bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using a citizen science approach for research-informed teaching of evolution and palaeontology
Authors: Lautenschlager Stephan 
Published: November 2024

AbstractOver the past few decades, citizen science has proven to be a valuable tool for research projects by involving the public in large-scale data collection. This collaboration between researchers and volunteers has been shown to be highly beneficial, allowing for efficient data collection in shorter time frames than possible for individual researchers. This study introduces a citizen science-inspired approach to teaching and curriculum design, involving undergraduate students directly in active research. Using a case study on dinosaur eye size, integrated into a first-year undergraduate course in Geology and Palaeontology at a UK university, the study presents the advantages of this approach for both teachers and learners. As part of the study, 22 undergraduate students were involved in data collection, analysis, and the subsequent publication processes, emphasizing active student participation in research. A subsequent survey demonstrated high student engagement and perceived relevance of the citizen science-style teaching approach. Results indicate that students found the approach appealing, engaging, and beneficial for understanding scientific concepts and methods. The case study shows that a citizen science approach to research-informed teaching can enhance student engagement and learning by providing meaningful, hands-on research experiences. This approach allows students to apply theoretical knowledge in a realistic context, fostering independence in quantitative research skills and collaborative learning. Despite potential challenges related to data accuracy and student motivation, the benefits of integrating such approaches into higher education curricula are substantial, offering a valuable model for teaching in Earth Sciences and beyond.
Limelight - An open, web-based tool for visualizing, sharing, and analyzing mass spectrometry data from DDA pipelines
Authors: Riffle Michael , Zelter Alex , Jaschob Daniel , Hoopmann Michael R. , Faivre Danielle A. , Moritz Robert L. , Davis Trisha N. , MacCoss Michael J. , Isoherranen Nina 
Published: November 2024

AbstractLiquid chromatography-tandem mass spectrometry employing data-dependent acquisition (DDA) is a mature, widely used proteomics technique routinely applied to proteome profiling, protein-protein interaction studies, biomarker discovery, and protein modification analysis. Numerous tools exist for searching DDA data and myriad file formats are output as results. While some search and post processing tools include data visualization features to aid biological interpretation, they are often limited or tied to specific software pipelines. This restricts the accessibility, sharing and interpretation of data, and hinders comparison of results between different software pipelines. We developed Limelight, an easy-to-use, open-source, freely available tool that provides data sharing, analysis and visualization and is not tied to any specific software pipeline. Limelight is a data visualization tool specifically designed to provide access to the whole “data stack”, from raw and annotated scan data to peptide-spectrum matches, quality control, peptides, proteins, and modifications. Limelight is designed from the ground up for sharing and collaboration and to support data from any DDA workflow. We provide tools to import data from many widely used open-mass and closed-mass search software workflows. Limelight helps maximize the utility of data by providing an easy-to-use interface for finding and interpreting data, all using the native scores from respective workflows.
Multimodal LLMs for Retinal Disease Diagnosis via OCT: Few-Shot vs Single-Shot Learning
Authors: Agbareia Reem , Omar Mahmud , Zloto Ofira , Glicksberg Benjamin S , Nadkarni Girish N , Klang Eyal 
Published: November 2024

AbstractBackground and AimMultimodal large language models (LLMs) have shown potential in processing both text and image data for clinical applications. This study evaluated their diagnostic performance in identifying retinal diseases from optical coherence tomography (OCT) images.MethodsWe assessed the diagnostic accuracy of GPT-4o and Claude Sonnet 3.5 using two public OCT datasets (OCTID, OCTDL) containing expert-labeled images of four pathological conditions and normal retinas. Both models were tested using single-shot and few-shot prompts, with an overall of 3088 models’ API calls. Statistical analyses were performed to evaluate differences in overall and condition-specific performance.ResultsGPT-4o’s accuracy improved from 56.29% with single-shot prompts to 73.08% with few-shot prompts (p < 0.001). Similarly, Claude Sonnet 3.5 increased from 40.03% to 70.98% using the same approach (p < 0.001). Condition-specific analyses revealed similar trends, with absolute improvements ranging from 2% to 64%. These findings were consistent across the validation dataset.ConclusionFew-shot prompted multimodal LLMs show promise for clinical integration, particularly in identifying normal retinas, which could help streamline referral processes in primary care. While these models fall short of the diagnostic accuracy reported in established deep learning literature, they offer simple, effective tools for assisting in routine retinal disease diagnosis. Future research should focus on further validation and integrating clinical text data with imaging.
Effect of Lymphocyte miRNA Expression on Influenza Vaccine-Induced Immunity
Authors: Haralambieva Iana H. , Ratishvili Tamar , Goergen Krista M. , Grill Diane E. , Simon Whitney L. , Chen Jun , Ovsyannikova Inna G. , Poland Gregory A. , Kennedy Richard B. 
Published: November 2024

AbstractAlterations of gene expression by miRNAs contribute substantially to genetic regulation and cellular functions.We conducted a comprehensive study in 53 individuals before and after the administration of the 2010-2011 seasonal inactivated influenza vaccine to characterize lymphocyte-specific miRNA expression (in purified B cells, CD4+ T cells, CD8+ T cells and NK cells) and its effect on influenza vaccine-induced immune outcomes (hemagglutination inhibition antibody titers/HAI, viral neutralizing antibody titers /VNA and memory B cell ELISPOT).Overall, we observed relatively stable miRNA expression before/after influenza vaccination. Our statistical analysis uncovered three baseline miRNAs (miR-150-3p, miR-629-5p and miR-4443) that were significantly correlated with influenza vaccine-induced immune outcomes in different cell types. Predictive modeling of influenza vaccine-induced HAI/VNA titers identified a set of specific baseline miRNAs in CD4+T cells as factors predictive of antibody responses. A pathway enrichment analysis on the putative target genes revealed several regulated signaling pathways and functions: TGF-β signaling, PI3K-Akt signaling, p53 signaling, MAPK signaling, TNF signaling and C-type lectin receptor signaling, as well as cell adhesion and adherens junctions, and antiviral host response.In conclusion, our study offers evidence for the role of epigenetic modification (miRNAs) on influenza vaccine-induced immunity. After validation, identified miRNAs may serve as potential biomarkers of immune response after influenza vaccination.HighlightsHost miRNA expression is relatively stable before and after influenza vaccinationmiR-150-3p, miR-629-5p and miR-4443 were correlated with immunity across cell typesSpecific CD4+T cell miRNAs are predictive of antibody responsesIdentified miRNAs may serve as biomarkers of immune response after influenza vaccine
Sample-specific network analysis identifies gene coexpression patterns of immunotherapy response in advanced kidney cancer
Authors: Yin Liangwei , Traversa Pietro , Elati Mohamed , Moreno Yamir , Marek-Trzonkowska Natalia , Battail Christophe 
Published: November 2024

AbstractImmunotherapies have recently emerged as a standard of care for advanced cancers, offering remarkable improvements in patient prognosis. However, only a small proportion of patients respond to the treatment, and no definitive molecular hallmark has been identified for clinical use in predicting treatment outcomes. Here, we propose a sample-specific weighted gene network approach to investigate the heterogeneity of patient clinical outcomes by leveraging multiscale network features derived from gene expression data. Our results show that patients exhibiting similar clinical benefits share comparable gene coexpression patterns. Increased gene connectivity and stronger negative gene-gene associations are also pivotal factors in patients with poor prognoses. Moreover, integrating pathway genes with topological measures enables the identification of the perturbed regulation of biological pathways associated with treatment responses. Additionally, sample-level network features enhance the prediction performance of gene expression values-based machine learning models. Collectively, our approach provides valuable guidance on the use of gene network information to stratify cancer patients and to optimize treatment strategies.
Requirement for Fc effector function is overcome by binding potency for broadly reactive anti-alphavirus antibodies
Authors: Callahan Victoria , Sutton Matthew S. , Gardner Christina L. , Kenchegowda Doreswamy , Dunagan Megan M. , Gosavi Mrunal , Green Courtney , Chen Tammy Y. , Prado-Smith Jessica , Long Daniel , Vogel Jodi L. , Kristie Thomas M. , Clancy Chad S. , Burke Crystal W. , Roederer Mario , Fox Julie M. 
Published: November 2024

ABSTRACTAlphaviruses are emerging public health threats. Broadly reactive anti-alphavirus monoclonal antibodies (mAbs) have been shown to be protective in mouse models of infection. However, the mechanism of Fc-dependent or Fc-independent heterologous protection remains ill-definedin vivo. Here, we used two vaccine-elicited, broadly reactive, anti-alphavirus mAbs, SKT05 and SKT20, to establish correlates of mAb-mediated protection during Venezuelan equine encephalitis virus (VEEV) challenge. SKT20 required Fc effector functions to prevent lethality. In contrast, SKT05-mediated survival was independent of Fc effector functions, which is likely linked to early viral control through potent egress inhibition. However, control of virus replication and spread with SKT05 was Fc-dependent; these findings extended to additionalin vivomodels with alternative VEEV subtypes and chikungunya virus. During therapeutic delivery of SKT05, Fc effector functions were only required at 3 days post-infection. The necessity of Fc effector functions for SKT20 was related to mAb binding avidity rather than epitope and could be overcome by increasing the dose of SKT20 relative to the functional avidity of SKT05. Collectively, this study identified antibody avidity as a correlate forin vivoefficacy and associated Fc-dependent mechanisms that can be leveraged for therapeutic development of monoclonal antibodies against alphaviruses.One sentence summaryFunctional avidity of broadly reactive anti-alphavirus antibodies dictates requirement for Fc-mediated protection.
Mapping of the viral shunt across widespread coccolithophore blooms using metabolic biomarkers
Authors: Kuhlisch Constanze , Schleyer Guy , Flores J. Michel , Vincent Flora , Vallet Marine , Schatz Daniella , Vardi Assaf 
Published: November 2024

AbstractThe viral shunt is a fundamental ecosystem process which diverts the flux of organic carbon from grazers to heterotrophic microorganisms. Through the extracellular release of metabolites, lytic viral infections supply 2-10% of photosynthetically fixed carbon in the ocean for bacterial respiration. Despite its significance for the carbon cycle, we lack tools to detect the viral shunt in the natural environment and assess its ecological impact. Here, we study the use of exometabolites as biomarkers for the viral shunt by applying molecular, metabolomics, and oceanographic tools in blooms of the cosmopolitan microalgaGephyrocapsa huxleyiacross the Atlantic Ocean, spanning four biogeochemical provinces between Iceland and Patagonia. We mapped the distinct metabolic footprint of its viral infections using exo- and endometabolomics approaches and detected nineteen organohalogen metabolites across the blooms, showing their global formation. Time-resolved comparison of particulate and dissolved metabolite pools during an induced mesocosm bloom indicated virocells – actively infected host cells – as the source of the halogenated metabolites. Three trichloro-iodo metabolites were present during demise of all virus-infected blooms, highlighting them as suitable metabolic biomarkers. The environmental stability of these halometabolites in the DOM pool over a few days can recapitulate viral infections at earlier stages of phytoplankton bloom succession.
Emergence of alternative offspring care patterns from the evolution of parental negotiation strategies
Authors: Zheng Jia , Weissing Franz J. , Baldan Davide 
Published: November 2024

AbstractThe evolution of parental cooperation is challenging to explain because caring for offspring is costly, and parents face an evolutionary conflict of interests over how much care each should contribute. Evolutionary game theory suggests that this conflict may be resolved through parental negotiation, where the parents make their care level dependent on the care provided by their partner. However, mathematical negotiation models typically predict a low level of parental cooperation. As these models are not dynamically explicit and tend to neglect stochasticity, we here investigate the evolution of parental negotiation strategies by means of individual-based simulations. Our results differ markedly from earlier analytical predictions. Parental negotiation strategies readily evolve, but replicate simulations result in four alternative care patterns: uniparental care, sex-biased care and egalitarian biparental care with constant or oscillatory care levels. Hence, pronounced sex differences in parental care can evolve even in the absence of sexual selection and uncertainty of paternity. Effective parental cooperation is the most frequent outcome, but in contrast to the earlier models, it is not based on parental compensation but on a tit-for-tat negotiation strategy. Actually, compensation destabilises cooperation and leads to less effective outcomes where one parental sex exploits the other one.
A Transformer-Based Approach to Survival Outcome Prediction
Authors: Mellors Ted , Schneider Matt , Spitmann Mehran 
Published: November 2024

AbstractAccurate prediction of patient survival outcomes is a critical challenge in cancer research, with the potential to inform personalized treatment strategies and improve patient care. We leveraged Geneformer, a state-of-the-art transformer model pre-trained on a massive single-cell RNA-seq dataset, to develop a model for the prediction of overall survival (OS). We adapted Geneformer for bulk tumor data analysis by appending a task-specific transformer layer and fine-tuning the model on RNA-seq data from The Cancer Genome Atlas (TCGA). Additionally, we employed a rank-value encoding scheme to prioritize informative genes and reduce noise. Our model demonstrated a robust correlation between predicted and true OS, with Pearson correlation coefficient of 0.72 (p<0.00001). Survival analysis revealed significant differences in survival between patient subgroups stratified based on the model’s predictions. The Geneformer-based model outperformed traditional machine learning approaches (Random Forest and Neural Network) in patient stratification tasks. Further analysis demonstrated the consistency of the model’s performance across different tumor stages and patient subgroups. Our study highlights the potential of leveraging pre-trained transformer models, originally developed for single-cell data analysis, to predict clinically relevant outcomes from bulk tumor gene expression data. The superior performance of our Geneformer-based model underscores its potential to enhance prognostication and treatment decision-making in cancer research. Future work will focus on refining the model architecture, incorporating multi-omics data, and validating its performance on external datasets to further advance its clinical utility.Short AbstractAccurate prediction of patient survival has important implications for cancer research as it enables the development of personalized treatment plans, guides clinical decision-making, and can be leveraged for clinical trial optimization. We utilized Geneformer, a transformer model pre-trained on single-cell RNA-seq data, to predict overall survival (OS) from bulk tumor gene expression. Adapting Geneformer for bulk tumor analysis and using rank-value encoding, we achieved strong correlations between predicted and true OS (r=0.72, p<0.00001). Our model outperformed traditional machine learning approaches in patient stratification, demonstrating consistent performance across tumor stages and subgroups. This study highlights the potential of pre-trained transformer models for prognostication in cancer, paving the way for refined, personalized treatment strategies.
Centromeric repeat diversity underlies non-Mendelian segregation pattern in hop (Humulus lupulus)
Authors: Horáková Lucie , Čegan Radim , Jedlička Pavel , Navrátilová Pavla , Tanaka Hiroyuki , Toyoda Atsushi , Itoh Takehiko , Akagi Takashi , Ono Eiichiro , Hudzieczek Vojtěch , Patzak Josef , Šafář Jan , Hobza Roman , Bačovský Václav 
Published: November 2024

SummaryAberrant meiosis in plants often leads to aneuploidy, genetic instability, and sterility. This can occur due to several factors, including chromosome misalignment, defective synapsis or environmental factors that may result in unusual genetic combinations in the offsprings. Unusual chromosome behavior during male meiosis inHumulus lupulusis linked to irregular chromosome segregation and genome instability. However, the origin of meiotic instability remains unclear.We analyzed the centromeric landscape ofH.lupulus to determine its role in aberrant chromosomal segregation during cell division. Using a combination of bioinformatic, molecular and cytogenetic approaches, we identified new centromeric repeats and revealed two types of centromeric organizations. Cytogenetic localization on metaphase chromosomes confirmed the genomic distribution of major repeat arrays and revealed unique features that contribute to aberrant segregation.Two centromeric types are composed of the major repeats SaazCEN and SaazCRM1 which are further accompanied by chromosome-specific centromeric satellites, Saaz40, Saaz293, Saaz85, and HuluTR120. Chromosome 2 displays unbalanced segregation during the cell division, implicating an important role for its centromere structure in segregation patterns. Moreover, Saaz293 is a new marker for studying aneuploidy in hop.Our findings provide new insights on chromosome segregation in hop and highlight the diversity and complexity of the centromere organization inH.lupulus.
Local ionic conditions modulate the aggregation propensity and influence the structural polymorphism of alpha-synuclein
Authors: Zacharopoulou Maria , Seetaloo Neeleema , Ross James , Stephens Amberley D. , Fusco Giuliana , McCoy Thomas M. , Dai Wenyue , Mela Ioanna , Fernandez Villegas Ana , Martel Anne , Routh Alexander F. , De Simone Alfonso , Phillips Jonathan J. , Schierle Gabriele S. Kaminski 
Published: November 2024

ABSTRACTParkinson’s Disease (PD) is characterized by the aggregation of alpha-synuclein (aSyn), a presynaptic protein that transitions from a disordered monomer into beta-sheet rich amyloid fibrils. The precise triggers and mechanisms underlying aSyn misfolding and aggregation remain unclear, hindering the development of effective therapeutics. Monomeric aSyn is an intrinsically disordered protein (IDP) with high conformational flexibility. Local environmental factors, such as ion concentrations, can influence the conformational ensemble of aSyn, impacting its aggregation propensity and resulting in fibril polymorphism. In this study, we explore the impact of physiologically relevant ions, mainly Ca2+and Na+, on the aggregation kinetics, monomer structural dynamics, and fibril polymorphism of aSyn. Using ThT fluorescence assays, we demonstrate that all ions accelerate aSyn aggregation, with Ca2+having the most significant effect. Using Heteronuclear Single Quantum Correlation Nuclear Magnetic Resonance (1H-15NHSQC NMR) spectroscopy, we validate the specific binding of Ca2+ions at the C-terminus, whereas Na+ions display non-specific interactions along the sequence of aSyn. Small-angle neutron scattering (SANS) and hydrogen-deuterium exchange mass spectrometry (HDX-MS) further reveal that Na+and Ca2+induce distinct conformational changes in the aSyn monomer, with Na+leading to more extended structures and Ca2+promoting a moderate extension of the protein. Molecular dynamics simulations (MD) corroborate these findings, showing that Na+ions increase the protein’s extension, particularly between the non-amyloid beta component (NAC) region and the C-terminus, whereas Ca2+ions bias the ensemble towards a more moderately elongated structure. Using MD, we further investigate the local environment and in particular the solvent effect and show the water persistence times in the hydration shell are also increased in the presence of Ca2+ions, indicating that the aggregation propensity of the monomer is due to a combination of conformational bias of the monomer and solvent mobility. Atomic force microscopy (AFM) of aSyn fibrils formed under these different ionic conditions reveal distinct fibril polymorphs, suggesting that ion-induced conformational biases in the monomer contribute to the diversity of fibril structures. Collectively, these findings underscore the pivotal influence of the local ionic milieu in shaping the structure and aggregation propensity of aSyn, thus offering valuable insights into the molecular underpinnings of PD and potential therapeutic avenues aimed at manipulating aSyn conformational dynamics.
qByte: Open-source isothermal fluorimeter for democratizing analysis of nucleic acids, proteins and cells
Authors: Quero Francisco J. , Aidelberg Guy , Vielfaure Hortense , de Kermadec Yann Huon , Cazaux Severine , Pandi Amir , Pascual-Garrigos Ana , Arce Anibal , Sakyi Samuel , Gaudenz Urs , Federici Fernan , Molloy Jennifer C. , Lindner Ariel B. 
Published: November 2024

AbstractAccess to affordable and reliable scientific instrumentation remains a significant barrier to the democratization of healthcare and scientific research. In the field of biotechnology, in particular, the complexity, cost, and infrastructure requirements of many instruments continue to limit their accessibility, especially in resource-limited environments. Despite the recent increase in the development of open-source tools, driven by advances in digital fabrication and electronic prototyping, few of these projects have reached large-scale implementation or validation in real-world settings. Here, we present qByte, an open-source, 8-tube isothermal fluorimeter designed to overcome these barriers by offering a cost-effective ($60) yet production-ready solution. qByte leverages standard digital manufacturing and Printed Circuit Board (PCB) assembly techniques and is designed to be portable, making it ideal for both laboratory and field use. The device has been benchmarked against commercial real-time thermocyclers and spectrophotometers, showing comparable results across four key applications: nucleic acid amplification and detection, analysis of protein activity and stability, genetic construct characterization, and bacterial viability tests. Further testing including the on-site diagnosis of human parasites in Kumasi, Ghana, and field work in Patagonia, Chile, validated qByte’s reliability in real-world conditions. Taken together, our results proved qByte as flexible and reliable equipment for a variety of biological tests and applications, while its affordability and open-source design simplify further development and allow adaptation to the needs of future users.