Comparative evaluation of interpretation methods in surface-based age prediction for neonates.

Significant changes in brain morphology occur during the third trimester of gestation. The capability of deep learning in leveraging these morphological features has enhanced the accuracy of brain age predictions for this critical period. Yet, the opaque nature of deep learning techniques, often described as "black box" approaches, limits their interpretability, posing challenges in clinical applications.

Volume development changes in the occipital lobe gyrus assessed by MRI in fetuses with isolated ventriculomegaly correlate with neurological development in infancy and early childhood.

Objective: This study was to systematically assess the occipital lobe gray and white matter volume of isolated ventriculomegaly (IVM) fetuses with MRI and to follow up the neurodevelopment of participants.

Method: MRI was used to evaluate 37 IVM fetuses and 37 control fetuses. The volume of gray and white matter in each fetal occipital gyrus was manually segmented and compared, and neurodevelopment was followed up and assessed in infancy and early childhood.

[Estimation and Critical Source Area Identification of Non-point Source Pollution Based on Improved Export Coefficient Models: A Case Study of the Upper Beiyun River Basin].

Non-point source pollution(NSP) poses a great threat to water ecosystem health. The quantitative estimation of spatial distribution characteristics and accurate identification of critical source areas(CSAs) of NSP are the basis for its efficient and accurate control. The export coefficient model(ECM) has been widely used to assess NSP, but this model should be improved because it ignores pollutant loss in transport processes.