An RNA polymerase II construct synthesizes short-hairpin RNA with a quantitative indicator and mediates highly efficient RNAi

RNA interference (RNAi) mediates gene silencing in many eukaryotes and has been widely used to investigate gene functions. A common method to induce sustained RNAi is introducing plasmids that synthesize short hairpin RNAs (shRNAs) using Pol III promoters. While these promoters synthesize shRNAs and elicit RNAi efficiently, they lack cell specificity. Monitoring shRNA expression levels in individual cells by Pol III promoters is also difficult. An alternative way to deliver RNAi is to use Pol II-directed synthesis of shRNA. Previous efforts in developing a Pol II system have been sparse and the results were conflicting, and the usefulness of those Pol II vectors has been limited due to low efficacy. Here we demonstrate a new Pol II system that directs efficient shRNA synthesis and mediates strong RNAi at levels that are comparable with the commonly used Pol III systems. In addition, this system synthesizes a marker protein under control of the same promoter as the shRNA, thus providing an unequivocal indicator, not only to the cells that express the shRNA, but also to the levels of the shRNA expression. This system may be adapted for in vivo shRNA expression and gene silencing

The effect of hidden color channels on nucleon-nucleon interaction in quark model

In the framework of constituent quark model, the effect of hidden color channels on the nucleon-nucleon ($NN$) interaction is studied. By adjusting the color confinement strength between the hidden color channels and color singlet channels and/or between the hidden color channels and hidden color channels, the experimental data of $S$ to $I$ partial-wave phase shifts of $NN$ scattering can be fitted well. The results show that the hidden color channel coupling might be important in producing the intermediate-range attraction of $NN$ interaction. The deuteron properties and dibaryon candidates have also been studied with this model.Comment: 11 pages, 9 figure

Risk Factors of Suicide Ideation in Chinese Graduate Students: CHAID Tree Analysis

The present study aims to identify the risk factors and develop a decision tree model of suicide ideation in Chinese graduate students. A chi-square automatic interaction detection tree analysis was conducted in a graduate students sample (N=1036). Measurements included University Personality Inventory (UPI), Symptom Checklist 90 (SCL-90), and Eysenck Personality Questionnaire (EPQ). Results showed that suicide incidence of Chinese graduate students was 1.15%, with males’ was higher than females. Seventeen potential variables were considered and only three of them (depression, obsession, and neuroticism) were found to be risk factors of suicide ideation in Chinese graduate students, and the interactions between them constructed a decision tree model. These findings should be helpful for school and mental health providers to detect graduate students with a high possibility of suicide ideation, which will aid in planning of early suicide intervention and prevention for at risk students

Optimization Method Based On Optimal Control

In this paper, we focus on a method based on optimal control to address the optimization problem. The objective is to find the optimal solution that minimizes the objective function. We transform the optimization problem into optimal control by designing an appropriate cost function. Using Pontryagin's Maximum Principle and the associated forward-backward difference equations (FBDEs), we derive the iterative update gain for the optimization. The steady system state can be considered as the solution to the optimization problem. Finally, we discuss the compelling characteristics of our method and further demonstrate its high precision, low oscillation, and applicability for finding different local minima of non-convex functions through several simulation examples

Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creative

Accurately predicting conversions in advertisements is generally a challenging task, because such conversions do not occur frequently. In this paper, we propose a new framework to support creating high-performing ad creatives, including the accurate prediction of ad creative text conversions before delivering to the consumer. The proposed framework includes three key ideas: multi-task learning, conditional attention, and attention highlighting. Multi-task learning is an idea for improving the prediction accuracy of conversion, which predicts clicks and conversions simultaneously, to solve the difficulty of data imbalance. Furthermore, conditional attention focuses attention of each ad creative with the consideration of its genre and target gender, thus improving conversion prediction accuracy. Attention highlighting visualizes important words and/or phrases based on conditional attention. We evaluated the proposed framework with actual delivery history data (14,000 creatives displayed more than a certain number of times from Gunosy Inc.), and confirmed that these ideas improve the prediction performance of conversions, and visualize noteworthy words according to the creatives' attributes.Comment: 9 pages, 6 figures. Accepted at The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019) as an applied data science pape