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AB-DOM: An Algorithmic Framework for Supporting Privacy-Preserving big data Publishing in big data Lakes

IEEE Transactions on big data 2025年

作者： Cuzzocrea, Alfredo Soufargi, Selim University of Calabria The Big Data Engineering and Analytics Laboratory (IDEA Lab) Rende 87036 Italy

With the emergence of new technologies that extend the capabilities of actual data collection methods, healthcare data are more and more amassed in the purpose of being later analyzed to serve the ultimate, well-known, goal of 4P medicine (Predictive, Preventive, Personalized, Participative). Given the sensitive nature of healthcare data, and in a matter of compliance with data protection and privacy regulations, there is a need to make data publishing more secure. This is one of the main goals of the EU H2020 QUALITOP research project, with particular regards to the issue of defining a big health data smart digital platform and the shared data lake. In this context, we design, implement and experimentally assess an innovative algorithmic framework called Advanced Privacy-Preserving big data Publishing in Hierarchical DOMains (AB-DOM). AB-DOM is based on state-of-the-art anonymization techniques mixed with a graph coloring algorithm and an integrated data sampling method to guarantee that sensitive data are highly secured. © 2015 IEEE.

关键词： big data Lakes Privacy-Preserving big data

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Contamination, biofilm formation, virulence and molecular characterization of multi–drug resistant Staphylococcus aureus isolated from pork under low temperature in Zhejiang Province, China

SSRN

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SSRN 2025年

作者： Zhai, Ruidong Liang, Yilun Lei, Lei Xu, Xinlu Wu, Qin Han, Yue Deng, Simin Xu, Jiali Xia, Jing Yang, Yongchun Cocolin, Luca Simone Rantsiou, Kalliopi Cheng, Changyong Song, Houhui Key Laboratory of Applied Biotechnology on Animal Science & Veterinary Medicine of Zhejiang Province Zhejiang Engineering Research Center for Veterinary Diagnostics & Advanced Technology Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management Belt and Road International Joint Laboratory for One Health and Food Safety China-Australia Joint Laboratory for Animal Health Big Data Analytics College of Veterinary Medicine of Zhejiang Agricultural and Forestry University Zhejiang Province Hangzhou China Department of Agricultural Forest and Food Sciences University of Turin Torino Italy

Staphylococcus aureus is considered to be an increasingly food safety concern. Low temperature is usually used to limit bacterial reproduction on meat. The aim of this study was to investigate the contamination and characterization about S. aureus on pork under low temperature at cold storage warehouses in Zhejiang Province, China. A total of 344 samples were collected to detect S. aureus, and All 52 S. aureus were isolated from 11.6% samples with present in 18 batches from 13 cold storage warehouses, of note, 51.9% isolates was enterotoxigenic, and 71.2% isolates were multi-drug resistant strains with strong biofilm formation, notably, 13 MRSA. Moreover, all 52 isolates were classified into 10 clusters of RAPD with 13 SPA types, especially the new frequently Spa-t20663. And ST398, ST188, ST6 types were ranked among the top three accompanied by close genetic relationships with other food-borne S. aureus in China. In addition, high virulence isolates were observed on Galleria mellonella, and ST188 S. aureus with skin abscesses in rabbits. Findings from this study highlight the contamination, long-term survival, virulence and diverse molecular characteristics of multi-drug resistant S. aureus isolated from frozen pork. Therefore, integrated surveillance and control measures are urgent to mitigate the associated risk. © 2025, The Authors. All rights reserved.

关键词： Meats

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Exploring the Dynamics of Dietary Self-Monitoring Adherence Among Participants in a Digital Behavioral Weight Loss Program: Model Development Study

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Journal of Medical Internet Research 2025年 27卷

作者： Lin, Hui Yang, Min Zhou, Zhiheng Zhang, Yu Deng, Ning Pingshan Hospital Southern Medical University Shenzhen China Pingshan District Peoples' Hospital of Shenzhen Shenzhen China School of Public Health Zhejiang University School of Medicine Hangzhou China Center of Clinical Big Data and Analytics The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China School of Biomedical Engineering Southern Medical University Guangzhou China Ministry of Education Key Laboratory of Biomedical Engineering College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China

Background: Self-monitoring of dietary behaviors is typically a central component of behavioral weight loss programs, and it is widely recognized for its effectiveness in promoting healthy behavior changes and improving health outcomes. However, understanding the adherence dynamics of self-monitoring of dietary behaviors remains a challenge. Objective: We aimed to develop a prognostic model for adherence to self-monitoring of dietary behaviors using the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture and to qualitatively investigate adherence dynamics and the impact of various interventions through model-based analyses. Methods: The modeling data were derived from a digital behavioral weight loss program targeting adults who expressed a willingness to improve their lifestyle. Participants were assigned to 1 of 3 intervention groups: self-management, tailored feedback, and intensive support. ACT-R, a cognitive architecture simulating human cognitive processes, was used to model adherence to self-monitoring of dietary behaviors over 21 days, focusing on the mechanisms of goal pursuit and habit formation. Predictor and outcome variables were defined as adjacent elements in the sequence of self-monitoring of dietary behaviors. Model performance was evaluated using mean square error, root mean square error (RMSE), and goodness of fit. Mechanistic contributions were visualized to analyze adherence patterns and the impacts of different interventions. Results: The total sample size for modeling was 97, with 49 in the self-management group, 23 in the tailored feedback group, and 25 in the intensive support group. The ACT-R model effectively captured the adherence trends of self-monitoring of dietary behaviors, with RMSE values of 0.099 for the self-management group, 0.084 for the tailored feedback group, and 0.091 for the intensive support group. The visualized results revealed that, across all groups, the goal pursuit mechanism remained dominant througho

关键词： ACT-R architecture adherence dynamics computational behavioral science digital health interventions goal pursuit habit formation self-monitoring of dietary behavior tailored feedback interventions weight loss program

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Massive data clustering by multi-scale psychological observations

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National Science Review 2022年第2期9卷 43-51页

作者： Shusen Yang Liwen Zhang Chen Xu Hanqiao Yu Jianqing Fan Zongben Xu National Engineering Laboratory of Big Data Analytics Xi'an Jiaotong University Industrial Artificial Intelligent Center Pazhou Laboratory Department of Mathematics and Statistics University of Ottawa Center for Statistics and Machine Learning Princeton University

Clustering is the discovery of latent group structure in data and is a fundamental problem in artificial intelligence,and a vital procedure in data-driven scientific research over all ***,existing methods have various limitations,especially we ak cognitive interpretability and poor computational scalability,when it comes to clustering massive datasets that are increasingly available in all ***,by simulating the multi-scale cognitive observation process of humans,we design a scalable algorithm to detect clusters hierarchically hidden in massive *** observation scale changes,following the Weber-Fechner law to capture the gradually emerging meaningful grouping *** validated our approach in real datasets with up to a billion records and 2000 dimensions,including taxi trajectories,single-cell gene expressions,face images,computer logs and *** approach outperformed popular methods in usability,efficiency,effectiveness and robustness across different domains.

关键词： massive data clustering psychological observation Weber–Fechner law cognitive interpretability computational scalability

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KNAS-TP: Domain Knowledge-driven Automated Construction of Uplink and Downlink Throughput Predictor in 5G 16

KNAS-TP: Domain Knowledge-driven Automated Construction of U...

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16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Yang, Yuqian Zhao, Cong Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics Xi'an China

ISBN: (纸本)9798331506322

The advent of 5G networks marks a revolutionary shift in wireless communications, offering unprecedented improvements in speed, latency, and connectivity. Accurate uplink and downlink throughput prediction is crucial for 5G network Operations and Maintenance (O&M) tasks, yet existing methods constructing intuitive predictors without structural optimization often fail to adapt to dynamic 5 G environments due to the lack of effective utilization of domain knowledge. To address this, we present KNAS-TP, the first domain knowledge-driven automated construction method for 5G throughput prediction based on Neural Architecture Search (NAS) and the Attention mechanism. KNAS-TP incorporates domain knowledge in NAS search space establishment and loss function definitions, which includes the match degree evaluation of the complexity of the searched neural network structure and corresponding knowledge. Additionally, KNAS-TP enhances model using an Attention mechanism to captures the uneven impacts of deep factors, thereby improving final model training and prediction accuracy. Extensive experimental results demonstrate that our model outperforms all comparative methods, achieving up to 8.5 × lower throughput prediction MSE. The integration of domain knowledge is essential for robust and accurate throughput predictions, validating the effectiveness of our methodology in complex 5G scenarios. © 2024 IEEE.

关键词： 5G mobile communication systems

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Improvement of semen quality in Longyou Partridge Chicken by dietary N-acetyl-L-glutamic acid and its mechanism study

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Poultry Science 2025年第8期104卷 105234页

作者： Du, Xue Tong, Xiaopeng Qian, Xintong Zhang, Yuanning Li, Duoxi Liu, Xiaojun Zheng, Xianzhong Zhao, Ayong Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management China-Australia Joint Laboratory for Animal Health Big Data Analytics College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University Zhejiang Hangzhou 311300 China Longyou Agriculture and Rural Bureau Zhejiang Quzhou 324499 China Zhejiang Longchang Agriculture Development Co. LTD Quzhou 324400 China

The aim of the study was to investigate the mechanisms of the N-acetyl-L-glutamic (NAG) diet in improving rooster semen quality after cryopreservation. A total of 60 individually housed chickens were randomly allocated to three groups: a basal diet group (C), a basal diet supplemented with 2 g/kg NAG group (NL), and a basal diet supplemented with 4 g/kg NAG group (NH). Semen quality was evaluated on days 15 and 30, including fresh sperm motility and cryopreservation efficacy. Serum biochemical parameters, hormone levels, immune indices, semen metabolites, and gut microbiota composition were assessed at 30 d. NAG supplementation significantly improved semen quality. At 15 and 30 d, both NL and NH groups showed a significant increase in the total motile sperm ratio (TM) in fresh semen compared to the control (P ≤ 0.05). At 30 d, group NH exhibited significantly higher percentage of sperm in a straight-line trajectory (LIN) in fresh semen than both the C and NL groups. After cryopreservation, the sperm TM and LIN from chickens with NAG dietary for 30 days were significantly better in the NL group compared to C and NH groups (P ≤ 0.05). Metabolomic analysis identified significant upregulation of metabolites, including melatonin and ferulic acid, in cryopreserved semen from NAG-supplemented groups, while 5-hydroxylysine and inosine-1-phosphate were significantly reduced. Serum antioxidant capacity, as measured by glutathione peroxidase (GSH-Px) was significantly higher, while malondialdehyde (MDA) levels lower in NAG groups compared to C (P ≤ 0.05). No significant differences in immune markers or reproductive hormones were observed across groups. Gut microbiota analysis revealed that NAG supplementation enhanced beneficial bacterial populations. In conclusion, NAG supplementation improved sperm motility and cryopreservation outcomes, potentially by increasing the levels of semen melatonin and ferulic acid and reducing the body oxidative stress. And it can improve the ove

关键词： Gut microbiota N-acetyl-L-glutamic acid Semen metabolites Semen quality Serum indicators

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LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning 50th

LightDiC: A Simple yet Effective Approach for Large-scale Di...

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50th International Conference on Very Large data Bases, VLDB 2024

作者： Li, Xunkai Liao, Meihao Wu, Zhengyu Su, Daohan Zhang, Wentao Li, Rong-Hua Wang, Guoren Beijing Institute of Technology China Peking University National Engineering Laboratory for Big Data Analytics and Applications China

Most existing graph neural networks (GNNs) are limited to undirected graphs, whose restricted scope of the captured relational information hinders their expressive capabilities and deployment. Compared with undirected graphs, directed graphs (digraphs) fit the demand for modeling more complex topological systems by capturing more intricate relationships between nodes. While some directed GNNs have been introduced, their inspiration mainly comes from deep learning architectures, which lead to redundant complexity and computation, making them inapplicable to large-scale databases. To address these issues, we propose LightDiC, a scalable variant of the digraph convolution based on the magnetic Laplacian. Since topology-related computations are conducted solely during offline pre-processing, LightDiC achieves exceptional scalability, enabling downstream predictions to be trained separately without incurring recursive computational costs. Theoretical analysis shows that LightDiC achieves message passing based on the complex field, which corresponds to the proximal gradient descent process of the Dirichlet energy optimization function from the perspective of digraph signal denoising, ensuring its expressiveness. Experimental results demonstrate that LightDiC performs comparably well or even outperforms other SOTA methods in various downstream tasks, with fewer learnable parameters and higher efficiency. © 2024, VLDB Endowment. All rights reserved.

关键词： Directed graphs

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Mechanistic Insights into Cadmium-Induced Nephrotoxicity: Nrf2-Driven Ho-1 Activation Promotes Ferroptosis Via Iron Overload and Oxidative Stress

SSRN

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SSRN 2025年

作者： Luo, Tongwang Song, Shengzhe Wang, Shujie Zhou, Bin Song, Quanjiang Jiang, Sheng Shen, Lingjun Wang, Xiaodu Song, Houhui Shao, Chunyan College of Veterinary Medicine Zhejiang A&F University Zhejiang Hangzhou311300 China Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province Zhejiang Hangzhou311300 China Zhejiang Engineering Research Center for Veterinary Diagnostics & Advanced Technology Zhejiang Hangzhou311300 China Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management Zhejiang Hangzhou311300 China Belt and Road International Joint Laboratory for One Health and Food Safety Zhejiang Hangzhou311300 China China-Australia Joint Laboratory for Animal Health Big Data Analytics Zhejiang Hangzhou311300 China College of Animal Science and Technology College of Animal Science and Technology College of Veterinary Medicine China

Cadmium (Cd), a pervasive environmental toxicant, poses significant threats to human and animal health through multi-organ toxicity. While ferroptosis has been implicated in Cd-induced pathologies, the molecular mechanisms underlying Cd-mediated nephrotoxicity remain poorly understood. This study elucidates the ferroptosis pathway in CdCl2-exposed PK-15 cells and murine kidney, characterized by iron overload, lipid peroxidation, and mitochondrial dysfunction, which were ameliorated by ferroptosis inhibitor ferrostatin-1. Transcriptomic analysis revealed substantial upregulation of heme oxygenase-1 (HO-1) following CdCl2 exposure. Mechanistically, CdCl2 triggered nuclear translocation of nuclear factor erythroid 2-related factor-2 (NRF2), subsequently activating Ho-1 transcription. Over-activated HO-1 catalyzes the decomposition of heme and releases free iron, accompanied with the degradation of ferritin heavy chain 1 (FTH1) induced by CdCl2 exposure, which leads to intracellular iron overload and excessive lipid peroxides production through Fenton reaction, resulting in ferroptosis ultimately. In vivo validation confirmed NRF2/HO-1-mediated ferroptosis in CdCl2-induced murine nephrotoxicity. Both pre-treatment with HO-1 competitive inhibitor Zinc protoporphyrin IX (ZnPP) and knockout of Ho-1 gene remarkably alleviated PK-15 cells against ferroptosis induced by CdCl2 treatment. Our findings demonstrate that Cd exposure initiates NRF2-mediated HO-1 overexpression, driving iron-dependent lipid peroxidation and subsequent ferroptosis. This mechanistic insight provides potential therapeutic targets for mitigating Cd-induced renal damage, advancing our understanding of heavy metal toxicity and its cellular consequences. © 2025, The Authors. All rights reserved.

关键词： Transcription

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Self-supervised probabilistic models for exploring shape memory alloys

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npj Computational Materials 2024年第1期10卷 1338-1347页

作者： Yiding Wang Tianqing Li Hongxiang Zong Xiangdong Ding Songhua Xu Jun Sun Turab Lookman State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an710049China National Engineering Laboratory for Big Data Analytics School of Mathematics and StatisticsXi’an Jiaotong UniversityXi’an710049China AiMaterials Research LLC Santa FeNM87501USA

Recent advancements in machine learning(ML)have revolutionized the field of high-performance materials ***,developing robust ML models to decipher intricate structure-property relationships in materials remains challenging,primarily due to the limited availability of labeled datasets with well-characterized crystal *** is particularly pronounced in materials where functional properties are closely intertwined with their crystallographic *** a selfsupervised probabilistic model(SSPM)that autonomously learns unbiased atomic representations and the likelihood of compounds with given crystal structures,utilizing solely the existing crystal structure data from materials *** significantly enhances the performance of downstream ML models by efficient atomic representations and accurately captures the probabilistic relationships between composition and crystal *** showcase SSPM’s capability by discovering shapememory alloys(SMAs).Amongst the top 50 predictions,23 have been confirmed as SMAs either experimentally or theoretically,and a previously unknown SMA candidate,MgAu,has been identified.

关键词： alloys probabilistic shape

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Machine Learning Techniques for Autism Spectrum Disorder: Current trends and future directions 4

Machine Learning Techniques for Autism Spectrum Disorder: Cu...

引用

4th International Conference on Innovative Trends in Information Technology, ICITIIT 2023

作者： Khan, Kainat Katarya, Rahul Delhi Technological University Big Data Analytics and Web Intelligence Laboratory Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9781665494144

ASD or autism spectrum disorder is a critical neuro-developmental disorder that hinders an individual's capability of social communication and interaction. This disorder has acquired considerable attention and importance due to its ubiquity among individuals covering all the countries worldwide. Individuals with ASD struggles in daily life activities. Detection of autism with the help of medical tests is a tedious and very costly task. However, detection and care of ASD still remains unfamiliar due to inadequate awareness, knowledge among the society, limited number of diagnostic devices and limited verbal therapy services for ASD patients. This paper investigates and displays reviews of various machine learning approaches on extracting useful data associated with distinctive characteristics of ASD such as brain functioning, hyperactivitperactivity, language disability, etc. Current researches reveal that analysis of biological traits by employing machine learning techniques have helped in the progress of early detection of ASD. ABIDE dataset is very much explored for the research in ASD. Additionally, numerous studies for the advancement of tools are still in progression. The presented research work can remarkably aid future studies on machine learning for ASD. © 2023 IEEE.

关键词： Supervised learning

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