Being Mum’s Confidant, a Boon or Bane? Examining Gender Differences in the Association of Maternal Disclosure with Adolescents’ Depressive Feelings

This article reports on a longitudinal study investigating gender differences in the association between maternal disclosure and adolescents’ depressive symptoms. Little research has examined the relationship of parental disclosure to adolescents’ depressive symptoms and research on sex differences is particularly lacking. In a sample of 428 families with a mean age of 13.36 (52% female) of the target adolescents, maternal and children’s disclosure and depressive symptoms were assessed twice with an interval of 4 years. Controlling for the quality of the parent–child relationship and levels of maternal depressive symptoms, the analyses revealed an interaction effect for child’s gender, moderating the effect of maternal disclosure on adolescents’ depressive symptoms. Higher levels of maternal disclosure were accompanied by lower levels of depressive symptoms in girls and higher levels of depressive symptoms in boys. Gender differences in socialization, communication, individuation and social networks might explain why daughters and sons are differently affected by maternal disclosure

Towards a model of contemporary parenting: The parenting behaviours and dimensions questionnaire

The assessment of parenting has been problematic due to theoretical disagreement, concerns over generalisability, and problems with the psychometric properties of current parenting measures. The aim of this study was to develop a comprehensive, psychometrically sound self-report parenting measure for use with parents of preadolescent children, and to use this empirical scale development process to identify the core dimensions of contemporary parenting behaviour. Following item generation and parent review, 846 parents completed an online survey comprising 116 parenting items. Exploratory and confirmatory factor analyses supported a six factor parenting model, comprising Emotional Warmth, Punitive Discipline, Anxious Intrusiveness, Autonomy Support, Permissive Discipline and Democratic Discipline. This measure will allow for the comprehensive and consistent assessment of parenting in future research and practice

Protein docking prediction using predicted protein-protein interface

<p>Abstract</p> <p>Background</p> <p>Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations.</p> <p>Results</p> <p>We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering.</p> <p>Conclusion</p> <p>We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.</p

Potentially pathogenic yeasts from soil of children’s recreational areas in the city of Łódź (Poland)

Objectives: Yeasts may become potential human and animal pathogens, particularly for individuals with a depressed immune system. Their presence in the environment, especially in soil, may favour their spread into human ontocenoses. Materials and Methods: Eighty-four soil samples obtained from 21 children's recreational sites in Łódź in autumn 2010 and spring 2011 were evaluated. The yeasts were isolated by classical microbiological methods and identified on the basis of morphological and biochemical features. Results: The fungi were found in 73.8% and in 69.0% of the examined samples collected in autumn and spring, respectively. Among 97 isolates of yeasts, the species potentially pathogenic to humans and animals were Candida colliculosa, C. guilliermondii, C. humicola, C. inconspicua, C. lambica, C. lusitaniae, C. pelliculosa, C. tropicalis, Cryptococcus albidus, C. laurentii, C. neoformans, C. terreus, Kloeckera japonica, Geotrichum candidum, G. penicillatum, Rhodotorula mucilaginosa, R. glutinis, Saccharomyces cerevisiae, Sporobolomyces salmonicolor and Trichosporon cutaneum. The most frequently isolated fungi included the genus Cryptococcus (38 isolates) and two species: Rhodotorula glutinis (15), Trichosporon cutaneum (14). C. neoformans, an etiological factor of cryptococcal meningitis, was present in the sandpits of 3 kindergartens. The Candida species were identified from park playgrounds and school sports fields mainly in autumn 2010 (14 isolates), in spring 2011 - only 1 isolate. The concentration of fungal species in particular samples varied considerably, but in the majority of samples, fungi were present at concentration of up to 1×102 CFU/1 g of soil. Conclusions: Yeasts were present in the soil of parks, schools and kindergarten recreational areas; the fact may pose a health risk to humans, especially to children, and this type of biological pollution should be regarded as a potential public health concern

Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6  ×  6  ×  6 m 3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation