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  1. Article ; Online: R

    Li, Ao / Zou, Xingfu

    Bulletin of mathematical biology

    2024  Volume 86, Issue 4, Page(s) 41

    Abstract: This paper examines the short-term or transient dynamics of SIR infectious disease models in patch environments. We employ reactivity of an equilibrium and amplification rates, concepts from ecology, to analyze how dispersals/travels between patches, ... ...

    Abstract This paper examines the short-term or transient dynamics of SIR infectious disease models in patch environments. We employ reactivity of an equilibrium and amplification rates, concepts from ecology, to analyze how dispersals/travels between patches, spatial heterogeneity, and other disease-related parameters impact short-term dynamics. Our findings reveal that in certain scenarios, due to the impact of spatial heterogeneity and the dispersals, the short-term disease dynamics over a patch environment may disagree with the long-term disease dynamics that is typically reflected by the basic reproduction number. Such an inconsistence can mislead the public, public healthy agencies and governments when making public health policy and decisions, and hence, these findings are of practical importance.
    MeSH term(s) Humans ; Epidemiological Models ; Models, Biological ; Mathematical Concepts ; Communicable Diseases/epidemiology ; Ecology ; Basic Reproduction Number ; Population Dynamics
    Language English
    Publishing date 2024-03-15
    Publishing country United States
    Document type Journal Article
    ZDB-ID 184905-0
    ISSN 1522-9602 ; 0007-4985 ; 0092-8240
    ISSN (online) 1522-9602
    ISSN 0007-4985 ; 0092-8240
    DOI 10.1007/s11538-024-01271-7
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  2. Article ; Online: R

    Jin, Tongtong / Yin, Jinlong / Wang, Tao / Xue, Song / Li, Bowen / Zong, Tingxuan / Yang, Yunhua / Liu, Hui / Liu, Mengzhuo / Xu, Kai / Wang, Liqun / Xing, Guangnan / Zhi, Haijian / Li, Kai

    Journal of integrative plant biology

    2022  Volume 65, Issue 3, Page(s) 838–853

    Abstract: Soybean mosaic virus (SMV) is one of the most devastating viral pathogens of soybean (Glycine max (L.) Merr). In total, 22 Chinese SMV strains (SC1-SC22) have been classified based on the responses of 10 soybean cultivars to these pathogens. However, ... ...

    Abstract Soybean mosaic virus (SMV) is one of the most devastating viral pathogens of soybean (Glycine max (L.) Merr). In total, 22 Chinese SMV strains (SC1-SC22) have been classified based on the responses of 10 soybean cultivars to these pathogens. However, although several SMV-resistance loci in soybean have been identified, no gene conferring SMV resistance in the resistant soybean cultivar (cv.) Kefeng No.1 has been cloned and verified. Here, using F
    MeSH term(s) Glycine max/genetics ; Viral Proteins ; Potyvirus/genetics ; Ribonucleases ; Plant Diseases/genetics
    Chemical Substances Viral Proteins ; Ribonucleases (EC 3.1.-)
    Language English
    Publishing date 2022-12-31
    Publishing country China (Republic : 1949- )
    Document type Journal Article
    ZDB-ID 2130095-1
    ISSN 1744-7909 ; 1672-9072
    ISSN (online) 1744-7909
    ISSN 1672-9072
    DOI 10.1111/jipb.13401
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  3. Article ; Online: R

    Kuang, Qihong / He, Dandan / Sun, Huarun / Hu, Huihui / Li, Fulin / Li, Wenya / Hu, Gongzheng / Wu, Hua / Yuan, Li

    Antimicrobial agents and chemotherapy

    2020  Volume 64, Issue 11

    Abstract: Here, the mechanisms of colistin heteroresistance (CHR) were assessed in ... ...

    Abstract Here, the mechanisms of colistin heteroresistance (CHR) were assessed in 12
    MeSH term(s) Acyltransferases ; Amino Acid Substitution ; Animals ; Anti-Bacterial Agents/pharmacology ; Bacterial Proteins/genetics ; China ; Colistin/pharmacology ; Drug Resistance, Bacterial/genetics ; Escherichia coli/genetics ; Escherichia coli Proteins/genetics ; Microbial Sensitivity Tests ; Swine ; Transcription Factors/genetics
    Chemical Substances Anti-Bacterial Agents ; Bacterial Proteins ; Escherichia coli Proteins ; MCR-1 protein, E coli ; Transcription Factors ; Acyltransferases (EC 2.3.-) ; PagP protein, E coli (EC 2.3.1.-) ; Colistin (Z67X93HJG1)
    Language English
    Publishing date 2020-10-20
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 217602-6
    ISSN 1098-6596 ; 0066-4804
    ISSN (online) 1098-6596
    ISSN 0066-4804
    DOI 10.1128/AAC.01509-20
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: R

    Wang, Xiunan / Wang, Hao / Li, Michael Y

    Mathematical biosciences

    2019  Volume 315, Page(s) 108225

    Abstract: ... differential equations with a time delay. We define the basic reproduction ratio R ...

    Abstract Coexistence and seasonal fluctuations of predator and prey populations are common and well documented in ecology. Under what conditions can predators coexist with prey in a seasonally changing environment? What factors drive long-term population cycles of some predator and prey species? To answer these questions, we investigate an improved predator-prey model based on the Rosenzweig-MacArthur [1] model. Our model incorporates seasonality and a predator maturation delay, leading to a system of periodic differential equations with a time delay. We define the basic reproduction ratio R
    MeSH term(s) Animals ; Basic Reproduction Number ; Daphnia/physiology ; Food Chain ; Models, Biological ; Seasons
    Language English
    Publishing date 2019-07-05
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1126-5
    ISSN 1879-3134 ; 0025-5564
    ISSN (online) 1879-3134
    ISSN 0025-5564
    DOI 10.1016/j.mbs.2019.108225
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  5. Article ; Online: R

    Gao, Quanxue / Xu, Sai / Chen, Fang / Ding, Chris / Gao, Xinbo / Li, Yunsong

    IEEE transactions on cybernetics

    2018  Volume 49, Issue 4, Page(s) 1212–1223

    Abstract: ... this problem, we present an efficient robust method, namely R ...

    Abstract 2-D principal component analysis (2-DPCA) is one of the successful dimensionality reduction approaches for image classification and representation. However, 2-DPCA is not robust to outliers. To tackle this problem, we present an efficient robust method, namely R
    Language English
    Publishing date 2018-02-08
    Publishing country United States
    Document type Journal Article
    ISSN 2168-2275
    ISSN (online) 2168-2275
    DOI 10.1109/TCYB.2018.2796642
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  6. Article: R-Loop Functions in

    Chiang, Huai-Chin / Qi, Leilei / Mitra, Payal / Hu, Yanfen / Li, Rong

    bioRxiv : the preprint server for biology

    2024  

    Abstract: Excessive R-loops, a DNA-RNA hybrid structure, are associated with genome instability and ...

    Abstract Excessive R-loops, a DNA-RNA hybrid structure, are associated with genome instability and
    Language English
    Publishing date 2024-02-16
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.02.14.580374
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  7. Book ; Online: R-Judge

    Yuan, Tongxin / He, Zhiwei / Dong, Lingzhong / Wang, Yiming / Zhao, Ruijie / Xia, Tian / Xu, Lizhen / Zhou, Binglin / Li, Fangqi / Zhang, Zhuosheng / Wang, Rui / Liu, Gongshen

    Benchmarking Safety Risk Awareness for LLM Agents

    2024  

    Abstract: ... within diverse environments. We introduce R-Judge, a benchmark crafted to evaluate the proficiency of LLMs ... in judging safety risks given agent interaction records. R-Judge comprises 162 agent interaction records ... consensus on safety with annotated safety risk labels and high-quality risk descriptions. Utilizing R-Judge ...

    Abstract Large language models (LLMs) have exhibited great potential in autonomously completing tasks across real-world applications. Despite this, these LLM agents introduce unexpected safety risks when operating in interactive environments. Instead of centering on LLM-generated content safety in most prior studies, this work addresses the imperative need for benchmarking the behavioral safety of LLM agents within diverse environments. We introduce R-Judge, a benchmark crafted to evaluate the proficiency of LLMs in judging safety risks given agent interaction records. R-Judge comprises 162 agent interaction records, encompassing 27 key risk scenarios among 7 application categories and 10 risk types. It incorporates human consensus on safety with annotated safety risk labels and high-quality risk descriptions. Utilizing R-Judge, we conduct a comprehensive evaluation of 8 prominent LLMs commonly employed as the backbone for agents. The best-performing model, GPT-4, achieves 72.29% in contrast to the human score of 89.38%, showing considerable room for enhancing the risk awareness of LLMs. Notably, leveraging risk descriptions as environment feedback significantly improves model performance, revealing the importance of salient safety risk feedback. Furthermore, we design an effective chain of safety analysis technique to help the judgment of safety risks and conduct an in-depth case study to facilitate future research. R-Judge is publicly available at https://github.com/Lordog/R-Judge.
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Publishing date 2024-01-18
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: R-MAE

    Nguyen, Duy-Kien / Aggarwal, Vaibhav / Li, Yanghao / Oswald, Martin R. / Kirillov, Alexander / Snoek, Cees G. M. / Chen, Xinlei

    Regions Meet Masked Autoencoders

    2023  

    Abstract: ... effective especially with high-quality regions. When integrated with MAE, our approach (R-MAE) demonstrates ... segmentation. The code is provided at https://github.com/facebookresearch/r-mae. ...

    Abstract In this work, we explore regions as a potential visual analogue of words for self-supervised image representation learning. Inspired by Masked Autoencoding (MAE), a generative pre-training baseline, we propose masked region autoencoding to learn from groups of pixels or regions. Specifically, we design an architecture which efficiently addresses the one-to-many mapping between images and regions, while being highly effective especially with high-quality regions. When integrated with MAE, our approach (R-MAE) demonstrates consistent improvements across various pre-training datasets and downstream detection and segmentation benchmarks, with negligible computational overheads. Beyond the quantitative evaluation, our analysis indicates the models pre-trained with masked region autoencoding unlock the potential for interactive segmentation. The code is provided at https://github.com/facebookresearch/r-mae.
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 006
    Publishing date 2023-06-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: R-Mixup

    Kan, Xuan / Li, Zimu / Cui, Hejie / Yu, Yue / Xu, Ran / Yu, Shaojun / Zhang, Zilong / Guo, Ying / Yang, Carl

    Riemannian Mixup for Biological Networks

    2023  

    Abstract: ... networks usually faces severe overfitting. In this work, we propose R-MIXUP, a Mixup-based data ... from biological networks with optimized training efficiency. The interpolation process in R-MIXUP leverages ... effect and arbitrarily incorrect label issues of vanilla Mixup. We demonstrate the effectiveness of R ...

    Abstract Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological entities. However, due to their characteristics of high dimensionality and low sample size, directly applying deep learning models on biological networks usually faces severe overfitting. In this work, we propose R-MIXUP, a Mixup-based data augmentation technique that suits the symmetric positive definite (SPD) property of adjacency matrices from biological networks with optimized training efficiency. The interpolation process in R-MIXUP leverages the log-Euclidean distance metrics from the Riemannian manifold, effectively addressing the swelling effect and arbitrarily incorrect label issues of vanilla Mixup. We demonstrate the effectiveness of R-MIXUP with five real-world biological network datasets on both regression and classification tasks. Besides, we derive a commonly ignored necessary condition for identifying the SPD matrices of biological networks and empirically study its influence on the model performance. The code implementation can be found in Appendix E.

    Comment: Accepted to KDD 2023
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Quantitative Biology - Quantitative Methods ; 68T07 ; 68T05 ; I.2.6 ; J.3
    Subject code 612 ; 006
    Publishing date 2023-06-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Measurement of the Ratios of Branching Fractions R(D^{*}) and R(D^{0}).

    Aaij, R / Abdelmotteleb, A S W / Abellan Beteta, C / Abudinén, F / Ackernley, T / Adeva, B / Adinolfi, M / Adlarson, P / Afsharnia, H / Agapopoulou, C / Aidala, C A / Ajaltouni, Z / Akar, S / Akiba, K / Albicocco, P / Albrecht, J / Alessio, F / Alexander, M / Alfonso Albero, A /
    Aliouche, Z / Alvarez Cartelle, P / Amalric, R / Amato, S / Amey, J L / Amhis, Y / An, L / Anderlini, L / Andersson, M / Andreianov, A / Andreotti, M / Andreou, D / Ao, D / Archilli, F / Artamonov, A / Artuso, M / Aslanides, E / Atzeni, M / Audurier, B / Bachiller Perea, I B / Bachmann, S / Bachmayer, M / Back, J J / Bailly-Reyre, A / Baladron Rodriguez, P / Balagura, V / Baldini, W / Baptista de Souza Leite, J / Barbetti, M / Barlow, R J / Barsuk, S / Barter, W / Bartolini, M / Baryshnikov, F / Basels, J M / Bassi, G / Batsukh, B / Battig, A / Bay, A / Beck, A / Becker, M / Bedeschi, F / Bediaga, I B / Beiter, A / Belin, S / Bellee, V / Belous, K / Belov, I / Belyaev, I / Benane, G / Bencivenni, G / Ben-Haim, E / Berezhnoy, A / Bernet, R / Bernet Andres, S / Berninghoff, D / Bernstein, H C / Bertella, C / Bertolin, A / Betancourt, C / Betti, F / Bezshyiko, Ia / Bhasin, S / Bhom, J / Bian, L / Bieker, M S / Biesuz, N V / Billoir, P / Biolchini, A / Birch, M / Bishop, F C R / Bitadze, A / Bizzeti, A / Blago, M P / Blake, T / Blanc, F / Blank, J E / Blusk, S / Bobulska, D / Boelhauve, J A / Boente Garcia, O / Boettcher, T / Boldyrev, A / Bolognani, C S / Bolzonella, R / Bondar, N / Borgato, F / Borghi, S / Borsato, M / Borsuk, J T / Bouchiba, S A / Bowcock, T J V / Boyer, A / Bozzi, C / Bradley, M J / Braun, S / Brea Rodriguez, A / Brodzicka, J / Brossa Gonzalo, A / Brown, J / Brundu, D / Buonaura, A / Buonincontri, L / Burke, A T / Burr, C / Bursche, A / Butkevich, A / Butter, J S / Buytaert, J / Byczynski, W / Cadeddu, S / Cai, H / Calabrese, R / Calefice, L / Cali, S / Calvi, M / Calvo Gomez, M / Campana, P / Campora Perez, D H / Campoverde Quezada, A F / Capelli, S / Capriotti, L / Carbone, A / Cardinale, R / Cardini, A / Carniti, P / Carus, L / Casais Vidal, A / Caspary, R / Casse, G / Cattaneo, M / Cavallero, G / Cavallini, V / Celani, S / Cerasoli, J / Cervenkov, D / Chadwick, A J / Chahrour, I / Chapman, M G / Charles, M / Charpentier, Ph / Chavez Barajas, C A / Chefdeville, M / Chen, C / Chen, S / Chernov, A / Chernyshenko, S / Chobanova, V / Cholak, S / Chrzaszcz, M / Chubykin, A / Chulikov, V / Ciambrone, P / Cicala, M F / Cid Vidal, X / Ciezarek, G / Cifra, P / Ciullo, G / Clarke, P E L / Clemencic, M / Cliff, H V / Closier, J / Cobbledick, J L / Coco, V / Coelho, J A B / Cogan, J / Cogneras, E / Cojocariu, L / Collins, P / Colombo, T / Congedo, L / Contu, A / Cooke, N / Corredoira, I / Corti, G / Couturier, B / Craik, D C / Cruz Torres, M / Currie, R / Da Silva, C L / Dadabaev, S / Dai, L / Dai, X / Dall'Occo, E / Dalseno, J / D'Ambrosio, C / Daniel, J / Danilina, A / d'Argent, P / Davies, J E / Davis, A / De Aguiar Francisco, O / de Boer, J / De Bruyn, K / De Capua, S / De Cian, M / De Freitas Carneiro Da Graca, U / De Lucia, E / De Miranda, J M / De Paula, L / De Serio, M / De Simone, D / De Simone, P / De Vellis, F / de Vries, J A / Dean, C T / Debernardis, F / Decamp, D / Dedu, V / Del Buono, L / Delaney, B / Dembinski, 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/ Jones, T P / Jost, B / Jurik, N / Juszczak, I / Kandybei, S / Kang, Y / Karacson, M / Karpenkov, D / Karpov, M / Kautz, J W / Keizer, F / Keller, D M / Kenzie, M / Ketel, T / Khanji, B / Kharisova, A / Kholodenko, S / Khreich, G / Kirn, T / Kirsebom, V S / Kitouni, O / Klaver, S / Kleijne, N / Klimaszewski, K / Kmiec, M R / Koliiev, S / Kolk, L / Kondybayeva, A / Konoplyannikov, A / Kopciewicz, P / Kopecna, R / Koppenburg, P / Korolev, M / Kostiuk, I / Kot, O / Kotriakhova, S / Kozachuk, A / Kravchenko, P / Kravchuk, L / Krawczyk, R D / Kreps, M / Kretzschmar, S / Krokovny, P / Krupa, W / Krzemien, W / Kubat, J / Kubis, S / Kucewicz, W / Kucharczyk, M / Kudryavtsev, V / Kulikova, E K / Kupsc, A / Lacarrere, D / Lafferty, G / Lai, A / Lampis, A / Lancierini, D / Landesa Gomez, C / Lane, J J / Lane, R / Langenbruch, C / Langer, J / Lantwin, O / Latham, T / Lazzari, F / Lazzaroni, M / Le Gac, R / Lee, S H / Lefèvre, R / Leflat, A / Legotin, S / Lenisa, P / Leroy, O / Lesiak, T / 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    Physical review letters

    2023  Volume 131, Issue 11, Page(s) 111802

    Abstract: The ratios of branching fractions R(D^{*})≡B(B[over ¯]→D^{*}τ^{-}ν[over ¯]_{τ})/B(B[over ¯]→D^{*}μ^ ... ν[over ¯]_{μ}) and R(D^{0})≡B(B^{-}→D^{0}τ^{-}ν[over ¯]_{τ})/B(B^{-}→D^{0}μ^{-}ν[over ¯]_{μ}) are ... is identified in the decay mode τ^{-}→μ^{-}ν_{τ}ν[over ¯]_{μ}. The measured values are R(D^{*})=0 ...

    Abstract The ratios of branching fractions R(D^{*})≡B(B[over ¯]→D^{*}τ^{-}ν[over ¯]_{τ})/B(B[over ¯]→D^{*}μ^{-}ν[over ¯]_{μ}) and R(D^{0})≡B(B^{-}→D^{0}τ^{-}ν[over ¯]_{τ})/B(B^{-}→D^{0}μ^{-}ν[over ¯]_{μ}) are measured, assuming isospin symmetry, using a sample of proton-proton collision data corresponding to 3.0  fb^{-1} of integrated luminosity recorded by the LHCb experiment during 2011 and 2012. The tau lepton is identified in the decay mode τ^{-}→μ^{-}ν_{τ}ν[over ¯]_{μ}. The measured values are R(D^{*})=0.281±0.018±0.024 and R(D^{0})=0.441±0.060±0.066, where the first uncertainty is statistical and the second is systematic. The correlation between these measurements is ρ=-0.43. The results are consistent with the current average of these quantities and are at a combined 1.9 standard deviations from the predictions based on lepton flavor universality in the standard model.
    Language English
    Publishing date 2023-09-29
    Publishing country United States
    Document type Journal Article
    ZDB-ID 208853-8
    ISSN 1079-7114 ; 0031-9007
    ISSN (online) 1079-7114
    ISSN 0031-9007
    DOI 10.1103/PhysRevLett.131.111802
    Database MEDical Literature Analysis and Retrieval System OnLINE

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