular design for drug discovery. In addition, we divide those models into two categories based on molecular representations in silico. Then these two classical types of models are reported in detail and discussed about both pros and cons. We also indicate the current challenges in deep generative models for de novo molecular design. De novo molecular design automatically is promising but a long road to be explored.
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  • 21
    Publication Date: 2021-08-19
    Description: DNA methylation may be regulated by genetic variants within a genomic region, referred to as methylation quantitative trait loci (mQTLs). The changes of methylation levels can further lead to alterations of gene expression, and influence the risk of various complex human diseases. Detecting mQTLs may provide insights into the underlying mechanism of how genotypic variations may influence the disease risk. In this article, we propose a methylation random field (MRF) method to detect mQTLs by testing the association between the methylation level of a CpG site and a set of genetic variants within a genomic region. The proposed MRF has two major advantages over existing approaches. First, it uses a beta distribution to characterize the bimodal and interval properties of the methylation trait at a CpG site. Second, it considers multiple common and rare genetic variants within a genomic region to identify mQTLs. Through simulations, we demonstrated that the MRF had improved power over other existing methods in detecting rare variants of relatively large effect, especially when the sample size is small. We further applied our method to a study of congenital heart defects with 83 cardiac tissue samples and identified two mQTL regions, MRPS10 and PSORS1C1, which were colocalized with expression QTL in cardiac tissue. In conclusion, the proposed MRF is a useful tool to identify novel mQTLs, especially for studies with limited sample sizes.
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  • 22
    Publication Date: 2021-08-20
    Description: While public research organisations (PROs) are increasingly expected to transfer knowledge to businesses and other stakeholders, their engagement in knowledge transfer (KT) activities is still under-researched. Better understanding of PROs’ KT engagement, including how it is shaped by PROs’ organisational characteristics, could lead to better tailored policies in support to PROs’ effort to transfer knowledge. We develop a conceptual framework linking PROs’ specialisation in different fields of knowledge to their profiles of KT engagement and validate it empirically using a six-year panel data set of 33 PROs in the UK. We use multidimensional scaling and cluster analysis techniques to identify three distinct KT profiles, which are stable over time, and strongly associated with the PROs’ knowledge field specialisation. We argue that these profiles may depend on the different market readiness and user specificity of knowledge outputs arising from different fields of knowledge and derive implications for theory, policy, and practice.
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    Topics: Nature of Science, Research, Systems of Higher Education, Museum Science
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  • 23
    Publication Date: 2021-06-29
    Description: Motivation The mathematically optimal solution in computational protein folding simulations does not always correspond to the native structure, due to the imperfection of the energy force fields. There is therefore a need to search for more diverse suboptimal solutions in order to identify the states close to the native. We propose a novel multimodal optimization protocol to improve the conformation sampling efficiency and modeling accuracy of de novo protein structure folding simulations. Results A distance-assisted multimodal optimization sampling algorithm, MMpred, is proposed for de novo protein structure prediction. The protocol consists of three stages: The first is a modal exploration stage, in which a structural similarity evaluation model DMscore is designed to control the diversity of conformations, generating a population of diverse structures in different low-energy basins. The second is a modal maintaining stage, where an adaptive clustering algorithm MNDcluster is proposed to divide the populations and merge the modal by adjusting the annealing temperature to locate the promising basins. In the last stage of modal exploitation, a greedy search strategy is used to accelerate the convergence of the modal. Distance constraint information is used to construct the conformation scoring model to guide sampling. MMpred is tested on a large set of 320 non-redundant proteins, where MMpred obtains models with TM-score≥0.5 on 291 cases, which is 28% higher than that of Rosetta guided with the same set of distance constraints. In addition, on 320 benchmark proteins, the enhanced version of MMpred (E-MMpred) has 167 targets better than trRosetta when the best of five models are evaluated. The average TM-score of the best model of E-MMpred is 0.732, which is comparable to trRosetta (0.730). Availability and implementation The source code and executable are freely available at https://github.com/iobio-zjut/MMpred. Supplementary information Supplementary data are available at Bioinformatics online.
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  • 24
    Publication Date: 2021-08-20
    Description: Antimicrobial resistance (AMR) poses a threat to global public health. To mitigate the impacts of AMR, it is important to identify the molecular mechanisms of AMR and thereby determine optimal therapy as early as possible. Conventional machine learning-based drug-resistance analyses assume genetic variations to be homogeneous, thus not distinguishing between coding and intergenic sequences. In this study, we represent genetic data from Mycobacterium tuberculosis as a graph, and then adopt a deep graph learning method—heterogeneous graph attention network (‘HGAT–AMR’)—to predict anti-tuberculosis (TB) drug resistance. The HGAT–AMR model is able to accommodate incomplete phenotypic profiles, as well as provide ‘attention scores’ of genes and single nucleotide polymorphisms (SNPs) both at a population level and for individual samples. These scores encode the inputs, which the model is ‘paying attention to’ in making its drug resistance predictions. The results show that the proposed model generated the best area under the receiver operating characteristic (AUROC) for isoniazid and rifampicin (98.53 and 99.10%), the best sensitivity for three first-line drugs (94.91% for isoniazid, 96.60% for ethambutol and 90.63% for pyrazinamide), and maintained performance when the data were associated with incomplete phenotypes (i.e. for those isolates for which phenotypic data for some drugs were missing). We also demonstrate that the model successfully identifies genes and SNPs associated with drug resistance, mitigating the impact of resistance profile while considering particular drug resistance, which is consistent with domain knowledge.
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  • 25
    Publication Date: 2021-08-20
    Description: Protein engineering and design principles employing the 20 standard amino acids have been extensively used to achieve stable protein scaffolds and deliver their specific activities. Although this confers some advantages, it often restricts the sequence, chemical space, and ultimately the functional diversity of proteins. Moreover, although site-specific incorporation of non-natural amino acids (nnAAs) has been proven to be a valuable strategy in protein engineering and therapeutics development, its utility in the affinity-maturation of nanobodies is not fully explored. Besides, current experimental methods do not routinely employ nnAAs due to their enormous library size and infinite combinations. To address this, we have developed an integrated computational pipeline employing structure-based protein design methodologies, molecular dynamics simulations and free energy calculations, for the binding affinity prediction of an nnAA-incorporated nanobody toward its target and selection of potent binders. We show that by incorporating halogenated tyrosines, the affinity of 9G8 nanobody can be improved toward epidermal growth factor receptor (EGFR), a crucial cancer target. Surface plasmon resonance (SPR) assays showed that the binding of several 3-chloro-l-tyrosine (3MY)-incorporated nanobodies were improved up to 6-fold into a picomolar range, and the computationally estimated binding affinities shared a Pearson’s r of 0.87 with SPR results. The improved affinity was found to be due to enhanced van der Waals interactions of key 3MY-proximate nanobody residues with EGFR, and an overall increase in the nanobody’s structural stability. In conclusion, we show that our method can facilitate screening large libraries and predict potent site-specific nnAA-incorporated nanobody binders against crucial disease-targets.
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  • 26
    Publication Date: 2021-08-20
    Description: Over the past few years, meta-analysis has become popular among biomedical researchers for detecting biomarkers across multiple cohort studies with increased predictive power. Combining datasets from different sources increases sample size, thus overcoming the issue related to limited sample size from each individual study and boosting the predictive power. This leads to an increased likelihood of more accurately predicting differentially expressed genes/proteins or significant biomarkers underlying the biological condition of interest. Currently, several meta-analysis methods and tools exist, each having its own strengths and limitations. In this paper, we survey existing meta-analysis methods, and assess the performance of different methods based on results from different datasets as well as assessment from prior knowledge of each method. This provides a reference summary of meta-analysis models and tools, which helps to guide end-users on the choice of appropriate models or tools for given types of datasets and enables developers to consider current advances when planning the development of new meta-analysis models and more practical integrative tools.
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  • 27
    Publication Date: 2021-08-12
    Description: Motivation Co-evolution analysis can be used to accurately predict residue–residue contacts from multiple sequence alignments. The introduction of machine-learning techniques has enabled substantial improvements in precision and a shift from predicting binary contacts to predict distances between pairs of residues. These developments have significantly improved the accuracy of de novo prediction of static protein structures. With AlphaFold2 lifting the accuracy of some predicted protein models close to experimental levels, structure prediction research will move on to other challenges. One of those areas is the prediction of more than one conformation of a protein. Here, we examine the potential of residue–residue distance predictions to be informative of protein flexibility rather than simply static structure. Results We used DMPfold to predict distance distributions for every residue pair in a set of proteins that showed both rigid and flexible behaviour. Residue pairs that were in contact in at least one reference structure were classified as rigid, flexible or neither. The predicted distance distribution of each residue pair was analysed for local maxima of probability indicating the most likely distance or distances between a pair of residues. We found that rigid residue pairs tended to have only a single local maximum in their predicted distance distributions while flexible residue pairs more often had multiple local maxima. These results suggest that the shape of predicted distance distributions contains information on the rigidity or flexibility of a protein and its constituent residues. Supplementary information Supplementary data are available at Bioinformatics online.
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  • 28
    Publication Date: 2021-08-16
    Description: Motivation The well-known fact that protein structures are more conserved than their sequences forms the basis of several areas of computational structural biology. Methods based on the structure analysis provide more complete information on residue conservation in evolutionary processes. This is crucial for the determination of evolutionary relationships between proteins and for the identification of recurrent structural patterns present in biomolecules involved in similar functions. However, algorithmic structural alignment is much more difficult than multiple sequence alignment. This study is devoted to the development and applications of DAMA—a novel effective environment capable to compute and analyze multiple structure alignments. Results DAMA is based on local structural similarities, using local 3D structure descriptors and thus accounts for nearest-neighbor molecular environments of aligned residues. It is constrained neither by protein topology nor by its global structure. DAMA is an extension of our previous study (DEDAL) which demonstrated the applicability of local descriptors to pairwise alignment problems. Since the multiple alignment problem is NP-complete, an effective heuristic approach has been developed without imposing any artificial constraints. The alignment algorithm searches for the largest, consistent ensemble of similar descriptors. The new method is capable to capture most of the biologically significant similarities present in canonical test sets and is discriminatory enough to prevent the emergence of larger, but meaningless, solutions. Tests performed on the test sets, including protein kinases, demonstrate DAMA’s capability of identifying equivalent residues, which should be very useful in discovering the biological nature of proteins similarity. Performance profiles show the advantage of DAMA over other methods, in particular when using a strict similarity measure QC, which is the ratio of correctly aligned columns, and when applying the methods to more difficult cases. Availability and implementation DAMA is available online at http://dworkowa.imdik.pan.pl/EP/DAMA. Linux binaries of the software are available upon request. Supplementary information Supplementary data are available at Bioinformatics online.
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  • 29
    Publication Date: 2021-02-25
    Description: We investigated the effects of principal instructional leadership (IL) on the frequency of two forms of teacher collaboration (TC) namely exchange and coordination of teaching (EXCHT) and professional collaboration (PROFCOLT) and their influence on student achievement (SA). Using data from the representative German PISA 2015 sample, we carry out structural equation modeling analysis to estimate the direct effects of IL on TC and of TC on SA, as well as the indirect effects of IL on SA. The analyses were conducted at the school level and only teachers belonging to the non-science group in PISA 2015 were included. After testing for the factorial validity of the instrument, only the collaboration form (EXCHT) could be retained. Our analysis suggests that principal instructional leadership can positively influence teachers’ collaboration frequency and that the remaining form of teacher collaboration is not positively related to student achievement. Our study builds on and extends research on student achievement by adding evidence about the relations between principal leadership and teacher collaboration in Germany.
    Electronic ISSN: 2504-284X
    Topics: Education , Nature of Science, Research, Systems of Higher Education, Museum Science
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  • 30
    Publication Date: 2021-02-26
    Description: Teachers’ diagnostic competences are regarded as highly important for classroom assessment and teacher decision making. Prior conceptualizations of diagnostic competences as judgement accuracy have been extended to include a wider understanding of what constitutes a diagnosis; novel models of teachers’ diagnostic competences explicitly include the diagnostic process as the core of diagnosing. In this context, domain-general and mathematics-specific research emphasizes the importance of tasks used to elicit student cognition. However, the role of (mathematical) tasks in diagnostic processes has not yet attracted much systematic empirical research interest. In particular, it is currently unclear whether teachers consider diagnostic task potential when selecting tasks for diagnostic interviews and how this relationship is shaped by their professional knowledge. This study focuses on pre-service mathematics teachers’ selection of tasks during one-to-one diagnostic interviews in live simulations. Each participant worked on two 30 mins interviews in the role of a teacher, diagnosing a student’s mathematical understanding of decimal fractions. The participants’ professional knowledge was measured afterward. Trained assistants played simulated students, who portrayed one of four student case profiles, each having different mathematical (mis-)conceptions of decimal fractions. For the interview, participants could select tasks from a set of 45 tasks with different diagnostic task potentials. Two aspects of task selection during the diagnostic processes were analyzed: participants’ sensitivity to the diagnostic potential, which was reflected in higher odds for selecting tasks with high potential than tasks with low potential, and the adaptive use of diagnostic task potential, which was reflected in task selection influenced by a task’s diagnostic potential in combination with previously collected information about the student’s understanding. The results show that participants vary in their sensitivity to diagnostic task potential, but not in their adaptive use. Moreover, participants’ content knowledge had a significant effect on their sensitivity. However, the effects of pedagogical content and pedagogical knowledge did not reach significance. The results highlight that pre-service teachers require further support to effectively attend to diagnostic task potential. Simulations were used for assessment purposes in this study, and they appear promising for this purpose because they allow for the creation of authentic yet controlled situations.
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    Topics: Education , Nature of Science, Research, Systems of Higher Education, Museum Science
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  • 31
    Publication Date: 2021-02-26
    Description: This paper discusses results from two successive rounds of virtual mines rescue training. The first round was conducted in a surround projection environment (360-VR), and the second round was conducted in desktop virtual reality (Desktop-VR). In the 360-VR condition, trainees participated as groups, making collective decisions. In the Desktop-VR condition, trainees could control their avatars individually. Overall, 372 participants took part in this study, including 284 mines rescuers who took part in 360-VR, and 243 in Desktop-VR. (155 rescuers experienced both.) Each rescuer who trained in 360-VR completed a battery of pre- and post-training questionnaires. Those who attended the Desktop-VR session only completed the post-training questionnaire. We performed principal components analysis on the questionnaire data, followed by a multiple regression analysis, the results of which suggest that the chief factor contributing to positive learning outcome was Learning Context, which extracted information about the quality of the learning content, the trainers, and their feedback. Subjective feedback from the Desktop-VR participants indicated that they preferred Desktop-VR to 360-VR for this training activity, which highlights the importance of choosing an appropriate platform for training applications, and links back to the importance of Learning Context. Overall, we conclude the following: 1) it is possible to train effectively using a variety of technologies but technology that is well-suited to the training task is more useful than technology that is “more advanced,” and 2) factors that have always been important in training, such as the quality of human trainers, remain critical for virtual reality training.
    Electronic ISSN: 2673-4192
    Topics: Computer Science
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  • 32
    Publication Date: 2021-02-26
    Description: The human ability of keeping balance during various locomotion tasks is attributed to our capability of withstanding complex interactions with the environment and coordinating whole-body movements. Despite this, several stability analysis methods are limited by the use of overly simplified biped and foot structures and corresponding contact models. As a result, existing stability criteria tend to be overly restrictive and do not represent the full balance capabilities of complex biped systems. The proposed methodology allows for the characterization of the balance capabilities of general biped models (ranging from reduced-order to whole-body) with segmented feet. Limits of dynamic balance are evaluated by the Boundary of Balance (BoB) and the associated novel balance indicators, both formulated in the Center of Mass (COM) state space. Intermittent heel, flat, and toe contacts are enabled by a contact model that maps discrete contact modes into corresponding center of pressure constraints. For demonstration purposes, the BoB and balance indicators are evaluated for a whole-body biped model with segmented feet representative of the human-like standing posture in the sagittal plane. The BoB is numerically constructed as the set of maximum allowable COM perturbations that the biped can sustain along a prescribed direction. For each point of the BoB, a constrained trajectory optimization algorithm generates the biped’s whole-body trajectory as it recovers from extreme COM velocity perturbations in the anterior–posterior direction. Balance capabilities for the cases of flat and segmented feet are compared, demonstrating the functional role the foot model plays in the limits of postural balance. The state-space evaluation of the BoB and balance indicators allows for a direct comparison between the proposed balance benchmark and existing stability criteria based on reduced-order models [e.g., Linear Inverted Pendulum (LIP)] and their associated stability metrics [e.g., Margin of Stability (MOS)]. The proposed characterization of balance capabilities provides an important benchmarking framework for the stability of general biped/foot systems.
    Electronic ISSN: 2296-9144
    Topics: Computer Science
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  • 33
    Publication Date: 2021-02-25
    Description: The COVID-19 pandemic led to school closures and a rapid transition to online classes. However, little is known about the impact of online learning in Canadian children with Attention-Deficit/Hyperactivity Disorder (ADHD). An online survey created on Qualtrics was distributed to families across Canada. Data collection was conducted over a total of five weeks in May and June 2020. We reviewed 587 surveys (4% margin of error using a 95% confidence interval) completed by caregivers/parents of children with ADHD (mean child age 10.14 years, SD = 3.06). Survey questions focused on hours of schoolwork completed and whether the learning needs of children with ADHD were met during school closures. Results indicated 90% of children with ADHD received web-based learning during the pandemic. Parents (41%) reported 〈 5 h of schoolwork per week, and 35% indicated between 5 to 10 h. Of the parents who said their child with ADHD had a modified curriculum (68%), 40% reported receiving educational materials that met their learning expectations during online classes. Parents (59%) reported that their child found it “very challenging” adjusting to online classes. The results indicated that children with ADHD faced significant challenges in adapting to online learning during the pandemic. Binary logistic regression indicated significant associations between depression severity, difficulties with starting and managing tasks and challenges adjusting to online learning. Long-term consequences of these challenges will need to be determined to ensure children with ADHD are able to meet their academic expectations.
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  • 34
    Publication Date: 2021-02-26
    Description: Background: The introduction of new visual technologies increases the risk of visually induced motion sickness (VIMS). The aim was to evaluate the 6-item Visually Induced Motion Sickness Susceptibility Questionnaire (VIMSSQ; also known as the VIMSSQ-short) and other predictors for individual susceptibility to VIMS.Methods: Healthy participants (10M + 20F), mean age 22.9 (SD 5.0) years, viewed a 360° panoramic city scene projected in the visual equivalent to the situation of rotating about an axis tilted from the vertical. The scene rotated at 0.2 Hz (72° s−1), with a ‘wobble’ produced by superimposed 18° tilt on the rotational axis, with a field of view of 83.5°. Exposure was 10 min or until moderate nausea was reported. Simulator Sickness Questionnaire (SSQ) was the index of VIMS. Predictors/correlates were VIMSSQ, Motion Sickness Susceptibility Questionnaire (MSSQ), migraine (scale), syncope, Social & Work Impact of Dizziness (SWID), sleep quality/disturbance, personality (“Big Five” TIPI), a prior multisensory Stepping-Vection test, and vection during exposure.Results: The VIMSSQ had good scale reliability (Cronbach’s alpha = 0.84) and correlated significantly with the SSQ (r = 0.58). Higher MSSQ, migraine, syncope, and SWID also correlated significantly with SSQ. Other variables had no significant relationships with SSQ. Regression models showed that the VIMSSQ predicted 34% of the individual variation of VIMS, increasing to 56% as MSSQ, migraine, syncope, and SWID were incorporated as additional predictors.Conclusion: The VIMSSQ is a useful adjunct to the MSSQ in predicting VIMS. Other predictors included migraine, syncope, and SWID. No significant relationship was observed between vection and VIMS.
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  • 35
    Publication Date: 2021-03-30
    Description: Increased participation in activities has been associated with improved positive mental health outcomes. However, there is much debate regarding the net effects of video games on individuals. Typified as a socially isolating activity, many games inherently contain socialization within the environment with game-generated characters or other players. Coinciding with the time of the initial pandemic/quarantine period was the release of a popular socializing and life simulation game, Animal Crossing: New Horizons. We investigated whether participation in this game was related to emotional outcomes associated with pandemics (e.g., loneliness and anxiety). The relationship between deleterious mental health and social gaming, amid a time of enforced reduction in socializing, would allow us to isolate the impact of the introduction of a social video game on improving the quality of life for players of this game. Participants (n = 1053) were asked about their time spent playing video games via an online survey, their socialization in game play, loneliness, and anxiety. We predicted that participants with higher levels of social interaction within the game would report less loneliness and anxiety. Utilizing multiple linear regression analyses, the research found that increased gaming and related activities were predictive of higher anxiety and somewhat related to increased loneliness. However, increased visits to another island were associated with lower levels of loneliness. As such, players may be utilizing gaming as a coping mechanism for anxiety. This research may inform generalized research regarding the influence that social games may have on feelings of loneliness and anxiety.
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  • 36
    Publication Date: 2021-03-31
    Description: BackgroundBlockchain is a new methodology involving a data structure with list of records, called blocks, which are linked using cryptography. The aim of the review is to overview the existing publication, projects, and platforms on the use of blockchain in Medicine and Neurology.MethodsWe searched the bibliographic database of MEDLINE and BASE. We also accessed ICObench, Coinmarketcap, and Mobihealthnews databases to explore upcoming, ongoing, and ended projects.ResultsIn medicine, there are many projects related to health care, disease prevention, and promotion of healthy life style. In neurology, only one project looks promising: Neuro, an ongoing scientific-technical project uniting scientists, engineers, and programmers for development of new architectures and algorithms of neural networks. Bibliographic searches found 117 publications on Medline and 203 publications on BASE referring to the use of blockchain technology in medicine. Most of them are presented as reviews (narrative, systematic, or minireview), opinions and hypotheses, commentaries, or perspectives. As for Neurology, only one publication refers to the use of blockchain, specifically to Parkinson’s disease.DiscussionAmong the problems related to medicine, there is the lack of information on the patient’s clinical history that could allow accurate diagnosis and treatment. The possibility of having a register based on blockchain technology could help doctors in many ways, including patient management, choosing and monitoring treatments, and standardization of clinical trials.ConclusionThe use of the blockchain technology in medicine has been repetitively proposed to solve different problems. In this article, we highlight the possible benefits of this technology, with attention to Neurology. Blockchain use can lead to quantifiable benefits in the treatment of neurodegenerative diseases, especially in clinical trials that can fail because of an incorrect patient recruitment.
    Electronic ISSN: 2624-7852
    Topics: Computer Science
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  • 37
    Publication Date: 2021-03-31
    Description: The purpose of this study is to investigate the use of an educational game to enhance student learning effectiveness. This study consisted of 56 college students majoring in physical education and recreation management (32 men, 24 women, age M = 21 years, SD = 1.72). Students used the educational computer game “PaGamO” to study the motor learning and development course. Students received rewards based on their individual and group “PaGamO” scores. Regression analysis indicated that “PaGamO” score was a significant (p 〈 0.01) predictor of multiple choice (MC) score in the final examination, there was a medium positive correlation (β = 0.354). The R2 suggests that 12.6% of MC score was explained by “PaGamO” score. Quantitative and qualitative mixed-method approach was used to gain insights into students’ perceptions and experiences of the educational game. The top three statements of a modified questionnaire from Riemer and Schrader (2015) are: (1) “In my opinion, the use of ‘PaGamO’ enables me to better prepare for the final examination” (M = 5.04, SD = 1.41), (2) “In my opinion, the use of ‘PaGamO’ enables me to understand learning contents” (M = 4.8, SD = 1.19), (3) “In my opinion, the use of ‘PaGamO’ allows me to apply knowledge” (M = 4.75, SD = 1.08). The top three motives to play “PaGamO” were “fun,” “self-learning,” and “want to get a higher grade in the final examination.” By using gamification as a tool for learning and studying, students did find “PaGamO” effective for their learning experience. Both intrinsic and extrinsic participation motives are reasons why students play “PaGamO.” Furthermore, due to its convenience, using mobile devices to play “PaGamO” is more popular than using computers and tablet devices. In conclusion, the combination of gamification and traditional learning methods can enhance students’ learning outcomes.
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  • 38
    Publication Date: 2021-03-31
    Description: This paper introduces the development of an anthropomorphic soft robotic hand integrated with multiple flexible force sensors in the fingers. By leveraging on the integrated force sensing mechanism, grip state estimation networks have been developed. The robotic hand was tasked to hold the given object on the table for 1.5 s and lift it up within 1 s. The object manipulation experiment of grasping and lifting the given objects were conducted with various pneumatic pressure (50, 80, and 120 kPa). Learning networks were developed to estimate occurrence of object instability and slippage due to acceleration of the robot or insufficient grasp strength. Hence the grip state estimation network can potentially feedback object stability status to the pneumatic control system. This would allow the pneumatic system to use suitable pneumatic pressure to efficiently handle different objects, i.e., lower pneumatic pressure (50 kPa) for lightweight objects which do not require high grasping strength. The learning process of the soft hand is made challenging by curating a diverse selection of daily objects, some of which displays dynamic change in shape upon grasping. To address the cost of collecting extensive training datasets, we adopted one-shot learning (OSL) technique with a long short-term memory (LSTM) recurrent neural network. OSL aims to allow the networks to learn based on limited training data. It also promotes the scalability of the network to accommodate more grasping objects in the future. Three types of LSTM-based networks have been developed and their performance has been evaluated in this study. Among the three LSTM networks, triplet network achieved overall stability estimation accuracy at 89.96%, followed by LSTM network with 88.00% and Siamese LSTM network with 85.16%.
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  • 39
    Publication Date: 2021-03-31
    Description: Techniques from artificial intelligence have been widely applied in optical communication and networks, evolving from early machine learning (ML) to the recent deep learning (DL). This paper focuses on state-of-the-art DL algorithms and aims to highlight the contributions of DL to optical communications. Considering the characteristics of different DL algorithms and data types, we review multiple DL-enabled solutions to optical communication. First, a convolutional neural network (CNN) is used for image recognition and a recurrent neural network (RNN) is applied for sequential data analysis. A variety of functions can be achieved by the corresponding DL algorithms through processing the different image data and sequential data collected from optical communication. A data-driven channel modeling method is also proposed to replace the conventional block-based modeling method and improve the end-to-end learning performance. Additionally, a generative adversarial network (GAN) is introduced for data augmentation to expand the training dataset from rare experimental data. Finally, deep reinforcement learning (DRL) is applied to perform self-configuration and adaptive allocation for optical networks.
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  • 40
    Publication Date: 2021-03-31
    Description: This article outlines a meta-analysis of the 10 learning techniques identified in Dunlosky et al. (2013a), and is based on 242 studies, 1,619 effects, 169,179 unique participants, with an overall mean of 0.56. The most effective techniques are Distributed Practice and Practice Testing and the least effective (but still with relatively high effects) are Underlining and Summarization. A major limitation was that the majority of studies in the meta-analysis were based on surface or factual outcomes, and there is caution needed when applying these findings to deeper and more relational outcomes. Other important moderators included the presence of feedback or not, near or far transfer, and the effects were much greater for lower than higher ability students. It is recommended that more attention be paid to when, under what conditions, each technique can be used, and how they can best be taught.
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  • 41
    Publication Date: 2021-03-31
    Description: Motivation Assigning new sequences to known protein families and subfamilies is a prerequisite for many functional, comparative and evolutionary genomics analyses. Such assignment is commonly achieved by looking for the closest sequence in a reference database, using a method such as BLAST. However, ignoring the gene phylogeny can be misleading because a query sequence does not necessarily belong to the same subfamily as its closest sequence. For example, a hemoglobin which branched out prior to the hemoglobin alpha/beta duplication could be closest to a hemoglobin alpha or beta sequence, whereas it is neither. To overcome this problem, phylogeny-driven tools have emerged but rely on gene trees, whose inference is computationally expensive. Results Here, we first show that in multiple animal and plant datasets, 18 to 62% of assignments by closest sequence are misassigned, typically to an over-specific subfamily. Then, we introduce OMAmer, a novel alignment-free protein subfamily assignment method, which limits over-specific subfamily assignments and is suited to phylogenomic databases with thousands of genomes. OMAmer is based on an innovative method using evolutionarily-informed k-mers for alignment-free mapping to ancestral protein subfamilies. Whilst able to reject non-homologous family-level assignments, we show that OMAmer provides better and quicker subfamily-level assignments than approaches relying on the closest sequence, whether inferred exactly by Smith-Waterman or by the fast heuristic DIAMOND. Availability OMAmer is available from the Python Package Index (as omamer), with the source code and a precomputed database available at https://github.com/DessimozLab/omamer. Supplementary information Supplementary data are available at Bioinformatics online.
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  • 42
    Publication Date: 2021-03-30
    Description: Social media is part of almost everyone's daily life. Its networking facilities redefines the way people connect and interact with each other. However, social media is reported being misused in different ways, especially the millennials. There is a need to elevate the teens' level of empowerment on the responsible use of social media. Other technological innovations like augmented reality (AR) and digital gamification provides pedagogical benefits. Digital gamification in the classroom is a teaching strategy that translates content and delivery into a game using digital technology. On the other hand, AR is an emerging technology to enlarge real-life situations in multimedia. Research shows both technologies increase interactivity well as attention span among the learners. Additionally, these technologies, including social media, are among the many useful tools in teaching twenty first century learners once correctly used. With this, a game-based mobile application was developed to advocate responsible use of social media among teens. The learning content was gamified in augmented reality to provide an innovative teaching and learning way at Silliman University. This paper describes the gamification design of the learning trail on the topic of responsible use of social media. Specifically, it presents the publishing process of the augmented reality mobile application about responsible use of social media. Kuhlmann's 3C Model (Challenge, Choices, Consequences) was utilized to formulate the learning content. It also demonstrates the design phases, game mechanics, and the general evaluation of the learning application. Four challenges were developed. These are (a) be familiar with security and privacy policies, (b) do not express concerns about others, even if you think you are anonymous, (c) respond to digital offenders, (d) do not tell the world about an upcoming vacation. These challenges were translated into graphics and animations. The animated material was compiled, programmed, and published to a server of the mobile application. The gamified learning trail on responsible use of social media is accessible through QR codes leading to the augmented reality interface. The design was validated and found to be relevant and engaging.
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    Topics: Education , Nature of Science, Research, Systems of Higher Education, Museum Science
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  • 43
    Publication Date: 2021-03-31
    Description: Summary VCF files with results of sequencing projects take a lot of space. We propose the VCFShark, which is able to compress VCF files up to an order of magnitude better than the de facto standards (gzipped VCF and BCF). The advantage over competitors is the greatest when compressing VCF files containing large amounts of genotype data. The processing speeds up to 100 MB/s and main memory requirements lower than 30 GB allow to use our tool at typical workstations even for large datasets. Availability and Implementation https://github.com/refresh-bio/vcfshark Supplementary information Supplementary data are available at publisher’s Web site.
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  • 44
  • 45
    Publication Date: 2021-03-30
    Description: The use of eye tracking to assess reading fluency has been proposed as a novel and efficient screening method for identifying school children with atypical reading development or risk of dyslexia. Currently, however, little is known about the relationship between the predictive outcomes produced by eye tracking screening systems and specialist cognitive assessments. Here we investigate this relationship in the context of a neuropsychological case study involving eight subjects (9–10 years) who were identified as being at risk of dyslexia by an eye tracking screening system. We analyze to what extent these subjects displayed cognitive limitations when assessed with a neuropsychological test battery, and to what extent cognitive difficulties were limited to reading and decoding, or whether they extended to other domains as well. As a group, the subjects performed on average significantly worse in reading and decoding than in other cognitive domains. Five subjects performed more than 1.25 standard deviations below the mean for age. In line with empirical evidence, co-occurring difficulties in the attention domain were also observed in a few struggling readers. Overall, the results support the view that eye tracking can be a useful tool to assess and monitor children’s reading development during the early school years.
    Electronic ISSN: 2504-284X
    Topics: Education , Nature of Science, Research, Systems of Higher Education, Museum Science
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  • 46
    Publication Date: 2021-03-28
    Description: Motivation As the generation of complex single-cell RNA sequencing datasets becomes more commonplace it is the responsibility of researchers to provide access to these data in a way that can be easily explored and shared. Whilst it is often the case that data is deposited for future bioinformatic analysis many studies do not release their data in a way that is easy to explore by non-computational researchers. Results In order to help address this we have developed ShinyCell, an R package that converts single-cell RNA sequencing datasets into explorable and shareable interactive interfaces. These interfaces can be easily customised in order to maximise their usability and can be easily uploaded to online platforms to facilitate wider access to published data. Availability ShinyCell is available at https://github.com/SGDDNB/ShinyCell and https://figshare.com/projects/ShinyCell/100439.
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  • 47
    Publication Date: 2021-03-28
    Description: Motivation Genomic selection (GS) is currently deemed the most effective approach to speed up breeding of agricultural varieties. It has been recognized that consideration of multiple traits in GS can improve accuracy of prediction for traits of low heritability. However, since GS forgoes statistical testing with the idea of improving predictions, it does not facilitate mechanistic understanding of the contribution of particular single nucleotide polymorphisms (SNP). Results Here we propose a L2,1-norm regularized multivariate regression model and devise a fast and efficient iterative optimization algorithm, called L2,1-joint, applicable in multi-trait GS. The usage of the L2,1-norm facilitates variable selection in a penalized multivariate regression that considers the relation between individuals, when the number of SNPs is much larger than the number of individuals. The capacity for variable selection allows us to define master regulators that can be used in a multi-trait GS setting to dissect the genetic architecture of the analyzed traits. Our comparative analyses demonstrate that the proposed model is a favorable candidate compared to existing state-of-the-art approaches. Prediction and variable selection with data sets from Brassica napus, wheat and Arabidopsis thaliana diversity panels are conducted to further showcase the performance of the proposed model. Availability and implementation The model is implemented using R programming language and the code is freely available from https://github.com/alainmbebi/L21-norm-GS. Supplementary information Supplementary data are available at Bioinformatics online.
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  • 48
    Publication Date: 2021-03-28
    Description: Summary Finding informative predictive features in high dimensional biological case-control datasets is challenging. The Extreme Pseudo-Sampling (EPS) algorithm offers a solution to the challenge of feature selection via a combination of deep learning and linear regression models. First, using a variational autoencoder, it generates complex latent representations for the samples. Second, it classifies the latent representations of cases and controls via logistic regression. Third, it generates new samples (pseudo-samples) around the extreme cases and controls in the regression model. Finally, it trains a new regression model over the upsampled space. The most significant variables in this regression are selected. We present an open-source implementation of the algorithm that is easy to set up, use, and customize. Our package enhances the original algorithm by providing new features and customizability for data preparation, model training and classification functionalities. We believe the new features will enable the adoption of the algorithm for a diverse range of datasets. Availability The software package for Python is available online at https://github.com/roohy/eps
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  • 49
    Publication Date: 2021-03-25
    Description: Research suggests Specific Learning Disabilities (SLD) are directly linked to specific neurocognitive deficits that result in unexpected learning delays in academic domains for children in schools. However, meta-analytic studies have failed to find supporting evidence for using neurocognitive tests and, consequently, have discouraged their inclusion in SLD identification policies. The current study critically reviews meta-analytic findings and the methodological validity of over 200 research studies used in previous meta-analytic studies to estimate the causal effect of neurocognitive tests on intervention outcomes. Results suggest that only a very small percentage (6–12%) of studies used in previous meta-analytic studies were methodologically valid to estimate a direct effect of cognitive tests on academic intervention outcomes, with the majority of studies having no causal link between neurocognitive tests and intervention outcomes. Additionally, significant reporting discrepancies and inaccurate effect size estimates were found that warranted legitimate concerns for conclusions and policy recommendations provided in several meta-analytic studies. Given the lack of methodological rigor linking cognitive testing to academic interventions in current research, removing neurocognitive testing from learning disability evaluations may be premature. Suggestions for future studies evaluating the impact of neurocognitive tests on intervention outcomes as well as guidelines for synthesizing meta-analytic findings are discussed.
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    Topics: Education , Nature of Science, Research, Systems of Higher Education, Museum Science
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  • 50
    Publication Date: 2021-03-25
    Description: Two of the major revolutions of this century are the Artificial Intelligence and Robotics. These technologies are penetrating through all disciplines and faculties at a very rapid pace. The application of these technologies in medicine, specifically in the context of Covid 19 is paramount. This article briefly reviews the commonly applied protocols in the Health Care System and provides a perspective in improving the efficiency and effectiveness of the current system. This article is not meant to provide a literature review of the current technology but rather provides a personal perspective of the author regarding what could happen in the ideal situation.
    Electronic ISSN: 2296-9144
    Topics: Computer Science
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  • 51
    Publication Date: 2021-03-24
    Description: The exponentially increasing advances in robotics and machine learning are facilitating the transition of robots from being confined to controlled industrial spaces to performing novel everyday tasks in domestic and urban environments. In order to make the presence of robots safe as well as comfortable for humans, and to facilitate their acceptance in public environments, they are often equipped with social abilities for navigation and interaction. Socially compliant robot navigation is increasingly being learned from human observations or demonstrations. We argue that these techniques that typically aim to mimic human behavior do not guarantee fair behavior. As a consequence, social navigation models can replicate, promote, and amplify societal unfairness, such as discrimination and segregation. In this work, we investigate a framework for diminishing bias in social robot navigation models so that robots are equipped with the capability to plan as well as adapt their paths based on both physical and social demands. Our proposed framework consists of two components: learning which incorporates social context into the learning process to account for safety and comfort, and relearning to detect and correct potentially harmful outcomes before the onset. We provide both technological and societal analysis using three diverse case studies in different social scenarios of interaction. Moreover, we present ethical implications of deploying robots in social environments and propose potential solutions. Through this study, we highlight the importance and advocate for fairness in human-robot interactions in order to promote more equitable social relationships, roles, and dynamics and consequently positively influence our society.
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  • 52
    Publication Date: 2021-03-22
    Description: The notion that blockchains offer decentralized, “trustless” guarantees of security through technology is a fundamental misconception held by many advocates. This misconception hampers participants from understanding the security differences between public and private blockchains and adopting blockchain technology in suitable contexts. This paper introduces the notion of “people security” to argue that blockchains hold inherent limitations in offering accurate security guarantees to people as participants in blockchain-based infrastructure, due to the differing nature of the threats to participants reliant on blockchain as secure digital infrastructure, as well as the technical limitations between different types of blockchain architecture. This paper applies a sociotechnical security framework to assess the social, software, and infrastructural layers of blockchain applications to reconceptualize “blockchain security” as “people security.” A sociotechnical security analysis of existing macrosocial level blockchain systems surfaces discrepancies between the social, technical, and infrastructural layers of a blockchain network, the technical and governance decisions that characterize the network, and the expectations of, and threats to, participants using the network. The results identify a number of security and trust assumptions against various blockchain architectures, participants, and applications. Findings indicate that private blockchains have serious limitations for securing the interests of users in macrosocial contexts, due to their centralized nature. In contrast, public blockchains reveal trust and security shortcomings at the micro and meso-organizational levels, yet there is a lack of suitable desktop case studies by which to analyze sociotechnical security at the macrosocial level. These assumptions need to be further investigated and addressed in order for blockchain security to more accurately provide “people security”.
    Electronic ISSN: 2624-9898
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  • 53
    Publication Date: 2021-03-22
    Description: In recent years, communication robots aiming to offer mental support to the elderly have attracted increasing attention. Dialogue systems consisting of two robots could provide the elderly with opportunities to hold longer conversations in care homes. In this study, we conducted an experiment to compare two types of scenario-based dialogue systems with different types of bodies—physical and virtual robots—to investigate the effects of embodying such dialogue systems. Forty elderly people aged from 65 to 84 interacted with either an embodied desktop-sized humanoid robot or computer graphic agent displayed on a monitor. The elderly participants were divided into groups depending on the success of the interactions. The results revealed that (i) in the group where the robots responded more successfully with the expected conversation flow, the elderly are more engaged in the conversation with the physical robots than the virtual robots, and (ii) the elderly in the group in which robots responded successfully are more engaged in the conversation with the physical robots than those in the group in which the robots responded with ambiguous responses owing to unexpected utterances from the elderly. These results suggest that having a physical body is advantageous in promoting high engagement, and the potential advantage appears depending on whether the system can handle the conversation flow. These findings provide new insight into the development of dialogue systems assisting elderly in maintaining a better mental health.
    Electronic ISSN: 2296-9144
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  • 54
    Publication Date: 2021-03-24
    Description: Motivation There are high demands for joint genotyping of structural variations with short-read sequencing, but efficient and accurate genotyping in population scale is a challenging task. Results We developed muCNV that aggregates per-sample summary pileups for joint genotyping of 〉 100,000 samples. Pilot results show very low Mendelian inconsistencies. Applications to large-scale projects in cloud show the computational efficiencies of muCNV genotyping pipeline. Availability muCNV is publicly available for download at: https://github.com/gjun/muCNV Supplementary information Supplementary data are available at Bioinformatics online.
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  • 55
    Publication Date: 2021-03-24
    Description: Alzheimer’s dementia (AD) is a type of neurodegenerative disease that is associated with a decline in memory. However, speech and language impairments are also common in Alzheimer’s dementia patients. This work is an extension of our previous work, where we had used spontaneous speech for Alzheimer’s dementia recognition employing log-Mel spectrogram and Mel-frequency cepstral coefficients (MFCC) as inputs to deep neural networks (DNN). In this work, we explore the transcriptions of spontaneous speech for dementia recognition and compare the results with several baseline results. We explore two models for dementia recognition: 1) fastText and 2) convolutional neural network (CNN) with a single convolutional layer, to capture the n-gram-based linguistic information from the input sentence. The fastText model uses a bag of bigrams and trigrams along with the input text to capture the local word orderings. In the CNN-based model, we try to capture different n-grams (we use n = 2, 3, 4, 5) present in the text by adapting the kernel sizes to n. In both fastText and CNN architectures, the word embeddings are initialized using pretrained GloVe vectors. We use bagging of 21 models in each of these architectures to arrive at the final model using which the performance on the test data is assessed. The best accuracies achieved with CNN and fastText models on the text data are 79.16 and 83.33%, respectively. The best root mean square errors (RMSE) on the prediction of mini-mental state examination (MMSE) score are 4.38 and 4.28 for CNN and fastText, respectively. The results suggest that the n-gram-based features are worth pursuing, for the task of AD detection. fastText models have competitive results when compared to several baseline methods. Also, fastText models are shallow in nature and have the advantage of being faster in training and evaluation, by several orders of magnitude, compared to deep models.
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  • 56
    Publication Date: 2021-03-26
    Description: Motivation Molecular property prediction is a hot topic in recent years. Existing graph-based models ignore the hierarchical structures of molecules. According to the knowledge of chemistry and pharmacy, the functional groups of molecules are closely related to its physio-chemical properties and binding affinities. So, it should be helpful to represent molecular graphs by fragments that contain functional groups for molecular property prediction. Results In this paper, to boost the performance of molecule property prediction, we first propose a definition of molecule graph fragments that may be or contain functional groups, which are relevant to molecular properties, then develop a fragment-oriented multi-scale graph attention network for molecular property prediction, which is called FraGAT. Experiments on several widely-used benchmarks are conducted to evaluate FraGAT. Experimental results show that FraGAT achieves state-of-the-art predictive performance in most cases. Furthermore, our case studies showthat when the fragments used to represent the molecule graphs contain functional groups, the model can make better predictions. This conforms to our expectation and demonstrates the interpretability of the proposed model. Availability and implementation The code and data underlying this work are available in GitHub, at https://github.com/ZiqiaoZhang/FraGAT. Supplementary information Supplementary data are available at Bioinformatics online.
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  • 57
    Publication Date: 2021-03-26
    Description: Motivation The Anatomical Therapeutic Chemical (ATC) system is an official classification system established by the World Health Organization for medicines. Correctly assigning ATC classes to given compounds is an important research problem in drug discovery, which can not only discover the possible active ingredients of the compounds, but also infer theirs therapeutic, pharmacological, and chemical properties. Results In this paper, we develop an end-to-end multi-label classifier called CGATCPred to predict 14 main ATC classes for given compounds. In order to extract rich features of each compound, we use the deep Convolutional Neural Network (CNN) and shortcut connections to represent and learn the seven association scores between the given compound and others. Moreover, we construct the correlation graph of ATC classes and then apply graph convolutional network (GCN) on the graph for label embedding abstraction. We use all label embedding to guide the learning process of compound representation. As a result, by using the Jackknife test, CGATCPred obtain reliable Aiming of 81.94%, Coverage of 82.88%, Accuracy 80.81%, Absolute True 76.58% and Absolute False 2.75%, yielding significantly improvements compared to exiting multi-label classifiers. Availability The codes of CGATCPred are available at https://github.com/zhc940702/CGATCPred and https://zenodo.org/record/4552917. Supplementary information Supplementary data are available at Bioinformatics online.
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  • 58
    Publication Date: 2021-03-24
    Description: Motivation Sequence motif discovery algorithms can identify novel sequence patterns that perform biological functions in DNA, RNA and protein sequences—for example, the binding site motifs of DNA-and RNA-binding proteins. Results The STREME algorithm presented here advances the state-of-the-art in ab initio motif discovery in terms of both accuracy and versatility. Using in vivo DNA (ChIP-seq) and RNA (CLIP-seq) data, and validating motifs with reference motifs derived from in vitro data, we show that STREME is more accurate, sensitive and thorough than several widely used algorithms (DREME, HOMER, MEME, Peak-motifs) and two other representative algorithms (ProSampler and Weeder). STREME’s capabilities include the ability to find motifs in datasets with hundreds of thousands of sequences, to find both short and long motifs (from 3 to 30 positions), to perform differential motif discovery in pairs of sequence datasets, and to find motifs in sequences over virtually any alphabet (DNA, RNA, protein and user-defined alphabets). Unlike most motif discovery algorithms, STREME reports a useful estimate of the statistical significance of each motif it discovers. STREME is easy to use individually via its web server or via the command line, and is completely integrated with the widely-used MEME Suite of sequence analysis tools. The name STREME stands for “Simple, Thorough, Rapid, Enriched Motif Elicitation”. Availability The STREME web server and source code are provided freely for non-commercial use at http://meme-suite.org.
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  • 59
    Publication Date: 2021-03-24
    Description: Motivation Understanding the mechanisms by which the zebrafish pectoral fin develops is expected to produce insights on how vertebrate limbs grow from a 2D cell layer to a 3D structure. Two mechanisms have been proposed to drive limb morphogenesis in tetrapods: a growth-based morphogenesis with a higher proliferation rate at the distal tip of the limb bud than at the proximal side, and directed cell behaviors that include elongation, division and migration in a nonrandom manner. Based on quantitative experimental biological data at the level of individual cells in the whole developing organ, we test the conditions for the dynamics of pectoral fin early morphogenesis. Results We found that during the development of the zebrafish pectoral fin, cells have a preferential elongation axis that gradually aligns along the proximodistal axis (PD) of the organ. Based on these quantitative observations, we build a center-based cell model enhanced with a polarity term and cell proliferation to simulate fin growth. Our simulations resulted in 3D fins similar in shape to the observed ones, suggesting that the existence of a preferential axis of cell polarization is essential to drive fin morphogenesis in zebrafish, as observed in the development of limbs in the mouse, but distal tip-based expansion is not. Availability Upon publication, biological data will be available at http://bioemergences.eu/modelingFin, and source code at https://github.com/guijoe/MaSoFin. Supplementary information Supplementary data are included in this manuscript.
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  • 60
    Publication Date: 2021-03-23
    Description: The COVID-19 pandemic has unleashed torrents of global suffering at a devastating scale, necessitating a strong response to alleviating suffering. This paper begins with noting that the conventional approach to suffering in North America is to be positive and not to be negative. The paper summarily explores the philosophy of positive psychology underlying the first- and the second-wave of positive psychology, commenting on the evolution from dualism and a binary conceptualization in the first wave (PP 1.0) to a non-dualism of integrating binaries in the second wave (PP 2.0). PP 2.0’s enhanced therapeutic efficacy is noted for its non-dual framework. The paper then explores and suggests a different conceptualization possibility of non-duality, fundamental non-duality, that is related to but distinct from the one in PP 2.0. A case is made that fundamental non-duality has a radical possibility of therapeutic efficacy. Being consistent with the philosophy of non-duality, further suggestions are made that non-duality of PP 2.0 and fundamental non-duality can be therapeutically deployed together for greatest efficacy. The exploration contained in the paper is largely philosophical, arts-based, and autobiographical, creating an enacted and lived experience of applying theory to practice.
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    Topics: Education , Nature of Science, Research, Systems of Higher Education, Museum Science
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  • 61
    Publication Date: 2021-03-23
    Description: In this research, the evolution of blockchain applied to supply chains has been mapped from the inception of the technology until June 2020, utilizing primarily public data sources. We have analyzed 271 blockchain projects on parameters such as their inception dates, types of blockchain, status, sectors applied to and type of organization that founded the project. We confirm generally understood trends in the blockchain market with new projects following the industry’s general hype and funding levels. We observe most activity in the Agriculture/Grocery sector and the Freight/Logistics sector. We see the shift of market interest from private companies (startups) to public companies and consortia and the change in blockchain adoption from Ethereum to Hyperledger. Finally, we observe more market-ready solutions and fewer inactive projects for Hyperledger-based projects than Ethereum-based projects.
    Electronic ISSN: 2624-7852
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  • 62
    Publication Date: 2021-03-23
    Description: The field of musical robotics presents an interesting case study of the intersection between creativity and robotics. While the potential for machines to express creativity represents an important issue in the field of robotics and AI, this subject is especially relevant in the case of machines that replicate human activities that are traditionally associated with creativity, such as music making. There are several different approaches that fall under the broad category of musical robotics, and creativity is expressed differently based on the design and goals of each approach. By exploring elements of anthropomorphic form, capacity for sonic nuance, control, and musical output, this article evaluates the locus of creativity in six of the most prominent approaches to musical robots, including: 1) nonspecialized anthropomorphic robots that can play musical instruments, 2) specialized anthropomorphic robots that model the physical actions of human musicians, 3) semi-anthropomorphic robotic musicians, 4) non-anthropomorphic robotic instruments, 5) cooperative musical robots, and 6) individual actuators used for their own sound production capabilities.
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  • 63
    Publication Date: 2021-03-24
    Description: Motivation Knowledge manipulation of Gene Ontology (GO) and Gene Ontology Annotation (GOA) can be done primarily by using vector representation of GO terms and genes. Previous studies have represented GO terms and genes or gene products in Euclidean space to measure their semantic similarity using an embedding method such as the Word2Vec-based method to represent entities as numeric vectors. However, this method has the limitation that embedding large graph-structured data in the Euclidean space cannot prevent a loss of information of latent hierarchies, thus precluding the semantics of GO and GOA from being captured optimally. On the other hand, hyperbolic spaces such as the Poincaré balls are more suitable for modeling hierarchies, as they have a geometric property in which the distance increases exponentially as it nears the boundary because of negative curvature. Results In this paper, we propose hierarchical representations of GO and genes (HiG2Vec) by applying Poincaré embedding specialized in the representation of hierarchy through a two-step procedure: GO embedding and gene embedding. Through experiments, we show that our model represents the hierarchical structure better than other approaches and predicts the interaction of genes or gene products similar to or better than previous studies. The results indicate that HiG2Vec is superior to other methods in capturing the GO and gene semantics and in data utilization as well. It can be robustly applied to manipulate various biological knowledge. Availability https://github.com/JaesikKim/HiG2Vec Supplementary information Supplementary data are available at Bioinformatics online.
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  • 64
    Publication Date: 2021-03-24
    Description: The purpose of this study was to investigate Chinese pre-service kindergarten teachers’ beliefs and behaviors inherent to a public health perspective in the time of coronavirus disease (COVID-19) pandemic. The study intended to deepen our understanding of the contextual factors that may influence their future implementation of public health education in early childhood settings. Forty-five participants were recruited from an early childhood teacher education program, with the majority (n = 41) reporting that they lived through the strict restrictions in different regions of China during the initial COVID-19 outbreak. Data were collected via a reflective writing task from an online course. The results indicated an increasing awareness of public health among the participants due to the COVID-19 crisis. Their personal hygiene behaviors included personal protection, cleaning, disinfection, physical distancing, avoidance, and ventilation. The pandemic also changed pre-service teachers’ beliefs about the importance of public health education. Although the participants seemed to have positive attitudes toward public health education, evidence of their limitations of knowledge and skills was further exposed in their ideas for teaching health-related topics. When there is a need to strengthen public health education in the ongoing fight against COVID-19, this paper calls for a refocus in pre-service teacher education to improve public health outcomes for young children.
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  • 65
    Publication Date: 2021-03-24
    Description: This paper presents a ranking method of operating sequences based on the actual condition of complex systems. This objective is achieved using the health checkup concept and the multiattribute utility theory. Our contribution is the proposal of sequences ranking process using data and experts’ judgments. The ranking results in a decision-making element; it allows experts to have an objective and concise overall ranking to be used for decision making. A case study is presented based on an experimental platform; it allows us to compare two aggregation operators: the weighted mean and the Choquet integral.
    Electronic ISSN: 2624-8212
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  • 66
    Publication Date: 2021-03-24
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  • 67
    Publication Date: 2021-03-24
    Description: Since its introduction in 1994, Milgram and Kishino's reality-virtuality (RV) continuum has been used to frame virtual and augmented reality research and development. While originally, the RV continuum and the three dimensions of the supporting taxonomy (extent of world knowledge, reproduction fidelity, and extent of presence metaphor) were intended to characterize the capabilities of visual display technology, researchers have embraced the RV continuum while largely ignoring the taxonomy. Considering the leaps in technology made over the last 25 years, revisiting the RV continuum and taxonomy is timely. In reexamining Milgram and Kishino's ideas, we realized, first, that the RV continuum is actually discontinuous; perfect virtual reality cannot be reached. Secondly, mixed reality is broader than previously believed, and, in fact, encompasses conventional virtual reality experiences. Finally, our revised taxonomy adds coherence, accounting for the role of users, which is critical to assessing modern mixed reality experiences. The 3D space created by our taxonomy incorporates familiar constructs such as presence and immersion, and also proposes new constructs that may be important as mixed reality technology matures.
    Electronic ISSN: 2673-4192
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  • 68
    Publication Date: 2021-03-23
    Description: Motivation Facing the increasing gap between high-throughput sequence data and limited functional insights, computational protein function annotation provides a high-throughput alternative to experimental approaches. However, current methods can have limited applicability while relying on protein data besides sequences, or lack generalizability to novel sequences, species and functions. Results To overcome aforementioned barriers in applicability and generalizability, we propose a novel deep learning model using only sequence information for proteins, named Transformer-based protein function Annotation through joint sequence–Label Embedding (TALE). For generalizability to novel sequences we use self attention-based transformers to capture global patterns in sequences. For generalizability to unseen or rarely seen functions (tail labels), we embed protein function labels (hierarchical GO terms on directed graphs) together with inputs/features (1D sequences) in a joint latent space. Combining TALE and a sequence similarity-based method, TALE+ outperformed competing methods when only sequence input is available. It even outperformed a state-of-the-art method using network information besides sequence, in two of the three gene ontologies. Furthermore, TALE and TALE+ showed superior generalizability to proteins of low similarity, new species, or rarely annotated functions compared to training data, revealing deep insights into the protein sequence–function relationship. Ablation studies elucidated contributions of algorithmic components toward the accuracy and the generalizability. Availability The data, source codes and models are available at https://github.com/Shen-Lab/TALE Supplementary information Supplementary data are available at Bioinformatics online.
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  • 69
    Publication Date: 2021-03-23
    Description: Motivation Random sampling of metabolic fluxes can provide a comprehensive description of the capabilities of a metabolic network. However, current sampling approaches do not model thermodynamics explicitly, leading to inaccurate predictions of an organism’s potential or actual metabolic operations. Results We present a probabilistic framework combining thermodynamic quantities with steady-state flux constraints to analyze the properties of a metabolic network. It includes methods for probabilistic metabolic optimization and for joint sampling of thermodynamic and flux spaces. Applied to a model of E. coli, we use the methods to reveal known and novel mechanisms of substrate channeling, and to accurately predict reaction directions and metabolite concentrations. Interestingly, predicted flux distributions are multimodal, leading to discrete hypotheses on E. coli’s metabolic capabilities. Availability Python and MATLAB packages available at https://gitlab.com/csb.ethz/pta. Supplementary information Supplementary data are available at Bioinformatics online.
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  • 70
    Publication Date: 2021-03-23
    Description: Background: Pulmonary rehabilitation (PR) has been proven effective but is not well accessed due to transport, time, cost, and physical limitations of patients. We have developed a mobile phone-based PR program (mPR) that could be offered as an alternative for those unable to attend in-person. This was developed following formative research with patients, their families and clinicians. mPR has a core text message program plus an app that includes an action plan, exercise videos, lung visualization, symptom score questionnaire and 1-min sit-to-stand test.Aims: To determine the feasibility of delivering pulmonary rehabilitation by mobile phone.Methods: A 9-week non-randomized (1-arm) pilot study was conducted. Participants were 26 adults with chronic obstructive pulmonary disease plus four family members, who were offered participation at first assessment or during group PR sessions. Outcomes included satisfaction, engagement with the program, and perceived impacts.Results: Eight people (31%) opted for text messages only, and 18 (69%) chose text messages plus the app. Three people stopped the program early, 20 said they would recommend it to others, 19 said it helped them to feel more supported, 17 said it helped them to change their behavior.Conclusion: It is feasible to deliver PR support via mobile phone, including exercise prescription and support. Our mPR program was appreciated by a small number of people with chronic respiratory disorders and family members. Suggestions for improvements are being used to inform the further development of the program, which will then be tested for effectiveness. Registered with the Australia New Zealand Clinical Trials Registry ACTRN12619000884101 (www.anzctr.org.au).
    Electronic ISSN: 2624-9898
    Topics: Computer Science
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  • 71
    Publication Date: 2021-03-23
    Description: Civic learning is an essential element of service learning, but one that is often underdeveloped in practice. This article surveys various conceptualizations of civic learning that are in use in higher education around the world, discusses approaches to designing service learning courses to generate civic learning outcomes, and proposes two methods for assessing student attainment of them. The intent is to build instructors’ capacities to cultivate the knowledge, skills, dispositions, and behaviors that lie at the very heart of civic learning and of public life in the ever-more complex and interconnected 21st century.
    Electronic ISSN: 2504-284X
    Topics: Education , Nature of Science, Research, Systems of Higher Education, Museum Science
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  • 72
    Publication Date: 2021-03-23
    Description: Entering the 5G/6G era, the core concept of human-centric communications has intensified the research effort into analytical frameworks for integrating technological and non-technological domains. Among non-technological domains, human behavioral, psychological, and socio-economic contexts are widely considered as indispensable elements for characterizing user experience (UE). In this study, we introduce the prospect theory as a promising methodology for modeling UE and perceptual measurements for human-centric communications. As the founding pillar of behavioral economics, the prospect theory proposes the non-linear quantity and probability perception of human psychology, which extends to five fundamental behavioral attributes that have profound implications for diverse disciplines. An example of applying the novel theoretic framework is also provided to illustrate how the prospect theory can be utilized to incorporate human factors and analyze human-centric communications. By expatiating on the prospect theoretic framework, we aim to provide a guideline for developing human-centric communications and articulate a novel interdisciplinary research area for further investigation.
    Electronic ISSN: 2673-530X
    Topics: Computer Science
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  • 73
    Publication Date: 2021-03-19
    Description: Ocean ecosystems have spatiotemporal variability and dynamic complexity that require a long-term deployment of an autonomous underwater vehicle for data collection. A new generation of long-range autonomous underwater vehicles (LRAUVs), such as the Slocum glider and Tethys-class AUV, has emerged with high endurance, long-range, and energy-aware capabilities. These new vehicles provide an effective solution to study different oceanic phenomena across multiple spatial and temporal scales. For these vehicles, the ocean environment has forces and moments from changing water currents which are generally on the order of magnitude of the operational vehicle velocity. Therefore, it is not practical to generate a simple trajectory from an initial location to a goal location in an uncertain ocean, as the vehicle can deviate significantly from the prescribed trajectory due to disturbances resulted from water currents. Since state estimation remains challenging in underwater conditions, feedback planning must incorporate state uncertainty that can be framed into a stochastic energy-aware path planning problem. This article presents an energy-aware feedback planning method for an LRAUV utilizing its kinematic model in an underwater environment under motion and sensor uncertainties. Our method uses ocean dynamics from a predictive ocean model to understand the water flow pattern and introduces a goal-constrained belief space to make the feedback plan synthesis computationally tractable. Energy-aware feedback plans for different water current layers are synthesized through sampling and ocean dynamics. The synthesized feedback plans provide strategies for the vehicle that drive it from an environment’s initial location toward the goal location. We validate our method through extensive simulations involving the Tethys vehicle’s kinematic model and incorporating actual ocean model prediction data.
    Electronic ISSN: 2296-9144
    Topics: Computer Science
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  • 74
    Publication Date: 2021-03-26
    Description: Motivation Thanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning models becomes possible. However, it remains largely an open problem how to effectively utilize large and noisy biomedical KG for DDI detection. Due to its sheer size and amount of noise in KGs, it is often less beneficial to directly integrate KGs with other smaller but higher quality data (e.g., experimental data). Most of existing approaches ignore KGs altogether. Some tries to directly integrate KGs with other data via graph neural networks with limited success. Furthermore most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is more meaningful but harder task. Results To fill the gaps, we propose a new method SumGNN: knowledge summarization graph neural network, which is enabled by a subgraph extraction module that can efficiently anchor on relevant subgraphs from a KG, a self-attention based subgraph summarization scheme to generate reasoning path within the subgraph, and a multi-channel knowledge and data integration module that utilizes massive external biomedical knowledge for significantly improved multi-typed DDI predictions. SumGNN outperforms the best baseline by up to 5.54%, and performance gain is particularly significant in low data relation types. In addition, SumGNN provides interpretable prediction via the generated reasoning paths for each prediction. Availability The code is available in the supplementary. Supplementary information Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 75
    Publication Date: 2021-03-25
    Description: Wrist disability caused by a series of diseases or injuries hinders the patient’s capability to perform activities of daily living (ADL). Rehabilitation devices for the wrist motor function have gained popularity among clinics and researchers due to the convenience of self-rehabilitation. The inherent compliance of soft robots enabled safe human-robot interaction and light-weight characteristics, providing new possibilities to develop wearable devices. Compared with the conventional apparatus, soft robotic wearable rehabilitation devices showed advantages in flexibility, cost, and comfort. In this work, a compact and low-profile soft robotic wrist brace was proposed by directly integrating eight soft origami-patterned actuators on the commercially available wrist brace. The linear motion of the actuators was defined by their origami pattern. The extensions of the actuators were constrained by the brace fabrics, deriving the motions of the wrist joint, i.e., extension/flexion, ulnar/radial deviation. The soft actuators were made of ethylene-vinyl acetate by blow molding, achieving mass-production capability, low cost, and high repeatability. The design and fabrication of the soft robotic wrist brace are presented in this work. The experiments on the range of motion, output force, wearing position adaptivity, and performance under disturbance have been carried out with results analyzed. The modular soft actuator approach of design and fabrication of the soft robotic wrist brace has a wide application potential in wearable devices.
    Electronic ISSN: 2296-9144
    Topics: Computer Science
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  • 76
    Publication Date: 2021-03-27
    Description: Motivation Most protein-structure superimposition tools consider only Cartesian coordinates. Yet, much of biology happens on the surface of proteins, which is why proteins with shared ancestry and similar function often have comparable surface shapes. Superposition of proteins based on surface shape can enable comparison of highly divergent proteins, identify convergent evolution and enable detailed comparison of surface features and binding sites. Results We present ZEAL, an interactive tool to superpose global and local protein structures based on their shape resemblance using 3D (Zernike-Canterakis) functions to represent the molecular surface. In a benchmark study of structures with the same fold, we show that ZEAL outperforms two other methods for shape-based superposition. In addition, alignments from ZEAL was of comparable quality to the coordinate-based superpositions provided by TM-align. For comparisons of proteins with limited sequence and backbone-fold similarity, where coordinate-based methods typically fail, ZEAL can often find alignments with substantial surface-shape correspondence. In combination with shape-based matching, ZEAL can be used as a general tool to study relationships between shape and protein function. We identify several categories of protein functions where global shape similarity is significantly more likely than expected by random chance, when comparing proteins with little similarity on the fold level. In particular, we find that global surface shape similarity is particular common among DNA binding proteins. Availability ZEAL can be used online at https://andrelab.org/zeal or as a standalone program with command line or graphical user interface. Source files and installers are available at https://github.com/Andre-lab/ZEAL Supplementary information Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 77
    Publication Date: 2021-03-25
    Description: The recent outbreak of the novel coronavirus (COVID-19) has infected millions of citizens worldwide and claimed many lives. This paper examines the impact of COVID-19 on Chinese e-commerce by analyzing behavioral changes observed on a large online shopping platform. We first conduct a time series analysis to identify product categories that faced the most extensive disruptions. The time-lagged analysis shows that behavioral patterns of shopping actions are highly responsive to the epidemic's development. Based on these findings, we present a consumer demand prediction method by encompassing the epidemic statistics and behavioral features of COVID-19-related products. Experimental results demonstrate that our predictions outperform existing baselines and further extend to long-term and province-level forecasts. Finally, we discuss how our market analysis and prediction can help better prepare for future pandemics by gaining extra time to launch preventive measures.
    Electronic ISSN: 2624-9898
    Topics: Computer Science
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  • 78
    Publication Date: 2021-03-23
    Description: This article details how the FALKE research project (Fachspezifische Lehrerkompetenzen im Erklären; Engl.: subject-specific teacher competency in explaining) integrates 14 heterogeneous disciplines in order to empirically examine the didactic quality of teacher explanations in eleven school subjects by bringing together trans-, multi-, and interdisciplinary perspectives. In order to illustrate the academic landscape of the FALKE project we briefly outline the nature of the transdisciplinary German “Fachdidaktiken” (Engl.: subject-matter didactics, i.e., special academic disciplines of teaching and learning specific school subjects). The FALKE project required the willingness of all researchers from eleven participating subject-matter didactics to rely on both the concepts and the methods of educational sciences as an overarching research framework (transdisciplinary aspect). All researchers of subject-matter didactics had to develop a shared conceptual, methodological, and administrative framework in order to empirically investigate commonalities in and differences between “good explanations” across the range of school subjects represented (multidisciplinary aspect). The additional perspectives of researchers in speech science and linguistics proved fruitful in recognizing rhetorical and linguistic aspects of teacher explanations (interdisciplinary aspect). Data management and statistical analysis were provided by the discipline methods of educational sciences. Rather than reporting empirical results, we here discuss opportunities and challenges as well as the lessons learned from the FALKE project regarding cognitive-epistemic reasoning, communication, and organization.
    Electronic ISSN: 2504-284X
    Topics: Education , Nature of Science, Research, Systems of Higher Education, Museum Science
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  • 79
    Publication Date: 2021-03-25
    Description: COVID-19, the illness caused by the SARS-CoV-2 virus, is now a worldwide pandemic with mortality in hundreds of thousands as infections continue to increase. Containing the spread of this viral infection and decreasing the mortality rate is a major challenge. Identifying appropriate antigenic epitopes from the viral proteins is a very important task for vaccine production and the development of diagnostic kits and antibody therapy. A novel antigenic epitope would be specific to the SARS-CoV-2 virus and can distinguish infections caused by common cold viruses. In this study two approaches are employed to identify both continuous and conformational B-cell antigenic epitopes. To achieve this goal, we modeled a complete structure of the receptor binding domain (RBD) of the spike protein using recently deposited coordinates (6vxx, 6vsb, and 6w41) in the protein data bank. In addition, we also modeled the RBD-ACE2 receptor complex for SARS-CoV-2 using the SARS-CoV RBD-ACE2 complex (3D0J) as a reference model. Finally, structure based predicted antigenic epitopes were compared to the ACE2 binding region of RBD of SARS-CoV-2. The identified conformational epitopes show overlaps with the ACE2-receptor binding region of the RBD of SARS-CoV-2. Strategies defined in the current study identified novel antigenic epitope that is specific to the SARS-CoV-2 virus. Integrating such approach in the diagnosis can distinguish infections caused by common cold viruses from SARS-CoV-2 virus.
    Electronic ISSN: 2624-8212
    Topics: Computer Science
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  • 80
    Publication Date: 2021-03-25
    Description: The solutions to many computer vision problems, including that of 6D object pose estimation, are dominated nowadays by the explosion of the learning-based paradigm. In this paper, we investigate 6D object pose estimation in a practical, real-word setting in which a mobile device (smartphone/tablet) needs to be localized in front of a museum exhibit, in support of an augmented-reality application scenario. In view of the constraints and the priorities set by this particular setting, we consider an appropriately tailored classical as well as a learning-based method. Moreover, we develop a hybrid method that consists of both classical and learning based components. All three methods are evaluated quantitatively on a standard, benchmark dataset, but also on a new dataset that is specific to the museum guidance scenario of interest.
    Electronic ISSN: 2673-4192
    Topics: Computer Science
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  • 81
    Publication Date: 2021-03-25
    Description: Parents play an important role in children’s academic achievement. The purpose of the present study was to explore the internal structure of an established parent survey and to investigate the relationships among different aspects of parental involvement in predicting children’s mathematics achievement. The study involved secondary data from 139 parents and math achievement scores of 121 elementary school-aged children. Guided by Hoover-Dempsey and Sandler’s Revised Parent Involvement Process model, a Principal Component Analysis with direct oblimin rotation was conducted on the parent survey, followed by path analysis to predict children’s math achievement. Five principal components were retained. Standardized results of the path analysis indicated that parental self-efficacy had the largest direct effect on children’s math achievement. Moreover, parental self-efficacy was favored directly by parental perceptions of specific school invitations to become involved. These findings shed light on the interplay between parental involvement and children’s achievement and underscore the importance of school-family collaboration, which can potentially link to parental self-efficacy.
    Electronic ISSN: 2504-284X
    Topics: Education , Nature of Science, Research, Systems of Higher Education, Museum Science
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  • 82
    Publication Date: 2021-03-25
    Description: AI is one of the most debated subjects of today and there seems little common understanding concerning the differences and similarities of human intelligence and artificial intelligence. Discussions on many relevant topics, such as trustworthiness, explainability, and ethics are characterized by implicit anthropocentric and anthropomorphistic conceptions and, for instance, the pursuit of human-like intelligence as the golden standard for Artificial Intelligence. In order to provide more agreement and to substantiate possible future research objectives, this paper presents three notions on the similarities and differences between human- and artificial intelligence: 1) the fundamental constraints of human (and artificial) intelligence, 2) human intelligence as one of many possible forms of general intelligence, and 3) the high potential impact of multiple (integrated) forms of narrow-hybrid AI applications. For the time being, AI systems will have fundamentally different cognitive qualities and abilities than biological systems. For this reason, a most prominent issue is how we can use (and “collaborate” with) these systems as effectively as possible? For what tasks and under what conditions, decisions are safe to leave to AI and when is human judgment required? How can we capitalize on the specific strengths of human- and artificial intelligence? How to deploy AI systems effectively to complement and compensate for the inherent constraints of human cognition (and vice versa)? Should we pursue the development of AI “partners” with human (-level) intelligence or should we focus more at supplementing human limitations? In order to answer these questions, humans working with AI systems in the workplace or in policy making have to develop an adequate mental model of the underlying ‘psychological’ mechanisms of AI. So, in order to obtain well-functioning human-AI systems, Intelligence Awareness in humans should be addressed more vigorously. For this purpose a first framework for educational content is proposed.
    Electronic ISSN: 2624-8212
    Topics: Computer Science
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  • 83
    Publication Date: 2021-03-10
    Description: The effectiveness of cyber security measures are often questioned in the wake of hard hitting security events. Despite much work being done in the field of cyber security, most of the focus seems to be concentrated on system usage. In this paper, we survey advancements made in the development and design of the human centric cyber security domain. We explore the increasing complexity of cyber security with a wider perspective, defining user, usage and usability (3U’s) as three essential components for cyber security consideration, and classify developmental efforts through existing research works based on the human centric security design, implementation and deployment of these components. Particularly, the focus is on studies that specifically illustrate the shift in paradigm from functional and usage centred cyber security, to user centred cyber security by considering the human aspects of users. The aim of this survey is to provide both users and system designers with insights into the workings and applications of human centric cyber security.
    Electronic ISSN: 2624-909X
    Topics: Computer Science
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  • 84
    Publication Date: 2021-03-10
    Description: Soft robots are ideal for underwater manipulation in sampling and other servicing applications. Their unique features of compliance, adaptability, and being naturally waterproof enable robotic designs to be compact and lightweight, while achieving uncompromized dexterity and flexibility. However, the inherent flexibility and high nonlinearity of soft materials also results in combined complex motions, which creates both soft actuator and sensor challenges for force output, modeling, and sensory feedback, especially under highly dynamic underwater environments. To tackle these limitations, a novel Soft Origami Optical-Sensing Actuator (SOSA) with actuation and sensing integration is proposed in this paper. Inspired by origami art, the proposed sensorized actuator enables a large force output, contraction/elongation/passive bending actuation by fluid, and hybrid motion sensing with optical waveguides. The SOSA design brings two major novelties over current designs. First, it involves a new actuation-sensing mode which enables a superior large payload output and a robust and accurate sensing performance by introducing the origami design, significantly facilitating the integration of sensing and actuating technology for wider applications. Secondly, it simplifies the fabrication process for harsh environment application by investigating the boundary features between optical waveguides and ambient water, meaning the external cladding layer of traditional sensors is unnecessary. With these merits, the proposed actuator could be applied to harsh environments for complex interaction/operation tasks. To showcase the performance of the proposed SOSA actuator, a hybrid underwater 3-DOFs manipulator has been developed. The entire workflow on concept design, fabrication, modeling, experimental validation, and application are presented in detail as reference for wider effective robot-environment applications.
    Electronic ISSN: 2296-9144
    Topics: Computer Science
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  • 85
    Publication Date: 2021-03-10
    Description: Numerous studies show positive effects of students’ malleable implicit theories of their abilities on their self-regulated learning and learning achievements (Yeager and Dweck, 2012; Burnette et al., 2013), especially when domain-specific implicit theories are assessed (Costa and Faria, 2018). Thinking of school improvement as a collective learning process for the teaching staff, it is reasonable to assume that this relationship also exists on the teacher level. Hence, this study aims to provide answers to the following overarching question: What role do teachers’ implicit theories of professional abilities play for school improvement? In a first step, a measurement instrument was developed to assess teachers’ implicit theories of professional abilities in the domain of school improvement. In a second step, we explored the link between these implicit theories and collective teacher learning in the area of further developing the school’s educational practices. In a sample of N = 1,483 Swiss primary school teachers at N = 59 schools, we analyzed how teachers’ malleable (vs. fixed) implicit theories of professional abilities are related to collective metacognitive and emotional-motivational regulation activities and to the perception that the school is on the right track to improvement. Results show that teachers’ implicit theories of professional abilities can be assessed reliably. Structural equation modeling analyses revealed that the more teachers view professional abilities as malleable and developable, the more positive their perceptions of the schools’ improvement were. This relation was mediated by collective emotional-motivational regulation activities. However, no significant effect of a malleable implicit theory on collective metacognitive regulation was found. It can be concluded that teachers have varying beliefs about the malleability of teachers’ professional abilities that are linked to their collective regulation. It therefore acknowledges the domain-specific effects of teachers’ implicit theories in the area of school improvement.
    Electronic ISSN: 2504-284X
    Topics: Education , Nature of Science, Research, Systems of Higher Education, Museum Science
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  • 86
    Publication Date: 2021-03-10
    Description: The main aim of this study was to determine the agreement in classification between the modified KörperKoordinations Test für Kinder (KTK3+) and the Athletic Skills Track (AST) for measuring fundamental movement skill levels (FMS) in 6- to 12-year old children. 3,107 Dutch children (of which 1,625 are girls) between 6 and 12 years of age (9.1 ± 1.8 years) were tested with the KTK3+ and the AST. The KTK3+ consists of three items from the KTK and the Faber hand-eye coordination test. Raw scores from each subtest were transformed into percentile scores based on all the data of each grade. The AST is an obstacle course consisting of 5 (grades 3 till 5, 6–9 years) or 7 (grades 6 till 8, 9–12 years) concatenated FMS that should be performed as quickly as possible. The outcome measure is the time needed to complete the track. A significant bivariate Pearson correlation coefficient of 0.51 was found between the percentile sum score of the KTK3+ and the time to complete the AST, indicating that both tests measure a similar construct to some extent. Based on their scores, children were classified into one of five categories: 95%. Cross tabs revealed an agreement of 58.8% with a Kappa value of 0.15 between both tests. Less than 1% of the children were classified more than two categories higher or lower. The moderate correlation between the KTK3+ and the AST and the low classification agreement into five categories of FMS stress the importance to further investigate the test choice and the measurement properties (i.e., validity and reliability) of both tools. PE teachers needs to be aware of the context in which the test will be conducted, know which construct of motor competence they want to measure and know what the purpose of testing is (e.g., screening or monitoring). Based on these considerations, the most appropriate assessment tool can be chosen.
    Electronic ISSN: 2504-284X
    Topics: Education , Nature of Science, Research, Systems of Higher Education, Museum Science
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  • 87
    Publication Date: 2021-03-17
    Description: Motivation For network-assisted analysis, which has become a popular method of data mining, network construction is a crucial task. Network construction relies on the accurate quantification of direct associations among variables. The existence of multiscale associations among variables presents several quantification challenges, especially when quantifying nonlinear direct interactions. Results In this study, the multiscale part mutual information (MPMI), based on part mutual information (PMI) and nonlinear partial association (NPA), was developed for effectively quantifying nonlinear direct associations among variables in networks with multiscale associations. First, we defined the MPMI in theory and derived its five important properties. Second, an experiment in a three-node network was carried out to numerically estimate its quantification ability under two cases of strong associations. Third, experiments of the MPMI and comparisons with the PMI, NPA and conditional mutual information were performed on simulated datasets and on datasets from DREAM challenge project. Finally, the MPMI was applied to real datasets of glioblastoma and lung adenocarcinoma to validate its effectiveness. Results showed that the MPMI is an effective alternative measure for quantifying nonlinear direct associations in networks, especially those with multiscale associations. Availability The source code of MPMI is available online at https://github.com/CDMB-lab/MPMI. Supplementary information Supplementary data are available at Bioinformatics online.
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  • 88
    Publication Date: 2021-03-17
    Description: This article discusses the impact of COVID-19 on a cohort of international students studying at one Australian university and the efforts made by social work academics to assist these students through a challenging and distressing time between December 2019 and July 2020. International social work students usually rely on scholarships and casual employment to support themselves while studying. Nonetheless, the Australian government made no financial provisions for international students when the COVID-19 pandemic struck leaving many students without any means of support. Students from all parts of the world attend Griffith University, located in south-east Queensland, Australia, including students from Wuhan, China, where the first known outbreak of COVID-19 was identified. Shame, anxiety, racism and concerns related to the wellbeing of families overseas were superimposed onto their own health concerns and day-to-day survival while still seeking to maintain academic progress. Material and emotional support were provided through the establishment of a food bank and the development of a case management model to address the needs of students. A COVID-19 Alternative Placement course and a field education student hub supported academic success during the health emergency. These interventions developed independently as a crisis response and were merged into a multi-pronged, coordinated approach that included collaborations with other sections of the university and the community. A School working committee was established to co-ordinate interventions to address individual student need that included crisis intervention, referrals to internal and external resources, and developing communities of support. The journey traveled by students and academics and the lessons learned from this experience are described, all of which are relevant to future health emergencies including the value of involving social work in university preparedness planning. The article concludes that intensive support can enhance resilience while supporting students’ own survival strategies, and, importantly, how such efforts can minimize, as far as possible, disruption to academic progress.
    Electronic ISSN: 2504-284X
    Topics: Education , Nature of Science, Research, Systems of Higher Education, Museum Science
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  • 89
    Publication Date: 2021-03-18
    Description: Emerging research shows that circular RNA (circRNA) plays a crucial role in the diagnosis, occurrence and prognosis of complex human diseases. Compared with traditional biological experiments, the computational method of fusing multi-source biological data to identify the association between circRNA and disease can effectively reduce cost and save time. Considering the limitations of existing computational models, we propose a semi-supervised generative adversarial network (GAN) model SGANRDA for predicting circRNA–disease association. This model first fused the natural language features of the circRNA sequence and the features of disease semantics, circRNA and disease Gaussian interaction profile kernel, and then used all circRNA–disease pairs to pre-train the GAN network, and fine-tune the network parameters through labeled samples. Finally, the extreme learning machine classifier is employed to obtain the prediction result. Compared with the previous supervision model, SGANRDA innovatively introduced circRNA sequences and utilized all the information of circRNA–disease pairs during the pre-training process. This step can increase the information content of the feature to some extent and reduce the impact of too few known associations on the model performance. SGANRDA obtained AUC scores of 0.9411 and 0.9223 in leave-one-out cross-validation and 5-fold cross-validation, respectively. Prediction results on the benchmark dataset show that SGANRDA outperforms other existing models. In addition, 25 of the top 30 circRNA–disease pairs with the highest scores of SGANRDA in case studies were verified by recent literature. These experimental results demonstrate that SGANRDA is a useful model to predict the circRNA–disease association and can provide reliable candidates for biological experiments.
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
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  • 90
    Publication Date: 2021-02-02
    Description: Tendon actuation is one of the most prominent actuation principles for continuum robots. To date, a wide variety of modelling approaches has been derived to describe the deformations of tendon-driven continuum robots. Motivated by the need for a comprehensive overview of existing methodologies, this work summarizes and outlines state-of-the-art modelling approaches. In particular, the most relevant models are classified based on backbone representations and kinematic as well as static assumptions. Numerical case studies are conducted to compare the performance of representative modelling approaches from the current state-of-the-art, considering varying robot parameters and scenarios. The approaches show different performances in terms of accuracy and computation time. Guidelines for the selection of the most suitable approach for given designs of tendon-driven continuum robots and applications are deduced from these results.
    Electronic ISSN: 2296-9144
    Topics: Computer Science
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  • 91
    Publication Date: 2021-02-03
    Description: Contracts regulate most of our professional and personal life: they enable modern society to operate. The term “Smart Contract,” coined in 1994 by Nick Szabo, means different things to different people. This editorial perspective explores the meanings of the term “smart contract” and the challenges about the legality of “smart contracts.” We are familiar with contracts written in natural language, yet our relationships with smart contracts is yet to be defined. The advent of blockchain technology seems to have accelerated the development and the opportunities for the adoption of smart contracts. The purpose of this editorial is to create an interdisciplinary section where computer scientists and members of the legal profession participate in a constructive debate around smart contracts to positively influence future development.
    Electronic ISSN: 2624-7852
    Topics: Computer Science
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  • 92
    Publication Date: 2021-03-11
    Description: We introduce a soft robot actuator composed of a pre-stressed elastomer film embedded with shape memory alloy (SMA) and a liquid metal (LM) curvature sensor. SMA-based actuators are commonly used as electrically-powered limbs to enable walking, crawling, and swimming of soft robots. However, they are susceptible to overheating and long-term degradation if they are electrically stimulated before they have time to mechanically recover from their previous activation cycle. Here, we address this by embedding the soft actuator with a capacitive LM sensor capable of measuring bending curvature. The soft sensor is thin and elastic and can track curvature changes without significantly altering the natural mechanical properties of the soft actuator. We show that the sensor can be incorporated into a closed-loop “bang-bang” controller to ensure that the actuator fully relaxes to its natural curvature before the next activation cycle. In this way, the activation frequency of the actuator can be dynamically adapted for continuous, cyclic actuation. Moreover, in the special case of slower, low power actuation, we can use the embedded curvature sensor as feedback for achieving partial actuation and limiting the amount of curvature change.
    Electronic ISSN: 2296-9144
    Topics: Computer Science
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  • 93
    Publication Date: 2021-03-11
    Description: Over the past two decades, scholars developed various unmanned sailboat platforms, but most of them have specialized designs and controllers. Whereas these robotic sailboats have good performance with open-source designs, it is actually hard for interested researchers or fans to follow and make their own sailboats with these open-source designs. Thus, in this paper, a generic and flexible unmanned sailboat platform with easy access to the hardware and software architectures is designed and tested. The commonly used 1-m class RC racing sailboat was employed to install Pixhawk V2.4.8, Arduino Mega 2,560, GPS module M8N, custom-designed wind direction sensor, and wireless 433 Mhz telegram. The widely used open-source hardware modules were selected to keep reliable and low-cost hardware setup to emphasize the generality and feasibility of the unmanned sailboat platform. In software architecture, the Pixhawk V2.4.8 provided reliable states’ feedback. The Arduino Mega 2,560 received estimated states from Pixhawk V2.4.8 and the wind vane sensor, and then controlled servo actuators of rudder and sail using simplified algorithms. Due to the complexity of introducing robot operating system and its packages, we designed a generic but real-time software architecture just using Arduino Mega 2,560. A suitable line-of-sight guidance strategy and PID-based controllers were used to let the autonomous sailboat sail at user-defined waypoints. Field tests validated the sailing performance in facing WRSC challenges. Results of fleet race, station keeping, and area scanning proved that our design and algorithms could control the 1-m class RC sailboat with acceptable accuracy. The proposed design and algorithms contributed to developing educational, low-cost, micro class autonomous sailboats with accessible, generic, and flexible hardware and software. Besides, our sailboat platform also facilitates readers to develop similar sailboats with more focus on their missions.
    Electronic ISSN: 2296-9144
    Topics: Computer Science
    Published by Frontiers Media
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  • 94
    Publication Date: 2021-03-11
    Description: Model-Based Reinforcement Learning (MBRL) algorithms have been shown to have an advantage on data-efficiency, but often overshadowed by state-of-the-art model-free methods in performance, especially when facing high-dimensional and complex problems. In this work, a novel MBRL method is proposed, called Risk-Aware Model-Based Control (RAMCO). It combines uncertainty-aware deep dynamics models and the risk assessment technique Conditional Value at Risk (CVaR). This mechanism is appropriate for real-world application since it takes epistemic risk into consideration. In addition, we use a model-free solver to produce warm-up training data, and this setting improves the performance in low-dimensional environments and covers the shortage of MBRL’s nature in the high-dimensional scenarios. In comparison with other state-of-the-art reinforcement learning algorithms, we show that it produces superior results on a walking robot model. We also evaluate the method with an Eidos environment, which is a novel experimental method with multi-dimensional randomly initialized deep neural networks to measure the performance of any reinforcement learning algorithm, and the advantages of RAMCO are highlighted.
    Electronic ISSN: 2296-9144
    Topics: Computer Science
    Published by Frontiers Media
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  • 95
    Publication Date: 2021-03-15
    Description: Motivation Ribosome Profiling (Ribo-seq) has revolutionized the study of RNA translation by providing information on ribosome positions across all translated RNAs with nucleotide-resolution. Yet several technical limitations restrict the sequencing depth of such experiments, the most common of which is the overabundance of rRNA fragments. Various strategies can be employed to tackle this issue, including the use of commercial rRNA depletion kits. However, as they are designed for more standardized RNAseq experiments, they may perform suboptimally in Ribo-seq. In order to overcome this, it is possible to use custom biotinylated oligos complementary to the most abundant rRNA fragments, however currently no computational framework exists to aid the design of optimal oligos. Results Here, we first show that a major confounding issue is that the rRNA fragments generated via Ribo-seq vary significantly with differing experimental conditions, suggesting that a “one-size-fits-all” approach may be inefficient. Therefore we developed Ribo-ODDR, an oligo design pipeline integrated with a user-friendly interface that assists in oligo selection for efficient experiment-specific rRNA depletion. Ribo-ODDR uses preliminary data to identify the most abundant rRNA fragments, and calculates the rRNA depletion efficiency of potential oligos. We experimentally show that Ribo-ODDR designed oligos outperform commercially available kits and lead to a significant increase in rRNA depletion in Ribo-seq. Availability Ribo-ODDR is freely accessible at https://github.com/fallerlab/Ribo-ODDR Supplementary information Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 96
    Publication Date: 2021-03-16
    Print ISSN: 0302-3427
    Electronic ISSN: 1471-5430
    Topics: Nature of Science, Research, Systems of Higher Education, Museum Science
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  • 97
    Publication Date: 2021-03-15
    Description: Summary Many experimental approaches have been developed to identify transcription start sites (TSS) from genomic scale data. However, experiment specific biases lead to large numbers of false-positive calls. Here, we present our integrative approach iTiSS, which is an accurate and generic TSS caller for any TSS profiling experiment in eukaryotes, and substantially reduces the number of false positives by a joint analysis of several complementary datasets. Availability and implementation iTiSS is platform independent and implemented in Java (v1.8) and is freely available at https://www.erhard-lab.de/software and https://github.com/erhard-lab/iTiSS. Supplementary information Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 98
    Publication Date: 2021-03-12
    Description: Living beings modulate the impedance of their joints to interact proficiently, robustly, and safely with the environment. These observations inspired the design of soft articulated robots with the development of Variable Impedance and Variable Stiffness Actuators. However, designing them remains a challenging task due to their mechanical complexity, encumbrance, and weight, but also due to the different specifications that the wide range of applications requires. For instance, as prostheses or parts of humanoid systems, there is currently a need for multi-degree-of-freedom joints that have abilities similar to those of human articulations. Toward this goal, we propose a new compact and configurable design for a two-degree-of-freedom variable stiffness joint that can match the passive behavior of a human wrist and ankle. Using only three motors, this joint can control its equilibrium orientation around two perpendicular axes and its overall stiffness as a one-dimensional parameter, like the co-contraction of human muscles. The kinematic architecture builds upon a state-of-the-art rigid parallel mechanism with the addition of nonlinear elastic elements to allow the control of the stiffness. The mechanical parameters of the proposed system can be optimized to match desired passive compliant behaviors and to fit various applications (e.g., prosthetic wrists or ankles, artificial wrists, etc.). After describing the joint structure, we detail the kinetostatic analysis to derive the compliant behavior as a function of the design parameters and to prove the variable stiffness ability of the system. Besides, we provide sets of design parameters to match the passive compliance of either a human wrist or ankle. Moreover, to show the versatility of the proposed joint architecture and as guidelines for the future designer, we describe the influence of the main design parameters on the system stiffness characteristic and show the potential of the design for more complex applications.
    Electronic ISSN: 2296-9144
    Topics: Computer Science
    Published by Frontiers Media
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  • 99
    Publication Date: 2021-03-12
    Description: An investigation of the relationships between empathy, music learning, and shared music values may provide new insights into the ways that musical meaningfulness and the symbiotic eudaimonic ideal – “flourishing of oneself and of others” – might be cultivated. We investigated the relationships between parent perceptions of children’s empathy, parent beliefs and values about music, and formal Suzuki training over a period of 6 months, to understand ways in which shared music learning experiences might relate to emotional and musical connections between parents and children. Participants were parents of 48 Suzuki students at a structured Suzuki school in the Midwestern United States. We designed a survey containing an adapted version of the Griffith Empathy Measure for children with additional questions regarding family demographics and values regarding music education. Participants completed the survey at the start of the lesson year, and again 6 months later. Children who persisted in group lessons throughout the study began with significantly higher empathy scores, compared to those children who did not participate in group lessons. Children’s initial empathy scores were significantly positively correlated with parents’ ratings of the importance of music instruction generally, and of Suzuki instruction particularly, to their family. A significant but moderate correlation was found between initial empathy scores and the belief that music would help the child succeed in other areas. This exploratory study highlights potential relationships between family music values, children’s empathy, and participation in group music-making. Our findings are consistent with research demonstrating that child and parent personalities predict the duration of formal music training for children. They also support the philosophy of musical meaningfulness, by suggesting ways in which meaningful connections may be forged simultaneously between people who care for one another, and between people and the activities they care about. Our results point to the importance of providing a variety of music learning activities and structures that reflect the personality, needs, and interests of children and parents. We offer specific recommendations for future research to draw further insights and implications about these relationships.
    Electronic ISSN: 2504-284X
    Topics: Education , Nature of Science, Research, Systems of Higher Education, Museum Science
    Published by Frontiers Media
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  • 100
    Publication Date: 2021-03-12
    Description: Higher education teaching practices experience in Mexico changed from face-to-face teaching to the emergency remote teaching derived from the health contingency by COVID-19. The change from face-to-face to an online modality in the Mexican education system represented a great challenge for teachers of all educational levels. In Mexico, the federal government declared on Tuesday March 24, 2020, Phase 2 of the plan to address the country’s Covid 19 pandemic. Governments in at least ten states decided to suspend their activities and services as of March 17, including the education system. On April 13, the Mexican education system began activities in the online modality exclusively; however, derived from the country’s technical adequacy and digital connectivity conditions, a large proportion of the educational campuses, academic programs, and the teaching staff were not prepared for this situation. This cross-sectional study was carried out to analyze the faculty members experiences about the change from face-to-face to an online modality in the health emergency context due to the COVID-19 outbreak. We designed a study to gather information on practice in the new online learning scenario. For this purpose, we collected data on the previous experience in the conduct of online courses, the technological tools used, the barriers faced while driving online courses, the current conditions of use of educational and technological tools, the vision for the future, and some indicators of physical and mental health.In a sample of 341 faculty members, those working in public institutions were on less favorable terms than their peers attached to private universities. In contrast, lecturers recorded better conditions for dealing with modality change than full-time teachers. Likewise, lecturers more often mentioned having their infrastructure to teach courses from their homes than full-time teachers, which indeed responds to less access to the universities facilities in which they teach. Another important aspect to highlight is the increased proportion of teachers in public institutions and lecturers who have other jobs, so their workload can be more intense than those of their peers.
    Electronic ISSN: 2504-284X
    Topics: Education , Nature of Science, Research, Systems of Higher Education, Museum Science
    Published by Frontiers Media
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