Towards a better negative sampling strategy for dynamic graphs.

As dynamic graphs have become indispensable in numerous fields due to their capacity to represent evolving relationships over time, there has been a concomitant increase in the development of Temporal Graph Neural Networks (TGNNs). When training TGNNs for dynamic graph link prediction, the commonly used negative sampling method often produces starkly contrasting samples, which can lead the model to overfit these pronounced differences and compromise its ability to generalize effectively to new data. To address this challenge, we introduce an innovative negative sampling approach named Enhanced Negative Sampling (ENS). This strategy takes into account two pervasive traits observed in dynamic graphs: (1) Historical dependence, indicating that nodes frequently reestablish connections they held in the past, and (2) Temporal proximity preference, which posits that nodes are more inclined to connect with those they have recently interacted with. Specifically, our technique employs a designed scheduling function to strategically control the progression of difficulty of the negative samples throughout the training. This ensures that the training progresses in a balanced manner, becoming incrementally challenging, and thereby enhancing TGNNs' proficiency in predicting links within dynamic graphs. In our empirical evaluation across multiple datasets, we discerned that our ENS, when integrated as a modular component, notably augments the performance of four SOTA baselines. Additionally, we further investigated the applicability of ENS in handling dynamic graphs of varied attributes. Our code is available at https://github.com/qqaazxddrr/ENS.

The Application of Computer Technology to Clinical Practice Guideline Implementation: A Scoping Review.

Implementation of clinical practice guidelines (CPG) is a complex and challenging task. Computer technology, including artificial intelligence (AI), has been explored to promote the CPG implementation. This study has reviewed the main domains where computer technology and AI has been applied to CPG implementation. PubMed, Embase, Web of science, the Cochrane Library, China National Knowledge Infrastructure database, WanFang DATA, VIP database, and China Biology Medicine disc database were searched from inception to December 2021. Studies involving the utilization of computer technology and AI to promote the implementation of CPGs were eligible for review. A total of 10429 published articles were identified, 117 met the inclusion criteria. 21 (17.9%) focused on the utilization of AI techniques to classify or extract the relative content of CPGs, such as recommendation sentence, condition-action sentences. 47 (40.2%) focused on the utilization of computer technology to represent guideline knowledge to make it understandable by computer. 15 (12.8%) focused on the utilization of AI techniques to verify the relative content of CPGs, such as conciliation of multiple single-disease guidelines for comorbid patients. 34 (29.1%) focused on the utilization of AI techniques to integrate guideline knowledge into different resources, such as clinical decision support systems. We conclude that the application of computer technology and AI to CPG implementation mainly concentrated on the guideline content classification and extraction, guideline knowledge representation, guideline knowledge verification, and guideline knowledge integration. The AI methods used for guideline content classification and extraction were pattern-based algorithm and machine learning. In guideline knowledge representation, guideline knowledge verification, and guideline knowledge integration, computer techniques of knowledge representation were the most used.

Clinic Efficacy and Action Mechanism of Promoting Repairment of Knee Osteoarthritis's Cartilage Corpuscle via Modified Duhuo Jisheng Mixture / 中国中医药信息杂志

Objective To discuss the clinic efficacy and its mechanism of action in promoting the cell proliferation of knee osteoarthritis's cartilage corpuscle via modified Duhuo Jisheng Mixture. Methods Totally 60 cases were divided into treatment group and control group by random number table method, with 30 cases in each group. Treatment group was given modified Duhuo Jisheng Mixture, 62.5 mL each time, twice a day, orally, and sulfoglycan sulfate capsules, 1 capsule a day, three times a day, orally; while control group was given diacerein capsules, 1 pill a day, twice a day, orally, and sulfoglycan sulfate capsules, the same as treatment group. Patients were required to do quadriceps muscle systolic and functional exercise, and keep warm. The treatment lasted for 6 weeks. VAS score and WOMAC of the two group were observed before and after treatment. IL-1, NO, Wnt5a, β-catenin, Sox9, Collagen Ⅱ in knee joint fluid were detected. Adverse reactions were monitored. Results Compared with pre-treatment, the contents of IL-1, NO, Wnt5a and β-catenin in two groups were reduces (P<0.05); while the contents of Sox9 and Collagen Ⅱ were raised (P<0.05). The VAS scores and WOMAC were reduced in two groups after treatment (P<0.05). There was no statistical significance between the two group in the above indexes (P>0.05). There were no obvious adverse reactions in both groups.Conclusion Modified Duhuo Jisheng Mixture can reduce inflammatory factors of the joint fluid and inhibit the Wnt signal pathway through regulating the expression of Sox9 and Collagen Ⅱ in the pathway, which can promote the cell proliferation of knee osteoarthritis's cartilage corpuscle and benefit the repairment of cartilage corpuscle in order to relieve pains and improve functions of knee joint.

Thickness-dependent transport channels in topological insulator Bi2Se3 thin films grown by magnetron sputtering.

We study the low-temperature transport properties of Bi2Se3 thin films grown by magnetron sputtering. A positive magnetoresistance resulting from the weak antilocalization (WAL) effect is observed at low temperatures. The observed WAL effect is two dimensional in nature. Applying the Hikami-Larkin-Nagaoka theory, we have obtained the dephasing length. It is found that the temperature dependence of the dephasing length cannot be described only by the Nyquist electron-electron dephasing, in conflict with prevailing experimental results. From the WAL effect, we extract the number of the transport channels, which is found to increase with increasing the thickness of the films, reflecting the thickness-dependent coupling between the top and bottom surface states in topological insulator. On the other hand, the electron-electron interaction (EEI) effect is observed in temperature-dependent conductivity. From the EEI effect, we also extract the number of the transport channel, which shows similar thickness dependence with that obtained from the analysis of the WAL effect. The EEI effect, therefore, can be used to analyze the coupling effect between the top and bottom surface states in topological insulator like the WAL effect.