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arXiv 2023年

作者： Zhang, Shiyun Jin, Weiwei Wang, Dong Xu, Ding Zhang, Jerry Shattuck, Mark D. O’Hern, Corey S. Department of Physics University of Science and Technology of China Anhui Hefei230026 China Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department The City College of New York New YorkNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

Strain-controlled isotropic compression gives rise to jammed packings of repulsive, frictionless disks with either positive or negative global shear moduli. We carry out computational studies to understand the contributions of the negative shear moduli to the mechanical response of jammed disk packings. We first decompose the ensemble-averaged, global shear modulus as hGi = (1 − F−)hG+i + F−hG−i, where F− is the fraction of jammed packings with negative shear moduli and hG+i and hG−i are the average values from packings with positive and negative moduli, respectively. We show that hG+i and h|G−|i obey different power-law scaling relations above and below pN2 ∼ 1. For pN2 > 1, both hG+iN and h|G−|iN ∼ (pN2)β, where β ∼ 0.5 for repulsive linear spring interactions. Despite this, hGiN ∼ (pN2)β0 with β0 & 0.5 due to the contributions from packings with negative shear moduli. We show further that the probability distribution of global shear moduli P(G) collapses at fixed pN2 and different values of p and N. We calculate analytically that P(G) is a Gamma distribution in the pN2 1 limit. As pN2 increases, the skewness of P(G) decreases and P(G) becomes a skew-normal distribution with negative skewness in the pN2 1 limit. We also partition jammed disk packings into subsystems using Delanunay triangulation of the disk centers to calculate local shear moduli. We show that the local shear moduli defined from groups of adjacent triangles can be negative even when G > 0. The spatial correlation function of local shear moduli C(~r) displays weak correlations for pn2sub −2, where nsub is the number of particles within each subsystem. However, C(~r) begins to develop long-ranged spatial correlations with four-fold angular symmetry for pn2sub & 10−2 © 2023, CC BY-NC-ND.

关键词： Elastic moduli

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Bacterial biofilms use chiral branches to escape crowded environments by tracking oxygen gradient

arXiv

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arXiv 2022年

作者： Basaran, Mustafa Yüce, Tevfik Can Yaman, Yusuf Ilker Vetter, Roman Kocabas, Askin Department of Physics Koç University Sarıyer Istanbul34450 Turkey Bio-Medical Sciences and Engineering Program Koç University Sarıyer Istanbul34450 Turkey Computational Physics for Engineering Materials ETH Zurich Zurich8093 Switzerland Koç University Surface Science and Technology Center Koç University Sarıyer Istanbul34450 Turkey Koç University Research Center for Translational Medicine Koç University Sarıyer Istanbul34450 Turkey

Bacterial biofilms collectively develop distinct and ordered structures, including fibers, bundles, and branches. Often, it is unclear how these structural motifs convey specific advantages to bacterial strains under challenging conditions. In oxygen-limited environments, dense bacterial aggregates generally deplete oxygen, which leads to arrest of bacterial growth. However, we observed that biofilm-forming Bacillus subtilis could use branching patterns to escape from these crowded regions by tracking the oxygen gradient. The process depends on the chain-forming ability of the biofilm. As a result of collective branching triggered by bending and mechanical buckling, bacteria can extend from the oxygen-depleted biofilm core to the oxygen available periphery. Remarkably, these bacterial branches are strongly chiral and curve in a clockwise direction. Our analysis revealed that the surface friction and axial rotation of the twisting cell wall break left-right symmetry on solid surfaces and drive chiral bending of bacterial chains. We further observed that the chirality of individual branches could propagate across a large scale and shape the macroscopic morphology of the colony under a limited spatiotemporal growth profile. Taken together, our results provide new insights into how simple physical interactions lead to bacterial collaboration and promote the survival of biofilm-forming strains in challenging environments. © 2022, CC0.

关键词： Biofilms

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Fluorescent imaging technologies for in situ measurement of drug target engagement and cell signaling pathways 4

Fluorescent imaging technologies for in situ measurement of ...

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Visualizing and Quantifying Drug Distribution in Tissue IV 2020

作者： McMahon, Nathan P. Solanki, Allison Jones, Jocelyn Kwon, Sunjong Chang, Young-Hwan Chin, Koei Nederlof, Michel A. Gray, Joe W. Gibbs, Summer L. Department of Biomedical Engineering Oregon Health and Science University PortlandOR97201 United States Knight Cancer Institute Oregon Health and Science University PortlandOR97201 United States Center for Spatial Systems Biomedicine Oregon Health and Science University PortlandOR97201 United States Computational Biology Program Oregon Health and Science University PortlandOR97201 United States Quantitative Imaging Systems PittsburghPA15238 United States

ISBN: (纸本)9781510632011

Successful cancer treatment continues to elude modern medicine and its arsenal of therapeutic strategies. Therapy resistance is driven by significant tumor heterogeneity, complex interactions between malignant, microenvironmental and immune cells and cross talk between signaling pathways. Advances in molecular characterization technologies such as next generation sequencing have helped unravel this network of interactions and identify druggable therapeutic targets. Tyrosine kinase inhibitors (TKI) are a class of drugs seeking to inhibit signaling pathways critical to sustaining proliferative signaling, resisting cell death, and the other hallmarks of cancer. While tumors may initially respond to TKI therapy, disease progression is near universal due to mechanisms of acquired resistance largely involving cellular signaling pathway reprogramming. With the ultimate goal of improved TKI therapeutic efficacy our group has developed intracellular paired agent imaging (iPAI) to quantify drug target interactions and oligonucleotide conjugated antibody (Ab-oligo) cyclic immunofluorescence (cycIF) imaging to characterize perturbed signaling pathways in response to therapy. iPAI uses spectrally distinct, fluorescently labeled targeted and untargeted drug derivatives, correcting for non-specific drug distribution and facilitating quantitative assessment of the drug binding before and after therapy. Ab-oligo cycIF exploits in situ hybridization of complementary oligonucleotides for biomarker labeling while oligonucleotide modifications facilitate signal removal for sequential rounds of fluorescent tagging and imaging. Aboligo CycIF is capable of generating extreme multi-parametric images for quantifying total and phosphorylated protein expression to quantify protein activation, expression, and spatial distribution. Together iPAI and Ab-oligo cycIF can be applied to interrogate drug uptake and target binding as well as changes to heterogenous cell populations within tumors that d

关键词： Fluorescence imaging

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Machine learning assisted phase and size-controlled synthesis of iron oxides

arXiv

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arXiv 2023年

作者： Liu, Juejing Zhang, Zimeng Li, Xiaoxu Zong, Meirong Wang, Yining Wang, Suyun Chen, Ping Wan, Zaoyan Zhao, Yatong Liu, Lili Liang, Yangang Wang, Wei Wang, Zheming Wang, Shiren Guo, Xiaofeng Saldanha, Emily G. Rosso, Kevin M. Zhang, Xin Physical and Computational Sciences Directorate Pacific Northwest National Laboratory RichlandWA99354 United States Department of Chemistry Washington State University PullmanWA99164 United States Materials Science and Engineering Program Washington State University PullmanWA99164 United States Department of Industrial and Systems Engineering Texas A&M University College StationTX77843 United States Energy and Environment Directorate Pacific Northwest National Laboratory RichlandWA99354 United States National Security Directorate Pacific Northwest National Laboratory RichlandWA99354 United States

The controllable synthesis of iron oxides particles is a critical issue for materials science, energy storage, biomedical applications, environmental science, and earth science. However, synthesis of iron oxides with desired phase and size are still a time-consuming and trial-and-error process. This study presents solutions for two fundamental challenges in materials synthesis: predicting the outcome of a synthesis from specified reaction parameters and correlating sets of parameters to obtain products with desired outcomes. Four machine learning algorithms, including random forest, logistic regression, support vector machine, and k-nearest neighbor, were trained to predict the phase and particle size of iron oxide based on experimental conditions. Among the models, random forest exhibited the best performance, achieving 96% and 81% accuracy when predicting the phase and size of iron oxides in the test dataset. Premutation feature importance analysis shows that most models (except logistic regression) rely on known features such as precursor concentration, pH, and temperature to predict the phases from synthesis conditions. The robustness of the random forest models was further verified by comparing prediction and experimental results based on 24 randomly generated methods in additive and non-additive systems not included in the datasets. The predictions of product phase and particle size from the models are in good agreement with the experimental results. Additionally, a searching and ranking algorithm was developed to recommend potential synthesis parameters for obtaining iron oxide products with desired phase and particle size from previous studies in the dataset. This study lays the foundation for a closed-loop approach in materials synthesis and preparation, from suggesting potential reaction parameters from the dataset and predicting potential outcomes, through conducting experiments and analysis, to enriching the dataset. Copyright © 2023, The Authors. All ri

关键词： Particle size

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Spatial atlas of the mouse central nervous system at molecular resolution (vol 622, pg 552, 2023)

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NATURE 2024年第7993期625卷 E6-E6页

作者： Shi, Hailing He, Yichun Zhou, Yiming Huang, Jiahao Maher, Kamal Wang, Brandon Tang, Zefang Luo, Shuchen Tan, Peng Wu, Morgan Lin, Zuwan Ren, Jingyi Thapa, Yaman Tang, Xin Chan, Ken Y. Deverman, Benjamin E. Shen, Hao Liu, Albert Liu, Jia Wang, Xiao Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry Massachusetts Institute of Technology Cambridge MA USA John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA USA Computational and Systems Biology PhD Program Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Biology Massachusetts Institute of Technology Cambridge MA USA Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry and Chemical Biology Harvard University Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA

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Assessment of scoring functions for computational models of protein-protein interfaces

arXiv

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arXiv 2024年

作者： Sumner, Jacob Meng, Grace Brandt, Naomi Grigas, Alex T. Córdoba, Andrés Shattuck, Mark D. O’Hern, Corey S. Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Department of Chemistry Duke University DurhamNC27708 United States Department of Physics Duke University DurhamNC27708 United States Benjamin Levich Institute Physics Department City College of New York New YorkNY10031 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

A goal of computational studies of protein-protein interfaces (PPIs) is to predict the binding site between two monomers that form a heterodimer. The simplest version of this problem is to rigidly re-dock the bound forms of the monomers, which involves generating computational models of the heterodimer and then scoring them to determine the most native-like models. Scoring functions have been assessed previously using rank- and classification-based metrics, however, these methods are sensitive to the number and quality of models in the scoring function training set. We assess the accuracy of seven PPI scoring functions by comparing their scores to a measure of structural similarity to the x-ray crystal structure (i.e. the DockQ score) for a non-redundant set of heterodimers from the Protein Data Bank. For each heterodimer, we generate re-docked models uniformly sampled over DockQ and calculate the Spearman correlation between the PPI scores and DockQ. For some targets, the scores and DockQ are highly correlated;however, for many targets, there are weak correlations. Several physical features can explain the difference between difficult- and easy-to-score targets. For example, strong correlations exist between the score and DockQ for targets with highly intertwined monomers and many interface contacts. We also develop a new score based on only three physical features that matches or exceeds the performance of current PPI scoring functions. These results emphasize that PPI prediction can be improved by focusing on correlations between the PPI score and DockQ and incorporating more discriminating physical features into PPI scoring functions. © 2024, CC BY.

关键词： Monomers

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What we should learn from pandemic publishing

arXiv

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arXiv 2024年

作者： Sikdar, Satyaki Venturini, Sara Charpignon, Marie-Laure Kumar, Sagar Rinaldi, Francesco Tudisco, Francesco Fortunato, Santo Majumder, Maimuna S. Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Computer Science Loyola University Chicago ChicagoIL United States Senseable City Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Mathematics "Tullio Levi-Civita" University of Padova Padova Italy Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Network Science Institute Northeastern University BostonMA United States School of Mathematics The University of Edinburgh Edinburgh United Kingdom School of Mathematics Gran Sasso Science Institute L’Aquila Italy Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children’s Hospital BostonMA United States

Since the emergence of COVID-19, discussions of ongoing pandemic-related research have accounted for an unprecedented share of media coverage and debate in the public sphere1. The urgency of the pandemic forced researchers to operate on an accelerated timeline, as both policymakers and the public relied on the most current evidence to guide their decisions and behaviors. With high demand for rapid pandemic-related insights and lower barriers to entry via preprint servers, the volume of COVID-19 articles skyrocketed2. The pressing need for research triggered the participation of many researchers with expertise in the science of infectious disease outbreaks (‘outbreak scientists’), who were joined by researchers from other disciplines (‘bellwethers’) and more junior researchers still in training (‘newcomers’) with the common goal of advancing the frontiers of pandemic science and informing policy decisions3. Please see Box 1 for details of this taxonomy. © 2024, CC BY-NC-ND.

关键词： COVID-19

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A growth model for water distribution networks with loops

arXiv

引用

arXiv 2021年

作者： Sugishita, Kashin Abdel-Mottaleb, Noha Zhang, Qiong Masuda, Naoki Department of Mathematics State University of New York at Buffalo BuffaloNY14260-2900 United States Department of Transdisciplinary Science and Engineering Tokyo Institute of Technology Tokyo152-8550 Japan Department of Civil and Environmental Engineering University of South Florida TampaFL33620 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

Water distribution networks (WDNs) expand their service areas over time. These growth dynamics are poorly understood. One facet of WDNs is that they have loops in general, and closing loops may be a functionally important process for enhancing their robustness and efficiency. We propose a growth model for WDNs which generates networks with loops and is applicable to networks with multiple water sources. We apply the proposed model to four empirical WDNs to show that it produces networks whose structure is similar to that of the empirical WDNs. The comparison between the empirical and modeled WDNs suggests that the empirical WDNs may realize a reasonable balance between cost, efficiency, and robustness in terms of the network structure. We also study the design of pipe diameters based on a biological positive feedback mechanism. Specifically, we apply a model inspired by Physarum polycephalum to find moderate positive correlations between the empirical and modeled pipe diameters. The difference between the empirical and modeled pipe diameters suggests that we may be able to improve the performance of WDNs by following organizing principles of biological flow networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Water distribution systems

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Simultaneous biomonitoring of volatile organic compounds’ metabolites in human urine samples using a novel in-syringe based fast urinary metabolites extraction (FaUMEx) technique coupled with UHPLC-MS/MS analysis

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Chemosphere 2023年第1期329卷 138667页

作者： Gurrani, Swapnil Prakasham, Karthikeyan Huang, Po-Chin Wu, Ming-Tsang Wu, Chia-Fang Lin, Yu-Chia Tsai, Bongee Krishnan, Anbarasu Tsai, Pei-Chien Ponnusamy, Vinoth Kumar Kaohsiung City807 Taiwan Miaoli County 35053 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung City Taiwan Kaohsiung City807 Taiwan Department of Public Health Kaohsiung Medical University Kaohsiung City Taiwan International Master Program of Translational Medicine College of Engineering and Science National United University Miaoli Taiwan Corporation No.392 Yucheng Rd. Zuoying District. Kaohsiung City813 Taiwan Department of Computational Biology Institute of Bioinformatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Kaohsiung City807 Taiwan Kaohsiung City807 Taiwan

Assessing the impact of human exposure to environmental toxicants is often crucial to biomonitoring the exposed dose. In this work, we report a novel fast urinary metabolites extraction (FaUMEx) technique coupled with UHPLC-MS/MS analysis for the highly sensitive and simultaneous biomonitoring of the five major urinary metabolites (thiodiglycolic acid, s-phenylmercapturic acid, t,t-muconic acid, mandelic acid, and phenyl glyoxylic acid) of common volatile organic compounds' (VOCs) exposure (vinyl chloride, benzene, styrene, and ethylbenzene) in human. FaUMEx technique comprises of two-steps, liquid-liquid microextraction was performed first in an extraction syringe using 1 mL of methanol (pH 3) as an extraction solvent and then, the extractant was passed through a clean-up syringe (pre-packed-with various sorbents including 500 mg anhydrous MgSO4, 50 mg C18, and 50 mg SiO2) to obtain the high order of matrice clean-up and preconcentration efficiency. The developed method displayed excellent linearity, and the correlation coefficients were >0.998 for all the target metabolites with detection and quantification limits of 0.02–0.24 ng mL−1 and 0.05–0.72 ng mL−1, respectively. Furthermore, the matrix effects were © 2023 Elsevier Ltd

关键词： Metabolites

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B(s)→D(s)(*)M decays in the presence of final-state interactions

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Physical Review D 2025年第9期111卷 095020-095020页

作者： Albertus Hariwangsa Panuluh Satoshi Tanaka Hiroyuki Umeeda Department of Physics Education Faculty of Teacher Training and Education Sanata Dharma University Paingan Maguwohardjo Sleman Yogyakarta 55282 Indonesia Physics Program Graduate School of Advanced Science and Engineering Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima 739-8526 Japan Scientific Computational Division Nippon Advanced Information Service (NAIS Co. Inc.) 416 Muramatsu Tokaimura Naka-gun Ibaraki 319-1112 Japan Center for Theoretical Physics and College of Physics Jilin University Changchun 130012 China China Center of Advanced Science and Technology Beijing 100190 China Institute of High Energy Physics Chinese Academy of Sciences Beijing 100049 China

In light of the recent data for B¯(s)→D(s)(*)P and B¯(s)→D(s)V decays, we perform a model-independent phenomenological analysis in the presence of quasielastic rescattering. With the Wilson coefficients including contributions beyond the standard model, the lifetimes of the B meson and Bd0−B¯d0 mixing are investigated to clarify correlations among the observables. We show that parameter regions for quasielastic rescattering, the size of color-suppressed tree amplitudes, and new physics are constrained due to the lifetime data. As a consequence, it is revealed that this scenario can be tested by the future LHCb measurement of the width difference in Bd0−B¯d0 mixing and semileptonic CP asymmetry.

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