bluuebunny/crossref_metadata_2025_split huggingface dataset:
Neural Processing of Health Information and Hypertension Self-Management in African Americans
Authors: Still Carolyn H. , Jack Anthony I. , Wright Kathy D. , Sattar Abdus , Moore Shirley M. 
Published: March 2022

Background
Uncontrolled blood pressure (BP) rates are persistently high among African Americans with hypertension. Although self-management is critical to controlling BP, little is known about the brain–behavior connections underlying the processing of health information and the performance of self-management activities.

Objectives
In this pilot study, we explored the associations among neural processing of two types of health information and a set of self-management cognitive processes (self-efficacy, activation, decision-making, and hypertension knowledge) and behaviors (physical activity, dietary intake, and medication taking) and health status indicators (BP, health-related quality of life, anxiety, and depression).

Methods
Using a descriptive cross-sectional design, 16 African Americans with uncontrolled hypertension (mean age = 57.5 years, 68.8% women) underwent functional magnetic resonance imaging to assess activation of two neural networks, the task-positive network and the default mode network, and a region in the ventromedial prefrontal cortex associated with emotion-focused and analytic-focused health information. Participants completed self-reports and clinical assessments of self-management processes, behaviors, and health status indicators.

Results
Our hypothesis that neural processing associated with different types of health information would correlate with self-management cognitive processes and behaviors and health status indicators was only partially supported. Home diastolic BP was positively associated with ventromedial prefrontal cortex activation (r = .536, p = .09); no other associations were found among the neural markers and self-management or health status variables. Expected relationships were found among the self-management processes and behaviors and health status indicators.

Discussion
To advance our understanding of the neural processes underlying health information processing and chronic illness self-management, future studies are needed that use larger samples with more heterogeneous populations and additional neuroimaging techniques.
Detection of bacterial co-infections and prediction of fatal outcomes in COVID-19 patients presenting to the emergency department using a 29 mRNA host response classifier
Authors: Ram-Mohan Nikhil , Rogers Angela J. , Blish Catherine A. , Nadeau Kari C. , Zudock Elizabeth J , Kim David , Quinn James V. , Sun Lixian , Liesenfeld Oliver ,   Stanford COVID-19 Biobank Study Group, Yang Samuel 
Published: March 2022

AbstractObjectiveClinicians in the emergency department (ED) face challenges in concurrently assessing patients with suspected COVID-19 infection, detecting bacterial co-infection, and determining illness severity since current practices require separate workflows. Here we explore the accuracy of the IMX-BVN-3/IMX-SEV-3 29 mRNA host response classifiers in simultaneously detecting SARS-CoV-2 infection, bacterial co-infections, and predicting clinical severity of COVID-19.Methods161 patients with PCR-confirmed COVID-19 (52.2% female, median age 50.0 years, 51% hospitalized, 5.6% deaths) were enrolled at the Stanford Hospital ED. RNA was extracted (2.5 mL whole blood in PAXgene Blood RNA) and 29 host mRNAs in response to the infection were quantified using Nanostring nCounter.ResultsThe IMX-BVN-3 classifier identified SARS-CoV-2 infection in 151 patients with a sensitivity of 93.8%. Six of 10 patients undetected by the classifier had positive COVID tests more than 9 days prior to enrolment and the remaining oscillated between positive and negative results in subsequent tests. The classifier also predicted that 6 (3.7%) patients had a bacterial co-infection. Clinical adjudication confirmed that 5/6 (83.3%) of the patients had bacterial infections, i.e. Clostridioides difficile colitis (n=1), urinary tract infection (n=1), and clinically diagnosed bacterial infections (n=3) for a specificity of 99.4%. 2/101 (2.8%) patients in the IMX-SEV-3 Low and 7/60 (11.7%) in the Moderate severity classifications died within thirty days of enrollment.ConclusionsIMX-BVN-3/IMX-SEV-3 classifiers accurately identified patients with COVID-19, bacterial co-infections, and predicted patients’ risk of death. A point-of-care version of these classifiers, under development, could improve ED patient management including more accurate treatment decisions and optimized resource utilization.
Like host, like parasite: intraspecific divergence in a polystomatid flatworm parasite across South Africa echoes that of its frog host
Authors: Schoeman Anneke Lincoln , Kmentová Nikol , Vanhove Maarten PM , Du Preez Louis Heyns 
Published: March 2022

ABSTRACTThe African Clawed FrogXenopus laevis, a global invader, exhibits marked phylogeographic divergence among native populations in southern Africa, which enhances its invasive potential. The polystomatid flatwormProtopolystoma xenopodis, as the frog’s most frequently co-introduced metazoan parasite, may be the ideal biological tag for the frog’s movement, if corresponding divergence can be demonstrated. In an integrative approach, we utilised morphometrics and molecular markers to assess divergence inP. xenopodisin its native range. We measured twelve key morphological characters from 23 flatworms and compared these statistically between flatworms collected to the north and south of the Great Escarpment Mountain Range in South Africa. Phylogenetic analyses were based on three concatenated markers, namely28Sand12S rDNAandCOX1, from six flatworms. The combination of five morphological characters, which involve egg size, gut morphology and size of the attachment hooks, differentiated northern and southern populations ofP. xenopodisin South Africa. The multilocus phylogenetic analyses supported these findings, showing a well-supported cluster of northernP. xenopodis. These findings suggest that taxonomic studies of polystomatid flatworms should make use of geographically representative data sets that consider both morphological and molecular evidence. Moreover, the findings demonstrate that the frog host and flatworm parasite exhibit corresponding phylogeographic structuring in the native range. Consequently, the phylogeography ofP. xenopodis, both in the native and invasive range of its host, may act as a key piece of evidence to reconstruct past invasion pathways ofX. laevis.
Evaluating the potential benefits and pitfalls of combining protein and expression quantitative trait loci in evidencing drug targets
Authors: Robinson Jamie W , Battram Thomas , Baird Denis A , Haycock Philip C , Zheng Jie , Hemani Gibran , Chen Chia-Yen , Gaunt Tom R 
Published: March 2022

AbstractMolecular quantitative trait loci (molQTL), which can provide functional evidence on the mechanisms underlying phenotype-genotype associations, are increasingly used in drug target validation and safety assessment. In particular, protein abundance QTLs (pQTLs) and gene expression QTLs (eQTLs) are the most commonly used for this purpose. However, questions remain on how to best consolidate results from pQTLs and eQTLs for target validation.In this study, we combined blood cell-derived eQTLs and plasma-derived pQTLs to form QTL pairs representing each gene and its product. We performed a series of enrichment analyses to identify features of QTL pairs that provide consistent evidence for drug targets based on the concordance of the direction of effect of the pQTL and eQTL. We repeated these analyses using eQŢLs derived in 49 tissues.We found that 25-30% of blood-cell derived QTL pairs have discordant effects. The difference in tissues of origin for molecular markers contributes to, but is not likely a major source of, this observed discordance. Finally, druggable genes were as likely to have discordant QTL pairs as concordant.Our analyses suggest combining and consolidating evidence from pQTLs and eQTLs for drug target validation is crucial and should be done whenever possible, as many potential drug targets show discordance between the two molecular phenotypes that could be misleading if only one is considered. We also encourage investigating QTL tissue-specificity in target validation applications to help identify reasons for discordance and emphasise that concordance and discordance of QTL pairs across tissues are both informative in target validation.
The DeepFaune initiative: a collaborative effort towards the automatic identification of French fauna in camera-trap images
Authors: Rigoudy Noa , Dussert Gaspard , Benyoub Abdelbaki , Besnard Aurélien , Birck Carole , Boyer Jérome , Bollet Yoann , Bunz Yoann , Caussimont Gérard , Chetouane Elias , Carriburu Jules Chiffard , Cornette Pierre , Delestrade Anne , De Backer Nina , Dispan Lucie , Le Barh Maden , Duhayer Jeanne , Elder Jean-François , Fanjul Jean-Baptiste , Fonderflick Jocelyn , Froustey Nicolas , Garel Mathieu , Gaudry William , Gérard Agathe , Gimenez Olivier , Hemery Arzhela , Hemon Audrey , Jullien Jean-Michel , Knitter Daniel , Malafosse Isabelle , Marginean Mircea , Ménard Louise , Ouvrier Alice , Pariset Gwennaelle , Prunet Vincent , Rabault Julien , Randon Malory , Raulet Yann , Régnier Antoine , Ribière Romain , Ricci Jean-Claude , Ruette Sandrine , Schneylin Yann , Sentilles Jérôme , Siefert Nathalie , Smith Bethany , Terpereau Guillaume , Touchet Pierrick , Thuiller Wilfried , Uzal Antonio , Vautrain Valentin , Vimal Ruppert , Weber Julian , Spataro Bruno , Miele Vincent , Chamaillé-Jammes Simon 
Published: March 2022

AbstractCamera traps have revolutionized how ecologists monitor wildlife, but their full potential is realized only when the hundreds of thousands of collected images can be readily classified with minimal human intervention. Deep-learning classification models have allowed extraordinary progress towards this end, but trained models remain rare and are only now emerging for European fauna. We report on the first milestone of the DeepFaune initiative (https://www.deepfaune.cnrs.fr), a large-scale collaboration between more than 50 partners involved in wildlife research, conservation and management in France. We developed a classification model trained to recognize 26 species or higher-level taxa. The classification model achieved 0.97 validation accuracy and often >0.95 precision and recall for many classes. These performances were generally higher than 0.90 when tested on independent out-of-sample datasets for which we used image redundancy contained in sequence of images. We implemented our model in a software to classify images stored locally on a personal computer, so as to provide a free, user-friendly and high-performance tool for wildlife practitioners to automatically classify camera-trap images.
metaPathwayMap: A tool to predict metabolic pathway neighborhoods from structural classes of untargeted metabolomics peaks
Authors: Moghe Gaurav , Strickler Susan 
Published: March 2022

AbstractSummaryThousands of peaks detected via untargeted tandem liquid chromatography mass spectrometry (LC-MS/MS) of natural extracts typically go unannotated, limiting our understanding of the metabolic pathways perturbed under different conditions. Current tools for predicting metabolic pathways from untargeted metabolomics data either require prior compound identification or are more focused on specific model species. metaPathwayMap makes use of recent advances in computational metabolomics to map peaks detected in untargeted LC-MS/MS experiments to MetaCyc pathway representations using their structural class predictions. This approach enables better insights into metabolomes of model and non-model species.Availability and ImplementationRequired Python scripts can be downloaded from the moghelab/metaPathwayMap GitHub repository and implemented on a Unix machine. This tool is also available for use through the SolCyc website (https://metapathwaymap.solgenomics.net) and via DockerHub (srs57/metapathwaymap).Contactgdm67@cornell.eduSupplementary InformationAdditional information is provided in Supplementary Methods, Supplementary Files 1-3 and on GitHub (https://github.com/moghelab/metaPathwayMap)
Feature-based attention multiplicatively scales the fMRI-BOLD contrast-response function
Authors: Foster Joshua J. , Ling Sam 
Published: March 2022

AbstractFunctional MRI (fMRI) plays a key role in the study of attention. However, there remains a puzzling discrepancy between attention effects measured with fMRI and with electrophysiological methods. While electrophysiological studies find that attention increases sensory gain, amplifying stimulus-evoked neural responses by multiplicatively scaling the contrast-response function (CRF), fMRI appears to be insensitive to these multiplicative effects. Instead, fMRI studies typically find that attention produces an additive baseline shift in the blood-oxygen-level-dependent (BOLD) signal. These findings suggest that attentional effects measured with fMRI reflect top-down inputs to visual cortex, rather than the modulation of sensory gain. If true, this drastically limits what fMRI can tell us about how attention improves sensory coding. Here, we re-examined whether fMRI is sensitive to multiplicative effects of attention using a feature-based attention paradigm designed to preclude any possible additive effects. We measured BOLD activity evoked by a probe stimulus in one visual hemifield while participants (6 male, 6 female) attended to the probe orientation (attended condition), or to an orthogonal orientation (unattended condition), in the other hemifield. To measure CRFs in visual areas V1-V3, we parametrically varied the contrast of the probe stimulus. In all three areas, feature-based attention increased contrast gain, improving sensitivity by shifting CRFs towards lower contrasts. For a subset of visual eccentricities, we also found an increase in response gain, an increase in the responsivity of the CRF. These results provide clear evidence that the fMRI-BOLD signal is sensitive to multiplicative effects of attention.Significance StatementFunctional MRI (fMRI) plays a central role in the study of attention because it allows researchers to precisely and non-invasively characterize the effects of attention throughout the brain. Electrophysiological studies have shown that attention increases sensory gain, amplifying stimulus-evoked neural responses. However, a growing body of work suggests that the BOLD signal that is measured with fMRI is not sensitive to these multiplicative effects of attention, calling into question what we can learn from fMRI about how attention improves sensory codes. Here, using a feature-based attention paradigm, we provide evidence that the BOLD signal can pick up multiplicative effects of attention.
Are trapping data still suited for home range estimation? An analysis with various estimators, asymptotic models and data ordering procedures
Authors: Socias-Martínez L. , Peckre L. R. , Noonan M. J. 
Published: March 2022

AbstractUnderstanding the size of animals’ home ranges is vital for studies in ecology and conservation. Trapping datasets are an important source of information when targeting the biodiversity of an area, inconspicuous species, or high numbers of individuals in contrast to more expensive telemetry-based methods such as radio- or GPS-collaring. Currently, studies relying on trapping lack an evaluation of the performance of existing home range estimation procedures comparable to those developed for telemetry. Using animal movement simulations, we evaluate three variables reflecting the trade-offs faced by ecologists when designing a trapping study, 1) the number of observations obtained per individual, 2) the trap density and 3) the proportion of the home range area falling inside of the trapping grid. We compare the performance of five estimators on these conditions, four commonly used (AKDE, KDE, MCP, LoCoH) and a possible alternative for situations with low trap density or high number of observations (bicubic interpolation). We further test suggested benefits of using asymptotic models (Michaelis-Menten and monomolecular) to assess the total home range area when information obtained per individual is scarce, as this situation might be common in trapping datasets. In addition, we propose sorting the observations based on the distance between locations to improve the performance of asymptotic models’ estimates. Using the results of the different procedures we constructed a generalized additive model (GAM) that allows predicting the bias in home range size under the different scenarios investigated. Our results show that the proportion of the area covered by the trapping grid and the number of observations were the most important factors predicting the accuracy and reliability of the estimates. The use of asymptotic models helped obtaining an accurate estimation at lower sample sizes and this effect was further improved by distance-ordering. The autocorrelation informed KDE was the estimator performing best under most conditions evaluated. Nevertheless, bicubic interpolation can be an alternative under common trapping conditions with low density of traps and low area covered. We provide the current results to the constructed GAM as a prospective tool for ecologists planning a new study or with already collected datasets that aim at assessing the potential biases in their estimates. Reliable and accurate home range estimates using trapping data can optimize monetary costs of home range studies, potentially enlarging the span of species, researchers and questions studied in ecology and conservation.
Robust group- but limited individual-level (longitudinal) reliability and insights into cross-phases response prediction of conditioned fear
Authors: Klingelhöfer-Jens Maren , Ehlers Mana R. , Kuhn Manuel , Keyaniyan Vincent , Lonsdorf Tina B. 
Published: March 2022

AbstractHere we follow the call to target measurement reliability as a key prerequisite for individual-level predictions in translational neuroscience by investigating i) longitudinal reliability at the individual and ii) group level, iii) cross-sectional reliability and iv) response predictability across experimental phases. 120 individuals performed a fear conditioning paradigm twice six month apart. Analyses of skin conductance responses, fear ratings and BOLD-fMRI with different data transformations and included numbers of trials were conducted. While longitudinal reliability was generally poor to moderate at the individual level, it was good for acquisition but not extinction at the group-level. Cross-sectional reliability was satisfactory. Higher responding in preceding phases predicted higher responding in subsequent experimental phases at a weak to moderate level depending on data specifications. In sum, the results suggest the feasibility of individual-level predictions for (very) short time intervals (e.g., cross-phases) while predictions for longer time intervals may be problematic.
Arginine 65 methylation of Neurogenin 3 by PRMT1 is a prerequisite for development of hESCs into pancreatic endocrine cells
Authors: Cho Gahyang , Hyun Kwangbeom , Choi Jieun , Shin Eunji , Kim Bumsoo , Kim Jaehoon , Kim Hail , Han Yong-Mahn 
Published: March 2022

AbstractIn the developing pancreas, Neurogenin 3 (NGN3) is a key transcription factor in the cell fate determination of endocrine progenitors (EPs). Although the activation and stability of NGN3 are regulated by phosphorylation, the role of arginine methylation of NGN3 is poorly understood. Here, we report arginine 65 methylation of NGN3 is absolutely required for the pancreatic lineage development of human embryonic stem cells (hESCs) in vitro. First, we found inducible protein arginine methyltransferase 1 (PRMT1)-knockout (P-iKO) hESCs did not differentiate from EPs to endocrine cells (ECs) in the presence of doxycycline. Loss of PRMT1 caused an accumulation of NGN3 in the cytoplasm of EPs and blocked NGN3’s transcriptional activity in PRMT1-KO EPs. We also found that PRMT1 specifically methylates NGN3 arginine 65, and this modification is a prerequisite for ubiquitin-mediated NGN3 degradation. Our findings indicate arginine 65 methylation of NGN3 is a key molecular switch in hESCs in vitro permitting the differentiation into pancreatic endocrine lineages.
Health service research definition builder: An R Shiny application for exploring diagnosis codes associated with services reported in routinely collected health data
Authors: Chalmers Kelsey , Gopinath Valérie , Elshaug Adam G 
Published: March 2022

AbstractMany administrative health data-based studies define patient cohorts using procedure and diagnosis codes. The impact these criteria have on a study’s final cohort is not always transparent to co-investigators or other audiences if access to the research data is restricted. We developed a SAS and R Shiny interactive research support tool which generates and displays the diagnosis code summaries associated with a selected medical service or procedure. This allows non-analyst users to interrogate claims data and groupings of reported diagnosis codes. The SAS program uses a tree classifier to find associated diagnosis codes with the service claims compared against a matched, random sample of claims without the service. Claims are grouped based on the overlap of these associated diagnosis codes. The Health Services Research (HSR) Definition Builder Shiny application uses this input to create interactive table and graphics, which updates estimated claim counts of the selected service as users select inclusion and exclusion criteria. This tool can help researchers develop preliminary and shareable definitions for cohorts for administrative health data research. It allows an additional validation step of examining frequency of all diagnosis codes associated with a service, reducing the risk of incorrect included or omitted codes from the final definition. In our results, we explore use of the application on three example services in 2016 US Medicare claims for patients aged over 65: knee arthroscopy, spinal fusion procedures and urinalysis. Readers can access the application at https://kelsey209.shinyapps.io/hsrdefbuilder/ and the code at https://github.com/kelsey209/hsrdefbuilder.
Validation of a rapid potency assay for cord blood stem cells using phospho flow cytometry: the IL-3-pSTAT5 assay
Authors: Simard Carl , Fournier Diane , Trépanier Patrick 
Published: March 2022

AbstractPublic cord blood banks (CBBs) are required to measure cord blood units (CBUs) potency before their release, allowing for the identification of units that may be unsuitable for hematopoietic transplantation. We have developed a rapid flow cytometry assay based on the measurement of STAT-5 phosphorylation of CD34+ stem cells in response to IL-3 stimulation: the IL-3-pSTAT5 assay.To adapt the assay from a research setting to its implementation within our CBB regulated operations, we proceeded with a full method validation and a correlation comparison of the IL-3-pSTAT5 assay results with the colony-forming unit assay (CFU) results. A total of 60 CBUs cryopreserved in vials were analyzed by flow cytometry to determine the sensitivity, specificity, intra-assay precision, robustness, reproducibility and inter-laboratory agreement of the assay. The CFU assay was also done on the same samples for comparison purposes.The assay threshold was established at 50% CD34+CD45+pSTAT5+, which provides a 100% sensitivity and a 98.3% specificity. An average intra-assay CV of 7.3% was determined. All results met our qualitative results acceptance criteria regarding the inter-user and inter-laboratory agreements, IL-3 stimulation time, post-thaw incubation delay and staining time. The IL-3-pSTAT5 assay results correlated well with the total CFU determined using the CFU assay (r2 = 0.82, n = 56).This study shows that our rapid flow cytometry assay can be successfully validated and that the potency data obtained display good sensitivity, specificity and robustness. These results demonstrate the feasibility of implementing this assay within CBB operations, as a validated CB potency assay.