Robust data-driven segmentation of pulsatile cerebral vessels using functional magnetic resonance imaging.

Functional magnetic resonance imaging (fMRI) captures rich physiological and neuronal information that can offer insights into neurofluid dynamics, vascular health, and waste clearance function. The availability of cerebral vessel segmentation could facilitate fluid dynamics research in fMRI. However, without magnetic resonance angiography scans, cerebral vessel segmentation is challenging and time-consuming. This study leverages cardiac-induced pulsatile fMRI signal to develop a data-driven, automatic segmentation of large cerebral arteries and the superior sagittal sinus (SSS). The method was validated in a local dataset by comparing it to ground truth cerebral artery and SSS segmentations. Using the Human Connectome Project (HCP) aging dataset, the method's reproducibility was tested on 422 participants aged 36 to 100 years, each with four repeated fMRI scans. The method demonstrated high reproducibility, with an intraclass correlation coefficient > 0.7 in both cerebral artery and SSS segmentation volumes. This study demonstrates that the large cerebral arteries and SSS can be reproducibly and automatically segmented in fMRI datasets, facilitating the investigation of fluid dynamics in these regions.

Establishing a river health evaluation index system for seven rivers in Jiamusi City of China.

The Jiamusi section of the Songhua River is one of the first 17 model river construction sections in China. The implementation of river health assessments can determine the health dynamics of rivers and test the management's effectiveness. Targeting seven rivers, this study conducted river zoning and monitoring point deployment to conduct sufficient field research and monitoring. The authors selected hydrological and water resources, physical structure, water quality, aquatic life, social service functions, and management as guideline layers and 15 indicator layers. Subsequently, the authors established an evaluation index system to evaluate and analyze the ecological status and social service status of each river. The results showed that the Yindamu, Alingda, and Gejie rivers scored well as healthy rivers, with health evaluation scores of 78.98, 76.06, and 75.83, respectively. The Wangsanwu, Lujiagang, and Lingdangmai rivers are generally sub-healthy rivers with scores of 71.55, 67.97, and 60.7, respectively. The Yinggetu River has a score of 54.52 and is therefore assessed as unhealthy. Based on the scientific evaluation index method, this study analyses the current river health state in Jiamusi City to provide the basis for the evaluation of the river chief's work and future river management.

Variation characteristics and the impact of urbanization of extreme precipitation in Shanghai.

With the rapid development of urbanization, the characteristics of extreme precipitation in urban areas have changed significantly. Revealing the spatial and temporal distribution of extreme precipitation under changing environment is the basis of scientific response to the urban flood. Trends of extreme precipitation at 95% and 99% thresholds in Shanghai and the influence of urbanization on them were analyzed. The results show that: (1) The precipitation threshold limit value for each site are 5.7 ~ 6.3 mm at 95% with a variation factor of 0.04, and 14.3 ~ 17.16 mm at 99% with a variation factor of 0.06. The precipitation thresholds under 99% conditions were more significantly different among stations. (2) The extreme precipitation at each site has been increasing over the past 50 years, and the growth rates of 95% and 99% extreme precipitation are 8.02~11.46%/10a and 7.11~16.86%/10a, respectively. The growth rate of extreme precipitation is significantly higher than that of average precipitation, while the extreme part of the precipitation probability distribution increases considerably. There is a strong variability in extreme precipitation in this region, while the 99% threshold precipitation varies more. (3) The extreme precipitation in Shanghai is significantly positively correlated with the urbanization of the area around the site. Urbanization has an increasing effect on regional extreme precipitation, with more extreme precipitation and greater growth rate in highly urbanized areas.