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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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Automated Detection of Poultry Farms from Aerial Images for Actionable AI System toward Biosecurity Applications

Automated Detection of Poultry Farms from Aerial Images for ...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Li, Yu Das, Rana Duong, Cuong Lim, Teng-Teeh Haithcoat, Timothy Ersoy, Ilker Shyu, Chi-Ren University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States University of Missouri Biological Engineering ColumbiaMO United States University of Missouri Plant Science and Technology Extension ColumbiaMO United States University of Missouri Institute for Data Science and Informatics Columbia United States University of Missouri Institute for Data Science and Informatics Electrical Eng. and Computer Science Dept. ColumbiaMO United States

ISBN: (纸本)9798350359527

Poultry productions have shifted towards larger farms and often cluster in certain regions. However, many of the smaller farms with a considerable amount of production are not considered concentrated animal feeding operation under regulation in many regions, and therefore are not required to obtain a permit. As a result, information of these operations is not being recorded and monitored, contributing to an increased risk for biosecurity issues. The development in computer vision and availability of high-resolution remote sensing imagery allows the possibility for automatic localization and operation estimation of the unregistered farms, filling in the missing information needed for biosecurity risk management. The farm information extracted from images can be incorporated with other available geospatial data, such as road networks, watersheds, etc., to build a comprehensive knowledge base for actionable plans. In the current study, we present a framework for automated localization, analysis, and visualization of poultry farms from remote sensing images. A robust machine learning method was developed to computationally link the visual features from remote sensing imagery to Missouri Department of Natural Resources Concentrated Animal Feeding Operation data, to identify the animal farm type, location, and potential capacity in the region. The developed process can be performed automatically on images of different timestamps to analyze temporal and spatial development of poultry production facilities of a region. This research contributes to the effective management and regulation of poultry and potentially other animal farms, providing insights into farm characteristics and capacities while facilitating informed decision-making. © 2023 IEEE.

关键词： Object detection

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Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis (vol 14, 3750, 2023)

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NATURE COMMUNICATIONS 2023年第1期14卷 1-16页

作者： Takada, Hitomi Sasagawa, Yohei Yoshimura, Mika Tanaka, Kaori Iwayama, Yoshimi Hayashi, Tetsutaro Isomura-Matoba, Ayako Nikaido, Itoshi Kurisaki, Akira 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 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

Defects in gastric progenitor cell differentiation are associated with various gastric disorders, including atrophic gastritis, intestinal metaplasia, and gastric cancer. However, the mechanisms underlying the multilineage differentiation of gastric progenitor cells during healthy homeostasis remain poorly understood. Here, using a single-cell RNA sequencing method, Quartz-Seq2, we analyzed the gene expression dynamics of progenitor cell differentiation toward pit cell, neck cell, and parietal cell lineages in healthy adult mouse corpus tissues. Enrichment analysis of pseudotime-dependent genes and a gastric organoid assay revealed that EGFR-ERK signaling promotes pit cell differentiation, whereas NF-κB signaling maintains gastric progenitor cells in an undifferentiated state. In addition, pharmacological inhibition of EGFR in vivo resulted in a decreased number of pit cells. Although activation of EGFR signaling in gastric progenitor cells has been suggested as one of the major inducers of gastric cancers, our findings unexpectedly identified that EGFR signaling exerts a differentiation-promoting function, not a mitogenic function, in normal gastric homeostasis.

关键词： Adult stem cells Stem-cell differentiation Stem-cell niche

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Improving Prediction Quality of Face Image Preference Using Combinatorial Fusion Algorithm 16th

Improving Prediction Quality of Face Image Preference Using...

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16th International Conference on Brain informatics, BI 2023

作者： Zhang, Zihan Schweikert, Christina Shimojo, Shinsuke Hsu, D. Frank Laboratory of Informatics and Data Mining Department of Computer and Information Science Fordham University YorkNY10023 United States Division of Computer Science Mathematics and Science St. John’s University QueensNY11439 United States Division of Biology and Biological Engineering/Computation and Neural Systems California Institute of Technology PasadenaCA91125 United States

ISBN: (纸本)9783031430749

When two face images are shown to a subject, the subject’s preference choice is influenced by many factors. Although it is a challenging task, one way to detect a subject’s preference is to analyze the eye movement gaze cascade in the recorded video sequence together with a set of extracted attributes (features). Combinatorial fusion algorithm (CFA) is a new information fusion and machine learning paradigm for combining multiple scoring systems using rank-score characteristic (RSC) function and cognitive diversity (CD). In this paper, we apply CFA in the analysis of the eye movement gaze cascade sequence. In particular, we characterize each of the attributes and measure the diversity between two attributes using RSC function and CD respectively. The novelty of the combinatorial fusion approach is the new paradigm of incorporating both score function and rank function as well as using both score combination and rank combination. In addition, our results demonstrate that weighted rank combination has some advantage over weighted score combination when the weight is measured using diversity strength. Since diversity strength depends only on cognitive diversity between two attributes but not on any specific sequence in the eye movement gaze cascade, our result facilitates learning and modeling in an unsupervised manner. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information fusion

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Automated Detection of Poultry Farms from Aerial Images for Actionable AI System toward Biosecurity Applications

Automated Detection of Poultry Farms from Aerial Images for ...

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Yu Li Rana Das Cuong Duong Teng-Teeh Lim Timothy Haithcoat Ilker Ersoy Chi-Ren Shyu Department of Electrical Engineering and Computer Science University of Missouri Columbia MO US Biological Engineering University of Missouri Columbia MO US Plant Science and Technology Extension University of Missouri Columbia MO US Institute for Data Science and Informatics University of Missouri Columbia US Institute for Data Science and Informatics University of Missouri Columbia MO US Electrical Eng. and Computer Science Dept. Institute for Data Science and Informatics University of Missouri Columbia MO US

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Exome-wide variation in a diverse barley panel reveals genetic associations with ten agronomic traits in Eastern landraces

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Journal of Genetics and Genomics 2023年第4期50卷 241-252页

作者： June-Sik Kim Kotaro Takahagi Komaki Inoue Minami Shimizu Yukiko Uehara-Yamaguchi Asaka Kanatani Daisuke Saisho Ryuei Nishii Alexander ELipka Takashi Hirayama Kazuhiro Sato Keiichi Mochida Bioproductivity Informatics Research Team RIKEN Center for Sustainable Resource ScienceYokohama 230-0045Japan Institute of Plant Science and Resources Okayama UniversityKurashiki 710-0046Japan Kihara Institute for Biological Research Yokohama City UniversityYokohama 244-0813Japan Graduate School of Nanobioscience Yokohama City UniversityYokohama 236-0027Japan School of Information and Data Sciences Nagasaki UniversityNagasaki 852-8521Japan Department of Crop Sciences University of IllinoisUrbanaIL 61801USA Microalgae Production Control Technology Laboratory RIKEN Baton Zone ProgramRIKEN Cluster for ScienceTechnology and Innovation HubYokohama 230-0045Japan

Barley(Hordeum vulgare ***)is one of the first crops to be domesticated and is adapted to a wide range of *** barley germplasm collections possess valuable allelic variations that could further improve barley *** barley genomics has offered a global picture of allelic variation among varieties and its association with various agronomic traits,polymorphisms from East Asian varieties remain *** this study,we analyze exome polymorphisms in a panel of 274 barley varieties collected worldwide,including 137 varieties from East Asian countries and *** reveal the underlying population structure and conduct genome-wide association studies for 10 agronomic ***,we examin genome-wide associations for traits related to grain size such as awn length and glume *** results demonstrate the value of diverse barley germplasm panels containing Eastern varieties,highlighting their distinct genomic signatures relative to Western subpopulations.

关键词： Barley Exome-seq Genome-wide association study(GWAS) Population structure Allelic variation

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Rapamycin as a preventive intervention for Alzheimer’s disease in APOE4 carriers:Targeting brain metabolic and vascular restoration

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Neural Regeneration Research 2026年第2期21卷 685-686页

作者： Ai-Ling Lin Chetan Aware Department of Radiology University of MissouriColumbiaMOUSA NextGen Precision Health University of MissouriColumbiaMOUSA Division of Biological Sciences University of MissouriColumbiaMOUSA Institute for Data Science and Informatics University of MissouriColumbiaMOUSA

Alzheimer’s disease(AD)is the most common form of dementia,affecting over 50 million people *** figure is projected to nearly double every 20 years,reaching 82 million by 2030 and 152 million by 2050(Alzheimer’s Disease International).The apolipoproteinε4(APOE4)allele is the strongest genetic risk factor for late-onset AD(after age 65 years).Apolipoprotein E,a lipid transporter,exists in three variants:ε2,ε3,andε***ε2(APOE2)is protective against AD,APOEε3(APOE3)is neutral,while APOE4 significantly increases the *** with one copy of APOE4 have a 4-fold greater risk of developing AD,and those with two copies face an 8-fold risk compared to *** in cognitively normal individuals,APOE4 carriers exhibit brain metabolic and vascular deficits decades before amyloid-beta(Aβ)plaques and neurofibrillary tau tangles emerge-the hallmark pathologies of AD(Reiman et al.,2001,2005;Thambisetty et al.,2010).Notably,studies have demonstrated reduced glucose uptake,or hypometabolism,in brain regions vulnerable to AD in asymptomatic middle-aged APOE4 carriers,long before clinical symptoms arise(Reiman et al.,2001,2005).

关键词： lipid transporterexists Dementia alzheimer s disease ad Rapamycin Brain metabolic Vascular restoration Amyloid beta plaques APOE

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Modeling Prototypical Preference Behavior and Diversity using Rank Score Characteristic Functions 21

Modeling Prototypical Preference Behavior and Diversity usin...

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21st IEEE International Conference on Cognitive informatics and Cognitive Computing, ICCI*CC 2022

作者： Schweikert, Christina Shimojo, Shinsuke Glasser, Hannah Hendsey, Rebecca Alsaber, Rami Hsu, D. Frank St. John's University QueensNY United States California Institute of Technology Division of Biology and Biological Engineering / Computation and Neural Systems PasadenaCA United States Fordham University Laboratory of Informatics and Data Mining Department of Computer and Information Science New YorkNY United States

ISBN: (纸本)9781665490849

When given two human face images to choose, a subject's decision process is recorded as a sequence of eye movement gaze points. This sequence is then analyzed to detect and predict the preference made by the subject. In an experiment with twelve subjects, each with sixty trials, we have analyzed the 720 sequences using five attributes and combinatorial fusion. Results are promising with good accuracy and efficiency. In this paper, we characterize the decision-making behavior of each subject and measure the cognitive diversity between each of these twelve subjects and a prototypical subject. Our study contributes to improving the data and predictive quality of the experiment and the computational modeling. © 2022 IEEE.

关键词： Eye movements

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MassSpecGym: A benchmark for the discovery and identification of molecules 38

MassSpecGym: A benchmark for the discovery and identificatio...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bushuiev, Roman Bushuiev, Anton de Jonge, Niek F. Young, Adamo Kretschmer, Fleming Samusevich, Raman Heirman, Janne Wang, Fei Zhang, Luke Dührkop, Kai Ludwig, Marcus Haupt, Nils A. Kalia, Apurva Brungs, Corinna Schmid, Robin Greiner, Russell Wang, Bo Wishart, David S. Liu, Li-Ping Rousu, Juho Bittremieux, Wout Rost, Hannes Mak, Tytus D. Hassoun, Soha Huber, Florian van der Hooft, Justin J.J. Stravs, Michael A. Böcker, Sebastian Sivic, Josef Pluskal, Tomáš Institute of Organic Chemistry and Biochemistry The Czech Academy of Sciences Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Republic Bioinformatics Group Wageningen University & Research Netherlands Department of Computer Science University of Toronto Canada Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Germany Department of Computer Science University of Antwerp Belgium Department of computing science University of Alberta Canada Alberta Machine Intelligence Institute Canada Department of Molecular Genetics University of Toronto Canada Bright Giant GmbH Department of Computer Science Tufts University United States Department of Biological Sciences University of Alberta Canada Department of Computer Science Aalto University Finland Mass Spectrometry Data Center National Institute of Standards and Technology United States Department of Chemical and Biological Engineering Tufts University United States Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Germany Department of Biochemistry University of Johannesburg South Africa Eawag Swiss Federal Institute of Aquatic Science and Technology Switzerland

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym. © 2024 Neural information processing systems foundation. All rights reserved.

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Mitochondrial Complex Mutants Can Modulate Responses to Stresses Associated with Reduced Water Availability

SSRN

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SSRN 2023年

作者： Zsigmond, Laura Alexandra Juhász-Erdélyi, Annabella Valkai, Ildikó Alexa, Dávid Rigó, Gábor Kant, Kamal Szepesi, Ágnes Fiorani, Fabio Körber, Niklas Kovács, László Szabados, László Institute of Plant Biology HUN-REN Biological Research Centre Szeged Hungary Department of Plant Biology Faculty of Science and Informatics University of Szeged Szeged Hungary Institut für Bio- und Geowissenschaften Forschungszentrum Jülich GmbH Jülich Germany Nunhems - BASF Vegetable Seeds Department of Data Science and Technology Roermond Netherlands

Mitochondria are important sources of energy in plants and are implicated in coordination of a number of metabolic and physiological processes including stabilization of redox balance, synthesis and turnover of several metabolites, and control of programmed cell death. Mitochondrial electron transport chain (mETC) is the backbone of the energy producing process which can influence other processes as well. Accumulating evidence suggests that mETC can affect responses to environmental stimuli and modulate tolerance to extreme conditions such as drought or salinity. Screening for stress responses of 13 Arabidopsis mitochondria-related T-DNA insertion mutants, we identified ndufs8.2-1 which has an increased ability to withstand osmotic and oxidative stresses compared to wild type plants. Insertion in ndufs8.2-1 disrupted the gene that encodes the NADH dehydrogenase [ubiquinone] fragment S subunit 8 (NDUFS8) subunit of Complex I of mETC. ndufs8.2-1 tolerated reduced water availability, retained photosynthetic activity and recovered from severe water stress with higher efficiency compared to wild type plants. Several mitochondrial functions were altered in the mutant including oxygen consumption, ROS production, ATP and ADP content as well as activities of genes encoding alternative oxidase 1A (AOX1A) and various alternative NAD(P)H dehydrogenases (ND). Our results suggest, that certain mETC components such as NDUFS8.2 are implicated in redox and energy homeostasis which can modulate responses to stresses associated with reduced water availability. © 2023, The Authors. All rights reserved.

关键词： Mitochondria

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