Big Data has proved to be vast and complex, without being efficiently manageable through traditional architectures, whereas data analysis is considered crucial for both technical and non-technical stakeholders. Current analytics platforms are siloed for specific domains, whereas the requirements to enhance their use and lower their technicalities are continuously increasing. This paper describes a domain-agnostic single access autoscaling Big Data analytics platform, namely Diastema, as a collection of efficient and scalable components, offering user-friendly analytics through graph data modelling, supporting technical and non-technical stakeholders. Diastema's applicability is evaluated in healthcare through a predicting classifier for a COVID19 dataset, considering real-world constraints.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.3233/SHTI220743 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Enhanced Genome Editing Activity with Novel Chimeric ScCas9 Variants in Rice.
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 PDFUnderstanding structural-functional connectivity coupling in patients with major depressive disorder: A white matter perspective.
J 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.
Spatiotemporal multi-feature fusion vehicle trajectory anomaly detection for intelligent transportation: An improved method combining autoencoders and dynamic Bayesian networks.
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 PDFMPIC: Exploring alternative approach to standard convolution in deep neural networks.
Neural 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 PDFCysteine-Specific F and NIR Dual Labeling of Peptides via Vinyltetrazine Bioorthogonal Conjugation for Molecular Imaging.
J 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 PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!