bluuebunny/crossref_metadata_2025_split huggingface dataset:
Taking Control of Your Functional Cognitive Symptoms Workbook: A Novel Intervention
Authors: Cotton E. , Mordecai K. L. , McWhirter L. , Cabreira V. , Van Patten R. , Silverberg N.D. , Kaat A.J. , LaFrance W.C. 
Published: October 2024

AbstractBackgroundFunctional cognitive disorder (FCD) is a subtype of functional neurological disorder (FND). FCD can present with various cognitive symptoms, precipitants, and comorbidities (other FNDs, concussion, fatigue, fibromyalgia, etc.). However, there are no widely available existing behavioral health interventions for FCD. The authors’ aim was to develop a therapist-guided treatment for FCD for widespread use among civilians and Veterans.MethodsWe adapted a well-known, evidence-based treatment for functional seizures (an adjacent condition often with cognitive symptoms), to fit the hypothesized mechanisms of FCD. The process is presented in GUIDED format (GUIDance for the rEporting of intervention Development). Key processes included determining broad intervention framework, detailed FCD specific content based on expert consensus, evidence, theory, target population centered approaches, specialty subgroup consideration and target population stakeholder feedback for Veteran and civilian populations.FindingsWe have created a comprehensive 14-chapter, manualized, therapist-guided neurobehavioral therapy protocol to target FCD symptoms independent of etiology – theTaking Control of Your Functional Cognitive Symptoms Workbook. Initial feasibility, tolerability, and utility were completed with 2 target population stakeholders with FCD (one civilian, one Veteran; both PGI-C rating = 1 “Very Much Improved”). The Template for Intervention Description and Replication (TIDieR) checklist is provided as a supplemental table.InterpretationThis new promising multi-modality behavioral health intervention can be considered Stage 1 (i.e. intervention generation, refinement, modification, adaptation, and pilot testing). Further pilot testing is being conducted and will need to be followed by traditional efficacy testing (Stage 2).Key MessagesFCD is an increasingly recognized subtype of FND. Despite existing models of FCD mechanisms, there are no widely available dedicated FCD-symptom focused behavioral health interventions.We have created theTaking Control of Your Functional Cognitive Symptoms: Workbook, a 14-chapter manualized therapist-guided neurobehavioral therapy protocol to target FCD symptoms regardless of cause / associated conditions, which shows early feasibility and promising clinical utility.
Study Research Protocol for Phenome India-CSIR Health Cohort Knowledgebase (PI-CHeCK): A Prospective multi-modal follow-up study on a nationwide employee cohort
Authors:   Phenome India Consortium, Sengupta Shantanu 
Published: October 2024

AbstractPredicting individual health trajectories based on risk scores can help formulate effective preventive strategies for diseases and their complications. Currently, most risk prediction algorithms rely on epidemiological data from the Caucasian population, which often do not translate well to the Indian population due to ethnic diversity, differing dietary and lifestyle habits, and unique risk profiles. In this multi-center prospective longitudinal study conducted across India, we aim to address these challenges by developing clinically relevant risk prediction scores for cardio-metabolic diseases specifically tailored to the Indian population. India, which accounts for nearly 18% of the global population, also has a significant diaspora worldwide. This program targets longitudinal collection and bio-banking of samples from over 10,000 employees both working and retirees of the Council of Scientific and Industrial Research and their spouses, with baseline sample collection already completed. During the baseline collection, we gathered multi-parametric data including clinical questionnaires, lifestyle and dietary habits, anthropometric parameters, lung function assessments, liver elastography by Fibroscan, electrocardiogram readings, biochemical data, and molecular assays, including but not limited to genomics, plasma proteomics, metabolomics, and fecal microbiome analysis. In addition to exploring associations between these parameters and their cardio-metabolic outcomes, we plan to employ artificial intelligence algorithms to develop predictive models for phenotypic conditions. This study could pave the way for precision medicine tailored to the Indian population, particularly for the middle-income strata, and help refine the normative values for health and disease indicators in India.
Bio-medical Big Data Operating System (Bio-OS): An Integrated Data Mining Environment for Data Intensive Scientific Research
Authors: Liu Ji-Long , Yang Li-Guang , Xiao Qing-Yu , Li Zhao-Qiang , Cheng Jin-Yu , Chen Zi-Ru , Zhou Jian-Wen , Wan Xiang , Chang Tsung-Hui , Zhang Xin , Li Yi-Xue 
Published: October 2024

AbstractThe advent of high throughput sequencing has ushered life science and clinical research into the era of big data, posing significant challenges for reproducibility due to the complexity of data integration and analysis. Although the FAIR principles advocate for the transparent and reliable sharing of scientific data, their implementation remains hampered by technical barriers. The Global Alliance for Genomics and Health (GA4GH) has made strides in standardizing data and tools, yet a comprehensive solution for reproducibility is lacking. In response, we present BioOS, an open source, cloud native Biomedical big data Operating System. This system encapsulates study components data, code, tools, and environments into workspaces, enhancing reproducibility and validation. BioOS employs JSON Schema for machine readability and includes a Hierarchy Hash Mechanism to ensure data integrity. Adhering to GA4GH protocols, BioOS simplifies complex technological implementations, making advanced research tools accessible. Demonstrated through representative workspaces, BioOS fosters seamless research replication, peer review, and editorial evaluation. Its cloud native infrastructure supports dynamic resource allocation, enabling efficient handling of large scale analyses. By integrating AI driven Large Language Models, BioOS enhances user interaction and operational flexibility. As an evolving open source platform, BioOS exemplifies a transformative approach to biomedical research, aligning with FAIR principles and advancing the AI for Science paradigm, thus promoting a more connected, efficient, and impactful research environment.
Template-driven dynamic functional network connectivity predicts medication response for major depression and bipolar disorders
Authors: Baker Bradley T. , Osuch Elizabeth , Langenecker Scott , Fournier Jay , Turner Jessica , Youngstrom Eric , Calhoun Vince D. 
Published: October 2024

ABSTRACTThe process of finding reliable treatment for major depression and bipolar disorder can be arduous. The myriad behavioral symptoms presented by patients and resistance to treatment from particular medication classes complicate standard diagnostic and prescription methodologies, often requiring multiple attempted treatments during which symptoms may still be present. Physiological information such as neuroimaging scans may help to alleviate some of the uncertainty surrounding diagnosis and treatment when incorporated into a clinical setting. Changes in functional magnetic resonance imaging show particular promise, as the incorporation of dynamical information may provide insights into physiological changes prior to static, structural changes. In this work, we present a novel method for generating robust and replicable dynamic functional network connectivity (dFNC) features from neuroimaging data using a template of dynamic states derived from a large, non-affected data set. We demonstrate that this template-driven dFNC approach expands on standard dFNC approaches by allowing for the derivation of a continuous state-contribution time series. We demonstrate that the derived biomarkers can support high predictive performance for the identification of medication class and non-responders while also expanding the set of biomarkers available for studying differences in mood disorder medication response.
Computational analysis of optogenetic inhibition of CA1 neurons using a data-efficient and interpretable potassium and chloride conducting opsin model
Authors: Weyn Laila , Tarnaud Thomas , Schoeters Ruben , De Becker Xavier , Joseph Wout , Raedt Robrecht , Tanghe Emmeric 
Published: October 2024

AbstractOptogenetic inhibition of excitatory populations has been suggested as a novel technique for the treatment of refractory epilepsy. While this approach holds significant potential, achieving seizure suppression in animal models using optogenetics has proven challenging. This difficulty can be attributed to a suboptimal stimulation method that involves numerous complex variables. To provide insight into these parameters, the behavior of the chloride conducting opsin, GtACR2, was fitted to a mathematical description and combined with a conductance-based model of a pyramidal CA1 neuron. The resulting model was adapted to add the ability to study potassium conducting opsins and used to demonstrate that the effect of an optogenetic modulation setup is highly dependent on its parameters and the physiological conditions of the neuronal environment. Stronger inhibition is observed at higher pulse repetition frequencies and duty cycles. Furthermore, potassium conducting opsins were shown to be more stable in use than chloride conducting ones. The dependence of these observations on the opsin model fit was found to be negligible. To determine this, a simplified model (22OMs) is proposed that permits easy implementation of the experimentally derived parameters describing the opsin’s opening and closing dynamics into its mathematical description. This model was also employed to determine that the impact of varying the opsin’s dynamics is insignificant when the opening and closing time constants are altered by a factor between 0.5 and 2. Consequently, this study provides insights into the stimulation and physiological parameters influencing the outcome of an optogenetic inhibition approach and offers a new tool that will facilitate future research into the development of an improved optogenetic modulation protocol for seizure suppression.
Darwin’s Naturalization Conundrum explained by gradients of environmental stress and disturbance
Authors: Galmán Andrea , Alexander Jake M. , Cavieres Lohengrin A. , Daehler Curtis C. , Seipel Tim , Winter Marten , Andersen Kelly M , Lembrechts Jonas J. , Aschero Valeria , Arévalo Sierra José Ramón , Barros Agustina , Buhaly Meike , Backes Amanda Ratier , Dar Pervaiz A. , Fuentes-Lillo Eduardo , Jiménez Alejandra , Küffer Christoph , Larson Christian D. , Mihoc Maritza , Milbau Ann , Morgan John W. , McDougall Keith , Müllerová Jana , Naylor Bridgett , Nuñez Martin A. , Padrón Mederos Miguel Antonio , Pauchard Aníbal , Rew Lisa J. , Reshi Zafar A. , Sandoya Veronica , Shah Manzoor A , Shannon James M. , Walsh Neville , Valencia Graciela , Vorstenbosch Tom , Wright Genevieve , Zong Shengwei , Haider Sylvia 
Published: October 2024

AbstractDarwin’s Naturalization Conundrum (DNC) states that non-native species closely related to the native community are either more likely to succeed because shared adaptations help them overcome environmental filtering, or less likely to succeed because of strong competition with their native relatives. Despite extensive research, no general patterns have so far emerged. One reason may be that the relative importance of competition and environmental filtering depends on environmental conditions. To test this hypothesis, we conducted a global assessment of DNC examining patterns of phylogenetic relatedness of non-native plant species to the native community along gradients of elevation and anthropogenic disturbance in mountains. Phylogenetic distance of non-native to native species decreased with increasing elevation and in disturbed plant communities. Our results help resolve DNC by showing that the environmental context sets expectations for patterns of relatedness between non-native and native species and helps illuminate the ecological and evolutionary processes generating these patterns.
Benzamidine-Mediated Inhibition of Human Lysozyme Aggregation: Differential Ligand Binding in Homologous Proteins
Authors: Hima Sree , Remya Chandran , Abhithaj J , Arun K.G. , Sabu A. , Vasudevan D. M. , Dileep K.V. 
Published: October 2024

AbstractAmyloid fibril formation is a hallmark of several protein misfolding diseases, including systemic hereditary amyloidosis (SHA), in which lysozyme aggregates into plaques, causing inflammation in various tissues. SHA is a rare disease with no current drug treatment options. In our efforts to identify potential therapeutics for SHA, we investigated the inhibitory effects of benzamidine (BEN) on the fibrillation of human lysozyme (HL). Multiple biophysical assays demonstrated BEN’s ability to effectively prevent amyloid formation. Intrinsic fluorescence measurements highlighted BEN’s interaction with HL. We inferred the binding mode of BEN to HL through ITC experiments, molecular docking, and molecular dynamics simulations, confirmed BEN’s binding at the active site, particularly near stretch-2 (residues 52-64), a key region in its anti-amyloidogenic activity. This interaction differed from the previously reported interaction with HEWL. Further, microscopy analyses, including scanning electron microscopy (SEM) and transmission electron microscopy (TEM), further supported these findings by showing reduced fibril formation and alterations in fibril morphology in the presence of BEN. Importantly, BEN exhibited no cytotoxic effects in HEK-293 cells, reinforcing its potential as a therapeutic candidate for amyloidosis. These results provide strong evidence of BEN’s anti-amyloidogenic activity and offer a foundation for future drug development targeting lysozyme amyloidosis.
Integrating target capture with whole genome sequencing of recent and natural history collections to explain the phylogeography of wild-growing and cultivatedCannabis
Authors: Balant Manica , Vitales Daniel , Wang Zhiqiang , Barina Zoltán , Fu Lin , Gao Tiangang , Garnatje Teresa , Gras Airy , Hayat Muhammad Q. , Oganesian Marine , Pellicer Jaume , Salami Alireza S. , Seregin Alexey P. , Stepanyan-Gandilyan Nina , Sultana Nusrat , Tsooj Shagdar , Urgamal Magsar , Vallès Joan , van Velzen Robin , Pokorny Lisa 
Published: October 2024

SummaryCannabishas provided important and versatile services to humans for millennia. Domestication and subsequent dispersal have resulted in various landraces and cultivars. Unravelling the phylogeography of this genus poses considerable challenges due to its complex history.We relied on a Hyb-Seq approach (combining target capture with shotgun sequencing), with the universal Angiosperms353 enrichment panel, to explore the genetic structure of wild-growing accessions and cultivars by implementing phylogenomic and population genomic workflows on the same Hyb-Seq data.Our findings support the treatment ofCannabisas a monotypic genus (C. sativaL.), structured into three main genetic groups—E Asia, Paleotropis, and Boreal—with clear phylogeographic signal despite significant levels of admixture. The E Asia group was sister to the Paleotropis and the Boreal groups. Individuals within the Paleotropis group could be further structured into three subgroups: Iranian Plateau, C & S China and Himalayas, and Indoafrica. Individuals from the Boreal group split into two subgroups: Eurosiberia and W Mongolia and Caucasus and Mediterranean. Hemp and drug-type landraces and cultivars consistently matched their putative geographic origin.These findings enhance our understanding of the genetic patterns inCannabisand provide a framework for future research into its current and past genetic diversity.
Evaluating the Biocontrol Potential of Pythium oligandrum and Serratia proteamaculans in Controlling Phytophthora plurivora in European beech (Fagus sylvatica)
Authors: Kelbessa Bekele Gelena , Samuelsson Anton , Ghadamgahi Farideh , Witzell Johanna , Whisson Stephen C. , Ortiz Rodomiro , Grenville-Briggs Laura J. , Vetukuri Ramesh R. 
Published: October 2024

AbstractIn recent years, the plant pathogenPhytophthora plurivorahas caused severe damage to beech forests in many European countries, including Sweden. In many affected areas, few protective measures are in place due to fears about potential negative impacts on the ecosystem or public health. The research presented in this article assesses the biocontrol potential of the oomycetePythium oligandrumand the bacteriumSerratia proteamaculansagainstP. plurivora, with experiments performed under bothin vitroand greenhouse conditions, which could represent a step forward in developing a safe treatment for European beech forests. Thein vitroresults revealed thatP. oligandrumandS. proteamaculanssignificantly inhibited pathogen growth and stimulated a shift in the hyphal growth pattern towards shorter, branched hyphae with many hyphal swellings and thickened cell walls. The experiments conducted in greenhouses showed that treating three-month-old beech seedlings withP. oligandrumandS. proteamaculanscounteracts theP. plurivorapathogen and reduces disease symptoms on the aerial and underground parts of the plant. GC-MS analysis detected the volatile organic compounds alpha-pinene, 2,5-dimethyl-pyrazine, and 3-methyl-1-butanol fromS. proteamaculans;these compounds have the potential to inhibit pathogen growth. The disease suppression demonstrated by these biocontrol agents could thus be related to the synergistic effect of competition for nutrients with the secretion of hydrolytic enzymes and VOCs; moreover, induced systemic resistance may be triggered under greenhouse conditions. In conclusion, usingP. oligandrumandS. proteamaculanscould represent an environmentally friendly strategy for effectively controlling the disease caused byP. plurivorain beech.
In silicogeneration of synthetic cancer genomes using generative AI
Authors: Díaz-Navarro Ander , Zhang Xindi , Jiao Wei , Wang Bo , Stein Lincoln 
Published: October 2024

AbstractCancer originates from alterations in the genome, and understanding how these changes lead to disease is crucial for achieving the goals of precision oncology. Connecting genomic alterations to health outcomes requires extensive computational analysis using accurate algorithms. Over the years, these algorithms have become increasingly sophisticated, but a severe shortage of open access gold-standard datasets presents a fundamental challenge. Since genomic data is considered personal health information, only an extremely limited number of deeply sequenced legacy cancer genomes can be shared and redistributed. As a result, tool benchmarking is often conducted on the same small set of genomes sequenced with older technologies and uncertain ground truths. This is a major obstacle to the development of improved analytic tools.To address this issue, we have developed OncoGAN, a novel generative AI tool that uses a combination of generative adversarial networks and tabular variational autoencoders to generate realistic but entirely synthetic cancer genomes based on training sets derived from large-scale genomic projects. Our results demonstrate that this approach accurately reproduces the scale, distribution, and characteristics of somatic point mutations, copy number alterations and structural variants across multiple common cancer types, while protecting donors’ privacy information. OncoGAN accurately recapitulates tumor type-specific mutational signatures as well as the positional distribution of somatic mutations. To evaluate the fidelity of the simulations, we tested the synthetic genomes using DeepTumour, a software capable of identifying tumor types based on mutational patterns, and demonstrated a high level of concordance between the synthetic genome tumor type and DeepTumour’s prediction of the type. We also showed that augmenting real donor data with OncoGAN-generated synthetic data could be used to train a more accurate version of DeepTumour.This tool will allow the generation of an extensive and realistic set of training and testing cancer genomes whose ground truth is known exactly. This advance provides computational biologists with the ability to develop realistic cancer genome benchmarking sets and make them available to the research community for the testing, development and enhancement of cancer genome analysis tools.
PFKFB3 protein in adipose tissue contributes to whole body glucose homeostasis
Authors: Griesel Beth A. , Olson Ann Louise 
Published: October 2024

AbstractAge-dependent changes in adipose tissue are thought to play a role in development of insulin resistance. A major age-dependent change in adipose tissue is the downregulation of key proteins involved in carbohydrate metabolism. In the current study, we investigate the role of 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3) a key governor of the rate of glycolysis in adipocytes via the synthesis of fructose-2,6-bisphosphate that was significantly down-regulated in aged mice. We employed an adipocyte-specific PFKFB3 mouse line to investigate the role of PFKFB3 on adipocyte function. In both aged mice and PFKFB3-knockout mice, we observed an increase in O-glcNAcylated proteins consistent with a shift in glucose metabolism toward the hexosamine biosynthetic pathway. Under chow-fed conditions, PFKFB3 knockout resulted in significantly smaller adipocyte area, but no difference in total fat mass. While glucose tolerance was unchanged under chow conditions, when mice were challenged with a 4 wk high fat feeding, PFKFB3 deletion led to a greater decrease in glucose tolerance as well as a significant increase in macrophage infiltration. These results indicate that perturbation of the glycolytic pathway in adipose tissue has multiple effects of adipocyte biology and may play a significant role in metabolic changes associated with aging. Results of this student support the notion that changes in glucose metabolism in adipose tissue impact whole body metabolism.