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This study evaluates the potential of machine learning algorithms for early-stage diabetes prediction. A dataset containing demographic information, medical history, and lab results was analyzed using Logistic Regression and Random Forest Classifier. The results showed that Random Forest algorithms were able to accurately predict diabetes at an early stage with high accuracy. The best-performing algorithm was found to be the Random Forest Classifier, with an accuracy of 98.0%. These findings suggest that machine-learning algorithms hold great promise for improving diabetes diagnosis and management. The results of this study provide valuable insights for future research in this area and may help inform the development of more effective and efficient screening and treatment strategies for diabetes. [ABSTRACT FROM AUTHOR]

Satellite images provide consistent and frequent information that can be used to estimate mineral resources over a large spatial extent. Advances in spaceborne hyperspectral remote sensing (HRS) and machine learning can help to support various remote-sensing-based applications, including mineral exploration. Leveraging these advances, the present study evaluates recently launched PRISMA spaceborne satellite images to map hydrothermally altered and weathered minerals using various machine-learning-based classification algorithms. The study was performed for the town of Jahazpur in Rajasthan, India (75°06′23.17″E, 25°25′23.37″N). The distribution map for minerals such as kaolinite, talc, and montmorillonite was generated using the spectral angle mapper technique. The resultant mineral distribution map was verified through an intensive field validation survey on surface exposures of the minerals. Furthermore, the obtained pixels of the end-members were used to develop the machine-learning-based classification models. Measures such as accuracy, kappa coefficient, F1 score, precision, recall, and ROC curve were employed to evaluate the performance of developed models. The results show that the stochastic gradient descent and artificial-neural-network-based multilayer perceptron classifiers were more accurate than other algorithms. Results confirm that the PRISMA dataset has enormous potential for mineral mapping in mountainous regions utilizing a machine-learning-based classification framework. [ABSTRACT FROM AUTHOR]

Byline: Neelam. Agrawal, Ankur. Sharma, Ravindra. Batra Sir, Congenital myopathies are a group of rare genetic disorders characterized by a defect in the contractile apparatus of the myocytes. [sup][1] We [...]

Metal nanoclusters have several applications in biological processes, medicine, cancer therapy, catalysis, etc. Iridium (Ir) nanoclusters exhibit excellent detection behavior compared to their bulk material. This work includes a deep insight into the interaction of Ir nanoclusters of four atoms (Ir 4 ) with amino acids and the analysis of Ir-amino acid (Ir-AAc) complexes. UV-visible spectroscopy of the Ir 4 nanocluster, amino acids, and their complexes was discussed as a way to detect amino acids with the help of the Ir 4 nanocluster. In UV-visible analysis, the UV-visible peak of phenylalanine (Phe) appeared at 204 nm with an excitation energy of 6.02 eV with a 0.0516 oscillator strength. Meanwhile, only in the Ir 4 nanocluster-phenylalanine (Ir-Phe) complex, the UV-visible peak was observed at 661 nm with an excitation energy of 1.87 eV and oscillator strength of 0.0051. This peak was observed due to the transition from HOMO-1 to LUMO+3. In the other complexes, no UV-visible peaks are observed. Thus, the results predict that the Ir 4 nanocluster can be used in the detection of Phe via UV-visible spectra., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

Research on amino acids is an attractive area because of their application in metabolism, cancer treatment, growth, and repair of body tissue, and RNA and DNA syntheses. Twenty amino acids are primarily responsible for protein synthesis. In our study, we used a Cu 6 nanocluster as an amino acid detector. For the investigation, we adsorbed amino acids on the Cu 6 nanocluster and studied their UV-visible spectra. It is observed that all of the Cu 6 -amino acid complexes have peaks at near 380 nm wavelength except the Cu-phenylalanine complex, where two UV-visible peaks are found at wavelengths 351 nm (excitation energy 3.49 eV) and 403 nm (excitation energy 3.02 eV), respectively, which originated from the HOMO - 2 to LUMO (28%) and HOMO - 1 to LUMO (38%) transitions. Due to this unique transition, the Cu 6 nanocluster can be used for the detection of the phenylalanine amino acid out of the 20 amino acids., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)