bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrating complexity in population modelling: from matrix to dynamic models using the jellyfishCarybdea marsupialis
Authors: Flores-García Adrián , Yañez-Dobson John , Fonfría Eva S. , García-García David , Bordehore César 
Published: January 2024

ABSTRACTMatrix models are widely used in population ecology studies and are valuable for analysing population dynamics. Nonetheless, this approach is somewhat rigid in terms of generating complex scenarios. Starting from the values of the transition matrix, we can build a dynamic model to incorporate more biological-based reality into the model (e.g. polyp stage) and provide a higher flexibility in generating scenarios. As an example, we used the transition matrix calculated for a time series data of a population of the box jellyfishCarybdea marsupialis(L. 1758) in the Western Mediterranean in a previously published study. Dynamic models can help us to better understand the complex relationships that drive populations, test different hypotheses and compare scenarios. The dynamic model was developed in STELLA Architect, calibrated and optimised and it has been used to simulate various scenarios of ecological interest, including a decline in food supply, jellyfish removal strategies, changes in drift currents and changes in substrate availability for planulae to settle. A sensitivity analysis showed that polyp strobilation rate and strobilation pattern were two of the most sensitive variables. This matrix-to-dynamic model approach could be useful to integrate more biological complexity into population models and, in turn, obtain a better fit to the field data.
The blobulator: a webtool for identification and visual exploration of hydrophobic modularity in protein sequences
Authors: Pitman Connor , Santiago-McRae Ezry , Lohia Ruchi , Bassi Kaitlin , Joseph Thomas T. , Hansen Matthew E.B. , Brannigan Grace 
Published: January 2024

ABSTRACTMotivationClusters of hydrophobic residues are known to promote structured protein stability and drive protein aggregation. Recent work has shown that identifying contiguous hydrophobic residue clusters (termed “blobs”) has proven useful in both intrinsically disordered protein (IDP) simulation and human genome studies. However, a graphical interface was unavailable.ResultsHere, we present the blobulator: an interactive and intuitive web interface to detect intrinsic modularity in any protein sequence based on hydrophobicity. We demonstrate three use cases of the blobulator and show how identifying blobs with biologically relevant parameters provides useful information about a globular protein, two orthologous membrane proteins, and an IDP. Other potential applications are discussed, including: predicting protein segments with critical roles in tertiary interactions, providing a definition of local order and disorder with clear edges, and aiding in predicting protein features from sequence.AvailabilityThe blobulator GUI can be found atwww.blobulator.branniganlab.org, and the source code with pip installable command line tool can be found on GitHub atwww.GitHub.com/BranniganLab/blobulator.
Alcohol Sipping Patterns, Personality, and Psychopathology in Children: Moderating Effects of Dorsal Anterior Cingulate Cortex (dACC)
Authors: Ferariu Ana , Taylor Alexei , Chang Hansoo , Zhang Fengqing 
Published: January 2024

ABSTRACTAlcohol, the most consumed drug in the United States, is associated with various psychological disorders and abnormal personality traits. Despite extensive research on adolescent alcohol consumption, the impact of early alcohol sipping patterns on changes in personality and mental health over time remains unclear. There is also limited information on the latent trajectory of early alcohol sipping, beginning as young as 9-10 years old. The dorsal anterior cingulate cortex (dACC) is crucial for cognitive control and response inhibition. However, the role of dACC remains unclear in the relationship between early alcohol sipping and mental health outcomes and personality traits over time. Utilizing the large data from the Adolescent Brain and Cognitive Development study, we aim to comprehensively examine the longitudinal impact of early alcohol sipping patterns on psychopathological measures and personality traits in adolescents, filling crucial gaps in the literature. We identified three latent alcohol-sipping groups, which demonstrate distinct personality traits and depression score trajectories. Bilateral dACC activation during the stop-signal task moderated the effect of early alcohol sipping on personality and depression over time. Additionally, bidirectional effects were observed between alcohol sipping and personality traits. This study provides insights into the impact of early alcohol consumption on adolescent development.
A Wireless Wearable Ecosystem for Social Network Analysis in Free-living Animals
Authors: Gaidica Matt , Zhang Mengxiao , Dantzer Ben 
Published: January 2024

AbstractUnderstanding the dynamics of animal social systems requires studying variation in contact and interaction, which is influenced by environmental conditions, resource availability, and predation risk, among other factors. Traditional (direct) observational methods have limitations, but advancements in sensor technologies and data analytics provide unprecedented opportunities to study these complex systems in naturalistic environments. Proximity logging and tracking devices, capturing movement, temperature, and social interactions, offer non-invasive means to quantify behavior and develop empirical models of animal social networks. However, challenges remain in integrating different data types, incorporating more sensor modalities, and addressing logistical constraints. To address these gaps, we developed a wireless wearable sensor system with novel features (called “Juxta”), including modular battery packs, memory management for combining data types, reconfigurable deployment modes, and a smartphone app for data collection. We present data from a pilot study on prairie voles (Microtus ochrogaster), which is a small mammal species that exhibits relatively complex social behavior. We demonstrate the potential for Juxta to increase our understanding of the social networks and behavior of free-living animals. Additionally, we propose a framework to guide future research in merging temporal, spatial, and event-driven data. By leveraging wireless technology, battery efficiency, and smart sensing modalities, our wearable ecosystem offers a scalable solution for real-time, high-resolution data capture and analysis in animal social network studies, opening new avenues for exploring complex social dynamics across species and environments.
Incremental Accumulation of Linguistic Context in Artificial and Biological Neural Networks
Authors: Tikochinski Refael , Goldstein Ariel , Meiri Yoav , Hasson Uri , Reichart Roi 
Published: January 2024

ABSTRACTAccumulated evidence suggests that Large Language Models (LLMs) are beneficial in predicting neural signals related to narrative processing. The way LLMs integrate context over large timescales, however, is fundamentally different from the way the brain does it. In this study, we show that unlike LLMs that apply parallel processing of large contextual windows, the incoming context to the brain is limited to short windows of a few tens of words. We hypothesize that whereas lower-level brain areas process short contextual windows, higher-order areas in the default-mode network (DMN) engage in an online incremental mechanism where the incoming short context is summarized and integrated with information accumulated across long timescales. Consequently, we introduce a novel LLM that instead of processing the entire context at once, it incrementally generates a concise summary of previous information. As predicted, we found that neural activities at the DMN were better predicted by the incremental model, and conversely, lower-level areas were better predicted with short-context-window LLM.
Profiling Clinical Researchers Effectively using Embeddings and Clustering
Authors: Sharma Anika , Edelson Maxim , Kuo Tsung-Ting 
Published: January 2024

AbstractEffective researcher profiling is key to support rapid research team formation. We developed a profiling method using (1) widely accessible publication titles, (2) document embedding vector representations to consider background, and (3) both general and specific types of datasets. Our results showed that the most similar researchers have cosine similarities of 0.287/0.258. Our preliminary results can support biomedical informaticians to expedite collaborative clinical studies, enhance research quality, and eventually improve patient healthcare outcomes.
Frailty, Mental Disorders, and Metabolic Syndrome: A Genetic Association and Mediation Mendelian Randomization Study
Authors: Deng Ming-Gang , Wang Kai , Nie Jia-Qi , Liu Fang , Liang Yuehui , Liu Jiewei 
Published: January 2024

AbstractObjectiveTo examine the genetic associations of metabolic syndrome (MetS) with frailty and mental disorders [depression, schizophrenia (SCZ), and bipolar disorder (BIP)], along with causality between frailty and MetS and the mediating role of mental disorders.MethodsThe summary-level datasets were obtained from recent genome-wide association studies. The genetic correlation was explored from the perspectives of global and local genetic correlation. Univariate Mendelian Randomization (UMR) was used to investigate the causal link between frailty and metabolic syndrome (MetS), followed by multivariate MR to address the confounding effects of body mass index (BMI) and physical activity (PA). Finally, two-step MR analyses were conducted to examine whether the causal relationship was mediated by mental disorders.ResultsThe global genetic correlation analyses presented MetS was positively associated with frailty and depression, but reversely related to SCZ. Similarly, MetS was locally correlated to frailty, depression, and SCZ in numerous genomic regions. The UMR demonstrated that fragile people have a higher likelihood of suffering from MetS (OR: 2.773, 95% CI: 1.455-5.286,p= 0.002), and reversely people with MetS tended to be more fragile (beta: 0.211, 95% CI: 0.180-0.241,p< 0.001). This bidirectional causal association still existed even after adjusting for BMI and PA. The mediation analyses implied this causality was mediated by depression, but not SCZ and BIP.ConclusionOur research provided evidence of genetic correlations between MetS and frailty, depression, and SCZ. Additionally, we discovered a bidirectional causality between frailty and MetS, with depression playing a significant mediating role.
cg05883128 (DDX60) and NR3C2 (CD4 T-cells and B-cells) to Be the Genetic Roots of Systemic Lupus Erythematosus
Authors: Zhang Zhengjun 
Published: January 2024

AbstractMotivationSystemic lupus erythematosus (SLE) is an autoimmune disease and a long-term condition affecting many body parts. Autoimmune diseases are affecting more people for reasons unknown, and the causes of these diseases remain a mystery.MethodA newly introduced robust competing (risk) max-logistic regression classifier that can simultaneously perform subtype clustering and classification for disease diagnoses and predictions becomes a new hope to solve the mystery. We use this method in the study to discover critical DNA methylation CpG sites and genome genes, which lead to the highest accuracy and interpretability.ResultsThe DNA methylation CpG site cg05883128 (DDX60) and gene NR3C2 are essentially responsible for SLE development. They can lead to 100% prediction accuracy together with a miniature set of other CpG sites and genes, respectively. cg05883128 (DDX60) reveals the LSE mechanism affecting many body parts. NR3C2 in CD4 T cells and B cells behaves reversely, leading to the cause of LSE and explaining the mechanism of the autoimmune disease.ConclusionsThis work represents a pioneering effort and intellectual discovery in applying the max-logistic competing risk factor model to identify critical genes for LSE, and the interpretability and reproducibility of the results across diverse populations suggest that the CpGs and DEGs identified can provide a comprehensive description of the transcriptomic features of SLE. The practical implications of this research include the potential for personalized risk assessment, precision diagnosis, and tailored treatment plans for patients.
The Impact of Normal Aging Out on Continuity of Treatment among Children and Adolescents Living with HIV in the PEPFAR Supported Program in Kenya
Authors: Odero Lydia , Chafetz Aaron , Gikura Mary , Goldstein Deborah , Golin Rachel , Kemunto Diana , Maina Nelly , Mutisya Immaculate , Muthoka Kennedy , Nganga Evelyn , Okegbe Tishina , Okutoyi Salome , Omoro Gonza , Siberry George , Wafula Rose , Achwoka Dunstan 
Published: January 2024

AbstractIntroductionTo reduce HIV-related morbidity and mortality among children and adolescents living with HIV, defined as those 0-14 years old, continuity of treatment is critical. Treatment continuity estimates among children and adolescents living with HIV lag adults. We sought to understand how aging out among children and adolescents living with HIV in Kenya impacts continuity of treatment.MethodsA retrospective cohort analysis was performed on de-identified individual-level data from the Kenya National Data Warehouse for all clients who initiated and/or received antiretroviral therapy between the periods of Oct 1, 2018 and Sep 30, 2022 (US Government fiscal years 2019-2022). Children and adolescents living with HIV previously on antiretroviral therapy and those newly initiating antiretroviral therapy were included in the analysis. Outcomes included aging out of the cohort after turning 15 years old, interruption in treatment, return to treatment, and remaining active on treatment.ResultsThe study analyzed client-level data for four US Government fiscal years 2019-2022. The number of active children and adolescents living with HIV on treatment at the end of fiscal year 2019 was 44,628. This changed to 48,218, 48,262, and 44,780 representing 8%, 0%, and -7% cohort growth/loss at the end of fiscal years 2020, 2021 and 2022, respectively. Among those who were on treatment at the beginning of each fiscal year, aging-out accounted for 47%, 39%, and 28% of the total losses for the periods 2020, 2021 and 2022, respectively. Interruptions in treatment accounted for proportions ranging from 5-9% among those active on treatment. Among the newly-initiated on treatment within each fiscal year, aging-out proportions ranged from 3%-5%. Among those who returned to treatment in each fiscal year, the proportions who remained active at the end of the fiscal year varied from 72%-76%.ConclusionsThis analysis suggests that normal aging-out results in underestimation of HIV treatment continuity for children and adolescents living with HIV. Routine aging out analyses can inform programs on their true rates of interruptions in treatment, as they work to achieve epidemic control among children and adolescents living with HIV.
Bystander intervention is associated with reduced mortality among injury victims in Cameroon
Authors: O’Connor Kathleen , Driban Matthew , Oke Rasheedat , Dissak-Delon Fanny Nadia , Magdalene Tanjong Signe Mary , Mirene Tchekep , Dieudonne Mbeya , Kinge Thompson , Njock Richard L. , Nkusu Daniel N. , Tsiagadigui Jean-Gustave , Edouka Cyrille , Wonja Catherine , Eisner Zachary , Delaney Peter , Julliard Catherine , Chichom-Mefire Alain , Christie S. Ariane 
Published: January 2024

ABSTRACTIntroductionDespite high injury mortality rates, Cameroon currently lacks a formal prehospital care system. In other sub-Saharan African low and middle-income countries, Lay First Responder (LFR) programs have trained non-medical professionals with high work-related exposure to injury in principles of basic trauma care. To develop a context-appropriate LFR program in Cameroon, we used trauma registry data to understand current layperson bystander involvement in prehospital care and explore associations between current non-formally trained bystander-provided prehospital care and clinical outcomes.MethodsThe Cameroon Trauma Registry (CTR) is a longitudinal, prospective, multisite trauma registry cohort capturing data on injured patients presenting to four hospitals in Cameroon. We assessed prevalence and patterns of prehospital scene care among all patients enrolled in a prospective, multisite trauma registry (Cameroon Trauma Registry (CTR)) in 2020. Associations between scene care, clinical status at presentation, and outcomes were tested using univariate and multivariate logistic regression. Injury severity was measured using the abbreviated injury score. Data were analyzed using Stata17.ResultsOf 2212 injured patients, 455 (21%) received prehospital care (PC) and 1699 (77%) did not receive care (NPC). Over 90% (424) of prehospital care was provided by persons without formal medical training. The most common prehospital interventions performed included bleeding control (370, 57%) and fracture immobilization (139, 21%). PC patients were more severely injured (p<0.001), had markers of increased socioeconomic status (p=0.01), and longer transport distances (p<0.001) compared to NPC patients. Despite increased severity of injury, patients who received PC were more likely to present with a palpable pulse (OR=6.2, p=0.02). Multivariate logistic regression adjusted for injury severity, socioeconomic status and travel distance found PC to be associated with reduced emergency department mortality (OR=0.14, p<0.0001).ConclusionsAlthough prehospital injury care in Cameroon is rarely performed and is provided almost entirely by persons without formal medical training, prehospital intervention is associated with increased early survival after injury. Implementation of LFR training to strengthen the frequency and quality of prehospital care has considerable potential to improve trauma survival. LFR training in Cameroon should target commercial drivers given high work-related exposure to the injured and emphasize bleeding control interventions.
Factors associated with viral Load re-suppression after enhanced adherence counseling among recipients of care with an Initial high viral load result in RISE-supported facilities in five States in Nigeria
Authors: Obasa Gbenga Benjamin , Ijaiya Mukhtar , Okwor Ejike , Dare Babafemi , Emerenini Franklin , Anyanwu Prince , Akinjeji Adewale , Brickson Kate , Zech Jennifer , Ogundare Yemisi , Atuma Emmanuel , Strachan Molly , Fayorsey Ruby , Curran Kelly 
Published: January 2024

AbstractThis study explores factors influencing viral re-suppression among 1,607 recipients of HIV care in Nigeria who completed Enhanced Adherence Counseling (EAC) between July 2021 and June 2022. The analysis, part of the Reaching Impact, Saturation, and Epidemic Control (RISE) project, utilized routine program data from 132 health facilities. Results showed a 91% re-suppression rate post-EAC, with age and ART regimen type identified as significant factors. Adolescents (10-19 years) had a higher likelihood of re-suppression, while ROC on second-line regimens exhibited lower resuppression rates. These findings inform programmatic decisions to enhance HIV treatment outcomes in Nigeria.
Characteristics of Walk-In Clinic Physicians and Patients in Ontario, Canada: A Cross-Sectional Study
Authors: Lapointe-Shaw Lauren , Salahub Christine , Austin Peter C. , Bai Li , Banwatt Sundeep , Berthelot Simon , Bhatia R. Sacha , Bird Cherryl , Desveaux Laura , Kiran Tara , Lofters Aisha , Maclure Malcolm , Martin Danielle , McBrien Kerry A. , McCracken Rita K. , Paterson J. Michael , Rahman Bahram , Shuldiner Jennifer , Tadrous Mina , Terpou Braeden A. , Thakkar Niels , Wang Ruoxi , Ivers Noah M. 
Published: January 2024

ABSTRACTObjectiveWe aimed to describe family physicians who primarily practice in a walk-in clinic setting and compare them to family physicians who provide longitudinal care.DesignA cross-sectional study that linked results from an annual physician survey (2019) to administrative healthcare data from Ontario, Canada. We compared the characteristics, practice patterns, and patients of physicians primarily working in a walk-in clinic setting, with family physicians providing longitudinal care.SettingOntario, Canada.ParticipantsPhysicians who primarily worked in a walk-in clinic setting in 2019, as indicated by an annual physician survey.ExposureWhether the physician was a walk-in clinic physician or a family physician who provided longitudinal care.Main MeasuresPhysician demographic and practice characteristics, as well as their patients’ demographic and healthcare utilization characteristics.ResultsCompared to the 9,137 family physicians practicing longitudinal care, the 597 physicians who self-identified as practicing primarily in walk-in clinics were more frequently male (67% vs. 49%) and could speak a language other than English or French (43% vs. 32%). Walk-in clinic physicians had more encounters with patients who were younger (M37 vs. 47 years), had lower levels of prior healthcare utilization (15% vs. 19% in highest band), who resided in large urban areas (87% vs. 77%), and in highly ethnically diverse neighborhoods (45% vs. 35%). Walk-in clinic physicians had more encounters with unattached patients (32% vs. 17%) and with patients attached to another physician outside their group (54% vs. 18%).ConclusionPhysicians who primarily work in walk-in clinics saw many patients from historically underserved groups, and many patients who were attached to another family physician.
Data leakage in deep learning studies of translational EEG
Authors: Brookshire Geoffrey , Kasper Jake , Blauch Nicholas , Wu Yunan “Charles” , Glatt Ryan , Merrill David A. , Gerrol Spencer , Yoder Keith J. , Quirk Colin , Lucero Ché 
Published: January 2024

AbstractA growing number of studies use deep neural networks (DNNs) to identify diseases from recordings of brain activity. DNN studies of electroencephalography (EEG) typically use cross-validation to test how accurately a model can predict the disease state of held-out test data. In these studies, segments of EEG data are often randomly assigned to the training or test sets. As a consequence, data from individual subjects appears in both training and test data. Could high test-set accuracy reflect leakage from subject-specific representations, rather than patterns that identify a disease? We address this question by testing the performance of DNN classifiers using segment-based holdout (where EEG segments from one subject can appear in both the training and test sets), and comparing this to their performance using subject-based holdout (where individual subjects’ data appears exclusively in either the training set or the test set). We compare segment-based and subject-based holdout in two EEG datasets: one classifying Alzheimer’s disease, and the other classifying epileptic seizures. In both datasets, we find that performance on previously-unseen subjects is strongly overestimated when models are trained using segment-based holdout. Next, we survey the literature and find that the majority of translational DNN-EEG studies use segment-based holdout, and therefore overestimate model performance on new subjects. In a hospital or doctor’s office, clinicians need to diagnose new patients whose data was not used in training the model; segment-based holdout, therefore, does not reflect the real-world performance of a translational DNN model. When evaluating how DNNs could be used for medical diagnosis, models must be tested on subjects whose data was not included in the training set.