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NH-MIL-125(Ti) and its functional nanomaterials - a versatile platform in the photocatalytic arena.
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Centre for Nano Science and Nano Technology, S 'O' A (Deemed to be University), Bhubaneswar-751 030, Odisha, India.
Titanium (Ti)-based MOFs are promising materials known for their porosity, stability, diverse valence states, and a lower conduction band (CB) than Zr-MOFs. These features support stable ligand-to-metal charge transfer (LMCT) transitions under photoirradiation, enhancing photocatalytic performance. However, Ti-MOF structures remain a challenge owing to the highly volatile and hydrophilic nature of ionic Ti precursors.
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Graduate School of Science and Technology, Shizuoka University, Shizuoka, Japan.
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View Article and Find Full Text PDFCharacterization of the host specificity of the SH3 cell wall binding domain of the staphylococcal phage 88 endolysin.
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Department of Microbiology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang, Selangor, 43400, Malaysia.
Bacteriophages produce endolysins at the end of the lytic cycle, which are crucial for lysing the host cells and releasing virion progeny. This lytic feature allows endolysins to act as effective antimicrobial alternatives when applied exogenously. Staphylococcal endolysins typically possess a modular structure with one or two enzymatically active N-terminal domains (EADs) and a C-terminal cell wall binding domain (CBD).
View Article and Find Full Text PDFQuantification of training-induced alterations in body composition via automated machine learning analysis of MRI images in the thigh region: A pilot study in young females.
Physiol Rep
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Motion and Exercise Science, University of Stuttgart, Stuttgart, Germany.
The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requires precise segmentation of structures. In this study we developed a novel automatic machine learning approach for volumetric segmentation and quantitative assessment of MRI volumes and investigated the efficacy of using a machine learning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bone volume of the thigh before and after a strength training.
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