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The Innovation 2020年第3期1卷 68-69页

作者： Wei Miao Lirong Song Sang Ba Longxian Zhang Guiquan Guan Zhang Zhang Kang Ning National Aquatic Biological Resource Center Institute of HydrobiologyChinese Academy of SciencesWuhanChina Laboratory of Wetland and Catchments Ecology in Tibetan Plateau Faculty of Natural SciencesTibet UniversityLhasaChina College of Veterinary Medicine Henan Agricultural UniversityZhengzhouChina State Key Laboratory of Veterinary Etiological Biology Key Laboratory of Veterinary Parasitology of Gansu ProvinceLanzhou Veterinary Research InstituteChinese Academy of Agricultural SciencesLanzhouChina National Genomics Data Center Beijing Institute of GenomicsChinese Academy of SciencesChina National Center for BioinformationBeijingChina Key Laboratory of Molecular Biophysics of the Ministry of Education Hubei Key Laboratory of Bioinformatics and Molecular-imagingCenter of AI BiologyDepartment of Bioinformatics and Systems BiologyCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhanChina

Protists are a highly diverse group of microscopic eukaryotic organisms that are not fungi,animals,or *** some of the microbes first visualized and described by Anton van Leeuwenhoek using themicroscope in the seventeenth *** that,the description and cataloging of these diverse microbial eukaryotes was pursued by microbiologists throughout the following centuries.1 So far,more than 60,000 protist species have been recorded in the NCBI taxonomy system,but many have yet to be *** have long been considered important models in fundamental biological studies,such as cell biology,genetics,ecology and evolution toxicology,and applied fields,including biofuels,nutritional supplements and aquaculture feed production,environmental monitoring and pollution treatment,protozoan parasitic disease treatment and prevention,as well as agriculture.

关键词： prevention taxonomy pollution

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Detecting Hemileia vastatrix using Vision AI as Supporting to Food Security for Smallholder Coffee Commodities

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IOP Conference Series: Earth and Environmental science 2022年第1期998卷

作者： Teddy Suparyanto Erick Firmansyah Tjeng Wawan Cenggoro Digdo Sudigyo Bens Pardamean Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia 55282 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The loss of food crops during the COVID-19 pandemic threatens food security in Indonesia as one of the world's top coffee producers. This affects the insecurity of coffee commodities which is influenced by several factors other than the pandemic such as pests, plant diseases, and extreme weather. Plant diseases, such as leaf rust, are a significant factor in the insecurity problem in coffee commodities. The fungus Hemileia vastatrix B et Br causes leaf rust disease, which is a pest that frequently damages coffee plants. This disease not only interferes with plant growth but also causes a decrease in coffee quality and quantity. This initial research aims to carry out early prevention of these diseases as supporting to food security in smallholder coffee commodities. An AI-based visual detection application is the result of this research. We collected 100 images of coffee leaves from various coffee plants. The image is reshaped to 256 x 256 pixels and randomly trimmed to 224 x 224 pixels to fulfill the size requirements of a standard Deep Learning technique. Each image was classified into two classes by a plant pest and disease specialist. The dataset was divided into training, validation, and testing series with a ratio of 60:20:20 for training procedures. The Convolutional Neural Network (CNN) research method used a variation of the ResNet CNN model with 18 layers. The best model validation was 59%.

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Artificial Intelligence for Predicting Recurrence of Clear Cell Renal Cell Carcinoma Using Multimodal data: A Multicenter Study

SSRN

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SSRN 2024年

作者： Zang, Xinyi Xia, Yujia Xiao, Haibing Luo, Haolun Si, Guiming Hou, Naiqiao Haoni, Axide Chen, Tianyi Pu, Xinyuan Zi, Xiangyu Xu, Lijun Zhu, Jin Xu, Zhipeng Wang, Jianning Wang, Zaoyu Xia, Jun Cao, Dengfeng Yin, Yu Wu, Xiaorong Kong, Wen Huang, Jiwei Zhang, Jin Chen, Yonghui Huang, Yiran Xue, Wei Wang, Keliang Liang, Chaozhao Zheng, Junhua Yu, Zhangsheng Zhai, Wei Department of Urology Renji Hospital School of Medicine in Shanghai Jiao Tong University 160 Pujian Road Shanghai200127 China Department of Bioinformatics and Biostatistics School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai China SJTU-Yale Joint Center for Biostatistics and Data Science School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai China Department of Urology The First Affiliated Hospital of Anhui Medical University Anhui Medical University Anhui Hefei China Department of Urology Kaohsiung Chang Gung Memorial Hospital Chang Gung University College of Medicine Kaohsiung Taiwan Department of Urology The Fourth Affiliated Hospital of Harbin Medical University Heilongjiang Key Laboratory of Scientific Research in Urology Harbin150001 China Department of Urology The Second Affiliated Hospital of Soochow University Jiangsu Suzhou215004 China Department of Urology The First Affiliated Hospital Shandong First Medical University Jinan China Department of Pathology Renji Hospital School of Medicine in Shanghai Jiao Tong University 160 Pujian Road Shanghai200127 China Department of Pathology The First Affiliated Hospital of Anhui Medical University Anhui Hefei China Clinical Research Institute Shanghai Jiao Tong University School of Medicine Shanghai China Shanghai Immune Therapy Institute State Key Laboratory of Oncogenes and Related Genes Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200127 China Department of Evidence-Based Medicine China

Background: Current prognostic models have limitations in predicting postoperative recurrence in renal clear cell carcinoma due to tumour heterogeneity. This study aims to develop and validate a multimodal predictive recurrence score (MPRS) to enhance recurrence prediction and patient stratification by integrating clinical features, CT imaging, and histological whole section images (WSIs). Methods: This multicenter retrospective study analyzed multimodal data from 1,145 ccRCC patients treated surgically across 6 centers in China and WSIs from 503 TCGA patients. The primary outcome was disease-free survival (DFS), defined as the time from surgery to the first recurrence or end of follow-up. Assessment of model efficacy using time-dependent ROC curves, calibration curves, survival curves. Assessing the impact of regions on the model using Grad-CAM visualisation analysis. Findings: MPRS performed better than other unimodal models in time-dependent ROC curves. At 3 years survival, the AUC values for MPRS were 0.944, 0.907, and 0.835;at 5 years survival, the AUC values of MPRS were 0.960, 0.897 and 0.829 for the training, validation, and test sets, respectively. There was strong agreement between MPRS predictions and observations in the calibration curves. The MPRS showed significant differences in subgroup analyses for both predicting DFS and various prognostic models. Interpretation: The MPRS is effective in predicting recurrence risk by integrating multimodal data and provides insights into the management of patients with ccRCC, potentially guiding adjuvant therapeutic decisions. Funding: This study was supported by National Natural science Foundation of China (82173214, 82473386);the National Key Research and Development Program of China (2022YFC2505305);Basic-Clinical Collaborative Innovation Project from Shanghai Immune Therapy Institute;Shanghai science and Technology Commission Medical Innovation Clinical Research Special Project (23Y21900400);Excellent Project o

关键词： Deep learning

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Feature Pyramid Networks for Crowd Counting

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Procedia Computer science 2019年 157卷 175-182页

作者： Tjeng Wawan Cenggoro Ayu Hidayah Aslamiah Ardian Yunanto Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

In this paper, we propose the use of Feature Pyramid Networks (FPN) for Crowd Counting problem. FPN previously has been used for retinanet, the state-of-the-art model for object detection. By using FPN, our proposed crowd counting model achieved a state-of-the-art performance for UCF CC 50 dataset with MAE 136.4 and MSE 223.6. The proposed model is also achieved a state-of-the-art MSE value of 7.6 for ShanghaiTech Part B dataset. The code can be accessed at https: //***/wawancenggoro/fpncc.

关键词： Crowd Counting Scale-Aware Deep Learning Feature Pyramid Networks

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Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice

Network and Systems Medicine

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Network and Systems Medicine 2020年第1期3卷 36-56页

作者： Stalidzans, Egils Zanin, Massimiliano Tieri, Paolo Castiglione, Filippo Polster, Annikka Scheiner, Stefan Pahle, Jürgen Stres, Blaž List, Markus Baumbach, Jan Lautizi, Manuela Van Steen, Kristel Schmidt, Harald H.H.W. Computational Systems Biology Group University of Latvia Riga Latvia Latvian Biomedical Reasearch and Study Centre Riga Latvia Centro de Tecnología Biomédica Universidad Politécnica de Madrid Pozuelo de Alarcón Spain CNR National Research Council IAC Institute for Applied Computing Rome Italy Oslo Norway Institute for Mechanics of Materials and Structures Vienna University of Technology Vienna Austria BioQuant Heidelberg University Heidelberg Germany Department of Animal Science University of Ljubljana Ljubljana Slovenia Faculty of Civil and Geodetic Engineering University of Ljubljana Ljubljana Slovenia Department of Automation Biocybernetics and Robotics Jozef Stefan Institute Ljubljana Slovenia Big Data in BioMedicine Research Group Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany Computational Systems Medicine Research Group Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany BIO-Systems Genetics GIGA-R University of Liège Liège Belgium BIO3 - Systems Medicine Department of Human Genetics KU Leuven Leuven Belgium Department of Pharmacology and Personalised Medicine Faculty of Health Medicine and Life Science Maastricht University Maastricht Netherlands

Drug research, therapy development, and other areas of pharmacology and medicine can benefit from simulations and optimization of mathematical models that contain a mathematical description of interactions between systems elements at the cellular, tissue, organ, body, and population level. This approach is the foundation of systems medicine and precision medicine. Here, simulated experiments are performed with computers (in silico) first, and they are then replicated through lab experiments (in vivo or in vitro) or clinical studies. In turn, these experiments and studies can be used to validate or improve the models. This iterative loop of dry and wet lab work is successful when biomedical researchers tightly collaborate with data scientists and modelers. From an educational point of view, the interdisciplinary research in systems biology can be sustained most effectively when specialists have been trained to have both a strong background in the disciplines of biology or modeling and strong communication skills, which make them able to communicate with other specialists. This overview addresses possible interdisciplinary communication gaps. Focusing our attention on biomedical researchers, we describe the reasons for using modeling and ways to collaborate with modelers, including their needs for specific biological expertise and data. This review includes an introduction to the principles of several widely used mechanistic modeling methods, focusing on their areas of applicability as well as their limitations. A potential complementary role of machine-learning methods in the development of mechanistic models is also discussed. The descriptions of the methods also include links to corresponding modeling software tools as well as practical examples of their application. Finally, we also explicitly address different aspects of multiscale modeling approaches that allow a more complete and holistic perspective of the human body. © Egils Stalidzans et al. 2020 Published b

关键词： Iterative methods

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iTReX: Interactive exploration of mono- and combination therapy dose response profiling data

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Pharmacological Research 2022年 175卷 105996-105996页

作者： ElHarouni, Dina Berker, Yannick Peterziel, Heike Gopisetty, Apurva Turunen, Laura Kreth, Sina Stainczyk, Sabine A. Oehme, Ina Pietiäinen, Vilja Jäger, Natalie Witt, Olaf Schlesner, Matthias Oppermann, Sina Bioinformatics and Omics Data Analytics German Cancer Research Center (DKFZ) Heidelberg Germany Hopp Children's Cancer Center (KiTZ) Heidelberg Germany Division of Pediatric Neurooncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Clinical Cooperation Unit Pediatric Oncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Institute for Molecular Medicine Finland (FIMM) Helsinki Institute of Life Science (HiLIFE) University of Helsinki Helsinki Finland Division of Neuroblastoma Genomics German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Department of Pediatric Oncology Hematology Immunology and Pulmonology Heidelberg University Hospital Heidelberg Germany Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg Germany

High throughput screening methods, measuring the sensitivity and resistance of tumor cells to drug treatments have been rapidly evolving. Not only do these screens allow correlating response profiles to tumor genomic features for developing novel predictors of treatment response, but they can also add evidence for therapy decision making in precision oncology. Recent analysis methods developed for either assessing single agents or combination drug efficacies enable quantification of dose-response curves with restricted symmetric fit settings. Here, we introduce iTReX, a user-friendly and interactive Shiny/R application, for both the analysis of mono- and combination therapy responses. The application features an extended version of the drug sensitivity score (DSS) based on the integral of an advanced five-parameter dose-response curve model and a differential DSS for combination therapy profiling. Additionally, iTReX includes modules that visualize drug target interaction networks and support the detection of matches between top therapy hits and the sample omics features to enable the identification of druggable targets and biomarkers. iTReX enables the analysis of various quantitative drug or therapy response readouts (e.g. luminescence, fluorescence microscopy) and multiple treatment strategies (drug treatments, radiation). Using iTReX we validate a cost-effective drug combination screening approach and reveal the application's ability to identify potential sample-specific biomarkers based on drug target interaction networks. The iTReX web application is accessible at https://*** © 2021 The Authors

关键词： Asymmetric sensitivity scoring Differential combination sensitivity scoring Drug target networks Interactive drug screen analysis Personalized medicine Therapy response profiling and exploration

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Author Correction: Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis

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Nature communications 2023年第1期14卷 6540页

作者： Hitomi Takada Yohei Sasagawa Mika Yoshimura Kaori Tanaka Yoshimi Iwayama Tetsutaro Hayashi Ayako Isomura-Matoba Itoshi Nikaido Akira Kurisaki Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan. Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan. Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan. Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan. itoshi.nikaido@riken.jp. Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan. itoshi.nikaido@riken.jp. Master's/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan. itoshi.nikaido@riken.jp. Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan. akikuri@bs.naist.jp.

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What makes a reaction network "chemical"?

arXiv

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arXiv 2022年

作者： Müller, Stefan Flamm, Christoph Stadler, Peter F. Faculty of Mathematics University of Vienna Oskar-Morgenstern-Platz 1 WienA-1090 Austria Department of Theoretical Chemistry University of Vienna Währinger Straße 17 WienA-1090 Austria Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Halle-Jena-Leipzig & Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases University Leipzig LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Faculdad de Ciencias Universidad Nacional de Colombia Sede Bogotá Ciudad Universitaria DC BogotáCOL-111321 Colombia Santa Fe Institute 1399 Hyde Park Road Santa FeNM87501 United States

Background: Reaction networks (RNs) comprise a set X of species and a set R of reactions Y → Y′, each converting a multiset of educts Y ⊆ X into a multiset Y′ ⊆ X of products. RNs are equivalent to directed hypergraphs. However, not all RNs necessarily admit a chemical interpretation. Instead, they might contradict fundamental principles of physics such as the conservation of energy and mass or the reversibility of chemical reactions. The consequences of these necessary conditions for the stoichiometric matrix S ∈ X×R have been discussed extensively in the chemical literature. Here, we provide sufficient conditions for S that guarantee the interpretation of RNs in terms of balanced sum formulas and structural formulas, respectively. Results: Chemically plausible RNs allow neither a perpetuum mobile, i.e., a "futile cycle" of reactions with non-vanishing energy production, nor the creation or annihilation of mass. Such RNs are said to be thermodynamically sound and conservative. For finite RNs, both conditions can be expressed equivalently as properties of the stoichiometric matrix S. The first condition is vacuous for reversible networks, but it excludes irreversible futile cycles and – in a stricter sense – futile cycles that even contain an irreversible reaction. The second condition is equivalent to the existence of a strictly positive reaction invariant. Furthermore, it is sufficient for the existence of a realization in terms of sum formulas, obeying conservation of "atoms". In particular, these realizations can be chosen such that any two species have distinct sum formulas, unless S implies that they are "obligatory isomers". In terms of structural formulas, every compound is a labeled multigraph, in essence a Lewis formula, and reactions comprise only a rearrangement of bonds such that the total bond order is preserved. In particular, for every conservative RN, there exists a Lewis realization, in which any two compounds are realized by pairwisely distinct mu

关键词： Energy conservation

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Increased Donor-Derived Cell-Free DNA as a Predictor for the Early Detection of Antibody-Mediated Rejection Following Heart Transplantation

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Clinical transplantation 2025年第6期39卷 e70209页

作者： Dana Dlouha Sarka Chytilova Jevgenija Vymetalova Eva Rohlova Marianna Lukasova Sarka Novakova Jaroslav Alois Hubacek Experimental Medicine Center Institute of Clinical and Experimental Medicine Prague Czech Republic. Department of Data Science Institute of Clinical and Experimental Medicine Prague Czech Republic. Cardio Center Institute of Clinical and Experimental Medicine Prague Czech Republic. Laboratory of Genomics and Bioinformatics Institute of Molecular Genetics of the Czech Academy of Sciences Prague Czech Republic. Third Department of Internal Medicine First Faculty of Medicine Charles University Prague Czech Republic.

Plasma circulating donor-derived cell-free DNA (ddcfDNA) can be used to noninvasively monitor acute rejection of heart transplants (HTx). This study utilized digital droplet PCR to analyze ddcfDNA concentrations (measured in copies per milliliter) and the fractional abundance (%ddcfDNA) to differentiate between donor and recipient DNA on the basis of single nucleotide polymorphism (SNP) homozygosity. Seventy-seven patients participated in a study, providing 300 plasma samples. Both markers, mean ddcfDNA (cp/mL) and %ddcfDNA, showed similar decreasing trends following the HTx, (R < 0.2; p < 0.001). Significantly higher levels of ddcfDNA (cp/mL) and %ddcfDNA were observed during episodes of acute rejection (AR) compared to non-rejection samples (p < 0.001). Additionally, antibody-mediated rejection (AMR) was associated with increased %ddcfDNA levels compared to non-rejection and to acute cellular rejection samples (p < 0.001 and p < 0.01). A logistic regression model identified %ddcfDNA as an early predictor of AMR risk 10-19 days post-heart transplant (odds ratio 158, p < 0.02). Performance analysis established an optimal %ddcfDNA threshold of 0.125% for AMR detection, correctly identifying all patients without subsequent AMR. These findings suggest that early %ddcfDNA measurements post-HTx can accurately identify individuals unlikely to develop AMR during the first posttransplant year.

关键词： SNP biomarker cfDNA rejection transplantation

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Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies

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Cell Genomics 2025年第5期5卷 100845-100845页

作者： Huang, Yu-Ning Munteanu, Viorel Love, Michael I. Ronkowski, Cynthia Flaire Deshpande, Dhrithi Wong-Beringer, Annie Corbett-Detig, Russell Dimian, Mihai Moore, Jason H. Garmire, Lana X. Reddy, T.B.K. Butte, Atul J. Robinson, Mark D. Eskin, Eleazar Abedalthagafi, Malak S. Mangul, Serghei Department of Clinical Pharmacy Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences University of Southern California Los Angeles 90089 CA United States Department of Computers Informatics and Microelectronics Technical University of Moldova Chisinau 2045 Moldova Department of Biological and Morphofunctional Sciences College of Medicine and Biological Sciences Stefan cel Mare University of Suceava Suceava 720229 Romania Department of Genetics University of North Carolina at Chapel Hill Chapel Hill 27514 NC United States Department of Biostatistics University of North Carolina at Chapel Hill Chapel Hill 27514 NC United States Department of Biomolecular Engineering University of California Santa Cruz Santa Cruz 95064 CA United States Genomics Institute University of California Santa Cruz Santa Cruz 95064 CA United States Department of Computers Electronics and Automation Stefan cel Mare University of Suceava Suceava 720229 Romania Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles 90069 CA United States Department of Computational Medicine and Bioinformatics University of Michigan Medical School Ann Arbor 48105 MI United States US Department of Energy Joint Genome Institute Lawrence Berkeley National Laboratory Berkeley 94720 CA United States Bakar Computational Health Sciences Institute University of California San Francisco (UCSF) San Francisco 94143 CA United States Center for Data-Driven Insights and Innovation University of California Oakland Oakland 94607 CA United States SIB Swiss Institute of Bioinformatics and Department of Molecular Life Sciences University of Zurich Zurich 8057 Switzerland Department of Computer Science University of California Los Angeles Los Angeles CA United States Department of Computational Medicine University of California Los Angeles Los Angeles CA United States Department of Human Genetics University of California Los Angeles Los Angeles CA United Stat

Metadata, or “data about data,” is essential for organizing, understanding, and managing large-scale omics datasets. It enhances data discovery, integration, and interpretation, enabling reproducibility, reusability, and secondary analysis. However, metadata sharing remains hindered by perceptual and technical barriers, including the lack of uniform standards, privacy concerns, study design limitations, insufficient incentives, inadequate infrastructure, and a shortage of trained personnel. These challenges compromise data reliability and obstruct integrative meta-analyses. Addressing these issues requires standardization, education, stronger roles for journals and funding agencies, and improved incentives and infrastructure. Looking ahead, emerging technologies such as artificial intelligence and machine learning may offer promising solutions to automate metadata processes, increasing accuracy and scalability. Fostering a collaborative culture of metadata sharing will maximize the value of omics data, accelerating innovation and scientific discovery. © 2025 The Authors

关键词： barriers in metadata sharing practices data metadata metadata completeness

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