Psychoeducational interventions in adolescent depression: A systematic review

Background: Adolescent depression is common and leads to distress and impairment for individuals/families. Treatment/prevention guidelines stress the need for good information and evidence-based psychosocial interventions. There has been growing interest in psychoeducational interventions (PIs), which broadly deliver accurate information about health issues and self-management. Objective, methods: Systematic search of targeted PIs as part of prevention/management approaches for adolescent depression. Searches were undertaken independently in PubMed, PsycINFO, EMBASE, guidelines, reviews (including Cochrane), and reference lists. Key authors were contacted. No restrictions regarding publishing dates. Results: Fifteen studies were included: seven targeted adolescents with depression/depressive symptoms, eight targeted adolescents ‘at risk' e.g. with a family history of depression. Most involved family/group programmes; others included individual, school-based and online approaches. PIs may affect understanding of depression, identification of symptoms, communication, engagement, and mental health outcomes. Conclusion, practice implications: PIs can have a role in preventing/managing adolescent depression, as a first-line or adjunctive approach. The limited number of studies, heterogeneity in formats and evaluation, and inconsistent approach to defining PI, make it difficult to compare programmes and measure overall effectiveness. Further work needs to establish an agreed definition of PI, develop/evaluate PIs in line with frameworks for complex interventions, and analyse their active components

Offspring of parents with recurrent depression: which features of parent depression index risk for offspring psychopathology?

Background: Parental depression is associated with an increased risk of psychiatric disorder in offspring, although outcomes vary. At present relatively little is known about how differences in episode timing, severity, and course of recurrentdepression relate to risk in children. The aim of this study was to consider the offspring of parents with recurrentdepression and examine whether a recent episode of parental depressionindexesrisk for offspringpsychopathology over and above these other parental depressionfeatures. <p/>Methods: Three hundred and thirty seven recurrently depressed parents and their offspring (aged 9–17) were interviewed as part of an ongoing study, the ‘Early Prediction of Adolescent Depression Study’. The Child and Adolescent Psychiatric Assessment was used to assess two child outcomes; presence of a DSM-IV psychiatric disorder and number of DSM-IV child-rated depression symptoms. <p/>Results: Children whose parents had experienced a recent episode of depression reported significantly more depression symptoms, and odds of child psychiatric disorder were doubled relative to children whose parents had not experienced a recent episode of depression. Past severity of parental depression was also significantly associated with child depression symptoms. <p/>Limitations: Statistical analyses preclude causal conclusions pertaining to parental depression influences on offspringpsychopathology; several features of parental depression were recalled retrospectively. <p/>Conclusions: This study suggests that particular features of parental depression, specifically past depression severity and presence of a recent episode, may be important indicators of risk for child psychiatric disorder and depressive symptoms

PVSNet: Palm Vein Authentication Siamese Network Trained using Triplet Loss and Adaptive Hard Mining by Learning Enforced Domain Specific Features

Designing an end-to-end deep learning network to match the biometric features with limited training samples is an extremely challenging task. To address this problem, we propose a new way to design an end-to-end deep CNN framework i.e., PVSNet that works in two major steps: first, an encoder-decoder network is used to learn generative domain-specific features followed by a Siamese network in which convolutional layers are pre-trained in an unsupervised fashion as an autoencoder. The proposed model is trained via triplet loss function that is adjusted for learning feature embeddings in a way that minimizes the distance between embedding-pairs from the same subject and maximizes the distance with those from different subjects, with a margin. In particular, a triplet Siamese matching network using an adaptive margin based hard negative mining has been suggested. The hyper-parameters associated with the training strategy, like the adaptive margin, have been tuned to make the learning more effective on biometric datasets. In extensive experimentation, the proposed network outperforms most of the existing deep learning solutions on three type of typical vein datasets which clearly demonstrates the effectiveness of our proposed method.Comment: Accepted in 5th IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), 2019, Hyderabad, Indi

Depression and blood pressure in high-risk children and adolescents: an investigation using two longitudinal cohorts

Objective: To examine the relationship between blood pressure and depressive disorder in children and adolescents at high risk for depression. Design: Multisample longitudinal design including a prospective longitudinal three-wave high-risk study of offspring of parents with recurrent depression and an on-going birth cohort for replication. Setting: Community-based studies. Participants: High-risk sample includes 281 families where children were aged 9-17 years at baseline and 10-19 years at the final data point. Replication cohort includes 4830 families where children were aged 11-14 years at baseline and 14-17 years at follow-up and a high-risk subsample of 612 offspring with mothers that had reported recurrent depression. Main outcome measures: The new-onset of Diagnostic and Statistical Manual of Mental Disorder, fourth edition defined depressive disorder in the offspring using established research diagnostic assessments-the Child and Adolescent Psychiatric Assessment in the high-risk sample and the Development and Wellbeing Assessment in the replication sample. Results: Blood pressure was standardised forage and gender to create SD scores and child's weight was statistically controlled in all analyses. In the high-risk sample, lower systolic blood pressure at wave 1 significantly predicted new-onset depressive disorder in children (OR=0.65, 95% CI 0.44 to 0.96; p=0.029) but diastolic blood pressure did not. Depressive disorder at wave 1 did not predict systolic blood pressure at wave 3. A significant association between lower systolic blood pressure and future depression was also found in the replication cohort in the second subset of high-risk children whose mothers had experienced recurrent depression in the past. Conclusions: Lower systolic blood pressure predicts new-onset depressive disorder in the offspring of parents with depression. Further studies are needed to investigate how this association arises

Applying a global optimisation algorithm to Fund of Hedge Funds portfolio optimisation

Portfolio optimisation for a Fund of Hedge Funds (“FoHF”) has to address the asymmetric, non-Gaussian nature of the underlying returns distributions. Furthermore, the objective functions and constraints are not necessarily convex or even smooth. Therefore traditional portfolio optimisation methods such as mean-variance optimisation are not appropriate for such problems and global search optimisation algorithms could serve better to address such problems. Also, in implementing such an approach the goal is to incorporate information as to the future expected outcomes to determine the optimised portfolio rather than optimise a portfolio on historic performance. In this paper, we consider the suitability of global search optimisation algorithms applied to FoHF portfolios, and using one of these algorithms to construct an optimal portfolio of investable hedge fund indices given forecast views of the future and our confidence in such views.portfolio optimisation; optimization; fund of hedge funds; global search optimisation; direct search; pgsl; hedge fund portfolio