Online interventions to prevent mental health problems implemented in school settings: the perspectives from key stakeholders in Austria and Spain

Background: Schools are key settings for delivering mental illness prevention in adolescents. Data on stakeholders’ attitudes and factors relevant for the implementation of Internet-based prevention programmes are scarce. Methods: Stakeholders in the school setting from Austria and Spain were consulted. Potential facilitators (e.g. teachers and school psychologists) completed an online questionnaire (N=50), policy makers (e.g. representatives of the ministry of education and health professional associations) participated in semi-structured interviews (N=9) and pupils (N=29, 14–19 years) participated in focus groups. Thematic analysis was used to identify experiences with, attitudes and needs towards Internet-based prevention programmes, underserved groups, as well as barriers and facilitators for reach, adoption, implementation and maintenance. Results: Experiences with Internet-based prevention programmes were low across all stakeholder groups. Better reach of the target groups was seen as main advantage whereas lack of personal contact, privacy concerns, risk for misuse and potential stigmatization when implemented during school hours were regarded as disadvantages. Relevant needs towards Internet-based programmes involved attributes of the development process, general requirements for safety and performance, presentation of content, media/tools and contact options of online programmes. Positive attitudes of school staff, low effort for schools and compatibility to schools’ curriculum were seen as key factors for successful adoption and implementation. A sound implementation of the programme in the school routine and continued improvement could facilitate maintenance of online prevention initiatives in schools. Conclusions: Attitudes towards Internet-based mental illness prevention programmes in school settings are positive across all stakeholder groups. However, especially safety concerns have to be considered

Beam-helicity asymmetries for single-hadron production in semi-inclusive deep-inelastic scattering from unpolarized hydrogen and deuterium targets

A measurement of beam-helicity asymmetries for single-hadron production in deep-inelastic scattering is presented. Data from the scattering of 27.6 GeV electrons and positrons off gaseous hydrogen and deuterium targets were collected by the HERMES experiment. The asymmetries are presented separately as a function of the Bjorken scaling variable, the hadron transverse momentum, and the fractional energy for charged pions and kaons as well as for protons and anti-protons. These asymmetries are also presented as a function of the three aforementioned kinematic variables simultaneously

Deep Learning for Single-Molecule Science

Exploring and making predictions based on single-molecule data can be challenging, not only due to the sheer size of the datasets, but also because a priori knowledge about the signal characteristics is typically limited and poor signal-to-noise ratio. For example, hypothesis-driven data exploration, informed by an expectation of the signal characteristics, can lead to interpretation bias or loss of information. Equally, even when the different data categories are known, e.g., the four bases in DNA sequencing, it is often difficult to know how to make best use of the available information content. The latest developments in Machine Learning (ML), so-called Deep Learning (DL) offers an interesting, new avenues to address such challenges. In some applications, such as speech and image recognition, DL has been able to outperform conventional Machine Learning strategies and even human performance. However, to date DL has not been applied much in single-molecule science, presumably in part because relatively little is known about the 'internal workings' of such DL tools within single-molecule science as a field. In this Tutorial, we make an attempt to illustrate in a step-by-step guide how one of those, a Convolutional Neural Network, may be used for base calling in DNA sequencing applications. We compare it with a Support Vector Machine as a more conventional ML method, and and discuss some of the strengths and weaknesses of the approach. In particular, a 'deep' neural network has many features of a 'black box', which has important implications on how we look at and interpret data

Transition from ion-coupled to electron-only reconnection: Basic physics and implications for plasma turbulence

Using kinetic particle-in-cell (PIC) simulations, we simulate reconnection conditions appropriate for the magnetosheath and solar wind, i.e., plasma beta (ratio of gas pressure to magnetic pressure) greater than 1 and low magnetic shear (strong guide field). Changing the simulation domain size, we find that the ion response varies greatly. For reconnecting regions with scales comparable to the ion Larmor radius, the ions do not respond to the reconnection dynamics leading to ''electron-only'' reconnection with very large quasi-steady reconnection rates. The transition to more traditional ''ion-coupled'' reconnection is gradual as the reconnection domain size increases, with the ions becoming frozen-in in the exhaust when the magnetic island width in the normal direction reaches many ion inertial lengths. During this transition, the quasi-steady reconnection rate decreases until the ions are fully coupled, ultimately reaching an asymptotic value. The scaling of the ion outflow velocity with exhaust width during this electron-only to ion-coupled transition is found to be consistent with a theoretical model of a newly reconnected field line. In order to have a fully frozen-in ion exhaust with ion flows comparable to the reconnection Alfv\'en speed, an exhaust width of at least several ion inertial lengths is needed. In turbulent systems with reconnection occurring between magnetic bubbles associated with fluctuations, using geometric arguments we estimate that fully ion-coupled reconnection requires magnetic bubble length scales of at least several tens of ion inertial lengths

Rewriting a Deep Generative Model

A deep generative model such as a GAN learns to model a rich set of semantic and physical rules about the target distribution, but up to now, it has been obscure how such rules are encoded in the network, or how a rule could be changed. In this paper, we introduce a new problem setting: manipulation of specific rules encoded by a deep generative model. To address the problem, we propose a formulation in which the desired rule is changed by manipulating a layer of a deep network as a linear associative memory. We derive an algorithm for modifying one entry of the associative memory, and we demonstrate that several interesting structural rules can be located and modified within the layers of state-of-the-art generative models. We present a user interface to enable users to interactively change the rules of a generative model to achieve desired effects, and we show several proof-of-concept applications. Finally, results on multiple datasets demonstrate the advantage of our method against standard fine-tuning methods and edit transfer algorithms.Comment: ECCV 2020 (oral). Code at https://github.com/davidbau/rewriting. For videos and demos see https://rewriting.csail.mit.edu

The effects of probiotics administration on the gut microbiome in adolescents with anorexia nervosa—A study protocol for a longitudinal, double-blind, randomized, placebo-controlled trial

Objective Knowledge on gut?brain interaction might help to develop new therapies for patients with anorexia nervosa (AN), as severe starvation-induced changes of the microbiome (MI) do not normalise with weight gain. We examine the effects of probiotics supplementation on the gut MI in patients with AN. Method This is a study protocol for a two-centre double-blind randomized-controlled trial comparing the clinical efficacy of multistrain probiotic administration in addition to treatment-as-usual compared to placebo in 60 patients with AN (13?19 years). Moreover, 60 sex- and age-matched healthy controls are included in order to record development-related changes. Assessments are conducted at baseline, discharge, 6 and 12 months after baseline. Assessments include measures of body mass index, psychopathology (including eating-disorder-related psychopathology, depression and anxiety), neuropsychological measures, serum and stool analyses. We hypothesise that probiotic administration will have positive effects on the gut microbiota and the treatment of AN by improvement of weight gain, gastrointestinal complaints and psychopathology, and reduction of inflammatory processes compared to placebo. Conclusions If probiotics could help to normalise the MI composition, reduce inflammation and gastrointestinal discomfort and increase body weight, its administration would be a readily applicable additional component of multi-modal AN treatment