School-Based Prevention Of Depressive Symptoms: A Randomized Controlled Study Of The Effectiveness And Specificity Of The Penn Resiliency Program

The authors investigated the effectiveness and specificity of the Penn Resiliency Program (PRP; J. E. Gillham, L. H. Jaycox, K. J. Reivich, M. E. P. Seligman, & T. Silver, 1990), a cognitive-behavioral depression prevention program. Children (N = 697) from 3 middle schools were randomly assigned to PRP, Control (CON), or the Penn Enhancement Program (PEP; K. J. Reivich, 1996; A. J. Shatté, 1997), an alternate intervention that controls for nonspecific intervention ingredients. Children\u27s depressive symptoms were assessed through 3 years of follow-up. There was no intervention effect on average levels of depressive symptoms in the full sample. Findings varied by school. In 2 schools, PRP significantly reduced depressive symptoms across the follow-up relative to both CON and PEP. In the 3rd school, PRP did not prevent depressive symptoms. The authors discuss the findings in relation to previous research on PRP and the dissemination of prevention programs. (PsycINFO Database Record (c) 2013 APA, all rights reserved)(journal abstract

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson's disease and schizophrenia

Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided

Usability Evaluation of a Cognitive-Behavioral App-Based Intervention for Binge Eating and Related Psychopathology: A Qualitative Study

Despite their promise as a scalable intervention modality for binge eating and related problems, reviews show that engagement of app-based interventions is variable. Issues with usability may account for this. App developers should undertake usability testing so that any problems can be identified and fixed prior to dissemination. We conducted a qualitative usability evaluation of a newly-developed app for binge eating in 14 individuals with a diagnostic- or subthreshold-level binge eating symptoms. Participants completed a semi-structured interview and self-report measures. Qualitative data were organized into six themes: usability, visual design, user engagement, content, therapeutic persuasiveness, and therapeutic alliance. Qualitative and quantitative results indicated that the app demonstrated good usability. Key advantages reported were its flexible content-delivery formats, level of interactivity, easy-to-understand information, and ability to track progress. Concerns with visual aesthetics and lack of professional feedback were raised. Findings will inform the optimal design of app-based interventions for eating disorder symptoms. </jats:p

Diversity in the sunflower: Puccinia helianthi pathosystem in Australia

Sunflower rust caused by Puccinia helianthi is the most important disease of sunflower in Australia with the potential to cause significant yield losses in susceptible hybrids. Rapid and frequent virulence changes in the rust fungus population limit the effective lifespan of commercial cultivars and impose constant pressure on breeding programs to identify and deploy new sources of resistance. This paper contains a synopsis of virulence data accumulated over 25 years, and more recent studies of genotypic diversity and sexual recombination. We have used this synopsis, generated from both published and unpublished data, to propose the origin, evolution and distribution of new pathotypes of P. helianthi. Virulence surveys revealed that diverse pathotypes of P. helianthi evolve in wild sunflower populations, most likely because sexual recombination and subsequent selection of recombinant pathotypes occurs there. Wild sunflower populations provide a continuum of genetically heterogeneous hosts on which P. helianthi can potentially complete its sexual cycle under suitable environmental conditions. Population genetics analysis of a worldwide collection of P. helianthi indicated that Australian isolates of the pathogen are more diverse than non-Australian isolates. Additionally, the presence of the same pathotype in different genotypic backgrounds supported evidence from virulence data that sexual recombination has occurred in the Australian population of P. helianthi at some time. A primary aim of the work described was to apply our knowledge of pathotype evolution to improve resistance in sunflower to sunflower rust. Molecular markers were identified for a number of previously uncharacterised sunflower rust R-genes. These markers have been used to detect resistance genes in breeding lines and wild sunflower germplasm. A number of virulence loci that do not recombine were identified in P. helianthi. The resistance gene combinations corresponding to these virulence loci are currently being introgressed with breeding lines to generate hybrids with durable resistance to sunflower rust

Critical measurement issues in the assessment of social media influence on body image

Progress towards understanding how social media impacts body image hinges on the use of appropriate measurement tools and methodologies. This review provides an overview of common (qualitative, self-report survey, lab-based experiments) and emerging (momentary assessment, computational) methodological approaches to the exploration of the impact of social media on body image. The potential of these methodologies is detailed, with examples illustrating current use as well as opportunities for expansion. A key theme from our review is that each methodology has provided insights for the body image research field, yet is insufficient in isolation to fully capture the nuance and complexity of social media experiences. Thus, in consideration of gaps in methodology, we emphasise the need for big picture thinking that leverages and combines the strengths of each of these methodologies to yield a more comprehensive, nuanced, and robust picture of the positive and negative impacts of social media

DNA markers linked to the R (2) rust resistance gene in sunflower (Helianthus annuus L.) facilitate anticipatory breeding for this disease variant

Pre-emptive breeding for host disease resistance is an effective strategy for combating and managing devastating incursions of plant pathogens. Comprehensive, long-term studies have revealed that virulence to the R sunflower (Helianthus annuus L.) rust resistance gene in the line MC29 does not exist in the Australian rust (Puccinia helianthi) population. We report in this study the identification of molecular markers linked to this gene. The three simple sequence repeat (SSR) markers ORS795, ORS882, and ORS938 were linked in coupling to the gene, while the SSR marker ORS333 was linked in repulsion. Reliable selection for homozygous-resistant individuals was efficient when the three markers, ORS795, ORS882, and ORS333, were used in combination. Phenotyping for this resistance gene is not possible in Australia without introducing a quarantinable race of the pathogen. Therefore, the availability of reliable and heritable DNA-based markers will enable the efficient deployment of this gene, permitting a more effective strategy for generating sustainable commercial cultivars containing this rust resistance gene