Lessons Learned From Developing Dashboards to Support Decision-Making for Community Opioid Response by Community Stakeholders: Mixed Methods and Multisite Study.

BACKGROUND: Data dashboards are published tools that present visualizations; they are increasingly used to display data about behavioral health, social determinants of health, and chronic and infectious disease risks to inform or support public health endeavors. Dashboards can be an evidence-based approach used by communities to influence decision-making in health care for specific populations. Despite widespread use, evidence on how to best design and use dashboards in the public health realm is limited. There is also a notable dearth of studies that examine and document the complexity and heterogeneity of dashboards in community settings. OBJECTIVE: Community stakeholders engaged in the community response to the opioid overdose crisis could benefit from the use of data dashboards for decision-making. As part of the Communities That HEAL (CTH) intervention, community data dashboards were created for stakeholders to support decision-making. We assessed stakeholders' perceptions of the usability and use of the CTH dashboards for decision-making. METHODS: We conducted a mixed methods assessment between June and July 2021 on the use of CTH dashboards. We administered the System Usability Scale (SUS) and conducted semistructured group interviews with users in 33 communities across 4 states of the United States. The SUS comprises 10 five-point Likert-scale questions measuring usability, each scored from 0 to 4. The interview guides were informed by the technology adoption model (TAM) and focused on perceived usefulness, perceived ease of use, intention to use, and contextual factors. RESULTS: Overall, 62 users of the CTH dashboards completed the SUS and interviews. SUS scores (grand mean 73, SD 4.6) indicated that CTH dashboards were within the acceptable range for usability. From the qualitative interview data, we inductively created subthemes within the 4 dimensions of the TAM to contextualize stakeholders' perceptions of the dashboard's usefulness and ease of use, their intention to use, and contextual factors. These data also highlighted gaps in knowledge, design, and use, which could help focus efforts to improve the use and comprehension of dashboards by stakeholders. CONCLUSIONS: We present a set of prioritized gaps identified by our national group and list a set of lessons learned for improved data dashboard design and use for community stakeholders. Findings from our novel application of both the SUS and TAM provide insights and highlight important gaps and lessons learned to inform the design of data dashboards for use by decision-making community stakeholders. TRIAL REGISTRATION: ClinicalTrials.gov NCT04111939; https://clinicaltrials.gov/study/NCT04111939.

Evaluating and Enhancing the Fitness-for-Purpose of Electronic Health Record Data: Qualitative Study on Current Practices and Pathway to an Automated Approach Within the Medical Informatics for Research and Care in University Medicine Consortium.

BACKGROUND: Leveraging electronic health record (EHR) data for clinical or research purposes heavily depends on data fitness. However, there is a lack of standardized frameworks to evaluate EHR data suitability, leading to inconsistent quality in data use projects (DUPs). This research focuses on the Medical Informatics for Research and Care in University Medicine (MIRACUM) Data Integration Centers (DICs) and examines empirical practices on assessing and automating the fitness-for-purpose of clinical data in German DIC settings. OBJECTIVE: The study aims (1) to capture and discuss how MIRACUM DICs evaluate and enhance the fitness-for-purpose of observational health care data and examine the alignment with existing recommendations and (2) to identify the requirements for designing and implementing a computer-assisted solution to evaluate EHR data fitness within MIRACUM DICs. METHODS: A qualitative approach was followed using an open-ended survey across DICs of 10 German university hospitals affiliated with MIRACUM. Data were analyzed using thematic analysis following an inductive qualitative method. RESULTS: All 10 MIRACUM DICs participated, with 17 participants revealing various approaches to assessing data fitness, including the 4-eyes principle and data consistency checks such as cross-system data value comparison. Common practices included a DUP-related feedback loop on data fitness and using self-designed dashboards for monitoring. Most experts had a computer science background and a master's degree, suggesting strong technological proficiency but potentially lacking clinical or statistical expertise. Nine key requirements for a computer-assisted solution were identified, including flexibility, understandability, extendibility, and practicability. Participants used heterogeneous data repositories for evaluating data quality criteria and practical strategies to communicate with research and clinical teams. CONCLUSIONS: The study identifies gaps between current practices in MIRACUM DICs and existing recommendations, offering insights into the complexities of assessing and reporting clinical data fitness. Additionally, a tripartite modular framework for fitness-for-purpose assessment was introduced to streamline the forthcoming implementation. It provides valuable input for developing and integrating an automated solution across multiple locations. This may include statistical comparisons to advanced machine learning algorithms for operationalizing frameworks such as the 3×3 data quality assessment framework. These findings provide foundational evidence for future design and implementation studies to enhance data quality assessments for specific DUPs in observational health care settings.

Designing visual hierarchies for the communication of health data.

OBJECTIVES: Visual hierarchy underlies all visual design decisions related to information presentation. This manuscript describes the experience of a multidisciplinary health data visualization and software design team in using visual hierarchy to redesign a hereditary colorectal cancer lab report. MATERIALS AND METHODS: A series of interviews with representative users were conducted to identify target user groups and determine information hierarchy for each user type. Visual elements (eg, size, color, contrast, etc.) were then assigned to mirror the information hierarchy and workflow for each user type. RESULTS: User research identified 2 distinct user groups as consumers of the redesigned lab report. An interactive design employing a 2-level page hierarchy was developed, which stratified the content to support the needs of each user type. CONCLUSIONS: The challenges related to displaying the complex nature of digital and personal health data can be addressed by applying foundational design methods such as visual hierarchy. DISCUSSION: Visual hierarchy, a foundational design principle, can be used by visualization teams to clearly and efficiently present complex datasets associated with healthcare.

Development of a Real-Time Dashboard for Overdose Touchpoints: User-Centered Design Approach.

BACKGROUND: Overdose Fatality Review (OFR) is an important public health tool for shaping overdose prevention strategies in communities. However, OFR teams review only a few cases at a time, which typically represent a small fraction of the total fatalities in their jurisdiction. Such limited review could result in a partial understanding of local overdose patterns, leading to policy recommendations that do not fully address the broader community needs. OBJECTIVE: This study explored the potential to enhance conventional OFRs with a data dashboard, incorporating visualizations of touchpoints-events that precede overdoses-to highlight prevention opportunities. METHODS: We conducted 2 focus groups and a survey of OFR experts to characterize their information needs and design a real-time dashboard that tracks and measures decedents' past interactions with services in Indiana. Experts (N=27) were engaged, yielding insights on essential data features to incorporate and providing feedback to guide the development of visualizations. RESULTS: The findings highlighted the importance of showing decedents' interactions with health services (emergency medical services) and the justice system (incarcerations). Emphasis was also placed on maintaining decedent anonymity, particularly in small communities, and the need for training OFR members in data interpretation. The developed dashboard summarizes key touchpoint metrics, including prevalence, interaction frequency, and time intervals between touchpoints and overdoses, with data viewable at the county and state levels. In an initial evaluation, the dashboard was well received for its comprehensive data coverage and its potential for enhancing OFR recommendations and case selection. CONCLUSIONS: The Indiana touchpoints dashboard is the first to display real-time visualizations that link administrative and overdose mortality data across the state. This resource equips local health officials and OFRs with timely, quantitative, and spatiotemporal insights into overdose risk factors in their communities, facilitating data-driven interventions and policy changes. However, fully integrating the dashboard into OFR practices will likely require training teams in data interpretation and decision-making.

Deception by obfuscation: Studnicki et al.'s retracted longitudinal cohort study of emergency room utilization following abortion.

OBJECTIVES: In November 2022, the anti-abortion advocacy group Alliance for Hippocratic Medicine filed a lawsuit against the U.S. Food and Drug Administration challenging the initial 2000 approval of mifepristone and its subsequent approvals, which removed unnecessary restrictions on its use, by disputing the medication's safety record. Such challenges relied on a study examining the incidence of emergency room visits following medication abortion with mifepristone and procedural abortion using Medicaid claims data from 1999-2015. In February 2024 that study was retracted by its publisher. In this paper, we analyzed the methods and presentations of the data used in the study. STUDY DESIGN: We drew upon commonly accepted principles in responsible epidemiologic and scientific research to evaluate the methods and presentations of the data and organized our findings into themes. RESULTS: We found multiple instances of methodological flaws, mischaracterizations, and obfuscations of data in this study, including use of a misleading research question and framing, analytic flaws, inappropriate use of an unvalidated proxy measure for outcomes of interest, and inappropriate and deceptive visualizations of data. In each instance, the resulting effect obfuscated and misrepresented the safety of medication abortion with mifepristone. CONCLUSIONS: The misrepresentation and exaggeration of data promoted and exacerbated misinterpretations about the study's findings, resulting in substantial harm before it was retracted. Recognizing that ongoing judicial proceedings threaten access to conventional reproductive health care in the United States, public health policies must be informed by scientific and medical literature that is comprehensive, methodologically sound, and absent any obfuscations or misrepresentations. IMPLICATIONS: Studnicki et al.'s study of emergency room visits after abortion misrepresented the safety of mifepristone with multiple instances of methodological flaws and obfuscations of data. While the study has now been retracted, it led to irrevocable harm, threatening access to medication abortion, which has an established safety record.

Deep Learning-Assisted Analysis of Immunopeptidomics Data.

Liquid chromatography-coupled mass spectrometry (LC-MS/MS) is the primary method to obtain direct evidence for the presentation of disease- or patient-specific human leukocyte antigen (HLA). However, compared to the analysis of tryptic peptides in proteomics, the analysis of HLA peptides still poses computational and statistical challenges. Recently, fragment ion intensity-based matching scores assessing the similarity between predicted and observed spectra were shown to substantially increase the number of confidently identified peptides, particularly in use cases where non-tryptic peptides are analyzed. In this chapter, we describe in detail three procedures on how to benefit from state-of-the-art deep learning models to analyze and validate single spectra, single measurements, and multiple measurements in mass spectrometry-based immunopeptidomics. For this, we explain how to use the Universal Spectrum Explorer (USE), online Oktoberfest, and offline Oktoberfest. For intensity-based scoring, Oktoberfest uses fragment ion intensity and retention time predictions from the deep learning framework Prosit, a deep neural network trained on a very large number of synthetic peptides and tandem mass spectra generated within the ProteomeTools project. The examples shown highlight how deep learning-assisted analysis can increase the number of identified HLA peptides, facilitate the discovery of confidently identified neo-epitopes, or provide assistance in the assessment of the presence of cryptic peptides, such as spliced peptides.

Communicating the results of risk-based breast cancer screening through visualizations of risk: a participatory design approach.

BACKGROUND: Risk-based breast cancer (BC) screening raises new questions regarding information provision and risk communication. This study aimed to: 1) investigate women's beliefs and knowledge (i.e., mental models) regarding BC risk and (risk-based) BC screening in view of implications for information development; 2) develop novel informational materials to communicate the screening result in risk-based BC screening, including risk visualizations of both quantitative and qualitative information, from a Human-Centered Design perspective. METHODS: Phase 1: Interviews were conducted (n = 15, 40-50 years, 5 lower health literate) on women's beliefs about BC risk and (risk-based) BC screening. Phase 2: In three participatory design sessions, women (n = 4-6 across sessions, 40-50 years, 2-3 lower health literate) made assignments and created and evaluated visualizations of risk information central to the screening result. Prototypes were evaluated in two additional sessions (n = 2, 54-62 years, 0-1 lower health literate). Phase 3: Experts (n = 5) and women (n = 9, 40-74 years) evaluated the resulting materials. Two other experts were consulted throughout the development process to ensure that the content of the information materials was accurate. Interviews were transcribed literally and analysed using qualitative thematic analysis, focusing on implications for information development. Notes, assignments and materials from the participatory design sessions were summarized and main themes were identified. RESULTS: Women in both interviews and design sessions were positive about risk-based BC screening, especially because personal risk factors would be taken into account. However, they emphasized that the rationale of risk-based screening and classification into a risk category should be clearly stated and visualized, especially for higher- and lower-risk categories (which may cause anxiety or feelings of unfairness due to a lower screening frequency). Women wanted to know their personal risk, preferably visualized in an icon array, and wanted advice on risk reduction and breast self-examination. However, most risk factors were considered modifiable by women, and the risk factor breast density was not known, implying that information should emphasize that BC risk depends on multiple factors, including breast density. CONCLUSIONS: The information materials, including risk visualizations of both quantitative and qualitative information, developed from a Human-Centered Design perspective and a mental model approach, were positively evaluated by the target group.

Exploring the Perspectives of Patients Living With Lupus: Retrospective Social Listening Study.

BACKGROUND: Systemic lupus erythematosus (SLE) is a chronic autoimmune inflammatory disease affecting various organs with a wide range of clinical manifestations. Cutaneous lupus erythematosus (CLE) can manifest as a feature of SLE or an independent skin ailment. Health-related quality of life (HRQoL) is frequently compromised in individuals living with lupus. Understanding patients' perspectives when living with a disease is crucial for effectively meeting their unmet needs. Social listening is a promising new method that can provide insights into the experiences of patients living with their disease (lupus) and leverage these insights to inform drug development strategies for addressing their unmet needs. OBJECTIVE: The objective of this study is to explore the experience of patients living with SLE and CLE, including their disease and treatment experiences, HRQoL, and unmet needs, as discussed in web-based social media platforms such as blogs and forums. METHODS: A retrospective exploratory social listening study was conducted across 13 publicly available English-language social media platforms from October 2019 to January 2022. Data were processed using natural language processing and knowledge graph tagging technology to clean, format, anonymize, and annotate them algorithmically before feeding them to Pharos, a Semalytix proprietary data visualization and analysis platform, for further analysis. Pharos was used to generate descriptive data statistics, providing insights into the magnitude of individual patient experience variables, their differences in the magnitude of variables, and the associations between algorithmically tagged variables. RESULTS: A total of 45,554 posts from 3834 individuals who were algorithmically identified as patients with lupus were included in this study. Among them, 1925 (authoring 5636 posts) and 106 (authoring 243 posts) patients were identified as having SLE and CLE, respectively. Patients frequently mentioned various symptoms in relation to SLE and CLE including pain, fatigue, and rashes; pain and fatigue were identified as the main drivers of HRQoL impairment. The most affected aspects of HRQoL included "mobility," "cognitive capabilities," "recreation and leisure," and "sleep and rest." Existing pharmacological interventions poorly managed the most burdensome symptoms of lupus. Conversely, nonpharmacological treatments, such as exercise and meditation, were frequently associated with HRQoL improvement. CONCLUSIONS: Patients with lupus reported a complex interplay of symptoms and HRQoL aspects that negatively influenced one another. This study demonstrates that social listening is an effective method to gather insights into patients' experiences, preferences, and unmet needs, which can be considered during the drug development process to develop effective therapies and improve disease management.

A pilot test of an infographic-based health communication intervention to enhance patient education among Latino persons with HIV.

OBJECTIVE: To pilot test an infographic-based health communication intervention that our team rigorously designed and explore whether its implementation leads to better health outcomes among Latino persons with HIV (PWH). MATERIALS AND METHODS: Latino PWH (N = 30) living in New York City received the intervention during health education sessions at 3 study visits that occurred approximately 3 months apart. At each visit, participants completed baseline or follow-up assessments and laboratory data were extracted from patient charts. We assessed 6 outcomes (HIV-related knowledge, self-efficacy to manage HIV, adherence to antiretroviral therapy, CD4 count, viral load, and current and overall health status) selected according to a conceptual model that describes pathways through which communication influences health outcomes. We assessed changes in outcomes over time using quantile and generalized linear regression models controlling for the coronavirus disease 2019 (COVID-19) research pause and new patient status (new/established) at the time of enrollment. RESULTS: Most participants were male (60%) and Spanish-speaking (60%); 40% of participants identified as Mixed Race/Mestizo, 13.3% as Black, 13.3% as White, and 33.3% as "other" race. Outcome measures generally improved after the second intervention exposure. Following the third intervention exposure (after the COVID-19 research pause), only the improvements in HIV-related knowledge and current health status were statistically significant. DISCUSSION AND CONCLUSION: Our infographic-based health communication intervention may lead to better health outcomes among Latino PWH, but larger trials are needed to establish efficacy. From this work, we contribute suggestions for effective infographic use for patient-provider communication to enhance patient education in clinical settings.

Unipept Desktop 2.0: Construction of Targeted Reference Protein Databases for Metaproteogenomics Analyses.

Unipept Desktop 2.0 is the most recent iteration of the Unipept Desktop tool that adds support for the analysis of metaproteogenomics datasets. Unipept Desktop now supports the automatic construction of targeted protein reference databases that only contain proteins (originating from the UniProtKB resource) associated with a predetermined list of taxa. This improves both the taxonomic and functional resolution of a metaproteomic analysis and yields several technical advantages. By limiting the proteins present in a reference database, it is also possible to perform (meta)proteogenomics analyses. Since the protein reference database resides on the user's local machine, they have complete control over the database used during an analysis. Data no longer need to be transmitted over the Internet, decreasing the time required for an analysis and better safeguarding privacy-sensitive data. As a proof of concept, we present a case study in which a human gut metaproteome dataset is analyzed with Unipept Desktop 2.0 using different targeted databases based on matched 16S rRNA gene sequencing data.

NetPlotBrain: A Python package for visualizing networks and brains.

Visualizations of networks are complex since they are multidimensional and generally convey large amounts of information. The layout of the visualization can communicate either network properties or spatial properties of the network. Generating such figures to effectively convey information and be accurate can be difficult and time-consuming, and it can require expert knowledge. Here, we introduce NetPlotBrain (short for network plots onto brains), a Python package for Python 3.9+. The package offers several advantages. First, NetPlotBrain provides a high-level interface to easily highlight and customize results of interest. Second, it presents a solution to promote accurate plots through its integration with TemplateFlow. Third, it integrates with other Python software, allowing for easy integration to include networks from NetworkX or implementations of network-based statistics. In sum, NetPlotBrain is a versatile but easy to use package designed to produce high-quality network figures while integrating with open research software for neuroimaging and network theory.

Micromorphological Sculptural Diversity in Foliar Epidermis and Trichomes Features among Invasive Species.

This study examines the role of light microscopic (LM) and scanning electron microscopic (SEM) micromorphological traits of the epidermis in identifying and classifying invasive plants. SEM was conducted to increase our understanding of microscopic qualities that are not visible in light microscopy and to elucidate unclear affinities among invasive species. The study examines invasive species' morphological and anatomical characteristics from the Pothohar Plateau of Pakistan for the first time. The results showed that various micromorphological features are very useful for species' accurate identification. Adaxial and abaxial surfaces of leaves showed variations in subsidiary cells, glands, anticlinal wall patterns, stomata, and epidermal cells. Epidermal cell shapes observed were irregular, elongated, rectangular, and polygonal. Epidermal cells having maximum length were calculated in Stellaria media (126.3 µm) on adaxial side. On the abaxial surface, the minimum length was noticed in Eucalyptus camaldulensis (28.5 µm). Both glandular and nonglandular trichomes were examined, ranging from unicellular to multicellular. Most of the investigated specimens of leaves were amphistomatic, while some were hypostomatic, like Alternanthera pungens, Calotropis procera, Cannabis sativa, Lantana camara, and Thevetia peruviana. Leaf epidermal morphology contains numerous useful systematic features for accurate identifications of plant species. The micromorphological attributes under observation provide a standard criterion to the researcher for identifications of invasive flora in future morpho-taxonomic studies.

Highly Porous Iridium Thin Electrodes with Low Loading and Improved Reaction Kinetics for Hydrogen Generation in PEM Electrolyzer Cells.

Highly efficient electrodes with simplified fabrication and low cost are highly desired for the commercialization of proton exchange membrane electrolyzer cells (PEMECs). Herein, highly porous Ir-coated thin/tunable liquid/gas diffusion layers with honeycomb-structured catalyst layers were fabricated as anode electrodes for PEMECs via integrating a facile and fast electroplating process with efficient template removal. Combined with a Nafion 117 membrane, a low cell voltage of 1.842 V at 2000 mA/cm2 and a high mass activity of 4.16 A/mgIr at 1.7 V were achieved with a low Ir loading of 0.27 mg/cm2, outperforming most of the recently reported anode catalysts. Moreover, the thin electrode shows outstanding stability at a high current density of 1800 mA/cm2 in the practical PEMEC. Moreover, with in-situ high-speed visualizations in PEMECs, the catalyst layer structure's impact on real-time electrochemical reactions and mass transport phenomena was investigated for the first time. Increased active sites and improved multiphase transport properties with favorable bubble detachment and water diffusion for the honeycomb-structured electrode are revealed. Overall, the significantly simplified ionomer-free honeycomb thin electrode with low catalyst loading and remarkable performance could efficiently accelerate the industrial application of PEMECs.

The Use of Visualizations to Improve Bayesian Reasoning: A Literature Review.

Decisions are often made under uncertainty. The most that one can do is use prior knowledge (e.g., base rates, prior probabilities, etc.) and make the most probable choice given the information we have. Unfortunately, most people struggle with Bayesian reasoning. Poor performance within Bayesian reasoning problems has led researchers to investigate ways to improve Bayesian reasoning. Many have found success in using natural frequencies instead of probabilities to frame problems. Beyond the quantitative format, there is growing literature on the use of visualizations or visual representations to improve Bayesian reasoning, which will be the focus of this review. In this review, we discuss studies that have found visualizations to be effective for improving Bayesian reasoning in a lab or classroom setting and discuss the considerations for using visualizations, paying special attention to individual differences. In addition, we will review the factors that influence Bayesian reasoning, such as natural frequencies vs. probabilities, problem format, individual differences, and interactivity. We also provide general and specific suggestions for future research.

Longitudinal Transition Patterns of Tobacco Use Among Youth and Young Adults Never Tobacco Product Users: Findings From the Population Assessment of Tobacco and Health Study, 2014-2019.

Aims: To identify, visualize, and describe the prevalence of within-product patterns of tobacco use behaviors for e-cigarettes, cigarettes, and hookah (TP) by 3 age groups (ie, 12-14-year-old, 15-17-year-old, and 18-20-year-old) with U.S. nationally representative data. Methods: In 2014-2015, never users of each (TP) and age group were followed-up longitudinally between 2015-2019 using five transition states: non-susceptible to (TP) use, susceptible to (TP) use, ever (TP) use, past 30-day (TP) use, and discontinued past 30-day (TP) use. Sankey diagrams were used to graphically visualize patterns in tobacco use behaviors across time. Results: Among 12-14-year-old who were never users and susceptible to each TP from 2014-2017, 7% initiated ever e-cigarette use and 9.4% first reported past 30-day use by 2018-2019; 5.8% initiated ever cigarette use and 3% first reported past 30-day cigarette use by 2018-2019; and, 4.5% initiated ever hookah use and 1.0% first reported past 30-day hookah use by 2018-2019. Among 15-17-year-old who were never users and susceptible to each TP from 2014-2017, 4.2% initiated ever e-cigarette use and 9.0% first reported past 30-day use by 2018-2019; 4.5% initiated ever cigarette use and 3% first reported past 30-day cigarette use by 2018-2019; and, 4.5% initiated ever hookah use and 2.4% first reported past 30-day hookah use by 2018-2019. Among 18-20-year-old who were never users and susceptible to each TP from 2014-2017, 3.2% initiated ever e-cigarette use and 3.6% first reported past 30-day e-cigarette use by 2018-2019; 3.0% initiated ever cigarette use and 2.3% first reported past 30-day cigarette use; and, 2.8% initiated ever hookah use and 1.0% first reported past 30-day hookah use by 2018-2019. Conclusions: From 2014 to 2019, onset and progression of e-cigarette, cigarette, and hookah use occurred more frequently in 12-14 and 15-17-year-old than in young adults 18-20-year-old.

Computational Study of Hemodynamic Field of an Occluded Artery Model with Anastomosis.

In this research work, the hemodynamic field of an occluded artery with anastomosis by means of computational simulation has been studied. The main objective of the current study is the investigation of 3D flow field phenomena in the by-pass region and the effect of the bypass graft to stenosis volume flow ratio on their formation. The anastomosis type was end-to-side with a 45° angle, while stenosis imposed a 75% area blockage of the aorta vessel and the total volume flow was 220 lt/h. The computational study of the flow field was utilized via a laminar flow model and three turbulence models (k-ε RNG, standard k-ω, and k-ω SST). Numerical results were compared qualitatively with experimental visualizations carried out under four different flow conditions, varying according to the flow ratio between the stenosis and the anastomotic graft. Comparison between computational results and experimental visualization findings exhibited a good agreement. Results showed that SST k-ω turbulence models reproduce better visually obtained flow patterns. Furthermore, cross-sectional velocity distributions demonstrated two distinct flow patterns down the bypass graft, depending on the flow ratio. Low values of flow ratio are characterized by fluid rolling up, whereas for high values fluid volume twisting was observed. Finally, areas with low wall shear stresses were mapped, as these are more prone to postoperative degradation of the bypass graft due to the development of subendothelial hyperplasia.

Hybrid Genome Assembly of Short and Long Reads in Galaxy.

Galaxy is a web browser-based data analysis platform that is widely used in biology. Public Galaxy instances allow the analysis of data and interpretation of results without requiring software installation. NanoGalaxy is a public Galaxy instance with tools and workflows for nanopore data analysis. This chapter describes the steps involved in performing genome assembly using short and long reads in NanoGalaxy.

Towards a temporospatial framework for measurements of disorganization in speech using semantic vectors.

Incoherent speech in schizophrenia has long been described as the mind making "leaps" of large distances between thoughts and ideas. Such a view seems intuitive, and for almost two decades, attempts to operationalize these conceptual "leaps" in spoken word meanings have used language-based embedding spaces. An embedding space represents meaning of words as numerical vectors where a greater proximity between word vectors represents more shared meaning. However, there are limitations with word vector-based operationalizations of coherence which can limit their appeal and utility in clinical practice. First, the use of esoteric word embeddings can be conceptually hard to grasp, and this is complicated by several different operationalizations of incoherent speech. This problem can be overcome by a better visualization of methods. Second, temporal information from the act of speaking has been largely neglected since models have been built using written text, yet speech is spoken in real time. This issue can be resolved by leveraging time stamped transcripts of speech. Third, contextual information - namely the situation of where something is spoken - has often only been inferred and never explicitly modeled. Addressing this situational issue opens up new possibilities for models with increased temporal resolution and contextual relevance. In this paper, direct visualizations of semantic distances are used to enable the inspection of examples of incoherent speech. Some common operationalizations of incoherence are illustrated, and suggestions are made for how temporal and spatial contextual information can be integrated in future implementations of measures of incoherence.