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Single-cell long-read sequencing-based mapping reveals specialized splicing patterns in developing and adult mouse and human brain

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Nature Neuroscience 2024年第6期27卷 1051-1063页

作者： Joglekar, Anoushka Hu, Wen Zhang, Bei Narykov, Oleksandr Diekhans, Mark Marrocco, Jordan Balacco, Jennifer Ndhlovu, Lishomwa C. Milner, Teresa A. Fedrigo, Olivier Jarvis, Erich D. Sheynkman, Gloria Korkin, Dmitry Ross, M. Elizabeth Tilgner, Hagen U. Feil Family Brain and Mind Research Institute Weill Cornell Medicine New York NY United States Center for Neurogenetics Weill Cornell Medicine New York NY United States New York Genome Center New York NY United States Spatial Genomics Inc. Pasadena CA United States Bioinformatics and Computational Biology Program Worcester Polytechnic Institute Worcester MA United States Computer Science Department Worcester Polytechnic Institute Worcester MA United States Data Science Program Worcester Polytechnic Institute Worcester MA United States UC Genomics Institute University of California Santa Cruz Santa Cruz CA United States Department of Biology Touro University New York NY United States Laboratory of Neuroendocrinology The Rockefeller University New York NY United States Vertebrate Genome Lab The Rockefeller University New York NY United States Department of Medicine Division of Infectious Diseases Weill Cornell Medicine New York NY United States Laboratory of Neurogenetics of Language The Rockefeller University New York NY United States Howard Hughes Medical Institute Chevy Chase MD United States Department of Molecular Physiology and Biological Physics University of Virginia Charlottesville VA United States Department of Biochemistry and Molecular Genetics University of Virginia Charlottesville VA United States Center for Public Health Genomics University of Virginia Charlottesville VA United States UVA Comprehensive Cancer Center University of Virginia Charlottesville VA United States

RNA isoforms influence cell identity and function. However, a comprehensive brain isoform map was lacking. We analyze single-cell RNA isoforms across brain regions, cell subtypes, developmental time points and species. For 72% of genes, full-length isoform expression varies along one or more axes. Splicing, transcription start and polyadenylation sites vary strongly between cell types, influence protein architecture and associate with disease-linked variation. Additionally, neurotransmitter transport and synapse turnover genes harbor cell-type variability across anatomical regions. Regulation of cell-type-specific splicing is pronounced in the postnatal day 21-to-postnatal day 28 adolescent transition. Developmental isoform regulation is stronger than regional regulation for the same cell type. Cell-type-specific isoform regulation in mice is mostly maintained in the human hippocampus, allowing extrapolation to the human brain. Conversely, the human brain harbors additional cell-type specificity, suggesting gain-of-function isoforms. Together, this detailed single-cell atlas of full-length isoform regulation across development, anatomical regions and species reveals an unappreciated degree of isoform variability across multiple axes. © The Author(s) 2024.

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Local Neighborhood-Based Adaptation of Weights in Multi-Objective Evolutionary Algorithms Based on Decomposition

Local Neighborhood-Based Adaptation of Weights in Multi-Obje...

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Congress on Evolutionary Computation

作者： Paulo Pinheiro Junqueira Ivan Reinaldo Meneghini Frederico Gadelha Guimarães Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais UFMG Belo Horizonte MG Brazil Federal Institute of Education Science and Technology of Minas Gerais Ibirité Brazil Machine Intelligence and Data Science Laboratory (MINDS) Universidade Federal de Minas Gerais Belo Horizonte Brazil

Multi-objective algorithms based on decomposition have become popular for the reason that a uniform distribution of weight vectors may result in a better distribution of solutions along the Pareto front. However, for more complex Pareto fronts with irregular shapes, the initial weight vectors may not be adequate. One alternative to overcome this problem, is to adapt the weight vectors during the evolutionary process. In this paper an adaptive version of Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is proposed to change the weight vectors based on the concept of local neighborhoods. The proposed method is called MOEA/D with local-neighborhood-based adaptation (MOEA/D-LNA). The proposed method is compared against a number of famous variants of MOEA/D in the literature. Initial experimental results have shown promising effectiveness on problems with irregular Pareto shapes.

关键词： Shape Heuristic algorithms Evolutionary computation Standards

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Perivascular space Identification Nnunet for Generalised Usage (PINGU)

arXiv

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arXiv 2024年

作者： Sinclair, Benjamin Vivash, Lucy Moses, Jasmine Lynch, Miranda Pham, William Dorfman, Karina Marotta, Cassandra Koh, Shaun Bunyamin, Jacob Rowsthorn, Ella Jarema, Alex Peiris, Himashi Chen, Zhaolin Shultz, Sandy R. Wright, David K. Kong, Dexiao Naismith, Sharon L. O’Brien, Terence J. Law, Meng Department of Neuroscience The School of Translational Medicine Monash University Melbourne Australia Department of Radiology Alfred Health Melbourne Australia Monash Biomedical Imaging. Monash University Melbourne Australia School of Psychology Faculty of Science University of Sydney Sydney Australia Department of Neurology Alfred Health Melbourne Australia Centre for Trauma and Mental Health Vancouver Island University Nanaimo Canada Department of Data Science and AI Monash University Melbourne Australia Department of Medicine The Royal Melbourne Hospital The University of Melbourne Parkville Australia Healthy Brain Ageing Program Brain and Mind Centre University of Sydney CamperdownNSW2050 Australia Charles Perkins Centre University of Sydney CamperdownNSW2050 Australia

Perivascular spaces (PVSs) form a central component of the brain’s waste clearance system, the glymphatic system. These structures are visible on MRIs when enlarged, and their morphology is associated with aging and neurological disease. Manual quantification of PVS is time consuming and subjective. Numerous deep learning methods for PVS segmentation have been developed for automated segmentation. However, the majority of these algorithms have been developed and evaluated on homogenous datasets and high resolution scans, perhaps limiting their applicability for the wide range of image qualities acquired in clinical and research settings. In this work we train a nnUNet, a top-performing task driven biomedical image segmentation deep learning algorithm, on a heterogenous training sample of manually segmented MRIs of a range of different qualities and resolutions from 6 different datasets acquired on 5 different scanners. These are compared to the two currently publicly available deep learning methods for 3D segmentation of PVS, evaluated on scans with a range of resolutions and qualities. The resulting model, PINGU (Perivascular space Identification Nnunet for Generalised Usage), achieved voxel and cluster level dice scores of 0.50(SD=0.15) and 0.63(0.17) in the white matter (WM), and 0.54 (0.11) and 0.66(0.17) in the basal ganglia (BG). Performance on unseen data was substantially lower for both PINGU (0.20-0.38 [WM, voxel], 0.29-0.58 [WM, cluster], 0.22-0.36 [BG, voxel], 0.46-0.60 [BG, cluster]) and the publicly available algorithms (0.18-0.30 [WM, voxel], 0.29-0.38 [WM cluster], 0.10-0.20 [BG, voxel], 0.15-0.37 [BG, cluster]). Nonetheless, PINGU strongly outperformed the publicly available algorithms, particularly in the BG. Finally, training PINGU on manual segmentations from a single site with homogenous scan properties (as opposed to training on the larger, multi-site dataset) gave marginally lower performances on internal cross-validation, but in some cases gav

关键词： Learning algorithms

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AI-based Automatic Segmentation of Prostate on Multi-modality Images: A Review

arXiv

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arXiv 2024年

作者： Jin, Rui Li, Derun Xiang, Dehui Zhang, Lei Zhou, Hailing Shi, Fei Zhu, Weifang Cai, Jing Peng, Tao Chen, Xinjian School of Future Science and Engineering Soochow University Suzhou China Department of Urology Peking University First Hospital Xicheng District Beijing100034 China MIPAV Lab The School of Electronic and Information Engineering Soochow University Jiangsu China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Jiangsu Kunshan215316 China Department of Mechanical Engineering and Product Design Engineering Swinburne University of Technology Melbourne Australia Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States The State Key Laboratory of Radiation Medicine and Protection Soochow University Jiangsu China

Prostate cancer represents a major threat to health. Early detection is vital in reducing the mortality rate among prostate cancer patients. One approach involves using multi-modality (CT, MRI, US, etc.) computer-aided diagnosis (CAD) systems for the prostate region. However, prostate segmentation is challenging due to imperfections in the images and the prostate’s complex tissue structure. The advent of precision medicine and a significant increase in clinical capacity have spurred the need for various data-driven tasks in the field of medical imaging. Recently, numerous machine learning and data mining tools have been integrated into various medical areas, including image segmentation. This article proposes a new classification method that differentiates supervision types, either in number or kind, during the training phase. Subsequently, we conducted a survey on artificial intelligence (AI)-based automatic prostate segmentation methods, examining the advantages and limitations of each. Additionally, we introduce variants of evaluation metrics for the verification and performance assessment of the segmentation method and summarize the current challenges. Finally, future research directions and development trends are discussed, reflecting the outcomes of our literature survey, suggesting high-precision detection and treatment of prostate cancer as a promising avenue. Impact Statement — Prostate cancer remains a significant health threat, necessitating early detection to reduce mortality rates. Current diagnostic approaches, particularly in medical imaging, face challenges in accurate prostate segmentation due to the organ's complex structure and imaging imperfections. Our review paper delves into AI-based automatic segmentation methods across various imaging modalities (CT, MRI, US), emphasizing their strengths, weaknesses, and the specific challenges they address. By classifying segmentation techniques based on supervision levels, we provide a comprehensive analys

关键词： Computer aided diagnosis

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Balanced background and explanation data are needed in explaining deep learning models with SHAP: An empirical study on clinical decision making

arXiv

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arXiv 2022年

作者： Liu, Mingxuan Ning, Yilin Yuan, Han Ong, Marcus Eng Hock Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Department of Emergency Medicine Singapore General Hospital Singapore SingHealth AI Health Program Singapore Health Services Singapore Institute of Data Science National University of Singapore Singapore

Objective: Shapley additive explanations (SHAP) is a popular post-hoc technique for explaining black box models. While the impact of data imbalance on predictive models has been extensively studied, it remains largely unknown with respect to SHAP-based model explanations. This study sought to investigate the effects of data imbalance on SHAP explanations for deep learning models, and to propose a strategy to mitigate these effects. Materials and Methods: We propose to adjust class distributions in the background and explanation data in SHAP when explaining black box models. Our data balancing strategy is to compose background data and explanation data with an equal distribution of classes. To evaluate the effects of data adjustment on model explanation, we propose to use the beeswarm plot as a qualitative tool to identify "abnormal" explanation artifacts, and quantitatively test the consistency between variable importance and prediction power. We demonstrated our proposed approach in an empirical study that predicted inpatient mortality using the Medical Information Mart for Intensive Care (MIMIC-III) data and a multilayer perceptron. Results: Using the data balancing strategy would allow us to reduce the number of the artifacts in the beeswarm plot, thus mitigating the negative effects of data imbalance. Additionally, with the balancing strategy, the top-ranked variables from the corresponding importance ranking demonstrated improved discrimination power. Discussion and Conclusion: Our findings suggest that balanced background and explanation data could help reduce the noise in explanation results induced by skewed data distribution and improve the reliability of variable importance ranking. Furthermore, these balancing procedures improve the potential of SHAP in identifying patients with abnormal characteristics in clinical applications. © 2022, CC BY.

关键词： Decision making

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Herb compounds screening as meningitis inhibitor candidates using neural network and random forest methods

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AIP Conference Proceedings 2025年第1期3250卷

作者： Riska Aprilia Mohammad Hamim Zajuli Al Faroby Muhammad Adib Kamali Muhammad Dzulfikar Fauzi Information Technology Study Program Faculty of Information Technology and Business Institut Teknologi Telkom Surabaya Surabaya Indonesia Data Science Study Program Faculty of Information Technology and Business Institut Teknologi Telkom Surabaya Surabaya Indonesia Informatics Study Program Faculty of Information Technology and Business Institut Teknologi Telkom Surabaya Surabaya Indonesia

Meningitis is an inflammation of the meninges that occurs in the protective lining of the brain and spinal cord caused by bacterial, viral, or fungal infections. This disease is difficult to recognize because it has initial symptoms like the flu where the patient has a fever and headache. Current efforts to prevent the disease by strengthening antibodies. Meanwhile, drug candidates for the treatment of this disease still have not found optimal results in reducing mortality due to meningitis. This study aims to find and analyses herbal compound candidates that might be inhibitors of meningitis. Compound data was acquired from a validated open database. The data acquired are smiles of the chemical bond structure of the compounds. In the data processing process, compound feature extraction is required by applying the concept of molecular fingerprint. The results of feature extraction are used as datasets to build classification models by applying the Multilayer Perceptron (MLP) and Random Forest algorithms. The two models are compared, and a more robust model is selected to be used as a prediction model for herbal compound search. The MLP model has a better accuracy of 0.97 compared to the Random Forest model. The results of screening using the MLP learning model obtained Symphytine, cis-Linalool oxide, and 3-O-Methylcalopocarpin compounds have the highest probability compared to thousands of other herbal compounds. This candidate compound can be used as a recommendation for drug discovery to treat patients who contract Meningitis.

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C2-GaMe: Classification of Cluster Galaxy Membership with Machine Learning

arXiv

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arXiv 2022年

作者： Farid, Daniel Aung, Han Nagai, Daisuke Farahi, Arya Rozo, Eduardo Program in Applied Mathematics Yale University New HavenCT06511 United States Department of Physics Yale University New HavenCT06520 United States Department of Statistics and Data Science The University of Texas AustinTX78712 United States Department of Physics University of Arizona TucsonAZ85721 United States

We present Classification of Cluster Galaxy Members (C2-GaMe), a classification algorithm based on a suite of machine learning models that differentiates galaxies into orbiting, infalling, and background (interloper) populations, using phase space information as input. We train and test C2-GaMe with the galaxies from UniverseMachine mock catalog based on Multi-Dark Planck 2 N-body simulations. We show that probabilistic classification is superior to deterministic classification in estimating the physical properties of clusters, including density profiles and velocity dispersion. We propose a set of estimators to get an unbiased estimation of cluster properties. We demonstrate that C2-GaMe can recover the distribution of orbiting and infalling galaxies' position and velocity distribution with Copyright © 2022, The Authors. All rights reserved.

关键词： Galaxies

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Epidemic dynamics on metapopulation networks with node2vec mobility

arXiv

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arXiv 2021年

作者： Meng, Lingqi Masuda, Naoki Department of Mathematics State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York BuffaloNY14260-5030 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

Metapopulation models have been a powerful tool for both theorizing and simulating epidemic dynamics. In a metapopulation model, one considers a network composed of subpopulations and their pairwise connections, and individuals are assumed to migrate from one subpopulation to another obeying a given mobility rule. While how different mobility rules affect epidemic dynamics in metapopulation models has been studied, there have been relatively few efforts on comparison of the effects of simple (i.e., unbiased) random walks and more complex mobility rules. Here we study a susceptible-infectious-susceptible (SIS) dynamics in a metapopulation model, in which individuals obey a parametric second-order random-walk mobility rule called the node2vec. We map the second-order mobility rule of the node2vec to a first-order random walk in a network whose each node is a directed edge connecting a pair of subpopulations and then derive the epidemic threshold. For various networks, we find that the epidemic threshold is large (therefore, epidemic spreading tends to be suppressed) when the individuals infrequently backtrack or infrequently visit the common neighbors of the currently visited and the last visited subpopulations than when the individuals obey the simple random walk. The amount of change in the epidemic threshold induced by the node2vec mobility is in general not as large as, but is sometimes comparable with, the one induced by the change in the diffusion rate for individuals. Copyright © 2021, The Authors. All rights reserved.

关键词： Random processes

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Interpretation and inference for altmetric indicators arising from sparse data statistics

arXiv

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arXiv 2022年

作者： Smolinsky, Lawrence Klingenberg, Bernhard Marx, Brian D. Department of Mathematics Louisiana State University Baton RougeLA United States Department of Mathematics and Statistics Williams College WilliamstownMA United States Graduate Program in Data Science New College of Florida SarasotaFL United States Department of Experimental Statistics Louisiana State University Baton RougeLA United States

In 2018 Bornmann and Haunschild (2018a) introduced a new indicator called the Mantel-Haenszel quotient (MHq) to measure alternative metrics (or altmetrics) of scientometric data. In this article we review the Mantel-Haenszel statistics, point out two errors in the literature, and introduce a new indicator. First, we correct the interpretation of MHq and mention that it is still a meaningful indicator. Second, we correct the variance formula for MHq, which leads to narrower confidence intervals. A simulation study shows the superior performance of our variance estimator and confidence intervals. Since MHq does not match its original description in the literature, we propose a new indicator, the Mantel-Haenszel row risk ratio (MHRR), to meet that need. Interpretation and statistical inference for MHRR are discussed. For both MHRR and MHq, a value greater (less) than one means performance is better (worse) than in the reference set called the *** Codes 62P25 © 2022, CC BY-NC-ND.

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Temporal motifs in patent opposition and collaboration networks

arXiv

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arXiv 2021年

作者： Liu, Penghang Masuda, Naoki Kito, Tomomi Sarıyüce, Ahmet Erdem Department of Computer Science and Engineering State University of New York at Buffalo BuffaloNY United States Department of Mathematics State University of New York at Buffalo BuffaloNY United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY United States Faculty of Science and Engineering Waseda University Tokyo Japan

Patents are intellectual properties that reflect innovative activities of companies and organizations. The literature is rich with the studies that analyze the citations among the patents and the collaboration relations among companies that own the patents. However, the adversarial relations between the patent owners are not as well investigated. One proxy to model such relations is the patent opposition, which is a legal activity in which a company challenges the validity of a patent. Characterizing the patent oppositions, collaborations, and the interplay between them can help better understand the companies’ business strategies. Temporality matters in this context as the order and frequency of oppositions and collaborations characterize their interplay. In this study, we construct a two-layer temporal network to model the patent oppositions and collaborations among the companies. We utilize temporal motifs to analyze the oppositions and collaborations from structural and temporal perspectives. We first characterize the frequent motifs in patent oppositions and investigate how often the companies of different sizes attack other companies. We show that large companies tend to engage in opposition with multiple companies. Then we analyze the temporal interplay between collaborations and oppositions. We find that two adversarial companies are more likely to collaborate in the future than two collaborating companies oppose each other in the future. © 2021, CC BY.

关键词： Patents and inventions

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