bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abnormal multisensory facilitation patterns relate to disorganized thinking severity and cognitive decline in schizophrenia
Authors: Rogge-Obando Kimberly K , Coffman Brian A , Stephen Julia M 
Published: July 2024

AbstractPast research has demonstrated that patients with schizophrenia (SP) have visual processing and multisensory integration deficits. Additional studies report that sensory abnormalities are related to positive symptoms. To further understand how multisensory abnormalities relate to positive symptoms, we administered a multisensory integration task requiring the evaluation of perceived distance from auditory, visual, and multisensory stimuli with varying synchrony as well as clinical and neurocognitive assessments. Overall, patients had greater facilitation than healthy controls and the near synchronous condition had the most facilitation in comparison to other conditions. To further examine how multisensory facilitation relates to symptom severity, we performed a Ward cluster analysis that grouped participants by their multisensory facilitation profile. In contrast to what was expected, none of the Ward clusters were populated by a single group. Patients in cluster 3 had a significantly greater disorganization factor score than those in cluster 1. Our in-depth comparison between Ward clusters and neuropsychological tests reveal patients with greater multisensory facilitation experience the most cognitive deficits. Overall, our results demonstrate that multisensory integration is related to behavioral and cognitive deficits in complex ways. Further research is needed to understand the relationship between multisensory integration and schizophrenia symptomology.HighlightsPatients with schizophrenia have greater multisensory facilitation in perceived synchronous conditionsMultisensory facilitation patterns are heterogenous across patients with Schizophrenia.Different multisensory patterns relate to cognitive decline and disorganized thinking in Schizophrenia.
Simple, universal rules predict trophic interaction strengths
Authors: Coblentz Kyle E. , Novak Mark , DeLong John P. 
Published: July 2024

AbstractMany critical drivers of ecological systems exhibit regular scaling relationships, yet the underlying mechanisms explaining these relationships are often unknown. Trophic interaction strengths, which underpin ecosystem stability and dynamics, are no exception, exhibiting statistical scaling relationships with predator and prey traits that lack causal, evolutionary explanations. Here we propose two universal rules to explain the scaling of trophic interaction strengths through the relationship between a predator’s feeding rate and its prey’s density --- the so-called predator functional response. First, functional responses must allow predators to meet their energetic demands when prey are rare. Second, functional responses should approach their maxima near the highest prey densities that predators experience. We show that independently parameterized mathematical equations derived from these two rules predict functional response parameters across over 2,100 functional response experiments. The rules further predict consistent patterns of feeding rate saturation among predators, a slow-fast continuum among functional response parameters, and the allometric scaling of those parameters. The two rules thereby offer a potential ultimate explanation for the determinants of trophic interaction strengths and their scaling, revealing the importance of ecologically realized constraints to the complex, adaptive nature of functional response evolution.
Automated model discovery for textile structures: The unique mechanical signature of warp knitted fabrics
Authors: McCulloch Jeremy A. , Kuhl Ellen 
Published: July 2024

AbstractTextile fabrics have unique mechanical properties, which make them ideal candidates for many engineering and medical applications: They are initially flexible, nonlinearly stiffening, and ultra-anisotropic. Various studies have characterized the response of textile structures to mechanical loading; yet, our understanding of their exceptional properties and functions remains incomplete. Here we integrate biaxial testing and constitutive neural networks to automatically discover the best model and parameters to characterize warp knitted polypropylene fabrics. We use experiments from different mounting orientations, and discover interpretable anisotropic models that perform well during both training and testing. Our study shows that constitutive models for warp knitted fabrics are highly sensitive to an accurate representation of the textile microstructure, and that models with three microstructural directions outperform classical orthotropic models with only two in-plane directions. Strikingly, out of 214=16,384 possible combinations of terms, we consistently discover models with two exponential linear fourth invariant terms that inherently capture the initial flexibility of the virgin mesh and the pronounced nonlinear stiffening as the loops of the mesh tighten. We anticipate that the tools we have developed and prototyped here will generalize naturally to other textile fabrics–woven or knitted, weft knit or warp knit, polymeric or metallic–and, ultimately, will enable the robust discovery of anisotropic constitutive models for a wide variety of textile structures. Beyond discovering constitutive models, we envision to exploit automated model discovery as a novel strategy for the generative material design of wearable devices, stretchable electronics, and smart fabrics, as programmable textile metamaterials with tunable properties and functions. Our source code, data, and examples are available athttps://github.com/LivingMatterLab/CANN.
Revamping the Day Hospital Program at North York General Hospital in response to COVID-related changes in patient demographics
Authors: Zhou Carrol , Doval Ishita , Liu Regina 
Published: July 2024

AbstractBackgroundThe Dynamic Sustainability Framework emphasizes the need for improving programs after implementation in response to the evolving environment. This report illustrates said framework and describes significant changes made to the Psychiatric Day Hospital (DH) at North York General Hospital (NYGH) in response to pandemic-related changes in participant demographic. Patient and staff satisfaction pre- and post-program modification are compared.ProblemThe COVID-19 pandemic resulted in increased DH referral acuity and patient affect dysregulation. The program needed to adapt to these changes and better serve the new DH patient population.MethodsDH participants and team member feedback was gathered. Five major areas of improvement were identified. Changes were systematically introduced from July 2021 to January 2022. Feedback post-implementation in 2022-2023 from patients and DH team members were gathered for comparison.InterventionsDialectical Behavioral Therapy (DBT) was adopted as the theoretical basis of the revamped Day Hospital Program. All Day Hospital staff underwent training in DBT skills, with the creation of new treatment schedules and materials. Two separate streams were created for differing patient illness severity. The program continued to run during implementation of new changes, without disruption to the existing clinical workload.ResultsThe program transitioned from a 3-week psychoeducational and rudimentary CBT program to a dual-stream 4-week DBT-based program to address patient acuity and higher prevalence of emotional dysregulation. Quantitative and qualitative feedback from new program participants have been positive.ConclusionsThe Day Hospital Program at NYGH made a successful transition in response to an evolving healthcare landscape.
Multimodal Large Language Model Passes Specialty Board Examination and Surpasses Human Test-Taker Scores: A Comparative Analysis Examining the Stepwise Impact of Model Prompting Strategies on Performance
Authors: Samaan Jamil S. , Margolis Samuel , Srinivasan Nitin , Srinivasan Apoorva , Yeo Yee Hui , Anand Rajsavi , Samaan Fadi S. , Mirocha James , Safavi-Naini Seyed Amir Ahmad , El Kurdi Bara , Soroush Ali , Watson Rabindra , Gaddam Srinivas , Elmore Joann G. , Spiegel Brennan M.R. , Tatonetti Nicholas P. 
Published: July 2024

ABSTRACTBackgroundLarge language models (LLMs) have shown promise in answering medical licensing examination-style questions. However, there is limited research on the performance of multimodal LLMs on subspecialty medical examinations. Our study benchmarks the performance of multimodal LLM’s enhanced by model prompting strategies on gastroenterology subspeciality examination-style questions and examines how these prompting strategies incrementally improve overall performance.MethodsWe used the 2022 American College of Gastroenterology (ACG) self-assessment examination (N=300). This test is typically completed by gastroenterology fellows and established gastroenterologists preparing for the gastroenterology subspeciality board examination. We employed a sequential implementation of model prompting strategies: prompt engineering, retrieval augmented generation (RAG), five-shot learning, and an LLM-powered answer validation revision model (AVRM). GPT-4 and Gemini Pro were tested.ResultsImplementing all prompting strategies improved the overall score of GPT-4 from 60.3% to 80.7% and Gemini Pro’s from 48.0% to 54.3%. GPT-4’s score surpassed the 70% passing threshold and 75% average human test-taker scores unlike Gemini Pro. Stratification of questions by difficulty showed the accuracy of both LLMs mirrored that of human examinees, demonstrating higher accuracy as human test-taker accuracy increased. The addition of the AVRM to prompt, RAG and 5-shot increased GPT-4’s accuracy by 4.4%. The incremental addition of model prompting strategies improved accuracy for both non-image (57.2% to 80.4%) and image-based (63.0% to 80.9%) questions for GPT-4, but not Gemini Pro.ConclusionsOur results underscore the value of model prompting strategies in improving LLM performance on subspecialty-level licensing exam questions. We also present a novel implementation of an LLM-powered reviewer model in the context of subspecialty medicine which further improved model performance when combined with other prompting strategies. Our findings highlight the potential future role of multimodal LLMs, particularly with the implementation of multiple model prompting strategies, as clinical decision support systems in subspecialty care for healthcare providers.
Evaluating phasic transcutaneous vagus nerve stimulation (taVNS) with pupil dilation: the importance of stimulation intensity and sensory perception
Authors: Ludwig Mareike , Pereira Calida , Keute Marius , Düzel Emrah , Betts Matthew J. , Hämmerer Dorothea 
Published: July 2024

AbstractThe efficacy of transcutaneous auricular vagus nerve stimulation (taVNS) as a non-invasive method to modulate physiological markers of noradrenergic activity of the Locus Coeruleus (LC), such as pupil dilation, is increasingly more discussed. However, taVNS studies show high heterogeneity of stimulation effects. Therefore, a taVNS setup was established here to test different frequencies (10 Hzand25 Hz) and intensities (3 mAand5 mA) during phasic stimulation (3 s) with time-synchronous recording of pupil dilation in younger adults. Specifically, phasic real taVNS and higher intensity led to increased pupil dilation, which is consistent with phasic invasive VNS studies in animals. The results also suggest that the influence of intensity on pupil dilation may be stronger than that of frequency. However, there was an attenuation of taVNS-induced pupil dilation when differences in perception of sensations were considered. Specifically, pupil dilation during phasic stimulation increased with perceived stimulation intensity. The extent to which the effect of taVNS induces pupil dilation and the involvement of sensory perception in the stimulation process are discussed here and require more extensive research. Additionally, it is crucial to strive for comparable stimulation sensations during systematic parameter testing in order to investigate possible effects of phasic taVNS on pupil dilation in more detail.
Lentivirus Transduced T Cell Lines Response to Pro-inflammatory and Antiviral Cytokines in the Presence of HIV
Authors: Ventocilla Kevin , Miranda Daniel , Sanchez David Jesse 
Published: July 2024

AbstractVarious studies have attempted to understand HIV infection under a diverse range of stimulants including cytokine stimulation. Pro-inflammatory cytokines, such as TNF-α, have been shown to reactivate HIV latency by inducing NF-κB mediated activation of the HIV LTR (long terminal repeats) that contain κB transcriptional binding sites. Interferon-alpha (IFN-α), an anti-viral cytokine, is not well studied as an inducer of HIV activation. However, previous work from our group has shown that HIV can block IFN-α signaling in CD4+ T cells presumably to allow for further viral replication. Initially using HEK 293T cells, we moved to CD4+ T cells lines to develop a system to determine how stimulation with different cytokines impacts signaling within T cell lines. We confirmed that in our system TNF-α triggers activation of NF-κB driven reporters but not in the presence of HIV. In addition, we show that the presence of HIV blocks IFN-α signaling. Taken together, our system demonstrates that HIV by TNF-α, will continue to block IFN-α signaling preventing it from impacting HIV activation. This system can now be used to screen for cytokine based and other molecule activators that may be influenced by the presence of HIV.
Immune Modulation in Solid Tumors: A Phase 1b Study of RO6870810 (BET Inhibitor) and Atezolizumab (PD-L1 Inhibitor)
Authors: Marbach Daniel , Brouer-Visser Jurriaan , Brennan Laura , Wilson Sabine , Davydov Iakov I , Staedler Nicolas , Duarte José , Quetglas Iris Martinez , Nüesch Eveline , Cañamero Marta , Chesné Evelyne , Au-Yeung George , Hamilton Erika , Lheureux Stephanie , Richardson Debra L , Spanggaard Iben , Gomes Bruno , Franjkovic Izolda , DeMario Mark , Kornacker Martin , Lechner Katharina 
Published: July 2024

ABSTRACTPurposeBromodomain and extra-terminal domain (BET) inhibitors (BETi) have demonstrated epigenetic modulation capabilities, specifically in transcriptional repression of oncogenic pathways. Preclinical assays suggest that BETi potentially attenuates the PD1/PD-L1 immune checkpoint axis, supporting its combination with immunomodulatory agents.Patients and MethodsA Phase 1b clinical trial was conducted to elucidate the pharmacokinetic and pharmacodynamic profiles of the BET inhibitor RO6870810, as monotherapy and in combination with the PD-L1 antagonist atezolizumab, in patients with advanced ovarian carcinomas and triple-negative breast cancer (TNBC). Endpoints included maximum tolerated dosages, adverse event profiling, pharmacokinetic evaluations, and antitumor activity. Pharmacodynamic and immunomodulatory effects were assessed in tumor tissue (by immunohistochemistry and RNA-seq) and in peripheral blood (by flow cytometry and cytokine analysis).ResultsThe study was terminated prematurely due to a pronounced incidence of immune-related adverse effects in patients receiving combination of RO6870810 and atezolizumab. Anti-tumor activity was limited to 2 patients (5.6%) showing partial response. Although target engagement was confirmed by established BETi pharmacodynamic markers in both blood and tumor samples, BETi failed to markedly decrease tumor PD-L1 expression and had a suppressive effect on anti-tumor immunity. Immune effector activation in tumor tissue was solely observed with the atezolizumab combination, aligning with this checkpoint inhibitor’s recognized biological effects.ConclusionsThe combination of BET inhibitor RO6870810 with the checkpoint inhibitor atezolizumab presents an unfavorable risk-benefit profile for ovarian cancer and TNBC (triple-negative breast cancer) patients due to the increased risk of augmented or exaggerated immune reactions, without evidence for synergistic anti-tumor effects.Trial registrationClinicalTrials.govIDNCT03292172
City-wide metagenomic surveillance of food centres reveals location-specific microbial signatures and enrichment of antibiotic resistance genes
Authors: Teo Jonathan J.Y. , Ho Eliza Xin Pei , Ng Amanda Hui Qi , How Shaun Hong Chuen , Chng Kern Rei , Ateş Yiğit Can , Fau’di Muhd Tarmidzi , Aung Kyaw Thu , Nagarajan Niranjan 
Published: July 2024

AbstractThe distribution of microorganisms in built environments with high human traffic, such as food centres, can potentially have a significant impact on public health, particularly in the context of increasing worldwide incidence of food and fomite-related outbreaks. In several major Asian cities, public food centres are the main venue for food consumption and yet we lack a baseline understanding of their environmental microbiomes. We conducted city-wide metagenomic surveillance of food-centre microbiomes in Singapore (16 centres, n=240 samples) to provide a detailed map of microbial (bacteria, archaea, fungi, viruses) as well as non-microbial DNA abundances across two timepoints. Food-centre microbiomes were found to be enriched in food-related DNA signatures compared to other environments such as hospitals and offices, with specific food-microbe associations (e.g. Enterobacteriaceae and fish) and food DNA providing a partial explanation for the microbial profiles observed (44% of variation explained). Machine learning analysis identified a small set of microbial species (n=22) that serve as highly accurate (>80%) location-specific signatures for various food centres, some of which persist even after 3 years. Profiling of antibiotic resistance genes (ARGs) and pathogens identified a surprising enrichment of ARGs in food centres relative to other non-healthcare environments (>2.5ξ), and an order of magnitude enrichment of key pathogenic species (e.g.Klebsiella pneumoniae,Enterobacter spp) even compared to hospital environments. These results highlight the contribution of diverse biotic and abiotic factors in shaping the unique microbiome profiles of different food-centre environments, and the potential for using metagenomic surveillance to understand the risk for infections and antibiotic resistance gene transmission.
Mind the Gap: Unraveling Mental Health Disparities in America’s Diverse Landscape
Authors: Fischer Margaret , Swint Jennifer , Zhang Wei , Zhang Xi 
Published: July 2024

AbstractBackgroundMental health disparities persist as a significant public health concern in the United States, with certain populations experiencing disproportionate burdens of mental illness and barriers to care. This systematic review aims to synthesize current evidence on mental health disparities across various demographic groups and identify key factors contributing to these inequities.MethodsWe conducted a comprehensive search of peer-reviewed literature published between 2010 and 2024 using PubMed, PsycINFO, and Scopus databases. Studies examining mental health outcomes, access to care, and treatment efficacy across racial/ethnic, socioeconomic, gender, sexual orientation, and geographic groups in the US were included. Two independent reviewers screened articles, extracted data, and assessed study quality.ResultsOf 2,345 initially identified studies, 127 met inclusion criteria. Consistent disparities were observed across multiple domains, with racial/ethnic minorities, low-income individuals, LGBTQ+ populations, and rural residents experiencing higher rates of mental health disorders, lower access to quality care, and poorer treatment outcomes. Key contributing factors included systemic racism, socioeconomic barriers, stigma, lack of culturally competent care, and inadequate insurance coverage.ConclusionThis review highlights persistent and multifaceted mental health disparities in the US. Addressing these inequities requires comprehensive policy interventions, increased funding for community-based mental health services, improved cultural competence in healthcare delivery, and targeted research to develop effective, culturally-tailored interventions for underserved populations.
SegColR: Deep Learning for Automated Segmentation and Color Extraction
Authors: Boyko James 
Published: July 2024

AbstractCitizen science platforms like iNaturalist generate biodiversity data at an unprecedented scale, with observations on the order of hundreds of millions. However, extracting phenotypic information from these images, such as color of organisms, at such a large scale poses unique challenges for biologists. Some of the challenges are that manual extraction of phenotypic information can be subjective and time-consuming. Fortunately, with the maturation of computer vision and deep learning, there is an opportunity to automate large parts of the image processing pipeline. Here, I present SegColR, a user-friendly software package that leverages two state-of-the-art deep learning models - GroundingDINO and SegmentAnything - to enable automated segmentation and color extraction from images. The SegColR package provides an R-based interface, making it more accessible to evolutionary biologists and ecologists who may not have extensive coding experience. The SegColR pipeline allows users to load images, automatically segment them based on text prompts, and extract color information from the segmented regions. The package also includes visualization and data summarization functions to facilitate downstream analysis and interpretation of the results.