The Prevention Of Depression: A Machine Learning Approach

Behavioral health disorders, specifically depression, are a serious health concern in the United States and worldwide. The consequences of unaddressed behavioral health conditions are multifaceted and have impact at the individual, relational, communal, and societal level. Despite the number of individuals who could benefit from treatment for behavioral health concerns, their difficulties are often unidentified and unaddressed through treatment. Technology carries unrealized potential to identify people at risk for behavioral health conditions and to inform prevention and intervention strategies. Drawing upon data from the National Longitudinal Study of Adolescent Health (Add Health, n=3782), this study has two aims related to advancing understanding of technology’s potential value in behavioral health: 1) to develop a forecasting procedure that can be used to identify youth who are at risk of reporting a depression diagnosis as adults based on a set of input variables; and 2) to understand the developmental trajectories of depression for youth. To address the first aim of this study, random forest methodology was used to derive the forecasting algorithm. The second aim was pursued with Generalized Additive Model analysis to estimate relationships between presence of a reported depression diagnosis as an adult and youth characteristics. Findings from this study indicate that it is feasible to use a forecasting tool to identify individuals at risk of being diagnosed with depression, which can facilitate early intervention and improved outcomes. Gender, race, and receiving counseling as a youth were the most important predictors of having a reported depression diagnosis as an adult. This dissertation addresses the role of health disparities, specifically gender and race, related to depression and mental health treatment. In sum, this dissertation highlights how a machine learning forecasting tool could be used to inform prevention strategies and understanding of factors associated with receiving a depression diagnosis. This study presents and discusses these findings in addition to offering important implications for future research and practice to identify and prevent behavioral health conditions such as depression

Multiple Family Groups to reduce child disruptive behavior difficulties: Moderating effects of child welfare status on child outcomes

Children who remain at home with their permanent caregivers following a child welfare (CW) involvement (e.g., investigation, out-of-home placement) manifest high rates of behavioral difficulties, which is a risk factor for further maltreatment and out-of-home placement if not treated effectively. A recently tested Multiple Family Group (MFG) service delivery model to treat youth Disruptive Behavior Disorders (DBDs) has demonstrated effectiveness in improving child behavior difficulties among hard-to-engage, socioeconomically disadvantaged families by addressing parenting skills, parent-child relationships, family communication and organization, social support, and stress. This exploratory study examines whether child behavioral outcomes for MFG differ for families with self-reported lifetime involvement in CW services compared to other families, as families with CW involvement struggle with additional stressors that can diminish treatment success. Youth (aged 7–11) and their families were assigned to MFG or services as usual (SAU) using a block comparison design. Caregivers reported on child behavior, social skills, and functional impairment. Mixed effects regression modeled multilevel outcomes across 4 assessment points (i.e., baseline, mid-test, post-test, 6 month follow-up). Among CW-involved families, MFG participants reported significantly reduced child oppositional defiant disorder symptoms at 6-month follow-up compared with SAU participants. No other differences were found in the effect of MFG treatment between CW and non-CW involved families. Findings suggest that MFG may be as effective in reducing child behavior difficulties for both CW and non-CW involved families. As a short-term, engaging, and efficient intervention, MFG may be a particularly salient service offering for families involved in the CW system

Fish Intelligence, Sentience and Ethics

Fish are one of the most highly utilised vertebrate taxa by humans; they are harvested from wild stocks as part of global fishing industries, grown under intensive aquaculture conditions, are the most common pet and are widely used for scientific research. But fish are seldom afforded the same level of compassion or welfare as warm-blooded vertebrates. Part of the problem is the large gap between people’s perception of fish intelligence and the scientific reality. This is an important issue because public perception guides government policy. The perception of an animal’s intelligence often drives our decision whether or not to include them in our moral circle. From a welfare perspective, most researchers would suggest that if an animal is sentient, then it can most likely suffer and should therefore be offered some form of formal protection. There has been a debate about fish welfare for decades which centres on the question of whether they are sentient or conscious. The implications for affording the same level of protection to fish as other vertebrates are great, not least because of fishing-related industries. Here, I review the current state of knowledge of fish cognition starting with their sensory perception and moving on to cognition. The review reveals that fish perception and cognitive abilities often match or exceed other vertebrates. A review of the evidence for pain perception strongly suggests that fish experience pain in a manner similar to the rest of the vertebrates. Although scientists cannot provide a definitive answer on the level of consciousness for any nonhuman vertebrate, the extensive evidence of fish behavioural and cognitive sophistication and pain perception suggests that best practice would be to lend fish the same level of protection as any other vertebrate

Multiple Family Groups for Child Behavior Difficulties Retention Among Child Welfare-Involved Caregivers.

Among children who remain at home with their permanent caregivers following a child welfare investigation, few who manifest emotional and behavioral difficulties actually engage in mental health treatment. The Multiple Family Group service delivery model to reduce childhood disruptive behavior disorders (MFG) has shown promise in engaging child welfare-involved families. This qualitative study examines caregiver perceptions of factors that influence retention in MFGs among child welfare-involved families. METHODS: Twenty-five predominantly Black and Hispanic adult (ages 26–57) female caregivers with child welfare services involvement participated in individual, in-depth interviews about their experience with MFGs. Transcribed interview data were thematically coded guided by grounded theory methodology. Emergent themes were subsequently organized into a conceptual framework. RESULTS: Within the overarching influence of child welfare services involvement, specific components of MFGs influencing retention included the quality of interaction among group members, group facilitators’ attentive approach with caregivers, supports designed to overcome logistical barriers (i.e., child care, transportation expenses, meals), and perceptions of MFG content and activities as fun and helpful. Caregiver factors, including their mental health and personal characteristics, as well as children’s behavior, (i.e., observed changes in behavioral difficulties) were also associated with retention. CONCLUSIONS: High acceptability suggest utility for implementing MFGs within settings serving child welfare involved families, with additional modifications to tailor to setting and client features

Vibration of snowboard decks

Vibrations of snowboards are closely related to their performance. The aim of this study was to investigate the frequencies of bending and torsional modes, the damping ratios and location of node lines in two boards with different torsional stiffness under free-free boundary conditions with a non-contact laser vibrometer. The frequencies of the first three bending modes were at 16, 37, and 65 Hz. The frequencies of the first three torsional modes were at 30, 54 and 86 Hz in one board, and 10% higher in the 2nd board. The damping ratios of the two boards investigated ranged between 0.3 and 0.6% for bending and between 0.6 and 1% for torsion. The location of the node lines was comparable to a free-free beam with constant cross-section. Vibration analysis should be a standard investigation for benchmarking of snowboards, in addition to mechanical and geometrical parameters.Franz Konstantin Fuss, Ben Cazzolato, Ashley Shepherd and Jason Hardin

Meta-Analysis of Immune Induced Gene Expression Changes in Diverse <i>Drosophila melanogaster</i> Innate Immune Responses

Organisms are commonly infected by a diverse array of pathogens and mount functionally distinct responses to each of these varied immune challenges. Host immune responses are characterized by the induction of gene expression, however, the extent to which expression changes are shared among responses to distinct pathogens is largely unknown. To examine this, we performed meta-analysis of gene expression data collected from Drosophila melanogaster following infection with a wide array of pathogens. We identified 62 genes that are significantly induced by infection. While many of these infection-induced genes encode known immune response factors, we also identified 21 genes that have not been previously associated with host immunity. Examination of the upstream flanking sequences of the infection-induced genes lead to the identification of two conserved enhancer sites. These sites correspond to conserved binding sites for GATA and nuclear factor κB (NFκB) family transcription factors and are associated with higher levels of transcript induction. We further identified 31 genes with predicted functions in metabolism and organismal development that are significantly downregulated following infection by diverse pathogens. Our study identifies conserved gene expression changes in Drosophila melanogaster following infection with varied pathogens, and transcription factor families that may regulate this immune induction