With the rapid increase in the number of scholarly publications on STEM education in recent years, reviews of the status and trends in STEM education research internationally support the development of the field. For this review, we conducted a systematic analysis of 798 articles in STEM education published between 2000 and the end of 2018 in 36 journals to get an overview about developments in STEM education scholarship. We examined those selected journal publications both quantitatively and qualitatively, including the number of articles published, journals in which the articles were published, authorship nationality, and research topic and methods over the years. The results show that research in STEM education is increasing in importance internationally and that the identity of STEM education journals is becoming clearer over time.

In this editorial, we conduct a systematic review of 144 items published in the International Journal of STEM Education over its first 5-year period from 2014 to 2018. We analyze publication quantities and types, authorship nationality, publication readership, research topic, and top 10 most accessed and top 10 most cited articles over the years. The results provide a snapshot of the research and readership development in multidisciplinary STEM education as an international field.

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world.

Background and Purpose: Breast cancer is one of the leading causes of death among women. RNA binding proteins (RBPs) play a vital role in the progression of many cancers. Functional investigation of RBPs may contribute to elucidating the mechanisms underlying tumor initiation, progression, and invasion, therefore providing novel insights into future diagnosis, treatment, and prognosis. Methods: We downloaded RNA sequencing data from the cancer genome atlas (TCGA) by UCSC Xena and identified relevant RBPs through an integrated bioinformatics analysis. We then analyzed biological processes of differentially expressed genes (DEGs) by DAVID, and established their interaction networks and performed pathway analysis through the STRING database to uncover potential biological effects of these RBPs. We also explored the relationship between these RBPs and the prognosis of breast cancer patients. Results: In the present study, we obtained 1092 breast tumor samples and 113 normal controls. After data analysis, we identified 90 upregulated and 115 downregulated RBPs in breast cancer. GO and KEGG pathway analysis indicated that these significantly changed genes were mainly involved in RNA processing, splicing, localization and RNA silencing, DNA transposition regulation and methylation, alkylation, mitochondrial gene expression, and transcription regulation. In addition, some RBPs were related to histone H3K27 methylation, estrogen response, inflammatory mediators, and translation regulation. Our study also identified five RBPs associated with breast cancer prognosis. Survival analysis found that overexpression of DCAF13, EZR, and MRPL13 showed worse survival, but overexpression of APOBEC3C and EIF4E3 showed better survival. Conclusion: In conclusion, we identified key RBPs of breast cancer through comprehensive bioinformatics analysis. These RBPs were involved in a variety of biological and molecular pathways in breast cancer. Furthermore, we identified five RBPs as a potential prognostic biomarker of breast cancer. Our study provided novel insights to understand breast cancer at a molecular level.

No abstract

No abstract

This paper describes the forensic and intelligence analysis capabilities of the Email Mining Toolkit (EMT) under development at the Columbia Intrusion Detection (IDS) Lab. EMT provides the means of loading, parsing and analyzing email logs, including content, in a wide range of formats. Many tools and techniques have been available from the fields of Information Retrieval (IR) and Natural Language Processing (NLP) for analyzing documents of various sorts, including emails. EMT, however, extends these kinds of analyses with an entirely new set of analyses that model "user behavior". EMT thus models the behavior of individual user email accounts, or groups of accounts, including the "social cliques" revealed by a user's email behavior. The application of this technology to diverse Internet objects and events (e.g., email and web transactions) allows for a broad range of behavior-based analyses including the detection of proxy email accounts and groups of user accounts that communicate with one another including covert group activities. Data mining applies machine learning and statistical techniques to automatically discover and detect misuse patterns, as well as anomalous activities in general. When applied to network-based activities and user account observations for the detection of errant or misuse behavior, these methods are referred to as behavior-based misuse detection. Behavior-based misuse detection can provide important new assistance for counter-terrorism intelligence. In addition to standard Internet misuse detection, these techniques will automatically detect certain patterns across user accounts that are indicative of covert, malicious or counter-intelligence activities. Moreover, behavior-based detection provides workbench functionalities to interactively assist an intelligence agent with targeted investigations and off-line forensics analyses. Intelligence officers have a myriad of tasks and problems confronting them each day. The sheer volume of source materials requires a means of honing in on those sources of maximal value to their mission. A variety of techniques can be applied drawing upon the research and technology developed in the field of Information Retrieval. There is, however, an additional source of information available that can used to aid even the simplest task of rank ordering and sorting documents for inspection: behavior models associated with the documents can be used to identify and group sources in interesting new ways. This is demonstrated by the Email Mining Toolkit that applies a variety of data mining techniques for profiling and behavior modeling of email sources. The deployment of behavior-based techniques for intelligence investigation and tracking tasks represents a significant qualitative step in the counter-intelligence "arms race". Because there is no way to predict what data mining will discover over any given data set, "counter-escalation" is particularly difficult. Behavior-based misuse detection is more robust against standard knowledgebased techniques. Behavior-b...

Do negative feelings in general trigger addictive behavior, or do specific emotions play a stronger role? Testing these alternative accounts of emotion and decision making, we drew on the Appraisal Tendency Framework to predict that sadness, specifically, rather than negative mood, generally, would 1) increase craving, impatience, and actual addictive substance use and 2) do so through mechanisms selectively heightened by sadness. Using a nationally representative, longitudinal survey, study 1 (n = 10,685) revealed that sadness, but not other negative emotions (i.e., fear, anger, shame), reliably predicted current smoking as well as relapsing 20 years later. Study 2 (n = 425) used an experimental design, and found further support for emotion specificity: Sadness, but not disgust, increased self-reported craving relative to a neutral state. Studies 3 and 4 (n = 918) introduced choice behavior as outcome variables, revealing that sadness causally increased impatience for cigarette puffs. Moreover, study 4 revealed that the effect of sadness on impatience was more fully explained by concomitant appraisals of self-focus, which are specific to sadness, than by concomitant appraisals of negative valence, which are general to all negative emotions. Importantly, study 4 also examined the topography of actual smoking behavior, finding that experimentally induced sadness (as compared to neutral emotion) causally increased the volume and duration of cigarette puffs inhaled. Together, the present studies provide support for a more nuanced model regarding the effects of emotion on tobacco use, in particular, as well as on addictive behavior, in general.