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PurposeLongitudinal patient tolerability data collected as part of randomized controlled trials are often summarized in a way that loses information and does not capture the treatment experience. To address this, we developed an interactive web application to empower clinicians and researchers to explore and visualize patient tolerability data.MethodsWe used adverse event (AE) data (Common Terminology Criteria for Adverse Events) and patient-reported outcomes (PROs) from the NSABP-B35 phase III clinical trial, which compared anastrozole with tamoxifen for breast cancer-free survival, to demonstrate the tools. An interactive web application was developed using R and the Shiny web application framework that generates Sankey diagrams to visualize AEs and PROs using four tools: AE Explorer, PRO Explorer, Cohort Explorer, and Custom Explorer.ResultsTo illustrate how users can use the interactive tool, examples for each of the four applications are presented using data from the NSABP-B35 phase III trial and the NSABP-B30 trial for the Custom Explorer. In the AE and PRO explorers, users can select AEs or PROs to visualize within specified time periods and compare across treatments. In the cohort explorer, users can select a subset of patients with a specific symptom, severity, and treatment received to visualize the trajectory over time within a specified time interval. With the custom explorer, users can upload and visualize structured longitudinal toxicity and tolerability data.ConclusionWe have created an interactive web application and tool for clinicians and researchers to explore and visualize clinical trial tolerability data. This adaptable tool can be extended for other clinical trial data visualization and incorporated into future patient-clinician interactions regarding treatment decisions.

The integration of Artificial Intelligence (AI) into healthcare has the potential to revolutionize medical diagnostics, particularly in specialized fields such as Ear, Nose, and Throat (ENT) medicine. However, the successful adoption of AI-assisted diagnostic tools in ENT practice depends on the understanding of various factors; these include influences on their effectiveness and acceptance among healthcare professionals. This cross-sectional study aimed to assess the usability and integration of AI tools in ENT practice, determine the clinical impact and accuracy of AI-assisted diagnostics in ENT, measure the trust and confidence of ENT professionals in AI tools, gauge the overall satisfaction and outlook on the future of AI in ENT diagnostics, and identify challenges, limitations, and areas for improvement in AI-assisted ENT diagnostics. A structured online questionnaire was distributed to 600 certified ENT professionals with at least one year of experience in the field. The questionnaire assessed participants' familiarity with AI tools, usability, clinical impact, trust, satisfaction, and identified challenges. A total of 458 respondents completed the questionnaire, resulting in a response rate of 91.7%. The majority of respondents reported familiarity with AI tools (60.7%) and perceived them as generally usable and clinically impactful. However, challenges such as integration with existing systems, user-friendliness, accuracy, and cost were identified. Trust and satisfaction levels varied among participants, with concerns regarding data privacy and support. Geographic and practice setting differences influenced perceptions and experiences. The study highlights the diverse perceptions and experiences of ENT professionals regarding AI-assisted diagnostics. While there is general enthusiasm for these tools, challenges related to integration, usability, trust, and cost need to be addressed for their widespread adoption. These findings provide valuable insights for developers, policymakers, and healthcare providers aiming to enhance the role of AI in ENT practice. [ABSTRACT FROM AUTHOR]

Detecting and avoiding obstacles while navigating can pose a challenge for people with low vision, but augmented reality (AR) has the potential to assist by enhancing obstacle visibility. Perceptual and user experience research is needed to understand how to craft effective AR visuals for this purpose. We developed a prototype AR application capable of displaying multiple kinds of visual cues for obstacles on an optical see-through head-mounted display. We assessed the usability of these cues via a study in which participants with low vision navigated an obstacle course. The results suggest that 3D world-locked AR cues were superior to directional heads-up cues for most participants during this activity.

Background: Electronic health record (EHR) nursing summaries have the potential to support nurses in locating and synthesizing patient information. However, nurses’ role-specific perspectives are often excluded in the design of the EHR system. The purpose of this study was to describe nurses’ current use of nursing summaries and vital sign information within them and glean their ideas for design improvements. Methods: This research followed Nielsen and Norman's 4-phased design cycle and focused on the discovery and exploration phases to identify the context of nurses' use of the nursing summary (discovery) and to create prototype design layout using physical materials (exploration). Results: Ten clinical inpatient nurses participated in interviews and co-design activities. Nurses hardly use the nursing summary to overview a comprehensive patient's health status. The current design of a nursing summary lacks comprehensive patient information and contains much irrelevant data. Nurses prefer vital signs to be prominently displayed on the summary screen for easy visibility. Conclusion: Involving nurses in the design process can lead to a nursing summary that better meets their needs.

Background: During the last decade, social media have affected people’s lives in an unprecedented way, today, different parts of people’s lives, from shopping to education, business, and electronic messages, are influenced by these media. In fact, social media have become an integral part of human life today, and in recent years this dependence has become even greater than before. Because following the spread of the COVID-19, people used social media more to work and study, to keep in touch with their loved ones, and to get information about Covid-19. The present study aimed to determine the positive and negative impact of social media in the Covid-19 pandemic.Methods: The present study was a brief review designed. From 01 to 10 January 2022, the researchers of this study examined 6 international databases: Medline/PubMed, ProQuest, Scopus, EMBASE, Google Scholar, and ISI Web of Science. After reviewing the titles and abstracts of the articles, 46 articles entered the next stage, in which the full text of the articles was reviewed.Results: Social media provides a space where health information is easily accessible to everyone. This capability is very efficient in global emergencies such as the outbreak of the Covid-19 pandemic so the use of social networks during the Covid-19 pandemic has increased significantly compared to previous data. In today’s world, social media has become a new field in business and marketing. With the involvement of social media in many aspects of life, we see its significant impact on various aspects of business, from brand development to guaranteeing buyer’s trust. Conclusion: Today, the role of social media in social communication, health, economy and other cultural fields is undeniable. However, it is important to consider the negative effects of these media. It seems that it is necessary that social media users be given the principal training before and during their use so that they do not become victims of its undesirable and unconscious effects.

Objectives Mobile health applications that are designed without considering usability criteria can lead to cognitive overload, resulting in the rejection of these apps. To avoid this problem, the user interface of mobile health applications should be evaluated for cognitive load. This evaluation can contribute to the improvement of the user interface and help prevent cognitive overload for the user. Methods In this study, we evaluated a mobile personal health records application using the cognitive task analysis method, specifically the goals, operators, methods, and selection rules (GOMS) approach, along with the related updated GOMS model and gesture-level model techniques. The GOMS method allowed us to determine the steps of the tasks and categorize them as physical or cognitive tasks. We then estimated the completion times of these tasks using the updated GOMS model and gesture-level model. Results All 10 identified tasks were split into 398 steps consisting of mental and physical operators. The time to complete all the tasks was 5.70 minutes and 5.45 minutes according to the updated GOMS model and gesture-level model, respectively. Mental operators covered 73% of the total fulfillment time of the tasks according to the updated GOMS model and 76% according to the gesture-level model. The inter-rater reliability analysis yielded an average of 0.80, indicating good reliability for the evaluation method. Conclusions The majority of the task execution times comprised mental operators, suggesting that the cognitive load on users is high. To enhance the application’s implementation, the number of mental operators should be reduced.

The awareness of individuals’ biological status is critical for creating interactive and adaptive environments that can actively assist the users to achieve optimal outcomes. Accordingly, specialized human–machine interfaces—equipped with bioperception and interpretation capabilities—are required. To this end, we devised a multimodal cryptographic bio-human–machine interface (CB-HMI), which seamlessly translates the user’s touch-based entries into encrypted biochemical, biophysical, and biometric indices. As its central component, the CB-HMI features thin hydrogel-coated chemical sensors and inference algorithms to noninvasively and inconspicuously acquire biochemical indices such as circulating molecules that partition onto the skin (here, ethanol and acetaminophen). Additionally, the CB-HMI hosts physical sensors and associated algorithms to simultaneously acquire the user’s heart rate, blood oxygen level, and fingerprint minutiae pattern. Supported by human subject studies, we demonstrated the CB-HMI’s capability in terms of acquiring physiologically relevant readouts of target bioindices, as well as user-identifying and biometrically encrypting/decrypting these indices in situ (leveraging the fingerprint feature). By upgrading the common surrounding objects with the CB-HMI, we created interactive solutions for driving safety and medication use. Specifically, we demonstrated a vehicle-activation system and a medication-dispensing system, where the integrated CB-HMI uniquely enabled user bioauthentication (on the basis of the user’s biological state and identity) prior to rendering the intended services. Harnessing the levels of bioperception achieved by the CB-HMI and other intelligent HMIs, we can equip our surroundings with a comprehensive and deep awareness of individuals’ psychophysiological state and needs.

Background: During the last decade, social media have affected people's lives in an unprecedented way, today, different parts of people's lives, from shopping to education, business, and electronic messages, are influenced by these media. In fact, social media have become an integral part of human life today, and in recent years this dependence has become even greater than before. Because following the spread of the COVID-19, people used social media more to work and study, to keep in touch with their loved ones, and to get information about Covid-19. The present study aimed to determine the positive and negative impact of social media in the Covid-19 pandemic. Methods: The present study was a brief review designed. From 01 to 10 January 2022, the researchers of this study examined 6 international databases: Medline/PubMed, ProQuest, Scopus, EMBASE, Google Scholar, and ISI Web of Science. After reviewing the titles and abstracts of the articles, 46 articles entered the next stage, in which the full text of the articles was reviewed. Results: Social media provides a space where health information is easily accessible to everyone. This capability is very efficient in global emergencies such as the outbreak of the Covid-19 pandemic so the use of social networks during the Covid-19 pandemic has increased significantly compared to previous data. In today's world, social media has become a new field in business and marketing. With the involvement of social media in many aspects of life, we see its significant impact on various aspects of business, from brand development to guaranteeing buyer's trust. Conclusion: Today, the role of social media in social communication, health, economy and other cultural fields is undeniable. However, it is important to consider the negative effects of these media. It seems that it is necessary that social media users be given the principal training before and during their use so that they do not become victims of its undesirable and unconscious effects. [ABSTRACT FROM AUTHOR]

Purpose: Despite known benefits of planning for end-of-life, no digital tool exists to help patients with advanced cancer and their loved ones plan for death comprehensively. To address this unmet need, we developed a preliminary version of an innovative website to help patients with advanced cancer prepare for end-of-life tasks. Methods: Guided by the Obesity-Related Behavioral Intervention Trials (ORBIT) model for behavioral intervention development, patients with advanced cancer (n = 10) and their caregivers (n = 10) participated in a “Think Aloud” exercise and usability protocols to optimize the end-of-life planning website. The website was iteratively refined throughout the study in collaboration with the partnering company, Peacefully, Inc. Participants also completed the Acceptability E-Scale and System Usability Scale, with a priori benchmarks established for acceptability (scores of ≥ 24 on the Acceptability E-Scale) and usability (scores of ≥ 68 on the System Usability Scale). Results: Patients (N = 10) and caregivers (N = 10) completed usability testing. Patients were majority female (80%), White (100%), and had a mean age of 58 years. Caregivers (N = 10) were majority male (60%), spouse/partner (90%), White (90%), and had a mean age of 59 years. For patients, a priori hypotheses were met for both acceptability (mean score of 24.7, SD = 4.35) and usability (mean score of 73.8, SD = 6.15). For caregivers, acceptability was just below the cutoff (mean score of 22.9, SD = 4.07) and usability exceeded the cutoff (mean score of 70.0, SD = 8.42). Overall, patients and caregivers reported high levels of satisfaction and found the website helpful, with specific suggestions for changes (e.g., add more information about information security, improve text legibility). Conclusions: The findings from this study will inform modifications to optimize an innovative website to support patients with advanced cancer to prepare holistically for end-of-life tasks. [ABSTRACT FROM AUTHOR]

This study aimed to assess whether prior knowledge of computer use determines performance of virtual reality tasks by postmenopausal women and whether menopausal symptoms, sociodemographic factors, lifestyle and cognition modify or interfere with their performance. This cross-sectional study included 152 postmenopausal women divided into two groups: computer users and non-users. Age, ethnicity, time of menopause, menopausal symptoms, female health status, level of physical activity and cognitive function were considered. The participants played a virtual reality game and were assessed for hits, errors, omissions and game time. The Mann–Whitney, chi-square and Fisher exact tests and multivariate linear regression analysis were used. Postmenopausal computer users play virtual reality games (p = 0.005) better than postmenopausal non-users of computers. Vasomotor symptoms were high in women who used computers compared to those who did not (p = 0.006). Multivariate linear regression analysis found that the best-fitting predictors for the number of hits – that is, age (p = 0.039), Mini-Mental State Examination score (p = 0.006) and the headache symptom (p = 0.021) – influence the performance of virtual reality tasks. Computer users performed virtual reality tasks better than non-users. Headache and age but not vasomotor symptoms negatively affected the postmenopausal women's performance. [ABSTRACT FROM AUTHOR]

The UCSC Genome Browser, https://genome.ucsc.edu, is a graphical viewer for exploring genome annotations. The website provides integrated tools for visualizing, comparing, analyzing, and sharing both publicly available and user-generated genomic datasets. Data highlights this year include a collection of easily accessible public hub assemblies on new organisms, now featuring BLAT alignment and PCR capabilities, and new and updated clinical tracks (gnomAD, DECIPHER, CADD, REVEL). We introduced a new Track Sets feature and enhanced variant displays to aid in the interpretation of clinical data. We also added a tool to rapidly place new SARS-CoV-2 genomes in a global phylogenetic tree enabling researchers to view the context of emerging mutations in our SARS-CoV-2 Genome Browser. Other new software focuses on usability features, including more informative mouseover displays and new fonts.

Tracking and quantifying the abundance and location of cells in the developing brain is essential in neuroscience research, enabling a greater understanding of mechanisms underlying nervous system morphogenesis. Widely used experimental methods to quantify cells labeled with fluorescent markers, such as immunohistochemistry (IHC), in situ hybridization, and expression of transgenes via stable lines or transient in utero electroporations (IUEs), depend on accurate and consistent quantification of images. Current methods to quantify fluorescently-labeled cells rely on labor-intensive manual counting approaches, such as the Fiji plugin Cell Counter, which requires custom macros to enable higher-throughput analyses. Here, we present RapID Cell Counter, a semi-automated cell-counting tool with an easy-to-implement graphical user interface (GUI), which facilitates quick and consistent quantifications of cell density within user-defined boundaries that can be divided into equally-partitioned segments. Compared with the standard manual counting approach, we show that RapID matched accuracy and consistency and only required ∼10% of user time relative to manual counting methods, when quantifying the distribution of fluorescently-labeled neurons in mouse IUE experiments. Using RapID, we recapitulated previously published work focusing on two genes, SRGAP2 and CUL5, important for projection neuron (PN) migration in the neocortex and used it to quantify PN displacement in a mouse knock-out model of RBX2 Moreover, RapID is capable of quantifying other cell types in the brain with complex cell morphologies, including astrocytes and dopaminergic neurons. We propose RapID as an efficient method for neuroscience researchers to process fluorescently-labeled brain images in a consistent, accurate, and mid-throughput manner.

BackgroundPapers on COVID-19 are being published at a high rate and concern many different topics. Innovative tools are needed to aid researchers to find patterns in this vast amount of literature to identify subsets of interest in an automated fashion.ObjectiveWe present a new online software resource with a friendly user interface that allows users to query and interact with visual representations of relationships between publications.MethodsWe publicly released an application called PLATIPUS (Publication Literature Analysis and Text Interaction Platform for User Studies) that allows researchers to interact with literature supplied by COVIDScholar via a visual analytics platform. This tool contains standard filtering capabilities based on authors, journals, high-level categories, and various research-specific details via natural language processing and dozens of customizable visualizations that dynamically update from a researcher's query.ResultsPLATIPUS is available online and currently links to over 100,000 publications and is still growing. This application has the potential to transform how COVID-19 researchers use public literature to enable their research.ConclusionsThe PLATIPUS application provides the end user with a variety of ways to search, filter, and visualize over 100,00 COVID-19 publications.

BACKGROUND: The evidence base supports effectiveness of dental sealants for prevention of childhood caries in school-aged children. OBJECTIVE: This study describes planning, development, usability testing and outcomes following implementation of DentaSeal, a web-based application designed to accurately track unique student data and generate reports for all Wisconsin school-based sealant placement (SP) programs. METHODS: Application software development was informed by a steering committee of representative stakeholders who were interviewed to inform design and provide feedback for design of DentaSeal during development and evaluation. Software development proceeded based on wireframes developed to build architectural design. Usability testing followed and informed any required adjustments to the application. The DentaSeal prototype was beta tested and fully implemented subsequently in the public health sector. RESULTS: The DentaSeal application demonstrated capacity to: 1) track unique student SP data and longitudinal encounter history, 2) generate reports and 3) support administrative tracking. In 2019, DentaSeal captured SP data of 47 school-based programs in Wisconsin that sponsored > 7,000 program visits for 184,000 children from 62 counties. Delivery of > 548,000 SP services were catalogued. CONCLUSIONS: For public health initiatives targeting reduction in caries incidence, web-based applications such as DentaSeal represent useful longitudinal tracking tools for cataloguing SP in school-based program participants. [ABSTRACT FROM AUTHOR]

Mouse cursor tracking has become a prominent method for characterizing cognitive processes, used in a wide variety of domains of psychological science. Researchers have demonstrated considerable ingenuity in the application of the approach, but the methodology has not undergone systematic analysis to facilitate the development of best practices. Furthermore, recent research has demonstrated effects of experimental design features on a number of mousetracking outcomes. We conducted a systematic review of the mouse-tracking literature to survey the reporting and spread of mouse variables (Cursor speed, Sampling rate, Training), physical characteristics of the experiments (Stimulus position, Response box position) and response requirements (Start procedure, Response procedure, Response deadline). This survey reveals that there is room for improvement in reporting practices, especially of subtler design features that researchers may have assumed would not impact research results (e.g., Cursor speed). We provide recommendations for future best practices in mouse-tracking studies and consider how best to standardize the mouse-tracking literature without excessively constraining the methodological flexibility that is essential to the field.

There is a wide, and continuously widening, gap between the number of proteins known only by their amino acid sequence versus those structurally characterized by direct experiment. To close this gap, we mostly rely on homology-based inference and modeling to reason about the structures of the uncharacterized proteins by using structures of homologous proteins as templates. With the rapidly growing size of the Protein Data Bank, there are often multiple choices of templates, including multiple sets of coordinates from the same protein. The substantial conformational differences observed between different experimental structures of the same protein often reflect function related structural flexibility. Thus, depending on the questions being asked, using distant homologs, or coordinate sets with lower resolution but solved in the appropriate functional form, as templates may be more informative. The ModFlex server (https://modflex.org/) addresses this seldom mentioned gap in the standard homology modeling approach by providing the user with an interface with multiple options and tools to select the most relevant template and explore the range of structural diversity in the available templates. ModFlex is closely integrated with a range of other programs and servers developed in our group for the analysis and visualization of protein structural flexibility and divergence.

The advent of single-cell chromatin accessibility profiling has accelerated the ability to map gene regulatory landscapes but has outpaced the development of scalable software to rapidly extract biological meaning from these data. Here we present a software suite for single-cell analysis of regulatory chromatin in R (ArchR; https://www.archrproject.com/ ) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses, including doublet removal, single-cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing (scRNA-seq). Enabling the analysis of over 1.2 million single cells within 8 h on a standard Unix laptop, ArchR is a comprehensive software suite for end-to-end analysis of single-cell chromatin accessibility that will accelerate the understanding of gene regulation at the resolution of individual cells.

In recent years, large-scale oceanic sequencing efforts have provided a deeper understanding of marine microbial communities and their dynamics. These research endeavors require the acquisition of complex and varied datasets through large, interdisciplinary and collaborative efforts. However, no unifying framework currently exists for the marine science community to integrate sequencing data with physical, geological, and geochemical datasets. Planet Microbe is a web-based platform that enables data discovery from curated historical and on-going oceanographic sequencing efforts. In Planet Microbe, each omics sample is linked with other biological and physiochemical measurements collected for the same water samples or during the same sample collection event, to provide a broader environmental context. This work highlights the need for curated aggregation efforts that can enable new insights into high-quality metagenomic datasets. Planet Microbe is freely accessible from https://www.planetmicrobe.org/.

The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations.

Viruses are integral components of all ecosystems and microbiomes on Earth. Through pervasive infections of their cellular hosts, viruses can reshape microbial community structure and drive global nutrient cycling. Over the past decade, viral sequences identified from genomes and metagenomes have provided an unprecedented view of viral genome diversity in nature. Since 2016, the IMG/VR database has provided access to the largest collection of viral sequences obtained from (meta)genomes. Here, we present the third version of IMG/VR, composed of 18 373 cultivated and 2 314 329 uncultivated viral genomes (UViGs), nearly tripling the total number of sequences compared to the previous version. These clustered into 935 362 viral Operational Taxonomic Units (vOTUs), including 188 930 with two or more members. UViGs in IMG/VR are now reported as single viral contigs, integrated proviruses or genome bins, and are annotated with a new standardized pipeline including genome quality estimation using CheckV, taxonomic classification reflecting the latest ICTV update, and expanded host taxonomy prediction. The new IMG/VR interface enables users to efficiently browse, search, and select UViGs based on genome features and/or sequence similarity. IMG/VR v3 is available at https://img.jgi.doe.gov/vr, and the underlying data are available to download at https://genome.jgi.doe.gov/portal/IMG_VR.

Algae are a diverse, polyphyletic group of photosynthetic eukaryotes spanning nearly all eukaryotic lineages of life and collectively responsible for ∼50% of photosynthesis on Earth. Sequenced algal genomes, critical to understanding their complex biology, are growing in number and require efficient tools for analysis. PhycoCosm (https://phycocosm.jgi.doe.gov) is an algal multi-omics portal, developed by the US Department of Energy Joint Genome Institute to support analysis and distribution of algal genome sequences and other 'omics' data. PhycoCosm provides integration of genome sequence and annotation for >100 algal genomes with available multi-omics data and interactive web-based tools to enable algal research in bioenergy and the environment, encouraging community engagement and data exchange, and fostering new sequencing projects that will further these research goals.

User experience (UX) is a quality aspect that considers the emotions evoked by the system, extending the usability concept beyond effectiveness, efficiency, and satisfaction. Practitioners and researchers are aware of the importance of evaluating UX. Thus, UX evaluation is a growing field with diverse approaches. Despite various approaches, most of them produce a general indication of the experience as a result and do not seek to capture the problem that gave rise to the bad UX. This information makes it difficult to obtain relevant results to improve the application, making it challenging to identify what caused a negative user experience. To address this gap, we developed a UX evaluation technique called UX-Tips. This paper presents UX-Tips and reports two empirical studies performed in an academic and an industrial setting to evaluate it. Our results show that UX-Tips had good performance in terms of efficiency and effectiveness, making it possible to identify the causes that led to a negative user experience, and it was easy to use. In this sense, we present a new technique suitable for use in both academic and industrial settings, allowing UX evaluation and finding the problems that may lead to a negative experience.

EEGLAB signal processing environment is currently the leading open-source software for processing electroencephalographic (EEG) data. The Neuroscience Gateway (NSG, nsgportal.org) is a web and API-based portal allowing users to easily run a variety of neuroscience-related software on high-performance computing (HPC) resources in the U.S. XSEDE network. We have reported recently (Delorme et al., 2019) on the Open EEGLAB Portal expansion of the free NSG services to allow the neuroscience community to build and run MATLAB pipelines using the EEGLAB tool environment. We are now releasing an EEGLAB plug-in, nsgportal, that interfaces EEGLAB with NSG directly from within EEGLAB running on MATLAB on any personal lab computer. The plug-in features a flexible MATLAB graphical user interface (GUI) that allows users to easily submit, interact with, and manage NSG jobs, and to retrieve and examine their results. Command line nsgportal tools supporting these GUI functionalities allow EEGLAB users and plug-in tool developers to build largely automated functions and workflows that include optional NSG job submission and processing. Here we present details on nsgportal implementation and documentation, provide user tutorials on example applications, and show sample test results comparing computation times using HPC versus laptop processing.

The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations.

In near-eye display systems that support three-dimensional (3D) augmented and virtual reality, a central factor in determining the user experience is the size of the eyebox. The eyebox refers to a volume where the eye receives an acceptable view of the image with respect to a set of criteria and thresholds. The size and location of this volume are primarily driven by optical architecture choices in which designers trade-off a number of constraints, such as field of view, image quality, and product design. It is thus important to clearly quantify how design decisions affect the properties of the eyebox. Recent work has started evaluating the eyebox in 3D based purely on optical criteria. However, such analyses do not incorporate perceptual criteria that determine visual quality, which are particularly important for binocular 3D systems. To address this limitation, we introduce the framework of a perceptual eyebox. The perceptual eyebox is the volume where the eye(s) must be located for the user to experience a visual percept falling within a perceptually-defined criterion. We combine optical and perceptual data to characterize an example perceptual eyebox for display visibility in augmented reality. The key contributions in this paper include: comparing the perceptual eyebox for monocular and binocular display designs, modeling the effects of user eye separation, and examining the effects of eye rotation on the eyebox volume.

BackgroundIon Torrent is one of the major next generation sequencing (NGS) technologies and it is frequently used in medical research and diagnosis. The built-in software for the Ion Torrent sequencing machines delivers the sequencing results in the BAM format. In addition to the usual SAM/BAM fields, the Ion Torrent BAM file includes technology-specific flow signal data. The flow signals occupy a big portion of the BAM file (about 75% for the human genome). Compressing SAM/BAM into CRAM format significantly reduces the space needed to store the NGS results. However, the tools for generating the CRAM formats are not designed to handle the flow signals. This missing feature has motivated us to develop a new program to improve the compression of the Ion Torrent files for long term archiving.ResultsIn this paper, we present IonCRAM, the first reference-based compression tool to compress Ion Torrent BAM files for long term archiving. For the BAM files, IonCRAM could achieve a space saving of about 43%. This space saving is superior to what achieved with the CRAM format by about 8-9%.ConclusionsReducing the space consumption of NGS data reduces the cost of storage and data transfer. Therefore, developing efficient compression software for clinical NGS data goes beyond the computational interest; as it ultimately contributes to the overall cost reduction of the clinical test. The space saving achieved by our tool is a practical step in this direction. The tool is open source and available at Code Ocean, github, and http://ioncram.saudigenomeproject.com .

The integration of Artificial Intelligence (AI) into healthcare has the potential to revolutionize medical diagnostics, particularly in specialized fields such as Ear, Nose, and Throat (ENT) medicine. However, the successful adoption of AI-assisted diagnostic tools in ENT practice depends on the understanding of various factors; these include influences on their effectiveness and acceptance among healthcare professionals. This cross-sectional study aimed to assess the usability and integration of AI tools in ENT practice, determine the clinical impact and accuracy of AI-assisted diagnostics in ENT, measure the trust and confidence of ENT professionals in AI tools, gauge the overall satisfaction and outlook on the future of AI in ENT diagnostics, and identify challenges, limitations, and areas for improvement in AI-assisted ENT diagnostics. A structured online questionnaire was distributed to 600 certified ENT professionals with at least one year of experience in the field. The questionnaire assessed participants’ familiarity with AI tools, usability, clinical impact, trust, satisfaction, and identified challenges. A total of 458 respondents completed the questionnaire, resulting in a response rate of 91.7%. The majority of respondents reported familiarity with AI tools (60.7%) and perceived them as generally usable and clinically impactful. However, challenges such as integration with existing systems, user-friendliness, accuracy, and cost were identified. Trust and satisfaction levels varied among participants, with concerns regarding data privacy and support. Geographic and practice setting differences influenced perceptions and experiences. The study highlights the diverse perceptions and experiences of ENT professionals regarding AI-assisted diagnostics. While there is general enthusiasm for these tools, challenges related to integration, usability, trust, and cost need to be addressed for their widespread adoption. These findings provide valuable insights for developers, policymakers, and healthcare providers aiming to enhance the role of AI in ENT practice.

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]

BACKGROUND: With the increased usage of dashboard reporting systems to monitor and track patient panels by clinical users, developers must ensure that the information displays they produce are accurate and intuitive. When evaluating usability of a clinical dashboard among potential end users, developers oftentimes rely on methods such as questionnaires as opposed to other, more time-intensive strategies that incorporate direct observation. OBJECTIVES: Prior to release of the potentially inappropriate medication (PIM) clinical dashboard, designed to facilitate completion of a quality improvement project by clinician scholars enrolled in the Veterans Affairs (VA) workforce development Geriatric Scholars Program (GSP), we evaluated the usability of the system. This article describes the process of usability testing a dashboard reporting system with clinicians using direct observation and think-aloud moderating techniques. METHODS: We developed a structured interview protocol that combines virtual observation, think-aloud moderating techniques, and retrospective questioning of the overall user experience, including use of the System Usability Scale (SUS). Thematic analysis was used to analyze field notes from the interviews of three GSP alumni. RESULTS: Our structured approach to usability testing identified specific functional problems with the dashboard reporting system that were missed by results from the SUS. Usability testing lead to overall improvements in the intuitive use of the system, increased data transparency, and clarification of the dashboards purpose. CONCLUSION: Reliance solely on questionnaires and surveys at the end stages of dashboard development can mask potential functional problems that will impede proper usage and lead to misinterpretation of results. A structured approach to usability testing in the developmental phase is an important tool for developers of clinician friendly systems for displaying easily digested information and tracking outcomes for the purpose of quality improvement.

Evaluation of cardiac function during periods of stress is of key importance for the perioperative setting. Non-invasive hemodynamic monitors provide markers of cardiac function. This pilot study sought to evaluate the ability of a non-invasive hemodynamic monitor to detect cardiac stress during formal stress echocardiography testing. The primary goal was to compare the change in hemodynamic values during the pre/during/post phases of stress echocardiography testing in patients who had results negative versus positive for myocardial ischemia. Adult patients scheduled for outpatient cardiac stress testing were screened. Only patients scheduled for stress-echocardiography testing were consented. Patients with history of arrhythmias were excluded. During the testing, patients wore a cuff-based hemodynamic sensor (Nexfin system, Edwards Lifesciences). Data from the hemodynamic sensor were compared to the findings of the stress study. A total of 37 patients were enrolled, with 31 patients included for analysis. Five patients had stress studies positive for coronary ischemia. Comparison of the hemodynamic variables between patients who had a positive stress study versus negative showed a significant reduction in the percentage change in dP/dt and stroke volume from baseline (p

The Pan-Cancer Analysis of Whole Genomes (PCAWG) project generated a vast amount of whole-genome cancer sequencing resource data. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we provide a user's guide to the five publicly available online data exploration and visualization tools introduced in the PCAWG marker paper. These tools are ICGC Data Portal, UCSC Xena, Chromothripsis Explorer, Expression Atlas, and PCAWG-Scout. We detail use cases and analyses for each tool, show how they incorporate outside resources from the larger genomics ecosystem, and demonstrate how the tools can be used together to understand the biology of cancers more deeply. Together, the tools enable researchers to query the complex genomic PCAWG data dynamically and integrate external information, enabling and enhancing interpretation.

BackgroundElectronic health records (EHRs) have potential to improve quality, health outcomes, and efficiency, but little is known about the mechanisms through which these improvements occur.PurposeOne potential mechanism could be that EHRs improve care team communication and coordination, leading to better outcomes. To test this hypothesis, we examine whether ease of EHR use is associated with better relational coordination (RC), a measure of team communication and coordination, among primary care team members.MethodologySurveys of adult primary care team members (n = 304) of 16 practices of two accountable care organizations in Chicago and Los Angeles were analyzed. The survey included a validated measure of RC and a measure of ease of EHR use from a national survey. Linear regression models estimated the association of ease of EHR use and RC, controlling for care site and patient demographics and accounting for cluster-robust standard errors. An interaction term tested a differential association of ease of EHR use and RC for primary care providers (PCPs) versus non-PCPs.ResultsEase of EHR use (mean = 3.5, SD = 0.6, range: 0-4) and RC were high (mean = 4.0, SD = 0.7, range: 0-5) but differed by occupation. In regression analyses, a 1-point increase in ease of EHR use was associated with a 0.36 point higher RC score (p = .001). The association of ease of EHR and RC use was stronger for non-PCPs than PCPs.ConclusionEase of EHR use is associated with better RC among primary care team members, and the benefits accrue more to non-PCPs than to PCPs.Practice implicationsEnsuring that clinicians and staff experience EHRs as easy to use for accessing and integrating data and for communication may produce gains in efficiency and outcomes through high RC. Future studies should examine whether interventions to improve EHR usability can lead to improved RC and patient outcomes.

Due to COVID-19, this year marked the first virtual Conference on Retroviruses and Opportunistic Infections (CROI) in the conference's 27-year history. There were important studies presented that provided new insights into the prevention, diagnosis, and treatment of tuberculosis (TB) and other HIV coinfections. Highlights related to TB and HIV coinfections from this year's meeting are reviewed below.

BackgroundChallenges to cardiac PET-CT include patient motion, prolonged image acquisition and a reduction of counts due to gating. We compared two analytical tools, FusionQuant and OsiriX, for quantification of gated cardiac 18F-sodium fluoride (18F-fluoride) PET-CT imaging.MethodsTwenty-seven patients with aortic stenosis were included, 15 of whom underwent repeated imaging 4 weeks apart. Agreement between analytical tools and scan-rescan reproducibility was determined using the Bland-Altman method and Lin's concordance correlation coefficients (CCC).ResultsImage analysis was faster with FusionQuant [median time (IQR) 7:10 (6:40-8:20) minutes] compared with OsiriX [8:30 (8:00-10:10) minutes, p = .002]. Agreement of uptake measurements between programs was excellent, CCC = 0.972 (95% CI 0.949-0.995) for mean tissue-to-background ratio (TBRmean) and 0.981 (95% CI 0.965-0.997) for maximum tissue-to-background ratio (TBRmax). Mean noise decreased from 11.7% in the diastolic gate to 6.7% in motion-corrected images (p = .002); SNR increased from 25.41 to 41.13 (p = .0001). Aortic valve scan-rescan reproducibility for TBRmax was improved with FusionQuant using motion correction compared to OsiriX (error ± 36% vs ± 13%, p

Little is known about the evolution of diaphragmatic function in the early post-cardiac surgery period. The main purpose of this work is to describe its evolution using ultrasound measurements of muscular excursion and thickening fraction (TF). Single-center prospective study of 79 consecutive uncomplicated elective cardiac surgery patients, using motion-mode during quiet unassisted breathing. Excursion and TF were measured sequentially for each patient [pre-operative (D1), 1 day (D2) and 5 days (D3) after surgery]. Pre-operative median for right and left hemidiaphragmatic excursions were 1.8 (IQR 1.6 to 2.1) cm and 1.7 (1.4 to 2.0) cm, respectively. Pre-operative median right and left thickening fractions were 28 (19 to 36) % and 33 (22 to 51) %, respectively. At D2, there was a reduction in both excursion (right: 1.5 (1.1 to 1.8) cm, p

ObjectiveTo describe the implementation of technological support important for optimizing clinical management of the COVID-19 pandemic.Materials and methodsOur health system has confirmed prior and current cases of COVID-19. An Incident Command Center was established early in the crisis and helped identify electronic health record (EHR)-based tools to support clinical care.ResultsWe outline the design and implementation of EHR-based rapid screening processes, laboratory testing, clinical decision support, reporting tools, and patient-facing technology related to COVID-19.DiscussionThe EHR is a useful tool to enable rapid deployment of standardized processes. UC San Diego Health built multiple COVID-19-specific tools to support outbreak management, including scripted triaging, electronic check-in, standard ordering and documentation, secure messaging, real-time data analytics, and telemedicine capabilities. Challenges included the need to frequently adjust build to meet rapidly evolving requirements, communication, and adoption, and to coordinate the needs of multiple stakeholders while maintaining high-quality, prepandemic medical care.ConclusionThe EHR is an essential tool in supporting the clinical needs of a health system managing the COVID-19 pandemic.

Phospholipase A2 (PLA2) enzymes play a major role in many diseases including the inflammatory cascade and specific potent small molecule inhibitors could be useful in studying their physiological role as well as for the development of drugs. In order to discover novel small molecule inhibitor platforms for members of the PLA2 superfamily of enzymes, we have applied computational approaches to determine the binding mode of potent inhibitors specific for particular PLA2s to the screening of chemical libraries. This has including the U.S. National Institutes of Health (NIH) National Cancer Institute (NCI) Diversity Set V and the ChemBridge commercial compound libraries. We have then subjected identified inhibitor structures to recently developed lipidomics based screening assays to determine the XI(50) and specificity of the identified compounds for specific PLA2s. Herein we review this approach and report the identity of initial hits for both the Group IVA cytosolic PLA2 and the Group VIA calcium-independent PLA2 that are worthy of further structural modification to develop novel platforms for inhibitor development.

Mosaic variants resulting from postzygotic mutations are prevalent in the human genome and play important roles in human diseases. However, except for cancer-related variants, there is no collection of postzygotic mosaic variants in noncancer disease-related and healthy individuals. Here, we present MosaicBase, a comprehensive database that includes 6698 mosaic variants related to 266 noncancer diseases and 27,991 mosaic variants identified in 422 healthy individuals. Genomic and phenotypic information of each variant was manually extracted and curated from 383 publications. MosaicBase supports the query of variants with Online Mendelian Inheritance in Man (OMIM) entries, genomic coordinates, gene symbols, or Entrez IDs. We also provide an integrated genome browser for users to easily access mosaic variants and their related annotations for any genomic region. By analyzing the variants collected in MosaicBase, we find that mosaic variants that directly contribute to disease phenotype show features distinct from those of variants in individuals with mild or no phenotypes, in terms of their genomic distribution, mutation signatures, and fraction of mutant cells. MosaicBase will not only assist clinicians in genetic counseling and diagnosis but also provide a useful resource to understand the genomic baseline of postzygotic mutations in the general human population. MosaicBase is publicly available at http://mosaicbase.com/ or http://49.4.21.8:8000.

ObjectiveWhile there has been a substantial increase in health information exchange, levels of outside records use by frontline providers are low. We assessed whether integration between outside data and local data results in increased viewing of outside records, overall and by encounter, provider, and patient type.Materials and methodsUsing data from UCSF Health, we measured change in outside record views after integrating the list of local (UCSF) and outside (other health systems on Epic [Epic Systems, Verona, WI]) encounters on the Chart Review tab. Previously, providers only viewed records from outside encounters on a separate tab. We used an interrupted time series design (with outside record viewing event counts aggregated to the week level) to measure changes in the level and trend over a 1-year period.ResultsThere was a large increase in the level of outside record views of 22 920 per week (P

ObjectiveThe United States faces an opioid crisis. Integrating prescription drug monitoring programs into electronic health records offers promise to improve opioid prescribing practices. This study aimed to evaluate 2 different user interface designs for prescription drug monitoring program and electronic health record integration.Materials and MethodsTwenty-four resident physicians participated in a randomized controlled experiment using 4 simulated patient cases. In the conventional condition, prescription opioid histories were presented in tabular format, and computerized clinical decision support (CDS) was provided via interruptive modal dialogs (ie, pop-ups). The alternative condition featured a graphical opioid history, a cue to visit that history, and noninterruptive CDS. Two attending pain specialists judged prescription appropriateness.ResultsParticipants in the alternative condition wrote more appropriate prescriptions. When asked after the experiment, most participants stated that they preferred the alternative design to the conventional design.ConclusionsHow patient information and CDS are presented appears to have a significant influence on opioid prescribing behavior.

High-dimensional single-cell technologies present new opportunities for biological discovery, but the complex nature of the resulting datasets makes it challenging to perform comprehensive analysis. One particular challenge is the analysis of single-cell time course datasets: how to identify unique cell populations and track how they change across time points. To facilitate this analysis, we developed FLOW-MAP, a graphical user interface (GUI)-based software tool that uses graph layout analysis with sequential time ordering to visualize cellular trajectories in high-dimensional single-cell datasets obtained from flow cytometry, mass cytometry or single-cell RNA sequencing (scRNAseq) experiments. Here we provide a detailed description of the FLOW-MAP algorithm and how to use the open-source R package FLOWMAPR via its GUI or with text-based commands. This approach can be applied to many dynamic processes, including in vitro stem cell differentiation, in vivo development, oncogenesis, the emergence of drug resistance and cell signaling dynamics. To demonstrate our approach, we perform a step-by-step analysis of a single-cell mass cytometry time course dataset from mouse embryonic stem cells differentiating into the three germ layers: endoderm, mesoderm and ectoderm. In addition, we demonstrate FLOW-MAP analysis of a previously published scRNAseq dataset. Using both synthetic and experimental datasets for comparison, we perform FLOW-MAP analysis side by side with other single-cell analysis methods, to illustrate when it is advantageous to use the FLOW-MAP approach. The protocol takes between 30 min and 1.5 h to complete.

Passive acoustic monitoring has become an important data collection method, yielding massive datasets replete with biological, environmental and anthropogenic information. Automated signal detectors and classifiers are needed to identify events within these datasets, such as the presence of species-specific sounds or anthropogenic noise. These automated methods, however, are rarely a complete substitute for expert analyst review. The ability to visualize and annotate acoustic events efficiently can enhance scientific insights from large, previously intractable datasets. A MATLAB-based graphical user interface, called DetEdit, was developed to accelerate the editing and annotating of automated detections from extensive acoustic datasets. This tool is highly-configurable and multipurpose, with uses ranging from annotation and classification of individual signals or signal-clusters and evaluation of signal properties, to identification of false detections and false positive rate estimation. DetEdit allows users to step through acoustic events, displaying a range of signal features, including time series of received levels, long-term spectral averages, time intervals between detections, and scatter plots of peak frequency, RMS, and peak-to-peak received levels. Additionally, it displays either individual, or averaged sound pressure waveforms, and power spectra within each acoustic event. These views simultaneously provide analysts with signal-level detail and encounter-level context. DetEdit creates datasets of signal labels for further analyses, such as training classifiers and quantifying occurrence, abundances, or trends. Although designed for evaluating underwater-recorded odontocete echolocation click detections, DetEdit can be adapted to almost any stereotyped impulsive signal. Our software package complements available tools for the bioacoustic community and is provided open source at https://github.com/MarineBioAcousticsRC/DetEdit.

In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven't been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics.