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To analyze the incidence of PICC associated venous thrombosis. To predict the risk factors of thrombosis. To validate the best predictive model in predicting PICC associated thrombosis. Consecutive oncology cases in 341 who initially naive intended to be inserted central catheter for chemotherapy, were recruited to our dedicated intravenous lab. All patients used the same gauge catheter, Primary endpoint was thrombosis formation, the secondary endpoint was infusion termination without thrombosis. Two patients were excluded. 339 patients were divided into thrombosis group in 59 (17.4%) and non-thrombosis Group in 280 (82.6%), retrospectively. Tumor, Sex, Age, Weight, Height, BMI, BSA, PS, WBC, BPC, PT, D-dimer, APTT, FIB, Smoking history, Location, Catheter length, Ratio and Number as independent variables were analyzed by Fisher’s scoring, then Logistic risk regression, ROC analysis and nomogram was introduced. Total incidence was 17.4%. Venous mural thrombosis in 2 (3.4%), “fibrin sleeves” in 55 (93.2%), mixed thrombus in 2 (3.4%), symptomatic thrombosis in 2 (3.4%), asymptomatic thrombosis in 57 (96.6%), respectively. Height (χ² = 4.48, P = 0.03), D-dimer (χ² = 37.81, P < 0.001), Location (χ² = 7.56, P = 0.006), Number (χ² = 43.64, P < 0.001), Ratio (χ² = 4.38, P = 0.04), and PS (χ² = 58.78, P < 0.001), were statistical differences between the two groups analyzed by Fisher’s scoring. Logistic risk regression revealed that Height (β = −0.05, HR = 0.95, 95%CI: 0.911–0.997, P = 0.038), PS (β = 1.07, HR = 2.91, 95%CI: 1.98–4.27, P < 0.001), D-dimer (β0.11, HR = 1.12, 95%CI: 1.045–1.200, P < 0.001), Number (β = 0.87, HR = 2.38, 95% CI: 1.619–3.512, P < 0.001) was independently associated with PICC associated thrombosis. The best prediction model, D-dimer + Number as a novel co-variable was validated in diagnosing PICC associated thrombosis before PICC. Our research revealed that variables PS, Number, D-dimer and Height were risk factors for PICC associated thrombosis, which were slightly associated with PICC related thrombosis, in which, PS was the relatively strongest independent risk factor of PICC related thrombosis.

We present JueWu-SL, the first supervisedlearning-based artificial intelligence (AI) program that achieves human-level performance in playing multiplayer online battle arena (MOBA) games. Unlike prior attempts, we integrate the macro-strategy and the micromanagement of MOBA-game-playing into neural networks in a supervised and end-to-end manner. Tested on Honor of Kings, the most popular MOBA at present, our AI performs competitively at the level of High King players in standard 5v5 games. Index Terms-Game artificial intelligence (AI), learning systems, macro-strategy, micromanagement, multiplayer online battle arena (MOBA), neural networks. I. INTRODUCTION M ULTIPLAYER online battle arena (MOBA) games, e.g., Dota, Honor of Kings, and League of Legends, have been considered as an important and suitable testbed for artificial intelligence (AI) research due to their considerable complexity and varied playing mechanics [1]-[4]. The standard game mode of MOBA is 5v5, where two opposing teams of five players each compete against each other. In this mode, each individual in a team has to control the actions of one hero in real time based on both the situation dynamics and the team strategy. During the game, a hero can grow stronger by killing enemy heroes, pushing turrets, killing creeps and monsters, and so on. The goal for players in a team is to destroy their enemy's main structure while protecting their own.

Ultrathin amorphous ZnSnO (a-ZTO) films and ultrathin amorphous ZnGeSnO (a-ZGTO) films with various Ge contents were deposited by pulsed laser deposition for ultra-thin-film transistors (UTFTs). The thicknesses of the channel layers are approximately 3.2 nm. The properties of these ultrathin films and behaviors of these UTFTs were comparatively studied in detail. The a-ZTO ultrathin film exhibited a low concentration of the oxygen vacancy (VO) compared to a-ZGTO ultrathin films. Among all the UTFTs, the a-ZTO UTFT demonstrated the undoubtedly best performance with an on/off current ratio of more than 107, the largest field-effect mobility of 23.2 cm2 V−1 s−1, a positive threshold voltage of 2.0 V, a very small subthreshold swing of 0.31 V/decade, and the best long-term stability under bias stress, suggesting that the introduction of VO suppressors is dispensable with such a small thickness. Above all, the concentration of the oxygen vacancy is easily controlled in the ultrathin a-ZTO nanofilms, leading to the UTFTs operating in the enhancement mode with a high field-effect mobility of 23.2 cm2 V−1 s−1 and excellent long-term stability. The a-ZTO ultrathin film and ultra-thin-film transistor are very potential for future electrical applications with their excellent properties.

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