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Retraction notice to "A strategic review on sustainable approaches in municipal solid waste management and energy recovery: Role of artificial intelligence, economic stability and life cycle assessment" [Bioresour. Technol. 379 (2023) 129044]

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Bioresource technology 2025年 421卷 132185页

作者： Rajendiran Naveenkumar Jayaraj Iyyappan Ravichandran Pravin Seifedine Kadry Jeehoon Han Raveendran Sindhu Mukesh Kumar Awasthi Samuel Lalthazuala Rokhum Gurunathan Baskar Biological Systems Engineering University of Wisconsin-Madison Madison WI 53706 United States Forest Products Laboratory USDA Forest Service Madison WI 53726 United States. Department of Biotechnology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai 602107 India. Department of Biotechnology St. Joseph's College of Engineering Chennai 600119 India. Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon. Department of Chemical Engineering Pohang University of Science and Technology Pohang 37673 Republic of Korea. Department of Food Technology TKM Institute of Technology Kollam Kerala India. College of Natural Resources and Environment Northwest A&F University Yangling Shaanxi Province 712100 China. Department of Chemistry National Institute of Technology Silchar 788010 Assam India. Department of Biotechnology St. Joseph's College of Engineering Chennai 600119 India Department of Applied Data Science Noroff University College Kristiansand Norway. Electronic address: basg2002@***.

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Magnolin Mitigates Skin Ageing Through the CXCL10/p38 Signalling Pathway

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Journal of cellular and molecular medicine 2025年第10期29卷 e70507页

作者： Cheng Wang Xiaoyun Hu Tianlin Song Fan Hu Le Du Chenqiong Yan Tianwei Shen Nihong Li Wei Yang Li Li Nian Deng Xingwu Jiang Yelin Wu Rui Ye Shanghai Tenth People's Hospital Tongji University School of Medicine Shanghai China. UNISKIN Research Institute on Skin Aging Inertia Shanghai Biotechnology Co. Ltd. Shanghai China. DermaHealth Shanghai Biotechnology Co. Ltd. Shanghai China. Department of Dermatology & Venerology Center of Cosmetic Safety and Efficacy Evaluation and NMPA Key Laboratory for Human Evaluation and Big Data of Cosmetics West China Hospital Sichuan University Chengdu China. Department of Materials Science and State Key Laboratory of Molecular Engineering of Polymers Fudan University Shanghai China.

Skin ageing accelerates systemic ageing and impairs the body's disease resistance. Inflammaging is a significant contributor to skin ageing. However, the mechanism by which inflammatory factors contribute to skin ageing remains unclear. We conducted screenings and identified CXC motif chemokine ligand 10 (CXCL10) as an inducer of skin ageing, which can be effectively inhibited by magnolin extracted from Magnolia biondii flower. Our results show that CXCL10 not only enhances beta-galactosidase (β-gal) activity but also upregulates the expression of p21/p16, along with matrix metalloproteinases (MMP1, MMP9 and MMP10), through activation of the C-X-C motif chemokine receptor 3 (CXCR3) in human primary fibroblasts. These findings suggest that CXCL10 drives skin ageing by inducing cellular senescence and extracellular matrix degradation. Importantly, treatment with magnolin significantly mitigates skin ageing phenotypes induced by CXCL10 via the p38 signalling pathway. Furthermore, our study demonstrates that magnolin significantly mitigates UV-induced skin ageing in ex vivo human skin samples as well as on human facial skin. This study provides insight into the role of chemokine CXCL10 in promoting inflammaging and proposes an innovative approach for preventing and treating skin ageing.

关键词： CXCL10 cell senescence chemokine magnolin skin ageing

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Sepsis on dementia risk: A population-based cohort study with dose-dependent analysis

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Journal of Critical Care 2025年 89卷 155100-155100页

作者： Sun, Mingyang Li, Fangfang Wang, Yangyang Miao, Mengrong Lu, Zhongyuan Chen, Wan-Ming Wu, Szu-Yuan Zhang, Jiaqiang Department of Anesthesiology and Perioperative Medicine People's Hospital of Zhengzhou University Henan Provincial People's Hospital Henan Zhengzhou China Academy of Medical Sciences of Zhengzhou University Henan Zhengzhou China Graduate Institute of Business Administration College of Management Fu Jen Catholic University Taipei Taiwan Artificial Intelligence Development Center Fu Jen Catholic University Taipei Taiwan Department of Food Nutrition and Health Biotechnology College of Medical and Health Science Asia University Taichung Taiwan Big Data Center Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Yilan Taiwan Division of Radiation Oncology Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Yilan Taiwan Department of Healthcare Administration College of Medical and Health Science Asia University Taichung Taiwan Cancer Center Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Yilan Taiwan Centers for Regional Anesthesia and Pain Medicine Taipei Municipal Wan Fang Hospital Taipei Medical University Taipei Taiwan

Purpose: Emerging evidence links sepsis-related inflammation to dementia risk, but the dose-dependent effects of recurrent sepsis episodes remain unclear. This study assessed whether sepsis increases dementia risk and explored a potential dose-response relationship between sepsis frequency and dementia. Methods: We conducted a retrospective cohort study using Taiwan's National Health Insurance Research database (2005–2022), enrolling patients aged ≥18 years hospitalized with sepsis. An index period (2008–2013) was used to identify patients without prior dementia. A fixed 12-month landmark period (calendar year 2014) was applied to assess the number of sepsis episodes. Patients who died during the landmark period were excluded. Propensity score matching was performed to generate well-balanced sepsis and non-sepsis groups. Follow-up for dementia and mortality began after the landmark period and continued for up to 64 months. Cox and Fine-Gray models were used to account for competing risks of death. Results: Sepsis was associated with a significantly increased risk of all-cause dementia (HR 1.59;95 % CI, 1.47–1.72;P < 0.0001). A dose-response relationship was observed: patients with multiple sepsis episodes had the highest dementia risk (sHR 1.63;95 % CI, 1.39–1.91). Vascular dementia showed the strongest association, with a higher incidence in the sepsis group (1.2 % vs. 0.6 %, P = 0.0003). Conclusions: Our findings provide robust evidence of a dose-dependent association between sepsis and increased dementia risk, particularly vascular dementia, even after adjusting for competing mortality risks. This study addresses the limitations of previous research by not only employing propensity score matching (PSM) to balance confounding factors between the sepsis and non-sepsis groups but also using an index period and landmark period design to better explore potential causal relationships. These periods ensure that dementia onset occurred after sepsis and allow for the exam

关键词： Dementia Neuroinflammation. Dose-response relationship Population-based cohort study Sepsis

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A Hybrid Optimized Model for Opinion Mining with Dual Weightage Aspects 7

A Hybrid Optimized Model for Opinion Mining with Dual Weight...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Gupta, Shilpi Singhal, Niraj Gupta, Gaurav Tomar, Abhishek Meerut250110 India Chaudhary Charan Singh University Sir Chhotu Ram Institute of Engineering & Technology Meerut250001 India Shoolini University of Biotechnology And Management Sciences Yogananda School of Artificial Intelligence Computers And Data Science HP Solan173229 India

ISBN: (纸本)9798350371406

Opinion mining, natural language processing technique task is used for extracting information from unstructured data available on the internet. Now a days, users' shared aspect-based online views have become a reliable source of information for customers making purchasing decisions and business purposes. In this paper, a new aspect/feature based opinion mining approach with four primary components: preprocessing, extraction of aspect sentiment, grouping of aspect term and Aspect Sentiment Classification are discussed. The preprocessing of the user's reviews is performed by tokenization, lemmatization, stemming and removal of stop words. The extraction of aspect sentiment is carried out in two stages: POS tagging and Rule-based lexicon extraction. Based on the estimated semantic similarity score, the retrieved characteristics are divided into primary and secondary aspects. Both the primary and secondary aspects are then multiplied by the weight function. Furthermore, the suggested Particle with Levy Updated Sparrow Search Algorithm model optimizes the primary and secondary weighting elements to make the categorization more suitable in terms of opinion. The hybrid optimization model is created by combining the conventional Particle Swarm Optimization (PSO) with sparrow search algorithm (SSA). Following that, the retrieved attributes for final classification take place via Neural Network. The opinions of the reviews are characterized as a positive, neutral or negative. © 2023 IEEE.

关键词： Sentiment analysis

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Transferable,highly crystalline covellite membrane for multifunctional thermoelectric systems

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InfoMat 2024年第11期6卷 66-80页

作者： Myungwoo Choi Geonhee Lee Yea-Lee Lee Hyejeong Lee Jin-Hoon Yang Hanhwi Jang Hyeonseok Han MinSoung Kang Seonggwang Yoo A-Rang Jang Yong Suk Oh Inkyu Park Min-Wook Oh Hosun Shin Seokwoo Jeon Jeong-O Lee Donghwi Cho Department of Materials Science and Engineering Korea UniversitySeoulRepublic of Korea Thin Film Materials Research Center Korea Research Institute of Chemical TechnologyDaejeonRepublic of Korea Chemical Data-Driven Research Center Korea Research Institute of Chemical TechnologyDaejeonRepublic of Korea Division of Chemical and Material Metrology Korea Research Institute of Standards and ScienceDaejeonRepublic of Korea Department of Materials Science and Engineering Korea Advanced Institute of Science and TechnologyDaejeonRepublic of Korea Department of Mechanical Engineering Korea Advanced Institute of Science and TechnologyDaejeonRepublic of Korea Therapeutics&Biotechnology Division Drug Discovery Platform Research CenterKorea Research Institute of Chemical TechnologyDaejeonRepublic of Korea Querry Simpson Institute for Bioelectronics Northwestern UniversityEvanstonIllinoisUSA Division of Electrical Electronic and Control EngineeringKongju National UniversityCheonanRepublic of Korea Department of Mechanical Engineering Changwon National UniversityChangwonRepublic of Korea Department of Materials Science and Engineering Hanbat National UniversityDaejeonRepublic of Korea Advanced Materials and Chemical Engineering University of Science and TechnologyDaejeonRepublic of Korea

Emerging freestanding membrane technologies,especially using inorganic thermoelectric materials,demonstrate the potential for advanced thermoelectric ***,using rare and toxic elements during material processing must be ***,we present a scalable method for synthesizing highly crystalline CuS membranes for thermoelectric *** sulfurizing crystalline Cu,we produce a highly percolated and easily transferable network of submicron CuS *** CuS membrane effectively separates thermal and electrical properties to achieve a power factor of 0.50 mW m^(-1) K^(-2) and thermal conductivity of 0.37 W m^(-1) K^(-1) at 650 K(estimated value).This yields a record-high dimensionless figure-of-merit of 0.91 at 650 K(estimated value)for ***,integrating 12 CuS devices into a module resulted in a power generation of4μW atΔT of 40 K despite using a straightforward configuration with only p-type ***,based on the temperature-dependent electrical characteristics of CuS,we develop a wearable temperature sensor with antibacterial properties.

关键词： copper sulfide flexible thermoelectric generator multifunctional thermoelectric systems sulfurization thermoelectric membrane

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Corrigendum to “Internal ploidy and heritable chromosome fragmentation in Parachlorella kessleri ” [Algal. Res. 89 (2025) 104047]

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Algal Research 2025年 90卷

作者： Kotaro Ishii Madoka Asano Yusuke Kazama Tsuyoshi Takeshita Kateřina Bišová Vilém Zachleder Atsushi Toyoda Tomoko Abe Shigeyuki Kawano RIKEN Nishina Center for Accelerator-Based Science 2-1 Hirosawa Wako Saitama 351-0198 Japan Department of Radiation Measurement and Dose Assessment Institute for Radiological Science National Institutes for Quantum Science and Technology 4-9-1 Anagawa Inage-ku Chiba 263-8555 Japan Department of Integrated Biosciences Graduate School of Frontier Sciences The University of Tokyo 5-1-5 Kashiwa-no-ha Kashiwa Chiba 277-8561 Japan Department of Bioscience and Biotechnology Fukui Prefectural University 4-1-1 Kenjojima Matsuoka Eiheiji-cho Yoshida-gun Fukui 910-1195 Japan Functional Biotechnology Project Life Science Data Research Center Graduate School of Frontier Sciences 503 Kashiwanoha Campus Station Satellite The University of Tokyo 178-4-4 Wakashiba Kashiwa Chiba 277-0871 Japan Laboratory of Cell Cycles of Algae Centre Algatech Institute of Microbiology of the Czech Academy of Sciences 37981 Třeboň Czech Republic Department of Genomics and Evolutionary Biology National Institute of Genetics 1111 Yata Mishima Shizuoka 411-8540 Japan

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Accurate Brain Tumor Segmentation and Detection using Multi-Task Learning with GlobalNet and FusionNet

Accurate Brain Tumor Segmentation and Detection using Multi-...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： S Shreyanth S Niveditha V Kathiroli Data Science and Engineering Birla Institute of Technology and Science Pilani India Department of Biotechnology Rajalakshmi Engineering College Chennai India Automotive Engineering Birla Institute of Technology and Science Pilani India

Accurate and quick brain tumor diagnosis and treatment are critical for the successful management of these disorders, which can have a substantial influence on an individual’s quality of life and even be deadly. This work describes a novel technique for brain tumor segmentation and classification that employs three deep learning architectures: GlobalNet, Multi-task Learning, and FusionNet. GlobalNet is a convolutional neural network that incorporates global context information into the model via a global pooling layer, whereas Multi-task Learning allows the model to do numerous tasks concurrently, enhancing each task’s performance by using shared knowledge among them. FusionNet is a deep fully residual convolutional neural network that combines information extracted from multiple CNNs trained on various medical imaging modalities to improve the accuracy and resilience of brain tumor segmentation. The proposed method is shown to be robust and accurate in segmenting and categorizing brain tumors, providing an effective solution with better accuracy in predicting the brain tumors and segmenting the affected part.

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Exploration of Activated Pathways for Improving Antifungal Agent FR901469 Productivity in Fungal Species No.11243 Using Comprehensive Pathway Model

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Journal of Biosciences and Medicines 2017年第7期5卷 16-31页

作者： Itaru Takeda Hiroya Itoh Makoto Matsui Takashi Shibata Masayuki Machida Sachiyo Aburatani Department of Biotechnology and Life Science Tokyo University of Agriculture and Technology Tokyo Japan Biotechnology Research Institute for Drug Discovery National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan Astellas Pharma Inc. Tsukuba Biotechnology Research Center Ibaraki Japan Bioproduction Research Institute National Institute of Advanced Industrial Science and Technology (AIST) Sapporo Japan Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) Tokyo Japan

Secondary metabolites are important for various industrial applications. The production of secondary metabolites is often improved by the activation of substrate supply pathways for biosynthesis. However, many important pathways have remained unclear. In this study, we explored possible pathways related to substrate supply for the biosynthesis of the antifungal agent FR901469 which is a nonribosomal peptide and a fungal secondary metabolite. To clarify the unknown activated pathways, we utilized the Comprehensive Pathway Model (CPM) which was developed in our previous study. We verified that the overexpression of the hypothetical beta-alanine-aminotransferase (BAL-AT), which was included in the explored pathways, improved the FR901469 productivity. The genes encoding the BAL metabolic enzymes are considered to be important for improving the FR901469 productivity.

关键词： Bioinformatics Metabolic Pathway Model Fungi Secondary Metabolites

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Predicting Malware Classification and Family using Machine Learning: A Cuckoo Environment Approach with Automated Feature Selection 2

Predicting Malware Classification and Family using Machine L...

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2nd International Conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Niveditha, S. Prianka, R.R. Sathya, K. Shreyanth, S. Subramani, Nandhagopal Deivasigamani, Balakrishnan Karthikeyan, S. Department of Biotechnology Rajalakshmi Engineering College Thandalam Tamil Nadu Chennai602105 India Department of AIML New Horizon College of Engineering Karnataka Bengaluru560103 India School of Computer Science and Engineering VIT Tamilnadu Vellore632014 India MTech in Data Science and Engineering Birla Institute of Technology and Science Rajasthan Pilani333031 India KPR Institute of Engineering and Technology Tamil Nadu Coimbatore641407 India Department of Information Technology KSR Institute for Engineering and Technology India

Malware's increasing menace in the digital realm needs the development of powerful detection and classification systems. This study presents a unique method for predicting malware category and family using machine learning, leveraging the Cuckoo environment and automated feature selection. To undertake an exhaustive examination of malware activity, we combined the Cuckoo environment with cutting-edge machine learning methods, such as the k-Nearest Neighbors (KNN) technique. The dynamic analysis of malware samples within the Cuckoo environment, which captured their interactions with the execution environment, provided remarkable insights into their malicious behaviors. The Boruta method aided automated feature selection, improving the feature set and optimizing model performance. A comparison with existing models yielded striking findings. Notably, Test Case 3 surpassed earlier cases by incorporating Automated Feature Selection + Cross-Validation. In identifying phishing attempts, it displayed outstanding specificity (90%), precision (93%), recall (96%), and an impressive F1-Score (92%), indicating its proficiency in reliably recognizing this frequent threat. Test Case 3 showed considerable increases as well, with a surprising 7.0% increase in precision and a notable 14.6% increase in recall when compared to Test Case 1. Furthermore, in the field of ransomware detection, Test Case 4, which solely focused on Automated Feature Selection, obtained excellent results, with 91% specificity, 92% accuracy, and a recall rate of 93%. These developments highlight the importance of the Boruta algorithm in optimizing the model's performance. © 2024 Elsevier B.V.. All rights reserved.

关键词： Forecasting

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Nonnegative spatial factorization

arXiv

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arXiv 2021年

作者： Townes, F. William Engelhardt, Barbara E. Department of Computer Science Princeton University PrincetonNJ United States Data Science and Biotechnology Institute Gladstone Institutes San FranciscoCA United States

Gaussian processes are widely used for the analysis of spatial data due to their nonparametric flexibility and ability to quantify uncertainty, and recently developed scalable approximations have facilitated application to massive datasets. For multivariate outcomes, linear models of coregionalization combine dimension reduction with spatial correlation. However, their real-valued latent factors and loadings are difficult to interpret because, unlike nonnegative models, they do not recover a parts-based representation. We present nonnegative spatial factorization (NSF), a spatially-aware probabilistic dimension reduction model that naturally encourages sparsity. We compare NSF to real-valued spatial factorizations such as MEFISTO (Velten et al., 2020) and nonspatial dimension reduction methods using simulations and high-dimensional spatial transcriptomics data. NSF identifies generalizable spatial patterns of gene expression. Since not all patterns of gene expression are spatial, we also propose a hybrid extension of NSF that combines spatial and nonspatial components, enabling quantification of spatial importance for both observations and features. A TensorFlow implementation of NSF is available from https://***/willtownes/nsf-paper. Copyright © 2021, The Authors. All rights reserved.

关键词： Factorization

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