ALBERT

Description: In this paper, the mechanical properties of nano-silica modified insulating paper under the combined action of mechanical vibration and temperature conditions are studied. Unmodified and nano-silica modified cellulose insulating paper with 2 wt% and 4 wt% were prepared, respectively, and a series of mechanical-thermal synergy experiments were carried out. With the same mechanical stress and temperature, and with the same aging duration of 144 h (6d), the tensile strength of modified insulating paper with 4 wt% nano-silica, increased 9.9 N and 5.5 N, respectively, compared with those of the unmodified and the 2 wt% nano-silica modified insulating paper. The experiments indicate that the nano-silica modification can effectively improve the mechanical properties of insulating paper. In this work, the modified mechanism of nano-silica is analyzed from the interface effect of modified polymer and the quantum effect of the modified polymer interface two aspects. It is shown that the interface formed in the modified insulating paper can transfer the mechanical stress acted on the insulating paper and prevent the cracks formed in the aging process of the test sample from further expansion, while the quantum effect discretizes the electron energy level, which can restrict the motion of the molecular chain segment to some extent. The conclusion can be used for reference to improve the performance of insulating paper.

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.

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.

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.

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.

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.

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/.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Description: A bi-level multi-target programming model is established for optimization of urban arterial road traffic signal coordination control. The performance index function for optimization is defined to reduce traffic delay and vehicle emissions at intersections. As a result, the optimization traffic signal control strategy can guarantee smooth traffic flow and minimum total emission in the road network. The result of simulation verifies the optimization effect of above model based on MATLAB and VISSIM.

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.

Description: This work presents a model for the simulation of plasmatic transmembrane ionic transport that may be exposed to a static gradient magnetic field. The simulation was carried out using the Monte Carlo method to simulate the transmembrane cell transport of five types of ions and obtain observables such as membrane potential, ionic current, and osmotic pressure. To implement the Monte Carlo method, a Hamiltonian was used that includes the contributions of the energy due to the cellular electric field, the electrostatic interaction between the ions, the friction force generated by moving the ion in the center and the contribution given by subduing a cell to a magnetic field gradient. The input parameters to carry out a simulation are the intra and extracellular concentrations of each ionic species, the length of the extracellular medium, the number of Monte Carlo steps (MCS) and the value of the magnetic gradient. The model was validated contrasting it with Gillespie’s algorithm to obtain variations less than 3 % in terms of membrane potential. The Monte Carlo Method combined with the Metropolis algorithm were considered for recreating the stochastic behavior of ion movement.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Description: Aiming at the low performance of classifying images under the computing model of single node. With GLCM (Gray Level Co-occurrence Matrix) which fuses gray level with texture of image, a parallel fuzzy C-means clustering method based on MapReduce is designed to classify massive images and improve the real-time performance of classification. The experimental results show that the speedup ratio of this method is more than 10% higher than that of the other two methods, moreover, the accuracy of image classification has not decreased. It shows that this method has high real-time processing efficiency in massive images classification.

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.

Description: The skylight on the roof of an atrium can be popular for commercial malls to illuminate the core area of the building. However, the solar radiation and its heat can get into the building together with the daylight, causing excessive cooling load. This paper studies the daylighting and energy performances of skylight coverage area for the air-conditioned atriums in the hot and humid regions. The energy performance with different atrium heights, glass types and the coverage ratios of the skylight are studied. The daylight performance was simulated by the ray-tracing Radiance and was transferred into EnergyPlus for energy evaluations. The finding suggested that, for hot and humid climates, the skylight coverage ratio should be controlled carefully to prevent the excessive solar heat gain. When the on/off lighting control is applied, the total energy consumption of the single-floor cases (or of the top floor for the multi-floor cases) leveled off when the coverage ratio of the skylight reached 9%. Thus, the skylight is favorable to the energy saving of the low-rise or single-floor commercial buildings only under the current assumptions, as the ground of the atrium cannot be well illuminated while the excessive solar radiation gets into the building. The skylight should be shaded in cooling seasons to prevent the excessive solar heat gains.

Description: The production of liquefied natural gas (LNG) is a high energy-consuming process. The study of ways to reduce energy consumption and consequently to reduce operational costs is imperative. Toward this purpose, this study proposes a hybrid system adopting a mixed refrigerant for the liquefaction of natural gas that is precooled with an ammonia/water absorption refrigeration (AR) cycle utilizing the exhaust heat of a molten carbonate fuel cell, 700°C and 2.74 bar, coupled with a gas turbine and a bottoming Brayton super-critical carbon dioxide cycle. The inauguration of the ammonia/water AR cycle to the LNG process increases the cooling load of the cycle by 10%, providing a 28.3-MW cooling load duty while having a 0.45 coefficient of performance. Employing the hybrid system reduces energy consumption, attaining 85% overall thermal efficiency, 53% electrical efficiency and 35% fuel cell efficiency. The hybrid system produces 6300 kg.mol.h−1 of LNG and 146.55 MW of electrical power. Thereafter, exergy and sensitivity analyses are implemented and, accordingly, the fuel cell had an 83% share of the exergy destruction and the whole system obtained a 95% exergy efficiency.

Description: High speed photography by Caustics method using Cranz-Schardin camera was studied for crack propagation and divergence in thermally tempered glass. Tempered 10 mm thick glass plates were used as a specimen. Two types of bifurcation and branching as the crack divergence could be observed and clarified even in 10 mm thick tempered glass. The difference of the shadow spot sizes between bifurcation type and branching type could be confirmed.

Description: In the component operated at elevated temperatures, the life evaluation should be made in consideration of both creep and fatigue (creep-fatigue) such as the linear damage summation rule. However, the concept of creep-fatigue life evaluation has not spread well in the industry. In order to consider the reason, a series of past creep-fatigue research was surveyed, namely experimental methods, life evaluation procedures and strength design guidelines. As a result, it was revealed that the mechanism of creep-fatigue interaction has not been fully clarified yet, which results in obscuring the necessity of creep-fatigue life evaluation. The necessity of creep-fatigue life evaluation was reviewed and consequently it proved to be necessary in two cases. One is the case where the creep-fatigue interaction is significant for some kinds of material, loading modes and temperatures. The other is one where the amount of creep damage is almost the same as that of fatigue damage even though the creep-fatigue interaction is insignificant.

Description: According to the regulation of European Union laws in 2014, it was inevitable to switch to low global warming potential (GWP) fluids in the refrigeration systems where the R404A working fluid is currently used. The GWP of R404A is very high, and the potential for ozone depletion is zero. In this study, energetic and exergetic performance assessment of a theoretical refrigeration system was carried out for R404 refrigerant and its alternatives, comparatively. The analyses were made for R448A, R449A, R452A and R404A. The results of the analysis were presented separately in the tables and graphs. According to the results, the cooling system working with R448A exhibited the best performance with a coefficient of performance (COP) value of 2.467 within the alternatives of R404A followed by R449A and R452A, where the COP values were calculated as 2.419 and 2.313, respectively. In addition, the exergy efficiencies of the system were calculated as 20.62%, 20.22% and 19.33% for R448A, R449A and R452A, respectively. For the base calculations made for R404A, the COP of the system was estimated as 2.477, where the exergy efficiency was 20.71%. Under the same operating conditions, the total exergy destruction rates for R404A, R448A, R449A and R452A working fluids were found to be 3.201 kW, 3.217 kW, 3.298 kW and 3.488 kW, respectively. Furthermore, parametric analyses were carried out in order to investigate the effects of different system parameters such as evaporator and condenser temperature.

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.

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.

Description: Summary Once folded, natural protein molecules have few energetic conflicts within their polypeptide chains. Many protein structures do however contain regions where energetic conflicts remain after folding, i.e. they are highly frustrated. These regions, kept in place over evolutionary and physiological timescales, are related to several functional aspects of natural proteins such as protein–protein interactions, small ligand recognition, catalytic sites and allostery. Here, we present FrustratometeR, an R package that easily computes local energetic frustration on a personal computer or a cluster. This package facilitates large scale analysis of local frustration, point mutants and molecular dynamics (MD) trajectories, allowing straightforward integration of local frustration analysis into pipelines for protein structural analysis. Availability and implementation https://github.com/proteinphysiologylab/frustratometeR. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Stored-grain temperature is the most important factor in grain storage. According to the measured data, the temperature in the grain pile can be effectively predicted, which can find problems in advance, reduce grain loss and increase grain quality. Long Short-Term memory (LSTM) can perform better in longer sequences than ordinary RNN. This paper is applied to the analysis of big data of grain storage and the early warning of grain storage temperature. In this paper, the selected LSTM is optimized and the early warning model of grain situation is established, and the analysis steps of the early warning model are given. In order to verify the availability of the improved LSTM network structure, RNN and three variants were used to predict the grain temperature under the same conditions, the prediction effect of the improved CLSTM is better.

Description: In the engineering applications, the distribution of objects is mostly random. Therefore, scattering analysis of randomly distributed objects has been one of the important problems in broadband electromagnetic calculation field. To resolve the problem, the Asymptotic Waveform Evaluation technique in conjunction with Monte Carlo Method is presented. First, the stochastic distribution is modeled by the Monte Carlo Method, and then the Asymptotic Waveform Evaluation technique using Padé approximation is utilized to achieve the Radar Cross Section at a wide frequency band. Numerical results show that the Asymptotic Waveform Evaluation technique can solve the random distributed object problems efficiently and accurately.

Description: By incorporating the merits of partitioned stator and flux modulation techniques, the partitioned stator flux modulation motors featuring high torque, have attracted much attention. This paper investigates four partitioned stator flux modulation motors with different types of PM arrays, including surface-mounted type, consequent-pole type, Halbach-array type, and half-Halbach type. The influences of key parameters on the average electromagnetic torque is conducted and compared. Then, based on the optimal designs, four motors are compared in terms of magnetic field distribution, back-electromotive force, torque characteristics, loss and overload ability. The result reveals that the half-Halbach motor is capable of utilizing more PM to improve electromagnetic torque. Also, the consequent-pole motor achieves the highest torque per PM usage. In addition, performance comparison under the same PM usage is carried out. The result shows that the half-Halbach motor not only exhibits the highest average torque but also achieves the highest PM utilization. Last, the prototype of half-Halbach motor is built for experimental verification.

Description: Summary MitoFlex is a linux-based mitochondrial genome analysis toolkit, which provides a complete workflow of raw data filtering, de novo assembly, mitochondrial genome identification and annotation for animal high throughput sequencing data. The overall performance was compared between MitoFlex and its analogue MitoZ, in terms of protein coding gene recovery, memory consumption and processing speed. Availability MitoFlex is available at https://github.com/Prunoideae/MitoFlex under GPLv3 license. Supplementary information Supplementary data are available at Bioinformatics online.

Description: A recommendation system is based on the user and the items, providing appropriate items to the user and effectively helping the user to find items that may be of interest. The most commonly used recommendation method is collaborative filtering. However, in this case, the recommendation system will be injected with false data to create false ratings to push or nuke specific items. This will affect the user’s trust in the recommendation system. After all, it is important that the recommendation system provides a trusted recommendation item. Therefore, there are many algorithms for detecting attacks. In this article, it proposes a method to detect attacks based on the beta distribution. Different researchers in the past assumed that the attacker only attacked one target item in the user data. This research simulated an attacker attacking multiple target items in the experiment. The result showed a detection rate of more than 80%, and the false rate was within 16%.

Description: The hub motor significantly increases the unsprung mass of electric in-wheel vehicles, which deteriorates the ride comfort and safety of vehicles and which can be effectively improved by optimizing the main suspension parameters of vehicles reasonably, so a multi-objective optimization method of main suspension parameters based on adaptive particle swarm algorithm is proposed and the dynamic model of a half in-wheel electric vehicle is established. Taking the stiffness coefficient of the suspension damping spring and damping coefficient of the damper as independent variables, the vertical acceleration of the body, the pitch acceleration and the vertical impact force of the hub motor as optimization variables, and the dynamic deflection of the suspension and the dynamic load of the wheel as constraint variables, the multi-objective optimization function is constructed, and the parameters are simulated and optimized under the compound pavement. The simulation results show that the vertical acceleration and pitch acceleration are reduced by 20.2% and 18.4% respectively, the vertical impact force of the front hub motor is reduced by 3.7%, and the ride comfort and safety are significantly improved.

Description: The short-term load forecast is an important part of power system operation, which is usually a nonlinear problem. The processing of load forecast data and the selection of forecasting methods are particularly important. In order to get accurate and effective prediction for power system load, this article proposes a hybrid multi-objective quantum particle swarm optimization (QPSO) algorithm for short-term load forecast of power system based on diagonal recursive neural network. Firstly, a multi-objective mathematical model for short-term load forecast is proposed. Secondly, the discrete particle swarm optimization (PSO) algorithm is used to select the characteristics of load data and screen out the appropriate data. Finally, the hybrid multi-objective QPSO algorithm is used to train diagonal recursive neural network. The experimental results show that the hybrid multi-objective QPSO for short-term load forecast based on diagonal recursive neural network is effective.

Description: In view of the multi-attribute decision making problems which the attribute values are in the forms of interval numbers, the paper presents an entropy method to obtain the attribute weights using the relative superiority concept. Firstly, the concept of this kind of problem is explained; Then in the light of the basic principle of the traditional entropy value method and train of thought, it given the calculation steps of weights using the relative superiority about the attribute value is interval number multiple attribute decision making problems. Its core is that relative superiority judgment matrix is obtained by comparing with two sets of interval numbers under the same indicator, which the group of interval numbers is equivalently mapped to the exact value form with the merits of relationship, then the weights of each indicator are calculated. Finally, the method is illustrated by giving an example.

Description: Motivation Breast cancer is one of the leading causes of cancer deaths among women worldwide. It is necessary to develop new breast cancer drugs because of the shortcomings of existing therapies. The traditional discovery process is time-consuming and expensive. Repositioning of clinically approved drugs has emerged as a novel approach for breast cancer therapy. However, serendipitous or experiential repurposing cannot be used as a routine method. Results In this study, we proposed a graph neural network model GraphRepur based on GraphSAGE for drug repurposing against breast cancer. GraphRepur integrated two major classes of computational methods, drug network-based and drug signature-based. The differentially expressed genes of disease, drug-exposure gene expression data, and the drug-drug links information were collected. By extracting the drug signatures and topological structure information contained in the drug relationships, GraphRepur can predict new drugs for breast cancer, outperforming previous state-of-the-art approaches and some classic machine learning methods. The high-ranked drugs have indeed been reported as new uses for breast cancer treatment recently. Availability The source code of our model and datasets are available at: https://github.com/cckamy/GraphRepur and https://figshare.com/articles/software/GraphRepur_Breast_Cancer_Drug_Repurposing/14220050 Supplementary information Supplementary data are available at Bioinformatics online.

Description: The accuracy of optical current sensors in power systems will be affected by the interference of the external environment. Among them, temperature has a significant influence on the accuracy of optical current sensors, which is one of the main factors that restrict the practicality of optical current sensors. In this paper, the temperature field distribution of the optical current sensor with iron core is calculated by simulation. The influence of its structural parameters on the temperature field distribution is studied. It is compared with the temperature field distribution of the optical current sensor of the commonly used magnetic ring structure. It is found that under the same current, the temperature of the magneto-optical material with the core optical current sensor is much lower than the optical current mutual inductance of the common magnetic ring structure. The geometric structure parameters of the optical current sensor with iron core have a great influence on its temperature field distribution. The temperature field distribution can be improved by optimizing the structure parameters, thereby improving the measurement accuracy. This article can provide a basis for the design optimization of optical current sensors.

Description: The prediction and recommendation of financial stocks are of great values. This study mainly analyzed the application of K-means clustering algorithm in stock forecasting and recommendation. Firstly, it introduced the k-means algorithm briefly and analyzed its advantages and disadvantages. Then, the k-means algorithm was optimized by introducing artificial fish swarm algorithm (AFSA) to obtain KAFSA. Then 100 stocks of listed companies were taken as the research subject and predicted by KAFSA designed in this study. The prediction results were verified through closing price, price earning ratio, earnings per share and return on net assets. The results showed that there were obvious differences between A and B stocks divided by KAFSA, and the differences of B stocks were significantly larger than those of A stocks. It shows that 100 stocks are well divided into high performance stocks and poor performance stocks through clustering, which provides a good reference for investors to invest in stocks and is worth of further application.

Description: In order to ensure the safety and reliability of power system, more and more monitoring and maintenance equipment on transmission lines are being used. However, these equipment would not work without the supply of power. At present, the current transformer has been widely used in the on line power acquisition device. As an important part of the current transformer, the performance magnetic core has great influence on the power acquisition. In this paper, the core parameters of the current transformer in the on-line power acquisition device are designed, and the parameters such as core material and air gap length are optimized and verified by simulation as well.

Description: A novel method of mirror motion recognition by rehabilitation robot with multi-channels sEMG signals is proposed, aiming to help the stroked patients to complete rehabilitation training movement. Firstly the bilateral mirror training is used and the model of muscle synergy with basic sEMG signals is established. Secondly, the constrained L1/2-NMF is used to extracted the main sEMG signals information which can also reduce the limb movement characteristics. Finally the relationship between sEMG signal characteristics and upper limb movement is described by TSSVD-ELM and it is applied to improve the model stability. The validity and feasibility of the proposed strategy are verified by the experiments in this paper, and the rehabilitation robot can move with the mirror upper limb. By comparing the method proposed in this paper with PCA and full-action feature extraction, it is confirmed that convergence speed is faster; the feature extraction accuracy is higher which can be used in rehabilitation robot systems.

Description: Summary LocusZoom.js is a JavaScript library for creating interactive web-based visualizations of genetic association study results. It can display one or more traits in the context of relevant biological data (such as gene models and other genomic annotation), and allows interactive refinement of analysis models (by selecting linkage disequilibrium reference panels, identifying sets of likely causal variants, or comparisons to the GWAS catalog). It can be embedded in web pages to enable data sharing and exploration. Views can be customized and extended to display other data types such as phenome-wide association study (PheWAS) results, chromatin co-accessibility, or eQTL measurements. A new web upload service harmonizes datasets, adds annotations, and makes it easy to explore user-provided result sets. Availability and implementation LocusZoom.js is open-source software under a permissive MIT license. Code and documentation are available at: https://github.com/statgen/locuszoom/. Installable packages for all versions are also distributed via NPM. Additional features are provided as standalone libraries to promote reuse. Use with your own GWAS results at https://my.locuszoom.org/. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Biometrics recognition takes advantage of feature extraction and pattern recognition to analyze the physical and behavioral characteristics of biological individuals to achieve the purpose of individual identification. As a typical biometric technology, palm print and palm vein have the characteristics of high recognition rate, stable features, easy location and good image quality, which have attracted the attention of researchers. This paper designs and develops a multispectral palm print and palm vein acquisition platform, which can quickly acquire palm spectrum and palm vein multispectral images with seven different wavelengths. We propose a multispectral palm print palmar vein recognition framework, and feature-level image fusion is performed after extracting features of palm print palmar vein images at different wavelengths. Through the multispectral palm print palm vein image fusion experiment, a more feasible multispectral palm print and palm vein image fusion scheme is proposed. Based on the results of image fusion, we further propose an improved convolutional neural network (CNN) for model training to achieve identity recognition based on multispectral palm print palm vein images. Finally, the effects of different CNN network structures and learning rates on the recognition results were analyzed and compared experimentally.

Description: In recent years, the research and development of 3D bioprinting device for artificial blood vessels has attracted great attention of researchers. In this paper, the research object is the control system of 3D bioprinting device for artificial blood vessel and the four-channel air pressure cooperative control technology for 3D bioprinting device double nozzles printing is mainly studied. Through the scheme design, working principle innovation, hardware selection and air pressure output calibration experiment of four-way air pressure cooperative control system, the hardware platform of 3D bioprinting device air pressure control system is built. Then, by using the embedded TwinCAT NC PTP software platform based on EtherCAT Ethernet bus protocol, the four-channel air pressure cooperative control is programmed and field experiments are carried out, which realizes the printing process of uniform liquid output and good continuity, and meets the requirements of 3D bioprinting for printing continuity and forming quality. The study of four-channel air pressure cooperative control system in this course is a worthy of application and promotion control method.

Description: This work reports numerical explorations in advection of one passive tracer by point vortices living in the unbounded plane. The main objective is to find the energy-optimal displacement of one passive particle (point vortex with zero circulation) surrounded by N point vortices. The direct formulation of the corresponding control problems is presented. The restrictions are due to (i) the ordinary differential equations that govern the displacement of the passive particle around the point vortices, (ii) the available time T to go from the initial position z0 to the final destination zf, and (iii) the maximum absolute value umax that is imposed on the control variables. The latter consist in staircase controls, i.e., the control is written as a finite linear combination of characteristic functions on the real interval. The resulting optimization problems are solved numerically. The numerical results shows the existence nearly/quasi optimal control for the cases of N=1, N=2, N=3, and N=4 vortices.

Description: Film shovel is the key device for mechanized operation of residual film recovery, in which the depth of soil breakage of the shovel is related to the degree of damage to the plastic film and the recovery efficiency of the residual film. However, the current mode of operation requires experience and visual inspection, which often leads to low level of mechanization and inefficient operation. The topic is to improve the existing residual film recovery device, focusing on the three-dimensional modeling and monitoring system of intelligent film lifting shovel. By optimizing the three-dimensional structure of the intelligent film-lifting shovel and kinematics simulation analysis based on Adams software, the design inclination angle of the loosening shovel is 60∘, and the breaking depth of the safe operation is 20–50 mm. Then through the use of Ethernet interface based on EtherCAT bus and the real-time bidirectional communication between BECKHOFF CX2030 controller and module and HMI, the on-line data transmission in the process of intelligent film shovel operation is realized. Field test results verify that the intelligent film-lifting shovel is feasible in the intelligent operation and recovery efficiency of the residual film recovery operation, which can greatly improve the efficiency of the whole machine operation.

Description: Load response characteristics of high thermal power units are critical to a safe and efficient grid-connected utilization of large-scale renewable energy with strong randomness. A condensate throttling regulation was reported to improve the units variable load rate. However, it is not verified by tests in different types of units. In the paper, the influence of the storage capacity of the steam generator’s condensate system on the load response characteristics is studied for four subcritical 330 MW heating units by simulation and an experimental test. The result shows the feasibility of the condensate throttling regulation, which is of great value to the practical engineering application in the future. In addition, this paper obtains the condensate regulation potential of units under different power generation load conditions through simulation and actual tests. These real data will help other units of the same type to carry out similar modifications or tests.

Description: In this article we prove the following results: (i) Every hemimaximal set has minimal $c_{1}$-degree, i.e. if $B$ is hemimaximal and $A$ is a c.e. set such that $A le _{c_{1}} B$ then either $B leq _{{c}_{1}} A$ or $A$ is computable. (ii) The $sQ$-degree of a c.e. set contains either only one or infinitely many c.e. $c$-degrees. (iii) If $A,B$ are c.e. cylinders in the same $sQ_{1}$-degree and $A

Description: Motivation R Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for storing one or more matrix-like assays along with associated row and column data. These objects have been used to facilitate the storage and analysis of high-throughput genomic data generated from technologies such as single-cell RNA sequencing. One common computational task in many genomics analysis workflows is to perform subsetting of the data matrix before applying down-stream analytical methods. For example, one may need to subset the columns of the assay matrix to exclude poor-quality samples or subset the rows of the matrix to select the most variable features. Traditionally, a second object is created that contains the desired subset of assay from the original object. However, this approach is inefficient as it requires the creation of an additional object containing a copy of the original assay and leads to challenges with data provenance. Results To overcome these challenges, we developed an R package called ExperimentSubset, which is a data container that implements classes for efficient storage and streamlined retrieval of assays that have been subsetted by rows and/or columns. These classes are able to inherently provide data provenance by maintaining the relationship between the subsetted and parent assays. We demonstrate the utility of this package on a single-cell RNA-seq dataset by storing and retrieving subsets at different stages of the analysis while maintaining a lower memory footprint. Overall, the ExperimentSubset is a flexible container for the efficient management of subsets. Availability and implementation ExperimentSubset package is available at Bioconductor: https://bioconductor.org/packages/ExperimentSubset/ and Github: https://github.com/campbio/ExperimentSubset. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary In this article, we introduce a hierarchical clustering and Gaussian mixture model with expectation-maximization (EM) algorithm for detecting copy number variants (CNVs) using whole exome sequencing (WES) data. The R shiny package ‘HCMMCNVs’ is also developed for processing user-provided bam files, running CNVs detection algorithm and conducting visualization. Through applying our approach to 325 cancer cell lines in 22 tumor types from Cancer Cell Line Encyclopedia (CCLE), we show that our algorithm is competitive with other existing methods and feasible in using multiple cancer cell lines for CNVs estimation. In addition, by applying our approach to WES data of 120 oral squamous cell carcinoma (OSCC) samples, our algorithm, using the tumor sample only, exhibits more power in detecting CNVs as compared with the methods using both tumors and matched normal counterparts. Availability and implementation HCMMCNVs R shiny software is freely available at github repository https://github.com/lunching/HCMM_CNVs.and Zenodo https://doi.org/10.5281/zenodo.4593371. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary The need for an efficient and cost-effective method is compelling in biomolecular NMR. To tackle this problem, we have developed the Poky suite, the revolutionized platform with boundless possibilities for advancing research and technology development in signal detection, resonance assignment, structure calculation, and relaxation studies with the help of many automation and user interface tools. This software is extensible and scalable by scripting and batching as well as providing modern graphical user interfaces and a diverse range of modules right out of the box. Availability Poky is freely available to non-commercial users at https://poky.clas.ucdenver.edu. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Aiming at the problem of complex method and low efficiency of fuzzy numbers in classification processing, a parallel Fuzzy CMeans (FCM) clustering method based on cut set is proposed. Firstly, according to the decomposition theorem, the fuzzy numbers are divided horizontally into the form of the union of interval numbers, and then the interval numbers are transformed into the determined “real” data, and the parallel FCM clustering algorithm is used to classify the fuzzy numbers. The theoretical analysis and application show that the method has good classification accuracy and efficiency for fuzzy data clustering.

Description: Intelligent underwater pollution cleaning robot is used to release microbial solution which can dissolve into water slowly into polluted river, so that the solution can react fully with pollutants, so as to achieve the purpose of river pollution control. The research of robot wireless monitoring system is based on the comprehensive application of wireless communication technology and intelligent control technology, in order to achieve real-time monitoring and centralized remote control of underwater pollution removal. Through the three-dimensional structure modeling of the intelligent underwater pollution cleaning robot, the overall scheme design and debugging test of the wireless monitoring system, it is proved that the intelligent underwater pollution cleaning robot is feasible in the intelligent and efficient underwater cleaning operation, and it is a research method worthy of reference and promotion.

Description: In the field of civil engineering, the meteorological data available usually do not have the detailed information of the wind near a certain site. However, the detailed information of the wind field during typhoon is important for the wind-resistant design of civil structures. Furthermore, the resolution of the meteorological data available by the civil engineers is too coarse to be applicable. Therefore it is meaningful to obtain the detailed information of the wind fields based on the meteorological data provided by the meteorological department. Therefore, in the present study, a one-way coupling method between WRF and CFD is adopted and a method to keep the mass conservation during the simulation in CFD is proposed. It is found that using the proposed one-way coupling method, the predicted wind speed is closer to the measurement. And the curvature of the wind streamline during typhoon is successfully reproduced.

Description: The stability and control of nonlinear time-delay systems of Takagi-Sugeno (T-S) fuzzy model are studied in this paper. The integral inequality of a free weight matrix is chosen to give a less conservative delay-dependent stability criterion in the form of linear matrix inequalities (LMIs). The premise mismatch strategy is applied, it is combined with Finsler lemma, a more flexible design method of fuzzy state feedback controller is proposed. This method does not require the controller and system to share the common premise membership function and the number of rules. The controller design strategy proposed in this paper can effectively solve the control problem of fuzzy systems when the number of state variables is not equal to the number of input variables (r≠c), or mi⁢(x⁢(t))≠hi⁢(x⁢(t)),i=1,2,…,r. Finally, two simulation examples are given to prove the advancement and effectiveness of the proposed theory.

Description: Motivation Breast cancer is a very heterogeneous disease and there is an urgent need to design computational methods that can accurately predict the prognosis of breast cancer for appropriate therapeutic regime. Recently, deep learning-based methods have achieved great success in prognosis prediction, but many of them directly combine features from different modalities that may ignore the complex inter-modality relations. In addition, existing deep learning-based methods do not take intra-modality relations into consideration that are also beneficial to prognosis prediction. Therefore, it is of great importance to develop a deep learning-based method that can take advantage of the complementary information between intra-modality and inter-modality by integrating data from different modalities for more accurate prognosis prediction of breast cancer. Results We present a novel unified framework named genomic and pathological deep bilinear network (GPDBN) for prognosis prediction of breast cancer by effectively integrating both genomic data and pathological images. In GPDBN, an inter-modality bilinear feature encoding module is proposed to model complex inter-modality relations for fully exploiting intrinsic relationship of the features across different modalities. Meanwhile, intra-modality relations that are also beneficial to prognosis prediction, are captured by two intra-modality bilinear feature encoding modules. Moreover, to take advantage of the complementary information between inter-modality and intra-modality relations, GPDBN further combines the inter- and intra-modality bilinear features by using a multi-layer deep neural network for final prognosis prediction. Comprehensive experiment results demonstrate that the proposed GPDBN significantly improves the performance of breast cancer prognosis prediction and compares favorably with existing methods. Availabilityand implementation GPDBN is freely available at https://github.com/isfj/GPDBN. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation Co-expression networks are a powerful gene expression analysis method to study how genes co-express together in clusters with functional coherence that usually resemble specific cell type behaviour for the genes involved. They can be applied to bulk-tissue gene expression profiling and assign function, and usually cell type specificity, to a high percentage of the gene pool used to construct the network. One of the limitations of this method is that each gene is predicted to play a role in a specific set of coherent functions in a single cell type (i.e. at most we get a single for each gene). We present here GMSCA (Gene Multifunctionality Secondary Co-expression Analysis), a software tool that exploits the co-expression paradigm to increase the number of functions and cell types ascribed to a gene in bulk-tissue co-expression networks. Results We applied GMSCA to 27 co-expression networks derived from bulk-tissue gene expression profiling of a variety of brain tissues. Neurons and glial cells (microglia, astrocytes and oligodendrocytes) were considered the main cell types. Applying this approach, we increase the overall number of predicted triplets by 46.73%. Moreover, GMSCA predicts that the SNCA gene, traditionally associated to work mainly in neurons, also plays a relevant function in oligodendrocytes. Availability The tool is available at GitHub, https://github.com/drlaguna/GMSCA as open-source software. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary MMseqs2 taxonomy is a new tool to assign taxonomic labels to metagenomic contigs. It extracts all possible protein fragments from each contig, quickly retains those that can contribute to taxonomic annotation, assigns them with robust labels and determines the contig’s taxonomic identity by weighted voting. Its fragment extraction step is suitable for the analysis of all domains of life. MMseqs2 taxonomy is 2–18× faster than state-of-the-art tools and also contains new modules for creating and manipulating taxonomic reference databases as well as reporting and visualizing taxonomic assignments. Availability and implementation MMseqs2 taxonomy is part of the MMseqs2 free open-source software package available for Linux, macOS and Windows at https://mmseqs.com. Supplementary information Supplementary data are available at Bioinformatics online.

Description: The paper contains some remarks on building automated counterpart of a comparison of some generalized rough approximations of sets, where the classical indiscernibility relation is generalized to arbitrary binary relation. Our focus was on translating rationality postulates for such operators by means of the Mizar system – the software and the database which allows for expressing and checking mathematical knowledge for the logical correctness. The main objective was the formal (and machine-checked) proof of Theorem 4.1 from A. Gomolińska’s paper “A Comparative Study of Some Generalized Rough Approximations”, hence the present title. We provide also the discussion on how to make the presentation more efficient to reuse the reasoning techniques of the Mizar verifier.

Description: One of the most popular families of techniques to boost classification are Ensemble methods. Random Forests, Bagging and Boosting are the most popular and widely used ones. This article presents a novel Ensemble Model, named Random Granular Reflections. The algorithm used in this new approach creates an ensemble of homogeneous granular decision systems. The first step of the learning process is to take the training system and cover it with random homogeneous granules (groups of objects from the same decision class that are as little indiscernible from each other as possible). Next, granular reflection is created, which is finally used in the classification process. Results obtained by our initial experiments show that this approach is promising and comparable with other tested methods. The main advantage of our new method is that it is not necessary to search for optimal parameters while looking for granular reflections in the subsequent iterations of our ensemble model.

Description: Gene regulatory networks represent the interactions among genes regulating the activation of specific cell functionalities and they have been successfully modeled using threshold Boolean networks. In this paper we propose a systematic translation of threshold Boolean networks into reaction systems. Our translation produces a non redundant set of rules with a minimal number of objects. This translation allows us to simulate the behavior of a Boolean network simply by executing the (closed) reaction system we obtain. This can be very useful for investigating the role of different genes simply by “playing” with the rules. We developed a tool able to systematically translate a threshold Boolean network into a reaction system. We use our tool to translate two well known Boolean networks modelling biological systems: the yeast-cell cycle and the SOS response in Escherichia coli. The resulting reaction systems can be used for investigating dynamic causalities among genes.

Description: Complex event processing (CEP) evaluates queries over streams of event data to detect situations of interest. If the event data are produced by geographically distributed sources, CEP may exploit in-network processing that distributes the evaluation of a query among the nodes of a network. To this end, a query is modularized and individual query operators are assigned to nodes, especially those that act as data sources. Existing solutions for such operator placement, however, are limited in that they assume all query results to be gathered at one designated node, commonly referred to as a sink. Hence, existing techniques postulate a hierarchical structure of the network that generates and processes the event data. This largely neglects the optimisation potential that stems from truly decentralised query evaluation with potentially many sinks. To address this gap, in this paper, we propose Multi-Sink Evaluation (MuSE) graphs as a formal computational model to evaluate common CEP queries in a decentralised manner. We further prove the completeness of query evaluation under this model. Striving for distributed CEP that can scale to large volumes of high-frequency event streams, we show how to reason on the network costs induced by distributed query evaluation and prune inefficient query execution plans. As such, our work lays the foundation for distributed CEP that is both, sound and efficient.

Description: In this article, we introduce a probabilistic verification algorithm for stochastic regular expressions over a probabilistic extension of the Action based Computation Tree Logic (ACTL*). The main results include a novel model checking algorithm and a semantics on the probabilistic action logic for stochastic regular expressions (SREs). Specific to our model checking algorithm is that SREs are defined via local probabilistic functions. Such functions are beneficial since they enable to verify properties locally for sub-components. This ability provides a flexibility to reuse the local results for the global verification of the system; hence, the framework can be used for iterative verification. We demonstrate how to model a system with an SRE and how to verify it with the probabilistic action based logic and present a preliminary performance evaluation with respect to the execution time of the reachability algorithm.

Description: Cause-effect structures are objects of a formal system devised for modeling, testing and verifying properties of tasks, where parallel execution of actions is the most characteristic feature. This is an algebraic system called a quasi-semiring. In this paper elementary cause-effect structures, a system behaviourally equivalent to 1-safe Petri nets, are extended by the following features: weighted edges, multi-valued nodes having capacities (counterpart of place/transition Petri nets), inhibitors and a model of time. The extensions are accomplished by modifying the notion of state and semantics, but leaving unchanged structure of the quasi-semiring expressions.

Description: Motivation The post-transcriptional epigenetic modification on mRNA is an emerging field to study the gene regulatory mechanism and their association with diseases. Recently developed high-throughput sequencing technology named Methylated RNA Immunoprecipitation Sequencing (MeRIP-seq) enables one to profile mRNA epigenetic modification transcriptome-wide. A few computational methods are available to identify transcriptome-wide mRNA modification, but they are either limited by over-simplified model ignoring the biological variance across replicates or suffer from low accuracy and efficiency. Results In this work, we develop a novel statistical method, based on an empirical Bayesian hierarchical model, to identify mRNA epigenetic modification regions from MeRIP-seq data. Our method accounts for various sources of variations in the data through rigorous modeling, and applies shrinkage estimation by borrowing informations from transcriptome-wide data to stabilize the parameter estimation. Simulation and real data analyses demonstrate that our method is more accurate, robust and efficient than the existing peak calling methods. Availability Our method TRES is implemented as an R package and is freely available on Github at https://github.com/ZhenxingGuo0015/TRES. Supplementary information Supplementary data are available at Bioinformatics online.

Description: We propose an internal calculus to check the satisfiability of a set of formulas in ${ oldsymbol {S4}}$. Our calculus directly supports model extraction and is designed so to implement a forward proof-search strategy that can be understood as a top-down construction of a model. We prove that the extracted models have minimal height.

Description: Quantifying the abnormal degree of each instance within data sets to detect outlying instances, is an issue in unsupervised anomaly detection research. In this paper, we propose a robust anomaly detection method based on principal component analysis (PCA). Traditional PCA-based detection algorithms commonly obtain a high false alarm for the outliers. The main reason is that ignores the difference of location and scale to each component of the outlier score, this leads to the cumulated outlier score deviates from the true values. To address the issue, we introduce the median and the Median Absolute Deviation (MAD) to rescale each outlier score that mapped onto the corresponding principal direction. And then, the true outlier scores of instances can be obtained as the sum of weighted squares of the rescaled scores. Also, the issue that the assignment of the weight for each outlier score will be solved. The main advantage of our new approach is easy to build with unsupervised data and the recognition performance is better than the classical PCA-based methods. We compare our method to the five different anomaly detection techniques, including two traditional PCA-based methods, in our experiment analysis. The experimental results show that the proposed method has a good performance for effectiveness, efficiency, and robustness.

Description: We present a novel kernel-free regressor, called quadratic hyper-surface kernel-free least squares support vector regression (QLSSVR), for some regression problems. The task of this approach is to find a quadratic function as the regression function, which is obtained by solving a quadratic programming problem with the equality constraints. Basically, the new model just needs to solve a system of linear equations to achieve the optimal solution instead of solving a quadratic programming problem. Therefore, compared with the standard support vector regression, our approach is much efficient due to kernel-free and solving a set of linear equations. Numerical results illustrate that our approach has better performance than other existing regression approaches in terms of regression criterion and CPU time.

Description: Sales forecasting is an important part of e-commerce and is critical to smart business decisions. The traditional forecasting methods mainly focus on building a forecasting model, training the model through historical data, and then using it to forecast future sales. Such methods are feasible and effective for the products with rich historical data while they are not performing as well for the newly listed products with little or no historical data. In this paper, with the idea of collaborative filtering, a similarity-based sales forecasting (S-SF) method is proposed. The implementation framework of S-SF includes three modules in order. The similarity module is responsible for generating top-k similar products of a given new product. We calculate the similarity based on two data types: time series data of sales and text data such as product attributes. In the learning module, we propose an attention-based ConvLSTM model which we called AttConvLSTM, and optimize its loss function with the convex function information entropy. Then AttConvLSTM is integrated with Facebook Prophet model to forecast top-k similar products sales based on their historical data. The prediction results of all top-k similar products will be fused in the forecasting module through operations of alignment and scaling to forecast the target products sales. The experimental results show that the proposed S-SF method can simultaneously adapt to the sales forecasting of mature products and new products, which shows excellent diversity, and the forecasting idea based on similar products improves the accuracy of sales forecasting.

Description: The energy load data in the micro-energy network are a time series with sequential and nonlinear characteristics. This paper proposes a model based on the encode-decode architecture and ConvLSTM for multi-scale prediction of multi-energy loads in the micro-energy network. We apply ConvLSTM, LSTM, attention mechanism and multi-task learning concepts to construct a model specifically for processing the energy load forecasting of the micro-energy network. In this paper, ConvLSTM is used to encode the input time series. The attention mechanism is used to assign different weights to the features, which are subsequently decoded by the decoder LSTM layer. Finally, the fully connected layer interprets the output. This model is applied to forecast the multi-energy load data of the micro-energy network in a certain area of Northwest China. The test results prove that our model is convergent, and the evaluation index value of the model is better than that of the multi-task FC-LSTM and the single-task FC-LSTM. In particular, the application of the attention mechanism makes the model converge faster and with higher precision.

Description: Time series similarity search is an essential operation in time series data mining and has received much higher interest along with the growing popularity of time series data. Although many algorithms to solve this problem have been investigated, there is a challenging demand for supporting similarity search in a fast and accurate way. In this paper, we present a novel approach, TS2BC, to perform time series similarity search efficiently and effectively. TS2BC uses binary code to represent time series and measures the similarity under the Hamming Distance. Our method is able to represent original data compactly and can handle shifted time series and work with time series of different lengths. Moreover, it can be performed with reasonably low complexity due to the efficiency of calculating the Hamming Distance. We extensively compare TS2BC with state-of-the-art algorithms in classification framework using 61 online datasets. Experimental results show that TS2BC achieves better or comparative performance than other the state-of-the-art in accuracy and is much faster than most existing algorithms. Furthermore, we propose an approximate version of TS2BC to speed up the query procedure and test its efficiency by experiment.

Description: In electric motorcycles and light electric cars, permanent magnetic synchronous motor (PMSM) of outer-rotor type are preferred, due to its concise structure and flexible control. Since the body mass and road impact causes vertical eccentricity on shaft bearings, vibration and noise take place during vehicle cruising along with a torque and speed ripple. The torque ripples contain significant features closely related to eccentricity, and those special components can be compensated by an injection current with proper frequency, phase and amplitude. In this paper, a novel control method is used to regulate the injection current to restrain torque ripples. By coarse turning with quadratic spline curve fitting and fine turning with successive approximation, the method can response sudden disturbance and drive the specific torque ripple to minimum in a very short time, so that the performance on ride comfort and handling stability of the vehicle could be improved. Experimental tests show that the method performs favorably with rapid convergence and validity.