[Ecological risk assessment and influencing factors in the Wuhan Metropolitan Area based on supply and demand bundles of ecosystem services]. / åŸºäºŽç”Ÿæ€ç³»ç»ŸæœåŠ¡ä¾›éœ€ç°‡çš„æ­¦æ±‰éƒ½å¸‚åœˆç”Ÿæ€é£Žé™©è¯„ä¼°åŠå½±å“å› ç´ .

In the context of rapid urbanization, metropolitan areas are facing the risk of supply-demand mismatches among ecosystem services. Investigating the patterns, relationships, and driving factors of multiple supply-demand risks is of great significance to support the efficient management of regional ecological risks. We quantified the single/comprehensive supply-demand risk rates of six ecosystem services in Wuhan Metropolitan Area at the township scale in 2000, 2010, and 2020. By applying the self-organizing feature map network and optimal parameter geo-detector, we identified supply-demand risks bundles of ecosystem services and influencing factors of comprehensive risks. The results showed significant spatial variations in the supply-demand risks of typical ecosystem services from 2000 to 2020. The supply-demand risk associated with grain production, water yield, carbon sequestration, and green space recreation increased, while soil conservation and water purification risks decreased. The comprehensive ecosystem services supply-demand risk increased from 0.41 to 0.45, indicating a 'core area increase and periphery decrease' trend. Throughout the study period, the area exhibited bundles of comprehensive extremely high-risk bundles (B1), comprehensive high-risk bundles (B2), water purification high-risk bundles (B3), and grain production-soil conservation risk bundles (B4). The transition of risk types from B3 to B2 and from B2 to B1 suggested an increase in the combination and intensity of supply-demand risk. Vegetation cover, nighttime light index, and population density were the main driving factors for spatial variations in comprehensive supply-demand risk. Ecologi-cal risk assessment based on ecosystem services supply-demand bundles could provide an effective and reliable way to regulate multiple regional risk issues.

The clinical and imaging data fusion model for single-period cerebral CTA collateral circulation assessment.

Background: The Chinese population ranks among the highest globally in terms of stroke prevalence. In the clinical diagnostic process, radiologists utilize computed tomography angiography (CTA) images for diagnosis, enabling a precise assessment of collateral circulation in the brains of stroke patients. Recent studies frequently combine imaging and machine learning methods to develop computer-aided diagnostic algorithms. However, in studies concerning collateral circulation assessment, the extracted imaging features are primarily composed of manually designed statistical features, which exhibit significant limitations in their representational capacity. Accurately assessing collateral circulation using image features in brain CTA images still presents challenges. Methods: To tackle this issue, considering the scarcity of publicly accessible medical datasets, we combined clinical data with imaging data to establish a dataset named RadiomicsClinicCTA. Moreover, we devised two collateral circulation assessment models to exploit the synergistic potential of patients' clinical information and imaging data for a more accurate assessment of collateral circulation: data-level fusion and feature-level fusion. To remove redundant features from the dataset, we employed Levene's test and T-test methods for feature pre-screening. Subsequently, we performed feature dimensionality reduction using the LASSO and random forest algorithms and trained classification models with various machine learning algorithms on the data-level fusion dataset after feature engineering. Results: Experimental results on the RadiomicsClinicCTA dataset demonstrate that the optimized data-level fusion model achieves an accuracy and AUC value exceeding 86% . Subsequently, we trained and assessed the performance of the feature-level fusion classification model. The results indicate the feature-level fusion classification model outperforms the optimized data-level fusion model. Comparative experiments show that the fused dataset better differentiates between good and bad side branch features relative to the pure radiomics dataset. Conclusions: Our study underscores the efficacy of integrating clinical and imaging data through fusion models, significantly enhancing the accuracy of collateral circulation assessment in stroke patients.

Radiomics derived from T2-FLAIR: the value of 2- and 3-classification tasks for different lesions in multiple sclerosis.

Background: White matter (WM) lesions can be classified into contrast enhancement lesions (CELs), iron rim lesions (IRLs), and non-iron rim lesions (NIRLs) based on different pathological mechanism in relapsing-remitting multiple sclerosis (RRMS). The application of radiomics established by T2-FLAIR to classify WM lesions in RRMS is limited, especially for 3-class classification among CELs, IRLs, and NIRLs. Methods: A total of 875 WM lesions (92 CELs, 367 IRLs, 416 NIRLs) were included in this study. The 2-class classification was only performed between IRLs and NIRLs. For the 2- and 3-class classification tasks, all the lesions were randomly divided into training and testing sets with a ratio of 8:2. We used least absolute shrinkage and selection operator (LASSO), reliefF algorithm, and mutual information (MI) for feature selection, then eXtreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM) were used to establish discrimination models. Finally, the area under the curve (AUC), accuracy, sensitivity, specificity, and precision were used to evaluate the performance of the models. Results: For the 2-class classification model, LASSO classifier with RF model showed the best discrimination performance with the AUC of 0.893 (95% CI: 0.838-0.942), accuracy of 0.813, sensitivity of 0.833, specificity of 0.781, and precision of 0.851. However, the 3-class classification model of LASSO with XGBoost displayed the highest performance with the AUC of 0.920 (95% CI: 0.887-0.950), accuracy of 0.796, sensitivity of 0.839, specificity of 0.881, and precision of 0.846. Conclusions: Radiomics models based on T2-FLAIR images have the potential for discriminating among CELs, IRLs, and NIRLs in RRMS.

LVONet: automatic classification model for large vessel occlusion based on the difference information between left and right hemispheres.

Objective.Stroke is a highly lethal condition, with intracranial vessel occlusion being one of its primary causes. Intracranial vessel occlusion can typically be categorized into four types, each requiring different intervention measures. Therefore, the automatic and accurate classification of intracranial vessel occlusions holds significant clinical importance for assessing vessel occlusion conditions. However, due to the visual similarities in shape and size among different vessels and variations in the degree of vessel occlusion, the automated classification of intracranial vessel occlusions remains a challenging task. Our study proposes an automatic classification model for large vessel occlusion (LVO) based on the difference information between the left and right hemispheres.Approach.Our approach is as follows. We first introduce a dual-branch attention module to learn long-range dependencies through spatial and channel attention, guiding the model to focus on vessel-specific features. Subsequently, based on the symmetry of vessel distribution, we design a differential information classification module to dynamically learn and fuse the differential information of vessel features between the two hemispheres, enhancing the sensitivity of the classification model to occluded vessels. To optimize the feature differential information among similar vessels, we further propose a novel cooperative learning loss function to minimize changes within classes and similarities between classes.Main results.We evaluate our proposed model on an intracranial LVO data set. Compared to state-of-the-art deep learning models, our model performs optimally, achieving a classification sensitivity of 93.73%, precision of 83.33%, accuracy of 89.91% and Macro-F1 score of 87.13%.Significance.This method can adaptively focus on occluded vessel regions and effectively train in scenarios with high inter-class similarity and intra-class variability, thereby improving the performance of LVO classification.

[Effect of Biogas Slurry Return to Field on Heavy Metal Accumulation in Soil-crop System: A Meta-analysis].

To investigate the effects of biogas slurry return-to-field methods, the duration of biogas slurry return to field and the amount of heavy metals brought in from biogas slurry on the accumulation of heavy metals in soil-crop systems, and the importance of factors influencing heavy metal accumulation, 41 papers and 1972 pairs of data were integrated and analyzed. The results showed that the application of biogas slurry alone significantly increased the accumulation of As, Cd, Cr, Cu, and Zn in soil and As and Cr in crops by 20.5%, 15.2%, 25.6%, 18.7%, and 26.3% and 14.6% and 39.5%, respectively, and it had no significant effect on the accumulation of other heavy metals in crops. The combined application of biogas slurry and chemical fertilizers significantly increased the accumulation of soil Cr and Zn by 8.05% and 4.70% and decreased the accumulation of As by crops. Correlation analysis showed that the accumulation rates of soil As, Cd, and Cr were highly significantly and positively correlated (P<0.01) with the duration of biogas slurry return to field and soil organic matter (SOM) content, with correlation coefficients of 0.30, 0.15, and 0.13 and 0.22, 0.27, and 0.22, respectively; they were highly significantly and negatively correlated (P<0.01) with soil pH, with correlation coefficients of 0.16, 0.13, and 0.11, respectively. The heavy metals brought in by biogas slurry return to field promoted the accumulation of As, Cd, and Cr in soil and As, Cd, Cr, and Zn in crops, whereas the accumulation of Cd, Cu, and Zn in soil promoted the accumulation of Cd, Cu, and Zn in crops, with correlation coefficients of 0.45, 0.58, and 0.42, respectively. The main factors of heavy metal accumulation in the soil-crop systems were the duration of biogas slurry return to field, SOM, and soil pH.

Aggregation modeling of the influence of pH on the aggregation of variably charged nanoparticles.

The aggregation of variably charged nanoparticles is usually induced by the changes in internal and external conditions, such as solution temperature, pH, particle size, van der Waals force, and electrostatic repulsion among particles. In order to explore the effect of pH on the aggregation of variable charge nanoparticles, this paper proposed an extended model based on the 3D on-lattice Cluster-Cluster Aggregation (CCA) model. The extended model successfully established the relationship between pH and sticking probability, and used Smoluchowski theory to calculate the aggregation rate of nanoparticles. The simulation results showed that: (1) the change of the aggregation rate of the variable charge nanoparticles with pH conforms to the Gaussian distribution, (2) the initial particle concentration has a significant effect on the aggregation rate of the nanoparticles, and (3) pH can affect the competition between van der Waals force and electrostatic repulsion between particles, thereby affecting the degree of openness of clusters. The research demonstrated the extended CCA model is valuable in studying the aggregation of the variably charged nanoparticles via transforming the corresponding influence factors into the influence on the sticking probability.

Coupling ITO3dE model and GIS for spatiotemporal evolution analysis of agricultural non-point source pollution risks in Chongqing in China.

To determine the risk state distribution, risk level, and risk evolution situation of agricultural non-point source pollution (AGNPS), we built an 'Input-Translate-Output' three-dimensional evaluation (ITO3dE) model that involved 12 factors under the support of GIS and analyzed the spatiotemporal evolution characteristics of AGNPS risks from 2005 to 2015 in Chongqing by using GIS space matrix, kernel density analysis, and Getis-Ord Gi* analysis. Land use changes during the 10 years had a certain influence on the AGNPS risk. The risk values in 2005, 2010, and 2015 were in the ranges of 0.40-2.28, 0.41-2.57, and 0.41-2.28, respectively, with the main distribution regions being the western regions of Chongqing (Dazu, Jiangjin, etc.) and other counties such as Dianjiang, Liangping, Kaizhou, Wanzhou, and Zhongxian. The spatiotemporal transition matrix could well exhibit the risk transition situation, and the risks generally showed no changes over time. The proportions of 'no-risk no-change', 'low-risk no-change', and 'medium-risk no-change' were 10.86%, 33.42%, and 17.25%, respectively, accounting for 61.53% of the coverage area of Chongqing. The proportions of risk increase, risk decline, and risk fluctuation were 13.45%, 17.66%, and 7.36%, respectively. Kernel density analysis was suitable to explore high-risk gathering areas. The peak values of kernel density in the three periods were around 1110, suggesting that the maximum gathering degree of medium-risk pattern spots basically showed no changes, but the spatial positions of high-risk gathering areas somehow changed. Getis-Ord Gi* analysis was suitable to explore the relationships between hot and cold spots. Counties with high pollution risks were Yongchuan, Shapingba, Dianjiang, Liangping, northwestern Fengdu, and Zhongxian, while counties with low risks were Chengkou, Wuxi, Wushan, Pengshui, and Rongchang. High-value hot spot zones gradually dominated in the northeast of Chongqing, while low-value cold spot zones gradually dominated in the Midwest. Our results provide a scientific base for the development of strategies to prevent and control AGNPS in Chongqing.

[Ion specificity during ion exchange equilibrium in natural clinoptilolite].

Zeolites have been widely applied in soil improvement and environment protection. The study on ion specificity during ion exchange equilibrium is of important significance for better use of zeolites. The maximum adsorption capacities of alkali ions during ion exchange equilibrium in the clinoptilolite showed obvious specificity. For alkali metal ions with equivalent valence, the differences in adsorption capacity increased with the decrease of ionic concentration. These results cannot be well explained by the classical theories including coulomb force, ionic size, hydration, dispersion force, classic induction force and surface complexation. We found that the coupling of polarization effects resulted from the quantum fluctuation of diverse alkali metal ions and electric field near the zeolite surface should be the primary reason for specific ion effect during ion exchange in zeolite. The result of this coupling effect was that the difference in the ion dipole moment increased with the increase of surface potential, which further expanded the difference in the adsorption ability between zeolite surface and ions, resulting in different ion exchange adsorption ability at the solid/liquid interface. Due to the high surface charge density of zeolite, ionic size also played an important role in the distribution of ions in the double diffuse layer, which led to an interesting result that distinct differences in exchange adsorption ability of various alkali metal ions were only detected at high surface potential (the absolute value was greater than 0.2 V), which was different from the ion exchange equilibrium result on the surface with low charge density.

Application of the cluster-cluster aggregation model to an open system.

Colloid aggregation is often induced by the change of internal or external conditions. In order to account for the dynamic features of the evolutional open system, a conceptual model for colloid aggregation in open systems was developed based on the classic Cluster-Cluster Aggregation (CCA) model. The extended model allows the important parameters of the classic CCA model, diffusion coefficient D(1) and sticking probability P(1) of primary particles, time-dependent. Consequently, the new model can be used to simulate colloid aggregation in open systems. To demonstrate the applicability of the extended model, the diffusion coefficient D(1) and sticking probability P(1) were defined as a function of solvent evaporation rate and aggregation time in this study. For the simplicity purpose, this study only evaluate D(1)(t) while kept P(1)(t) as a constant for the simulations. Simulation results indicate that the solvent evaporation altered the aggregation mechanism in various degrees depending on the solvent evaporation rate. This research shows that the extended model based on the classic CCA model is valuable and applicable to open systems.