With the advance of internet and wireless communication technology, the fields of ecology and environment have entered a new digital era with the amount of data growing explosively and big data technologies attracting more and more attention. The eco-environmental big data is based airborne and space-/land-based observations of ecological and environmental factors and its ultimate goal is to integrate multi-source and multi-scale data for information mining by taking advantages of cloud computation, artificial intelligence, and modeling technologies. In comparison with other fields, the eco-environmental big data has its own characteristics, such as diverse data formats and sources, data collected with various protocols and standards, and serving different clients and organizations with special requirements. Big data technology has been applied worldwide in ecological and environmental fields including global climate prediction, ecological network observation and modeling, and regional air pollution control. The development of eco-environmental big data in China is facing many problems, such as data sharing issues, outdated monitoring facilities and techno-logies, and insufficient data mining capacity. Despite all this, big data technology is critical to solving eco-environmental problems, improving prediction and warning accuracy on eco-environmental catastrophes, and boosting scientific research in the field in China. We expected that the eco-environmental big data would contribute significantly to policy making and environmental services and management, and thus the sustainable development and eco-civilization construction in China in the coming decades.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.13287/j.1001-9332.201705.001 | DOI Listing |
Adv Sci (Weinh)
January 2025
Research Institute of Big Data Science and Industry, Shanxi University, Taiyuan, Shanxi, 030006, China.
The Streptococcus canis Cas9 protein (ScCas9) recognizes the NNG protospacer adjacent motif (PAM), offering a wider range of targets than that offered by the commonly used S. pyogenes Cas9 protein (SpCas9). However, both ScCas9 and its evolved Sc++ variant still exhibit low genome editing efficiency in plants, particularly at the less preferred NTG and NCG PAM targets.
View Article and Find Full Text PDFJ Affect Disord
January 2025
Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China; Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases (Yantai Yuhuangding Hospital), Yantai, Shandong 264000, PR China; Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China. Electronic address:
Purpose: To elucidate the structural-functional connectivity (SC-FC) coupling in white matter (WM) tracts in patients with major depressive disorder (MDD).
Methods: A total of 178 individuals diagnosed with MDD and 173 healthy controls (HCs) were recruited for this study. The Euclidean distance was calculated to assess SC-FC coupling.
Accid Anal Prev
January 2025
School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK.
With the continuous development of intelligent transportation systems, traffic safety has become a major societal concern, and vehicle trajectory anomaly detection technology has emerged as a crucial method to ensure safety. However, current technologies face significant challenges in handling spatiotemporal data and multi-feature fusion, including difficulties in big data processing, and have room for improvement in these areas. To address these issues, this paper proposes a novel method that combines autoencoders, Mahalanobis distance, and dynamic Bayesian networks for anomaly detection.
View Article and Find Full Text PDFNeural Netw
December 2024
Institute of Automation, Chinese Academy of Sciences, MAIS, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 101408, China.
In the rapidly evolving field of deep learning, Convolutional Neural Networks (CNNs) retain their unique strengths and applicability in processing grid-structured data such as images, despite the surge of Transformer architectures. This paper explores alternatives to the standard convolution, with the objective of augmenting its feature extraction prowess while maintaining a similar parameter count. We propose innovative solutions targeting depthwise separable convolution and standard convolution, culminating in our Multi-scale Progressive Inference Convolution (MPIC).
View Article and Find Full Text PDFJ Med Chem
January 2025
Department of Respiratory and Critical Care Medicine, Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, Frontiers Science Center for Disease-Related Molecular Network, Precision Medicine Key Laboratory of Sichuan Province & Precision Medicine Center, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital of Sichuan University, Chengdu 610041, China.
Radiolabeled peptides are vital for positron emission tomography (PET) imaging, yet the F-labeling peptides remain challenging due to harsh conditions and time-consuming premodification requirements. Herein, we developed a novel vinyltetrazine-mediated bioorthogonal approach for highly efficient F-radiolabeling of a native peptide under mild conditions. This approach enabled radiosynthesis of various tumor-targeting PET tracers, including targeting the neurofibromin receptor (), the integrin αβ (), and the platelet-derived growth factor receptor β (), with a radiochemical yield exceeding 90%.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!