ALBERT

Description: Motivation Constraint-based modeling approaches allow the estimation of maximal in vivo enzyme catalytic rates that can serve as proxies for enzyme turnover numbers. Yet, genome-scale flux profiling remains a challenge in deploying these approaches to catalogue proxies for enzyme catalytic rates across organisms. Results Here, we formulate a constraint-based approach, termed NIDLE-flux, to estimate fluxes at a genome-scale level by using the principle of efficient usage of expressed enzymes. Using proteomics data from Escherichia coli, we show that the fluxes estimated by NIDLE-flux and the existing approaches are in excellent qualitative agreement (Pearson correlation 〉 0.9). We also find that the maximal in vivo catalytic rates estimated by NIDLE-flux exhibits a Pearson correlation of 0.74 with in vitro enzyme turnover numbers. However, NIDLE-flux results in a 1.4-fold increase in the size of the estimated maximal in vivo catalytic rates in comparison to the contenders. Integration of the maximum in vivo catalytic rates with publically available proteomics and metabolomics data provide a better match to fluxes estimated by NIDLE-flux. Therefore, NIDLE-flux facilitates more effective usage of proteomics data to estimate proxies for kcatomes. Availability and implementation https://github.com/Rudan-X/NIDLE-flux-code. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation Tumour heterogeneity is being increasingly recognized as an important characteristic of cancer and as a determinant of prognosis and treatment outcome. Emerging spatial transcriptomics data hold the potential to further our understanding of tumour heterogeneity and its implications. However, existing statistical tools are not sufficiently powerful to capture heterogeneity in the complex setting of spatial molecular biology. Results We provide a statistical solution, the HeTerogeneity Average index (HTA), specifically designed to handle the multivariate nature of spatial transcriptomics. We prove that HTA has an approximately normal distribution, therefore lending itself to efficient statistical assessment and inference. We first demonstrate that HTA accurately reflects the level of heterogeneity in simulated data. We then use HTA to analyze heterogeneity in two cancer spatial transcriptomics datasets: spatial RNA sequencing by 10x Genomics and spatial transcriptomics inferred from H&E. Finally, we demonstrate that HTA also applies to 3D spatial data using brain MRI. In spatial RNA sequencing, we use a known combination of molecular traits to assert that HTA aligns with the expected outcome for this combination. We also show that HTA captures immune-cell infiltration at multiple resolutions. In digital pathology, we show how HTA can be used in survival analysis and demonstrate that high levels of heterogeneity may be linked to poor survival. In brain MRI, we show that HTA differentiates between normal ageing, Alzheimer’s disease and two tumours. HTA also extends beyond molecular biology and medical imaging, and can be applied to many domains, including GIS. Availability and implementation Python package and source code are available at: https://github.com/alonalj/hta. Supplementary information Supplementary data are available at Bioinformatics online.

Description: The offshore Greenland halibut (Reinhardtius hippoglossoides) fishery, west Greenland, employs demersal trawl gear at depths of 800–1400 m. In contrast to many deep-sea fisheries, the target stock appears stable and the fishery is of significant economic importance. Recent Marine Stewardship Council certification of this fishery highlighted the paucity of knowledge of benthic habitats and trawling impacts, which this study aimed to address using a towed benthic video sled. The spatially discrete northern and southern areas of the fishery were found to be distinct in terms of the communities present, which non-metric multidimensional scaling suggests is primarily driven by temperature. Extensive physical evidence of trawling was observed. Trawling effort was significantly linked with community composition, with a negative association between trawling effort and abundance of some taxa, including some vulnerable marine ecosystem (VME) indicator species. Three potential VMEs are identified: (i) Flabellum alabastrum cup coral meadows; (ii) a Halipteris finmarchica sea pen field; and (iii) areas exhibiting mixed assemblages of VME indicators. Of immediate conservation concern is a H. finmarchica field, which seems to be at least regionally rare, is situated within the fringes of existing trawling effort and is currently afforded no protection by management measures.

Description: A pressing challenge for climate-vulnerable fisheries is how to manage now for present and future climate change. In contrast to climate forecasting approaches, we track integrated signals of change for example populations in a climatically forced region and use stochastic dynamic programming to compare the performance of a range of management-ready policies over all possible future states. Our main results highlight: (i) that biomass-linked harvest control rules (HCRs) can partially compensate for changing production, even if the HCR is time invariant; and (ii) that the form of utility (e.g. risk neutral or risk averse) can result in remarkably different optimal decision paths. Performance over future horizons degrades marginally from dynamic HCRs to static HCRs (except at low productivity where differences are more pronounced) but markedly when the biomass level is ignored altogether, as is the case in many managed fish populations globally. Understanding the processes whereby climate affects productivity is important for interpreting past data, but forecasts are not needed for tactical decision making now. Instead, we argue that the priorities for managing fish stocks influenced by climate change are to: measure the current productivity, assess the current abundance of the stock, and respond with a dynamic HCR.

Description: In 2020, the developing COVID-19 pandemic disrupted fisheries surveys to an unprecedented extent. Many surveys were cancelled, including those for walleye pollock (Gadus chalcogrammus) in the eastern Bering Sea (EBS), the largest fishery in the United States. To partially mitigate the loss of survey information, we deployed three uncrewed surface vehicles (USVs) equipped with echosounders to extend the ship-based acoustic-trawl time series of pollock abundance. Trawling was not possible from USVs, so an empirical relationship between pollock backscatter and biomass established from previous surveys was developed to convert USV backscatter observations into pollock abundance. The EBS is well suited for this approach since pollock dominate midwater fishes in the survey area. Acoustic data from the USVs were combined with historical surveys to provide a consistent fishery-independent index in 2020. This application demonstrates the unique capabilities of USVs and how they could be rapidly deployed to collect information on pollock abundance and distribution when a ship-based survey was not feasible. We note the limitations of this approach (e.g. higher uncertainty relative to previous ship-based surveys), but found the USV survey to be useful in informing the stock assessment in a situation where ship-based surveys were not possible.

Description: The fishery for Northern Atlantic cod (Gadus morhua) off Newfoundland and Labrador, Eastern Canada, presents the most spectacular case of an exploited stock crashed in a few decades by an industrial bottom trawl fishery under a seemingly sophisticated management regime after half a millennium of sustainable fishing. The fishery, which had generated annual catches of 100000 to 200000 tonnes from the beginning of the 16th century to the 1950s,  peaked in 1968 at 810000 tonnes, followed by a devastating collapse and closure 24 years later. Since then, stock recovery may have been hindered by premature openings, with vessels targeting the remains of the cod population. Previous research paid little attention towards using multicentury time series to inform sustainable catches and recovery plans. Here, we show that a simple stock assessment model can be used to model the cod population trajectory for the entire period from 1508 to 2019 for which catch estimates are available. The model suggests that if fishing effort and mortality had been stabilized in the 1980s,  precautionary annual yields of about 200000 tonnes could have been sustained. Our analysis demonstrates the value of incorporating prior knowledge to counteract shifting baseline effects on reference points and contemporary perceptions of historical stock status.

Description: Non-negative matrix factorization (NMF) is a powerful tool for data science researchers, and it has been successfully applied to data mining and machine learning community, due to its advantages such as simple form, good interpretability and less storage space. In this paper, we give a detailed survey on existing NMF methods, including a comprehensive analysis of their design principles, characteristics and drawbacks. In addition, we also discuss various variants of NMF methods and analyse properties and applications of these variants. Finally, we evaluate the performance of nine NMF methods through numerical experiments, and the results show that NMF methods perform well in clustering tasks.

Description: The task of question generation (QG) aims to create valid questions and correlated answers from the given text. Despite the neural QG approaches have achieved promising results, they are typically developed for languages with rich annotated training data. Because of the high annotation cost, it is difficult to deploy to other low-resource languages. Besides, different samples have their own characteristics on the aspects of text contextual structure, question type and correlations. Without capturing these diversified characteristics, the traditional one-size-fits-all model is hard to generate the best results. To address this problem, we study the task of cross-lingual QG from an adaptive learning perspective. Concretely, we first build a basic QG model on a multilingual space using the labelled data. In this way, we can transfer the supervision from the high-resource language to the language lacking labelled data. We then design a task-specific meta-learner to optimize the basic QG model. Each sample and its similar instances are viewed as a pseudo-QG task. The asking patterns and logical forms contained in the similar samples can be used as a guide to fine-tune the model fitly and produce the optimal results accordingly. Considering that each sample contains the text, question and answer, with unknown semantic correlations among them, we propose a context-dependent retriever to measure the similarity of such structured inputs. Experimental results on three languages of three typical data sets show the effectiveness of our approach.

Description: This article investigates the local economic cost of hosting refugees. Using administrative data in France, we show that the opening of small housing centers for refugees decreases the economic activity in hosting municipalities. We demonstrate that this downturn is related to a decline in the population by around 2% due to fewer people moving to hosting municipalities. We show that this avoidance behavior of natives results from prejudices, and is unlikely to be driven by a labor market supply shock from the arrival of refugees. We also estimate the aggregate cost of hosting refugees.

Description: Motivation Genomic DNA replicates according to a reproducible spatiotemporal program, with some loci replicating early in S phase while others replicate late. Despite being a central cellular process, DNA replication timing studies have been limited in scale due to technical challenges. Results We present TIGER (Timing Inferred from Genome Replication), a computational approach for extracting DNA replication timing information from whole genome sequence data obtained from proliferating cell samples. The presence of replicating cells in a biological specimen leads to non-uniform representation of genomic DNA that depends on the timing of replication of different genomic loci. Replication dynamics can hence be observed in genome sequence data by analyzing DNA copy number along chromosomes while accounting for other sources of sequence coverage variation. TIGER is applicable to any species with a contiguous genome assembly and rivals the quality of experimental measurements of DNA replication timing. It provides a straightforward approach for measuring replication timing and can readily be applied at scale. Availability and implementation TIGER is available at https://github.com/TheKorenLab/TIGER. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary We describe improvements to BAli-Phy, a Markov chain Monte Carlo (MCMC) program that jointly estimates phylogeny, alignment and other parameters from unaligned sequence data. Version 3 is substantially faster for large trees, and implements covarion models, additional codon models and other new models. It implements ancestral state reconstruction, allows prior selection for all model parameters, and can also analyze multiple genes simultaneously. Availability and implementation Software is available for download at http://www.bali-phy.org. C++ source code is freely available on Github under the GPL2 License. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Vulnerable marine ecosystems (VMEs) are typically fragile and slow to recover, making them likely to be substantially altered by disturbance. In the High Seas, regional fishery management organizations (RFMOs) are required to implement measures to prevent significant adverse impacts on VMEs. The objectives of the present study were to: update distribution models of VME indicator taxa in the South Pacific RFMO Convention Area; evaluate these against newly-collated independent field data to test the reliability of the presence-only habitat suitability models; and assess how well the updated models were able to predict into unsampled space. Ensemble habitat suitability models of 10 VME indicator taxa performed well using the newly collated data (AUC 〉 0.95, TSS 〉 0.76, and RMSE  0.93, TSS 〉 0.71, and RMSE

Description: Incorporating ecological covariates into fishery stock assessments may improve estimates, but most covariates are estimated with error. Model selection criteria are often used to identify support for covariates, have some limitations and rely on assumptions that are often violated. For a more rigorous evaluation of ecological covariates, we used four popular selection criteria to identify covariates influencing natural mortality or recruitment in a Bayesian stock assessment of Pacific herring (Clupea pallasii) in Prince William Sound, Alaska. Within this framework, covariates were incorporated either as fixed effects or as latent variables (i.e. covariates have associated error). We found most support for pink salmon increasing natural mortality, which was selected by three of four criteria. There was ambiguous support for other fixed effects on natural mortality (walleye pollock and the North Pacific Gyre Oscillation) and recruitment (hatchery-released juvenile pink salmon and a 1989 regime shift). Generally, similar criteria values among covariates suggest no clear evidence for a consistent effect of any covariate. Models with covariates as latent variables were sensitive to prior specification and may provide potentially very different results. We recommend using multiple criteria and exploring different statistical assumptions about covariates for their use in stock assessment.

Description: Catch data from bottom trawl surveys are used in various ways (e.g. stock assessments, fisheries management, and ecosystem studies) to represent trends in fish populations across space, time, season, or size. Relative abundance indices assume constant capture efficiency, and area-swept abundance requires an estimate of capture efficiency. Therefore, it is important to develop a predictive understanding of the interaction between fish and survey gear. We conducted experiments to test two primary factors that influence the efficiency of survey trawls at capturing demersal groundfish: (1) footrope escapement—estimated by attaching a collection bag beneath the primary trawl, and (2) herding of the sweeps/doors—estimated by varying sweep length. Random forest models were used to disentangle the herding effect from patterns caused by environmental variables. Contrary to common assumptions, footrope efficiency was incomplete (〈 100%) and herding was non-trivial (〉 0%), which introduces a bias in population metrics that rely on such assumptions. This bias varied by species and depended upon the relative strength of the counteracting effects of footrope escapement and herding. Our findings suggest that trawl efficiency should be estimated (not assumed) to derive area-swept abundance, and relative abundance indices should account for size-based efficiency and changing size compositions.

Description: Motivation Identifying proteins that interact with drugs plays an important role in the initial period of developing drugs, which helps to reduce the development cost and time. Recent methods for predicting drug–protein interactions mainly focus on exploiting various data about drugs and proteins. These methods failed to completely learn and integrate the attribute information of a pair of drug and protein nodes and their attribute distribution. Results We present a new prediction method, GVDTI, to encode multiple pairwise representations, including attention-enhanced topological representation, attribute representation and attribute distribution. First, a framework based on graph convolutional autoencoder is constructed to learn attention-enhanced topological embedding that integrates the topology structure of a drug–protein network for each drug and protein nodes. The topological embeddings of each drug and each protein are then combined and fused by multi-layer convolution neural networks to obtain the pairwise topological representation, which reveals the hidden topological relationships between drug and protein nodes. The proposed attribute-wise attention mechanism learns and adjusts the importance of individual attribute in each topological embedding of drug and protein nodes. Secondly, a tri-layer heterogeneous network composed of drug, protein and disease nodes is created to associate the similarities, interactions and associations across the heterogeneous nodes. The attribute distribution of the drug–protein node pair is encoded by a variational autoencoder. The pairwise attribute representation is learned via a multi-layer convolutional neural network to deeply integrate the attributes of drug and protein nodes. Finally, the three pairwise representations are fused by convolutional and fully connected neural networks for drug–protein interaction prediction. The experimental results show that GVDTI outperformed other seven state-of-the-art methods in comparison. The improved recall rates indicate that GVDTI retrieved more actual drug–protein interactions in the top ranked candidates than conventional methods. Case studies on five drugs further confirm GVDTI’s ability in discovering the potential candidate drug-related proteins. Contact zhang@hlju.edu.cn  Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.

Description: Motivation Over the past decades, vast amounts of genome sequencing data have been produced, requiring an enormous level of storage capacity. The time and resources needed to store and transfer such data cause bottlenecks in genome sequencing analysis. To resolve this issue, various compression techniques have been proposed to reduce the size of original FASTQ raw sequencing data, but these remain suboptimal. Long-read sequencing has become dominant in genomics, whereas most existing compression methods focus on short-read sequencing only. Results We designed a compression algorithm based on read reordering using a novel scoring model for reducing FASTQ file size with no information loss. We integrated all data processing steps into a software package called FastqCLS and provided it as a Docker image for ease of installation and execution to help users easily install and run. We compared our method with existing major FASTQ compression tools using benchmark datasets. We also included new long-read sequencing data in this validation. As a result, FastqCLS outperformed in terms of compression ratios for storing long-read sequencing data. Availability and implementation FastqCLS can be downloaded from https://github.com/krlucete/FastqCLS. Supplementary information Supplementary data are available at Bioinformatics online.

Description: This paper plans to develop the optimal brain tumor classification model with diverse intelligent methods. The main phases of the proposed model are ‘(a) image pre-processing, (b) skull stripping, (c) tumor segmentation, (d) feature extraction and (e) classification’. At first, pre-processing of the image is performed by converting the image from red green blue to gray followed by median filtering. Further, skull stripping is done for removing the extra-meningeal tissue from the head image, which is done by Otsu thresholding. As the main contribution, the tumor segmentation is done by the optimized threshold-based tumor segmentation using multi-objective randomly updated beetle swarm and multi-verse optimization (RBS-MVO). The objective constraints considered for the segmentation of the tumor is entropy and variance. Next, the feature extraction techniques like gray level co-occurrence matrix, local binary pattern and gray-level run length matrix is accomplished to extract the set of features. The classification side uses the combination of neural network (NN) and deep learning model called convolutional neural network (CNN) for tumor classification. The extracted features are subjected to NN, and the segmented image is taken as input to CNN. In addition, the weight function of NN and hidden neurons of CNN is optimized by the RBS-MVO.

Description: Motivation Knowing the number and the exact locations of multiple change points in genomic sequences serves several biological needs. The cumulative segmented algorithm (cumSeg) has been recently proposed as a computationally efficient approach for multiple change-points detection, which is based on a simple transformation of data and provides results quite robust to model mis-specifications. However, the errors are also accumulated in the transformed model so that heteroscedasticity and serial correlation will show up, and thus the variations of the estimated change points will be quite different, while the locations of the change points should be of the same importance in the original genomic sequences. Results In this study, we develop two new change-points detection procedures in the framework of cumulative segmented regression. Simulations reveal that the proposed methods not only improve the efficiency of each change point estimator substantially but also provide the estimators with similar variations for all the change points. By applying these proposed algorithms to Coriel and SNP genotyping data, we illustrate their performance on detecting copy number variations. Supplementary information The proposed algorithms are implemented in R program and are available at Bioinformatics online.

Description: As our understanding of the microbiome has expanded, so has the recognition of its critical role in human health and disease, thereby emphasizing the importance of testing whether microbes are associated with environmental factors or clinical outcomes. However, many of the fundamental challenges that concern microbiome surveys arise from statistical and experimental design issues, such as the sparse and overdispersed nature of microbiome count data and the complex correlation structure among samples. For example, in the human microbiome project (HMP) dataset, the repeated observations across time points (level 1) are nested within body sites (level 2), which are further nested within subjects (level 3). Therefore, there is a great need for the development of specialized and sophisticated statistical tests. In this paper, we propose multilevel zero-inflated negative-binomial models for association analysis in microbiome surveys. We develop a variational approximation method for maximum likelihood estimation and inference. It uses optimization, rather than sampling, to approximate the log-likelihood and compute parameter estimates, provides a robust estimate of the covariance of parameter estimates and constructs a Wald-type test statistic for association testing. We evaluate and demonstrate the performance of our method using extensive simulation studies and an application to the HMP dataset. We have developed an R package MZINBVA to implement the proposed method, which is available from the GitHub repository https://github.com/liudoubletian/MZINBVA.

Description: Summary Studying biological systems generally relies on computational modeling and simulation, e.g., model-driven discovery and hypothesis testing. Progress in standardization efforts led to the development of interrelated file formats to exchange and reuse models in systems biology, such as SBML, the Simulation Experiment Description Markup Language (SED-ML) or the Open Modeling EXchange format. Conducting simulation experiments based on these formats requires efficient and reusable implementations to make them accessible to the broader scientific community and to ensure the reproducibility of the results. The Systems Biology Simulation Core Library (SBSCL) provides interpreters and solvers for these standards as a versatile open-source API in JavaTM. The library simulates even complex bio-models and supports deterministic Ordinary Differential Equations; Stochastic Differential Equations; constraint-based analyses; recent SBML and SED-ML versions; exchange of results, and visualization of in silico experiments; open modeling exchange formats (COMBINE archives); hierarchically structured models; and compatibility with standard testing systems, including the Systems Biology Test Suite and published models from the BioModels and BiGG databases. Availability and implementation SBSCL is freely available at https://draeger-lab.github.io/SBSCL/ and via Maven Central. Supplementary information Supplementary data are available at Bioinformatics online.

Description: This article analyzes the entry of corn-ethanol plants in the Midwestern USA, where the majority of corn in the USA is grown, during the second US ethanol boom. In particular, we examine whether the presence of existing ethanol plants affects ethanol plant entry decisions at the county level using discrete response panel models. There are two main channels through which existing ethanol plants may affect ethanol plant entry decisions: a competition effect and an agglomeration effect. Our results show that existing ethanol plants have a negative effect on the probability of ethanol plant entry in a given county. The net negative competition effect dissipates with distance. We also find that existing conglomerates and large ethanol producing firms in neighboring counties have a positive effect on ethanol plant entry, while existing singlet plants in neighboring counties do not. These results provide evidence for both local competition among ethanol plants within counties, as well as possible agglomeration benefits from existing conglomerates and large ethanol producing firms in neighboring counties.

Description: Summary We present the LipidQuant 1.0 tool for automated data processing workflows in lipidomic quantitation based on lipid class separation coupled with high-resolution mass spectrometry. Lipid class separation workflows, such as hydrophilic interaction liquid chromatography or supercritical fluid chromatography, should be preferred in lipidomic quantitation due to the coionization of lipid class internal standards with analytes from the same class. The individual steps in the LipidQuant workflow are explained, including lipid identification, quantitation, isotopic correction and reporting results. We show the application of LipidQuant data processing to a small cohort of human serum samples. Availability and implementation The LipidQuant 1.0 is freely available at Zenodo https://doi.org/10.5281/zenodo.5151201 and https://holcapek.upce.cz/lipidquant.php. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation Genetic variations of expression quantitative trait loci (eQTLs) play a critical role in influencing complex traits and diseases development. Two main factors that affect the statistical power of detecting eQTLs are: (i) relatively small size of samples available, and (ii) heavy burden of multiple testing due to a very large number of variants to be tested. The later issue is particularly severe when one tries to identify trans-eQTLs that are far away from the genes they influence. If one can exploit co-expressed genes jointly in eQTL-mapping, effective sample size can be increased. Furthermore, using the structure of the gene regulatory network (GRN) may help to identify trans-eQTLs without increasing multiple testing burden. Results In this article, we use the structure equation model (SEM) to model both GRN and effect of eQTLs on gene expression, and then develop a novel algorithm, named sparse SEM for eQTL mapping (SSEMQ), to conduct joint eQTL mapping and GRN inference. The SEM can exploit co-expressed genes jointly in eQTL mapping and also use GRN to determine trans-eQTLs. Computer simulations demonstrate that our SSEMQ significantly outperforms nine existing eQTL mapping methods. SSEMQ is further used to analyze two real datasets of human breast and whole blood tissues, yielding a number of cis- and trans-eQTLs. Availability and implementation R package ssemQr is available at https://github.com/Ivis4ml/ssemQr.git. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Fishers reporting all of their catch is key to estimating population viabilities of vulnerable, highly migratory fish stocks. However, fishery managers find it difficult to ensure that this reporting behavior takes place consistently. Wild Atlantic salmon (Salmo salar) are a highly migratory and internationally contested species with a threatened conservation status. Greenland manages a fishery for Atlantic salmon, and its coastline serves as a key feeding ground in the life history of Atlantic salmon. However, salmon catch is underreported by fishers, even though they are required to report. Deterring noncompliant behavior with penalties and sending short message service (SMS) messages have been shown to increase compliance, but no known studies test their effect on compliance with catch reporting requirements. We evaluated two interventions for their effect on salmon catch reporting behavior among Greenland's salmon fishers. Salmon fishers were 41% more likely to report (p

Description: Exploitation is one of the major drivers of change in marine ecosystems. Following discovery in 1775, South Georgia saw sequential overexploitation of living resources, including seals, whales, and fish. Although exploitation is now tightly regulated, the ecosystem is still recovering. Marbled rockcod, Notothenia rossii (Richardson 1844), was the first fish species to be commercially exploited and high catches between 1967 and 1972 resulted in dramatic stock decline. Here, we use 30 years of trawl survey data to provide the first evidence of a sustained increase in the N. rossii population starting two decades after the prohibition of targeted fishing in 1985. The way species respond to change is mediated in part by trophic relationships with other organisms. We present the first multi-year, spatially-resolved comparison of adult N. rossii diet at South Georgia, highlighting a variable diet with less reliance on Antarctic krill than previously thought. Life history factors and possible heavy predation on early life stages might have delayed their recovery while diet plasticity potentially supported recent population growth. Due to the dynamic ecosystem at South Georgia and questions over catch reports from the period of heaviest exploitation, it is unlikely the current ecosystem could support a recovery to estimated pre-exploitation levels.

Description: In this article, we make use of large-scale municipal border changes in Germany to provide the first evidence on the effect of local border changes on the distribution of activity in space. To allow for a comparison of economic activity within unique geographical units over time, we use geo-coded light data as well as local land-use data. Applying a difference-in-differences approach, we find evidence that municipalities absorbing their merger partners and hosting the new administrative center experience a significant increase in local activity, while the municipalities that are being absorbed and are losing the administrative center experience a decrease in such activity. The difference between the gains in activity from absorbing municipalities and the losses from absorbed ones is positive. These previously undocumented results point to the importance of distance to the administrative center as a determinant of the spatial distribution of economic activity.

Description: Clustering is a widely used technique in data mining applications and various pattern recognition applications, in which data objects are divided into groups. K-means algorithm is one of the most classical clustering algorithms. In this algorithm, the initial clustering centers are randomly selected, this results in unstable clustering results. To solve this problem, an optimized algorithm to select the initial centers is proposed. In the proposed algorithm, dispersion degree is defined, which is based on entropy. In the algorithm, all the objects are firstly grouped into a big cluster, and the object that has the maximum dispersion degree and the object that has the minimum dispersion degree are selected as the initial clustering centers from the initial big cluster. And then other objects in the biggest cluster are partitioned to the initial clusters to which the objects are nearest. The partition process will be repeated until the cluster number is equal to the specified value k. Finally, the partitioned k clusters and their cluster centers are applied to k-means algorithm as initial clusters and centers. Several experiments are conducted on real data sets to evaluate the proposed algorithm. The proposed algorithm is compared with traditional k-means algorithm and max-min distance clustering algorithm, and experimental results show that the improved k-means algorithm is stable in selecting initial clustering, because it can select unique initial clustering centers. The optimized algorithm’s effectiveness and feasibility are also verified by experiments, and the algorithm can reduce the times of iterations and has more stable clustering results and higher accuracy.

Description: Privacy protection is one of the key concerns of users in recommender system-based consumer markets. Popular recommendation frameworks such as collaborative filtering (CF) suffer from several privacy issues. Federated learning has emerged as an optimistic approach for collaborative and privacy-preserved learning. Users in a federated learning environment train a local model on a self-maintained item log and collaboratively train a global model by exchanging model parameters instead of personalized preferences. In this research, we proposed a federated learning-based privacy-preserving CF model for context-aware recommender systems that work with a user-defined collaboration protocol to ensure users’ privacy. Instead of crawling users’ personal information into a central server, the whole data are divided into two disjoint parts, i.e. user data and sharable item information. The inbuilt power of federated architecture ensures the users’ privacy concerns while providing considerably accurate recommendations. We evaluated the performance of the proposed algorithm with two publicly available datasets through both the prediction and ranking perspectives. Despite the federated cost and lack of open collaboration, the overall performance achieved through the proposed technique is comparable with popular recommendation models and satisfactory while providing significant privacy guarantees.

Description: Ecological interactions among marine zooplankton are poorly described because conventional sampling gears, such as plankton nets and traps, obscure the physical and biological environment that individuals experience. With in situ imagery, however, it is possible to resolve these interactions and potentially convert snapshot distributions into process-oriented oceanographic and ecological understanding. We describe a variety of imagery-detected ecological interactions with high spatial resolution in the northern Gulf of Mexico shelf waters (20–35 m bottom depth), providing new evidence of parasitism, predation, and life stage spatial structuring for different zooplankton groups. Chaetognaths were infected with an anteriorly attached, parasitic polychaete (1.1% of 33 824 individuals), and these infected chaetognaths were more common further offshore, south of a nearshore patch where unparasitized individuals reached concentrations of ∼90 m–3. Predation by Liriope spp. hydromedusae tended to occur in the shallowest 10–15 m, and doliolids formed distinct patches of different life stages, indicating that the environment is replete with sharp transitions among various ecological processes. Similar patterns in other marine ecosystems likely exist, and we encourage hybrid (machine/human expertise) approaches that broaden the scope for analysis of plankton images, which are rich sources of new ecological information and hypotheses yet to be examined quantitatively.

Description: Aspect sentiment classification is an important research topic in natural language processing and computational linguistics, assisting in automatically review analysis and emotional tendency judgement. Different from extant methods that focus on text sequence representations, this paper presents a network framework to learn representation from concurrence-words relation graph (LRCWG), so as to improve the Macro-F1 and accuracy. The LRCWG first employs the multi-head attention mechanism to capture the sentiment representation from the sentences which can learn the importance of text sequence representation. And then, it leverages the priori sentiment dictionary information to construct the concurrence relations of sentiment words with Graph Convolution Network (GCN). This assists in that the learnt context representation can keep both the semantics integrity and the features of sentiment concurrence-words relations. The designed algorithm is experimentally evaluated with all the five benchmark datasets and demonstrated that the proposed aspect sentiment classification can significantly improve the prediction performance of learning task.

Description: Since 1996, the European Union has required that fishery products of 35 fish species or groups of species, including crustaceans and cephalopods, be graded before being landed on the basis of commercial size-categories. A multi-stage sampling scheme to estimate catch-at-length and catch-at-age compositions of total annual landings has been conducted in Portuguese waters since 2009. All species and their size-categories are sampled concurrently from random trips within representatively selected site-days annually. In an effort to improve cost-efficiency of the biological catch sampling of commercial landings in Portugal, a size- category sampling scheme was tested during 2017, taking advantage of the stratification of horse mackerel landings, induced by the mandatory grading of landings by size-category. The total number of site-days (primary sampling units), fish boxes sampled, and specimens of horse mackerel sampled for length and age across strata during the pilot study were 30%, 57%, and 12% of the sampling carried out under the standard concurrent scheme, respectively. The assignment of horse mackerel length to the six commercial size-categories was highly consistent across site-days. Our study shows that the concurrent sampling, where trips are subsampled within site-days, could miss some size-categories by chance, resulting in poor estimates of catch-at-length especially for small fish present at the market during a site-day. The size-category sampling scheme ensured subsamples of fish from all size-categories within a site-day, and achieved data on catch-at-age that are fit-for purpose at 24% of the total cost for the standard concurrent sampling. The effect of sampling designs and sample sizes on horse mackerel stock assessment outputs showed that the onshore biological sampling to estimate catch-at-age compositions for stock assessment is optimized with the size-category sampling scheme. Pilot field experiments should be carried out for selected species that evidence consistent size grading among commercial categories.

Description: Spawning timing in fish is generally cyclical in temperate regions in order to increase the probability of matching larval occurrence with ideal environmental conditions. The capelin stock in Northwest Atlantic Fisheries Organization Divisions 2J3KL collapsed in 1990–1991 and has not recovered. This collapse was concomitant with collapses in groundfish stocks and cold oceanographic conditions. Using citizen science data, newspaper archives, grey and primary literature, and monitoring data, a century of capelin beach spawning times were compiled. Capelin beach spawning has been persistently 3 weeks later since the stock collapse. To identify potential predictors of capelin spawning timing, an exploratory analysis was conducted using environmental and biological variables and a period factor that categorized a year as either pre-collapse (1990 and earlier) or post-collapse (post-1990) in a step-wise multiple regression model. Spawning timing was predicted to be delayed in the post-collapse period when there were negative anomalies in the Newfoundland and Labrador Climate Index and summer (June–August) North Atlantic Oscillation, and when there was a decrease in mean length of the spawning population. The production of weak year-classes is predicted when spawning is delayed, suggesting that late spawning is severely inhibiting the recovery of the stock.

Description: While the importance of early life survival and growth variations for population dynamics is well documented, there is still a relatively limited understanding of how survival and growth is affected by the species’ spatial distribution. Using Barents Sea spatial bottom survey data (1994–2018), we study the spatiotemporal variability of the juvenile Atlantic cod (Gadus morhua) growth and survival. We used indices of the spatial distribution of juvenile cod at age-1 to study the role of distribution for the change in abundance and mean body size through their second winter of life (from age-1 to age-2). Over the 24 years analysed, we found that the location where the age-1 cod are in the Barents Sea matters for their growth and survival. We found that year-classes growing up in the western Barents Sea have higher mortality but faster growth than year-classes distributed farther east. Our results indicate that the biotic and abiotic conditions encountered at the settlement location may influence the spatial survival and growth of age-1 cod and subsequently the population dynamics. Our results underscore the importance of distribution for survival and growth early in life and by providing this essential information has implications for stock assessment and spatial fisheries management.

Description: Motivation Personalized medicine aims at providing patient-tailored therapeutics based on multi-type data toward improved treatment outcomes. Chronotherapy that consists in adapting drug administration to the patient’s circadian rhythms may be improved by such approach. Recent clinical studies demonstrated large variability in patients’ circadian coordination and optimal drug timing. Consequently, new eHealth platforms allow the monitoring of circadian biomarkers in individual patients through wearable technologies (rest-activity, body temperature), blood or salivary samples (melatonin, cortisol) and daily questionnaires (food intake, symptoms). A current clinical challenge involves designing a methodology predicting from circadian biomarkers the patient peripheral circadian clocks and associated optimal drug timing. The mammalian circadian timing system being largely conserved between mouse and humans yet with phase opposition, the study was developed using available mouse datasets. Results We investigated at the molecular scale the influence of systemic regulators (e.g. temperature, hormones) on peripheral clocks, through a model learning approach involving systems biology models based on ordinary differential equations. Using as prior knowledge our existing circadian clock model, we derived an approximation for the action of systemic regulators on the expression of three core-clock genes: Bmal1, Per2 and Rev-Erbα. These time profiles were then fitted with a population of models, based on linear regression. Best models involved a modulation of either Bmal1 or Per2 transcription most likely by temperature or nutrient exposure cycles. This agreed with biological knowledge on temperature-dependent control of Per2 transcription. The strengths of systemic regulations were found to be significantly different according to mouse sex and genetic background. Availability and implementation https://gitlab.inria.fr/julmarti/model-learning-mb21eccb. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Recently, significant breakthroughs have been achieved in the field of object detection. However, existing methods mostly focus on the generic object detection task. Performance degradation can be unavoidable when applying the existing methods to some specific situations directly, e.g. a low-light environment. To address this issue, we propose a single-shot real-time object Detector based on Low-light image Enhancement, namely LEDet. LEDet adapts itself to the low-light detection task in three aspects. First, a low-light enhancement module is introduced as the image preprocessor, producing the augmented inputs from the low-light images. Second, two modules, i.e. low-light and enhanced features fusion module and the scale-aware channel attention dilated convolution module are designed. These two modules aim at learning robust and discriminative features from objects of various sizes hidden in the darkness. In experiments, we validate the effectiveness of each part of our LEDet model via several ablation studies. We also compare LEDet with various methods on the Exclusively Dark dataset, showing that our model achieves the state-of-the-art performance on the balance between speed and accuracy.

Description: Spectral clustering is widely applied in real applications, as it utilizes a graph matrix to consider the similarity relationship of subjects. The quality of graph structure is usually important to the robustness of the clustering task. However, existing spectral clustering methods consider either the local structure or the global structure, which can not provide comprehensive information for clustering tasks. Moreover, previous clustering methods only consider the simple similarity relationship, which may not output the optimal clustering performance. To solve these problems, we propose a novel clustering method considering both the local structure and the global structure for conducting nonlinear clustering. Specifically, our proposed method simultaneously considers (i) preserving the local structure and the global structure of subjects to provide comprehensive information for clustering tasks, (ii) exploring the nonlinear similarity relationship to capture the complex and inherent correlation of subjects and (iii) embedding dimensionality reduction techniques and a low-rank constraint in the framework of adaptive graph learning to reduce clustering biases. These constraints are considered in a unified optimization framework to result in one-step clustering. Experimental results on real data sets demonstrate that our method achieved competitive clustering performance in comparison with state-of-the-art clustering methods.

Description: Motivation Immune cells are important components of the immune system and are crucial for disease initiation, progression, prognosis, and survival. Although several computational methods have been designed for predicting the abundance of immune cells, very few tools are applicable to mouse. Given that mouse is the most widely used animal model in biomedical research, there is an urgent need to develop a precise algorithm for predicting mouse immune cells. Results We developed a tool named ImmuCellAI-mouse (Immune Cell Abundance Identifier for mouse), for estimating the abundance of 36 immune cell (sub)types from gene expression data in a hierarchical strategy of three layers. Reference expression profile and robust marker gene sets of immune cell types were curated. The abundance of cells in three layers was predicted separately by calculating the ssGSEA enrichment score of the expression deviation profile per cell type. Benchmark results showed high accuracy of ImmuCellAI-mouse in predicting most immune cell types, with correlation coefficients between predicted value and real cell proportion of most cell types being larger than 0.8. We applied ImmuCellAI-mouse to a mouse breast tumor dataset and revealed the dynamic change of immune cell infiltration during treatment, which is consistent with the findings of the original study but with more details. We also constructed an online server for ImmuCellAI-mouse, on which users can upload expression matrices for analysis. ImmuCellAI-mouse will be a useful tool for studying the immune microenvironment, cancer immunology, and immunotherapy in mouse models, providing an indispensable supplement for human disease studies. Availability Software is available at http://bioinfo.life.hust.edu.cn/ImmuCellAI-mouse/ Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation As one of the most important post-translational modifications (PTMs), protein lysine crotonylation (Kcr) has attracted wide attention, which involves in important physiological activities, such as cell differentiation and metabolism. However, experimental methods are expensive and time-consuming for Kcr identification. Instead, computational methods can predict Kcr sites in silico with high efficiency and low cost. Results In this study, we proposed a novel predictor, BERT-Kcr, for protein Kcr sites prediction, which was developed by using a transfer learning method with pre-trained bidirectional encoder representations from transformers (BERT) models. These models were originally used for natural language processing (NLP) tasks, such as sentence classification. Here, we transferred each amino acid into a word as the input information to the pre-trained BERT model. The features encoded by BERT were extracted and then fed to a BiLSTM network to build our final model. Compared with the models built by other machine learning and deep learning classifiers, BERT-Kcr achieved the best performance with AUROC of 0.983 for 10-fold cross-validation. Further evaluation on the independent test set indicates that BERT-Kcr outperforms the state-of-the-art model Deep-Kcr with an improvement of about 5% for AUROC. The results of our experiment indicate that the direct use of sequence information and advanced pre-trained models of natural language processing could be an effective way for identifying post-translational modification sites of proteins. Availability The BERT-Kcr model is publicly available on http://zhulab.org.cn/BERT-Kcr_models/. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary We present several recent improvements to minimap2, a versatile pairwise aligner for nucleotide sequences. Now minimap2 v2.22 can more accurately map long reads to highly repetitive regions and align through insertions or deletions up to 100 kb by default, addressing major weakness in minimap2 v2.18 or earlier. Availability and implementation https://github.com/lh3/minimap2.

Description: Motivation Deciphering nucleosome–nucleosome interactions is an important step toward mesoscale description of chromatin organization but computational tools to perform such analyses are not publicly available. Results We developed iNucs, a user-friendly and efficient Python-based bioinformatics tool to compute and visualize nucleosome-resolved interactions using standard pairs format input generated from pairtools. Availabilityand implementation https://github.com/Karimi-Lab/inucs/. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Marine predatory fish face unpredictable prey environments, ranging from abundance to scarcity of food. Dimensioning their assimilative system to accommodate gorging and fasting is therefore a central life history choice. Assimilative capacity experiments typically operate with sustained feeding to satiation, and therefore ignore the fluctuations in natural feeding opportunities. A more relevant description of the adaptive response is the episodic capacity associated with binge feeding (hyperphagia). We develop the theoretical foundation to define episodic and sustained capacity and its allometry. Extensive empirical evidence on marine piscivorous fish at higher latitudes confirms that the episodic capacity scales almost linearly with predator body mass (exponent approximately 0.95), producing an increasing factorial hyperphagic scope (exponent approximately 0.20). Our synthesis overturns the reigning steady state perspective on assimilative capacity. The fish can utilize an episodic capacity, typically twice the size of the sustained capacity, resulting in local dynamics of functional responses with profound implications for scaling-up to ecosystem level.

Description: Target identification of small molecules is an important and still changeling work in the area of drug discovery, especially for botanical drug development. Indistinct understanding of the relationships of ligand–protein interactions is one of the main obstacles for drug repurposing and identification of off-targets. In this study, we collected 9063 crystal structures of ligand-binding proteins released from January, 1995 to April, 2021 in PDB bank, and split the complexes into 5133 interaction pairs of ligand atoms and protein fragments (covalently linked three heavy atoms) with interatomic distance ≤5 Å. The interaction pairs were grouped into ligand atoms with the same SYBYL atom type surrounding each type of protein fragment, which were further clustered via Bayesian Gaussian Mixture Model (BGMM). Gaussian distributions with ligand atoms ≥20 were identified as significant interaction patterns. Reliability of the significant interaction patterns was validated by comparing the difference of number of significant interaction patterns between the docked poses with higher and lower similarity to the native crystal structures. Fifty-one candidate targets of brucine, strychnine and icajine involved in Semen Strychni (Mǎ Qián Zǐ) and eight candidate targets of astragaloside-IV, formononetin and calycosin-7-glucoside involved in Astragalus (Huáng Qí) were predicted by the significant interaction patterns, in combination with docking, which were consistent with the therapeutic effects of Semen Strychni and Astragalus for cancer and chronic pain. The new strategy in this study improves the accuracy of target identification for small molecules, which will facilitate discovery of botanical drugs.

Description: The amount of data generated is increasing day by day due to the development in remote sensors, and thus it needs concern to increase the accuracy in the classification of the big data. Many classification methods are in practice; however, they limit due to many reasons like its nature for data loss, time complexity, efficiency and accuracy. This paper proposes an effective and optimal data classification approach using the proposed Ant Cat Swarm Optimization-enabled Deep Recurrent Neural Network (ACSO-enabled Deep RNN) by Map Reduce framework, which is the incorporation of Ant Lion Optimization approach and the Cat Swarm Optimization technique. To process feature selection and big data classification, Map Reduce framework is used. The feature selection is performed using Pearson correlation-based Black hole entropy fuzzy clustering. The classification in reducer part is performed using Deep RNN that is trained using a developed ACSO scheme. It classifies the big data based on the reduced dimension features to produce a satisfactory result. The proposed ACSO-based Deep RNN showed improved results with maximal specificity of 0.884, highest accuracy of 0.893, maximal sensitivity of 0.900 and the maximum threat score of 0.827 based on the Cleveland dataset.

Description: The productivity of Atlantic salmon (Salmo salar) has declined markedly since the 1980s,  in part because of changing ocean conditions, but mechanisms driving this decline remain unclear. Previous research has suggested differential recruitment dynamics between the continental stock groups, with post-smolt growth influencing the survival of populations in Europe, but not North America. We used a large, representative archive of North American, multi sea-winter salmon scales to reconstruct long-term changes in growth between 1968 and 2018. We then modeled relationships between annual growth indices, estimates of maturation rates, and post-smolt survival, while allowing for the possibility of non-stationary dynamics. We found that marine growth of MSW salmon has changed over the past 50 years, generally increasing despite declining survival. However, we found strong evidence of a non-stationary influence of post-smolt growth on survival. Prior to a period of rapid change in the ocean environment during the late 1980s,  post-smolt growth was positively related with survival, similar to the pattern observed in European populations. These findings suggest that the mechanisms determining marine survival of North American and European salmon populations may have diverged around 1990. More generally, our results highlight the importance of considering non-stationary dynamics when evaluating linkages between the environment, growth, and survival of Atlantic salmon.

Description: Summary Large scale pre-trained language models (PLMs) have advanced state-of-the-art (SOTA) performance on various biomedical text mining tasks. The power of such PLMs can be combined with the advantages of deep generative models. These are examples of these combinations. However, they are trained only on general domain text, and biomedical models are still missing. In this work, we describe BioVAE, the first large scale pre-trained latent variable language model for the biomedical domain, which uses the OPTIMUS framework to train on large volumes of biomedical text. The model shows SOTA performance on several biomedical text mining tasks when compared to existing publicly available biomedical PLMs. Additionally, our model can generate more accurate biomedical sentences than the original OPTIMUS output. Availability Our source code and pre-trained models are freely available: https://github.com/aistairc/BioVAE Supplementary information Supplementary data are available at Bioinformatics online.

Description: The tremendous progress of single-cell sequencing technology has given researchers the opportunity to study cell development and differentiation processes at single-cell resolution. Assay of Transposase-Accessible Chromatin by deep sequencing (ATAC-seq) was proposed for genome-wide analysis of chromatin accessibility. Due to technical limitations or other reasons, dropout events are almost a common occurrence for extremely sparse single-cell ATAC-seq data, leading to confusion in downstream analysis (such as clustering). Although considerable progress has been made in the estimation of scRNA-seq data, there is currently no specific method for the inference of dropout events in single-cell ATAC-seq data. In this paper, we select several state-of-the-art scRNA-seq imputation methods (including MAGIC, SAVER, scImpute, deepImpute, PRIME, bayNorm and knn-smoothing) in recent years to infer dropout peaks in scATAC-seq data, and perform a systematic evaluation of these methods through several downstream analyses. Specifically, we benchmarked these methods in terms of correlation with meta-cell, clustering, subpopulations distance analysis, imputation performance for corruption datasets, identification of TF motifs and computation time. The experimental results indicated that most of the imputed peaks increased the correlation with the reference meta-cell, while the performance of different methods on different datasets varied greatly in different downstream analyses, thus should be used with caution. In general, MAGIC performed better than the other methods most consistently across all assessments. Our source code is freely available at https://github.com/yueyueliu/scATAC-master.

Description: Summary Non-coding RNAs are often neglected during genome annotation due to their difficulty of detection relative to protein coding genes. FindNonCoding takes a pattern mining approach to capture the essential sequence motifs and hairpin loops representing a non-coding RNA family and quickly identify matches in genomes. FindNonCoding was designed for ease of use and accurately finds non-coding RNAs with a low false discovery rate. Availability and implementation FindNonCoding is implemented within the DECIPHER package (v2.19.3) for R (v4.1) available from Bioconductor. Pre-trained models of common non-coding RNA families are included for bacteria, archaea and eukarya. Supplementary information Supplementary data are available at Bioinformatics online.

Description: The state-space assessment model (SAM) is extended by allowing a functional relationship between observation variance and the corresponding prediction. An estimated relationship between observation variance and predicted value for each individual observation allows the model to assign smaller (or larger) variance to predicted larger log-observations. This relation is different from the usual assumption of constant variance of log-observations within age groups. The prediction–variance link is implemented and compared to the usual constant variance assumption for the official assessments of North East Arctic cod and haddock. For both of these stocks, the prediction–variance link is found to give a significant improvement.

Description: Motivation Genome-wide association studies (GWAS) summary statistics have popularized and accelerated genetic research. However, a lack of standardization of the file formats used has proven problematic when running secondary analysis tools or performing meta-analysis studies. Results To address this issue, we have developed MungeSumstats, a Bioconductor R package for the standardization and quality control of GWAS summary statistics. MungeSumstats can handle the most common summary statistic formats, including variant call format (VCF) producing a reformatted, standardized, tabular summary statistic file, VCF or R native data object. Availability and implementation MungeSumstats is available on Bioconductor (v 3.13) and can also be found on Github at: https://neurogenomics.github.io/MungeSumstats. Supplementary information Supplementary data are available at Bioinformatics online.

Description: The side effects of drugs present growing concern attention in the healthcare system. Accurately identifying the side effects of drugs is very important for drug development and risk assessment. Some computational models have been developed to predict the potential side effects of drugs and provided satisfactory performance. However, most existing methods can only predict whether side effects will occur and cannot determine the frequency of side effects. Although a few existing methods can predict the frequency of drug side effects, they strongly depend on the known drug-side effect relationships. Therefore, they cannot be applied to new drugs without known side effect frequency information. In this paper, we develop a novel similarity-based deep learning method, named SDPred, for determining the frequencies of drug side effects. Compared with the existing state-of-the-art models, SDPred integrates rich features and can be applied to predict the side effect frequencies of new drugs without any known drug-side effect association or frequency information. To our knowledge, this is the first work that can predict the side effect frequencies of new drugs in the population. The comparison results indicate that SDPred is much superior to all previously reported models. In addition, some case studies also demonstrate the effectiveness of our proposed method in practical applications. The SDPred software and data are freely available at https://github.com/zhc940702/SDPred, https://zenodo.org/record/5112573 and https://hub.docker.com/r/zhc940702/sdpred.

Description: Mobile edge computing (MEC) is a key feature of next-generation heterogeneous networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the user equipment. In this research, we investigated on connection management approaches in multi-access edge computing systems. This paper presents joint radio resource allocation and MEC optimization in a multi-layer NOMA HetNet in order to maximize system’s energy efficiency. The continues carrier allocation and handoff decision variables, in addition to the interference incorporated in the goal function, modifies the primary optimization problem to a mixed integer nonlinear programming. Network selection is done statically based on the Analytic Hierarchy Process, and station selection is done dynamically based on the Data Envelope Analysis method. Also, an effective feedback mechanism has been designed in collaboration with the server resource manager to solve a global optimization problem in order to load balancing and meet the users quality of service constraints simultaneously. To reduce the computational complexity and to achieve a locally optimal solution, we applied variable relaxation and majorization minimization approach in which offloading decision and multi-part Markov noise analysis have been developed to model users’ preferences without the need for explicit information from the users. Based on the simulations, the proposed approach not only results in a significant increase of system’s energy efficiency but also enhances the system throughput in multiple-source scenarios.

Description: Summary Recent efforts to identify novel bacterial structured noncoding RNA (ncRNA) motifs through searching long, GC-rich intergenic regions (IGRs) have revealed several new classes, including the recently validated HMP-PP riboswitch. The DIMPL (Discovery of Intergenic Motifs PipeLine) discovery pipeline described herein enables rapid extraction and selection of bacterial IGRs that are enriched for structured ncRNAs. Moreover, DIMPL automates the subsequent computational steps necessary for their functional identification. Availability and implementation The DIMPL pipeline is freely available as a Docker image with an accompanying set of Jupyter notebooks. Full instructions for download and use are available at https://github.com/breakerlab/dimpl. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Nowadays, Apache Hadoop and Apache Spark are two of the most prominent distributed solutions for processing big data applications on the market. Since in many cases these frameworks are adopted to support business critical activities, it is often important to predict with fair confidence the execution time of submitted applications, for instance when service-level agreements are established with end-users. In this work, we propose and validate a hybrid approach for the performance prediction of big data applications running on clouds, which exploits both analytical modeling and machine learning (ML) techniques and it is able to achieve a good accuracy without too many time consuming and costly experiments on a real setup. The experimental results show how the proposed approach attains improvement in accuracy, number of experiments to be run on the operational system and cost over applying ML techniques without any support from analytical models. Moreover, we compare our approach with Ernest, an ML-based technique proposed in the literature by the Spark inventors. Experiments show that Ernest can accurately estimate the performance in interpolating scenarios while it fails to predict the performance when configurations with increasing number of cores are considered. Finally, a comparison with a similar hybrid approach proposed in the literature demonstrates how our approach significantly reduce prediction errors especially when few experiments on the real system are performed.

Description: Whole exome sequencing (WES) is a powerful approach for discovering sequence variants in cancer cells but its time effectiveness is limited by the complexity and issues of WES data analysis. Here we present iWhale, a customizable pipeline based on Docker and SCons, reliably detecting somatic variants by three complementary callers (MuTect2, Strelka2 and VarScan2). The results are combined to obtain a single variant call format file for each sample and variants are annotated by integrating a wide range of information extracted from several reference databases, ultimately allowing variant and gene prioritization according to different criteria. iWhale allows users to conduct a complex series of WES analyses with a powerful yet customizable and easy-to-use tool, running on most operating systems (macOs, GNU/Linux and Windows). iWhale code is freely available at https://github.com/alexcoppe/iWhale and the docker image is downloadable from https://hub.docker.com/r/alexcoppe/iwhale.

Description: Qualitative Network Models (QNMs), Fuzzy Cognitive Maps (FCMs), and Bayesian Belief Networks (BBNs) have been proposed as methods to formalize conceptual models of social–ecological systems and project system responses to management interventions or environmental change. To explore how these different methods might influence conclusions about system dynamics, we assembled conceptual models representing three different coastal systems, adapted them to the network approaches, and evaluated outcomes under scenarios representing increased fishing effort and environmental warming. The sign of projected change was the same across the three network models for 31–60% of system variables on average. Pairwise agreement between network models was higher, ranging from 33 to 92%; average levels of similarity were comparable between network pairs. Agreement measures based on both the sign and strength of change were substantially worse for all model comparisons. These general patterns were similar across systems and scenarios. Different outcomes between models led to different inferences regarding trade-offs under the scenarios. We recommend deployment of all three methods, when feasible, to better characterize structural uncertainty and leverage insights gained under one framework to inform the others. Improvements in precision will require model refinement through data integration and model validation.

Description: Motivation Discover is an algorithm developed to identify mutually exclusive genomic events. Its main contribution is a statistical analysis based on the Poisson-Binomial (PB) distribution to take into account the mutation rate of genes and samples. Discover is very effective for identifying mutually exclusive mutations at the expense of speed in large datasets: the Poisson-Binomial is computationally costly to estimate, and checking all the potential mutually exclusive alterations requires millions of tests. Results We have implemented a new version of the package called Rediscover that implements exact and approximate computations of the PB. Rediscover exact implementation is slightly faster than Discover for large and medium-sized datasets. The approximation is 100 to 1,000 times faster for them making it possible to get results in less than a minute with a standard desktop. The memory footprint is also smaller in Rediscover. The new package is available at CRAN and provides some functions to integrate its usage with other R packages such as maftools and TCGAbiolinks. Availability Rediscover is available at CRAN (https://cran.r-project.org/web/packages/Rediscover/index.html). Supplementary information Supplementary data are available at Bioinformatics online.

Description: Food webs are central entities mediating processes and external pressures in marine ecosystems. They are essential to understand and predict ecosystem dynamics and provision of ecosystem services. Paradoxically, utilization of food web knowledge in marine environmental conservation and resource management is limited. To better understand the use of knowledge and barriers to incorporation in management, we assess its application related to the management of eutrophication, chemical contamination, fish stocks, and non-indigenous species. We focus on the Baltic, a severely impacted, but also intensely studied and actively managed semi-enclosed sea. Our assessment shows food web processes playing a central role in all four areas, but application varies strongly, from formalized integration in management decisions, to support in selecting indicators and setting threshold values, to informal knowledge explaining ecosystem dynamics and management performance. Barriers for integration are complexity of involved ecological processes and that management frameworks are not designed to handle such information. We provide a categorization of the multi-faceted uses of food web knowledge and benefits of future incorporation in management, especially moving towards ecosystem-based approaches as guiding principle in present marine policies and directives. We close with perspectives on research needs to support this move considering global and regional change.

Description: As industrialized fishing activities have moved into deeper water, the recognition of Vulnerable Marine Ecosystems (VMEs) has become important for the protection of the deep-sea. Our limited knowledge on the past and present distribution of VMEs hinders our ability to manage bottom fisheries effectively. This study investigated whether accounting for bottom fishing intensity (derived from Vessel Monitoring System records) as a predictor in habitat suitability models can (1) improve predictions of, and (2) provide estimates for a pre-fishing baseline for the distribution and biomass of a VME indicator taxon. Random Forest models were applied to presence/absence and biomass of Geodia sponges and environmental variables with and without bottom fishing intensity. The models including fishing were further used to predict distribution and biomass of Geodia to a pre-fishing scenario. Inclusion of fishing pressure as a predictive term significantly improved model performance for both sponge presence and biomass. This study has demonstrated a way to produce a more accurate picture of the current distribution of VMEs in the study area. The pre-fishing scenario predictions also identified areas of suitable Geodia habitat that are currently impacted by fishing, suggesting that sponge habitat and biomass have been impacted by bottom trawling activities.

Description: Since graph learning could preserve the structure information of the samples to improve the learning ability, it has been widely applied in both shallow learning and deep learning. However, the current graph learning methods still suffer from the issues such as outlier influence and model robustness. In this paper, we propose a new dynamic graph neural network (DGCN) method to conduct semi-supervised classification on multi-view data by jointly conducting the graph learning and the classification task in a unified framework. Specifically, our method investigates three strategies to improve the quality of the graph before feeding it into the GCN model: (i) employing robust statistics to consider the sample importance for reducing the outlier influence, i.e. assigning every sample with soft weights so that the important samples are with large weights and outliers are with small or even zero weights; (ii) learning the common representation across all views to improve the quality of the graph for every view; and (iii) learning the complementary information from all initial graphs on multi-view data to further improve the learning of the graph for every view. As a result, each of the strategies could improve the robustness of the DGCN model. Moreover, they are complementary for reducing outlier influence from different aspects, i.e. the sample importance reduces the weights of the outliers, both the common representation and the complementary information improve the quality of the graph for every view. Experimental result on real data sets demonstrates the effectiveness of our method, compared to the comparison methods, in terms of multi-class classification performance.

Description: Two end-to-end ecosystem models, NORWECOM.E2E and NoBa Atlantis, have been used to explore a selection of indicators from the Barents Sea Management plans (BSMP). The indicators included in the BSMP are a combination of simple (e.g. temperature, biomass, and abundance) and complex (e.g. trophic level and biomass of functional groups). The abiotic indicators are found to serve more as a tool to report on climate trends rather than being ecological indicators. It is shown that the selected indicators give a good overview of the ecosystem state, but that overarching management targets and lack of connection between indicators and management actions makes it questionable if the indicator system is suitable for direct use in management as such. The lack of socio-economic and economic indicators prevents a holistic view of the system, and an inclusion of these in future management plans is recommended. The evaluated indicators perform well as an assessment of the ecosystem, but consistency and representativeness are extremely dependent on the time and in what area they are sampled. This conclusion strongly supports the inclusion of an observing system simulation experiment in management plans, to make sure that the observations represent the properties that the indicators need.

Description: Laplacian support vector machine (LapSVM) is an extremely popular classification method and relies on a small number of labels and a Laplacian regularization to complete the training of the support vector machine (SVM). However, the training of SVM model and Laplacian matrix construction are usually two independent process. Therefore, In this paper, we propose a new adaptive LapSVM method to realize semi-supervised learning with a primal solution. Specifically, the hinge loss of unlabelled data is considered to maximize the distance between unlabelled samples from different classes and the process of dealing with labelled data are similar to other LapSVM methods. Besides, the proposed method embeds the Laplacian matrix acquisition into the SVM training process to improve the effectiveness of Laplacian matrix and the accuracy of new SVM model. Moreover, a novel optimization algorithm considering primal solver is proposed to our adaptive LapSVM model. Experimental results showed that our method outperformed all comparison methods in terms of different evaluation metrics on both real datasets and synthetic datasets.

Description: Many management bodies require applying the precautionary approach when managing marine fisheries resources to achieve sustainability and avoid exceeding limits. For data-limited stocks, however, defining and achieving management objectives can be difficult. Management procedures can be optimized towards specific management objectives with genetic algorithms. We explored the feasibility of including an objective that limited the risk of a stock falling below various limit reference points in the optimization routine for an empirical data-limited control rule that uses a biomass index, mean catch length, and includes constraints (the “rfb-rule”). This was tested through management strategy evaluation on several fish stocks representing various life-history traits. We show that risk objectives could be met, but more restrictive risk limits can lead to a potential loss of yield. Outcomes were sensitive to simulation conditions such as observation uncertainty, which can be highly uncertain in data-limited situations. The rfb-rule outperforms the method currently applied by ICES, particularly when risk limitation objectives are considered. We conclude that the application of explicit precautionary levels is useful to avoid overfishing. However, we caution against the indiscriminate use of arbitrary risk limits without scientific evaluation to analyse their impact on stock yields and sustainability.

Description: The mesopelagic zone (200–1000 m depth) contains high fish species diversity but biomass and abundances are uncertain yet essential to understand ecosystem functioning. Hull-mounted acoustic systems (usually 38 kHz) often make assumptions on average target strength (TS) of mesopelagic fish assemblages when estimating biomass/abundance. Here, an unsupervised clustering algorithm was applied on broadband acoustic data (54–78 kHz), collected by a towed instrumented platform in the central Northeast Atlantic, to identify different mesopelagic target types based on similarity of individual TS spectra. Numerical density estimates from echo-counting showed spatial differences in vertical distribution patterns of the different target types and TS spectra data suggested that 〉30% of the gas-bearing targets had high resonance frequencies (〉60 kHz) with low scattering strength at 38 kHz. This conceptual study highlights the importance of separating targets into different target groups to obtain correct backscatter information and to account for all relevant scatterers when estimating average TS at 38 kHz, in order to achieve more accurate biomass/abundance estimates. It furthermore demonstrates the use of a towed broadband acoustic platform for fine-scale numerical density estimates as a complementary method to hull-mounted acoustic data to increase knowledge on mesopelagic ecosystem structure.

Description: China's mariculture is an indispensable part of the world's aquaculture and helps address food security issues in China and around the globe. However, this industry is facing a grand challenge from global warming. Therefore, it is urgent to assess the sensitivity of the main mariculture species and production modes to the increasing temperature. Here, we first extracted the coastal temperature data from 1465 grid cells (0.25 × 0.25 arcdegree) in the mariculture regions, and then compiled an upper thermal limit as well as culturing modes dataset of forty-two commercially important mariculture species. With these two datasets, we calculated the thermal safety margin (TSM) for each species across its aquaculture regions. Our results showed that several species with low TSMs were particularly sensitive to the current conditions and future warming, and some culturing regions face catastrophic consequences caused by high temperature and potential heatwaves. It is also noted that several mariculture modes like pond farming and mudflat ranching were more vulnerable compared to other mariculture modes. In summary, China's mariculture industry is sensitive to global warming at present and in the future. Our present study also provided tools to assess the risks in mariculture production and suggested solutions for future mitigation and adaptations.

Description: Accurate estimates of growth and mortality are needed to understand drivers of production and cohort success. Existing methods for estimating mortality rates, such as catch-curves, require large sample sizes, as they work by grouping individuals into age-bins to determine a frequency distribution. Yet, sampling enough larvae is often not possible at fine scales within the constraints of research projects, due to low density of larvae in pelagic environments. Here, we develop a novel method to simultaneously estimate growth and mortality rates of fish larvae as a continuous function of size using theory of size-structured populations, eliminating the need to group data into age-bins. We compare the effectiveness of our model to existing methods by generating data from a known distribution. This comparison demonstrates that while all models recover correct parameter values under ideal circumstances, our new method performs better than existing methods when sample sizes are low. Additionally, our method can accommodate non-linear growth and mortality functions, while also allowing growth and mortality to vary as functions of environmental co-variates. This increased accuracy and flexibility of our method should improve our ability to relate variability in larval production to environmental fluctuations at finer spatial scales.

Description: Motivation Deep learning approaches have empowered single-cell omics data analysis in many ways and generated new insights from complex cellular systems. As there is an increasing need for single cell omics data to be integrated across sources, types, and features of data, the challenges of integrating single-cell omics data are rising. Here, we present an unsupervised deep learning algorithm that learns discriminative representations for single-cell data via maximizing mutual information, SMILE (Single-cell Mutual Information Learning). Results Using a unique cell-pairing design, SMILE successfully integrates multi-source single-cell transcriptome data, removing batch effects and projecting similar cell types, even from different tissues, into the shared space. SMILE can also integrate data from two or more modalities, such as joint profiling technologies using single-cell ATAC-seq, RNA-seq, DNA methylation, Hi-C, and ChIP data. When paired cells are known, SMILE can integrate data with unmatched feature, such as genes for RNA-seq and genome wide peaks for ATAC-seq. Integrated representations learned from joint profiling technologies can then be used as a framework for comparing independent single source data. Supplementary information Supplementary data are available at Bioinformatics online. The source code of SMILE including analyses of key results in the study can be found at: https://github.com/rpmccordlab/SMILE.

Description: Motivation Alignment-free (AF) distance/similarity functions are a key tool for sequence analysis. Experimental studies on real datasets abound and, to some extent, there are also studies regarding their control of false positive rate (Type I error). However, assessment of their power, i.e., their ability to identify true similarity, has been limited to some members of the D2 family. The corresponding experimental studies have concentrated on short sequences, a scenario no longer adequate for current applications, where sequence lengths may vary considerably. Such a State of the Art is methodologically problematic, since information regarding a key feature such as power is either missing or limited. Results By concentrating on a representative set of word-frequency based AF functions, we perform the first coherent and uniform evaluation of the power, involving also Type I error for completeness. Two Alternative models of important genomic features (CIS Regulatory Modules and Horizontal Gene Transfer), a wide range of sequence lengths from a few thousand to millions, and different values of k have been used. As a result, we provide a characterization of those AF functions that is novel and informative. Indeed, we identify weak and strong points of each function considered, which may be used as a guide to choose one for analysis tasks. Remarkably, of the fifteen functions that we have considered, only four stand out, with small differences between small and short sequence length scenarios. Finally, in order to encourage the use of our methodology for validation of future AF functions, the Big Data platform supporting it is public. Availability The software is available at: https://github.com/pipp8/power_statistics Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary Distributed Acoustic Sensing (DAS) networks promise to revolutionize observational seismology by providing cost-effective, highly dense spatial sampling of the seismic wavefield, especially by utilizing pre-deployed telecomm fiber in urban settings for which dense seismic network deployments are difficult to construct. However, each DAS channel is sensitive only to one projection of the horizontal strain tensor and therefore gives an incomplete picture of the horizontal seismic wavefield, limiting our ability to make a holistic analysis of instrument response. This analysis has therefore been largely restricted to pointwise comparisons where a fortuitious coincidence of reference three-component seismometers and co-located DAS cable allows. We evaluate DAS instrument response by comparing DAS measurements from the PoroTomo experiment with strain-rate wavefield reconstructed from the nodal seismic array deployed in the same experiment, allowing us to treat the entire DAS array in a systematic fashion irrespective of cable geometry relative to the location of nodes. We found that, while the phase differences are in general small, the amplitude differences between predicted and observed DAS strain-rates average a factor of 2 across the array and correlate with near-surface geology, suggesting that careful assessment of DAS deployments is essential for applications that require reliable assessments of amplitude. We further discuss strategies for empirical gain corrections and optimal placement of point sensor deployments to generate the best combined sensitivity with an already deployed DAS cable, from a wavefield reconstruction perspective.

Description: Summary We have studied the active and recent tectonics of New Guinea, using earthquake source modelling, analysis of gravity anomalies, seismic reflection profiles, and thermal and mechanical models. Our aim is to investigate the behaviour and evolution of a young continental deformation belt, and to explore the effects of lateral variations in foreland rheology on the deformation. We find that along-strike gradients in the lithosphere thickness of the southern foreland have resulted in correlated changes in seismogenic thickness, likely due to the effects on the temperature structure of the crust. The resulting variation in the strength of the foreland means that in the east, the foreland is broken through on thrust faults, whereas in the west it is relatively intact. The lack of correlation between the elevation of the mountain belt and the seismogenic thickness of the foreland is likely to be due to the time taken to thicken the crust in the mountains following changes in the rheology of the underthrusting foreland, as the thinned passive margin of northern Australia is consumed. The along-strike variation in whether the force exerted between the mountains and the lowlands is able to break the foreland crust enables us to estimate the effective coefficient of friction on foreland faults to be in the range of 0.01-0.28. We use force-balance calculations to show that the recent tectonic re-organisation in western New Guinea is likely to be due to the development of increasing curvature in the Banda Arc, and that the impingement of continental material on the subduction zone may explain the unusually low force it exerts on western New Guinea.

Description: Summary On 2020 December 29, the Mw 6.4 Petrinja earthquake hit the Kupa Valley region and set a record for the largest earthquake in northwestern (NW) Croatia. The coseismic surface displacements are well obtained on three pairs of interferometric synthetic aperture radar (InSAR) images from Sentinel-1 satellites. The interferograms exhibit coseismic ground deformation with a maximum line-of-sight (LOS) displacement of 0.4 m. Based on the coseismic deformation field, we investigate both the fault geometry and the coseismic slip distribution. The results show a dextral event with a peak slip of 3.50 m at a depth of 3.47 km. The shallow depth and unusually large coseismic slip correspond to obvious ground deformation and serious damage in the epicentral zone. The 2020 earthquake highlights an unmapped, steeply dipping strike-slip fault, which possibly enabled a potential ‘curve cut-off’ process on the bending segment of the Pokupsko fault in the context of ∼N-S compression in NW Croatia. The large coseismic slip and high stress drop associated with the Mw 6.4 Petrinja earthquake are likely products of the geometrically complex fault zones and immature seismotectonic environment in NW Croatia.

Description: Motivation Coiled-coil is composed of two or more helices that are wound around each other. It widely exists in proteins and has been discovered to play a variety of critical roles in biology processes. Generally, there are three types of structural features in coiled-coil: coiled-coil domain (CCD), oligomeric state, and register. However, most of the existing computational tools only focus on one of them. Results Here, we describe a new deep learning model, CoCoPRED, which is based on convolutional layers, bidirectional long short-term memory, and attention mechanism. It has three networks, i.e., CCD network, oligomeric state network, and register network, corresponding to the three types of structural features in coiled-coil. This means CoCoPRED has the ability of fulfilling comprehensive prediction for coiled-coil proteins. Through the 5-fold cross-validation experiment, we demonstrate that CoCoPRED can achieve better performance than the state-of-the-art models on both CCD prediction and oligomeric state prediction. Further analysis suggests the CCD prediction may be a performance indicator of the oligomeric state prediction in CoCoPRED. The attention heads in CoCoPRED indicate that registers a, b, and e are more crucial for the oligomeric state prediction. Availability CoCoPRED is available at http://www.csbio.sjtu.edu.cn/bioinf/CoCoPRED. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation Inference of Identity-by-descent (IBD) sharing along the genome between pairs of individuals has important uses. But all existing inference methods are based on genotypes, which is not ideal for low-depth Next Generation Sequencing (NGS) data from which genotypes can only be called with high uncertainty. Results We present a new probabilistic software tool, LocalNgsRelate, for inferring IBD sharing along the genome between pairs of individuals from low-depth NGS data. Its inference is based on genotype likelihoods instead of genotypes, and thereby it takes the uncertainty of the genotype calling into account. Using real data from the 1000 Genomes project, we show that LocalNgsRelate provides more accurate IBD inference for low-depth NGS data than two state-of-the-art genotype based methods, Albrechtsen et al. (2009) and hap-IBD. We also show that the method works well for NGS data down to a depth of 2X. Availability LocalNgsRelate is freely available at https://github.com/idamoltke/LocalNgsRelate Supplementary Data Supplementary data are available at Bioinformatics online.

Description: Summary This article proposes the use of geostatistical techniques to estimate dispersion curves between other known ones. To do it, we introduce two novel methodologies: the stacking method and the group-velocity mapping method. We obtain our set of group-velocity fundamental mode dispersion curves from seismic noise correlation. Consequently, we first assign their attribution point at the mid-distance between the stations used for the dispersion curves calculation. The stacking method uses the range of the omnidirectional semivariogram of a regionalized variable that quantifies the similarity between dispersion curves to stack them according to their spatial correlation. We test this technique with dispersion curves obtained in Mexico City and get a range of ∼400 m for the omnidirectional semivariogram. We also calculate directional semivariograms and observe a maximum range (∼500 m) in the NW-SE direction, agreeing with the city's spatial distribution of natural periods. On the other hand, the group-velocity mapping method uses the ordinary kriging estimator in the group velocities for all the ranges of periods to generate maps and then dispersion curves. Estimated dispersion curves retrieved from both, the stacking and the group-velocity mapping method, were compared with those obtained with the fast marching tomographic method. We also establish analogies between getting group-velocity maps with the tomographic method and with the group-velocity mapping method. Finally, we observe that the range of the omnidirectional semivariogram used in the stacking method may be related to the tomographic method resolution.

Description: Summary The Bayesian slip inversion offers a powerful tool for modeling the earthquake source mechanism. It can provide a fully probabilistic result and thus permits us to quantitively assess the inversion uncertainty. The Bayesian problem is usually solved with Monte Carlo methods, but they are computationally expensive and are inapplicable for high-dimensional and large-scale problems. Variational inference is an alternative solver to the Bayesian problem. It turns Bayesian inference into an optimization task and thus enjoys better computational performances. In this study, we introduce a general variational inference algorithm, automatic differentiation variational inference (ADVI), to the Bayesian slip inversion and compare it with the classic Metropolis-Hastings (MH) sampling method. The synthetic test shows that the two methods generate nearly identical mean slip distributions and standard deviation maps. In the real case study, the two methods produce highly consistent mean slip distributions, but the ADVI-derived standard deviation map differs from that produced by the MH method, possibly because of the limitation of the Gaussian approximation in the ADVI method. In both cases, ADVI can give comparable results to the MH method but with a significantly lower computational cost. Our results show that ADVI is a promising and competitive method for the Bayesian slip inversion.

Description: Summary Whole genome assembly (WGA) of bacterial genomes with short reads is a quite common task as DNA sequencing has become cheaper with the advances of its technology. The process of assembling a genome has no absolute golden standard and it requires to perform a sequence of steps each of which can involve combinations of many different tools. However, the quality of the final assembly is always strongly related to the quality of the input data. With this in mind we built WGA-LP, a package that connects state-of-the-art programs for microbial analysis and novel scripts to check and improve the quality of both samples and resulting assemblies. WGA-LP, with its conservative decontamination approach, has shown to be capable of creating high quality assemblies even in the case of contaminated reads. Availability and implementation WGA-LP is available on GitHub (https://github.com/redsnic/WGA-LP) and Docker Hub (https://hub.docker.com/r/redsnic/wgalp). The web app for node visualization is hosted by shinyapps.io (https://redsnic.shinyapps.io/ContigCoverageVisualizer/). Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation The adoption of current single-cell DNA methylation sequencing protocols is hindered by incomplete coverage, outlining the need for effective imputation techniques. The task of imputing single-cell (methylation) data requires models to build an understanding of underlying biological processes. Results We adapt the transformer neural network architecture to operate on methylation matrices through combining axial attention with sliding window self-attention. The obtained CpG Transformer displays state-of-the-art performances on a wide range of scBS-seq and scRRBS-seq datasets. Furthermore, we demonstrate the interpretability of CpG Transformer and illustrate its rapid transfer learning properties, allowing practitioners to train models on new datasets with a limited computational and time budget. Availability and Implementation CpG Transformer is freely available at https://github.com/gdewael/cpg-transformer. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary Constitutive theory for viscoelasticity has broad application to solid mantle or ice deformations driven by tides, surface mass variations, and post-seismic flow. Geophysical models using higher order viscoelasticity can better accommodate geodetic observations than lower-order theory, typically provided by tensor versions of Maxwell, 4-parameter Burgers or standard linear (Zener) rheology. We derive, for the first time, a mathematical description of a compressible version of the extended Burgers material (EBM) model paradigm which has a distribution function of relaxation spectra. The latter model is often used for parameterizing high temperature background transient responses in the rock physics and mechanics laboratory setting and have demonstrated application to low frequency seismic wave attenuation. A new generalization of this practical anelastic model is presented and applied to the glacial isostatic adjustment momentum equations, thus providing useful guidance for generating initial-value boundary problem-solving software for quite general coding strategies. The solutions for the vertical motion response to a suddenly imposed surface load reveal a short-term transience of substantial amplitude.

Description: Motivation With the advancement of sequencing technologies, genomic data sets are constantly being expanded by high volumes of different data types. One recently introduced data type in genomic science is genomic signals, which are usually short-read coverage measurements over the genome. To understand and evaluate the results of such studies, one needs to understand and analyze the characteristics of the input data. Results SigTools is an R-based genomic signals visualization package developed with two objectives: 1) to facilitate genomic signals exploration in order to uncover insights for later model training, refinement, and development by including distribution and autocorrelation plots. 2) to enable genomic signals interpretation by including correlation, and aggregation plots. In addition, our corresponding web application, SigTools-Shiny, extends the accessibility scope of these modules to people who are more comfortable working with graphical user interfaces instead of command-line tools. Availability SigTools source code, installation guide, and manual is freely available on http://github.com/shohre73.

Description: Summary Stoneley modes are a special subset of normal modes whose energy is confined along the core-mantle boundary. As such, they offer a unique glimpse into Earth structure at the base of the mantle. They are often observed through coupling with mantle modes due to rotation, ellipticity and lateral heterogeneity, though they can be detected without such coupling. In this study, we explore the relative sensitivities of seismic spectra of two low-frequency Stoneley modes to several factors, taking as reference the fully coupled computation up to 3 mHz in model S20RTS. The factors considered are (i) theoretical, by exploring the extent to which various coupling approximations can accurately reproduce reference spectra; and (ii) model-based, by exploring how various Earth parameters such as core-mantle boundary topography, attenuation, and S-wave and P-wave structures, and the seismic source solution may influence the spectra. We find that mode-pair coupling is insufficiently accurate, but coupling modes within a range of ±0.1 mHz produces acceptable spectra, compared to full coupling. This has important implications for splitting function measurements, which are computed under the assumption of isolated modes or at best, mode-pair or group coupling. We find that uncertainties in the P-wave velocity mantle model dominate compared to other model parameters. In addition, we also test several hypothetical models of mantle density structure against real data. These tests indicate that, with the low-frequency Stoneley mode spectral data considered here, it is difficult to make any firm statement on whether the large-low-shear-velocity-provinces are denser or lighter than their surroundings. We conclude that better constraints on long wavelength elastic mantle structure, particularly P-wave velocity, need to be obtained, before making further statements on deep mantle density heterogeneity. In particular, a dense anomaly confined to a thin layer at the base of the mantle (less than ∼100-200 km) may not be resolvable using the two Stoneley modes tested here, while the ability of higher frequency Stoneley modes to resolve it requires further investigations.

Description: Nephrops (Nephrops norvegicus) is an economically valuable target species in the North Sea. Although individual Nephrops populations are scattered, the crustacean is managed regionally by the European Union (EU). The spatial competition for fisheries in the North Sea is growing especially due to expanding offshore wind farms (OWF) and newly implemented marine protected areas (MPA). Moreover, the Brexit affects the availability of EU fishing quotas and adds to the overall uncertainty EU fishers face. We compare landings and catches to scientifically advised quantities and perform an overlap analysis of fishing grounds with current and future OWFs and MPAs. Furthermore, we explore the German Nephrops fleet using high-resolution spatial fishing effort and catch data. Our results confirm earlier studies showing that Nephrops stocks have been fished above scientific advice. Present OWFs and MPAs marginally overlap with Nephrops fishing grounds, whereas German fishing grounds are covered up to 45% in future scenarios. Co-use strategies with OWFs could mitigate the loss of fishing opportunities. Decreased cod quotas due to Brexit and worse stock conditions, lowers Germany's capability to swap Nephrops quotas with the UK. We support the call for a new management strategy of individual Nephrops populations and the promotion of selective fishing gears.

Description: Assessment of zooplankton abundance, distribution, community composition, and temporal variability is critical to understanding the effects of climate variability and change on lower trophic level production and availability for consumption by larger consumers. Zooplankton sampling is performed across the Canadian continental shelf system by Fisheries and Oceans Canada's Atlantic Zone Monitoring Programme (AZMP). Sampling includes semi-monthly to monthly collection of zooplankton using vertical net tows (VNTs) deployed from near-bottom to surface at stations on the central Scotian Shelf (Stn 2, 150 m depth) and Newfoundland Shelf (Stn 27, 175 m depth), and by Continuous Plankton Recorders (CPRs) in the near-surface layers along routes over the Scotian and Newfoundland shelves (0–10 m depth). Here, we compare abundance metrics for 11 copepod taxa collected using both gear types in both regions between 1999 and 2015. Seasonal cycles of VNT and CPR abundance were similar for near-surface residents. VNT: CPR abundance ratios varied year-round for vertical migrants, as ontogenetic migrants shifted their vertical distribution, and as diel migrants changed their migratory behaviour. For some taxa, differences in annual average VNT: CPR abundance ratios between regions suggest differences in vertical distribution, while for others differences in inter-annual variability for VNT and CPR abundances suggest differences in the dynamics of the near- and sub-surface components of the populations.

Description: The origin of the eclogites that reside in cratonic mantle roots has long been debated. In the classic Roberts Victor kimberlite locality in South Africa, the strongly contrasting textural and geochemical features of two types of eclogites have led to different genetic models. We studied a new suite of 63 eclogite xenoliths from the former Roberts Victor Mine. In addition to major- and trace-element compositions for all new samples, we determined 18O/16O for garnet from 34 eclogites. Based on geochemical and textural characteristics we identify a large suite of Type I eclogites (n = 53) consistent with previous interpretations that these rocks originate from metamorphosed basaltic-picritic lavas or gabbroic cumulates from oceanic crust, crystallised from melts of depleted MORB mantle. We identify a smaller set of Type II eclogites (n = 10) based on geochemical and textural similarity to eclogites in published literature. We infer their range to very low δ18O values combined with their varied, often very low Zr/Hf ratios and LREE-depleted nature to indicate a protolith origin via low-pressure clinopyroxene-bearing oceanic cumulates formed from melts that were more depleted in incompatible elements than N-MORB. These compositions are indicative of derivation from a residual mantle source that experienced preferential extraction of incompatible elements and fractionation of Zr-Hf during previous melting.

Description: Summary In an attempt to overcome the difficulties of the full waveform inversion (FWI), several alternative objective functions have been proposed over the last few years. Many of them are based on the assumption that the residuals (differences between modelled and observed seismic data) follow specific probability distributions when, in fact, the true probability distribution is unknown. This leads FWI to converge to an incorrect probability distribution if the assumed probability distribution is different from the real one and, consequently it may lead the FWI to achieve biased models of the subsurface. In this work, we propose an objective function which does not force the residuals to follow a specific probability distribution. Instead, we propose to use the non-parametric kernel density estimation technique (KDE) (which imposes the least possible assumptions about the residuals) to explore the probability distribution that may be more suitable. As evidenced by the results obtained in a synthetic model and in a typical P-wave velocity model of the Brazilian pre-salt fields, the proposed FWI reveals a greater potential to overcome more adverse situations (such as cycle-skipping) and also a lower sensitivity to noise in the observed data than conventional L2 and L1-norm objective functions and thus making it possible to obtain more accurate models of the subsurface. This greater potential is also illustrated by the smoother and less sinuous shape of the proposed objective function with fewer local minima compared with the conventional objective functions.

Description: Summary Finite-difference (FD) modeling of seismic waves in the vicinity of dipping interfaces gives rise to artifacts. Examples are phase and amplitude errors, as well as staircase diffractions. Such errors can be reduced in two general ways. In the first approach, the interface can be anti-aliased (i.e., with an anti-aliased step-function, or a lowpass filter). Alternatively, the interface may be replaced with an equivalent medium (i.e., using Schoenberg & Muir (SM) calculus or orthorhombic averaging). We test these strategies in acoustic, elastic isotropic, and elastic anisotropic settings. Computed FD solutions are compared to analytical solutions. We find that in acoustic media, anti-aliasing methods lead to the smallest errors. Conversely, in elastic media, the SM calculus provides the best accuracy. The downside of the SM calculus is that it requires an anisotropic FD solver even to model an interface between two isotropic materials. As a result, the computational cost increases compared to when using isotropic FD solvers. However, since coarser grid spacings can be used to represent the dipping interfaces, the two effects (an expensive FD solver on a coarser FD grid) equal out. Hence, the SM calculus can provide an efficient means to reduce errors, also in elastic isotropic media.

Description: Motivation Adverse Outcome Pathways (AOPs) are a conceptual framework developed to support the use of alternative toxicology approaches in the risk assessment. AOPs are structured linear organizations of existing knowledge illustrating causal pathways from the initial molecular perturbation triggered by various stressors, through key events (KEs) at different levels of biology, to the ultimate health or ecotoxicological adverse outcome. Results Artificial intelligence can be used to systematically explore available toxicological data that can be parsed in the scientific literature. Recently a tool called AOP-helpFinder was developed to identify associations between stressors and KEs supporting thus documentation of AOPs. To facilitate the utilization of this advanced bioinformatics tool by the scientific and the regulatory community, a webserver was created. The proposed AOP-helpFinder webserver uses better performing version of the tool which reduces the need for manual curation of the obtained results. As an example, the server was successfully applied to explore relationships of a set of endocrine disruptors with metabolic-related events. The AOP-helpFinder webserver assists in a rapid evaluation of existing knowledge stored in the PubMed database, a global resource of scientific information, to build AOPs and Adverse Outcome Networks (AONs) supporting the chemical risk assessment. Availability and implementation AOP-helpFinder is available at http://aop-helpfinder.u-paris-sciences.fr/index.php

Description: Motivation Differential network inference is a fundamental and challenging problem to reveal gene interactions and regulation relationships under different conditions. Many algorithms have been developed for this problem; however, they do not consider the differences between the importance of genes, which may not fit the real-world situation. Different genes have different mutation probabilities, and the vital genes associated with basic life activities have less fault tolerance to mutation. Equally treating all genes may bias the results of differential network inference. Thus, it is necessary to consider the importance of genes in the models of differential network inference. Results Based on the Gaussian graphical model with adaptive gene importance regularization, we develop a novel importance-penalized joint graphical Lasso method, IPJGL, for differential network inference. The presented method is validated by the simulation experiments as well as the real datasets. Furthermore, to precisely evaluate the results of differential network inference, we propose a new metric named APC2 for the differential levels of gene pairs. We apply IPJGL to analyze the TCGA colorectal and breast cancer datasets and find some candidate cancer genes with significant survival analysis results, including SOST for colorectal cancer and RBBP8 for breast cancer. We also conduct further analysis based on the interactions in the Reactome database and confirm the utility of our method. Availability R source code of importance-penalized joint graphical lasso is freely available at https://github.com/Wu-Lab/IPJGL. Supplementary information Supplementary materials are available at Bioinformatics online.

Description: Motivation DNA Methylation plays a key role in a variety of biological processes. Recently, Nanopore long-read sequencing has enabled direct detection of these modifications. As a consequence, a range of computational methods have been developed to exploit Nanopore data for methylation detection. However, current approaches rely on a human-defined threshold to detect the methylation status of a genomic position and are not optimized to detect sites methylated at low frequency. Furthermore, most methods employ either the Nanopore signals or the basecalling errors as the model input and do not take advantage of their combination. Results Here we present DeepMP, a convolutional neural network (CNN)-based model that takes information from Nanopore signals and basecalling errors to detect whether a given motif in a read is methylated or not. Besides, DeepMP introduces a threshold-free position modification calling model sensitive to sites methylated at low frequency across cells. We comprehensively benchmarked DeepMP against state-of-the-art methods on E. coli, human and pUC19 datasets. DeepMP outperforms current approaches at read-based and position-based methylation detection across sites methylated at different frequencies in the three datasets. Availability DeepMP is implemented and freely available under MIT license at https://github.com/pepebonet/DeepMP Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary We deployed a seismic network near the source region of the 2017 Mw 6.5 Jiuzhaigou earthquake to monitor aftershock activity and to investigate the local fault structure. An aftershock deployment of Array of small Arrays (AsA) and a Geometric Mean Envelop (GME) algorithm are adopted to enhance detection performance. We also adopt a set of association, relocation, and matched-filter techniques to obtain a detailed regional catalog. 16,742 events are detected and relocated, including 1,279 aftershocks following the Mw 4.8 aftershock. We develop a joint inversion algorithm utilizing locations of event clusters and focal mechanisms to determine the geometry of planar faults. Six segments were finally determined, in which three segments are related to the Huya fault reflecting a change in fault dip direction near the mainshock hypocenter, while the other segments reflect branches showing orthogonal and conjugate geometries with the Huya fault. Aftershocks were active on branching faults between the Huya and Minjiang faults indicating that the mainshock may have ruptured both major faults. We also resolve a fault portion with ‘weak strength’ near the mainshock hypocenter, which is characterized by limited co-seismic slips, concentrated afterslip, low aftershock activities, high b-value, and high sensitivity to stress changes. These phenomena can be explained by fault frictional properties at conditional stable sliding status, which may be related to the localized high pore-fluid pressure produced by the fluid intrusion.

Description: Summary The numerical simulation of seismic wave propagation in realistic heterogeneous media, as sedimentary basins, is a key element of seismic hazard estimation. Many numerical methods in two dimensions are based on unstructured triangular meshes and explicit time schemes. However, the presence of thin layers and tangential stratigraphic contacts in sedimentary basins entails poorly shaped mesh elements: some triangle heights are extremely small compared to the edge lengths, which requires small time steps in the simulations and thus leads to prohibitive computation times. We compare manual and automatic geological model simplification techniques to modify problematic areas of the domain, so as to improve the quality of the triangulated mesh. We modify the shape and the connectivity between rock units in the basin, with the objective to reduce the computation time without significantly changing the physical response of the geological medium. These simplification techniques are applied in an investigation of site effects in the lower Var valley, a densely urbanized area located near the city of Nice (South-East of France). Numerical simulations of plane wave propagation in a heterogeneous 2D profile are carried out with a discontinuous Galerkin finite element method. Five simplified meshes are generated and the impacts of the simplifications are analyzed in comparison to the reference model. We compare the time solutions and the transfer functions obtained on the surface of the basin. The results show that the simplification procedures, in particular automatic modifications of the model, yield a significant performance gain, with a ratio higher than 55, while having a negligible impact on the ground motion response.

Description: Since the first report of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019, over 100 million people have been infected by COVID-19, millions of whom have died. In the latest year, a large number of omics data have sprung up and helped researchers broadly study the sequence, chemical structure and function of SARS-CoV-2, as well as molecular abnormal mechanisms of COVID-19 patients. Though some successes have been achieved in these areas, it is necessary to analyze and mine omics data for comprehensively understanding SARS-CoV-2 and COVID-19. Hence, we reviewed the current advantages and limitations of the integration of omics data herein. Firstly, we sorted out the sequence resources and database resources of SARS-CoV-2, including protein chemical structure, potential drug information and research literature resources. Next, we collected omics data of the COVID-19 hosts, including genomics, transcriptomics, microbiology and potential drug information data. And subsequently, based on the integration of omics data, we summarized the existing data analysis methods and the related research results of COVID-19 multi-omics data in recent years. Finally, we put forward SARS-CoV-2 (COVID-19) multi-omics data integration research direction and gave a case study to mine deeper for the disease mechanisms of COVID-19.

Description: Motivation Reverse engineering of gene regulatory networks (GRNs) has long been an attractive research topic in system biology. Computational prediction of gene regulatory interactions has remained a challenging problem due to the complexity of gene expression and scarce information resources. The high-throughput spatial gene expression data, like in situ hybridization images that exhibit temporal and spatial expression patterns, has provided abundant and reliable information for the inference of GRNs. However, computational tools for analyzing the spatial gene expression data are highly underdeveloped. Results In this study, we develop a new method for identifying gene regulatory interactions from gene expression images, called ConGRI. The method is featured by a contrastive learning scheme and deep Siamese convolutional neural network architecture, which automatically learns high-level feature embeddings for the expression images and then feeds the embeddings to an artificial neural network to determine whether or not the interaction exists. We apply the method to a Drosophila embryogenesis dataset and identify GRNs of eye development and mesoderm development. Experimental results show that ConGRI outperforms previous traditional and deep learning methods by a large margin, which achieves accuracies of 76.7% and 68.7% for the GRNs of early eye development and mesoderm development, respectively. It also reveals some master regulators for Drosophila eye development. Availabilityand implementation https://github.com/lugimzheng/ConGRI. Supplementary information Supplementary data are available at Bioinformatics online.