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Background: Immunotherapy is important for the treatment of esophagogastric cancer. The purpose of this study is to compare the efficacy and safety of PD-(L)1 antibody, chemotherapy, and supportive treatment in the management of pretreated advanced esophagogastric cancer. Methods:The randomized controlled trials were identified by searching electronic databases including PubMed, Cochrane Library and Embase database. The network meta-analysis (NMA) was carried out using software R 3.3.2. Main outcomes including overall survival (OS), progression-free survival (PFS), all grades and serious treatment-related adverse events (TRAEs) were extracted and analyzed. The ranking results for all outcomes were performed to identify the best treatments.Results: Seven high-quality RCTs involving 1,891 patients were taken into analysis. Compared with supportive treatment, PD-(L)1 antibody and chemotherapy both had a significantly longer OS time.Chemotherapy could obvious improve PFS than supportive treatment, but it had more all grades and serious TRAEs than PD-(L)1 antibody and supportive treatment. No significant difference was found in other comparisons. The probabilities of rank plot showed that PD-(L)1 antibody was the best in the outcome of OS. Chemotherapy ranked first in PFS and ranked last in all grades and serious TRAEs.Conclusions: According to our results, PD-(L)1 antibody had excellent survival benefits and tolerable TRAEs for pretreated advanced esophagogastric cancer. It might be a suitable potential choice, especially for patients with high PDL1 CPS or with gastroesophageal junction cancer.

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The development of artificial intelligence technology has promoted the rapid improvement of human-computer interaction. This system uses the Kinect visual image sensor to identify human bone data and complete the recognition of the operator's movements. Through the filtering process of real-time data by the host computer platform with computer software as the core, the algorithm is programmed to realize the conversion from data to control signals. The system transmits the signal to the lower computer platform with Arduino as the core through the transmission mode of the serial communication, thereby completing the control of the steering gear. In order to verify the feasibility of the theory, the team built a 4-DOF robotic arm control system and completed software development. It can display other functions such as the current bone angle and motion status in real time on the computer operation interface. The experimental data shows that the Kinect-based motion recognition method can effectively complete the tracking of the expected motion and complete the grasping and transfer of the specified objects, which has extremely high operability.

Network traffic classification has been widely studied to fundamentally advance network measurement and management. Machine Learning is one of the effective approaches for network traffic classification. Specifically, Deep Learning (DL) has attracted much attention from the researchers due to its effectiveness even in encrypted network traffic without compromising neither user privacy nor network security. However, most of the existing models are created from closed-world datasets, thus they can only classify those existing classes previously sampled and labeled. In this case, unknown classes cannot be correctly classified. To tackle this issue, an autonomous learning framework is proposed to effectively update DL-based traffic classification models during active operations. The core of the proposed framework consists of a DL-based classifier, a selflearned discriminator, and an autonomous self-labeling model. The discriminator and self-labeling process can generate new dataset during active operations to support classifier update. Evaluation of the proposed framework is performed on an open dataset, i.e., ISCX VPN-nonVPN, and independently collected data packets. The results demonstrate that the proposed autonomous learning framework can filter packets from unknown classes and provide accurate labels. Thus, corresponding DLbased classification models can be updated successfully with the autonomously generated dataset.