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Development of PFAS-Free Locally Concentrated Ionic Liquid Electrolytes for High-Energy Lithium and Aluminum Metal Batteries.
Acc Chem Res
January 2025
Helmholtz Institute Ulm (HIU) Electrochemical Energy Storage, Helmholtzstrasse 11, 89081 Ulm, Germany.
ConspectusLithium-ion batteries (LIBs) based on graphite anodes are a widely used state-of-the-art battery technology, but their energy density is approaching theoretical limits, prompting interest in lithium-metal batteries (LMBs) that can achieve higher energy density. In addition, the limited availability of lithium reserves raises supply concerns; therefore, research on postlithium metal batteries is underway. A major issue with these metal anodes, including lithium, is dendritic formation and insufficient reversibility, which leads to safety risks due to short circuits and the use of flammable electrolytes.
View Article and Find Full Text PDFHDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning.
BMC Bioinformatics
January 2025
School of Computer Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei, 230027, China.
Background: Drug-drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functional groups and substructures on drug properties. However, existing approaches face challenges regarding the insufficient interpretability of identified substructures and the isolation of chemical substructures.
View Article and Find Full Text PDFFinding a needle in a haystack: functional screening for novel targets in cancer immunology and immunotherapies.
Oncogene
January 2025
Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, West China Second Hospital, State Key Laboratory of Biotherapy, and Department of Neurosurgery, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, P. R. China.
Genome-wide functional genetic screening has been widely used in the biomedicine field, which makes it possible to find a needle in a haystack at the genetic level. In cancer research, gene mutations are closely related to tumor development, metastasis, and recurrence, and the use of state-of-the-art powerful screening technologies, such as clustered regularly interspaced short palindromic repeat (CRISPR), to search for the most critical genes or coding products provides us with a new possibility to further refine the cancer mapping and provide new possibilities for the treatment of cancer patients. The use of CRISPR screening for the most critical genes or coding products has further refined the cancer atlas and provided new possibilities for the treatment of cancer patients.
View Article and Find Full Text PDFAn automatic cervical cell classification model based on improved DenseNet121.
Sci Rep
January 2025
Department of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.
The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network.
View Article and Find Full Text PDFEarly identification of dropouts during the special forces selection program.
Sci Rep
January 2025
Department of Psychology, Faculty of Behavioural and Social Sciences, University of Groningen, Grote Kruisstraat 2/1, 9712TS, Groningen, The Netherlands.
Recruits are exposed to high levels of psychological and physical stress during the special forces selection period, resulting in dropout rates of up to 80%. To identify who likely drops out, we assessed a group of 249 recruits, every week of the selection program, on their self-efficacy, motivation, experienced psychological and physical stress, and recovery. Using linear regression as well as state-of-the-art machine learning techniques, we aimed to build a model that could meaningfully predict dropout while remaining interpretable.
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