bluuebunny/crossref_metadata_2025_split huggingface dataset:
Darwin’s Overlooked Radiation: genomic evidence points to the early stages of a radiation in the Galápagos prickly pear cactus (Opuntia,Cactaceae)
Authors: Zapata Felipe , Cerca José , McCarney Dana , Henriquez Claudia L. , Tiamiyu Bashir B. , McCormack John E. , Reckling Kelsey R. , Chaves Jaime A. , Rivas-Torres Gonzalo 
Published: October 2023

SUMMARYThe Galápagos Islands are a prime example of a natural laboratory for the study of evolutionary radiations. While much attention has been devoted to iconic species like Darwin’s finches1–4, the islands offer an equally unique but often overlooked opportunity for plant radiations5. Yet, compared to their animal counterparts, our understanding of the patterns and processes underpinning Galápagos plant radiations remains relatively limited6,7. We present evidence of the early stages of a radiation in prickly-pear cactus (Opuntia, Cactaceae), a plant lineage widespread across the archipelago. Phylogenomic and population genomic analyses show that notwithstanding overall low genetic differentiation across populations, there is marked geographic structure that is broadly consistent with current taxonomy and the dynamic paleogeography of the Galápagos. Because such low genetic differentiation stands in stark contrast to the exceptional eco-phenotypic diversity displayed by cacti across islands, it is plausible that phenotypic plasticity precedes genetic divergence and is the source of adaptive evolution, or that introgression between populations facilitates local adaptation. Models of population relationships including admixture indicate that gene flow is common between certain islands, likely facilitated by dispersal via animals known to feed onOpuntiaflowers, fruits, and seeds across the archipelago. Scans of genetic differentiation between populations reveal candidate loci associated with seed traits and environmental stressors, suggesting that a combination of biotic interactions and abiotic pressures due to the harsh conditions characterizing island life in a volcanic, equatorial archipelago may underlie the diversification of prickly-pear cacti. Considered in concert, these results are relevant to both the mechanisms of plant eco-phenotypic differentiation and the evolutionary history and conservation of the Galápagos biota.
FedGMMAT: Federated Generalized Linear Mixed Model Association Tests
Authors: Li Wentao , Chen Han , Jiang Xiaoqian , Harmanci Arif 
Published: October 2023

AbstractIncreasing genetic and phenotypic data size is critical for understanding the genetic determinants of diseases. Evidently, establishing practical means for collaboration and data sharing among institutions is a fundamental methodological barrier for performing high-powered studies. As the sample sizes become more heterogeneous, complex statistical approaches, such as generalized linear mixed effects models, must be used to correct for confounders that may bias results. On another front, due to the privacy concerns around Protected Health Information (PHI), genetic information is restrictively protected by sharing according to regulations such as Health Insurance Portability and Accountability Act (HIPAA). This limits data sharing among institutions and hampers efforts around executing high-powered collaborative studies. Federated approaches are promising to alleviate the issues around privacy and performance, since sensitive data never leaves the local sites.Motivated by these, we developed FedGMMAT, a federated genetic association testing tool that utilizes a federated statistical testing approach for efficient association tests that can correct for arbitrary fixed and random effects among different collaborating sites. Genetic data is never shared among collaborating sites, and the intermediate statistics are protected by homomorphic encryption. Using simulated and real datasets, we demonstrate FedGMMAT can achieve the virtually same results as pooled analysis under a privacy-preserving framework with practical resource requirements.
Accurate prediction of protein tertiary structural changes induced by single-site mutations with equivariant graph neural networks
Authors: Mahmud Sajid , Morehead Alex , Cheng Jianlin 
Published: October 2023

AbstractPredicting the change of protein tertiary structure caused by singlesite mutations is important for studying protein structure, function, and interaction. Even though computational protein structure prediction methods such as AlphaFold can predict the overall tertiary structures of most proteins rather accurately, they are not sensitive enough to accurately predict the structural changes induced by single-site amino acid mutations on proteins. Specialized mutation prediction methods mostly focus on predicting the overall stability or function changes caused by mutations without attempting to predict the exact mutation-induced structural changes, limiting their use in protein mutation study. In this work, we develop the first deep learning method based on equivariant graph neural networks (EGNN) to directly predict the tertiary structural changes caused by single-site mutations and the tertiary structure of any protein mutant from the structure of its wild-type counterpart. The results show that it performs substantially better in predicting the tertiary structures of protein mutants than the widely used protein structure prediction method AlphaFold.
Laminar architecture of visual responses in supplementary eye field of macaques
Authors: Thirunavukkarasu Pranavan , Errington Steven P. , Sajad Amirsaman , Schall Jeffrey D. 
Published: October 2023

ABSTRACTPreviously, we have described the laminar organization of neurons in the supplementary eye field (SEF) that signal error, reward gain and loss, conflict, event timing, and goal maintenance. Here we describe the laminar organization of visually responsive neurons that were active during performance of a saccade stop-signal task. Nearly 40% of isolated neurons exhibited enhanced or suppressed responses to a visual target for a potential saccade, with the majority exhibiting enhanced activity and three-quarters with broad spikes. Visually responsive neurons were observed in all layers but were less common in layers 5 and 6. Response latencies were comparable to those reported previously, which are significantly later than those measured in occipital and temporal visual areas but overlapping those measured in cingulate cortex. Task-related visual response latency varied across cortical layers. Response latency was significantly earlier for neurons with narrow spikes. Neurons with task-related visual responses discharged until after saccade production. Around three-fifths of visually responsive neurons were most sensitive to the visual target appearing in one hemifield. Many neurons in layer 2 had ipsilateral receptive fields. Laminar current-source density aligned on visual target presentation revealed the earliest sink in layers 3 followed by a prolonged strong sink more superficially coupled with a weaker prolonged sink in layer 5 and a transient sink in layer 6. The current sink in layers 2 and 3 was stronger for ipsilateral stimuli. These findings reveal new details about visual processing in medial frontal cortex and complete the first catalogue of laminar organization of functional signals in a frontal lobe area.
Identifying key underlying regulatory networks and predicting targets of orphan C/D boxSNORD116snoRNAs in Prader-Willi syndrome
Authors: Gilmore Rachel B. , Liu Yaling , Stoddard Christopher E. , Chung Michael S. , Carmichael Gordon G. , Cotney Justin 
Published: October 2023

AbstractPrader-Willi syndrome (PWS) is a rare neurodevelopmental disorder characterized principally by initial symptoms of neonatal hypotonia and failure-to-thrive in infancy, followed by hyperphagia and obesity. It is well established that PWS is caused by loss of paternal expression of the imprinted region on chromosome 15q11-q13. While most PWS cases exhibit megabase-scale deletions of the paternal chromosome 15q11-q13 allele, several PWS patients have been identified harboring a much smaller deletion encompassing primarilySNORD116. This finding suggestsSNORD116is a direct driver of PWS phenotypes. TheSNORD116gene cluster is composed of 30 copies of individualSNORD116C/D box small nucleolar RNAs (snoRNAs). Many C/D box snoRNAs have been shown to guide chemical modifications of other RNA molecules, often ribosomal RNA (rRNA). However,SNORD116snoRNAs are termed ‘orphans’ because no verified targets have been identified and their sequences show no significant complementarity to rRNA. It is crucial to identify the targets and functions ofSNORD116snoRNAs because all reported PWS cases lack their expression. To address this, we engineered two different deletions modelling PWS in two distinct human embryonic stem cell (hESC) lines to control for effects of genetic background. Utilizing an inducible expression system enabled quick, reproducible differentiation of these lines into neurons. Systematic comparisons of neuronal gene expression across deletion types and genetic backgrounds revealed a novel list of 42 consistently dysregulated genes. Employing the recently described computational tool snoGloBe, we discovered these dysregulated genes are significantly enriched for predictedSNORD116targeting versus multiple control analyses. Importantly, our results showed it is critical to use multiple isogenic cell line pairs, as this eliminated many spuriously differentially expressed genes. Our results indicate a novel gene regulatory network controlled bySNORD116is likely perturbed in PWS patients.
Rummagene: Mining Gene Sets from Supporting Materials of PMC Publications
Authors: Clarke Daniel J. B. , Marino Giacomo B. , Deng Eden Z. , Xie Zhuorui , Evangelista John Erol , Ma’ayan Avi 
Published: October 2023

AbstractEvery week thousands of biomedical research papers are published with a portion of them containing supporting tables with data about genes, transcripts, variants, and proteins. For example, supporting tables may contain differentially expressed genes and proteins from transcriptomics and proteomics assays, targets of transcription factors from ChIP-seq experiments, hits from genome-wide CRISPR screens, or genes identified to harbor mutations from GWAS studies. Because these gene sets are commonly buried in the supplemental tables of research publications, they are not widely available for search and reuse. Rummagene, available fromhttps://rummagene.com, is a web server application that provides access to hundreds of thousands human and mouse gene sets extracted from supporting materials of publications listed on PubMed Central (PMC). To create Rummagene, we first developed a softbot that extracts human and mouse gene sets from supporting tables of PMC publications. So far, the softbot has scanned 5,448,589 PMC articles to find 121,237 articles that contain 642,389 gene sets. These gene sets are served for enrichment analysis, free text, and table title search. Users of Rummagene can submit their own gene sets to find matching gene sets ranked by their overlap with the input gene set. In addition to providing the extracted gene sets for search, we investigated the massive corpus of these gene sets for statistical patterns. We show that the number of gene sets reported in publications is rapidly increasing, containing both short sets that are highly enriched in highly studied genes, and long sets from omics profiling. We also demonstrate that the gene sets in Rummagene can be used for transcription factor and kinase enrichment analyses, and for gene function predictions. By combining gene set similarity with abstract similarity, Rummagene can be used to find surprising relationships between unexpected biological processes, concepts, and named entities. Finally, by overlaying the Rummagene gene set space with the Enrichr gene set space we can discover areas of biological and biomedical knowledge unique to each resource.
Systematic Investigation of Machine Learning on Limited Data: A Study on Predicting Protein-Protein Binding Strength
Authors: Zheng Feifan , Jiang Xin , Wen Yuhao , Yang Yan , Li Minghui 
Published: October 2023

AbstractThe application of machine learning techniques in biological research, especially when dealing with limited data availability, poses significant challenges. In this study, we leveraged advancements in method development for predicting protein-protein binding strength to conduct a systematic investigation into the application of machine learning on limited data. The binding strength, quantitatively measured as binding affinity, is vital for understanding the processes of recognition, association, and dysfunction that occur within protein complexes. By incorporating transfer learning, integrating domain knowledge, and employing both deep learning and traditional machine learning algorithms, we mitigate the impact of data limitations and make significant advancements in predicting protein-protein binding affinity. In particular, we developed over 20 models, ultimately selecting three representative best-performing ones that belong to distinct categories. The first model is structure-based, consisting of a random forest regression and thirteen handcrafted features. The second model is sequence-based, employing an architecture that combines transferred embedding features with a multilayer perceptron. Finally, we created an ensemble model by averaging the predictions of the two aforementioned models. The comparison with other predictors on three independent datasets confirmed the significant improvements achieved by our models in predicting protein-protein binding affinity. The source codes for these three models are available athttps://github.com/minghuilab/BindPPI.
Automated evaluation of multiple sequence alignment methods to handle third generation sequencing errors
Authors: Rohmer Coralie , Touzet Hélène , Limasset Antoine 
Published: October 2023

ABSTRACTMost third-generation sequencing (TGS) processing tools rely on multiple sequence alignment (MSA) methods to manage sequencing errors. Despite the broad range of MSA approaches available, a limited selection of implementations are commonly used in practice for this type of application, and no comprehensive comparative assessment of existing tools has been under-taken to date. In this context, we have developed an automatic pipeline, named MSA_Limit, designed to facilitate the execution and evaluation of diverse MSA methods across a spectrum of conditions representative of TGS reads. MSA_Limit offers insights into alignment accuracy, time efficiency, and memory utilization. It serves as a valuable resource for both users and developers, aiding in the assessment of algorithmic performance and assisting users in selecting the most appropriate tool for their specific experimental settings. Through a series of experiments using real and simulated data, we demonstrate the value of such exploration. Our findings reveal that in certain scenarios, popular methods may not consistently exhibit optimal efficiency and that the choice of the most effective method varies depending on factors such as sequencing depth, genome characteristics, and read error patterns. MSA_Limit is open source is freely available atgitlab.cristal.univ-lille.fr/crohmer/msa-limitand all presented results and necessary information to reproduce the experiments are available atgitlab.cristal.univ-lille.fr/crohmer/msa-limit
Visual selectivity and motor role of action observation/execution neurons in macaque ventral premotor cortex
Authors: Sofie De Schrijver , Thomas Decramer , Peter Janssen 
Published: October 2023

SummaryNeurons responding during action execution and action observation were discovered in the ventral premotor cortex three decades ago. However, neither the minimum visual features nor the motor involvement of Action Observation/Execution Neurons (AOENs) have been revealed at present. We investigated the minimal visual stimulus for activation and the involvement in motor behavior of AOENs in ventral premotor area F5c of four macaques. The large majority of AOENs showed highly phasic responses during the action videos, and also responded to an abstract shape moving towards but not interacting with an object, even when the shape moved on a scrambled background, implying that most AOENs in F5c do not require the perception of causality or a meaningful action. Additionally, electrical microstimulation of AOEN sites invariably elicited motor responses and frequently interfered with visually guided object grasping. Our findings suggest that an important role of F5c AOENs in visuomotor control during object grasping.
Reproducible spectral CT thermometry with liver-mimicking phantoms for image-guided thermal ablation
Authors: Liu Leening P. , Pua Rizza , Rosario-Berrios Derick N. , Sandvold Olivia F. , Perkins Amy E. , Cormode David P. , Shapira Nadav , Soulen Michael C. , Noël Peter B. 
Published: October 2023

AbstractObjectivesEvaluate the reproducibility, temperature sensitivity, and radiation dose requirements of spectral CT thermometry in tissue-mimicking phantoms to establish its utility for non-invasive temperature monitoring of thermal ablations.Materials and MethodsThree liver mimicking phantoms embedded with temperature sensors were individually scanned with a dual-layer spectral CT at different radiation dose levels during heating and cooling (35 to 80 °C). Physical density maps were reconstructed from spectral results using a range of reconstruction parameters. Thermal volumetric expansion was then measured at each temperature sensor every 5°C in order to establish a correlation between physical density and temperature. Linear regressions were applied based on thermal volumetric expansion for each phantom, and coefficient of variation for fit parameters was calculated to characterize reproducibility of spectral CT thermometry. Additionally, temperature sensitivity was determined to evaluate the effect of acquisition parameters, reconstruction parameters, and image denoising. The resulting minimum radiation dose to meet the clinical temperature sensitivity requirement was determined for each slice thickness, both with and without additional denoising.ResultsThermal volumetric expansion was robustly replicated in all three phantoms, with a correlation coefficient variation of only 0.43%. Similarly, the coefficient of variation for the slope and intercept were 9.6% and 0.08%, respectively, indicating reproducibility of the spectral CT thermometry. Temperature sensitivity ranged from 2 to 23 °C, decreasing with increased radiation dose, slice thickness, and iterative reconstruction level. To meet the clinical requirement for temperature sensitivity, the minimum required radiation dose ranged from 20, 30, and 57 mGy for slice thickness of 2, 3, and 5 mm, respectively, but was reduced to 2 mGy with additional denoising.ConclusionsSpectral CT thermometry demonstrated reproducibility across three liver-mimicking phantoms and illustrated the clinical requirement for temperature sensitivity can be met for different slice thicknesses. Moreover, additional denoising enables the use of more clinically relevant radiation doses, facilitating the clinical translation of spectral CT thermometry. The reproducibility and temperature accuracy of spectral CT thermometry enable its clinical application for non-invasive temperature monitoring of thermal ablation.
Automated Extraction and Classification of Drug Prescriptions in Electronic Health Records: Introducing the PRESNER Pipeline
Authors: Colón-Ruiz Cristóbal , Fitzgerald Tomas , Segura-Bedmar Isabel , Birney Ewan , Herrero-Zazo Maria 
Published: October 2023

AbstractElectronic health record (EHR) systems with prescription data offer vast potential in pharmacoepidemiology and pharmacogenomics. The large amount of clinical data recorded in these systems requires automatic processing to extract relevant information. This paper introduces PRESNER, a name entity recognition (NER) and classification pipeline for EHR prescription data.The pipeline uses the pre-trained transformer Bio-ClinicalBERT fine-tuned on UK Biobank prescription entries manually annotated with medication-related information (drug name, route of administration, pharmaceutical form, strength, and dosage) as the core NER system. Moreover, PRESNER also maps drugs to the Anatomical Therapeutic and Chemical (ATC) classification system and distinguishes between systemic and non-systemic drug products. It outperformed a baseline model combining the state-of-the-art Med7 and a dictionary-based approach from the ChEMBL database with a macro-average F1-score of 0.95 vs 0.71. In addition to UK Biobank prescription data, PRESNER can also be applied to other English prescription datasets, making it a versatile tool for researchers in the field.
COVID-19 and Influenza Infection Prevention and Control Measures in U.S. Jails, Prisons, and Detention Centers: A Scoping Review
Authors: Westgard Leo Knudsen , Ngassa Yvane , Grussing Emily , Wurcel Alysse G. , Tiako Max Jordan Nguemeni 
Published: October 2023

AbstractBackgroundPeople who are incarcerated or detained are at a high risk of infection and death due to viruses spread through droplets, including SARS-CoV-2 (COVID-19) and influenza. Increased risk is largely associated with structural and institutional conditions of detainment, including but not limited to congregate settings, poor ventilation, and barriers to accessing care. The lessons learned from managing previous infectious outbreaks can inform preventative measures for future emerging outbreaks.AimsOur project aims to analyze infection prevention and control measures for COVID-19 and influenza in U.S. correctional facilities.MethodsWe searched the PubMed and Embase databases for manuscripts evaluating the effectiveness of influenza or COVID-19 mitigation measures. Then, we reviewed the reference lists from our results to identify manuscripts not captured by the initial search.ResultsOut of the 553 articles initially found, only 28 met the inclusion criteria, with 27 focusing on COVID-19. Two additional studies were added after reviewing reference lists. Effective measures to prevent the spread of both infections included vaccines, testing, de-densification, limiting movement, masks, and contact tracing.ConclusionImproved infrastructure, stakeholder collaboration, and research on sustainable responses are needed to address disproportionate impacts on people who are incarcerated or detained.