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arXiv 2020年

作者： Lawson, Brodie A.J. dos Santos, Rodrigo Weber Turner, Ian W. Bueno-Orovio, Alfonso Burrage, Pamela Burrage, Kevin Centre for Data Science Queensland University of Technology QLD Australia ARC Centre of Excellence for Mathematical and Statistical Frontiers Queensland University of Technology QLD Australia Graduate Program on Computational Modeling Universidade de Juiz de Fora Minas Gerais Brazil Department of Computer Science University of Oxford Oxfordshire United Kingdom

MSC Codes 35B27computational models in cardiac electrophysiology are notorious for long runtimes, restricting the numbers of nodes and mesh elements in the numerical discretisations used for their solution. This makes it particularly challenging to incorporate structural heterogeneities on small spatial scales, preventing a full understanding of the critical arrhythmogenic effects of conditions such as cardiac fibrosis. In this work, we explore the technique of homogenisation by volume averaging for the inclusion of non-conductive micro-structures into larger-scale cardiac meshes with minor computational overhead. Importantly, our approach is not restricted to periodic patterns, enabling homogenised models to represent, for example, the intricate patterns of collagen deposition present in different types of fibrosis. We first highlight the importance of appropriate boundary condition choice for the closure problems that define the parameters of homogenised models. Then, we demonstrate the technique’s ability to correctly upscale the effects of fibrotic patterns with a spatial resolution of 10 µm into much larger numerical mesh sizes of 100-250 µm. The homogenised models using these coarser meshes correctly predict critical pro-arrhythmic effects of fibrosis, including slowed conduction, source/sink mismatch, and stabilisation of re-entrant activation patterns. As such, this approach to homogenisation represents a significant step towards whole organ simulations that unravel the effects of microscopic cardiac tissue heterogeneities. © 2020, CC BY.

关键词： Heart

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METTL4 is an snRNA m6 Am methyltransferase that regulates RNA splicing

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细胞研究（英文版） 2020年第6期30卷 544-547页

作者： Hao Chen Lei Gu Esteban A.Orellana Yuanyuan Wang Jiaojiao Guo Qi Liu Longfei Wang Zhangfei Shen Hao Wu Richard I.Gregory Yi Xing Yang Shi Division of Newborn Medicine and Program in Epigenetics Boston Children's Hospital Harvard Medical School 300 Longwood AvenueBoston MA 02115 USA Stem Cell Program Division of Hematology/Oncology Boston Children's Hospital Department of Biological Chemistry and Molecular Pharmacology Harvard Medical SchoolBoston MA 02115 USA Bioinformatics Interdepartmental Graduate Program University of California Los Angeles Los Angeles CA 90095USA College of Science Northeastern University 360 Huntington Ave. Boston MA 02115 USA Program in Cellular and Molecular Medicine Boston Children's Hospital Boston MA 02115 USA Department of Biological Chemistry and Molecular Pharmacology Harvard Medical School Boston MA 02115 USA Division of Newborn Medicine and Program in Epigenetics Boston Children's Hospital Harvard Medical School 300 Longwood AvenueBoston MA 02115 USA Program in Cellular and Molecular Medicine Boston Children's Hospital Boston MA 02115 USA Department of Biological Chemistry and Molecular Pharmacology Harvard Medical School Boston MA 02115 USA Center for Computational and Genomic Medicine The Children's Hospital of Philadelphia Philadelphia PA 19104 USA Department of Pathology and Laboratory Medicine University of PennsylvaniaPhiladelphia PA 19104 USA

Dear Editor, Protein coding mRNAs and non-coding RNAs are extensively modified by chemical modifications,1 including methylation of the N6 position of adenosine (m6A).2 While mRNA internal m6A methylation is mediated by METTL3 and METTL14, which are members of the MT-A70 family,2 N6-methylation of cap-adjacent N6,2'-O-dimethyladenosine (m6Am) is catalyzed by PCIF1.3-6 Non-coding RNAs are also m6Am methylated internally,7 but the corresponding enzyme remains *** we provide evidence that METTL4 mediates internal m6Am methylation of U2 small nuclear RNA (snRNA).

关键词： Epigenetics RNA modification RNA splicing

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Improved Accuracy of Cardiac Tissue-Level Simulations by Considering Membrane Resistance as a Cellular-Level Optimization Objective

Improved Accuracy of Cardiac Tissue-Level Simulations by Con...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： E. Pouranbarani L. A. Berg R. S. Oliveira R. W. dos Santos A. Nygren Department of Electrical and Computer Engineering University of Calgary Alberta Canada Department of Computer Science and the Graduate Program of Computational Modeling Federal University of Juiz de Fora Juiz de Fora Brazil Department of Computer Science University of São João del-Rei São João del-Rei Brazil

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Cardiac cellular models are utilized as the building blocks for tissue simulation. One of the imprecisions of conventional cellular modeling, especially when the models are used in tissue-level modeling, stems from the mere consideration of cellular properties (e.g., action potential shape) in parameter tuning of the model. In our previous work, we put forward an accurate framework in which membrane resistance (R m ) reflecting inter-cellular characteristics, i.e., electrotonic effects, was considered alongside cellular features in cellular model fitting. This paper, for the first time, examines the hypothesis that considering R m as an additional optimization objective improves the accuracy of tissue-level modeling. To study this hypothesis, after cellular-level optimization of a well-known model, source-sink mismatch configurations in a 2-dimensional model are investigated. The results demonstrate that including R m in the optimization protocol yields a substantial improvement in the relative error of the critical transition border which is defined as the minimum window size between source and sink that wave propagates. Model developers can utilize the proposed concept during parameter tuning to increase the accuracy of models.

关键词： Mathematical model Optimization computational modeling Resistance Tuning Fitting Protocols

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Exponential acceleration of macroscopic quantum tunneling in a Floquet Ising model

arXiv

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arXiv 2023年

作者： Grattan, George Barton, Brandon A. Feeney, Sean Mossi, Gianni Patnaik, Pratik Sagal, Jacob C. Carr, Lincoln D. Oganesyan, Vadim Kapit, Eliot Quantum Engineering Program Colorado School of Mines 1523 Illinois St CO Golden80401 United States Department of Computer Science Colorado School of Mines 1500 Illinois St CO Golden80401 United States Department of Applied Mathematics and Statistics Colorado School of Mines 1500 Illinois St CO Golden80401 United States Department of Physics Colorado School of Mines 1523 Illinois St CO Golden80401 United States KBR Inc. 601 Jefferson St. HoustonTX77002 United States NASA Ames Research Center Moffett FieldCA94035 United States Department of Physics and Astronomy College of Staten Island CUNY Staten IslandNY10314 United States Physics program and Initiative for the Theoretical Sciences The Graduate Center CUNY New YorkNY10016 United States Center for Computational Quantum Physics Flatiron Institute 162 5th Avenue New YorkNY10010 United States

The exponential suppression of macroscopic quantum tunneling (MQT) in the number of elements to be reconfigured is an essential element of broken symmetry phases. Slow MQT is also a core bottleneck in quantum algorithms, such as traversing an energy landscape in optimization, and adiabatic state preparation more generally. In this work, we demonstrate the possibility to accelerate MQT through Floquet engineering with the application of a uniform, high frequency transverse drive field. Using the ferromagnetic phase of the transverse field Ising model in one and two dimensions as a prototypical example, we identify three qualitatively distinct regimes as a function of drive strength: (i) for weak drive, the system exhibits exponentially slow MQT alongside robust magnetic order, as expected;(ii) at intermediate drive strength, we find polynomial decay of rates alongside vanishing magnetic order consistent with critical or paramagnetic state;(iii) at very strong drive strengths both the tunnelling rate and time-averaged magnetic order remain finite with increasing system size. We support these claims with extensive full wavefunction and matrix-product state numerical simulations, and theoretical analysis. An experimental test of these results presents a technologically important and novel scientific question accessible on NISQ-era quantum computers. © 2023, CC BY.

关键词： Ising model

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Enriching and Characterizing T-Cell Repertoires from 3' Barcoded Single-Cell Whole Transcriptome Amplification Products

arXiv

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arXiv 2022年

作者： Jivanjee, Tasneem Ibrahim, Samira Nyquist, Sarah K. James Gatter, G. Bromley, Joshua D. Jaiswal, Swati Berger, Bonnie Behar, Samuel M. Christopher Love, J. Shalek, Alex K. Institute for Medical Engineering & Science Department of Chemistry Massachusetts Institute of Technology CambridgeMA United States Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology CambridgeMA United States Ragon Institute of MGH MIT and Harvard CambridgeMA United States Broad Institute of MIT and Harvard CambridgeMA United States Massachusetts Institute of Technology CambridgeMA United States Program in Computational and Systems Biology Massachusetts Institute of Technology CambridgeMA United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States Microbiology Graduate Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Microbiology and Physiological Systems University of Massachusetts Medical School WorcesterMA United States Department of Mathematics Massachusetts Institute of Technology CambridgeMA United States Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA United States

Antigen-specific T cells play an essential role in immunoregulation and many diseases such as cancer. Characterizing the T cell receptor (TCR) sequences that encode T cell specificity is critical for elucidating the antigenic determinants of immunological diseases and designing therapeutic remedies. However, methods of obtaining single-cell TCR sequencing data are labor and cost intensive, typically requiring both cell sorting and full length single-cell RNA-sequencing (scRNA-seq). New high-throughput 3' cell-barcoding scRNA-seq methods can simplify and scale this process;however, they do not routinely capture TCR sequences during library preparation and sequencing. While 5' cell-barcoding scRNA-seq methods can be used to examine TCR repertoire at single-cell resolution, doing so requires specialized reagents which cannot be applied to samples previously processed using 3' cell-barcoding methods. Here, we outline a method for sequencing TCRα and TCRβ transcripts from samples already processed using 3' cell-barcoding scRNA-seq platforms, ensuring TCR recovery at a single-cell resolution. In short, a fraction of the 3' barcoded whole transcriptome amplification (WTA) product typically used to generate a massively parallel 3' scRNA-seq library is enriched for TCR transcripts using biotinylated probes, and further amplified using the same universal primer sequence from WTA. Primer extension using TCR V-region primers and targeted PCR amplification using a second universal primer results in a 3' barcoded single-cell CDR3-enriched library that can be sequenced with custom sequencing primers. Coupled with 3' scRNA-seq of the same WTA, this method enables simultaneous analysis of single-cell transcriptomes and TCR sequences which can help interpret inherent heterogeneity among antigen-specific T cells and salient disease biology. The method presented here can also be adapted readily to enrich and sequence other transcripts of interest from both 3' and 5' barcoded scRNA-seq

关键词： RNA

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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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Stochastic Petri Net Models for the Study of Macrophage Reprogramming

Stochastic Petri Net Models for the Study of Macrophage Repr...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Elvis H. Ribeiro Alexandre B. Pigozzo Carolina R. Xavier Rodrigo W. dos Santos Marcelo Lobosco Department of Computer Science Federal University of Sao Joao Del Rei Graduate Program in Computational Modeling Federal University of Juiz de Fora

ISBN: (纸本)9781728118680

In recent years, Petri nets are being used for modeling complex biological systems. Examples include the modeling of biochemical reactions, protein interaction networks, gene regulation networks and the immune system. In this work, Petri nets are used to model the innate immune response to a bacterial infection. The objective of this work is to study how changes in macrophage phenotype impact the regulation and resolution of the immune response. From simulations performed, we have observed that the conversion between a M1 proinflammatory phenotype to a M2 regulatory phenotype during an inflammatory response is an important process to avoid a state of persistent inflammation.

关键词： stochastic Petri net innate immune response macrophage reprogramming chronic inflammation Petri nets Innate Immune Response Chronic inflammation Macrophages reprogramming Inflammatory Response Modeling Immune System

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EMPRESS. XI. SDSS and JWST Search for Local and z ∼ 4–5 Extremely Metal-Poor Galaxies (EMPGs): Clustering and Chemical Properties of Local EMPGs

arXiv

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arXiv 2023年

作者： Nishigaki, Moka Ouchi, Masami Nakajima, Kimihiko Ono, Yoshiaki Rauch, Michael Isobe, Yuki Harikane, Yuichi Narita, Kanako Zahedy, Fakhri Xu, Yi Yajima, Hidenobu Fukushima, Hajime Hirai, Yutaka Kim, Ji Hoon Inoue, Shigeki Kusakabe, Haruka Lee, Chien-Hsiu Nagao, Tohru Onodera, Masato Department of Astronomical Science The Graduate University for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-8588 Japan 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 Kashiwa-no-Ha Kashiwa Chiba277-8582 Japan University of Tokyo Chiba Kashiwa277-8583 Japan Observatories of the Carnegie Institution for Science 813 Santa Barbara Street PasadenaCA91101 United States 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 Center for Computational Sciences University of Tsukuba Ten-nodai 1-1-1 Tsukuba Ibaraki305-8577 Japan 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 Sendai Miyagi980-8578 Japan Astronomy Program Department of Physics and Astronomy Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of SNU Astronomy Research Center Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Faculty of Science Hokkaido University Hokkaido Sapporo060-0810 Japan Observatoire de Genève Université de Genève 51 Chemin de Pégase Versoix1290 Switzerland W. M. Keck Observatory 65-1120 Mamalahoa Hwy KamuelaHI96743 United States Research Center for Space and Cosmic Evolution Ehime University Ehime Matsuyama790-8577 Japan 650 North A’ohoku Place HiloHI96720 United States

We search for local extremely metal-poor galaxies (EMPGs), selecting photometric candidates by broadband color excess and machine-learning techniques with the SDSS photometric data. After removing stellar contaminants by shallow spectroscopy with Seimei and Nayuta telescopes, we confirm that three candidates are EMPGs with 0.05–0.1 Z☉ by deep Magellan/MagE spectroscopy for faint [Oiii]λ4363 lines. Using a statistical sample consisting of 105 spectroscopically-confirmed EMPGs taken from our study and the literature, we calculate cross-correlation function (CCF) of the EMPGs and all SDSS galaxies to quantify environments of EMPGs. Comparing another CCF of all SDSS galaxies and comparison SDSS galaxies in the same stellar mass range (107.0 − 108.4M☉), we find no significant (> 1σ) difference between these two CCFs. We also compare mass-metallicity relations (MZRs) of the EMPGs and those of galaxies at z ∼ 0–4 with a steady chemical evolution model and find that the EMPG MZR is comparable with the model prediction on average. These clustering and chemical properties of EMPGs are explained by a scenario of stochastic metal-poor gas accretion on metal-rich galaxies showing metal-poor star formation. Extending the broadband color-excess technique to a high-z EMPG search, we select 17 candidates of z ∼ 4–5 EMPGs with the deep (' 30 mag) near-infrared JWST/NIRCam images obtained by ERO and ERS programs. We find galaxy candidates with negligible [Oiii]λλ4959,5007 emission weaker than the local EMPGs and known high-z galaxies, suggesting that some of these candidates may fall in 0–0.01 Z☉, which potentially break the lowest metallicity limit known to date. Copyright © 2023, The Authors. All rights reserved.

关键词： Infrared devices

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Theoretical study on adsorption and reaction of polymeric formic acid on the Cu(111) surface

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Physical Review Materials 2021年第7期5卷 075801-075801页

作者： Septia Eka Marsha Putra Fahdzi Muttaqien Yuji Hamamoto Kouji Inagaki Akitoshi Shiotari Jun Yoshinobu Yoshitada Morikawa Ikutaro Hamada Department of Precision Engineering Graduate School of Engineering Osaka University 2-1 Yamada-oka Suita Osaka 565-0871 Japan Department of Engineering Physics Institut Teknologi Sumatera Jalan Terusan Ryacudu Way Hui Jati Agung South Lampung 35365 Indonesia Instrumentation and Computational Physics Research Group Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jalan Ganesha 10 Bandung West Java 40132 Indonesia Master Program in Computational Science Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jalan Ganesha 10 Bandung West Java 40132 Indonesia Elements Strategy Initiative for Catalysts and Batteries (ESICB) Kyoto University Goryo-Ohara Nishikyou-ku Katsura Kyoto 615-8245 Japan Department of Advanced Materials Science The University of Tokyo 5-1-5 Kashiwanoha Kashiwa Chiba 277-8561 Japan The Institute for Solid State Physics The University of Tokyo 5-1-5 Kashiwanoha Kashiwa Chiba 277-8581 Japan Research Center for Precision Engineering Graduate School of Engineering Osaka University 2-1 Yamada-oka Suita Osaka 565-0871 Japan

We investigate the polymeric adsorption of formic acid (HCOOH) on the Cu(111) surface by means of density functional theory. We present structural models for the polymeric form of HCOOH on Cu(111) and characterize their stability, electronic, and vibrational properties. Based on the energetics and dynamics, as well as simulated scanning tunneling microscopy and atomic force microscopy images, we propose that the α polymorph is likely to be formed on the Cu(111) surface and can explain the experimental findings. We also study the initial step of the catalytic dehydrogenation of HCOOH and find that the O-H bond dissociation at the edge of the polymer is facilitated, rather than that forming hydrogen bonding, agreeing well with the experiments.

关键词： Adsorption Chemical bonding Chemical kinetics, dynamics & catalysis Electronic structure Surface & interfacial phenomena Surfaces Density functional theory First-principles calculations

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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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