Pub Date : 2024-01-01Epub Date: 2024-10-21DOI: 10.4310/cis.241021213225
Jun Li, Nils G Walter, Shi-Jie Chen
Single-molecule Förster Resonance Energy Transfer (smFRET) is a powerful biophysical technique that utilizes the distance-dependent energy transfer between donor and acceptor dyes linked to individual molecules, providing insights into molecular conformational changes and interactions at the single-molecule level. Prior investigations leveraged smFRET to study the conformational dynamics of single truncated Ubc4 pre-mRNA molecules during splicing, yet these efforts did not prioritize structural modeling. In this study, we develop an smFRET-assisted RNA prediction method to predict the 2D and 3D structures of this pre-mRNA. To achieve this, we initiate the process by generating RNA structural ensembles through coarse-grained molecular dynamics (MD) simulations. Subsequently, inter-dye distances are calculated for these RNA structural ensembles by performing all-atom MD simulations of the dye groups. The ultimate determination of the 2D and 3D structures for the pre-mRNA is achieved by comparing the calculated inter-dye distances with experimental counterparts. Notably, our computational results demonstrate a significant alignment with experimental findings, which involve a conformational change at the 2D level.
{"title":"smFRET-assisted RNA structure prediction.","authors":"Jun Li, Nils G Walter, Shi-Jie Chen","doi":"10.4310/cis.241021213225","DOIUrl":"https://doi.org/10.4310/cis.241021213225","url":null,"abstract":"<p><p>Single-molecule Förster Resonance Energy Transfer (smFRET) is a powerful biophysical technique that utilizes the distance-dependent energy transfer between donor and acceptor dyes linked to individual molecules, providing insights into molecular conformational changes and interactions at the single-molecule level. Prior investigations leveraged smFRET to study the conformational dynamics of single truncated Ubc4 pre-mRNA molecules during splicing, yet these efforts did not prioritize structural modeling. In this study, we develop an smFRET-assisted RNA prediction method to predict the 2D and 3D structures of this pre-mRNA. To achieve this, we initiate the process by generating RNA structural ensembles through coarse-grained molecular dynamics (MD) simulations. Subsequently, inter-dye distances are calculated for these RNA structural ensembles by performing all-atom MD simulations of the dye groups. The ultimate determination of the 2D and 3D structures for the pre-mRNA is achieved by comparing the calculated inter-dye distances with experimental counterparts. Notably, our computational results demonstrate a significant alignment with experimental findings, which involve a conformational change at the 2D level.</p>","PeriodicalId":45018,"journal":{"name":"Communications in Information and Systems","volume":"24 3","pages":"163-179"},"PeriodicalIF":0.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11545564/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.4310/cis.2023.v23.n3.a4
King-Yeung Lam, Yuan Lou, Shizhao Ma
{"title":"Exploring the evolutionary dynamics of infectious diseases through SIS epidemic models","authors":"King-Yeung Lam, Yuan Lou, Shizhao Ma","doi":"10.4310/cis.2023.v23.n3.a4","DOIUrl":"https://doi.org/10.4310/cis.2023.v23.n3.a4","url":null,"abstract":"","PeriodicalId":45018,"journal":{"name":"Communications in Information and Systems","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136302408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.4310/cis.2023.v23.n3.a1
Jong-Shenq Guo, Bei Hu, Robert Jensen, Stephen S.-T. Yau
{"title":"Preface of special issue dedicated to Professor Avner Friedman’s 90th birthday","authors":"Jong-Shenq Guo, Bei Hu, Robert Jensen, Stephen S.-T. Yau","doi":"10.4310/cis.2023.v23.n3.a1","DOIUrl":"https://doi.org/10.4310/cis.2023.v23.n3.a1","url":null,"abstract":"","PeriodicalId":45018,"journal":{"name":"Communications in Information and Systems","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136302435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.4310/cis.2023.v23.n2.a1
Nan Zhao, Xinqi Gong
{"title":"A review of computational models for predicting protein-protein interaction and non-interaction","authors":"Nan Zhao, Xinqi Gong","doi":"10.4310/cis.2023.v23.n2.a1","DOIUrl":"https://doi.org/10.4310/cis.2023.v23.n2.a1","url":null,"abstract":"","PeriodicalId":45018,"journal":{"name":"Communications in Information and Systems","volume":"1 1","pages":"15-184"},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70404387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.4310/cis.2023.v23.n3.a2
Jong-Shenq Guo, Karen Guo, Paul Schrater
{"title":"Prediction of human looking behavior using interest-based image representations","authors":"Jong-Shenq Guo, Karen Guo, Paul Schrater","doi":"10.4310/cis.2023.v23.n3.a2","DOIUrl":"https://doi.org/10.4310/cis.2023.v23.n3.a2","url":null,"abstract":"","PeriodicalId":45018,"journal":{"name":"Communications in Information and Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136302095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.4310/cis.2023.v23.n3.a3
Yaodan Huang, Bei Hu
{"title":"Periodic solution for a free boundary problem modeling small plaques","authors":"Yaodan Huang, Bei Hu","doi":"10.4310/cis.2023.v23.n3.a3","DOIUrl":"https://doi.org/10.4310/cis.2023.v23.n3.a3","url":null,"abstract":"","PeriodicalId":45018,"journal":{"name":"Communications in Information and Systems","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136302393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.4310/cis.2023.v23.n3.a5
Allen Montz, Hamid Bellout, Frederick Bloom
{"title":"Flow of an incompressible nonlinear bipolar viscous fluid in a sinusoidally constricted channel","authors":"Allen Montz, Hamid Bellout, Frederick Bloom","doi":"10.4310/cis.2023.v23.n3.a5","DOIUrl":"https://doi.org/10.4310/cis.2023.v23.n3.a5","url":null,"abstract":"","PeriodicalId":45018,"journal":{"name":"Communications in Information and Systems","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136302444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.4310/cis.2023.v23.n2.a3
Xu-Qiang Hu, Zi-Xin Zou, Dinesh Manocha
We present a novel solution, TSDFFilter, for remote 3D reconstruction to relieve the high bandwidth requirement problem. Our approach is designed for scenarios where agents are used to collect data using an RGB-D camera and then transmit the information over the regular network to a high-performance server, where a global, dense, and volumetric model of a real-world scene is reconstructed. Our approach uses a content-aware communication planning framework in which agents can prune the gathered RGB-D information according to the transmission policy generated by the server. To generate the transmission policy, we introduce a confidence value to estimate how much each RGB-D pixel contributes to the reconstruction quality, and present an algorithm to find the confidence value. As a result, agents can transmit less RGB-D information without blindly compromising the reconstruction quality as the key-frame method and down-sampling method do. We implement our TSDFFilter framework to achieve real-time agent-assisted 3D reconstruction. Extensive evaluations show that comparing with the key-frame and down-sampling methods, our TSDFFil-ter framework can reduce the bandwidth requirement by up to 36% with similar reconstruction Chamfer distance, and reduce the reconstruction Chamfer distance by up to 78% with similar bandwidth requirement.
{"title":"TSDFFilter: content-aware communication planning for remote 3D reconstruction","authors":"Xu-Qiang Hu, Zi-Xin Zou, Dinesh Manocha","doi":"10.4310/cis.2023.v23.n2.a3","DOIUrl":"https://doi.org/10.4310/cis.2023.v23.n2.a3","url":null,"abstract":"We present a novel solution, TSDFFilter, for remote 3D reconstruction to relieve the high bandwidth requirement problem. Our approach is designed for scenarios where agents are used to collect data using an RGB-D camera and then transmit the information over the regular network to a high-performance server, where a global, dense, and volumetric model of a real-world scene is reconstructed. Our approach uses a content-aware communication planning framework in which agents can prune the gathered RGB-D information according to the transmission policy generated by the server. To generate the transmission policy, we introduce a confidence value to estimate how much each RGB-D pixel contributes to the reconstruction quality, and present an algorithm to find the confidence value. As a result, agents can transmit less RGB-D information without blindly compromising the reconstruction quality as the key-frame method and down-sampling method do. We implement our TSDFFilter framework to achieve real-time agent-assisted 3D reconstruction. Extensive evaluations show that comparing with the key-frame and down-sampling methods, our TSDFFil-ter framework can reduce the bandwidth requirement by up to 36% with similar reconstruction Chamfer distance, and reduce the reconstruction Chamfer distance by up to 78% with similar bandwidth requirement.","PeriodicalId":45018,"journal":{"name":"Communications in Information and Systems","volume":"1 1","pages":"213-239"},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70404404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.4310/cis.2023.v23.n2.a2
Hongmei Yang, Yanqi Liu, Xiaoqiu Jiang, Xinqi Gong
{"title":"Quantitative anatomy of characteristics and influencing factors of PM2.5 and O3 in Liaoning province of China","authors":"Hongmei Yang, Yanqi Liu, Xiaoqiu Jiang, Xinqi Gong","doi":"10.4310/cis.2023.v23.n2.a2","DOIUrl":"https://doi.org/10.4310/cis.2023.v23.n2.a2","url":null,"abstract":"","PeriodicalId":45018,"journal":{"name":"Communications in Information and Systems","volume":"1 1","pages":"185-212"},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70404396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01Epub Date: 2022-07-22DOI: 10.4310/cis.2022.v22.n3.a3
Jiahui Chen, Guo-Wei Wei
Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have compromised existing vaccines and posed a grand challenge to coronavirus disease 2019 (COVID-19) prevention, control, and global economic recovery. For COVID-19 patients, one of the most effective COVID-19 medications is monoclonal antibody (mAb) therapies. The United States Food and Drug Administration (U.S. FDA) has given the emergency use authorization (EUA) to a few mAbs, including those from Regeneron, Eli Elly, etc. However, they are also undermined by SARS-CoV-2 mutations. It is imperative to develop effective mutation-proof mAbs for treating COVID-19 patients infected by all emerging variants and/or the original SARS-CoV-2. We carry out a deep mutational scanning to present the blueprint of such mAbs using algebraic topology and artificial intelligence (AI). To reduce the risk of clinical trial-related failure, we select five mAbs either with FDA EUA or in clinical trials as our starting point. We demonstrate that topological AI-designed mAbs are effective for variants of concerns and variants of interest designated by the World Health Organization (WHO), as well as the original SARS-CoV-2. Our topological AI methodologies have been validated by tens of thousands of deep mutational data and their predictions have been confirmed by results from tens of experimental laboratories and population-level statistics of genome isolates from hundreds of thousands of patients.
{"title":"Mathematical artificial intelligence design of mutation-proof COVID-19 monoclonal antibodies.","authors":"Jiahui Chen, Guo-Wei Wei","doi":"10.4310/cis.2022.v22.n3.a3","DOIUrl":"10.4310/cis.2022.v22.n3.a3","url":null,"abstract":"<p><p>Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have compromised existing vaccines and posed a grand challenge to coronavirus disease 2019 (COVID-19) prevention, control, and global economic recovery. For COVID-19 patients, one of the most effective COVID-19 medications is monoclonal antibody (mAb) therapies. The United States Food and Drug Administration (U.S. FDA) has given the emergency use authorization (EUA) to a few mAbs, including those from Regeneron, Eli Elly, etc. However, they are also undermined by SARS-CoV-2 mutations. It is imperative to develop effective mutation-proof mAbs for treating COVID-19 patients infected by all emerging variants and/or the original SARS-CoV-2. We carry out a deep mutational scanning to present the blueprint of such mAbs using algebraic topology and artificial intelligence (AI). To reduce the risk of clinical trial-related failure, we select five mAbs either with FDA EUA or in clinical trials as our starting point. We demonstrate that topological AI-designed mAbs are effective for variants of concerns and variants of interest designated by the World Health Organization (WHO), as well as the original SARS-CoV-2. Our topological AI methodologies have been validated by tens of thousands of deep mutational data and their predictions have been confirmed by results from tens of experimental laboratories and population-level statistics of genome isolates from hundreds of thousands of patients.</p>","PeriodicalId":45018,"journal":{"name":"Communications in Information and Systems","volume":"22 3","pages":"339-361"},"PeriodicalIF":0.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9881605/pdf/nihms-1825681.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10695510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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