[Coronavirus Pandemic - #STAYHOME: How Are You Holding Up? Questions And Tips For 11-18-Year-Olds To Make It Better].

PURPOSE: Adolescents have to cope with several challenges and restrictions due to the COVID-19 pandemic, with many of those incongruent with the typical developmental tasks of adolescent age. Some adolescents might be particularly vulnerable in this situation, including those who are deprived of psychological, social or health care services and/or are exposed to abuse or neglect in their home environment. The aims of the current international multicentre follow-up study are to: 1. collect data on the mental health and quality of life of adolescents during and after the pandemic; 2. improve their mental health by providing an online prevention program that addresses their actual needs; 3. accelerate the development of culturally adapted prevention programs by involving an international team, and 4. to contribute to adequate preparation for any potentially occurring, similar situationin the future. METHODS: Participants aged 11-18 years and their parents/caregivers from diff erent parts of Europe and non-European countries are recruited online. Data are collected regularly in a follow-up study by means of structured self-administered online questionnaires on adolescents' mental health, quality of life and current attitudes and needs. The baseline data collection was in March 2020 at first restrictions of the COVID pandemic in Europe. It is followed up several times (at the beginning weekly, later monthly, bi-monthly, three-monthly) to study changes in mental health, quality of life and attitudes of children and adolescents during the coronavirus disease pandemic. Data were collected by means of structured questionnaires (see below). The time frame of the study is set to one year from study start, March 2021. The last data collection was done in December 2020. The prevention program is developed and provided based on continuously analysed incoming data. CONCLUSIONS: Prevention based on the results of the study is expected to contribute to maintaining adolescents' mental health, improve their quality of life, increase their and their environment's cooperation with the necessary restrictions during the pandemic, and to make reintegration easier once the restrictions are over. Furthermore, the study has the potential to inform on the wellbeing of children and adolescents in extreme situations in general, thus contribute to future preventive measures and policymaking. Implications and Contribution: The proposed international online follow-up study is expected to provide scientifi c evidence for 1. possible changes in the mental health and quality of life of adolescents during and after a pandemic situation, 2. the eff ectiveness of a culturally adapted prevention program developed to address challenges associated with these changes.

A Feature Ranking and Selection Algorithm for Brain Tumor Segmentation in Multi-Spectral Magnetic Resonance Image Data.

Accuracy is the most important quality marker in medical image segmentation. However, when the task is to handle large volumes of data, the relevance of processing speed rises. In machine learning solutions the optimization of the feature set can significantly reduce the computational load. This paper presents a method for feature selection and applies it in the context of a brain tumor detection and segmentation problem in multi-spectral magnetic resonance image data. Starting from an initial set of 104 features involved in an existing ensemble learning solution that employs binary decision trees, a reduced set of features is obtained using a iterative algorithm based on a composite criterion. In each iteration, features are ranked according to the frequency of usage and the correctness of the decisions to which they contribute. Lowest ranked features are iteratively eliminated as long as the segmentation accuracy is not damaged. The final reduced set of 13 features provide the same accuracy in the whole tumor segmentation process as the initial one, but three times faster.