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Soil environmental issues in the red bed region are increasingly conspicuous, underscoring the critical importance of assessing soil quality for the region’s sustainable development and ecosystem security. This study examines six distinct land use types of soils—agricultural land (AL), woodland (WL), shrubland (SL), grassland (GL), bare rock land (BRL), and red bed erosion land (REL)—in the Nanxiong Basin of northern Guangdong Province. This area typifies red bed desertification in South China. Principal component analysis (PCA) was employed to establish a minimum data set (MDS) for calculating the soil quality index (SQI), evaluating soil quality, analyzing influencing factors, and providing suggestions for ecological restoration in desertification areas. The study findings indicate that a minimal data set comprising soil organic matter (SOM), pH, available phosphorus (AP), exchangeable calcium (Ca2+), and available copper (A-Cu) is most suitable for evaluating soil quality in the red bed desertification areas of the humid region in South China. Additionally, we emphasize that exchangeable salt ions and available trace elements should be pivotal considerations in assessing soil quality within desertification areas. Regarding comprehensive soil quality indicators across various land use types, the red bed erosion soils exhibited the lowest quality, followed by those in bare rock areas and forest land. Within the minimal data set, Ca2+ and pH contributed the most to overall soil quality, underscoring the significance of parent rock mineral composition in the red bed desertification areas. Moreover, the combined effects of SOM, A-Cu, and AP on soil quality indicate that anthropogenic land management and use, including fertilization methods and vegetation types, are crucial factors influencing soil quality. Our research holds significant implications for the scientific assessment, application, and enhancement of soil quality in desertification areas.

Coronary heart disease (CHD) is the leading cause of mortality in the world. Early detection and treatment of CHD are crucial. Currently, coronary CT angiography (CCTA) has been the prior choice for CHD screening and diagnosis, but it cannot meet the clinical needs in terms of examination quality, the accuracy of reporting, and the accuracy of prognosis analysis. In recent years, artificial intelligence (AI) has developed rapidly in the field of medicine; it played a key role in auxiliary diagnosis, disease mechanism analysis, and prognosis assessment, including a series of studies related to CHD. In this article, the application and research status of AI in CCTA were summarized and the prospects of this field were also described.

Breast cancer is one of the diseases with the highest incidence and mortality among women in the world, which has posed a serious threat to women’s health. The appearance of clustered calcifications is one of the important signs of breast cancer, and thus how to classify clustered calcifications comes to be a key breakthrough in controlling breast cancer. In this study, the discriminant model based on image convolution is used to learn the image features related to the classification of clustered microcalcifications, and the graph convolutional network (GCN) based on topological graph is used to learn the spatial distribution characteristics of clustered microcalcifications. These two models are fused to obtain a complementary model of image information and spatial information. The results show that the performance of the fusion model proposed in this paper is obviously superior to that of the two classification models in the classification of clustered microcalcification.

Discarding Non-Maximum Suppression (NMS) post-processing and realizing fully end-to-end object detection is a recent research focus. Previous works have proved that the one-to-one label assignment strategy provides the chance to eliminate NMS during inference. However, this strategy might also result in multiple predictions with high scores due to the inconsistency of label assignment during training. Thus, how to adaptively identify only one positive sample as a final prediction for each Ground-Truth instance remains important. In this paper, we propose an Enhanced Positive Sample Filter (EPSF) to filter out the single positive sample for each Ground-Truth instance and lower the confidence of other negative samples. This is mainly achieved with two components: a Dual-stream Feature Enhancement module (DsFE) and a Disentangled Max Pooling Filter (DeMF). DsFE makes full use of representations trained with different targets so as to provide rich information clues for positive sample selection, while DeMF enhances the feature discriminability in potential foreground regions with disentangled pooling. With the proposed methods, our end-to-end detector achieves a better performances against existing NMS-free object detectors on COCO, PASCAL VOC, CrowdHuman and Caltech datasets.

The effects of Ti modification on the structural properties and catalytic performance of vanadia on hexagonal mesoporous silica (V-HMS) catalysts are studied for selective methanol-to-dimethoxymethane oxidation. Characterizations including N2 adsorption–desorption (SBET), X-ray diffraction (XRD), UV-Vis diffuse reflectance spectroscopy (DRS UV-Vis), Micro-Raman spectroscopy, FTIR spectroscopy, and H2 temperature-programmed reduction (H2-TPR) were carried out to investigate the property and structure of the catalysts. The results show that Ti can be successfully incorporated into the HMS framework in a wide range of Si/Ti ratios from 50 to 10. Ti modification can effectively improve the distribution of vanadium species and thus enhance the overall redox properties and catalytic performance of the catalysts. The catalytic activity of the V-Ti-HMS catalysts with the Si/Ti ratio of 30 is approximately two times higher than that of V-HMS catalysts with comparable selectivity. The enhanced activity exhibited by the V-Ti-HMS catalyst is attributed to the improved dispersion and reducibility of vanadium oxides.