Timely and accurate information on rural settlements is essential for rural development planning. Remote sensing has become an important means for accurately mapping large scale rural settlements. Nevertheless, numerous difficulties remain in accurate and efficient rural settlement extraction. In this study, by combining multi-dimensional features derived from Sentinel-1/2 images, Visible Infrared Imaging Radiometer Suite supporting a Day-Night Band (VIIRS-DNB) dataset, and Digital Elevation Model (DEM) data using the Google Earth Engine (GEE) platform, we proposed an efficient framework with good transferability for mapping rural settlements in the Yangtze River Delta. To avoid the time-consuming selection of a large number of training samples in the whole study area, we employed four random forest models obtained from the training samples in respective training municipal districts in four different regions to classify other municipal districts in their corresponding region. We found that different features play diverse vital roles in the extraction of rural settlements in various regions. Compared to results only using optical data, accuracies obtained by the proposed method were significantly improved. The average user’s accuracy, producer’s accuracy, overall accuracy, and Kappa coefficient increased by 16.75%, 17.75%, 11.50%, and 14.50% in the four training municipal administrative areas, respectively. The overall accuracy and Kappa coefficient were 96% and 0.84, respectively. By contrast, our classification results are superior to other public datasets. The final mapping results provided a detailed spatial distribution of the rural settlements in the Yangtze River Delta and revealed that the total area of rural settlements is approximately 32,121.1 km2, accounting for 17.41% of the total area. The high-density rural settlements are mainly distributed in the Northern Plain and East Coast, while the low-density rural settlements are located in the Central Hills and Southern Mountain.

Deep learning techniques have greatly improved the efficiency and accuracy of building extraction using remote sensing images. However, high-quality building outline extraction results that can be applied to the field of surveying and mapping remain a significant challenge. In practice, most building extraction tasks are manually executed. Therefore, an automated procedure of a building outline with a precise position is required. In this study, we directly used the U2-net semantic segmentation model to extract the building outline. The extraction results showed that the U2-net model can provide the building outline with better accuracy and a more precise position than other models based on comparisons with semantic segmentation models (Segnet, U-Net, and FCN) and edge detection models (RCF, HED, and DexiNed) applied for two datasets (Nanjing and Wuhan University (WHU)). We also modified the binary cross-entropy loss function in the U2-net model into a multiclass cross-entropy loss function to directly generate the binary map with the building outline and background. We achieved a further refined outline of the building, thus showing that with the modified U2-net model, it is not necessary to use non-maximum suppression as a post-processing step, as in the other edge detection models, to refine the edge map. Moreover, the modified model is less affected by the sample imbalance problem. Finally, we created an image-to-image program to further validate the modified U2-net semantic segmentation model for building outline extraction.

Background The use of multimodal pharmacological prophylactic regimes has decreased postoperative nausea and vomiting (PONV) in general but it still occurs in over 60% of female patients after bariatric surgery. This study aimed to evaluate the efficacy of ST36 acupoint injection with anisodamine in prevention of PONV among female patients after bariatric surgery. Methods Ninety patients undergoing laparoscopic sleeve gastrectomy were randomly allocated to anisodamine or control group at the ratio of 2:1. Anisodamine or normal saline was injected into Zusanli (ST36) bilaterally after induction of general anesthesia. The incidence and severity of PONV were assessed during the first 3 postoperative days and at 3 months. The quality of early recovery of anesthesia, gastrointestinal function, sleep quality, anxiety, depression, and complications were also evaluated. Results Baseline and perioperative characteristics were comparable between two groups. In the anisodamine group, 25 patients (42.4%) experienced vomiting within postoperative 24 h compared with 21 (72.4%) in the control group (relative risk 0.59; 95% confidence interval 0.40–0.85). Time to first rescue antiemetic was 6.5 h in anisodamine group, and 1.7 h in the control group (P = 0.011). Less rescue antiemetic was required during the first 24 h in the anisodamine group (P = 0.024). There were no differences in either postoperative nausea or other recovery characteristics. Conclusions The addition of ST36 acupoint injection with anisodamine significantly reduced postoperative vomiting without affecting nausea in female patients with obesity undergoing laparoscopic sleeve gastrectomy.

Background: Two systematic reviews summarized the efficacy and safety of pharmacological prophylaxis for venous thromboembolism (VTE) after hepatic resection, but both lacked a discussion of the differences in the pharmacological prophylaxis of VTE in different ethnicities. Therefore, we aimed to evaluate the efficacy and safety of low-molecular-weight heparin (LMWH) or unfractionated heparin (UFH) for VTE prophylaxis in Asian and Caucasian patients who have undergone hepatic resection. Methods: We searched PubMed, Web of Science, Embase, China National Knowledge Infrastructure, Wanfang Data, and VIP databases for studies reporting the primary outcomes of VTE incidence, bleeding events, and all-cause mortality from January 2000 to July 2022. Results: Ten studies involving 4318 participants who had undergone hepatic resection were included: 6 in Asians and 4 in Caucasians. A significant difference in VTE incidence was observed between the experimental and control groups (odds ratio [OR] = 0.39, 95% confidence interval [CI]: 0.20, 0.74, P = .004). No significant difference in bleeding events and all-cause mortality was observed (OR = 1.29, 95% CI: 0.80, 2.09, P = .30; OR = 0.71, 95% CI: 0.36, 1.42, P = .33, respectively). Subgroup analyses stratified by ethnicity showed a significant difference in the incidence of VTE in Asians (OR = 0.16, 95% CI: 0.06, 0.39, P < .0001), but not in Caucasians (OR = 0.69, 95% CI: 0.39, 1.23, P = .21). No significant differences in bleeding events were found between Asians (OR = 1.60, 95% CI: 0.48, 5.37, P = .45) and Caucasians (OR = 1.11, 95% CI: 0.58, 2.12, P = .75). The sensitivity analysis showed that Ejaz’s study was the main source of heterogeneity, and when Ejaz’s study was excluded, a significant difference in VTE incidence was found in Caucasians (OR = 0.58, 95% CI: 0.36, 0.93, P = .02). Conclusion: This study’s findings indicate that the application of UFH or LMWH for VTE prophylaxis after hepatic resection is efficacious and safe in Asians and Caucasians. It is necessary for Asians to receive drug prophylaxis for VTE after hepatic resection. This study can provide a reference for the development of guidelines in the future, especially regarding the pharmacological prevention of VTE in different ethnicities.