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development and validation of a nomogram model for predicting overall survival in patients with gastric carcinoma

World Journal of Gastrointestinal Oncology 2025年第2期17卷 132-143页

作者： Guan-Zhong Liang Xiao-Sheng Li Zu-Hai Hu Qian-Jie Xu Fang Wu Xiang-Lin Wu Hai-Ke Lei Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment Chongqing University Cancer HospitalChongqing 400030China Chongqing Cancer Multi-omics Big Data Application Engineering Research Center Chongqing University Cancer HospitalChongqing 400030China Department of Health Statistics School of Public HealthChongqing Medical UniversityChongqing 400016China Research Center for Medicine and Social Development School of Public HealthChongqing Medical UniversityChongqing 400016China The Research Center of Big Data Chongqing University Cancer HospitalChongqing 400030China

BACKGROUND The prevalence and mortality rates of gastric carcinoma are disproportionately elevated in China,with the disease's intricate and varied characteristics further amplifying its health *** forecasting of overall survival(OS)is of paramount importance for the clinical management of individuals afflicted with this *** To develop and validate a nomogram model that provides precise gastric cancer prevention and treatment guidance and more accurate survival outcome prediction for patients with gastric *** data analysis was conducted on samples collected from hospitalized gastric cancer patients between 2018 and *** absolute shrinkage and selection operator,univariate,and multivariate Cox regression analyses were employed to identify independent prognostic factors.A nomogram model was developed to predict gastric cancer patient *** model's predictability and discriminative ability were evaluated via receiver operating characteristic *** evaluate the clinical utility of the model,Kaplan-Meier and decision curve analyses were *** A total of ten independent prognostic factors were identified,including body mass index,tumor-node-metastasis(TNM)stage,radiation,chemotherapy,surgery,albumin,globulin,neutrophil count,lactate dehydrogenase,and platelet-to-lymphocyte *** area under the curve(AUC)values for the 1-,3-,and 5-year survival prediction in the training set were 0.843,0.850,and 0.821,*** AUC values were 0.864,0.820,and 0.786 for the 1-,3-,and 5-year survival prediction in the validation set,*** model exhibited strong discriminative ability,with both the time AUC and time C-index exceeding *** with TNM staging,the model demonstrated superior clinical ***,a nomogram was developed via a web-based *** This study established and validated a novel nomogram model for predicting the OS of gastric cancer patients,which demonstrat

关键词： Gastric carcinoma Prediction Overall survival Nomogram Prospective

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Hydrogen Energy Demand Management in China:A Department Scenario Analysis Method

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Energy Engineering 2025年第3期122卷 971-983页

作者： Zhongxun Li Bing Wang Xiaolin Liu Center for Sustainable Development and Energy Policy Research School of Energy and Mining EngineeringChina University of Mining and Technology-BeijingBeijing100083China Research Institute of Decision-Making Science and Big Data School of ManagementChina University of Mining and Technology-BeijingBeijing100083China

The proposal of carbon neutrality target makes decarbonization and hydrogenation typical features of future energy development in *** a wide range of application scenarios,hydrogen energy will experience rapid growth in production and *** formulate an effective hydrogen energy development strategy for the future of China,this study employs the departmental scenario analysis method to calculate and evaluate the future consumption of hydrogen energy in China’s heavy industry,transportation,electricity,and other related *** technical parameters are selected and predicted accurately and reliably in combination with different development *** findings indicate that the period from 2030 to 2050 will enjoy rapid development of hydrogen energy,having an average annual growth rate of 2%to 4%.The technological progress and breakthroughs scenario has the greatest potential for hydrogen demand scale among the four development *** this scenario,the total demand for hydrogen energy is expected to reach 446.37Mt in *** sector will be the sector with the greatest potential for hydrogen deployment growth from 2023 to 2060,which is expected to rise from 0.038Mt to about 163.18Mt,with the ambitious growth in the ***,hydrogen energy has a considerable development potential in the steel sector,and the trend of de-refueling coke by hydrogenation in this sector will be imperative for this energy-intensive industries.

关键词： Hydrogen demand management department scenario analysis carbon neutrality

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Geological big data utilization in public health

Geological big data utilization in public health

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2024 International Conference on Remote Sensing and Digital Earth, RSDE 2024

作者： Wang, Liang Jia, Liqiong Yang, Shuqi Li, Sixia Yan, Hongwei Natural Resources Integrated Survey Command Center China Geological Survey Beijing100055 China Applied Geology Data Research Center Laboratory for Big Data and Decision Beijing100055 China Development and Research Center China Geological Survey Beijing100037 China Research Center of Applied Geology China Geological Survey Sichuan Chengdu610036 China

ISBN: (数字)9781510688223

ISBN: (纸本)9781510688216

This research delves into the uses of big data, in public health and emphasizes its crucial role in assessing health risks,data control of disease transmission and allocation of public health *** big data,serving as an resource could shed light on the complex connection between environmental factors and public health introducing new predictive models for health risks evidence based strategies, for mitigating diseases and sensible distribution of public health resources. Facing challenges related to data accuracy and privacy protection as technical processing issues doesn't diminish the undeniable value of using this technology for making well informed decisions, in public health matters. Looking ahead to the synergy of advancements and regulatory improvements in disciplines and anticipating the integration of extensive geological data, into public health initiatives promises to enhance the precision and effectiveness of managing healthcare systems in the future. © 2025 SPIE.

关键词： Electronic health record

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Decoding maize meristems maintenance and differentiation:integrating single-cell and spatial omics

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Journal of Genetics and Genomics 2025年第3期52卷 319-333页

作者： Bin Li Wenhao Liu Jie Xu Xuxu Huang Long Yang Fang Xu The Key Laboratory of Plant Development and Environmental Adaptation Biology Ministry of EducationShandong Key Laboratory of Precision Molecular Crop Design and Breeding School of Life SciencesShandong UniversityQingdaoShandong 266237China Housing and Urban Rural Development Bureau of Jimo District QingdaoShandong 266200China Agricultural Big-Data Research Center and College of Plant Protection Shandong Agricultural UniversityTai’anShandong 271018China

All plant organs are derived from stem cell-containing *** maize,the shoot apical meristem(SAM)is responsible for generating all above-ground structures,including the male and female inflorescence meristems(IMs),which give rise to tassel and ear,*** and reverse genetic studies on maize meristem mutants have driven forward our fundamental understanding of meristem maintenance and differentiation ***,the high genetic redundancy of the maize genome has impeded progress in functional *** review comprehensively summarizes recent advancements in understanding maize meristem development,with a focus on the integration of single-cell and spatial *** discuss the mechanisms governing stem cell maintenance and differentiation in SAM and IM,emphasizing the roles of gene regulatory networks,hormonal pathways,and cellular omics insights into stress responses and *** directions include cross-species comparisons,multi-omics integration,and the application of these technologies to precision breeding and stress adaptation research,with the ultimate goal of translating our understanding of meristem into the development of higher yield varieties.

关键词： Maize meristem Stem cell Single-cell and spatial technologies Meristem maintenance Regulatory mechanism

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Hybrid forecasting of demand flexibility: A top-down approach for thermostatically controlled loads

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Energy and AI 2025年 20卷

作者： Massidda, Luca Marrocu, Marino CRS4 Center for Advanced Studies Research and Development in Sardinia Italy National Research Center in High Performance Computing Big Data and Quantum Computing Italy

Demand-side flexibility is crucial to balancing supply and demand, as renewable energy sources are increasingly integrated into the energy mix, and heating and transport systems are becoming more and more electrified. Historically, this balancing has been managed from the supply side. However, the shift towards renewable energy sources limits the controllability of traditional fossil fuel plants, increasing the importance of demand response (DR) techniques to achieve the required flexibility. Aggregators participating in flexibility markets need to accurately forecast the adaptability they can offer, a task complicated by numerous influencing variables. Based on a top-down approach, this study addresses the problem of forecasting electricity demand in the presence of flexibility from thermostatically controlled loads. We propose a hybrid model that combines data-driven techniques for probabilistic estimation of electricity consumption with a disaggregation of electricity consumption to identify the fraction of thermal loads, subject to flexibility, which is simulated by a virtual battery model. The technique is applied to a synthetic dataset that simulates the response of a European neighborhood to demand response interventions. The results demonstrate the model's ability to accurately predict both the reduction in electricity demand during DR events and the subsequent rebound in consumption. The model achieves a mean absolute percentage error (MAPE) lower than 17.0%, comparable to the accuracy without flexibility. The results obtained are compared with a direct data-driven approach, demonstrating the validity and effectiveness of our model. © 2025

关键词： Demand response

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Monitoring crop growth at Redstar Farm based on Sentinel-2 6

Monitoring crop growth at Redstar Farm based on Sentinel-2

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6th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Hua, Zhiming Meng, Jihua Jing, Juanli Lin, Zhenxin You, Xinyan He, Rongpeng College of Geomatics and Geoinformation Guilin University of Technology Guangxi Guilin541004 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China

ISBN: (纸本)9781510688018

Using remote sensing technology to monitor crop growth is one of the very important research topics in precision agricultural remote sensing. Crop growth monitoring refers to monitoring crop growth status and predicting its development trend through satellite remote sensing. Real-time knowledge of crop growth status can provide timely and reliable information for field management, lay the foundation for estimating crop yields, and provide the National Agricultural Department with a basis for food management and decision-making. This paper takes the Redstar Farm of the Beian Branch of the Heilongjiang General Administration of Agricultural Reclamation as the research area, selects the Sentinel-2 image data and vector data files in the area in 2023, and uses basic software such as ENVI5.3 and ArcMap10.4 to obtain the crop phenological phase growth information map of the study area. By comparing and analyzing monthly growth charts, we can obtain the characteristics of changes in time and space of crop growth in Redstar Farm in 2023 and analyze the main reasons for these changes. © 2025 SPIE.

关键词： Crop rotation

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Increase in global per capita cropland imbalance across countries from 1985 to 2022:A threat to achieving Sustainable development Goals

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Geography and Sustainability 2025年第2期6卷 253-264页

作者： Tingting Zhao Xiao Zhang Wendi Liu Jinqing Wang Zhehua Li Liangyun Liu School of Geography and Ocean Science Nanjing UniversityNanjing 210023China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100049China

Sustainable development Goal 2(SDG 2,zero hunger)highlights that global hunger and food insecurity have worsened since 2015,driven in part by growing *** the challenge of achieving SDG 2 in the face of rapid global population growth requires sustained attention to global and national cropland *** quantifying the correlation between population and cropland area(i.e.,SDG 2.4.1 per capita cropland)and analyzing the trends of global cropland imbalance are essential for a comprehensive understanding of SDG *** this study,we utilized a new global 30 m land-cover dynamic dataset(GLC_FCS30D)to analyze cropland dynamics,quantify per capita cropland and its changes across various countries and levels of *** results indicate that the global cropland area expanded by 0.944 million km^(2)from 1985 to 2022,with an average expansion rate of 2.42×10^(4)km^(2)/***,the global per capita cropland area decreased from 0.347 ha in 1985 to 0.217 ha in 2022,mainly due to a higher population increase of nearly 65%in the same *** the context of globalization,cropland expansion and per capita cropland exhibited spatial imbalances globally,particularly in developing *** countries saw an increase in total cropland area by 7.09%but a significant decrease in per capita cropland area by 37.38%.From a temporal perspective,the global imbalance has been steadily increasing with the Gini index rising from 0.895 in 1985 to 0.909 in ***,this study reveals an increasing imbalance of global per capita cropland across various countries,which threatens the attainment of the targets of SDG 2.

关键词： Sustainable development Goals GLC_FCS30D Cropland changes Population Imbalance

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SPDGrNet: A Lightweight and Efficient Image Classification Network for Zea mays Diseases

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Journal of Crop Health 2025年第3期77卷 1-12页

作者： Xu, Laixiang Cao, Bingxu Su, Jing feng Zhang, Fengyue Song, Hongyun Zhao, Junmin School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Henan University of Urban Construction Pingdingshan China School of Information Engineering Luohe Vocational Technology College Luohe China Institute of Technology University of Technology Luohe Luohe China School of Computer and Data Science Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan China School of Computer and Data Science Henan University of Urban Construction Pingdingshan China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Research Center of Intelligent Campus Application Engineering of Henan Province Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan China

Controlling diseases on the foliage of cultivated plants has a major impact on crop yield and quality. Leaf symptoms and environmental information are the basis of crop disease and pest identification. Due to the complexity and diversity of disease leaf symptoms and environmental information, the accuracy of crop disease detection methods has decreased. Aiming at low disease identification efficiency and difficult feature modeling, we propose a new deep learning algorithm and obtain ideal results. We used improved convolutional neural networks (CNNs) SPDNet and GrNet (SPDGrNet) to perform Zea mays disease identification. The proposed research and analysis focus on four categories of Zea mays symptoms: healthy, leaf blight, southern leaf blight and grey spot. The experimental results and analysis confirm that the proposed approach outperforms other excellent plant disease recognition algorithms and achieves an overall classification accuracy of 98.96%. When compared to AlexNet, VGG-16, Inception-V4, ShuffleNet-V3, and Efficient-B7, the proposed model achieves accuracy improvements of 27.78%, 19.05%, 14.59%, 10.28%, and 7.06%, respectively, with corresponding accuracies of 71.18%, 79.91%, 84.37%, 88.68%, and 91.9%. The proposed model has outstanding generalization ability in open-source databases, such as the PlantifyDr, Plant Pathology, and New Plant Diseases dataset. The suggested strategy can provide ideas for transplanting models into mobile disease detection equipment. This is very helpful in the intelligent development of agricultural systems and crop leaf disease analysis.

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Impact of urban sprawl on land surface temperature in the Mashhad City,Iran:A deep learning and cloudbased remote sensing analysis

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Journal of Arid Land 2025年第3期17卷 285-303页

作者： Komeh ZINAT Hamzeh SAEID Memarian HADI Attarchi SARA LU Linlin Naboureh AMIN Alavipanah KAZEM SEYED Department of Remote Sensing and GIS(Geographic Information System) University of TehranTehran 14178-53933Iran Department of Watershed Management University of BirjandBirjand 97174-34765Iran Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Research Center for Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610041China University of Chinese Academy of Sciences Beijing 100049China

The evolution of land use patterns and the emergence of urban heat islands(UHI)over time are critical issues in city development *** study aims to establish a model that maps the correlation between changes in land use and land surface temperature(LST)in the Mashhad City,northeastern *** the Google Earth Engine(GEE)platform,we calculated the LST and extracted land use maps from 1985 to *** convolutional neural network(CNN)approach was utilized to deeply explore the relationship between the LST and land *** obtained results were compared with the standard machine learning(ML)methods such as support vector machine(SVM),random forest(RF),and linear *** results revealed a 1.00°C–2.00°C increase in the LST across various land use *** variation in temperature increases across different land use types suggested that,in addition to global warming and climatic changes,temperature rise was strongly influenced by land use *** LST surge in built-up lands in the Mashhad City was estimated to be 1.75°C,while forest lands experienced the smallest increase of 1.19°*** developed CNN demonstrated an overall prediction accuracy of 91.60%,significantly outperforming linear regression and standard ML methods,due to the ability to extract higher level ***,the deep neural network(DNN)modeling indicated that the urban lands,comprising 69.57%and 71.34%of the studied area,were projected to experience extreme temperatures above 41.00°C and 42.00°C in the years 2025 and 2030,*** conclusion,the LST predictioin framework,combining the GEE platform and CNN method,provided an effective approach to inform urban planning and to mitigate the impacts of UHI.

关键词： convolutional neural network machine learning Google Earth Engine land use change random forest

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Nnmamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model

Nnmamba: 3D Biomedical Image Segmentation, Classification an...

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IEEE International Symposium on Biomedical Imaging

作者： Haifan Gong Luoyao Kang Yitao Wang Yihan Wang Xiang Wan Xusheng Wu Haofeng Li Shenzhen Research Institute of Big Data Shenzhen China The Chinese University of Hong Kong (Shenzhen) Shenzhen China Shenzhen Health Development Research and Data Management Center Shenzhen China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

In biomedical image analysis, developing architectures that effectively capture long-range dependencies is crucial. Traditional Convolutional Neural Networks (CNNs) are constrained by their local receptive fields, while Transformers, though proficient in global context integration, are computationally demanding for high-dimensional medical images. Here, we present nnMamba, a novel architecture that combines the strengths of CNNs with the long-range modeling capabilities of State Space Models (SSMs). We introduce the Mamba-In-Convolution with Channel-Spatial Siamese learning (MICCSS) block to model long-range voxel relationships. Additionally, we implement channel scaling and channel-sequential learning methods to enhance performance in dense prediction and classification tasks. Extensive experiments on seven datasets demonstrate that nnMamba outperforms current state-of-the-art methods in 3D image segmentation, classification, and landmark detection. nnMamba effectively integrates CNNs' local representation with SSMs' global con-text processing, establishing a new benchmark for long-range dependency modeling in medical image analysis. Code is available at https://***/lhaof/nnMamba.

关键词： Learning systems Solid modeling Image segmentation Three-dimensional displays Image analysis Computational modeling Biological system modeling Computer architecture Transformers Biomedical imaging

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