Measurement errors in body size of sea scallops (Placopecten magellanicus) and their effect on stock assessment models

Body-size measurement errors are usually ignored in stock assessments, but may be important when body-size data (e.g., from visual sur veys) are imprecise. We used experiments and models to quantify measurement errors and their effects on assessment models for sea scallops (Placopecten magellanicus). Errors in size data obscured modes from strong year classes and increased frequency and size of the largest and smallest sizes, potentially biasing growth, mortality, and biomass estimates. Modeling techniques for errors in age data proved useful for errors in size data. In terms of a goodness of model fit to the assessment data, it was more important to accommodate variance than bias. Models that accommodated size errors fitted size data substantially better. We recommend experimental quantification of errors along with a modeling approach that accommodates measurement errors because a direct algebraic approach was not robust and because error parameters were diff icult to estimate in our assessment model. The importance of measurement errors depends on many factors and should be evaluated on a case by case basis

National Center for Biomedical Ontology: Advancing biomedicine through structured organization of scientific knowledge

The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) to create new software tools so that scientists can use ontologies to annotate and analyze biomedical data, (3) to provide a national resource for the ongoing evaluation, integration, and evolution of biomedical ontologies and associated tools and theories in the context of driving biomedical projects (DBPs), and (4) to disseminate the tools and resources of the Center and to identify, evaluate, and communicate best practices of ontology development to the biomedical community. Through the research activities within the Center, collaborations with the DBPs, and interactions with the biomedical community, our goal is to help scientists to work more effectively in the e-science paradigm, enhancing experiment design, experiment execution, data analysis, information synthesis, hypothesis generation and testing, and understand human disease

Analyzing historical diagnosis code data from NIH N3C and RECOVER Programs using deep learning to determine risk factors for Long Covid

Post-acute sequelae of SARS-CoV-2 infection (PASC) or Long COVID is an emerging medical condition that has been observed in several patients with a positive diagnosis for COVID-19. Historical Electronic Health Records (EHR) like diagnosis codes, lab results and clinical notes have been analyzed using deep learning and have been used to predict future clinical events. In this paper, we propose an interpretable deep learning approach to analyze historical diagnosis code data from the National COVID Cohort Collective (N3C) to find the risk factors contributing to developing Long COVID. Using our deep learning approach, we are able to predict if a patient is suffering from Long COVID from a temporally ordered list of diagnosis codes up to 45 days post the first COVID positive test or diagnosis for each patient, with an accuracy of 70.48\%. We are then able to examine the trained model using Gradient-weighted Class Activation Mapping (GradCAM) to give each input diagnoses a score. The highest scored diagnosis were deemed to be the most important for making the correct prediction for a patient. We also propose a way to summarize these top diagnoses for each patient in our cohort and look at their temporal trends to determine which codes contribute towards a positive Long COVID diagnosis

Characterizing Long COVID: Deep Phenotype of a Complex Condition

BACKGROUND: Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or long COVID ), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies. METHODS: The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19. FINDINGS: We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies. INTERPRETATION: Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID. FUNDING: U24TR002306; UL1TR001439; P30AG024832; GBMF4552; R01HG010067; UL1TR002535; K23HL128909; UL1TR002389; K99GM145411

A Vulnerability Assessment of Fish and Invertebrates to Climate Change on the Northeast U.S. Continental Shelf

Climate change and decadal variability are impacting marine fish and invertebrate species worldwide and these impacts will continue for the foreseeable future. Quantitative approaches have been developed to examine climate impacts on productivity, abundance, and distribution of various marine fish and invertebrate species. However, it is difficult to apply these approaches to large numbers of species owing to the lack of mechanistic understanding sufficient for quantitative analyses, as well as the lack of scientific infrastructure to support these more detailed studies. Vulnerability assessments provide a framework for evaluating climate impacts over a broad range of species with existing information. These methods combine the exposure of a species to a stressor (climate change and decadal variability) and the sensitivity of species to the stressor. These two components are then combined to estimate an overall vulnerability. Quantitative data are used when available, but qualitative information and expert opinion are used when quantitative data is lacking. Here we conduct a climate vulnerability assessment on 82 fish and invertebrate species in the Northeast U.S. Shelf including exploited, forage, and protected species. We define climate vulnerability as the extent to which abundance or productivity of a species in the region could be impacted by climate change and decadal variability. We find that the overall climate vulnerability is high to very high for approximately half the species assessed; diadromous and benthic invertebrate species exhibit the greatest vulnerability. In addition, the majority of species included in the assessment have a high potential for a change in distribution in response to projected changes in climate. Negative effects of climate change are expected for approximately half of the species assessed, but some species are expected to be positively affected (e.g., increase in productivity or move into the region). These results will inform research and management activities related to understanding and adapting marine fisheries management and conservation to climate change and decadal variability

The Impact of Digital Storytelling on Social Agency: Early Experience at an Online University

Digital Storytelling\u27 is a term often used to refer to a number of different types of digital narrative including web-based stories, hypertexts, videoblogs and computer games. This emergent form of creative work has found an outlet in a wide variety of different domains ranging from community social history, to cookbooks, to the classroom. It is the latter domain that provides the focus for this paper, specifically the online classroom at the tertiary level...Early feedback from students suggests that listening to and telling \u27true stories\u27 was a compelling and emotionally-engaging experience, providing an opportunity for \u27transformative reflection\u27 (Lambert 2000). By including multimedia, learners were able to build upon the fundamentals, presenting content in an easy-to-absorb and compelling way. In terms of team assignments students learned to become more effective actors in collaborative work environments

NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study.

BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. METHODS: A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. RESULTS: Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. CONCLUSIONS: Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database

Relationship of age, gender, race, and body size to infrarenal aortic diameter

AbstractPurpose: To assess the effects of age, gender, race, and body size on infrarenal aortic diameter (IAD) and to determine expected values for IAD on the basis of these factors.Methods: Veterans aged 50 to 79 years at 15 Department of Veterans Affairs medical centers were invited to undergo ultrasound measurement of IAD and complete a prescreening questionnaire. We report here on 69,905 subjects who had no previous history of abdominal aortic aneurysm (AAA) and no ultrasound evidence of AAA (defined as IAD ≥ 3.0 cm).Results: Although age, gender, black race, height, weight, body mass index, and body surface area were associated with IAD by multivariate linear regression (all p < 0.001), the effects were small. Female sex was associated with a 0.14 cm reduction in IAD and black race with a 0.01 cm increase in IAD. A 0.1 cm change in IAD was associated with large changes in the independent variables: 29 years in age, 19 cm or 40 cm in height, 35 kg in weight, 11 kg/m2 in body mass index, and 0.35 m2 in body surface area. Nearly all height-weight groups were within 0.1 cm of the gender means, and the unadjusted gender means differed by only 0.23 cm. The variation among medical centers had more influence on IAD than did the combination of age, gender, race, and body size.Conclusions: Age, gender, race, and body size have statistically significant but small effects on IAD. Use of these parameters to define AAA may not offer sufficient advantage over simpler definitions (such as an IAD ≥3.0 cm) to be warranted. (J Vasc Surg 1997;26:595-601.

Progress in the management and outcome of small-cell lung cancer in a French region from 1981 to 1994

Recent analyses of series of small-cell lung cancer (SCLC) patients included in clinical trials have shown improved survival over time, but it has been impossible to determine whether this was due to selection biases, stage migration, or true therapeutic improvement. To determine if there has been a true improvement of survival over time, we reviewed the medical records of all consecutive patients diagnosed with SCLC between 1981 and 1994 in the Bas-Rhin in France. Among the 787 patients (median age 63), there was no significant period effect for sex, age, or stage. Staging work-ups became increasingly thorough (significant period effect). The mean number of investigations and of tumour sites detected correlated significantly. The chemotherapy rate increased (from 76.4% in 1981–1983 to 91.7% in 1993–1994, P = 10−5) and mediastinal irradiation decreased (to roughly 25% of patients after 1983). Median survival time increased for the overall population from 6.6 months in 1981–1983 to 11.3 months in 1993–1994 (P = 10−5), for patients with limited disease (LD) from 9.2 (P = 0.002) months to 14.0 months, and for those with extensive (ED) disease from 3.5 months to 9.6 months (P = 10−5). Significant independent prognostic factors were disease extent, clinical trial participation, period, type of chemotherapy, and mediastinal irradiation in LD. Survival time has truly improved as ‘state of the art' management of SCLC has changed. © 2001 Cancer Research Campaignhttp://www.bjcancer.co

Hierarchies of Pain

Trauma has become a pervasive cultural model for representing individual and collective injuries and suffering. This process has produced what may be called a trauma aesthetic, a set of recognizable tropes in widespread use in trauma narratives. This chapter examines the adoption of this aesthetic in graphic narratives, focusing on the special capacities of the form. Familiar tropes, such as dissociation and the somatic trace, are presented in complex combinations of visual and textual components, often exploiting the differential appearance of text and image to introduce a dynamic of belatedness or disarticulation. This chapter analyses five works ordered according to their diminishing reliance on ‘trauma’. The trauma aesthetic is used, though not explicitly, in Catherine Meurisse’s La Légèreté (2016) about the Charlie Hebdo attack, Jean-Philip Stassen’s Déogratias (2000/2006) about the genocide in Rwanda, and Emmanuel Lepage’s Un printemps à Tchernobyl (2012) about the aftermath of the Chernobyl nuclear disaster. By contrast, it is absent from Mazen Kerbaj’s Beirut Won’t Cry (2007/2017) about the Israel-Hezbollah conflict and Josh Neufeld’s A.D. about Hurricane Katrina (2009). These works’ reliance on formalized and sanctioned trauma tropes not only is influenced by narrative characteristics, such as temporal distance from the event or the presence of a single narrator-protagonist but may also be motivated by the prestige conferred by trauma as recognized suffering, affecting the canonization and translatability of the graphic narratives in question