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Background: There is lack of data on bipolar disorder (BD) type II from India.

Aim: To compare the demographic and clinical characteristics of patients with BD-I and BD-II using the data of the Bipolar Disorder Course and Outcome study from India (BiD-CoIN study).

Methodology: Using the data of the BiD-CoIN study, patients with BD-I and BD-II were compared for demographic and clinical variables.

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To improve the scientific accuracy and precision of children's physical fitness evaluations, this study proposes a model that combines self-organizing maps (SOM) neural networks with cluster analysis. Existing evaluation methods often rely on traditional, single statistical analyses, which struggle to handle the complexity of high-dimensional, nonlinear data, resulting in a lack of precision and personalization. This study uses the SOM neural network to reduce the dimensionality of high-dimensional health data.

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The human cerebral cortex is known for its hemispheric specialization, which underpins a variety of functions and activities. However, it is not well understood if similar lateralization exists within the deep gray matter nuclei, such as the basal ganglia (BG) and thalamus, and their associated arteries, including the lenticulostriate arteries (LSAs). To explore this, we analyzed images from 7T MRI scans of 40 healthy young individuals.

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The degradation of plastic waste is a major research challenge due to the adverse impacts of microplastic weathering on the environment and ecosystems. As a major source of plastic contamination comes from urban hydrosystems, studying MP degradation prior to their environmental dissemination is crucial. Through a combination of field sampling and laboratory experiments, this study provides a thorough statistical degradation comparison analysis between polyethylene in situ environmentally aged microplastics and artificially aged films.

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Working memory is associated with general intelligence and is crucial for performing complex cognitive tasks. Neuroimaging investigations have recognized that working memory is supported by a distribution of activity in regions across the entire brain. Identification of these regions has come primarily from general linear model analyses of statistical parametric maps to reveal brain regions whose activation is linearly related to working memory task conditions.

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