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Microbial secondary metabolism is a reservoir of bioactive compounds of immense biotechnological and biomedical potential. The biosynthetic machinery responsible for the production of these secondary metabolites (SMs) (also called natural products) is often encoded by collocated groups of genes called biosynthetic gene clusters (BGCs). High-throughput genome sequencing of both isolates and metagenomic samples combined with the development of specialized computational workflows is enabling systematic identification of BGCs and the discovery of novel SMs. In order to advance exploration of microbial secondary metabolism and its diversity, we developed the largest publicly available database of predicted BGCs combined with experimentally verified BGCs, the Integrated Microbial Genomes Atlas of Biosynthetic gene Clusters (IMG-ABC) (https://img.jgi.doe.gov/abc-public). Here we describe the first major content update of the IMG-ABC knowledgebase, since its initial release in 2015, refreshing the BGC prediction pipeline with the latest version of antiSMASH (v5) as well as presenting the data in the context of underlying environmental metadata sourced from GOLD (https://gold.jgi.doe.gov/). This update has greatly improved the quality and expanded the types of predicted BGCs compared to the previous version.

The BiGG Models knowledge base (http://bigg.ucsd.edu) is a centralized repository for high-quality genome-scale metabolic models. For the past 12 years, the website has allowed users to browse and search metabolic models. Within this update, we detail new content and features in the repository, continuing the original effort to connect each model to genome annotations and external databases as well as standardization of reactions and metabolites. We describe the addition of 31 new models that expand the portion of the phylogenetic tree covered by BiGG Models. We also describe new functionality for hosting multi-strain models, which have proven to be insightful in a variety of studies centered on comparisons of related strains. Finally, the models in the knowledge base have been benchmarked using Memote, a new community-developed validator for genome-scale models to demonstrate the improving quality and transparency of model content in BiGG Models.

Understanding behavior is the first step to truly understanding neural mechanisms in the brain that drive it. Traditional behavioral analysis methods often do not capture the richness inherent to the natural behavior. Here, we provide detailed step-by-step instructions with visualizations of our recent methodology, DeepBehavior. The DeepBehavior toolbox uses deep learning frameworks built with convolutional neural networks to rapidly process and analyze behavioral videos. This protocol demonstrates three different frameworks for single object detection, multiple object detection, and three-dimensional (3D) human joint pose tracking. These frameworks return cartesian coordinates of the object of interest for each frame of the behavior video. Data collected from the DeepBehavior toolbox contain much more detail than traditional behavior analysis methods and provides detailed insights to the behavior dynamics. DeepBehavior quantifies behavior tasks in a robust, automated, and precise way. Following the identification of behavior, post-processing code is provided to extract information and visualizations from the behavioral videos.

Promoting health and prolonging independence in the home is a priority for older adults, caregivers, clinicians, and society at large. Rapidly developing robotics technology provides a platform for interventions, with the fields of physically and socially assistive robots expanding in recent years. However, less attention has been paid to using robots to enhance the cognitive health of older adults. The goal of this review is to synthesize the current literature on home-based cognitively assistive robots (CAR) in older adults without dementia and to provide suggestions to improve the quality of the scientific evidence in this subfield. First, we set the stage for CAR by: a) introducing the field of robotics to improve health, b) summarizing evidence emphasizing the importance of home-based interventions for older adults, c) reviewing literature on robot acceptability in older adults, d) highlighting important ethical issues in healthcare robotics, and e) reviewing current findings on socially assistive robots, with a focus on translating findings to the CAR context. With this foundation in place, we then review the literature on CAR, identifying gaps and limitations of current evidence, and proposing future directions for research. We conclude that CAR is promising and feasible and that there is a need for more methodologically rigorous evaluations of CAR to promote prolonged home-based independence in older adults.

Previous research has studied medical professionals' perception of artificial intelligence (AI). However, there has been a limited understanding of how healthcare consumers perceive and use AI-powered technologies such as mobile health apps. We collected 40 popular mobile health apps that claim to have adopted AI, to study how AI is explained in these apps' descriptions, and how users react to it through app reviews. We found that four AI features (Recommendation, Conversational Agent, Recognition, and Prediction) are frequently used across seven health domains, including Fitness, Mental Health, Meditation and Sleep, Nutrition and Diet, etc. Our results show that (1) users have unique expectations toward each AI features, such as including feedback for recommendations, humanlike experience for conversational agents, and accuracy for recognition and prediction; (2) when AI is not adequately described, users make their own attempts to understand AI and to find out how (well) it works.

In today's world of big data, computational analysis has become a key driver of biomedical research. High-performance computational facilities are capable of processing considerable volumes of data, yet often lack an easy-to-use interface to guide the user in supervising and adjusting bioinformatics analysis via a tablet or smartphone. To address this gap we proposed Telescope, a novel tool that interfaces with high-performance computational clusters to deliver an intuitive user interface for controlling and monitoring bioinformatics analyses in real-time. By leveraging last generation technology now ubiquitous to most researchers (such as smartphones), Telescope delivers a friendly user experience and manages conectivity and encryption under the hood. Telescope helps to mitigate the digital divide between wet and computational laboratories in contemporary biology. By delivering convenience and ease of use through a user experience not relying on expertise with computational clusters, Telescope can help researchers close the feedback loop between bioinformatics and experimental work with minimal impact on the performance of computational tools. Telescope is freely available at https://github.com/Mangul-Lab-USC/telescope.

The Alliance of Genome Resources (Alliance) is a consortium of the major model organism databases and the Gene Ontology that is guided by the vision of facilitating exploration of related genes in human and well-studied model organisms by providing a highly integrated and comprehensive platform that enables researchers to leverage the extensive body of genetic and genomic studies in these organisms. Initiated in 2016, the Alliance is building a central portal (www.alliancegenome.org) for access to data for the primary model organisms along with gene ontology data and human data. All data types represented in the Alliance portal (e.g. genomic data and phenotype descriptions) have common data models and workflows for curation. All data are open and freely available via a variety of mechanisms. Long-term plans for the Alliance project include a focus on coverage of additional model organisms including those without dedicated curation communities, and the inclusion of new data types with a particular focus on providing data and tools for the non-model-organism researcher that support enhanced discovery about human health and disease. Here we review current progress and present immediate plans for this new bioinformatics resource.

Infrared thermography is a potential method to improve efficiency for early detection of breast cancer. This technique does not use ionizing radiation and is feasible for screening in men and for detecting changes in young women. In this study, ninety-eight infrared images were used to create a database to develop a computer-aided diagnosis system. Typically, this kind of system is associated with graphical interfaces to facilitate users' work. In this study, the computer-aided diagnosis was implemented based on statistical classifiers for analysis of four classes: Malignant Tumor, Benign Tumor, Cyst and Healthy. The region of interest was segmented in automatic and semiautomatic ways, which is respectively associated with the Support Vector Machine classifier and Mahalanobis classifier. To evaluate the performance of the proposed classifiers, a confusion matrix was applied to each result obtained. Using the proposed GUI-CAD tool, it was possible to carry out individual and unsupervised classification of patients, with 93% sensitivity. [ABSTRACT FROM AUTHOR]

Objective: To prove the potentialities of an integrated and sensorized childbirth platform as an innovative simulator for education of inexperienced gynecological and obstetrical medical students. Methods: A total of 152 inexperienced medical students were recruited to a simulation program on labor progression evaluation. After an introductory lecture on basic concepts of labor and birth given by an expert gynecologist, three different gynecologic scenarios were simulated using both a traditional obstetric simulator and the innovative proposed platform, for a total of six tests for each student. A score was assigned for each performed scenario, based on its correctness. Self‐assessment questionnaires were compiled before and after the simulation program for additional subjective assessment. Results: Median score of the simulations performed with our platform was significantly higher than that of the simulations performed with a traditional simulator, for all the three experimented scenarios (P < 0.001). Conclusions: The use of a sensorized platform for labor progression allowed for an accurate and faster diagnosis if compared with a traditional simulator even for inexperienced operators, supporting its use in clinical training, which could be realistically introduced into the clinical practice for medical student education. Synopsis: Use of a sensorized platform as an innovative approach for training and education purposes has been demonstrated within a simulation program with inexperienced medical students. [ABSTRACT FROM AUTHOR]

In the highly competitive injection molding industry, the ability to effectively collect information from various sensors installed in molds and machines is of the utmost relevance, enabling the development of data-based Industry 4.0 algorithms. In this work, an alternative to commercially available monitoring systems used in the industry was developed and tested in the scope of the TOOLING 4G project. The novelty of this system is its affordability, simplicity, real-time data acquisition and display in an intuitive Graphical User Interface (GUI), while being open-source firmware and software-based. These characteristics, and their combinations have been present in previous works, but, to the authors' knowledge, not all of them simultaneously. The system used an Arduino microcontroller-based data acquisition module that can be connected to any computer via a USB port. Software was developed, including a GUI, prepared to receive data from both the Arduino module and a second module. In the current state of development, data corresponding to a maximum of six sensors can be visualized, at a rate of 10 Hz, and recorded for later usage. These capabilities were verified under real-world conditions for monitoring an injection mold with the objective of creating the basis of a platform to deploy predictive maintenance. Mold temperature, cavity pressure, 3-axis acceleration, and extraction force data showed the system can successfully monitor the mold and allowed the clear distinction between normal and abnormal operating patterns. [ABSTRACT FROM AUTHOR]

In medical data science, FHIR provides an increasingly used information model, which will lead to the creation of FHIR warehouses in the future. To efficiently work with a FHIR-based representation, users need a visual representation. The modern UI framework ReactAdmin (RA) enhances usability by leveraging current web standards such as React and Material Design. Rapid development and implementation of usable modern UIs is made possible by its high modularity and many widgets available in the framework. For data connection to different data sources RA needs a DataProvider (DP), which maps the communication from the server to the provided components. In this work, we present a DataProvider for FHIR that enables future UI developments for FHIR servers using RA. A demo application demonstrates the DP's capabilities. The code is published under MIT license. [ABSTRACT FROM AUTHOR]

Amide hydrogen-deuterium exchange mass spectrometry (HDX-MS) has become widely popular for mapping protein-ligand interfaces, for understanding protein-protein interactions, and for discovering dynamic allostery. Several platforms are now available which provide large data sets of amide hydrogen/deuterium exchange mass spectrometry (HDX-MS) data. Although many of these platforms provide some down-stream processing, a comprehensive software that provides the most commonly used down-stream processing tools such as automatic back-exchange correction options, analysis of overlapping peptides, calculations of relative deuterium uptake into regions of the protein after such corrections, rigorous statistical analysis of the significance of uptake differences, and generation of high quality figures for data presentation is not yet available. Here we describe the Deuterium Exchange Correction and Analysis (DECA) software package, which provides all these downstream processing options for data from the most popular mass spectrometry platforms. The major functions of the software are demonstrated on sample data.

PurposeCancer pathology findings are critical for many aspects of care but are often locked away as unstructured free text. Our objective was to develop a natural language processing (NLP) system to extract prostate pathology details from postoperative pathology reports and a parallel structured data entry process for use by urologists during routine documentation care and compare accuracy when compared with manual abstraction and concordance between NLP and clinician-entered approaches.Materials and methodsFrom February 2016, clinicians used note templates with custom structured data elements (SDEs) during routine clinical care for men with prostate cancer. We also developed an NLP algorithm to parse radical prostatectomy pathology reports and extract structured data. We compared accuracy of clinician-entered SDEs and NLP-parsed data to manual abstraction as a gold standard and compared concordance (Cohen's κ) between approaches assuming no gold standard.ResultsThere were 523 patients with NLP-extracted data, 319 with SDE data, and 555 with manually abstracted data. For Gleason scores, NLP and clinician SDE accuracy was 95.6% and 95.8%, respectively, compared with manual abstraction, with concordance of 0.93 (95% CI, 0.89 to 0.98). For margin status, extracapsular extension, and seminal vesicle invasion, stage, and lymph node status, NLP accuracy was 94.8% to 100%, SDE accuracy was 87.7% to 100%, and concordance between NLP and SDE ranged from 0.92 to 1.0.ConclusionWe show that a real-world deployment of an NLP algorithm to extract pathology data and structured data entry by clinicians during routine clinical care in a busy clinical practice can generate accurate data when compared with manual abstraction for some, but not all, components of a prostate pathology report.

BackgroundResearchers commonly analyze lists of differentially expressed entities (DEEs), such as differentially expressed genes (DEGs), differentially expressed proteins (DEPs), and differentially methylated positions/regions (DMPs/DMRs), across multiple pairwise comparisons. Large biological studies can involve multiple conditions, tissues, and timepoints that result in dozens of pairwise comparisons. Manually filtering and comparing lists of DEEs across multiple pairwise comparisons, typically done by writing custom code, is a cumbersome task that can be streamlined and standardized.ResultsA-Lister is a lightweight command line and graphical user interface tool written in Python. It can be executed in a differential expression mode or generic name list mode. In differential expression mode, A-Lister accepts as input delimited text files that are output by differential expression tools such as DESeq2, edgeR, Cuffdiff, and limma. To allow for the most flexibility in input ID types, to avoid database installation requirements, and to allow for secure offline use, A-Lister does not validate or impose restrictions on entity ID names. Users can specify thresholds to filter the input file(s) by column(s) such as p-value, q-value, and fold change. Additionally, users can filter the pairwise comparisons within the input files by fold change direction (sign). Queries composed of intersection, fuzzy intersection, difference, and union set operations can also be performed on any number of pairwise comparisons. Thus, the user can filter and compare any number of pairwise comparisons within a single A-Lister differential expression command. In generic name list mode, A-Lister accepts delimited text files containing lists of names as input. Queries composed of intersection, fuzzy intersection, difference, and union set operations can then be performed across these lists of names.ConclusionsA-Lister is a flexible tool that enables the user to rapidly narrow down large lists of DEEs to a small number of most significant entities. These entities can then be further analyzed using visualization, pathway analysis, and other bioinformatics tools.

How is the complex audiomotor skill of speaking learned? To what extent does it depend on the specific characteristics of the vocal tract? Here, we developed a touchscreen-based speech synthesizer to examine learning of speech production independent of the vocal tract. Participants were trained to reproduce heard vowel targets by reaching to locations on the screen without visual feedback and receiving endpoint vowel sound auditory feedback that depended continuously on touch location. Participants demonstrated learning as evidenced by rapid increases in accuracy and consistency in the production of trained targets. This learning generalized to productions of novel vowel targets. Subsequent to learning, sensorimotor adaptation was observed in response to changes in the location-sound mapping. These findings suggest that participants learned adaptable sensorimotor maps allowing them to produce desired vowel sounds. These results have broad implications for understanding the acquisition of speech motor control.

A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.

INTRODUCTION: As specialized medical professionals such as radiologists, pathologists, and cardiologists gain education and experience, their diagnostic efficiency and accuracy change, and they show altered eye movement patterns during medical image interpretation. Existing research in this area is limited to interpretation of static medical images, such as digitized whole slide biopsies, making it difficult to understand how expertise development might manifest during dynamic image interpretation, such as with angiograms or volumetric scans. METHODS: A two-group (novice, expert) comparative pilot study examined the feasibility and utility of tracking and interpreting eye movement patterns while cardiologists viewed video-based coronary angiograms. A non-invasive eye tracking system recorded cardiologists (n = 8) visual behaviour while they viewed and diagnosed a series of eight angiogram videos. Analyses assessed frame-by-frame video navigation behaviour, eye fixation behaviour, and resulting diagnostic decision making. RESULTS: Relative to novices, expert cardiologists demonstrated shorter and less variable video review times, fewer eye fixations and saccadic eye movements, and less time spent paused on individual video frames. Novices showed repeated eye fixations on critical image frames and regions, though these were not predictive of accurate diagnostic decisions. DISCUSSION: These preliminary results demonstrate interpretive decision errors among novices, suggesting they identify and process critical diagnostic features, but sometimes fail to accurately interpret those features. Results also showcase the feasibility of tracking and understanding eye movements during video-based coronary angiogram interpretation and suggest that eye tracking may be valuable for informing assessments of competency progression during medical education and training.

ObjectiveThe US CDC identified prescription drug monitoring programs (PDMPs) as a tool to address the contemporary opioid crisis, but few studies have investigated PDMP usability and effectiveness from the users' perspective. Even fewer have considered how practices differ across medical domains. In this study, we aimed to address these gaps, soliciting perspectives on PDMPs from providers contending with the opioid crisis: physicians working in emergency departments (EDs) and pain management clinics. We aimed to provide practical design recommendations to improve PDMP workflow integration, as well as controlled substance history retrieval, interpretation, and decision support.MethodsWe conducted 16 in-depth semi-structured interviews with practicing emergency and pain physicians regarding their procedures, problems, and proposed solutions surrounding their use of CURES, California's PDMP. We investigated design problems in CURES by combining users' feedback with our usability inspection, drawing upon an extensive body of design literature. Then, we generated alternatives using design methods.ResultsWe found CURES's design did not accommodate the unique information needs of different medical domains. Further, clinicians had trouble accessing CURES and retrieving patients' controlled substance histories, mainly due to usability problems that could be addressed with little technical adjustment. Additionally, CURES rendered patient histories in large, cluttered tables, devoid of overview or context, making interpretation difficult and precarious. Lastly, our interviewees had rarely noticed or used advanced features, such as decision support.Discussion and conclusionUsability barriers inhibited adoption and effective use. We provide practical recommendations for improving opioid control by way of improving PDMP design, based on interviewees' suggestions and research-based design principles. Our findings have implications for other disciplines, including surgery and primary care.

Genome annotation is the process of identifying the location and function of a genome's encoded features. Improving the biological accuracy of annotation is a complex and iterative process requiring researchers to review and incorporate multiple sources of information such as transcriptome alignments, predictive models based on sequence profiles, and comparisons to features found in related organisms. Because rapidly decreasing costs are enabling an ever-growing number of scientists to incorporate sequencing as a routine laboratory technique, there is widespread demand for tools that can assist in the deliberative analytical review of genomic information. To this end, we present Apollo, an open source software package that enables researchers to efficiently inspect and refine the precise structure and role of genomic features in a graphical browser-based platform. Some of Apollo's newer user interface features include support for real-time collaboration, allowing distributed users to simultaneously edit the same encoded features while also instantly seeing the updates made by other researchers on the same region in a manner similar to Google Docs. Its technical architecture enables Apollo to be integrated into multiple existing genomic analysis pipelines and heterogeneous laboratory workflow platforms. Finally, we consider the implications that Apollo and related applications may have on how the results of genome research are published and made accessible.

Protein networks have become a popular tool for analyzing and visualizing the often long lists of proteins or genes obtained from proteomics and other high-throughput technologies. One of the most popular sources of such networks is the STRING database, which provides protein networks for more than 2000 organisms, including both physical interactions from experimental data and functional associations from curated pathways, automatic text mining, and prediction methods. However, its web interface is mainly intended for inspection of small networks and their underlying evidence. The Cytoscape software, on the other hand, is much better suited for working with large networks and offers greater flexibility in terms of network analysis, import, and visualization of additional data. To include both resources in the same workflow, we created stringApp, a Cytoscape app that makes it easy to import STRING networks into Cytoscape, retains the appearance and many of the features of STRING, and integrates data from associated databases. Here, we introduce many of the stringApp features and show how they can be used to carry out complex network analysis and visualization tasks on a typical proteomics data set, all through the Cytoscape user interface. stringApp is freely available from the Cytoscape app store: http://apps.cytoscape.org/apps/stringapp .

The TOPAS Monte Carlo (MC) system is used in radiation therapy and medical imaging research, having played a significant role in making Monte Carlo simulations widely available for proton therapy related research. While TOPAS provides detailed simulations of patient scale properties, the fundamental unit of the biological response to radiation is a cell. Thus, our goal was to develop TOPAS-nBio, an extension of TOPAS dedicated to advance understanding of radiobiological effects at the (sub-)cellular, (i.e., the cellular and sub-cellular) scale. TOPAS-nBio was designed as a set of open source classes that extends TOPAS to model radiobiological experiments. TOPAS-nBio is based on and extends Geant4-DNA, which extends the Geant4 toolkit, the basis of TOPAS, to include very low-energy interactions of particles down to vibrational energies, explicitly simulates every particle interaction (i.e., without using condensed histories) and propagates radiolysis products. To further facilitate the use of TOPAS-nBio, a graphical user interface was developed. TOPAS-nBio offers full track-structure Monte Carlo simulations, integration of chemical reactions within the first millisecond, an extensive catalogue of specialized cell geometries as well as sub-cellular structures such as DNA and mitochondria, and interfaces to mechanistic models of DNA repair kinetics. We compared TOPAS-nBio simulations to measured and published data of energy deposition patterns and chemical reaction rates (G values). Our simulations agreed well within the experimental uncertainties. Additionally, we expanded the chemical reactions and species provided in Geant4-DNA and developed a new method based on independent reaction times (IRT), including a total of 72 reactions classified into 6 types between neutral and charged species. Chemical stage simulations using IRT were a factor of 145 faster than with step-by-step tracking. Finally, we applied the geometric/chemical modeling to obtain initial yields of double-strand breaks (DSBs) in DNA fibers for proton irradiations of 3 and 50 MeV and compared the effect of including chemical reactions on the number and complexity of DSB induction. Over half of the DSBs were found to include chemical reactions with approximately 5% of DSBs caused only by chemical reactions. In conclusion, the TOPAS-nBio extension to the TOPAS MC application offers access to accurate and detailed multiscale simulations, from a macroscopic description of the radiation field to microscopic description of biological outcome for selected cells. TOPAS-nBio offers detailed physics and chemistry simulations of radiobiological experiments on cells simulating the initially induced damage and links to models of DNA repair kinetics.

People typically rely heavily on visual information when finding their way to unfamiliar locations. For individuals with reduced vision, there are a variety of navigational tools available to assist with this task if needed. However, for wayfinding in unfamiliar indoor environments the applicability of existing tools is limited. One potential approach to assist with this task is to enhance visual information about the location and content of existing signage in the environment. With this aim, we developed a prototype software application, which runs on a consumer head-mounted augmented reality (AR) device, to assist visually impaired users with sign-reading. The sign-reading assistant identifies real-world text (e.g., signs and room numbers) on command, highlights the text location, converts it to high contrast AR lettering, and optionally reads the content aloud via text-to-speech. We assessed the usability of this application in a behavioral experiment. Participants with simulated visual impairment were asked to locate a particular office within a hallway, either with or without AR assistance (referred to as the AR group and control group, respectively). Subjective assessments indicated that participants in the AR group found the application helpful for this task, and an analysis of walking paths indicated that these participants took more direct routes compared to the control group. However, participants in the AR group also walked more slowly and took more time to complete the task than the control group. The results point to several specific future goals for usability and system performance in AR-based assistive tools.

The Evidence and Conclusion Ontology (ECO) contains terms (classes) that describe types of evidence and assertion methods. ECO terms are used in the process of biocuration to capture the evidence that supports biological assertions (e.g. gene product X has function Y as supported by evidence Z). Capture of this information allows tracking of annotation provenance, establishment of quality control measures and query of evidence. ECO contains over 1500 terms and is in use by many leading biological resources including the Gene Ontology, UniProt and several model organism databases. ECO is continually being expanded and revised based on the needs of the biocuration community. The ontology is freely available for download from GitHub (https://github.com/evidenceontology/) or the project's website (http://evidenceontology.org/). Users can request new terms or changes to existing terms through the project's GitHub site. ECO is released into the public domain under CC0 1.0 Universal.

Since its 2015 update, MaizeGDB, the Maize Genetics and Genomics database, has expanded to support the sequenced genomes of many maize inbred lines in addition to the B73 reference genome assembly. Curation and development efforts have targeted high quality datasets and tools to support maize trait analysis, germplasm analysis, genetic studies, and breeding. MaizeGDB hosts a wide range of data including recent support of new data types including genome metadata, RNA-seq, proteomics, synteny, and large-scale diversity. To improve access and visualization of data types several new tools have been implemented to: access large-scale maize diversity data (SNPversity), download and compare gene expression data (qTeller), visualize pedigree data (Pedigree Viewer), link genes with phenotype images (MaizeDIG), and enable flexible user-specified queries to the MaizeGDB database (MaizeMine). MaizeGDB also continues to be the community hub for maize research, coordinating activities and providing technical support to the maize research community. Here we report the changes MaizeGDB has made within the last three years to keep pace with recent software and research advances, as well as the pan-genomic landscape that cheaper and better sequencing technologies have made possible. MaizeGDB is accessible online at https://www.maizegdb.org.

The KnotProt 2.0 database (the updated version of the KnotProt database) collects information about proteins which form knots and other entangled structures. New features in KnotProt 2.0 include the characterization of both probabilistic and deterministic entanglements which can be formed by disulfide bonds and interactions via ions, a refined characterization of entanglement in terms of knotoids, the identification of the so-called cysteine knots, the possibility to analyze all or a non-redundant set of proteins, and various technical updates. The KnotProt 2.0 database classifies all entangled proteins, represents their complexity in the form of a knotting fingerprint, and presents many biological and geometrical statistics based on these results. Currently the database contains >2000 entangled structures, and it regularly self-updates based on proteins deposited in the Protein Data Bank (PDB).

Increasing evidence reveals that circular RNAs (circRNAs) are widespread in eukaryotes and play important roles in diverse biological processes. However, a comprehensive functionally annotated circRNA database is still lacking. CircFunBase is a web-accessible database that aims to provide a high-quality functional circRNA resource including experimentally validated and computationally predicted functions. The current version of CircFunBase documents more than 7000 manually curated functional circRNA entries, mainly including Homo sapiens, Mus musculus etc. CircFunBase provides visualized circRNA-miRNA interaction networks. In addition, a genome browser is provided to visualize the genome context of circRNAs. As a biological information platform for circRNAs, CircFunBase will contribute for circRNA studies and bridge the gap between circRNAs and their functions.

Animals housed in naturalistic social groups with access to automated cognitive testing vary in whether and how much they participate in cognitive testing. Understanding how demographic, seasonal, and social factors relate to participation is essential to evaluating the usefulness of these systems for studying cognition and in assessing the data produced. We evaluated how sex, age, reproductive experience, seasonality, and rank related to patterns of participation in a naturalistic group of rhesus monkeys over a 4-year period. Females interacted with the touchscreen systems more than males and were more likely to complete initial training. Age was positively correlated with touchscreen activity through adolescence in females, at which point seasonality and reproductive experience were stronger associates of participation. While monkeys in different rank categories did not differ in how much they interacted with the touchscreen systems, monkeys of different ranks tended not to work at the same times, perhaps reflecting avoidance of high ranking animals by those of lower rank. Automated cognitive testing systems for naturalistic social groups of rhesus monkeys can yield quality cognitive data from individuals of all ages and ranks, but participation biases may make it difficult to study sex differences or seasonal variation in cognition.

We present a tutorial on using Pycellerator for biomolecular simulations. Models are described in human readable (and editable) text files (UTF8 or ASCII) containing collections of reactions, assignments, initial conditions, function definitions, and rate constants. These models are then converted into a Python program that can optionally solve the system, e.g., as a system of differential equations using ODEINT, or be run by another program. The input language implements an extended version of the Cellerator arrow notation, including mass action, Hill functions, S-Systems, MWC, and reactions with user-defined kinetic laws. Simple flux balance analysis is also implemented. We will demonstrate the implementation and analysis of progressively more complex models, starting from simple mass action through indexed cascades. Pycellerator can be used as a library that is integrated into other programs, run as a command line program, or in iPython notebooks. It is implemented in Python 2.7 and available under an open source GPL license.

Studying the biology of sleep requires the accurate assessment of the state of experimental subjects, and manual analysis of relevant data is a major bottleneck. Recently, deep learning applied to electroencephalogram and electromyogram data has shown great promise as a sleep scoring method, approaching the limits of inter-rater reliability. As with any machine learning algorithm, the inputs to a sleep scoring classifier are typically standardized in order to remove distributional shift caused by variability in the signal collection process. However, in scientific data, experimental manipulations introduce variability that should not be removed. For example, in sleep scoring, the fraction of time spent in each arousal state can vary between control and experimental subjects. We introduce a standardization method, mixture z-scoring, that preserves this crucial form of distributional shift. Using both a simulated experiment and mouse in vivo data, we demonstrate that a common standardization method used by state-of-the-art sleep scoring algorithms introduces systematic bias, but that mixture z-scoring does not. We present a free, open-source user interface that uses a compact neural network and mixture z-scoring to allow for rapid sleep scoring with accuracy that compares well to contemporary methods. This work provides a set of computational tools for the robust automation of sleep scoring.

Successful open source software (OSS) projects comprise freely observable, task-oriented social networks with hundreds or thousands of participants and large amounts of (textual and technical) discussion. The sheer volume of interactions and participants makes it challenging for participants to find relevant tasks, discussions and people. Tagging (e.g., @AmySmith) is a socio-technical practice that enables more focused discussion. By tagging important and relevant people, discussions can be advanced more effectively. However, for all but a few insiders, it can be difficult to identify important and/or relevant people. In this paper we study tagging in OSS projects from a socio-linguistics perspective. First we argue that textual content per se reveals a great deal about the status and identity of who is speaking and who is being addressed. Next, we suggest that this phenomenon can be usefully modeled using modern deep-learning methods. Finally, we illustrate the value of these approaches with tools that could assist people to find the important and relevant people for a discussion.

Introduction. In the context of the Covid-19 pandemic and due to physical distancing restrictions, universities were motivated to set up alternative teaching methods to facilitate the training of health professionals. Objective. To describe the pedagogical considerations for the design and development of a virtual learning environment integrated with technology from a remote laboratory for practical purposes in the training of health professionals on mechanical ventilation issues. Methodology. It is based on a variation of the ADDIE model of instructional design, on which from the analysis of the context, needs and the review of previous experiences, the pedagogical elements are designed for the integration of a Virtual Learning Environment and an emulation system of ventilatory mechanics in an environment called ventilab. Results. Development of an environment for the training of health professionals, designed for the student to apply knowledge of cardiopulmonary pathophysiology in the operation of an invasive mechanical ventilation system. The environment considers educational resources, learning activities, communication, evaluation and interaction with the remote laboratory. Conclusion. The interdisciplinary work for the pedagogical proposal represented a challenge of communication and assembly of different knowledge. This work opens doors for reflection towards the possibility of new teaching practices in university education on issues of mechanical ventilation, as well as methodological application. in other areas of knowledge that require practice. [ABSTRACT FROM AUTHOR]

Background: In the federal state of Salzburg, the harmonization of nursing internship took place from 2014-2019 as a joint effort of educational and internship providers. Currently the handling of these mostly paper pencil-based documents involves a manual process through different institutions and people. Objectives: The project provides the basis for the implementation of the "internship platform". In the future, this is to be the digital, state-of-the-art one-stop shop for the state-wide practical nursing training (in the form of immanent internships) at all training levels. Methods: The process is influenced by modern requirements engineering techniques: As-is analysis of the internship process and related documents, contextual inquires in different internship providers, iterative focus group discussions focusing first on user stories, then on interface designs, and final user testing. Results: Standardized workflow and authorization concept for all user groups, mandatory requirements for the software system, tested user interfaces, tender documents for EU-wide tender procedure. Conclusion: Positive feedback from all involved user groups on project goal, results and involvement in the process. [ABSTRACT FROM AUTHOR]

Abstract Background There are differences of opinion regarding the selection of the most practical usability evaluation method among different methods. The present study aimed to compare two expert-based evaluation methods in order to assess a nursing module as the most widely used module of a Hospital Information System (HIS). Methods Five independent evaluators used the Heuristic Evaluation (HE) and Cognitive Walkthrough (CW) methods to evaluate the nursing module of Shafa HIS. In this regard, the number and severity of the recognized problems according to the usability attributes were compared using two evaluation methods. Results The HE and CW evaluation methods resulted in the identification of 104 and 24 unique problems, respectively, of which 33.3% of recognized problems in the CW evaluation method overlapped with the HE method. The average severity of the recognized problems was considered to be minor (2.34) in the HE method and major (2.77) in the CW evaluation method. There was a significant difference in terms of the total number and average severity of the recognized problems by these methods (P

Abstract Background Over the past 50 years, dental informatics has developed significantly in the field of health information systems. Accordingly, several studies have been conducted on standardized clinical coding systems, data capture, and clinical data reuse in dentistry. Methods Based on the definition of health information systems, the literature search was divided into three specific sub-searches: “standardized clinical coding systems,” “data capture,” and “reuse of routine patient care data.” PubMed and Web of Science were searched for peer-reviewed articles. The review was conducted following the PRISMA-ScR protocol. Results A total of 44 articles were identified for inclusion in the review. Of these, 15 were related to “standardized clinical coding systems,” 15 to “data capture,” and 14 to “reuse of routine patient care data.” Articles related to standardized clinical coding systems focused on the design and/or development of proposed systems, on their evaluation and validation, on their adoption in academic settings, and on user perception. Articles related to data capture addressed the issue of data completeness, evaluated user interfaces and workflow integration, and proposed technical solutions. Finally, articles related to reuse of routine patient care data focused on clinical decision support systems centered on patient care, institutional or population-based health monitoring support systems, and clinical research. Conclusions While the development of health information systems, and especially standardized clinical coding systems, has led to significant progress in research and quality measures, most reviewed articles were published in the US. Clinical decision support systems that reuse EDR data have been little studied. Likewise, few studies have examined the working environment of dental practitioners or the pedagogical value of using health information systems in dentistry.

Objective: To recognize the usefulness of incorporating Three-Dimensional models of standardized humans in electronic health records, in the context of the development of a teledentistry web platform designed for the attention of the elderly population in COVID-19 pandemic context. Material and Methods: A teledentistry web platform designed with different modules for clinical records. Through a new user-computer interface with a standardized virtual 3D phantom, an extraoral physical examination, an intraoral examination section was modeled. A label-associated marker is allowed to record descriptive aspects of the findings. A 3D odontogram represents multiple patient’s conditions for each of the 32 dental positions. Results: From a total of 135 patients registered on the platform, 51 markers and 33 photographs associated with the surface of the virtual 3D phantoms were recorded. For the Location parameter: Hard palate 27.6%, inserted gingiva 15.7%, tongue 15.6%. For the Type of lesion parameter (according to the information entered in the pathology selector): unidentified 35.3%, sub-prosthetic stomatitis 23.5%, irritative fibroma 9.8%. Through the registration of the exact location of the finding in the virtual phantom by a 3D marker, the 3D modeling of the oral pathologies contributed to a better diagnosis, improving the remote communication between the attending dentist and specialists. Conclusion: The combination of the 3D modeling and anatomical-referencing in a teledentistry platform can become a powerful tool for the dental practice, due to their utility and specificity.

Transferring the contralateral C7 nerve root to the median or radial nerve has become an important means of repairing brachial plexus nerve injury. However, outcomes have been disappointing. Electroencephalography (EEG)-based human-machine interfaces have achieved promising results in promoting neurological recovery by controlling a distal exoskeleton to perform functional limb exercises early after nerve injury, which maintains target muscle activity and promotes the neurological rehabilitation effect. This review summarizes the progress of research in EEG-based human-machine interface combined with contralateral C7 transfer repair of brachial plexus nerve injury. Nerve transfer may result in loss of nerve function in the donor area, so only nerves with minimal impact on the donor area, such as the C7 nerve, should be selected as the donor. Single tendon transfer does not fully restore optimal joint function, so multiple functions often need to be reestablished simultaneously. Compared with traditional manual rehabilitation, EEG-based human-machine interfaces have the potential to maximize patient initiative and promote nerve regeneration and cortical remodeling, which facilitates neurological recovery. In the early stages of brachial plexus injury treatment, the use of an EEG-based human-machine interface combined with contralateral C7 transfer can facilitate postoperative neurological recovery by making full use of the brain’s computational capabilities and actively controlling functional exercise with the aid of external machinery. It can also prevent disuse atrophy of muscles and target organs and maintain neuromuscular junction effectiveness. Promoting cortical remodeling is also particularly important for neurological recovery after contralateral C7 transfer. Future studies are needed to investigate the mechanism by which early movement delays neuromuscular junction damage and promotes cortical remodeling. Understanding this mechanism should help guide the development of neurological rehabilitation strategies for patients with brachial plexus injury.

Background: Despite the challenges the COVID-19 pandemic placed on many libraries’ existing workflows and operations, many librarians developed and debuted new services that addressed novel needs that emerged during the pandemic. This report describes how two electronic resource librarians at regional hospitals within a healthcare corporation used exhibition platforms to showcase resident research in an online format as a complement to in-person resident research programming. Case Presentation: Over the course of the pandemic, two exhibition platform variants were implemented, one year apart. This case report describes how each platform was developed. The first online event was conducted using a virtual exhibit platform to minimize in-person contact. The second online event, held the following year, blended a traditional live event with virtual elements using the online exhibit platform. To ensure completion of tasks, project management techniques were adopted throughout the event planning process. Conclusions: The pandemic created opportunities for hospitals to explore transforming meetings from primarily live and onsite into hybrid and fully virtual events. While many corporate hospitals have transitioned backed to primarily in-person programming, newly adopted online practices such as online judging platforms and automation of continuing medical education tasks will likely remain. As in-person restrictions within healthcare settings are lifted or eased at uneven rates, organizations may continue to explore the value of in-person in-person meetings versus the video conference experience of the same meeting.

BackgroundInformatics for Integrating Biology and the Bedside (i2b2) is an open source clinical data analytics platform used at over 200 healthcare institutions for querying patient data. The i2b2 platform has several components with numerous dependencies and configuration parameters, which renders the task of installing or upgrading i2b2 a challenging one. Even with the availability of extensive documentation and tutorials, new users often require several weeks to correctly install a functional i2b2 platform. The goal of this work is to simplify the installation and upgrade process for i2b2. Specifically, we have containerized the core components of the platform, and evaluated the containers for ease of installation.ResultsWe developed three Docker container images: WildFly, database, and web, to encapsulate the three major deployment components of i2b2. These containers isolate the core functionalities of the i2b2 platform, and work in unison to provide its functionalities. Our evaluations indicate that i2b2 containers function successfully on the Linux platform. Our results demonstrate that the containerized components work out-of-the-box, with minimal configuration.ConclusionsContainerization offers the potential to package the i2b2 platform components into standalone executable packages that are agnostic to the underlying host operating system. By releasing i2b2 as a Docker container, we anticipate that users will be able to create a working i2b2 hive installation without the need to download, compile, and configure individual components that constitute the i2b2 cells, thus making this platform accessible to a greater number of institutions.

BackgroundThe clinical sequencing of cancer genomes to personalize therapy is becoming routine across the world. However, concerns over patient re-identification from these data lead to questions about how tightly access should be controlled. It is not thought to be possible to re-identify patients from somatic variant data. However, somatic variant detection pipelines can mistakenly identify germline variants as somatic ones, a process called "germline leakage". The rate of germline leakage across different somatic variant detection pipelines is not well-understood, and it is uncertain whether or not somatic variant calls should be considered re-identifiable. To fill this gap, we quantified germline leakage across 259 sets of whole-genome somatic single nucleotide variant (SNVs) predictions made by 21 teams as part of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge.ResultsThe median somatic SNV prediction set contained 4325 somatic SNVs and leaked one germline polymorphism. The level of germline leakage was inversely correlated with somatic SNV prediction accuracy and positively correlated with the amount of infiltrating normal cells. The specific germline variants leaked differed by tumour and algorithm. To aid in quantitation and correction of leakage, we created a tool, called GermlineFilter, for use in public-facing somatic SNV databases.ConclusionsThe potential for patient re-identification from leaked germline variants in somatic SNV predictions has led to divergent open data access policies, based on different assessments of the risks. Indeed, a single, well-publicized re-identification event could reshape public perceptions of the values of genomic data sharing. We find that modern somatic SNV prediction pipelines have low germline-leakage rates, which can be further reduced, especially for cloud-sharing, using pre-filtering software.

Growing popularity and diversity of genomic data demand portable and versatile genome browsers. Here, we present an open source programming library called GIVE that facilitates the creation of personalized genome browsers without requiring a system administrator. By inserting HTML tags, one can add to a personal webpage interactive visualization of multiple types of genomics data, including genome annotation, "linear" quantitative data, and genome interaction data. GIVE includes a graphical interface called HUG (HTML Universal Generator) that automatically generates HTML code for displaying user chosen data, which can be copy-pasted into user's personal website or saved and shared with collaborators. GIVE is available at: https://www.givengine.org/ .

BackgroundFlux balance analysis (FBA) is a widely-used method for analyzing metabolic networks. However, most existing tools that implement FBA require downloading software and writing code. Furthermore, FBA generates predictions for metabolic networks with thousands of components, so meaningful changes in FBA solutions can be difficult to identify. These challenges make it difficult for beginners to learn how FBA works.ResultsTo meet this need, we present Escher-FBA, a web application for interactive FBA simulations within a pathway visualization. Escher-FBA allows users to set flux bounds, knock out reactions, change objective functions, upload metabolic models, and generate high-quality figures without downloading software or writing code. We provide detailed instructions on how to use Escher-FBA to replicate several FBA simulations that generate real scientific hypotheses.ConclusionsWe designed Escher-FBA to be as intuitive as possible so that users can quickly and easily understand the core concepts of FBA. The web application can be accessed at https://sbrg.github.io/escher-fba .

Can virtual reality be useful for visualizing and analyzing molecular structures and three-dimensional (3D) microscopy? Uses we are exploring include studies of drug binding to proteins and the effects of mutations, building accurate atomic models in electron microscopy and x-ray density maps, understanding how immune system cells move using 3D light microscopy, and teaching schoolchildren about biomolecules that are the machinery of life. Virtual reality (VR) offers immersive display with a wide field of view and head tracking for better perception of molecular architectures and uses 6-degree-of-freedom hand controllers for simple manipulation of 3D data. Conventional computer displays with trackpad, mouse and keyboard excel at two-dimensional tasks such as writing and studying research literature, uses for which VR technology is at present far inferior. Adding VR to the conventional computing environment could improve 3D capabilities if new user-interface problems can be solved. We have developed three VR applications: ChimeraX for analyzing molecular structures and electron and light microscopy data, AltPDB for collaborative discussions around atomic models, and Molecular Zoo for teaching young students characteristics of biomolecules. Investigations over three decades have produced an extensive literature evaluating the potential of VR in research and education. Consumer VR headsets are now affordable to researchers and educators, allowing direct tests of whether the technology is valuable in these areas. We survey here advantages and disadvantages of VR for molecular biology in the context of affordable and dramatically more powerful VR and graphics hardware than has been available in the past.

Multi-omic insights into microbiome function and composition typically advance one study at a time. However, in order for relationships across studies to be fully understood, data must be aggregated into meta-analyses. This makes it possible to generate new hypotheses by finding features that are reproducible across biospecimens and data layers. Qiita dramatically accelerates such integration tasks in a web-based microbiome-comparison platform, which we demonstrate with Human Microbiome Project and Integrative Human Microbiome Project (iHMP) data.

Microbial communities are critical to ecosystem function. A key objective of metagenomic studies is to analyse organism-specific metabolic pathways and reconstruct community interaction networks. This requires accurate assignment of assembled genome fragments to genomes. Existing binning methods often fail to reconstruct a reasonable number of genomes and report many bins of low quality and completeness. Furthermore, the performance of existing algorithms varies between samples and biotopes. Here, we present a dereplication, aggregation and scoring strategy, DAS Tool, that combines the strengths of a flexible set of established binning algorithms. DAS Tool applied to a constructed community generated more accurate bins than any automated method. Indeed, when applied to environmental and host-associated samples of different complexity, DAS Tool recovered substantially more near-complete genomes, including previously unreported lineages, than any single binning method alone. The ability to reconstruct many near-complete genomes from metagenomics data will greatly advance genome-centric analyses of ecosystems.

Whereas 400 million distinct compounds are now purchasable within the span of a few weeks, the biological activities of most are unknown. To facilitate access to new chemistry for biology, we have combined the Similarity Ensemble Approach (SEA) with the maximum Tanimoto similarity to the nearest bioactive to predict activity for every commercially available molecule in ZINC. This method, which we label SEA+TC, outperforms both SEA and a naïve-Bayesian classifier via predictive performance on a 5-fold cross-validation of ChEMBL's bioactivity data set (version 21). Using this method, predictions for over 40% of compounds (>160 million) have either high significance (pSEA ≥ 40), high similarity (ECFP4MaxTc ≥ 0.4), or both, for one or more of 1382 targets well described by ligands in the literature. Using a further 1347 less-well-described targets, we predict activities for an additional 11 million compounds. To gauge whether these predictions are sensible, we investigate 75 predictions for 50 drugs lacking a binding affinity annotation in ChEMBL. The 535 million predictions for over 171 million compounds at 2629 targets are linked to purchasing information and evidence to support each prediction and are freely available via https://zinc15.docking.org and https://files.docking.org .

The UCSC Genome Browser (https://genome.ucsc.edu) provides a web interface for exploring annotated genome assemblies. The assemblies and annotation tracks are updated on an ongoing basis-12 assemblies and more than 28 tracks were added in the past year. Two recent additions are a display of CRISPR/Cas9 guide sequences and an interactive navigator for gene interactions. Other upgrades from the past year include a command-line version of the Variant Annotation Integrator, support for Human Genome Variation Society variant nomenclature input and output, and a revised highlighting tool that now supports multiple simultaneous regions and colors.