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the World Health Organization (WHO) was alerted about a cluster of pneumonia cases of unknown etiology in Wuhan, China [1]. By January 12, 2020, China had shared the genetic sequence of a novel coronavirus [2], later named severe acute coronavirus syndrome 2 (SARS-CoV-2), the etiological agent of Coronavirus Disease 2019 (COVID-19) [3]. Until today, the virus has spread to more than 200 countries, causing over 1.5 million cases and over 100,000 deaths [4]. COVID-19 was declared first a Public Health Emergency of International Concern (PHEIC) [5] and later a pandemic disease [6] by the WHO.While recent published evidence describes the physical health impacts of COVID-19, there is paucity of research [7] regarding COVID-19erelated mental health outcomes. Previously, quarantine measures had led to post-traumatic stress symptoms, confusion, and anger [8]. Although it is likely that COVID-19erelated mental health impacts will only be manifested in the future, we can act today to prevent exposed adolescents and young adults (i.e., youths) from carrying mental health complications for decades after COVID-19. In the following paragraphs, we will explore the unique mental health risk and protective factors of Nepalese youths, who have been in complete lockdown since March 23 [9].

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Stress is a serious concern facing our world today, motivating the development of a better objective understanding through the use of non-intrusive means for stress recognition by reducing restrictions to natural human behavior. As an initial step in computer vision-based stress detection, this paper proposes a temporal thermal spectrum (TS) and visible spectrum (VS) video database ANUStressDB -a major contribution to stress research. The database contains videos of 35 subjects watching stressed and not-stressed film clips validated by the subjects. We present the experiment and the process conducted to acquire videos of subjects' faces while they watched the films for the ANUStressDB. Further, a baseline model based on computing local binary patterns on three orthogonal planes (LBP-TOP) descriptor on VS and TS videos for stress detection is presented. A LBP-TOP-inspired descriptor was used to capture dynamic thermal patterns in histograms (HDTP) which exploited spatio-temporal characteristics in TS videos. Support vector machines were used for our stress detection model. A genetic algorithm was used to select salient facial block divisions for stress classification and to determine whether certain regions of the face of subjects showed better stress patterns. Results showed that a fusion of facial patterns from VS and TS videos produced statistically significantly better stress recognition rates than patterns from VS or TS videos used in isolation. Moreover, the genetic algorithm selection method led to statistically significantly better stress detection rates than classifiers that used all the facial block divisions. In addition, the best stress recognition rate was obtained from HDTP features fused with LBP-TOP features for TS and VS videos using a hybrid of a genetic algorithm and a support vector machine stress detection model. The model produced an accuracy of 86%.

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In TOF patients undergoing PVR, older age was associated with increased spontaneous VA risk. Lower QRSVm predicted spontaneous VA or VAI risk with high negative predictive values. QRSVm is the only independent predictor of VAI. These clinical features may help further risk stratify TOF patients requiring therapies to prevent sudden death.

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