bluuebunny/crossref_metadata_2025_split huggingface dataset:
“Spectromics”: Holistic Optical Assessment of Human Cartilage via Complementary Vibrational Spectroscopy for Osteoarthritis Diagnosis
Authors: Cook Hiroki , Crisford Anna , Bourdakos Konstantinos , Dunlop Douglas , Oreffo Richard OC , Mahajan Sumeet 
Published: December 2023

AbstractOsteoarthritis (OA) is the most common degenerative joint disease, presented as wearing down of articular cartilage and resulting in pain and limited mobility for 1 in 10 adults in the UK.1There is an unmet need for patient friendly paradigms for clinical assessment that do not require ionising radiation (CT), exogenous contrast enhancing dyes (MRI), biopsy, and/or instrumentation approaches (arthroscopy or endoscopy). Hence, techniques that use non-destructive, near- and shortwave infrared light (NIR, SWIR) may be ideal providing for non-invasive, label-free and deep tissue interrogation. This study demonstrates multimodal “spectromics”, low-level abstraction data fusion of non-destructive NIR Raman scattering spectroscopy and NIR-SWIR absorption spectroscopy, providing an enhanced, interpretable “fingerprint” for diagnosis of OA in human cartilage. Samples were excised from femoral heads post hip arthroplasty from OA patients (n=13) and age-matched control (osteoporosis) patients (n=14). Under multivariate statistical analysis and supervised machine learning, tissue was classified to high precision: 100% segregation of tissue classes, and a classification accuracy of 95% (control) and 80% (OA), using the combined vibrational data. There was a marked performance improvement (5 to 6-fold for multivariate analysis) using the spectromics fingerprint compared to results obtained from solely Raman or NIR-SWIR data. Furthermore, discriminatory spectral features in the enhanced fingerprint elucidated clinically relevant tissue components (OA biomarkers). In summary, spectromics provides comprehensive information for early OA detection and disease stratification, imperative for effective intervention in treating the degenerative onset disease for an aging demographic.
One LLM is not Enough: Harnessing the Power of Ensemble Learning for Medical Question Answering
Authors: Yang Han , Li Mingchen , Zhou Huixue , Xiao Yongkang , Fang Qian , Zhang Rui 
Published: December 2023

ABSTRACTObjectiveTo enhance the accuracy and reliability of diverse medical question-answering (QA) tasks and investigate efficient approaches deploying the Large Language Models (LLM) technologies, We developed a novel ensemble learning pipeline by utilizing state-of-the-art LLMs, focusing on improving performance on diverse medical QA datasets.Materials and MethodsOur study employs three medical QA datasets: PubMedQA, MedQA-USMLE, and MedMCQA, each presenting unique challenges in biomedical question-answering. The proposed LLM-Synergy framework, focusing exclusively on zero-shot cases using LLMs, incorporates two primary ensemble methods. The first is a Boosting-based weighted majority vote ensemble, where decision-making is expedited and refined by assigning variable weights to different LLMs through a boosting algorithm. The second method is Cluster-based Dynamic Model Selection, which dynamically selects the most suitable LLM votes for each query, based on the characteristics of question contexts, using a clustering approach.ResultsThe Majority Weighted Vote and Dynamic Model Selection methods demonstrate superior performance compared to individual LLMs across three medical QA datasets. Specifically, the accuracies are 35.84%, 96.21%, and 37.26% for MedMCQA, PubMedQA, and MedQA-USMLE, respectively, with the Majority Weighted Vote. Correspondingly, the Dynamic Model Selection yields slightly higher accuracies of 38.01%, 96.36%, and 38.13%.ConclusionThe LLM-Synergy framework with two ensemble methods, represents a significant advancement in leveraging LLMs for medical QA tasks and provides an innovative way of efficiently utilizing the development with LLM Technologies, customing for both existing and potentially future challenge tasks in biomedical and health informatics research.
State-wise Economic Burden of Road Traffic Accidents in India
Authors: Sajith Kumar S , Bagepally Bhavani Shankara , Sasidharan Akhil , Jagadeesh Kayala Venkata , Ponniah Manickam 
Published: December 2023

AbstractIntroductionRoad traffic accidents (RTA) cause multifarious detrimental consequences, including fatality and serious injuries. RTAs cause considerable financial strain on healthcare systems with high costs of medical care, long-term care for the injured, and lost productivity. To assess this burden, we estimated state-wise economic costs of RTA in India. We followed CHEERS Checklist 2022 to present study’s methods and outcomes.MethodsData were obtained from Global Burden of Disease Injuries and Risk Factors study, Government of India’s budget documents, annual reports of National Health Authority, controller general of accounts, and Economic Surveys. Cost per disability-adjusted life years (DALY), direct costs, indirect costs and total costs associated with RTA burden were estimated for India and by its states.ResultsRTA burden declined in all Indian states except Jammu and Kashmir (with a minor increase from 1,458 to 1,564 DALYs per 100,000) and Rajasthan (from 1,275 to 1,394 DALYs per 100,000) during 2010-19 period. Estimated mean cost per DALY in India due to RTA (95%CI) was □1,821(□1,606 to □2,036) [US$ 22(19 to 24)] with a median (IQR) of □1,609(551) [US$ 19 (7)]. As of 2019, the estimated mean total cost of RTA burden in India was □1,017 billion [US$ 12 billion], with a median (IQR) of □731 billion(1,220 billion) [US$ 9 billion(15 billion)].ConclusionBurden of RTAs declined across the Indian States during 2010-2019, and same was not observed with fiscal implications. The study reiterates the need for strategic road safety interventions to ameliorate socio-economic and health impacts of RTAs in India.
In vivovalidation of late-onset Alzheimer’s disease genetic risk factors
Authors: Sasner Michael , Preuss Christoph , Pandey Ravi S. , Uyar Asli , Garceau Dylan , Kotredes Kevin P. , Williams Harriet , Oblak Adrian L. , Lin Peter Bor-Chian , Perkins Bridget , Soni Disha , Ingraham Cindy , Lee-Gosselin Audrey , Lamb Bruce T. , Howell Gareth R. , Carter Gregory W. 
Published: December 2023

Structured AbstractIntroductionGenome-wide association studies have identified over 70 genetic loci associated with late-onset Alzheimer’s disease (LOAD), but few candidate polymorphisms have been functionally assessed for disease relevance and mechanism of action.MethodsCandidate genetic risk variants were informatically prioritized and individually engineered into a LOAD-sensitized mouse model that carries the AD risk variants APOE4 and Trem2*R47H. Potential disease relevance of each model was assessed by comparing brain transcriptomes measured with the Nanostring Mouse AD Panel at 4 and 12 months of age with human study cohorts.ResultsWe created new models for 11 coding and loss-of-function risk variants. Transcriptomic effects from multiple genetic variants recapitulated a variety of human gene expression patterns observed in LOAD study cohorts. Specific models matched to emerging molecular LOAD subtypes.DiscussionThese results provide an initial functionalization of 11 candidate risk variants and identify potential preclinical models for testing targeted therapeutics.
Egg-laying hormone expression in identified neurons across developmental stages and reproductive states of the nudibranchBerghia stephanieae
Authors: Tait Cheyenne C. , Ramirez M. Desmond , Katz Paul S. 
Published: December 2023

AbstractNeuropeptides play essential roles in coordinating reproduction. Egg-laying hormone (ELH) is conserved in genetic sequence and behavioral function across molluscs, where neuronal clusters secrete ELH to modulate and induce egg-laying. Here we investigated ELH in the nudibranch mollusc,Berghia stephanieae. ELH preprohormone gene orthologs, which showed clade-specific differences at the C-terminus of the predicted bioactive peptide, were identified in brain transcriptomes across several nudipleuran species, includingB. stephanieae. Injection of synthesizedB. stephanieaeELH peptide into mature individuals induced egg-laying. ELH gene expression in the brain and body was mapped usingin-situhybridization chain reaction. Across the adult brain, 300-400 neurons expressed ELH. Twenty-one different cell types were identified in adults, three of which were located unilaterally on the right side, which corresponds to the location of the reproductive organs. Ten cell types were present in pre-reproductive juvenile stages. An asymmetric cluster of approximately 100 small neurons appeared in the right pedal ganglion of late-stage juveniles. Additional neurons in the pleural and pedal ganglia expressed ELH only in adults that were actively laying eggs and sub-adults that were on the verge of doing so, implicating their direct role in reproduction. Outside the brain, ELH was expressed on sensory appendages, including in presumptive sensory neurons. ELH shares deep homology with the corticotropin-releasing hormone gene family, which has roles broadly in stress response. Its widespread expression in the nudibranchB. stephanieaesuggests that ELH plays a role beyond reproduction in gastropod molluscs.HighlightsEgg-laying hormone (ELH) preprohormone sequences were identified in the transcriptomes of several nudipleuran molluscs.Many ELH-expressing neurons and clusters in the brain could be individually identified based on soma position and morphology.Some identified ELH-expressing neurons were not observed until later juvenile stages.Some neurons expressed ELH in adults only when they were actively laying eggs.Many hundreds of ELH-expressing neurons were present in peripheral appendages.
Classifying breast cancer and fibroadenoma tissue biopsies from paraffined stain-free slides by fractal biomarkers in Fourier Ptychographic Microscopy
Authors: Bianco Vittorio , Valentino Marika , Pirone Daniele , Miccio Lisa , Memmolo Pasquale , Brancato Valentina , Coppola Luigi , Smaldone Giovanni , D’Aiuto Massimiliano , Mossetti Gennaro , Salvatore Marco , Ferraro Pietro 
Published: December 2023

AbstractBreast cancer is one of the most spread and monitored pathologies in high-income countries. After breast biopsy, histological tissue is stored in paraffin, sectioned and mounted. Conventional inspection of tissue slides under benchtop light microscopes involves paraffin removal and staining, typically with H&E. Then, expert pathologists are called to judge the stained slides. However, paraffin removal and staining are operator-dependent, time and resources consuming processes that can generate ambiguities due to non-uniform staining. Here we propose a novel method that can work directly on paraffined stain-free slides. The method is automatic, independent from the operator, and provides classification of different portions of the tissue image with very high accuracy. Besides, it returns a guide map to help pathologist to judge the different tissue portions based on the likelihood these can be associated to a breast cancer or fibroadenoma biomarker. We use Fourier Ptychography as a quantitative phase-contrast microscopy method, which allows accessing a very wide field of view (i.e., square millimeters) in one single image while guaranteeing at the same time high lateral resolution (i.e., 0.5 microns). This imaging method is multi-scale, since it enables looking at the big picture, i.e. the complex tissue structure and connections, with the possibility to zoom-in up to the single cell level. In order to handle this informative image content, we introduce elements of fractal geometry as a multi-scale analysis method. We show the effectiveness of fractal features in describing fibroadenoma and breast cancer from six patients with very high accuracy. The proposed method could significantly simplify the steps of tissue analysis and make it independent from the sample preparation, the skills of the lab operator and the pathologist.
Biodiversity in mountain soils above the treeline
Authors: Praeg Nadine , Steinwandter Michael , Urbach Davnah , Snethlage Mark A. , Alves Rodrigo P. , Apple Martha E. , Britton Andrea J. , Bruni Estelle P. , Chen Ting-Wen , Dumack Kenneth , Fernandez-Mendoza Fernando , Freppaz Michele , Frey Beat , Fromin Nathalie , Geisen Stefan , Grube Martin , Guariento Elia , Guisan Antoine , Ji Qiao-Qiao , Jiménez Juan J. , Maier Stefanie , Malard Lucie A. , Minor Maria A. , Mc Lean Cowan C. , Mitchell Edward A. D. , Peham Thomas , Pizzolotto Roberto , Taylor Andy F. S. , Vernon Philippe , van Tol Johan J. , Wu Yunga , Wu Donghui , Xie Zhijing , Weber Bettina , Illmer Paul , Seeber Julia 
Published: December 2023

ABSTRACTDespite the importance of healthy soils for human livelihood, wellbeing, and safety, current gaps in our knowledge and understanding of biodiversity in soil are numerous, undermining conservation efforts. These gaps are particularly wide in mountain regions where healthy soils are especially important for human safety and yet evidence is accumulating of ongoing degradation, posing significant threats to ecosystem functioning and human settlements.To analyse these gaps in detail, we synthesise current research on the global diversity of microorganisms, cryptogams, and invertebrates in mountain soils above the treeline. This synthesis is based on a semi-quantitative survey of the literature and an expert-based analysis. Our work reveals not only deficiencies in geographic cover but also significant gaps in taxonomic coverage, particularly among soil protists and invertebrates, and a lack of (functional and ecological) description of the uncultivated majority of prokaryotes, fungi, and protists. We subsequently build on this overview to highlight opportunities for research on mountain soils as systems of co-occurring species that interact in complex environmental matrices to fulfil critical functions and make essential contributions to life on land.Closing gaps in biodiversity research in mountain soil is crucial to enhance our understanding and to promote laws and guidelines advancing international soil biodiversity conservation targets in mountains. Addressing sparse and biased data, recognizing the impact of environmental changes on mountain ecosystems, and advocating dedicated policies are essential strategies to safeguard mountain soils and their biodiversity.GLOSSARY
The human pupil and face encode sound affect and provide objective signatures of tinnitus and auditory hypersensitivity disorders
Authors: Smith Samuel S. , Jahn Kelly N. , Sugai Jenna A. , Hancock Ken E. , Polley Daniel B. 
Published: December 2023

SummarySound is jointly processed along acoustic and emotional dimensions. These dimensions can become distorted and entangled in persons with sensory disorders, producing a spectrum of loudness hypersensitivity, phantom percepts, and – in some cases – debilitating sound aversion. Here, we looked for objective signatures of disordered hearing (DH) in the human face. Pupil dilations and micro facial movement amplitudes scaled with sound valence in neurotypical listeners but not DH participants with chronic tinnitus (phantom ringing) and sound sensitivity. In DH participants, emotionally evocative sounds elicited abnormally large pupil dilations but blunted and invariant facial reactions that jointly provided an accurate prediction of individual tinnitus and hyperacusis questionnaire handicap scores. By contrast, EEG measures of central auditory gain identified steeper neural response growth functions but no association with symptom severity. These findings highlight dysregulated affective sound processing in persons with bothersome tinnitus and sound sensitivity disorders and introduce approaches for their objective measurement.
ANTIPASTI: interpretable prediction of antibody binding affinity exploiting Normal Modes and Deep Learning
Authors: Michalewicz Kevin , Barahona Mauricio , Bravi Barbara 
Published: December 2023

SummaryThe high binding affinity of antibodies towards their cognate targets is key to eliciting effective immune responses, as well as to the use of antibodies as research and therapeutic tools. Here, we propose ANTIPASTI, a Convolutional Neural Network model that achieves state-of-the-art performance in the prediction of antibody binding affinity using as input a representation of antibody-antigen structures in terms of Normal Mode correlation maps derived from Elastic Network Models. This representation captures not only structural features but energetic patterns of local and global residue fluctuations. The learnt representations are interpretable: they reveal similarities of binding patterns among antibodies targeting the same antigen type, and can be used to quantify the importance of antibody regions contributing to binding affinity. Our results show the importance of the antigen imprint in the Normal Mode landscape, and the dominance of cooperative effects and long-range correlations between antibody regions to determine binding affinity.
The clouded mother-of-pearlProtogoniomorpha anacardiiLinnaeus, 1758 (Lepidoptera: Nymphalidae) – an updated record of butterfly for Saudi Arabia
Authors: Alfakih Ateah , Al Faify Eisa Ali 
Published: December 2023

AbstractThe record of the clouded mother-of-pearlProtogoniomorpha anacardiiLinnaeus, 1758 (Lepidoptera: Nymphalidae) was updated in Saudi Arabia. At this stage, we confirmed the presence of this species in the southernmost part of Saudi Arabia based on photographs taken between 2020–2023 from Fayfa and the adjacent mountains in Jazan. The photographs match the descriptions and remarks made by Dr Torben Larsen in multiple publications, which suggest that this taxon represents the widespread subspeciesP. a. nebulosaTrimen, 1881. Here, we discuss the Saudi Arabian populations ofP. a. nebulosaand its conservation in depth and encourage more research on butterflies in Saudi Arabia.
Horizontal transfers between fungalFusariumspecies contributed to successive outbreaks of coffee wilt disease
Authors: Peck Lily D. , Llewellyn Theo , Bennetot Bastien , O’Donnell Samuel , Nowell Reuben W. , Ryan Matthew J. , Flood Julie , Rodŕıguez de la Vega Ricardo C. , Ropars Jeanne , Giraud Tatiana , Spanu Pietro D. , Barraclough Timothy G. 
Published: December 2023

AbstractOutbreaks of fungal disease have devastated plants and animals throughout history. Over the past century, the repeated emergence of coffee wilt disease caused by the fungal pathogenFusarium xylarioidesseverely impacted coffee production across sub-Saharan Africa. To improve the disease management of such pathogens, it is crucial to understand their genetic structure and evolutionary potential. We compared the genomes of 13 historic strains spanning six decades and multiple disease outbreaks to investigate population structure and host specialisation. We foundF. xylarioidescomprises at least four distinct lineages: one host-specific toCoffea arabica, one toC. canephoravar.robusta, and two historic lineages isolated from variousCoffeaspecies. Mapping variation onto a new long-read reference genome showed that host-specificity appears to be acquired through horizontal transfer of effector genes from members of theF. oxysporumspecies complex. This species complex is known to cause wilt disease in over 100 plant species. Multiple transfers into theF. xylarioidespopulations matched to different parts of theF. oxysporummobile pathogenicity chromosome and were enriched in effector genes and transposons. Effector genes in this region and other horizontally transferred carbohydrate-active enzymes important in the breakdown of plant cell walls were shown by transcriptomics to be highly expressed during infection ofC. arabicaby the fungal arabica strains. Widespread sharing of specific transposons betweenF. xylarioidesandF. oxysporum, and the presence of largeStarshipelements, indicate that transposons were involved in horizontal transfers. Our results support the hypothesis that horizontal gene transfers contributed to the repeated emergence of this fungal disease.
The Brain Image Library: A Community-Contributed Microscopy Resource for Neuroscientists
Authors: Kenney Mariah , Vasylieva Iaroslavna , Hood Greg , Cao-Berg Ivan , Tuite Luke , Laghaei Rozita , Smith Megan C. , Watson Alan M. , Ropelewski Alexander J. 
Published: December 2023

ABSTRACTAdvancements in microscopy techniques and computing technologies have enabled researchers to digitally reconstruct brains at micron scale. As a result, community efforts like the BRAIN Initiative Cell Census Network (BICCN) have generated thousands of whole-brain imaging datasets to trace neuronal circuitry and comprehensively map cell types. This data holds valuable information that extends beyond initial analyses, opening avenues for variation studies and robust classification of cell types in specific brain regions. However, the size and heterogeneity of these imaging data have historically made storage, sharing, and analysis difficult for individual investigators and impractical on a broad community scale. Here, we introduce the Brain Image Library (BIL), a public resource serving the neuroscience community that provides a persistent centralized repository for brain microscopy data. BIL currently holds thousands of brain datasets and provides an integrated analysis ecosystem, allowing for exploration, visualization, and data access without the need to download, thus encouraging scientific discovery and data reuse.
Single-nucleus transcriptome atlas of orbitofrontal cortex in amyotrophic lateral sclerosis with a deep learning-based decoding of alternative polyadenylation mechanisms
Authors: McKeever Paul M. , Sababi Aiden M. , Sharma Raghav , Xu Zhiyu , Xiao Shangxi , McGoldrick Philip , Ketela Troy , Sato Christine , Moreno Danielle , Visanji Naomi , Kovacs Gabor G. , Keith Julia , Zinman Lorne , Rogaeva Ekaterina , Goodarzi Hani , Bader Gary D. , Robertson Janice 
Published: December 2023

AbstractAmyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are two age-related and fatal neurodegenerative disorders that lie on a shared disease spectrum. While both disorders involve complex interactions between neuronal and glial cells, the specific cell-type alterations and their contributions to disease pathophysiology remain incompletely understood. Here, we applied single-nucleus RNA sequencing of the orbitofrontal cortex, a region affected in ALS-FTLD, to map cell-type specific transcriptional signatures in C9orf72-related ALS (with and without FTLD) and sporadic ALS cases. Our findings reveal disease- and cell-type-specific transcriptional changes, with neurons exhibiting the most pronounced alterations, primarily affecting mitochondrial function, protein homeostasis, and chromatin remodeling. A comparison with independent datasets from different cortical regions of C9orf72 and sporadic ALS cases showed concordance in several pathways, with neuronal STMN2 and NEFL showing consistent up-regulation between brain regions and disease subtypes. We also interrogated alternative polyadenylation (APA) as an additional layer of transcriptional regulation, demonstrating that APA events are not correlated with identified gene expression changes. To interpret these events, we developed APA-Net, a deep learning model that integrates transcript sequences with RNA-binding protein expression profiles, revealing cell type-specific patterns of APA regulation. Our atlas illuminates cell type-specific pathomechanisms of ALS/FTLD, providing a valuable resource for further investigation.