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Research progress on the directional breeding of function protein producing strain of edible fungi based on the nutritional proteomics

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Food Bioscience 2025年

作者： Nan Miao Chun Xiao Longhua Huang Xiangmin Li Yuanchao Liu Manjun Cai Wei Zhong Huixiang Tian Qinzhao Ruan Shaodan Chen Tianqiao Yon Huiping Hu Qingping WU Institute of Microbiology Guangdong Academy of Sciences State Key Laboratory of Applied Microbiology Southern China Guangdong Provincial Key Laboratory of Microbial Safety and Health National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food Key Laboratory of Big Data Technologies for Food Microbiological Safety State Administration for Market Regulation NHC Specialty Laboratory of Food Safety Risk Assessment and Standard Development Guangdong-Hong Kong-Macao Greater Bay Area Microbiological Safety and Health International Science and Technology Innovation Center Guangzhou 510070 China

Edible fungi, as a natural ingredient, not only contain abundant proteins but also exhibit a rich diversity of amino acids. More importantly, they possess functional activities that are not found in other proteins, such as anti-tumor, hypoglycemic, immune regulation,etc., which endows them with great potential for development and broad application prospects. These unique nutritional and functional activities highlight the importance of targeted selection and breeding of functional protein strains. This article reviews the amino acid content and protein composition of edible fungi, summarizes the nutritional and functional activities of edible fungi proteins in various health fields, and delves into the latest research progress in high-yield protein strain selection and fermentation technologies aimed at enhancing the protein content and functional activities of edible fungi. Finally, the development prospects of edible fungi proteins are discussed.

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Climate and topographic drivers of species and functional diversity in subtropical shrublands of Guangdong, China

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Ecological Indicators 2025年 176卷

作者： Xuan Luo Mengmeng Yang Muhammad Sadiq Khan Wanxuan Huang Shizhong Liu Feiyong Liao Yuelin Li Central South University of Forestry and Technology Changsha 410004 China Guangdong Provincial Key Laboratory of Applied Botany South China Botanical Garden Chinese Academy of Sciences Guangzhou 510650 China Hunan Big Data Engineering Technology Research Center of Natural Protected Areas Landscape Resources Changsha 410004 China University of Chinese Academy of Sciences Beijing 100049 China

Subtropical shrubland ecosystems in southern China are increasingly threatened by degradation under global climate change. However, the mechanisms by which environmental gradients shape species and functional diversity remain poorly understood. In this study, we examined spatial patterns of biodiversity across 15 shrubland communities in Guangdong Province using taxonomic and functional diversity indices, regression analysis, Pearson correlation, Redundancy analysis (RDA), and MaxEnt modeling. We found that species and functional diversity declined significantly along the longitudinal gradient, while species diversity increased and functional diversity decreased with increasing latitude. Precipitation during the wettest month explained 55.2 % of the variation in species diversity ( p < 0.05). For functional diversity, precipitation during the driest month and the minimum temperature of the coldest month accounted for 55.2 % and 10.2 % of the variance, respectively ( p < 0.05). Soil nutrients and topographic variables also contributed to spatial heterogeneity in diversity patterns. MaxEnt models predicted species distributions with high accuracy (mean AUC > 0.96). These findings highlight the dominant roles of climatic and edaphic factors in structuring shrubland biodiversity, providing a robust foundation for conservation planning and sustainable ecosystem management in subtropical regions.

关键词： Subtropical shrublands Species diversity Functional diversity Environmental gradients MaxEnt modeling Guangdong Province

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Anisotropic and Isotropic Implicit Obstacle Problems with Nonlocal Terms and Multivalued Boundary Conditions

SSRN

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SSRN 2022年

作者： Zeng, Shengda Gasinski, Leszek Radulescu, Vicentiu D. Winkert, Patrick Guangxi Colleges Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Guangxi Yulin537000 China Jagiellonian University in Krakow Faculty of Mathematics and Computer Science ul. Lojasiewicza 6 Krakow30-348 Poland Department of Mathematics Pedagogical University of Cracow Podchorazych 2 Cracow30-084 Poland Faculty of Applied Mathematics AGH University of Science and Technology Kraków30-059 Poland Department of Mathematics University of Craiova Craiova200585 Romania China-Romania Research Center in Applied Mathematics Romania Technische Universität Berlin Institut für Mathematik Straße des 17. Juni 136 Berlin10623 Germany

In this paper we study an anisotropic implicit obstacle problem driven by the $(p(\cdot),q(\cdot))$-Laplacian and an isotropic implicit obstacle problem involving a nonlinear convection term (a reaction term depending on the gradient) which contain several interesting and challenging untreated problems. These two implicit obstacle problems have both highly nonlinear and nonlocal functions and three multivalued terms where two of them are appearing on the boundary and the other one is formulated in the domain. Under very general assumptions on the data, we develop general frameworks to examine the nonemptiness and compactness of the set of weak solutions to the problems under consideration. The proofs of our main results use the theory of nonsmooth analysis, Tychonoff's fixed point theorem for multivalued operators, the theory of pseudomonotone operators and variational approach. © 2022, The Authors. All rights reserved.

关键词： Anisotropy

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Bilinear Semi-Tensor Product Attention (BSTPA) model for visual question answering

Bilinear Semi-Tensor Product Attention (BSTPA) model for vis...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zongwen Bai Ying Li Meili Zhou Di Li Dong Wang Dawid Połap Marcin Woźniak School of Computer Science Northwestern Polytechnical University Xi’an CHINA Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan’an CHINA School of Physics and Electronic Information Yan’an University Yan’an CHINA Faculty of Applied Mathematics Silesian University of Technology Gliwice POLAND

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

We propose a semi-tensor product attention network model as a visual question answering tool for complex interaction over image features. Proposed model performs matrix multiplication of two arbitrary dimensions, which is used to overcome possible dimensional limitations and improve recognition flexibility. In used block-wise operation we preserve spatial and temporal information but reduce the number of parameters by using low-rank pooling scheme. applied BERT pre-train model is tuned to recognize question features. The proposed model is evaluated on the VQA2.0 dataset. Research results show that our model has good accuracy and easy reconfiguration for future research.

关键词： Feature extraction Visualization Knowledge discovery Cognition Task analysis Computational modeling Bit error rate

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Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm

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Chemosphere 2024年 352卷 141329页

作者： Yaseen, Zaher Mundher Melini Wan Mohtar, Wan Hanna Homod, Raad Z. Alawi, Omer A. Abba, Sani I. Oudah, Atheer Y. Togun, Hussein Goliatt, Leonardo Ul Hassan Kazmi, Syed Shabi Tao, Hai Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Membranes and Water Security King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia Department of Civil Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia UKM Selangor Bangi 43600 Malaysia Environmental Management Centre Institute of Climate Change Universiti Kebangsaan Malaysia Selangor UKM Bangi 43600 Malaysia Department of Oil and Gas Engineering Basrah University for Oil and Gas Basra Iraq Department of Thermofluids School of Mechanical Engineering Universiti Teknologi Malaysia UTM Skudai Johor Bahru 81310 Malaysia Department of Computer Sciences College of Education for Pure Science University of Thi-Qar Nasiriyah 64001 Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Nasiriyah 64001 Iraq Department of Mechanical Engineering College of Engineering University of Baghdad Baghdad Iraq Computational and Applied Mechanics Department Federal University of Juiz de Fora 36036-900 Brazil Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention and Guangdong Provincial Key Laboratory of Marine Biotechnology Shantou University Shantou 515063 China School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Institute of Big Data Application and Artificial Intelligence Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Faculty of Data Science and Information Technology INTI International University 71800 Malaysia

This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies. © 2024 Elsevier Ltd

关键词： Artificial intelligence Heavy metals Sensitivity analysis Soil contamination

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One for Multiple: Physics-informed Synthetic data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

arXiv

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arXiv 2023年

作者： Wang, Zi Yu, Xiaotong Wang, Chengyan Chen, Weibo Wang, Jiazheng Chu, Ying-Hua Sun, Hongwei Li, Rushuai Li, Peiyong Yang, Fan Han, Haiwei Kang, Taishan Lin, Jianzhong Yang, Chen Chang, Shufu Shi, Zhang Hua, Sha Li, Yan Hu, Juan Zhu, Liuhong Zhou, Jianjun Lin, Meijing Guo, Jiefeng Cai, Congbo Chen, Zhong Guo, Di Yang, Guang Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China Human Phenome Institute Fudan University China Philips Healthcare China Siemens Healthineers Ltd. China United Imaging Research Institute of Intelligent Imaging China Department of Nuclear Medicine Nanjing First Hospital China Shandong Aoxin Medical Technology Company China Department of Radiology The First Affiliated Hospital of Xiamen University China Department of Radiology Zhongshan Hospital Affiliated to Xiamen University China China Department of Cardiology Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University China Department of Radiology Zhongshan Hospital Fudan University China Department of Cardiovascular Medicine Heart Failure Center Ruijin Hospital Lu Wan Branch Shanghai Jiaotong University School of Medicine China Department of Radiology Ruijin Hospital Shanghai Jiaotong University School of Medicine China Medical Imaging Department The First Affiliated Hospital of Kunming Medical University China Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence China Department of Applied Marine Physics and Engineering Xiamen University China Department of Microelectronics and Integrated Circuit Xiamen University China School of Computer and Information Engineering Xiamen University of Technology China Department of Bioengineering Imperial College London United Kingdom

Magnetic resonance imaging (MRI) is a widely used radiological modality renowned for its radiation-free, comprehensive insights into the human body, facilitating medical diagnoses. However, the drawback of prolonged scan times hinders its accessibility. The k-space undersampling offers a solution, yet the resultant artifacts necessitate meticulous removal during image reconstruction. Although Deep Learning (DL) has proven effective for fast MRI image reconstruction, its broader applicability across various imaging scenarios has been constrained. Challenges include the high cost and privacy restrictions associated with acquiring large-scale, diverse training data, coupled with the inherent difficulty of addressing mismatches between training and target data in existing DL methodologies. Here, we present a novel Physics-Informed Synthetic data learning framework for Fast MRI, called PISF. PISF marks a breakthrough by enabling generalized DL for multi-scenario MRI reconstruction through a single trained model. Our approach separates the reconstruction of a 2D image into many 1D basic problems, commencing with 1D data synthesis to facilitate generalization. We demonstrate that training DL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MRI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MRI data by up to 96%. Additionally, PISF exhibits remarkable generalizability across multiple vendors and imaging centers. Its adaptability to diverse patient populations has been validated through evaluations by ten experienced medical professionals. PISF presents a feasible and cost-effective way to significantly boost the widespread adoption of DL in various fast MRI applications. Importantly, it alleviates the ethical and practical challenges associated with in vivo human data acquisitions, opening new horizons for advancing fast MRI technologies. Copyright © 20

关键词： Magnetic resonance imaging

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Ⅲ–Ⅴ compounds as single photon emitters

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Journal of Semiconductors 2019年第7期40卷 63-74页

作者： Xu Wang Lei Xu Yun Jiang Zhouyang Yin Christopher C. S. Chan Chaoyong Deng Robert A. Taylor Key Laboratory of Electronic Functional Composite Materials of Guizhou Province Guizhou University Guiyang 550025 China Guizhou Institute of Quantum Information and Big Data Applied Technology Guiyang 550002 China Department of Physics The Hong Kong University of Science and Technology Hong Kong 999077 China Department of Physics University of Oxford Clarendon Laboratory Oxford OX1 3PU UK

Single-photon emitters (SPEs) are one of the key components in quantum information applications. The ideal SPEs emit a single photon or a photon-pair on demand, with high purity and distinguishability. SPEs can also be integrated in photonic circuits for scalable quantum communication and quantum computer systems. Quantum dots made from Ⅲ-Ⅴ compounds such as InGaAs or GaN have been found to be particularly attractive SPE sources due to their well studied optical performance and state of the art industrial flexibility in fabrication and integration. Here, we review the optical and optoelectronic properties and growth methods of general SPEs. Subsequently, a brief summary of the latest advantages in Ⅲ-Ⅴ compound SPEs and the research progress achieved in the past few years will be discussed. We finally describe frontier challenges and conclude with the latest SPE fabrication science and technology that can open new possibilities for quantum information applications.

关键词： single photon emitters solid-states quantum dots 2D materials

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A class of elliptic mixed boundary value problems with (p,q)-Laplacian: existence, comparison and optimal control

arXiv

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arXiv 2023年

作者： Zeng, Shengda Migórski, Stanislaw Tarzia, Domingo A. Zou, Lang Nguyen, Van Thien Guangxi Colleges Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Yulin537000 China Department of Mathematics Nanjing University Jiangsu Nanjing210093 China Jagiellonian University in Krakow Faculty of Mathematics and Computer Science ul. Lojasiewicza 6 Krakow30348 Poland College of Applied Mathematics Chengdu University of Information Technology Sichuan Province Chengdu610225 China Jagiellonian University in Krakow Chair of Optimization and Control ul. Lojasiewicza 6 Krakow30348 Poland Depto. Matemática FCE Universidad Austral Paraguay 1950 RosarioS2000FZF Argentina CONICET Argentina School of Mathematics and Computational Science Xiangtan University Hunan Province Xiangtan City210000 China Department of Mathematics FPT University Education zone Hoa Lac High Tech Park Km29 Thang Long Highway Thach That ward Hanoi Viet Nam

The paper deals with two nonlinear elliptic equations with (p, q)-Laplacian and the Dirichlet-Neumann-Dirichlet (DND) boundary conditions, and Dirichlet-Neumann-Neumann (DNN) boundary conditions, respectively. Under mild hypotheses, we prove the unique weak solvability of the elliptic mixed boundary value problems. Then, a comparison and a monotonicity results for the solutions of elliptic mixed boundary value problems are established. We examine a convergence result which shows that the solution of (DND) can be approached by the solution of (DNN). Moreover, two optimal control problems governed by (DND) and (DNN), respectively, are considered, and an existence result for optimal control problems is obtained. Finally, we provide a result on asymptotic behavior of optimal controls and system states, when a parameter tends to *** Codes 35J25, 35J66, 35J92, 49J20, 35Bxx © 2023, CC BY.

关键词： Nonlinear equations

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A Stacked Ensemble Learning Framework with Heterogeneous Feature Combinations for Predicting ncRNA-Protein Interaction

A Stacked Ensemble Learning Framework with Heterogeneous Fea...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiguo Dai Zhaowei Wang Jinmiao Song Xiaodong Duan Maozu Guo Zhen Tian School of Computer Science and Engineering Dalian Minzu University Dalian China Key Laboratory of Big Data Applied Technology State Ethnic Affairs Commission Dalian Minzu University Dalian China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

The interaction between ncRNA and protein is a kind of crucial molecular activities in a cell. Developing computational methods to predict ncRNA-protein interactions has attracted increasing attentions in recent years. In this work, a novel stacked ensemble learning framework is presented for predicting ncRNA-protein interaction based on heterogeneous feature combinations, named HFC-RPI. Firstly, the compositional features of k-mer with different orders were extracted from the primary sequence and secondary structure of RNA and protein respectively. Secondly, we trained a set of base learners using a variety of heterogeneous combinations of the extracted features respectively. Thirdly, the prediction results of these base learners were employed to train the stacked learner, which output the final prediction result at the higher layer in HFC-RPI. Moreover, in order to improve the generalization of HFC-RPI, when training the base learners, a cross-validation based method was applied. Extensive experimental results showed that the proposed learning framework HFC-RPI was effective and feasible for predicting the interaction of ncRNA and protein. By comparing with state-of-the-art methods, HFC-RPI was superior to them on most performance evaluation metrics.

关键词： Proteins Feature extraction RNA Training Predictive models Protein engineering Deep learning

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Author Correction: Mapping annual 10-m soybean cropland with spatiotemporal sample migration

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Scientific data 2024年第1期11卷 565页

作者： Hongchi Zhang Zihang Lou Dailiang Peng Bing Zhang Wang Luo Jianxi Huang Xiaoyang Zhang Le Yu Fumin Wang Linsheng Huang Guohua Liu Shuang Gao Jinkang Hu Songlin Yang Enhui Cheng Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China. International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China. University of Chinese Academy of Sciences Beijing 100094 China. Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China. pengdl@***. International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China. pengdl@***. Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China. zhangbing@***. University of Chinese Academy of Sciences Beijing 100094 China. zhangbing@***. Jiangxi Nuclearindustry Surveying and Mapping Institute Group Co. Ltd Nanchang 330038 China. College of Land Science and Technology China Agricultural University Beijing 100083 China. Geospatial Sciences Center of Excellence Department of Geography Geospatial Sciences South Dakota State University Brookings SD 57007 USA. Department of Earth System Science Tsinghua University Beijing 100084 China. Institute of Applied Remote Sensing & Information Technology Zhejiang University Hangzhou 310058 China. National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei 230601 China. Innovation Academy for Microsatellites Chinese Academy of Sciences Shanghai 200120 China.

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