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Genome of the enigmatic watering-pot shell and morphological adaptations for anchoring in sediment

BMC Genomics 2025年第1期26卷 1-10页

作者： Sigwart, Julia D. Wong, Nur Leena W.S. González, Vanessa Liz Machado, Fabrizio Marcondes Greve, Carola Schell, Tilman Chen, Zeyuan Senckenberg Research Institute and Natural History Museum Frankfurt Frankfurt Germany Institute of Ecology Evolution & Diversity Goethe University Frankfurt Germany International Institute of Aquaculture and Aquatic Sciences Universiti Putra Malaysia Port Dickson Malaysia Informatics and Data Science Center Smithsonian Institution National Museum of Natural History Washington DC United States Departamento de Biologia Animal Universidade Estadual de Campinas (UNICAMP) São Paulo Brazil LOEWE Centre for Translational Biodiversity Genomics Frankfurt Germany

Background: In this study, we present the first chromosome-scale genome of Verpa penis (Linnaeus, 1758), and the first for the bivalve clade Anomalodesmata. The present study has two separate foci. Primarily, we provide the genetic resource to bridge further studies from genome to phenome and propose hypotheses to guide future empirical investigations. Secondarily, based on morphology, we outline a conceptual exploration to address their adaptation. Watering-pot shells have been called “the weirdest bivalves” for their fused tubular shell resembling the spout of a watering can. This adventitious tube arose twice convergently in clavagelloidean bivalves. However, previous literature has never provided a convincing adaptive pathway. Results: The genome assembly of V. penis was about 507 Mb, with contig N50 of 5.33 Mb, and has 96.5% of sequences anchored onto 19 pseudochromosomes. Phylogenomic analyses of this new genome in context of other bivalves confirms the placement for Anomalodesmata as sister to the clade Imparidentia. Contrary to expectations from its highly modified body plan, there is no evidence of chromosome reduction compared to the ancestral karyotype of heterodont bivalves (1 N = 19). Drawing on established principles from engineering as well as morphology, the thought experiment about the adventitious tube seeks to extend current understanding by exploring parallels with other built structures. A new hypothesis explains one possible interpretation of the adaptive significance of this body form: it is potentially structurally optimised for vertical stability in relatively soft sediments, with parallels to the engineering principles of a suction anchor. Conclusions: While the conclusions presented here on morphological interpretations are theoretical, this serves as a foundation for further empirical validation and refinement. Our study offers new insights to a long-standing mystery in molluscan body forms and provides genomic resources that are relevant

关键词： Bivalvia Clavagelloidea Phylogenomics Suction Caisson

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Spontaneous Brain Dynamics Associated with Acceleration of Long-Term Functional Connectome in Postnatal Development

Spontaneous Brain Dynamics Associated with Acceleration of L...

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IEEE International Symposium on Biomedical Imaging

作者： Liang Ma Sarah Shultz Zening Fu Masoud Seraji Armin Iraji Vince Calhoun Tri-Institutional Georgia State University/Georgia Institute of Technology/Emory University Center for Translational Research in Neuroimaging and Data Science Atlanta GA USA Marcus Autism Center Children's Healthcare of Atlanta Atlanta GA USA Department of Pediatrics Division of Autism & Related Disabilities Emory University School of Medicine Atlanta GA USA School of Psychology University of Texas at Austin Austin USA Department of Computer Science Georgia State University Atlanta GA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The first six postnatal months are a critical period for brain development, marked by rapid changes in functional neural circuits. However, long-term changes in neonatal functional connectome lacks an interpretive imaging indicator for the future development due to the non-linearity characteristics. In this study, we introduce an approach to extract intrinsic brain states from short-term brain dynamics to study the long-term (longitudinal) development. We found a high association (r=0.460) between the co-activated pattern of specific brain state and the acceleration pattern of non-linear development of static functional connectome. The fractional occupancy, self-sustaining probability of this short-term state share the similar age tendency with the long-term change rate within the majority of the function connectome. These findings suggest that short-term brain dynamics could serve as potential biomarkers for predicting the long-term development of functional connectome.

关键词： Pediatrics Neural circuits Independent component analysis Biomarkers Biomedical imaging

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Large language models:game-changers in the healthcare industry

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science Bulletin 2025年第3期70卷 283-286页

作者： Bin Dong Li Zhang Jiajia Yuan Yang Chen Quanzheng Li Lin Shen Beijing International Center for Mathematical Research Peking UniversityBeijing 100871China Center for Machine Learning Research Peking UniversityBeijing 100871China Peking University Changsha Institute for Computing and Digital Economy Changsha 410205China Center for Data Science Peking UniversityBeijing 100871China Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing)Peking University Cancer Hospital and InstituteBeijing 100142China Massachusetts General Hospital Boston 02114-2696USA Harvard Medical School Boston 02115USA

The healthcare industry faces core challenges,including increasingly complex operational processes and entities,the rapid development of medical knowledge,and the rising demand for interdisciplinary *** complexity of medical processes is evident in every aspect,from patient appointments,diagnoses,and treatments to follow-up tasks,all of which involve intricate data processing and decision-making procedures.

关键词： industry expertise evident

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AI-DRIVEN AUTOMATED TOOL FOR ABDOMINAL CT BODY COMPOSITION ANALYSIS IN GASTROINTESTINAL CANCER MANAGEMENT

arXiv

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arXiv 2025年

作者： Nan, Xinyu He, Meng Chen, Zifan Dong, Bin Tang, Lei Zhang, Li Center for Data Science Peking University China Department of Radiology Key Laboratory of Carcinogenesis and Translational Research Ministry of Education Peking University Cancer Hospital and Institute Beijing China Peking University Beijing China Center for Machine Learning Research Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China

The incidence of gastrointestinal cancers remains significantly high, particularly in China, emphasizing the importance of accurate prognostic assessments and effective treatment strategies. research shows a strong correlation between abdominal muscle and fat tissue composition and patient outcomes. However, existing manual methods for analyzing abdominal tissue composition are time-consuming and costly, limiting clinical research scalability. To address these challenges, we developed an AI-driven tool for automated analysis of abdominal CT scans to effectively identify and segment muscle, subcutaneous fat, and visceral fat. Our tool integrates a multi-view localization model and a high-precision 2D nnUNet-based segmentation model, demonstrating a localization accuracy of 90% and a Dice Score Coefficient of 0.967 for segmentation. Furthermore, it features an interactive interface that allows clinicians to refine the segmentation results, ensuring high-quality outcomes effectively. Our tool offers a standardized method for effectively extracting critical abdominal tissues, potentially enhancing the management and treatment for gastrointestinal cancers. The code is available at https://***/NanXinyu/***. Copyright © 2025, The Authors. All rights reserved.

关键词： Diseases

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Ai-Driven Automated Tool for Abdominal CT Body Composition Analysis in Gastrointestinal Cancer Management

Ai-Driven Automated Tool for Abdominal CT Body Composition A...

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IEEE International Symposium on Biomedical Imaging

作者： Xinyu Nan Meng He Zifan Chen Bin Dong Lei Tang Li Zhang Center for Data Science Peking University China Department of Radiology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education) Peking University Cancer Hospital and Institute Beijing China Beijing International Center for Mathematical Research (BICMR) Peking University Beijing China Center for Machine Learning Research Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

关键词： Location awareness Analytical models Accuracy Computed tomography Scalability Manuals Muscles Gastrointestinal tract Fats Cancer

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B-type natriuretic peptide efficacy compared to fragmented QRS for diastolic dysfunction screening in patients with type 2 diabetes

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World Journal of Diabetes 2025年第4期16卷 84-95页

作者： Kunimasa Yagi Daisuke Chujo Isao Usui Jian-Hui Liu Atsushi Nohara Asako Enkaku Shirozu Akiko Takikawa Hisae Honoki Shiho Fujisaka Hideki Origasa Hayato Tada Department of Internal Medicine Kanazawa Medical University HospitalKahoku 920-0293IshikawaJapan Center for Clinical and Translational Research Toyama University HospitalToyama 930-0152ToyamaJapan Department of Endocrinology and Metabolism Dokkyo Medical UniversityUtsunomiya 321-0293TochigiJapan Department of Cardiology Ningbo Medical Center of Lihuili HospitalNingbo 315041Zhejiang ProvinceChina Department of Clinical Genetics Ishikawa Prefectural Central HospitalKanazawa 920-8530IshikawaJapan The First Department of Internal Medicine Toyama University HospitalToyama 930-0152ToyamaJapan Data Science and AI Innovation Research Promotion Center Institute of Statistical MathematicsShiga UniversityHikone 525-0034ShigaJapan Division of Cardiovascular Medicine Kanazawa UniversityGraduate School of MedicineKanazawa 920-8640IshikawaJapan

BACKGROUND Early diagnosis of left ventricular diastolic dysfunction(LVDD)is essential for preventing heart failure.B-type natriuretic peptide(BNP)is a viable marker for predicting LVDD,as elevated BNP levels have been associated with worsening LVDD in patients with diabetes over ***,the utility of BNP as a diagnostic marker in diabetes is controversial,as BNP levels are often low in overweight *** To examine the effectiveness of BNP levels and fragmented QRS(fQRS)on electrocardiography for diagnosing LVDD in patients with type 2 *** This retrospective cohort study included 303 patients with type 2 diabetes(67.4±12.3 years old)with preserved ejection fraction(EF)≥50%admitted to Toyama University Hospital for glycemic management and comorbidity evaluation between November 2017 and April *** participants underwent plasma BNP measurement,electrocardiography,and *** who were blinded to the BNP results assessed the electrocardiograms and *** analyses were conducted for overweight *** Receiver operating characteristic(ROC)curve analysis determined optimal BNP cut-off values of 34.8 pg/mL and 7.2 pg/mL for diagnosing LVDD in non-overweight[area under the ROC curve(AUC):0.70]and overweight(AUC:0.55)groups,respectively(P=0.040).In the overweight subgroup,fQRS showed greater diagnostic accuracy for LVDD(AUC:0.67),indicating moderate diagnostic utility compared with the low performance of the BNP cutoff of 35 pg/mL(AUC:0.52)(P=0.010).Multivariate analyses confirmed that fQRS was superior to BNP for LVDD diagnosis regardless of the patient’s *** A BNP level≥35 pg/mL in non-overweight individuals may be a reliable LVDD ***,fQRS was more effective than BNP in diagnosing LVDD irrespective of the patient’s *** can complement BNP in the early detection of LVDD,especially in overweight patients,potentially improving early detection and mitigatin

关键词： B-type natriuretic peptide Diastolic dysfunction Fragmented QRS Heart failure with preserved ejection fraction Overweight Type 2 diabetes

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THIDS: Triplet High-Order Individual Differential Structural Brain Network

THIDS: Triplet High-Order Individual Differential Structural...

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IEEE International Symposium on Biomedical Imaging

作者： Dailin Wen Chunzhi Zhao Tulay Adali Daoqiang Zhang Vince D. Calhoun Shile Qi Department of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China Department of CSEE University of Maryland Baltimore MD USA Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The human brain is a highly integrated network, and the brain network models serve as a simplified representation of this complex system. However, traditional brain network approaches are limited to group-level mean comparisons and first-order interactions, failing to capture the individual differences with higher-order interactions among brain regions in both brain development and heterogeneous psychiatric disorders. Here, we propose a triplet high-order individual differential structural brain network (THIDS), which is a linear combination of the differential (reference-perturbed) and perturbed networks, while utilizing the interaction information among all triplets of brain regions to construct an individual difference structured brain network. Results show that THIDS consistently achieves the highest accuracy in schizophrenia diagnosis, gender classification and cognition prediction across two datasets, four classification models and seven cognitive domains, compared with four alternative brain network construction methods. These results demonstrate that the proposed THIDS approach reliably captures individual differential features in the brain, showing great potential to investigate especially heterogeneous brain disorders and individual brain development.

关键词： Biometrics Accuracy Perturbation methods Schizophrenia Predictive models Brain modeling Cognition Robustness Complex systems Biomedical imaging

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Single-cell technologies: current and near future

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science China(Life sciences) 2025年第1期68卷 1-4页

作者： Chenfei Wang Qi Liu Xiaohui Fan Tieliu Shi KeyLaboratory of Spine and Spinal CordInjury Repairand Regeneration Ministry of EducationOrthopedics DepartmentTongji HospitalBioinformatics DepartmentSchoolofLifeSciencesandTechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(MOE) School of StatisticsEast China Normal UniversityShanghai 200062China

Single-cell technologies enable the indepth exploration of multiple biological hierarchies at the scale of individual cells,which have deepened our knowledge of cellular diversity,tissue organization,and overall organism function(Sun et al.,2024).In 2009,single-cell RNA sequencing(scRNA-seq)was first developed as a powerful tool to dissect gene expression and uncover transcriptional *** the rapid advancement of technology,single-cell approaches have expanded to encompass other omics,such as genomics,epigenomics,proteomics,and ***,multimodal technologies including spatially resolved transcriptomics and clustered regularly interspaced short palindromic repeats(CRISPR)screening at the single-cell level further advanced our ability to comprehensively understand cellular ***-cell methods provide deeper insights into disease mechanisms across various conditions,including cancer,developmental disorders,and aging-associated *** this issue,we focus on topics related to single-cell methods,covering the following four aspects:(i)single-cell multi-omic sequencing technology;(ii)single-cell spatial technology;(iii)singlecell CRISPR screening technology;(iv)applications of single-cell technology.

关键词： expanded enable insight

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Mapping minds not averages: a scalable subject-specific manifold learning framework for neuroimaging data

arXiv

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arXiv 2025年

作者： Geenjaar, Eloy Calhoun, Vince Georgia Institute of Technology United States Translational Research in Neuroimaging & Data Science Center Georgia State University Georgia Institute of Technology Emory University United States

Mental and cognitive representations are believed to reside on low-dimensional, non-linear manifolds embedded within high-dimensional brain activity. Uncovering these manifolds is key to understanding individual differences in brain function, yet most existing machine learning methods either rely on population-level spatial alignment or assume data that is temporally structured, either because data is aligned among subjects or because event timings are known. We introduce a manifold learning framework that can capture subject-specific spatial variations across both structured and temporally unstructured neuroimaging data. On simulated data and two naturalistic fMRI datasets (Sherlock and Forrest Gump), our framework outperforms group-based baselines by recovering more accurate and individualized representations. We further show that the framework scales efficiently to large datasets and generalizes well to new subjects. To test this, we apply the framework to temporally unstructured resting-state fMRI data from individuals with schizophrenia and healthy controls. We further apply our method to a large resting-state fMRI dataset comprising individuals with schizophrenia and controls. In this setting, we demonstrate that the framework scales efficiently to large populations and generalizes robustly to unseen subjects. The learned subject-specific spatial maps our model finds reveal clinically relevant patterns, including increased activation in the basal ganglia, visual, auditory, and somatosensory regions, and decreased activation in the insula, inferior frontal gyrus, and angular gyrus. These findings suggest that our framework can uncover clinically relevant subject-specific brain activity patterns. Our approach thus provides a scalable and individualized framework for modeling brain activity, with applications in computational neuroscience and clinical research. © 2025, CC BY.

关键词： Brain mapping

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Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

Longitudinal Segmentation of MS Lesions via Temporal Diffe...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Rokuss, Maximilian R. Kirchhoff, Yannick Roy, Saikat Kovacs, Balint Ulrich, Constantin Wald, Tassilo Zenk, Maximilian Denner, Stefan Isensee, Fabian Vollmuth, Philipp Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Helmholtz Imaging German Cancer Research Center Heidelberg Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Neuroimaging Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031845246

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level F1 score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at: ***/MIC-DKFZ/Longitudinal-Difference-Weighting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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