Dynamics of Air-Fluidized Granular System Measured by the Modulated Gradient Spin-echo

The power spectrum of displacement fluctuation of beads in the air-fluidized granular system is measured by a novel NMR technique of modulated gradient spin-echo. The results of measurement together with the related spectrum of the velocity fluctuation autocorrelation function fit well to an empiric formula based on to the model of bead caging between nearest neighbours; the cage breaks up after a few collisions \cite{Menon1}. The fit yields the characteristic collision time, the size of bead caging and the diffusion-like constant for different degrees of system fluidization. The resulting mean squared displacement increases proportionally to the second power of time in the short-time ballistic regime and increases linearly with time in the long-time diffusion regime as already confirmed by other experiments and simulations.Comment: 4 figures. Submited to Physical Review Letters, April 200

A new view of the spin echo diffusive diffraction on porous structures

Analysis with the characteristic functional of stochastic motion is used for the gradient spin echo measurement of restricted motion to clarify details of the diffraction-like effect in a porous structure. It gives the diffusive diffraction as an interference of spin phase shifts due to the back-flow of spins bouncing at the boundaries, when mean displacement of scattered spins is equal to the spin phase grating prepared by applied magnetic field gradients. The diffraction patterns convey information about morphology of the surrounding media at times long enough that opposite boundaries are restricting displacements. The method explains the dependence of diffraction on the time and width of gradient pulses, as observed at the experiments and the simulations. It also enlightens the analysis of transport properties by the spin echo, particularly in systems, where the motion is restricted by structure or configuration

Mediators and theories of change in psychotherapy with adolescents: a systematic review protocol

Introduction Approximately 75% of mental disorders emerge before the age of 25 years but less than half receive appropriate treatment. Little is known about the mechanisms underlying the therapeutic change of adolescents in psychotherapy. The 'European Network of Individualised Psychotherapy Treatment of Young People with Mental Disorders', funded by the European Cooperation in Science and Technology, will conduct the first systematic review to summarise the existing knowledge on mediators and theories of change in psychotherapy for adolescents. Method A systematic review will be conducted, conforming to the reporting guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement recommendations. Electronic databases (PubMed and PsycINFO) have been systematically searched on 23 February 2020, for prospective, longitudinal and case-control designs which examine mediators of change. Participants will be adolescents between 10 and 19 years of age who suffer from a mental disorder or psychological difficulties and receive an intervention that aims at preventing, ameliorating and/or treating psychological problems. Ethics and dissemination Ethical approval is not required for this systematic review as no primary data will be collected. The results will be published in a peer-reviewed journals and at conference presentations and will be shared with stakeholder groups. The whole data set will be offered to other research groups following recommendations of the open science initiative. Databases with the systematic search will be made openly available following open science initiatives. PROSPERO registration number CRD42020177535

Theories of Change and Mediators of Psychotherapy Effectiveness in Adolescents With Externalising Behaviours: A Systematic Review

bstract Background Externalising behaviours are becoming a remarkably prevalent problem during adolescence, often precipitating both externalising and internalising disorders in later adulthood. Psychological treatments aim to increase the social functioning of adolescents in order for them to live a more balanced life and prevent these negative trajectories. However, little is known of the intervening variables and mediators involved in these treatments' change mechanisms. We conducted a systematic review, exploring the available evidence on mediators of psychological treatments for externalising behaviours and symptoms amongst adolescents (10 to 19 years old). Methods A systematic search was performed on Medline and PsycINFO databases, which identified studies from inception to February 23, 2020. Eligible studies included randomised controlled trials that enrolled adolescents with externalising symptoms and behaviours as, at least, one of the primary outcomes. A group of 20 reviewers from the COST-Action TREATme (CA16102) were divided into 10 pairs. Each pair independently screened studies for inclusion, extracted information from the included studies, and assessed the methodological quality of the included studies and the requirements for mediators, following Kazdin's criteria. Risk of bias of RCTs was assessed by the Mixed Methods Appraisal Tool. Extracted data from the included studies were reported using a narrative synthesis. Results Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA), after removing duplicates, 3,660 articles were screened. Disagreements were resolved by consensus. In a second stage, 965 full-text articles were assessed for eligibility. A total of 14 studies fulfilled all inclusion criteria. The majority were related to systemic psychological treatment approaches. Two types of mediators were identified as potentially being involved in the mechanisms of change for better social improvements of adolescents: to increase healthier parent–adolescent relationships and parental discipline. However, there were significant and non-significant results amongst the same mediators, which led to discussing the results tentatively. Conclusions Family variables were found to be the largest group of investigated mediators, followed by relational, behavioural, and emotional variables. No cognitive or treatment-specific mediators were identified. Both adequate behavioural control of adolescents' peer behaviour and a better positive balance in their relationships with their parents seemed to buffer the effects of externalising behaviours in adolescents. Several methodological limitations concerning mediation testing design, outcome measures, and mediator selection have been identified. Ethics and Dissemination Ethical approval was not required. PROSPERO registration number: CRD42021231835

Semi-supervised regression trees with application to QSAR modelling

Despite the ease of collecting abundance of data about various phenomena, obtaining labeled data needed for learning models with high predictive performance remains a difficult and expensive task in many domains. This issue is particularly present in the case of the analysis of scientific data where obtaining labeled data typically requires expensive experiments. Moreover, in the analysis of scientific data, another issue is of fundamental importance: the interpretability of the models and the explainability of their decisions. By taking into account these considerations, we propose a novel semi-supervised method to learn regression trees. Thanks to the semi-supervised machine learning approach, the method is able to exploit information coming not only from labeled data, but also from unlabeled data, thus alleviating the issue of lack of labeled data. The method is based on the predictive clustering trees paradigm that extends regression trees towards structured output prediction. This allows us to obtain interpretable regression trees. The method we propose is particularly suited for the chemoinformatics task of quantitative structure-activity relationship (QSAR) modeling, which is the main application context considered in this paper. Specifically, we evaluate the proposed method on 4 QSAR modelling datasets and illustrate its use on a case study of predicting farnesyltransferase inhibitors. Additionally, we also evaluate our approach on 10 benchmark datasets not related to the QSAR modeling problem. The evaluation reveals the following: semi-supervised trees and ensembles thereof have better predictive performance than their supervised counterparts (especially when the number of labeled examples is very small); different datasets and different amounts of labeled data require different amounts of unlabeled data to be included in the learning process; and the learned semi-supervised regression trees can be used to better understand the problem at hand and the way predictions are being made