Background: Little is known about the associations between healthy dietary patterns and metabolic dysfunction-associated fatty liver disease (MAFLD) in less-developed ethnic minority regions (LEMRs), where the prevalence of MAFLD is increasing rapidly and dietary habits are quite different from those in developed countries. Moreover, a significant subset of MAFLD individuals in LEMRs are nonobese, but the efficacy of dietary patterns on MAFLD individuals with different obese statuses is also unclear. We aimed to test the associations of two wildly recommended a priori dietary patterns—Alternate Mediterranean diet (AMED) and Dietary Approaches to Stop Hypertension (DASH)—with the risk of MAFLD in the total population, and further in nonobese and obese individuals. Methods: We recruited 99,556 participants in the China Multi-Ethnic Cohort Study, an ongoing cohort study in less-developed southwest China. Using validated food frequency questionnaire, each participant was assigned an AMED score and a DASH score. MAFLD was ascertained as hepatic steatosis on ultrasound together with diabetes, overweight/obesity, or two other metabolic risk factors. We performed logistic regression with inverse probability of exposure weighting (IPEW) to examine associations between two dietary patterns and MAFLD, adjusting for potential confounders under the guidance of directed acyclic graphs. Further, analyses were stratified by body mass index. Results: We included 66,526 participants (age 49.5±11.0; 62.6% women), and the prevalence of MAFLD was 16.1%. Participants in the highest quintile of DASH score showed strong inverse associations with risks of MAFLD (OR = 0.85; 95% CI, 0.80-0.91; P_trend < 0.001) compared with participants in the lowest quintile. The association between DASH and nonobese MAFLD (OR = 0.69; 95% CI, 0.61-0.78; P_trend < 0.001) was stronger (I² = 78.5 % ; P_{heterogeneity} = 0.001) than that with obese MAFLD (OR = 0.90; 95% CI, 0.83-0.98; P_trend = 0.002). There was a null association between AMED and MAFLD risk. Conclusions: In LEMRs, a DASH diet but not AMED was associated with MAFLD. The relationship appeared to be more pronounced in nonobese MAFLD individuals than in obese MAFLD individuals.

The rational allocation of functional areas is the foundation for addressing the sustainable development of cities. Efficient and accurate identification methods of urban functional areas are of great significance to the adjustment and testing of urban planning and industrial layout optimization. Firstly, by employing multisource geographic data, an identification method of urban functional areas was developed. A quantitative measurement approach of the urban functional area was then established considering the comprehensive effects of human‐land, space‐time, and thematic information to present the covering area of ground objects, public awareness, and empirical research. Finally, the Zhengzhou city, which locates in Henan province of central China, was used to test the method. The results show that the developed method is efficient, accurate, and universal and can identify urban functional areas quickly and accurately. We found that the overall distribution of Zhengzhou’s functional areas presents a spatial pattern of single and multimixed coordinated development. The city’s commercial functional areas and commercial‐based mixed functional areas are located in the city’s central area. The green square’s function area occupies relatively low and is mainly distributed in the city’s fringe.

Public health facility planning is one of the important contents of national land planning, which needs to balance geospatial equity and service capacity. However, assessment models and data acquisition methods based on a geosystemic analysis perspective have been lacking for a long time. By focusing on urban public toilets and taking the highly urbanized city of Shenyang, China as the study area, this study developed a new data strategy for urban public facilities with points of interests (POI) big data as the main data source, and subsequently corrected the POI data and analyzed the errors through a field survey, and conducted an empirical assessment oriented toward spatial equity and service capacity to discover the development dynamics of urban facilities over the past ten years and the impacting factors. We found that the integrated population and spatial elements could more accurately evaluate the service capacity of public toilets. Meanwhile, POI data have value in the research of public health facilities, but there are some errors in data quality and data access. The study empirically explores the geographic analysis methods of field research data (small data) and POI data (big data) with empirical contributions.

The quantitative and qualitative assessment of post-disaster vegetation damage and recovery in the core area of the Wenchuan earthquake is of great significance for the restoration and reconstruction of natural ecosystems and the construction of human settlements in China. This study used time series analysis to determine the time of MODIS data and used the data to study the vegetation damage and restoration in the core area of the Wenchuan earthquake. The determined MODIS images were used to quantitatively analyze a series of vegetation damage changes and the vegetation recovery rate in the core area of the Wenchuan earthquake before and after the earthquake. By applying the topographic factors, we analyzed the spatial and temporal characteristics of the dynamic changes of vegetation damage and the recovery rate in the disaster area. The results show that the study area’s vegetation damage was correlated to topographic factors and distance from towns. Besides, the overall vegetation restoration after the disaster was relatively optimistic. In some areas, the vegetation restoration level even exceeded the vegetation coverage level before the disaster. The recovery study of MODIS-NDVI showed a specific lag delay effect on the image of vegetation cover. The vegetation damage and the recovery rate of vegetation cover were significantly correlated with the distance from towns and the topographic factor. Overall, the results contribute to the theoretical support for the damage and recovery of vegetation in the core area affected by the earthquake.

Vegetation indicators and spatial distribution characteristics are the core and basis to study the complex human-natural coupled system. In this paper, with Landsat 5 and Landsat 8 remote sensing data, we quantitatively estimated vegetation coverage in Henan Province, China. According to the urbanization rate, altitude, slope degree, and slope exposure, we analyzed spatial and temporal variation laws of vegetation coverage under the action of different factors to provide a reference for the improvement of the ecological environment and the quality assessment of Chinese granary. From 2000 to 2013, the vegetation coverage in Henan Province declined by 30.49% and the ecological environment deteriorated. The spatial change of vegetation coverage was evenly distributed in Henan Province. The vegetation coverage was increased in the west, south, and southwest parts of Henan Province and slightly decreased in the central, east, and the eastern part of Taihang Mountain. Vegetation coverage in a city was related to its population urbanization rate. The population urbanization rate was often negatively correlated with the vegetation coverage. According to the results of terrain factors based analysis, the low-altitude areas were in a good vegetation cover condition with the high vegetation coverage grade; the areas with a smaller slope degree had the large vegetation coverage and the coverage decreased with the increase in the slope degree; the coverage showed no significant difference between sunny and shady slopes and was less limited by light, temperature, and humidity.

Stellera chamaejasme L. (S. chamaejasme) is one of the primary toxic grass species (poisonous plants) distributed in the alpine meadows of Qinghai Province, China. In this study, according to the distinctive phenological characteristics of S. chamaejasme, the spectral differences between S. chamaejasme in the full-bloom stage and other pasture grasses were analyzed and the red, blue, and near-infrared bands of IKONOS image were determined as the diagnostic bands of S. chamaejasme recognition. Feature indexes related to S. chamaejasme were established using the diagnostic bands, and \(NDVI_{blue} = (\rho_{nir} − \rho_{blue})/(\rho_{nir} + \rho_{blue})\) obtained as S. chamaejasme sensitive index based on the linear regression analysis between the indexes derived from field spectra and the actual cover fraction of S. chamaejasme communities. The distribution area of S. chamaejasme was extracted by using the index \(NDVI_{blue}\) derived from IKONOS multispectral image in Qilian County of Qinghai Province, China and the verified result reached an overall accuracy of 90.71%. The study indicated that high resolution multispectral satellite images (such as IKONOS images) had significant potential in remote sensing recognition of toxic grass species.