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Echinococcus multilocularis, the causative agent of alveolar echinococcosis (AE), severely threats human health and livestock farming. The first line of chemotherapeutic drug for AE is albendazole, which limits rapid extension of E. multilocularis metacestodes, but is rarely curative for AE, with severe side effects in long-term use, thus development of new anti-echinococcal drugs is mandated. Pseudolaric acid B (PAB) has long been used to treat fungal-infected dermatosis, and exerted anti-tumor, -fertility, -angiogenesis, -tubulin and antiparasitic activity. However, the effect of PAB against Echinococcus spp. remains unclear. The present study is to understand the effect of PAB against E. multilocularis in vitro and in vivo, and identify potential anti-echinococcal mechanism, as well as its toxicity. After exposure to PAB at 20 μg/ml, significant reduction of the survival rate and substantial ultrastructural destructions in E. multilocularis protoscoleces were observed in vitro. Furthermore, the wet weight of E. multilocularis cysts in the infected mice was significantly decreased after treatment with PAB (40, 20 or 10 mg/kg) for 12 weeks. Meanwhile, significant increase of both protein and mRNA expression of transforming growth factor beta 1 (TGF-β1) was detected in the serum and liver of the infected mice, whereas PAB administration lowered its expression significantly. The toxicity tests demonstrated that PAB displayed lower cytotoxicity to human liver and kidney cells (HL-7702 and HK-2 cell) with IC50 = 25.29 and 42.94 μg/ml than albendazole with IC50 = 3.71 and 21.22 μg/ml in vitro, and caused lower hepatoxicity and nephrotoxicity in mice than ABZ. Our findings indicated that PAB possesses potent anti-echinococcal effect, with lower toxicity than albendazole, implying a potential chemotherapeutic agent for AE. Additionally, the present study demonstrated that the suppressive effect of PAB on the parasite may involve down-regulation of TGF-β1 signaling.

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.

Although previous works have proposed sophisticatedly probabilistic models that has strong capability of extracting features from remote sensing data (e.g., convolutional neural networks, CNN), the efforts that focus on exploring the human’s semantics on the object to be recognized are required more explorations. Moreover, interpretability of feature extraction becomes a major disadvantage of the state-of-the-art CNN. Especially for the complex urban objects, which varies in geometrical shapes, functional structures, environmental contexts, etc, due to the heterogeneity between low-level data features and high-level semantics, the features derived from remote sensing data alone are limited to facilitate an accurate recognition. In this paper, we present an ontology-based methodology framework for enabling object recognition through rules extracted from the high-level semantics, rather than unexplainable features extracted from a CNN. Firstly, we semantically organize the descriptions and definitions of the object as semantics (RDF-triple rules) through our developed domain ontology. Secondly, we exploit semantic web rule language to propose an encoder model for decomposing the RDF-triple rules based on a multilayer strategy. Then, we map the low-level data features, which are defined from optical satellite image and LiDAR height, to the decomposed parts of RDF-triple rules. Eventually, we apply a probabilistic belief network (PBN) to probabilistically represent the relationships between low-level data features and high-level semantics, as well as a modified TanH function is used to optimize the recognition result. The experimental results on lacking of the training process based on data samples show that our proposed approach can reach an accurate recognition with high-level semantics. This work is conducive to the development of complex urban object recognition toward the fields including multilayer learning algorithms and knowledge graph-based relational reinforcement learning.

The industrial spatial relationship is a cross-cutting subject of economic geography and geographical information science; it is of considerable significance to scientifically investigate and induce industrial space relationships for promoting the sustainable planning and development of the regional economic system. Taking Shenyang City as the research area and using the location information of manufacturing units and automobile sales outlets extracted from points of interest (POI), based on the spatial statistical analysis methods, we respectively investigated the spatial relationship between two industries from integration, correlation and coordination perspective. The characteristics of the spatial relationship between the equipment manufacturing industry and the automobile sales industry in Shenyang were spatial complementary integration, weak spatial correlation, and coordination with scale dependence and spatial heterogeneity in 2018. This distribution characteristic is attributed to local policy factors that industrial land is required to be concentrated in the secondary city outside the Second Ring Road, and the economic factors that the degree of dependence of the equipment manufacturing industry and automobile sales industry on externalities and factor costs. The results shed light on the improving industrial spatial relationship analysis framework based on POI big data, and industrial coordinated development monitoring and evaluation based on according to the industrial spatial relationship mechanism and effect theory, in order to accelerate the coordinated development of manufacturing and service industries and promote the construction of the industrial ecosystem.

Additional file 1: Supplementary Text 1. Diet-related questionnaires (excerpt from the CMEC questionnaires). Supplementary Table 1. Scoring criteria for the AMED score in the CMEC study. Supplementary Table 2. Scoring criteria for the DASH score in the CMEC study. Supplementary Table 3. Exchange values of fatty acids for food groups included in the CMEC studya. Supplementary Methods: directed acyclic graphs (DAG). Supplementary Figure 1. The final constructed DAG. Supplementary Table 4. Estimated associations using 24 kg/m2 and 28 kg/m2 as the BMI cutoff value of lean or overweight or obese individuals. Supplementary Table 5. Estimated associations in participants without exclusion of self-reported physician diagnosed chronic hepatitis/cirrhosis, diabetes, hypertension, hyperlipidemia, coronary heart disease, stroke or cancer. Supplementary Table 6. Estimated associations using logistic regression. Supplementary Table 7. Estimated associations with the complete case instead of imputed data. Supplementary Table 8. Adjusted associations between AMED and DASH and NAFLD.

The high-resolution regional division of mountainous area types has important scientific significance for promoting precise management of land space and regional sustainable development. At present, the classification method of mountainous area types is mainly at the county level and above, while classifications for towns and villages are nearly nonexistent, which poses a technical challenge for rural revitalization and the construction of ecological civilization in mountainous areas. We used Yuxi city, Yunnan Province, as the target area of this research, which was based on GIS technology and Digital Elevation Model (DEM) data and socioeconomic environmental monitoring data. The most appropriate statistical unit (e.g., 2.8224 km2) for topographic relief was defined, and the study area was divided into six mountain types: flatlands, hills, low mountains, medium-low mountains, midmountains, and subhigh mountains. Based on the township scale, an index system and classification method dominated by the plain comprehensive index were established to carry out mountain area classifications at township scales. The 75 towns of Yuxi city can be classified into 27 plain towns, 23 mountain-plain towns, and 25 mountain towns from an empirical application perspective, which can provide strong data support and a reference basis for studying the evolution characteristics of land use in different geographical spaces and their interrelationships as well as differentiated land space planning and governance.