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Objective: Based on the environment-individual interaction model and the "evaluation-coping" theory, the relationship between Resilience, Coping Style, Core Self-evaluation and psychological health of disadvantaged children was explored to provide some theoretical support for psychological health intervention research. Methods: Resilience Scale for Chinese Adolescent (RSCA), core self-evaluation Scale (CSES), Simplified Coping Style Questionnaire (SCSQ), General Health Questionnaire (GHQ-12) were used to conduct a questionnaire survey among 618 middle school students in South Xinjiang. Results: First, GHQ-12 scores were negatively correlated with RSCA, CSES, and SCSQ scores (r=-0.57/r=-0.56/r=-0.49, P

Ammonia (NH3) is an important alkaline reactive nitrogen (Nr) species which is involved in global nitrogen (N) biogeochemical cycling, but which has negative impacts on the environment and human health. In order to better understand and control the NH3 loss potential in soil-upland crop systems in China, an integrated data analysis including 1302 observations from 236 published articles between 1980 and 2021 was conducted. The typical NH3 volatilization rate (AVR) and the main factors influencing AVR in the major Chinese upland crops (maize, wheat, openfield vegetables and greenhouse vegetables and others) were estimated and analyzed. The mean AVR for maize, wheat, openfield vegetables and greenhouse vegetables were 7.8%, 5.3%, 8.4% and 1.8%. The most important influencing factors were fertilizer placement, meteorological conditions (especially temperature and rainfall) and soil properties (especially SOM). Subsurface N application produced a significantly lower AVR compared to surface application. High N recovery efficiency and N agronomic efficiency were generally associated with low AVRs. In conclusion, high N application rates, inefficient application methods and the use of loss-prone N fertilizer types are the main factors responsible for high AVRs in major Chinese croplands

Cervical cancer is one of the most common female malignancies. Human papillomaviruses (HPV) are the main causative agents of virtually all cervical carcinomas. Nevertheless, emerging evidence has demonstrated that a small proportion of cervical cancer patients are HPV negative. Long noncoding RNAs (lncRNAs) have been identified to play a crucial role in cervical cancer development. Here, this review describes the incidence and development of HPV-negative cervical cancer. Moreover, HPV-negative cervical cancers are more likely diagnosed at non-squamous type, older ages, more advanced stage and metastases, and associated with poorer prognosis as compared to HPV-positive cervical cancer. Furthermore, the significant role and functions of lncRNAs underlying HPV-negative cervical cancer is clarified.

Ferroptosis exhibits a potent antitumor effect and dihydroorotate dehydrogenase (DHODH) has recently been identified as a novel ferroptosis defender. However, the role of DHODH inhibition in cervical cancer cells is unclear, particularly in synergy with cisplatin via ferroptosis. Herein, shRNA and brequinar were used to knock down DHODH and directly inhibit DHODH, respectively. Immunohistochemistry and Western blotting assays were performed to measure the expression of proteins. CCK-8 and colony formation assays were employed to assess the cell viability and proliferation. Ferroptosis was monitored through flow cytometry, the malondialdehyde assay kit and JC-1 staining analyses. The nude mouse xenograft model was generated to examine the effect of combination of DHODH inhibition and cisplatin on tumor growth in vivo. The expression of DHODH was increased in cervical cancer tissues. DHODH inhibition inhibited the proliferation and promoted the ferroptosis in cervical cancer cells. A combination of DHODH inhibition and cisplatin synergistically induced both in vitro and in vivo ferroptosis and downregulated the ferroptosis defender mTOR pathway. Therefore, the combination of DHODH inhibition and cisplatin exhibits synergistic effects on ferroptosis induction via inhibiting the mTOR pathway could provide a promising way for cervical cancer therapy.

Neural precursor cell expressed developmentally downregulated 4-like (NEDD4L) is an E3 ubiquitin ligase that has been reported to participate in multiple cellular procedures by regulating of substrate ubiquitination and subsequent protein degradation. A great amount of evidence has demonstrated that NEDD4L mainly functions as a tumor suppressor in most cancer types, while it also acts as an oncogene in a few cancers. In this review, we summarize the potential role of NEDD4L in carcinogenesis and the related underlying molecular mechanism to improve our understanding of its functions in the tumorigenesis of human malignancies. Developing clinical drugs targeting NEDD4L could be a potential therapeutic strategy for cancer therapy in the future.

Human papillomavirus (HPV)-negative tumors distinguish from cancers associated with HPV infection. Due to its high rate of lymph node metastasis and difficulty in inchoate discover and diagnosis, the treatment efficacy of HPV-negative cancers is unsatisfactory. Epidemiological evidence suggests that HPV-negative tumor patients have a poor prognosis, and the mortality is higher than that of cancer patients caused by HPV infection. Evidence has demonstrated that noncoding RNAs (ncRNAs) play a crucial role in regulation of physiological and developmental processes. Therefore, dysregulated ncRNAs are involved in the occurrence of diversified diseases, including cancer. In cumulative studies, ncRNAs are concerned with pathogenetic mechanisms of HPV-negative tumors via regulating gene expression and signal transduction. It is important to decipher the functions of ncRNAs in HPV-negative cancers and identify the potential biomarkers, which will bring new treatment strategies for improving outcome of cancer therapy. In this review, we demonstrated the effects of ncRNAs via regulating the development and progression of HPV- negative tumors by directly or indirectly acting on target molecules, which provide a basis for future tumor targeted therapy by targeting ncRNAs for HPV-negative cancers.

Active distribution network is one of the important means to promote the safe consumption of clean energy and realize the sustainable development of power grid in the future. This paper presents a new non-iterative method for collaborative optimization model considering coordination of active distribution network and multi agents. Firstly, taking the technical economy of active distribution network as the objective, the optimization model of active distribution network is established, and the second-order cone relaxation is used to realize the non-convex constraint transformation of the model. Secondly, the optimization model of agent is established with the goal of the lowest total energy consumption cost, and then a three-layer optimization architecture of multi agents is proposed. Thirdly, fully considering the active distribution network and multi-agent information energy interaction, a non iterative collaborative optimization method based on the theory of parametric linear programming is proposed. Finally, the effectiveness of the model and algorithm proposed in this paper is verified through example analysis.

News reprint analysis is gradually becoming a hot research topic. Most existing studies in news reprint analysis mainly focus on mining news reprint relations, there is little study exploring the detection of the patterns of news reprint yet. To fill this gap, we aim to identify news reprint patterns such as word variant, sentence conversion, and topic restatement, which can provide deep insights into the news propagation mechanism. The challenge of this question lies in how to dig domain information and obtain the deep semantic representation of news to discover the writing styles of various reprint patterns. This paper proposes a media domain information-driven multi-level semantic fusion (MDID-MLSF) approach. It simultaneously considers news media information and extracts word-sentence-paragraph-level news semantic information by the interactive matching mechanism. Specifically, Word Mover's Distance (WMD) algorithm and Multilayer Perceptron (MLP) are employed to obtain semantic information at the word level and sentence level. Then, the model utilizes attention-based hierarchical Bi-LSTM to obtain the paragraph level semantic information. Finally, news media information and different levels' semantic information are jointly modeled to detect the patterns of news reprint. We empirically evaluate the performance of the proposed model on a real-world dataset, the experimental results demonstrate the efficacy of the model.

High ammonia (NH

BACKGROUND Stopping the epidemic of e-cigarette use among youth has become the common goal of both regulatory authorities and health departments. JUUL is currently the most popular e-cigarette brand on the market. Young people usually obtain and exchange information about JUUL with the help of social media platforms. Along with the rising prevalence of JUUL, posts about underage JUUL buying and selling have appeared on social media platforms such as Reddit, which sharply increase the risk of minors being exposed to JUUL. OBJECTIVE This study aims to analyze Reddit messages about JUUL buying and selling among the users of the UnderageJuul subreddit, and to further summarize the characteristics of those messages. The findings and insights can contribute to a better understanding of the patterns of underage JUUL use, and help public health officials provide timely education and guidance to minors who have intentions of accessing JUUL. METHODS We used a novel cross-subreddit method to analyze the Reddit messages on 2 subreddits. From July 9, 2017, to January 7, 2018, we collected data from the UnderageJuul subreddit, which was created for underage JUUL use discussion. The data set included 716 threads, 2935 comments, and 844 Reddit users (ie, Redditors). We collected our second data set, comprising 23,840 threads and 162,106 comments posted between July 9, 2017, and January 8, 2019, from the JUUL subreddit. We conducted analyses including the following: (1) annotation of users with buying/selling intention, (2) posting patterns discovery and topic comparison, and (3) posting activeness observation of discovered Redditors. Term frequency–inverse document frequency and regular expression-enhanced keyword search methods were applied during the content analysis to extract the posting patterns. The public posting records of the discovered users on the JUUL subreddit during the year after the UnderageJuul subreddit was shut down were analyzed to determine whether they were still active and interested in obtaining JUUL. RESULTS Our study revealed the following: (1) Among the 716 threads on the UnderageJuul subreddit, there were 214 threads related to JUUL sale and 168 threads related to JUUL purchase, which accounted for 53.5% (382/714) of threads. (2) Among the 844 Redditors of the UnderageJuul subreddit, 23.82% (201/844) of users were annotated with buying intention, and 21.10% (178/844) of users were annotated with selling intention. There were 34 users with buying/selling intention that self-reported as being CONCLUSIONS There were overlapping users active in the JUUL and UnderageJuul subreddits. The buying/selling-related content appeared in multiple venues with certain posting patterns from July 9, 2017, to January 7, 2018. Such content might lead to a high risk of health problems for minors, such as nicotine addiction. Based on these findings, this study provided some insights and suggestions that might contribute to the decision-making processes of regulators and public health officials.

JUUL has become a widely used brand of e-cigarettes which takes more than 70% of the market. Social media provides a popular platform for users to discuss the preference and perceptions of JUUL. The discussions are valuable for real-time monitoring of JUUL use. Current research on topic analysis of JUUL discussions mainly relies on human work, which takes much time and effort. This paper adopts a Semantics-assisted NMF topic analysis model to automatically discover topics from JUUL-related short posts on Reddit. By successfully merging the semantic relationships into traditional NMF, this model outperforms in discovering topics with keywords that are important but have a lower word frequency among the posts. Experimental results show the potential of this model in JUUL surveillance and control practice.

Background Stopping the epidemic of e-cigarette use among youth has become the common goal of both regulatory authorities and health departments. JUUL is currently the most popular e-cigarette brand on the market. Young people usually obtain and exchange information about JUUL with the help of social media platforms. Along with the rising prevalence of JUUL, posts about underage JUUL buying and selling have appeared on social media platforms such as Reddit, which sharply increase the risk of minors being exposed to JUUL. Objective This study aims to analyze Reddit messages about JUUL buying and selling among the users of the UnderageJuul subreddit, and to further summarize the characteristics of those messages. The findings and insights can contribute to a better understanding of the patterns of underage JUUL use, and help public health officials provide timely education and guidance to minors who have intentions of accessing JUUL. Methods We used a novel cross-subreddit method to analyze the Reddit messages on 2 subreddits. From July 9, 2017, to January 7, 2018, we collected data from the UnderageJuul subreddit, which was created for underage JUUL use discussion. The data set included 716 threads, 2935 comments, and 844 Reddit users (ie, Redditors). We collected our second data set, comprising 23,840 threads and 162,106 comments posted between July 9, 2017, and January 8, 2019, from the JUUL subreddit. We conducted analyses including the following: (1) annotation of users with buying/selling intention, (2) posting patterns discovery and topic comparison, and (3) posting activeness observation of discovered Redditors. Term frequency–inverse document frequency and regular expression-enhanced keyword search methods were applied during the content analysis to extract the posting patterns. The public posting records of the discovered users on the JUUL subreddit during the year after the UnderageJuul subreddit was shut down were analyzed to determine whether they were still active and interested in obtaining JUUL. Results Our study revealed the following: (1) Among the 716 threads on the UnderageJuul subreddit, there were 214 threads related to JUUL sale and 168 threads related to JUUL purchase, which accounted for 53.5% (382/714) of threads. (2) Among the 844 Redditors of the UnderageJuul subreddit, 23.82% (201/844) of users were annotated with buying intention, and 21.10% (178/844) of users were annotated with selling intention. There were 34 users with buying/selling intention that self-reported as being Conclusions There were overlapping users active in the JUUL and UnderageJuul subreddits. The buying/selling-related content appeared in multiple venues with certain posting patterns from July 9, 2017, to January 7, 2018. Such content might lead to a high risk of health problems for minors, such as nicotine addiction. Based on these findings, this study provided some insights and suggestions that might contribute to the decision-making processes of regulators and public health officials.

BACKGROUND Drug abuse is a severe challenge for the United States. Social media such as Reddit has become one of the most efficient platforms for drug users to share experiences and communicate with each other. By analyzing users’ perceptions and behavior patterns concerning causes and symptoms of drug usage on social media, public health researchers and agencies can have innovative real time situational awareness and surveillance capabilities to tackle the drug abuse crisis. OBJECTIVE This paper aims to develop a social media-based approach to analyze users’ causes and symptoms of drug usage across different age groups, then deeply gain insights into users’ behavior pattern. METHODS We collected 163,610 posts on the Reddit /r/drugs community from February 2008 to December 2017. Firstly, topics for drugs, causes, and symptoms based on word vector learning (word2vec) were extracted. Then, we designed a method to automatically extract age information from posts. Finally, the relationships between age and drugs, causes, and symptoms were established. RESULTS We found that: (1) Drug topics contained 6 categories including alcoholic, tobaccos, prescriptions, hallucinogens, ecstasies, and other highly addictive drugs. Tobaccos (n=11,947), hallucinogens (n=9244), and other highly addictive drugs (n=5733) were the most discussed drug categories. (2) 9646 (65% of the total posts that had age information) posts on Reddit were sent by users between 15 and 25 years old. There was relevance between age and drug addiction. People tended to change their drug usage from a primary drug (e.g. marijuana) to other highly addictive drugs (e.g. heroin) as they aged. The age groups age CONCLUSIONS This is the first study aimed at deeply gaining insights into users’ behavior patterns, which can help public health researchers and agencies provide personalized health regulatory services for people at different ages.