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arXiv 2024年

作者： Guo, Boyi Ling, Wodan Kwon, Sang Ho Panwar, Pratibha Ghazanfar, Shila Martinowich, Keri Hicks, Stephanie C. Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States Division of Biostatistics Department of Population Health Sciences Weill Cornell Medicine NY United States Lieber Institute for Brain Development Johns Hopkins Medical Campus BaltimoreMD United States Solomon H. Snyder Department of Neuroscience Johns Hopkins School of Medicine BaltimoreMD United States Biochemistry Cellular and Molecular Biology Graduate Program Johns Hopkins School of Medicine BaltimoreMD United States School of Mathematics and Statistics The University of Sydney NSW2006 Australia Sydney Precision Data Science Centre University of Sydney NSW2006 Australia Charles Perkins Centre The University of Sydney NSW2006 Australia Department of Psychiatry and Behavioral Sciences Johns Hopkins School of Medicine BaltimoreMD United States Johns Hopkins Kavli Neuroscience Discovery Institute BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Center for Computational Biology Johns Hopkins University BaltimoreMD United States Malone Center for Engineering in Healthcare Johns Hopkins University BaltimoreMD United States

Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the development of new computational analysis methods to unlock biological insights. As the cost of generating these data decreases, these technologies provide an exciting opportunity to create large-scale atlases that integrate SRT data across multiple tissues, individuals, species, or phenotypes to perform population-level analyses. Here, we describe unique challenges of varying spatial resolutions in SRT data, as well as highlight the opportunities for standardized preprocessing methods along with computational algorithms amenable to atlas-scale datasets leading to improved sensitivity and reproducibility in the future. © 2024, CC BY-NC-ND.

关键词： Biotic

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Approaching maximum resolution in structured illumination microscopy via accurate noise modeling

Npj imaging

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Npj imaging 2025年第1期3卷 5页

作者： Ayush Saurabh Peter T Brown J Shepard Bryan Iv Zachary R Fox Rory Kruithoff Cristopher Thompson Comert Kural Douglas P Shepherd Steve Pressé Center for Biological Physics Arizona State University Tempe AZ USA. Department of Physics Arizona State University Tempe AZ USA. Computational Science and Engineering Division Oak Ridge National Laboratory Oak Ridge TN USA. Department of Physics The Ohio State University Columbus OH USA. Interdisciplinary Biophysics Graduate Program The Ohio State University Columbus OH USA. School of Molecular Sciences Arizona State University Tempe AZ USA.

Biological images captured by microscopes are characterized by heterogeneous signal-to-noise ratios (SNRs) due to spatially varying photon emission across the field of view convoluted with camera noise. State-of-the-art unsupervised structured illumination microscopy (SIM) reconstruction methods, commonly implemented in the Fourier domain, often do not accurately model this noise. Such methods therefore suffer from high-frequency artifacts, user-dependent choices of smoothness constraints making assumptions on biological features, and unphysical negative values in the recovered fluorescence intensity map. On the other hand, supervised algorithms rely on large datasets for training, and often require retraining for new sample structures. Consequently, achieving high contrast near the maximum theoretical resolution in an unsupervised, physically principled manner remains an open problem. Here, we propose Bayesian-SIM (B-SIM), a Bayesian framework to quantitatively reconstruct SIM data, rectifying these shortcomings by accurately incorporating known noise sources in the spatial domain. To accelerate the reconstruction process, we use the finite extent of the point-spread-function to devise a parallelized Monte Carlo strategy involving chunking and restitching of the inferred fluorescence intensity. We benchmark our framework on both simulated and experimental images, and demonstrate improved contrast permitting feature recovery at up to 25% shorter length scales over state-of-the-art methods at both high- and low SNR. B-SIM enables unsupervised, quantitative, physically accurate reconstruction without the need for labeled training data, democratizing high-quality SIM reconstruction and expands the capabilities of live-cell SIM to lower SNR, potentially revealing biological features in previously inaccessible regimes.

关键词： Biophysics Image processing Imaging and sensing Super-resolution microscopy

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The Waiting-Time Paradox

arXiv

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arXiv 2020年

作者： Masuda, Naoki Porter, Mason A. 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 Department of Mathematics University of California Los Angeles Los AngelesCA United States

Suppose that you’re going to school and arrive at a bus stop. How long do you have to wait before the next bus arrives? Surprisingly, it is longer — possibly much longer — than what the bus schedule suggests intuitively. This phenomenon, called the waiting-time paradox, has a purely mathematical origin. Different buses arrive with different intervals, leading to this paradox. In this article, we look at the waiting-time paradox, explain why it happens, and explore some of its implications (beyond the possibility of being late for school). Copyright © 2020, The Authors. All rights reserved.

关键词： Buses

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BPS2025 - Structural basis of potassium selectivity in kalium channelrhodopsins

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Biophysical Journal 2025年第3期124卷 64a-65a页

作者： Seiya Tajima Yoon Seok Kim YoungJu Jo Peter Y. Wang Joseph M. Paggi Charu Ramakrishnan Shunki Takaramoto Takashi Nagata Masae Konno Kota Katayama Hideki Kandori Ron O. Dror Keiichi Inoue Karl Deisseroth Hideaki Kato Research Center for Advanced Science and Technology The University of Tokyo Tokyo Japan Department of Bioengineering Stanford University Stanford CA USA Department of Computer Science Stanford University Stanford CA USA Cracking the Neural Code Program Stanford University Stanford CA USA The Institute for Solid State Physics The University of Tokyo Kashiwa Japan PRESTO Japan Science and Technology Agency Kawaguchi Japan Department of Life Science and Applied Chemistry Nagoya Institute of Technology Nagoya Japan OptoBioTechnology Research Center Nagoya Institute of Technology Nagoya Japan Institute for Computational and Mathematical Engineering Stanford University Stanford CA USA Howard Hughes Medical Institute Stanford University Stanford CA USA Graduate School of Arts and Sciences The University of Tokyo Tokyo Japan

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Author Correction: Supervised learning of high-confidence phenotypic subpopulations from single-cell data

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Nature Machine Intelligence 2023年 676页

作者： Tao Ren Ling-Yun Wu Canping Chen Susan Liu Shunyi Du Zheng Xia Alexey V. Danilov Xiwei Wu Xiangnan Guan Mara H. Sherman Lisa M. Coussens Paul T. Spellman Andrew C. Adey Gordon B. Mills Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Computational Biology Program Oregon Health & Science University Portland OR USA Department of Biomedical Engineering Oregon Health & Science University Portland OR USA Knight Cancer Institute Oregon Health & Science University Portland OR USA City of Hope National Medical Center Duarte CA USA Department of Oncology Biomarker Development Genentech Inc South San Francisco CA USA Department of Cell Developmental & Cancer Biology Oregon Health & Science University Portland OR USA Cancer Biology & Genetics Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Molecular and Medical Genetics Oregon Health & Science University Portland OR USA Division of Oncological Sciences Knight Cancer Institute Oregon Health & Science University Portland OR USA

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Metrics for benchmarking and uncertainty quantification: Quality, applicability, and a path to best practices for machine learning in chemistry

arXiv

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arXiv 2020年

作者： Vishwakarma, Gaurav Sonpal, Aditya Hachmann, Johannes Department of Chemical and Biological Engineering University at Buffalo State University of New York BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Graduate Program University at Buffalo State University of New York BuffaloNY14260 United States New York State Center of Excellence in Materials Informatics BuffaloNY14203 United States

This review aims to draw attention to two issues of concern when we set out to make machine learning work in the chemical and materials domain, i.e., statistical loss function metrics for the validation and benchmarking of data-derived models, and the uncertainty quantification of predictions made by them. They are often overlooked or underappreciated topics as chemists typically only have limited training in statistics. Aside from helping to assess the quality, reliability, and applicability of a given model, these metrics are also key to comparing the performance of different models and thus for developing guidelines and best practices for the successful application of machine learning in chemistry. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

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On Performance Prediction of Big Data Transfer in High-performance Networks

On Performance Prediction of Big Data Transfer in High-perfo...

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IEEE International Conference on Communications (ICC)

作者： Wuji Liu Daqing Yun Chase Q. Wu Nageswara S.V. Rao Aiqin Hou Wei Shen Department of Computer Science New Jersey Institute of Technology Newark NJ USA Computer and Information Sciences Program Harrisburg University Harrisburg PA USA Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge TN USA School of Information Science and Technology Northwest University Xi’an Shaanxi China School of Informatics Science and Technology Zhejiang Sci-Tech University Hangzhou Zhejiang China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Big data generated by large-scale scientific and industrial applications need to be transferred between different geographical locations for remote storage, processing, and analysis. High-speed dedicated connections provisioned in High-performance Networks (HPNs) are increasingly utilized to carry out such big data transfer. HPN management highly relies on an important capability of performance (mainly throughput) prediction to reserve sufficient bandwidth and meanwhile avoid over-provisioning that may result in unnecessary resource waste. This capability is critical to improving the resource (mainly bandwidth) utilization of dedicated connections and meeting various user requests for data transfer. Conventional methods conduct performance prediction by fitting prior observed transfer history with predefined loss functions, without considering unobservable latent factors such as competing loads on end hosts. Such latent factors also have a significant impact on the application-level data transfer performance, which may result in an inaccurate prediction model. In this paper, we first investigate the impact of latent factors and propose a clustering-based method to eliminate their negative impact on performance prediction. We then develop a robust machine learning-based performance predictor by: i) incorporating the proposed latent factor elimination method into data preprocessing, and ii) adopting a customized domain guided loss function. Extensive experimental results show that our predictor achieves significantly higher prediction accuracy than several other state-of-the-art methods.

关键词： Data transfer Big Data Throughput Bandwidth Measurement Predictive models Protocols

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Immuno-Metabolic Pathways: Investigating Mediators of Major Depressive Disorder and Atherosclerotic Cardiovascular Disease comorbidity

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Biological Psychiatry Global Open science 2025年

作者： Angela Koloi Nabila P.R. Siregar Rick Quax Antonis I. Sakellarios Femke Lamers Arja Rydin Kevin Dobretz Costas Papaloukas Dimitrios I. Fotiadis Jos A. Bosch Unit of Medical Technology and Intelligent Information Systems Department of Materials Science and Engineering University of Ioannina Ioannina Greece Department of Biological Applications and Technology University of Ioannina Ioannina Greece Department of Clinical Psychology University of Amsterdam Amsterdam The Netherlands Computational Science Lab Institute of Informatics University of Amsterdam Amsterdam The Netherlands Biomedical Engineering of the Department of Mechanical Engineering and Aeronautics University of Patras Patras Greece Department of Psychiatry Amsterdam UMC location Vrije Universiteit Amsterdam Boelelaan 1117 Amsterdam The Netherlands Amsterdam Public Health Mental Health Program Amsterdam The Netherlands Cardiology Geneva University Hospitals Geneva Switzerland Department of Medicine University of Geneva Geneva Switzerland Biomedical Research Institute Foundation for Research and Technology - Hellas (FORTH) Ioannina Greece Department of medical Psychology Amsterdam University Medical Centers Amsterdam The Netherlands

Background Major depressive disorder (MDD) and cardiovascular diseases (CVD) often co-occur whereby comorbidity results in poorer clinical outcomes, presumably due to shared immune-metabolic pathways. Identifying shared biomarkers for MDD-CVD comorbidity may provide targets for prevention or treatment. Methods Using data from the Netherlands Study of Depression and Anxiety (NESDA: N=2,256, 66.3% female, mean age 41.75 ± 13.11 years), validated with the UK Biobank data (UKB, N=35,668, 56.14% female, mean age 63.95 ± 7.74 years), this study aimed to identify (i) biomarkers, closely associated with current MDD, and (ii) longitudinal pathways linking MDD and atherosclerotic CVD. Plasma metabolites (NMR, Nightingale) and inflammatory markers were used as exposures within a Machine Learning framework. Influential biomarkers were integrated into a temporal network analysis linking MDD to subsequent CVD, exploring longitudinal pathways through Causal Discovery, validated by sensitivity analysis and centrality assessment. External validation included mediation and regression analysis adjusting for covariates. Results Network analysis identified stable direct paths from MDD to CVD via TNF-α, tyrosine, and fatty acids, and indirect paths via acetate, HDL diameter, IL-6, alpha-1-acid glycoprotein (AGP), hs-CRP, and LDL triglycerides. Among these, acetate, tyrosine, AGP, and HDL diameter potentially mediate the MDD-CVD connection, as these were identified as key nodes within the network. UKB validation confirmed HDL diameter ( β = 0.004) and AGP ( β = 0.003) as significant depression-CVD mediators (both p < .001), after adjusting for age, sex, deprivation index, alcohol consumption, smoking status, physical activity, and BMI. Conclusions These analyses identified biomarkers shared in MDD and CVD and may drive comorbid pathology risk.

关键词： MDD biomarkers CVD Machine Learning mediators

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EMPRESS. X. Spatially resolved mass-metallicity relation in extremely metal-poor galaxies: Evidence of episodic star-formation fueled by a metal-poor gas infall

arXiv

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arXiv 2024年

作者： Nakajima, Kimihiko Ouchi, Masami Isobe, Yuki Xu, Yi Ozaki, Shinobu Nagao, Tohru Inoue, Akio K. Rauch, Michael Kusakabe, Haruka Onodera, Masato Nishigaki, Moka Ono, Yoshiaki Sugahara, Yuma Hattori, Takashi Hirai, Yutaka Hashimoto, Takuya Kim, Ji Hoon Moriya, Takashi J. Yanagisawa, Hiroto Aoyama, Shohei Fujimoto, Seiji Fukushima, Hajime Fukushima, Keita Harikane, Yuichi Hatano, Shun Hayashi, Kohei Ishigaki, Tsuyoshi Kawasaki, Masahiro Kojima, Takashi Komiyama, Yutaka Koyama, Shuhei Koyama, Yusei Lee, Chien-Hsiu Matsumoto, Akinori Mawatari, Ken Motohara, Kentaro Murai, Kai Nagamine, Kentaro Nakane, Minami Saito, Tomoki Sasaki, Rin Shibuya, Takatoshi Suzuki, Akihiro Takeuchi, Tsutomu T. Umeda, Hiroya Umemura, Masayuki Watanabe, Kuria Yabe, Kiyoto Yajima, Hidenobu Zhang, Yechi National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan Osawa 2-21-1 Mitaka Tokyo181-8588 Japan University of Tokyo Chiba Kashiwa277-8583 Japan Kavli Institute for Cosmology University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Cavendish Laboratory University of Cambridge 19 JJ Thomson Avenue CambridgeCB3 0HE United Kingdom Waseda Research Institute for Science and Engineering Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan Department of Physics Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan Department of Astronomy Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan Research Center for Space and Cosmic Evolution Ehime University Bunkyo-cho 2-5 Ehime Matsuyama790-8577 Japan Department of Physics School of Advanced Science and Engineering Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan Observatories of the Carnegie Institution for Science 813 Santa Barbara St. PasadenaCA91101 United States Observatoire de Genève Université de Genève 51 Chemin de Pégase Versoix1290 Switzerland 650 North Aohoku Place HiloHI96720 United States Department of Physics and Astronomy University of Notre Dame 225 Nieuwland Science Hall Notre DameIN46556 United States Astronomical Institute Tohoku University 6-3 Aoba Aramaki Aoba-ku Miyagi Sendai980-8578 Japan Division of Physics Faculty of Pure and Applied Sciences University of Tsukuba Ibaraki Tsukuba305-8571 Japan Faculty of Pure and Applied Sciences University of Tsukuba Ibaraki Tsukuba305-8571 Japan Astronomy Program Department of Physics and Astronomy Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of SNU Astronomy Research Cent

Using the Subaru/FOCAS IFU capability, we examine the spatially resolved relationships between gas-phase metallicity, stellar mass, and star-formation rate surface densities (Σ★ and ΣSFR, respectively) in extremely metal-poor galaxies (EMPGs) in the local universe. Our analysis includes 24 EMPGs, comprising 9,177 spaxels, which span a unique parameter space of local metallicity (12 + log(O/H) 6.9 to 7.9) and stellar mass surface density (Σ★ ∼ 105 to 107 Mo kpc−2), extending beyond the range of existing large integral-field spectroscopic surveys. Through spatially resolved emission line diagnostics based on the [N ii] BPT-diagram, we verify the absence of evolved active galactic nuclei in these EMPGs. Our findings reveal that, while the resolved mass-metallicity relation exhibits significant scatter in the low-mass regime, this scatter is closely correlated with local star-formation surface density. Specifically, metallicity decreases as ΣSFR increases for a given Σ★. Notably, half of the EMPGs show a distinct metal-poor horizontal branch on the resolved mass-metallicity relation. This feature typically appears at the peak clump with the highest Σ★ and ΣSFR and is surrounded by a relatively metal-enriched ambient region. These findings support a scenario in which metal-poor gas infall fuels episodic star formation in EMPGs, consistent with the kinematic properties observed in these systems. In addition, we identify four EMPGs with exceptionally low central metallicities (12 + log(O/H) 7.2), which display only a metal-poor clump without a surrounding metal-rich region. This suggests that such ultra-low metallicity EMPGs, at less than a few percent of the solar metallicity, may serve as valuable analogs for galaxies in the early stages of galaxy evolution. Copyright © 2024, The Authors. All rights reserved.

关键词： Spectroscopic analysis

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The structure of the stellar halo of the Andromeda galaxy explored with the NB515 for Subaru/HSC. I.: New insights on the stellar halo up to 120 kpc

arXiv

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arXiv 2024年

作者： Ogami, Itsuki Tanaka, Mikito Komiyama, Yutaka Chiba, Masashi Guhathakurta, Puragra Kirby, Evan N. Wyse, Rosemary F.G. Filion, Carrie Gilbert, Karoline M. Escala, Ivanna Mori, Masao Kirihara, Takanobu Tanaka, Masayuki Ishigaki, Miho N. Hayashi, Kohei Lee, Myung Gyoon Sharma, Sanjib Kalirai, Jason S. Lupton, Robert H. 2-21-1 Osawa Mitaka Tokyo181-8588 Japan National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Department of Advanced Sciences Faculty of Science and Engineering Hosei University 3-7-2 Kajino-cho Koganei Tokyo184-8584 Japan Astronomical Institute Tohoku University Aoba-ku Miyagi Sendai980-8578 Japan Department of Astronomy and Astrophysics University of California Santa Cruz University of California Observatories 1156 High Street Santa CruzCA95064 United States Department of Physics and Astronomy University of Notre Dame Notre DameIN46556 United States Department of Physics and Astronomy Johns Hopkins University BaltimoreMD21218 United States Center for Computational Astrophysics Flatiron Institute New YorkNY10010 United States John Hopkins Applied Physics Laboratory 11100 Johns Hopkins Road LaurelMD20723 United States Space Telescope Science Institute 3700 San Martin Dr. BaltimoreMD21218 United States The Observatories of the Carnegie Institution for Science 813 Santa Barbara Street PasadenaCA91101 United States Faculty of Pure and Applied Physics University of Tsukuba Tennodai 1-1-1 Ibaraki Tsukuba305-8577 Japan Center for Computational Sciences University of Tsukuba Tennodai 1-1-1 Ibaraki Tsukuba305-8577 Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan National Institute of Technology Sendai College Miyagi Natori981-1239 Japan Astronomy Program Department of Physics and Astronomy SNU Astronomy Research Center Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States

We analyse the M31 halo and its substructure within a projected radius of 120 kpc using a combination of Subaru/HSC NB515 and CFHT/MegaCam g- & i-bands. We succeed in separating M31’s halo stars from foreground contamination with ∼ 90 % accuracy by using the surface gravity sensitive NB515 filter. Based on the selected M31 halo stars, we discover three new substructures, which associate with the Giant Southern Stream (GSS) based on their photometric metallicity estimates. We also produce the distance and photometric metallicity estimates for the known substructures. While these quantities for the GSS are reproduced in our study, we find that the North-Western stream shows a steeper distance gradient than found in an earlier study, suggesting that it is likely to have formed in an orbit closer to the Milky Way. For two streams in the eastern halo (Stream C and D), we identify distance gradients that had not been resolved. Finally, we investigate the global halo photometric metallicity distribution and surface brightness profile using the NB515-selected halo stars. We find that the surface brightness of the metal-poor and metal-rich halo populations, and the all population can be fitted to a power-law profile with an index of α = −1.65 ± 0.02, −2.82 ± 0.01, and −2.44 ± 0.01, respectively. In contrast to the relative smoothness of the halo profile, its photometric metallicity distribution appears to be spatially non-uniform with nonmonotonic trends with radius, suggesting that the halo population had insufficient time to dynamically homogenize the accreted populations. Copyright © 2024, The Authors. All rights reserved.

关键词： Luminance

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