bluuebunny/crossref_metadata_2025_split huggingface dataset:
What incentives encourage local communities to collect and upload mosquito sound data by using smartphones? A case study in the Democratic Republic of the Congo
Authors: Storer Kieran E. , Messina Jane P. , Herreros-Moya Eva , Metelo Emery , Zanga Josué , Mvuama Nono M. , Muzinga Soleil , Dam Rinita , Sinka Marianne , Kiskin Ivan , Everett Josh , Li Yunpeng , Roberts Stephen , Willis Katherine J. 
Published: November 2024

AbstractMalaria is one of the deadliest vector borne diseases effecting sub-Saharan Africa. A suite of systems are being used to monitor and manage malaria risk and disease incidence, with an increasing focus on technological interventions that allow private citizens to remotely record and upload data. However, data collected by citizen scientists must be standardised and consistent if it is to be used for scientific analysis. Studies that aim to improve data collection quality and quantity have often included incentivisation, providing citizen scientists with monetary or other benefits for their participation in data collection. We tested whether monetary incentives enhance participation and data collection in a study trialling an acoustic mosquito sensor. Working with the community in two health districts in the Democratic Republic of Congo, we measured data collection participation, completeness, and community responses. Our results showed mixed responses to the incentive, with more participants interested in the social status and monetary value of the technology used than the monetary incentive itself. The effect of incentives on data collection varied over the course of the trial, increasing participation in the start of the trial but with no effect in the latter half of the trial. Feedback from participants showed that opinions on technology, research objectives, and incentives varied between communities, and was associated with differences in data collection quantity and quality, suggesting that differences in community interest in data collection and the incentives may be more important than the incentive value itself. These results suggest that though there is an initial benefit, extrinsic motivations do not override differences in intrinsic motivations over time, and enhanced communication and dialogue with participants may improve citizen science participation and attitudes.AttributionKES contributed to data curation, formal analysis, methodology, visualization, and writing the original draft. JPM contributed to formal analysis and visualization. EHM contributed to conceptualization, investigation, methodology, project administration, supervision, visualization, and the review and editing of the manuscript. EM, JZ, NMM, and SM contributed to conceptualization, investigation, methodology, data collection, and review and editing of the manuscript. RD, MS, IK, JE, YL, and SR contributed to conceptualization, methodology, and review and editing of the manuscript. KJW contributed to funding acquisition, methodology, supervision, and the review and editing of the manuscript.FundingThis study was funded by the Bill and Melinda Gates Foundation Grant Award Number: OPP1209888.
A multicentre prospective observational study to investigate the prevalence and short-term impact of frailty, multi-morbidity and sarcopenia in chronic limb threatening ischaemia (CLTI) The FraiLTI - (Frailty in chronic Limb Threatening Ischaemia) Study
Authors: Sivaharan Ashwin , Hickson Blaise , El-Sayed Tamer , Shelmerdine Lauren , Wunnava Sai , Baljer Bence , Bhullar Dilraj , Nesbitt Craig I , Witham Miles D , Nandhra Sandip 
Published: November 2024

AbstractBackgroundCLTI is a is a life and limb threatening condition that is associated with a gradual overall decline in health that is under explored. We aim to measure the prevalence of correlated conditions (frailty, sarcopenia and anaemia) in patients with CLTI to assess their ability to perform activities of daily living (ADLs) alongside multimorbidity, polypharmacy and clinical outcomes.MethodsFraiLTI is a multi-centre, prospective, observational study, funded by the National Institute for Health Research (NIHR) and supported by the Vascular and Endovascular Research (VERN). All dedicated vascular centres were eligible to participate. Ethical approval (21/PR/0750) was granted on 13/07/2021. Primary outcome was identification of prevalence rates of frailty, sarcopenia and anaemia. Secondary outcomes included prevalence of polypharmacy and multimorbidity. Analyses included associations between frailty and sarcopenia, with clinical outcomes.ResultsBy the Rockwood Clinical Frailty Scale, 43 (51.8%) patients were frail. By grip strength, 27 (33.3%) patients were sarcopenic. We found that 43 (53.8%) of patients were anaemic, with an overwhelming majority (92.1%) having normocytic anaemia. Multimorbidity was seen in 57 (67.9%) patients and 67 (79.8%) patients met the definition of polypharmacy. Patients who were frail, were more likely to require a walking aid (p<0.001), be on more medications (p=0.027), have more co-morbidities (p=0.002), more likely to be diabetic (p=0.016), be sarcopenic (p=0.018), fail the walk speed test (p=0.040) and self-report low activity (p<0.001). Patients who were considered sarcopenic by grip strength tended to be older (p=0.031), have more co-morbidities (p=0.049), and were more likely to be anaemic (p=0.004).ConclusionThis study shows that frailty, sarcopenia and anaemia are highly prevalent in those with CLTI. There also appears to be an important interaction between sarcopenia and anaemia, but not frailty and anaemia. The management of CLTI patients extends beyond revascularisation alone.
AI-Powered Test Question Generation in Medical Education: The DailyMed Approach
Authors: van Uhm J. , van Haelst M.M. , Jansen P.R. 
Published: November 2024

ABSTRACTIntroductionLarge language models (LLMs) presents opportunities to improve the efficiency and quality of tools in medical education, such as the generation of multiple-choice questions (MCQs). However, ensuring that these questions are clinically relevant, accurate, and easily accesible and reusable remains challenging. Here, we developed DailyMed, an online automated pipeline using LLMs to generate high-quality medical MCQs.MethodsOur DailyMed pipeline involves several key steps: 1) topic generation, 2) question creation, 3) validation using Semantic Scholar, 4) difficulty grading, 5) iterative improvement of simpler questions, and 6) final human review. The Chain-of-Thought (CoT) prompting technique was applied to enhance LLM reasoning. Three state-of the art LLMs—OpenBioLLM-70B, GPT-4o, and Claude 3.5 Sonnet—were evaluated within the area of clinical genetics, and the generated questions were rated by clinical experts for validity, clarity, originality, relevance, and difficulty.ResultsGPT-4o produced the highest-rated questions, excelling in validity, originality, clarity, and relevance. Although OpenBioLLM was more cost-efficient, it consistently scored lower in all categories. GPT-4o also achieved the greatest topic diversity (89.8%), followed by Claude Sonnet (86.9%) and OpenBioLLM (80.0%). In terms of cost and performance, GPT-4o was the most efficient model, with an average cost of $0.51 per quiz and a runtime of 16 seconds per question.ConclusionsOur pipeline provides a scalable, effective and online-accessible solution for generating diverse, clinically relevant MCQs. GPT-4o demonstrated the highest overall performance, making it the preferred model for this task, while OpenBioLLM offers a cost-effective alternative.
Reproducibility of electroencephalography alpha band biomarkers for diagnosis of major depressive disorder
Authors: Hollenbenders Y. , Maier C. , Reichenbach A. 
Published: November 2024

AbstractMajor depressive disorder (MDD) and other psychiatric diseases can greatly benefit from objective decision support in diagnosis and therapy. Machine learning approaches based on electroencephalography (EEG) have the potential to serve as low-cost decision support systems. Despite the successful demonstration of this approach, contradictory findings regarding the diagnostic value of those biomarkers hamper their deployment in a clinical setting. Therefore, thereproducibilityandrobustnessof these biomarkers needs to be established first. We employ a multiverse analysis to systematically investigate variations in five data processing steps, which may be one source of contradictory findings. These steps are normalization, time-series segment length, biomarker from the alpha band, aggregation, and classification algorithm. Forreplicabilityof our results, we utilize two publicly available EEG data sets with eyes-closed resting-state data containing 16/19 MDD patients and 14/14 healthy control subjects. The diagnostic classifiers range from chance level up to 85%, dependent on dataset and combination of processing steps. We find a large influence of choice of processing steps and their combinations. However, only the biomarker has an overall significant effect on both datasets. We find one biomarker candidate that has shown arobustandreproduciblehigh performance for MDD diagnostic support, therelative centroid frequency. Overall, thereplicabilityof our findings with the two datasets is rather inconsistent. This study is a showcase for the advantages of employing a multiverse approach in EEG data analysis and advocates for larger, well-curated data sets to further neuroscience research that can be translated to clinical practice.
Single cell long read whole genome sequencing reveals somatic transposon activity in human brain
Authors: Izydorczyk Michal B , Kalef-Ezra Ester , Horner Dominic W , Zheng Xinchang , Holmes Nadine , Toffoli Marco , Sahin Zeliha Gozde , Han Yi , Mehta Heer H , Muzny Donna M , Ameur Adam , Sedlazeck Fritz J , Proukakis Christos 
Published: November 2024

AbstractThe advent of single cell DNA sequencing revealed astonishing dynamics of genomic variability, but failed at characterizing smaller to mid size variants that on the germline level have a profound impact. In this work we discover novel dynamics in three brains utilizing single cell long-read sequencing. This provides key insights into the dynamic of the genomes of individual cells and further highlights brain specific activity of transposable elements.
Extensive binding of uncharacterized human transcription factors to genomic dark matter
Authors: Razavi Rozita , Fathi Ali , Yellan Isaac , Brechalov Alexander , Laverty Kaitlin U. , Jolma Arttu , Hernandez-Corchado Aldo , Zheng Hong , Yang Ally W.H. , Albu Mihai , Barazandeh Marjan , Hu Chun , Vorontsov Ilya E. , Patel Zain M. ,   The Codebook Consortium, Kulakovskiy Ivan V. , Bucher Philipp , Morris Quaid , Najafabadi Hamed S. , Hughes Timothy R. 
Published: November 2024

SUMMARYMost of the human genome is thought to be non-functional, and includes large segments often referred to as “dark matter” DNA. The genome also encodes hundreds of putative and poorly characterized transcription factors (TFs). We determined genomic binding locations of 166 uncharacterized human TFs in living cells. Nearly half of them associated strongly with known regulatory regions such as promoters and enhancers, often at conserved motif matches and co-localizing with each other. Surprisingly, the other half often associated with genomic dark matter, at largely unique sites, via intrinsic sequence recognition. Dozens of these, which we term “Dark TFs”, mainly bind within regions of closed chromatin. Dark TF binding sites are enriched for transposable elements, and are rarely under purifying selection. Some Dark TFs are KZNFs, which contain the repressive KRAB domain, but many are not: the Dark TFs also include known or potential pioneer TFs. Compiled literature information supports that the Dark TFs exert diverse functions ranging from early development to tumor suppression. Thus, our results sheds light on a large fraction of previously uncharacterized human TFs and their unappreciated activities within the dark matter genome.
scExplorer: A Comprehensive Web Server for Single-Cell RNA Sequencing Data Analysis
Authors: Herńandez-Galaz Sergio , Hernández-Oliveras Andrés , Villanelo Felipe , Lladser Alvaro , Martin Alberto J.M. 
Published: November 2024

AbstractSingle-cell RNA sequencing (scRNA-seq) has redefined our ability to investigate cellular heterogeneity at unprecedented resolution. However, the computational analysis required for scRNA-seq data interpretation remains a significant barrier for many researchers. We present scExplorer, an interactive web application that streamlines the analysis workflow from preprocessing through dimensional reduction to differential expression analysis. The platform provides an intuitive interface for real-time data exploration and visualization while maintaining compatibility with established analysis frameworks in both R and Python environments. scExplorer significantly reduces the technical knowledge and computational requirements needed to perform scRNA-seq analysis, enabling researchers to focus on biological insights rather than computational complexity. The application modular design ensures scalability and robust performance, making it a valuable tool for both small-scale and large-scale single-cell studies.Graphical Abstract
Natural Language Prompts Guide the Design of Novel Functional Protein Sequences
Authors: Praljak Nikša , Yeh Hugh , Moore Miranda , Socolich Michael , Ranganathan Rama , Ferguson Andrew L. 
Published: November 2024

AbstractThe advent of natural language interaction with machines has ushered in new innovations in text-guided generation of images, audio, video, and more. In this arena, we introduceBiologicalMulti-ModalModel (BioM3), as a novel framework for designing functional proteins via natural language prompts. This framework integrates natural language with protein design through a three-stage process: aligning protein and text representations in a joint embedding space learned using contrastive learning, refinement of the text embeddings, and conditional generation of protein sequences via a discrete autoregressive diffusion model. BioM3 synthe-sizes protein sequences with detailed descriptions of the protein structure, lineage, and function from text annotations to enable the conditional generation of novel sequences with desired attributes through natural language prompts. We presentin silicovalidation of the model predictions for subcellular localization prediction, reaction classification, remote homology detection, scaffold in-painting, and structural plausibility, andin vivoandin vitroexperimental tests of natural language prompt-designed synthetic analogs of Src-homology 3 (SH3) domain proteins that mediate signaling in the Sho1 osmotic stress response pathway in baker’s yeast. BioM3 possesses state-of-the-art performance in zero-shot prediction and homology detection tasks, and generates proteins with native-like tertiary folds and wild-type levels of experimentally assayed function.
Relationships between migraine and interoception: normal accuracy but altered sensibility and behaviour
Authors: Jones Siobhan , Komes Jessica , Benzaquén Ester , Vuong Quoc C. , Sedley William 
Published: November 2024

AbstractBackgroundMigraine is typically precipitated by unaccustomed changes in one’s internal state and/or external environment. Migraine symptoms largely involve increased, noxious, awareness of bodily changes and external stimuli. Links have been proposed between migraine and interoception (sensing and interpreting internal states), but direct evidence is limited.MethodsUnmedicated, otherwise healthy, age-matched female participants were grouped by migraine tendency: control (no unprovoked headaches, n=19); low-frequency (<=3 migraines/month, n=20); high-frequency (>=4 migraines/month, n=19). Interoception was assessed, interictally, with standardised questionnaires such as MAIA-2 and a widely used heartbeat counting task.ResultsThe notable significant questionnaire-based difference was in the ‘noticing’, ‘not distracting’ domain of the MAIA-2; controls were least likely to continue activities despite experiencing physical discomfort, and high-frequency migraineurs most likely. Follow-up questioning clarified that this behaviour related predominantly to migraine-related symptoms. The heartbeat task found no differences in accuracy, but lower confidence in the low-frequency migraine group than the control and high-frequency groups.ConclusionsWe suggest that low interoceptive confidence is a risk factor for migraine, whilst amplification of interoceptive signals caused by migraine restores this confidence, but at the price of migraine’s disabling symptoms. Self-reported tendency to deliberately ignore physical discomfort, including that caused by migraine, may result in more migraine attacks.
AlphaBind, a Domain-Specific Model to Predict and Optimize Antibody-Antigen Binding Affinity
Authors: Agarwal Aditya A. , Harrang James , Noble David , McGowan Kerry L. , Lange Adrian W. , Engelhart Emily , Lahman Miranda C. , Adamo Jeffrey , Yu Xin , Serang Oliver , Minch Kyle J. , Wellman Kimberly Y. , Younger David A. , Lopez Randolph M. , Emerson Ryan O. 
Published: November 2024

AbstractAntibodies are versatile therapeutic molecules that utilize combinatorial sequence diversity to cover a vast fitness landscape. However, designing optimal antibody sequences remains a major challenge. Recent advances in deep learning provide opportunities to address this challenge by learning sequence-function relationships to accurately predict fitness landscapes. These models enable efficientin silicoprescreening and optimization of antibody candidates. By focusing experimental efforts on the most promising candidates guided by deep learning predictions, antibodies with optimal properties can be designed more quickly and effectively.Here we present AlphaBind, a domain-specific model that utilizes protein language model embeddings and pre-training on millions of quantitative laboratory measurements of antibody-antigen binding strength to achieve state-of-the-art performance for guided affinity optimization of parental antibodies. We demonstrate that an AlphaBind-powered antibody optimization pipeline can deliver candidates with substantially improved binding affinity across four parental antibodies (some of which were already affinity-matured) and using two different types of training data. Resulting candidates, ranging up to 11 mutations from parental sequence, yield a sequence diversity that allows for optimization of other biophysical characteristics, all while using only a single round of data generation for each parental antibody. AlphaBind weights and code are publicly available at:https://github.com/A-Alpha-Bio/alphabind.
Climate change shapes richness-evenness relationships in a subalpine grassland experiment
Authors: Mühlbauer Lina K. , Klingler Andreas , Gaier Lukas , Schaumberger Andreas , Clark Adam Thomas 
Published: November 2024

AbstractGrasslands face an uncertain future due to climate change. Although there is increased interest in the interdependencies of different biodiversity components, the effects of climate change on these relationships remains understudied. One of these is the richness-evenness relationship (RER), which is sensitive to altered species abundances in relation to richness. This relationship may be important as evenness and richness jointly shape diverse ecosystem functions, such as stability and productivity. As evenness effects productivity differently in low and high richness communities, the richness-evenness relationship is important to investigate, especially under climate change.Here, we assess the effects of increased CO2concentrations, temperature, and drought on the RER in a subalpine long-term (2010 - ongoing) grassland climate change experiment, and test whether these effects can be buffered by reseeding. We provide evidence that climate change alters the RER in our experiment, and that these changes occur independently from changes in richness and evenness separately. Reseeding erases the differences in RER between treatments and controls but fails to restore the negative RER initially found in controls. Further, we show that the dominant grass species in our system(Arrhenatherum elatius)responds differently to each climate change factor, with opposite effects in high vs. low richness plots, thereby largely determining the direction of the RER.These results suggest that the RER can reveal additional insights on community responses to climate change and represents a different signal than evenness or richness alone. A more nuanced approach integrating evenness and maximizing richness in seed mixtures could be an important step forward to better match restoration treatments to particular community types and global change drivers.
Graph Autoencoder and StrNN based Causal Analysis of Mortality in Heart Failure Patients
Authors: Dae young Kim 
Published: November 2024

AbstractThough analyzed for decades, dissecting and finding mechanisms of cardiovascular diseases, especially heart failures, are still an on-going task for many researchers. However, through recent floods of machine learning and deep learning algorithms to replace traditional approaches, and their applications in diverse cardiovascular research areas, it seems plausible to say that conquering or preventing heart failure catastrophes might no longer be a delusional task within a few more years. To accelerate the arrival of a new era, this research implemented several cuttingedge algorithms currently introduced in causal deep learning to observational heart disease patient data to find key mechanisms that lead to cardiac deaths under a highly flexible framework. Extracting latent causal DAGs from observational data using Graph Auto Encoder, and finding specific causal relationships and interventional effects under Structured Neural Networks (StrNN), novel findings regarding key causes of deaths in heart failure patients were found in numerous aspects. Specifically, existence of intervals where average treatment effects due to causal interventions in platelets, ejection fraction, and serum creatinine levels dramatically decrease or increase was found among heart patients, which can lead to significant eliminations or additions of practical clinical treatments in terms of reducing cardiac death event probability after cardiac failure.
The contribution of mitochondria-associated ER membranes to cholesterol homeostasis
Authors: Montesinos J. , Kabra K. , Uceda M. , Larrea D. , Agrawal R.R. , Tamucci K.A. , Pera M. , Ferre A.C. , Gomez-Lopez N. , Yun T.D. , Velasco K.R. , Schon E.A. , Area-Gomez E. 
Published: November 2024

ABSTRACTCellular demands for cholesterol are met by a balance between its biosynthesis in the endoplasmic reticulum (ER) and its uptake from lipoproteins. Cholesterol levels in intracellular membranes form a gradient maintained by a complex network of mechanisms including the control of the expression, compartmentalization and allosteric modulation of the enzymes that balance endogenous and exogenous sources of cholesterol. Low-density lipoproteins (LDLs) are internalized and delivered to lysosomal compartments to release their cholesterol content, which is then distributed within cellular membranes. High-density lipoproteins (HDLs), on the other hand, can transfer their cholesterol content directly into cellular membranes through the action of receptors such as the scavenger receptor B type 1 (SR-B1; geneSCARB1). We show here that SR-B1-mediated exogenous cholesterol internalization from HDL stimulates the formation of lipid-raft subdomains in the ER known as mitochondria-associated ER membranes (MAM), that, in turn, suppressde novocholesterol biosynthesis machinery. We propose that MAM is a regulatory hub for cholesterol homeostasis that offers a novel dimension for understanding the intracellular regulation of this important lipid.