Social mining for sustainable cities: thematic study of gender-based violence coverage in news articles and domestic violence in relation to COVID-19

The Media and Sexual Violence Among Adolescents: Findings from a Qualitative Study of Educators Across Vietnam

Growing access to technology and media has presented new avenues of influence on youth attitudes and norms regarding sexuality and sexual violence, as well as new technological pathways through which to perpetrate sexual violence. The aim of this research was to understand contextual influences on and needs for scale-up of sexual violence prevention programming in the media-violence context of Vietnam. We conducted 45 interviews with high school teachers (n = 15), university lecturers (n = 15), and affiliates from youth-focused community service organizations (n = 15) from across Vietnam. Additionally, we conducted four sector-specific focus groups with a sub-sample of interview participants (k = 4, n = 22). Media and technology were brought up consistently in relation to sexual violence prevention and sexual health information. Key informants noted that, in Vietnam, generational differences in acceptability of sex and lack of comprehensive sexuality education intersect with new technological opportunities for exposure to sexual information and media. This creates a complex landscape that can promote sexual violence through priming processes, instigate mimicry of violent media, and presents new opportunities for the perpetration of sexual violence though technology. Development of comprehensive sexual education, including violence prevention education, is imperative, with consideration of age-specific needs for Vietnamese youth.

The psychological impact of the rise in media reporting of sexual violence after COVID-19 pandemic on Pakistani women's mental health

PURPOSE

Pandemics usually have inequitable effects on the most vulnerable groups of society. Since the start of COVID-19, there has been a horrifying upsurge in cases of sexual and gender-based violence against women, globally. Consequently, frequent breaking news of sexual violence in media aggravated mental distress and worry among women. In this cross-sectional study, we investigated the impact of the rise in active circulation of news of sexual violence on the mental health of working women and students using the validated DASS-21 questionnaire.

METHODS

A total of 303 women with diverse socio-demographic backgrounds participated in the study. We performed a chi-square test to analyze the association of increase in media reporting with DASS-21total and sub-categories scores. Multivariate linear regression was performed on propensity score-matched subjects to identify psychosocial predictors of mental distress.

RESULTS

Increased self-reported worries in response to rise in media reports of sexual violence was found to be significant predictor of mental distress (p < 0.002). Moreover, highly significant correlation between the increase in media reports and scores of depression, anxiety, and stress was observed (p < 0.001).

CONCLUSIONS

The rise in media reporting of sexual violence in the after math of COVID-19 was found to have a significant psychological impact on the mental health of Pakistani women. This is the first study of its kind on the subject and provides fundamental findings for shaping policy change on responsible media reporting of sexual violence.

4mCBERT: A computing tool for the identification of DNA N4-methylcytosine sites by sequence- and chemical-derived information based on ensemble learning strategies

N4-methylcytosine (4mC) is an important DNA chemical modification pattern which is a new methylation modification discovered in recent years and plays critical roles in gene expression regulation, defense against invading genetic elements, genomic imprinting, and so on. Identifying 4mC site from DNA sequence segment contributes to discovering more novel modification patterns. In this paper, we present a model called 4mCBERT that encodes DNA sequence segments by sequence characteristics including one-hot, electron-ion interaction pseudopotential, nucleotide chemical property, word2vec and chemical information containing physicochemical properties (PCP), chemical bidirectional encoder representations from transformers (chemical BERT) and employs ensemble learning framework to develop a prediction model. PCP and chemical BERT features are firstly constructed and applied to predict 4mC sites and show positive contributions to identifying 4mC. For the Matthew's Correlation Coefficient, 4mCBERT significantly outperformed other state-of-the-art models on six independent benchmark datasets including A. thaliana, C. elegans, D. melanogaster, E. coli, G. Pickering, and G. subterraneous by 4.32 % to 24.39 %, 2.52 % to 31.65 %, 2 % to 16.49 %, 6.63 % to 35.15, 8.59 % to 61.85 %, and 8.45 % to 34.45 %. Moreover, 4mCBERT is designed to allow users to predict 4mC sites and retrain 4mC prediction models. In brief, 4mCBERT shows higher performance on six benchmark datasets by incorporating sequence- and chemical-driven information and is available at http://cczubio.top/4mCBERT and https://github.com/abcair/4mCBERT.