Publications by authors named "J X Mo"

Intercorrelated aboveground traits associated with costs and plant growth have been widely used to predict vegetation in response to environmental changes. However, whether underground traits exhibit consistent responses remains unclear, particularly in N-rich subtropical forests. Responses of foliar and root morphological and physiological traits of tree and herb species after 8-year N, P, and combined N and P treatments (50 kg N, P, N and P ha year) were examined in leguminous Acacia auriculiformis (AA) and nonleguminous Eucalyptus urophylla (EU) forests in southern China.

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Background: The growing aging population has led to an increase in the prevalence of Alzheimer's disease (AD) and osteoporosis (OP), both of which significantly impair quality of life. The comorbid nature of these conditions suggests a shared genetic etiology, the understanding of which is crucial for developing targeted therapies.

Objective: This study aims to explore the shared genetic etiology underlying AD and OP, using a system biology approach to identify potential therapeutic targets and natural compounds for treatment.

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The subjective perceptions of passengers are intricately linked to the noise levels in subway stations of urban rail transit systems. In this study, comprehensive measurement was conducted on the noise environment on the platforms during operational hours at four distinct types of subway stations within an urban metro network. To ensure precise data collection, four different noise and sound pressure measurement points were strategically positioned in the middle and at both ends of each station platform.

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Utilizing network pharmacology and molecular docking, we evaluated the possible pharmacological mechanism of Danggui Sini Decoction (DGSND) for treating erectile dysfunction (ED). DGSND's chemical components and targets were found utilizing the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Disease-related genes associated with ED were identified through GeneCards, OMIM, TTD, DrugBank, and DisGeNET databases.

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Background Multiparametric MRI, including contrast-enhanced sequences, is recommended for evaluating suspected prostate cancer, but concerns have been raised regarding potential contrast agent accumulation and toxicity. Purpose To evaluate the feasibility of generating simulated contrast-enhanced MRI from noncontrast MRI sequences using deep learning and to explore their potential value for assessing clinically significant prostate cancer using Prostate Imaging Reporting and Data System (PI-RADS) version 2.1.

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