rpus atrophic gastritis patients without intestinal metaplasia. Twenty-six patients with UGI-SELs indicated for EUS-FNA were randomly assigned to the dry-first arm using the dry technique for the first two passes or the wet-first arm using the wet technique for the first two passes using a cross-over design with a ratio of 1  1. The primary endpoint was the cellularity score of the EUS-FNA specimens rated on a 4-point scale (0-3). The secondary endpoints were the factors influencing cellularity in each suction technique. The mean cellularity score was 1.65 ± 1.20 for the wet technique and 2.00 ± 0.98 for the dry technique ( = 0.068). Logistic regression analysis showed that higher cellularity may be related to the final diagnosis of gastrointestinal stromal tumors in the dry technique and the SEL location in the upper stomach in the wet technique. The wet EUS-FNA technique failed to show a potential for improved cellularity of specimens compared to the dry technique for UGI-SELs.The wet EUS-FNA technique failed to show a potential for improved cellularity of specimens compared to the dry technique for UGI-SELs.Previous studies have shown that different frequency band oscillations are associated with cognitive processing such as working memory (WM). Electroencephalogram (EEG) coherence and graph theory can be used to measure functional connections between different brain regions and information interaction between different clusters of neurons. At the same time, it was found that better cognitive performance of individuals indicated stronger small-world characteristics of resting-state WM networks. However, little is known about the neural synchronization of the retention stage during ongoing WM tasks (i.e., online WM) by training on the whole-brain network level. Therefore, combining EEG coherence and graph theory analysis, the present study examined the topological changes of WM networks before and after training based on the whole brain and constructed differential networks with different frequency band oscillations (i.e., theta, alpha, and beta). The results showed that after WM training, the subjects' WM networks had higher clustering coefficients and shorter optimal path lengths than before training during the retention period. Moreover, the increased synchronization of the frontal theta oscillations seemed to reflect the improved executive ability of WM and the more mature resource deployment; the enhanced alpha oscillatory synchronization in the frontoparietal and fronto-occipital regions may reflect the enhanced ability to suppress irrelevant information during the delay and pay attention to memory guidance; the enhanced beta oscillatory synchronization in the temporoparietal and frontoparietal regions may indicate active memory maintenance and preparation for memory-guided attention. The findings may add new evidence to understand the neural mechanisms of WM on the changes of network topological attributes in the task-related mode.In recent years, more and more scholars devoted themselves to the research of the target detection algorithm due to the continuous development of deep learning. Among them, the detection and recognition of small and complex targets are still a problem to be solved. The authors of this article have understood the shortcomings of the deep learning detection algorithm in detecting small and complex defect targets and would like to share a new improved target detection algorithm in steel surface defect detection. The steel surface defects will affect the quality of steel seriously. We find that most of the current detection algorithms for NEU-DET dataset detection accuracy are low, so we choose to verify a steel surface defect detection algorithm based on machine vision on this dataset for the problem of defect detection in steel production. A series of improvement measures are carried out in the traditional Faster R-CNN algorithm, such as reconstructing the network structure of Faster R-CNN. Based on the small features of the target, we train the network with multiscale fusion. For the complex features of the target, we replace part of the conventional convolution network with a deformable convolution network. The experimental results show that the deep learning network model trained by the proposed method has good detection performance, and the mean average precision is 0.752, which is 0.128 higher than the original algorithm. Among them, the average precision of crazing, inclusion, patches, pitted surface, rolled in scale and scratches is 0.501, 0.791, 0.792, 0.874, 0.649, and 0.905, respectively. The detection method is able to identify small target defects on the steel surface effectively, which can provide a reference for the automatic detection of steel defects.The COVID-19 pandemic has swept across the world over the past few months. Many articles have been published on the safety of anesthetic medications and procedures used in COVID-19 positive patients presenting for surgery. Several other articles covered the chronic pain management aspect during the pandemic. Our review aimed to focus on perioperative pain management for COVID-19 patients. We conducted a literature search for pertinent recent articles that cover considerations and recommendations concerning perioperative pain management in COVID-19 patients. We also searched the literature for the relevant adverse effects of the commonly used medications in the treatment of COVID-19, and their potential drug-drug interactions with the common medications used in perioperative pain management. Professional societies recommend prioritizing regional anesthesia techniques, which have many benefits over other perioperative pain management options. When neuraxial and continuous peripheral nerve block catheters are not an option, patient-controlled analgesia (PCA) should be considered if applicable. selleck chemicals Many of the medications used for the treatment of COVID-19 and its symptoms can interfere with the metabolism of medications used in perioperative pain management. We formulated an up-to-date guide for anesthesia providers to help them manage perioperative pain in COVID-19 patients presenting for surgery.