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School psychologists have training and expertise in consultation and evidence-based interventions that position them well to support early career teachers (ECTs). The current study involved iterative development and pilot testing of an intervention to help ECTs become more effective in classroom management and engaging learners, as well as more connected to colleagues. The intervention included group seminars, professional learning communities, and coaching. The sample included 15 ECTs and 57 school personnel in three high-poverty, urban schools. Feasibility and initial promise of the intervention were examined using a mixed-method design, which yielded promising trends in ECTs' effectiveness and connectedness. ECTs described facilitators to effectiveness and connectedness associated with the intervention and barriers associated with the structural realities of schools and gaps in their training. ECTs described effectiveness as neither static nor global and perceived meaningful progress, leveraging individual relationships and group formats to receive instrumental and emotional support.

Identifying biomarkers is essential to obtain the optimal therapeutic benefit while treating patients with late-life depression (LLD). We compare LLD patients with healthy controls (HC) using resting-state functional magnetic resonance and diffusion tensor imaging data to identify neuroimaging biomarkers that may be potentially associated with the underlying pathophysiology of LLD. We implement a Bayesian multimodal local false discovery rate approach for functional connectivity, borrowing strength from structural connectivity to identify disrupted functional connectivity of LLD compared to HC. In the Bayesian framework, we develop an algorithm to control the overall false discovery rate of our findings. We compare our findings with the literature and show that our approach can better detect some regions never discovered before for LLD patients. The Hub of our discovery related to various neurobehavioral disorders can be used to develop behavioral interventions to treat LLD patients who do not respond to antidepressants. [ABSTRACT FROM AUTHOR]

Objective: To examine the merits of using microRNAs (miRNAs) as biomarkers of disorders of consciousness (DoC) due to traumatic brain injury (TBI). Settings: Acute and subacute beds. Participants: Patients remaining in vegetative and minimally conscious states (VS, MCS), an average of 1.5 years after TBI, and enrolled in a randomized clinical trial (n = 6). Persons without a diagnosed central nervous system disorder, neurotypical controls (n = 5). Design: Comparison of whole blood miRNA profiles between patients and age/gender-matched controls. For patients, correlational analyses between miRNA profiles and measures of neurobehavioral function. Main Measures: Baseline measures of whole blood miRNAs isolated from the cellular and fluid components of blood and measured using miRNA-seq and real-time polymerase chain reaction (RT-PCR). Baseline neurobehavioral measures derived from 7 tests. Results: For patients, relative to controls, 48 miRNA were significantly (P <.05)/differentially expressed. Cluster analysis showed that neurotypical controls were most similar to each other and with 2 patients (VS: n = 1; and MCS: n = 1). Three patients, all in MCS, clustered separately. The only female in the sample, also in MCS, formed an independent group. For the 48 miRNAs, the enriched pathways identified are implicated in secondary brain damage and 26 miRNAs were significantly (P <.05) correlated with measures of neurobehavioral function. Conclusions: Patients remaining in states of DoC an average of 1.5 years after TBI showed a different and reproducible pattern of miRNA expression relative to age/gender-matched neurotypical controls. The phenotypes, defined by miRNA profiles relative to persisting neurobehavioral impairments, provide the basis for future research to determine the miRNA profiles differentiating states of DoC and the basis for future research using miRNA to detect treatment effects, predict treatment responsiveness, and developing targeted interventions. If future research confirms and advances reported findings, then miRNA profiles will provide the foundation for patient-centric DoC neurorehabilitation. [ABSTRACT FROM AUTHOR]

A plausible factorial structure for many types of psychological and educational tests exhibits a general factor and one or more group or method factors. This structure can be represented by a bifactor model. The bifactor structure results from the constraint that each item has a nonzero loading on the primary dimension and, at most, one of the group factors. The authors develop estimation procedures for fitting the graded response model when the data follow the bifactor structure. Using maximum marginal likelihood estimation of item parameters, the bifactor restriction leads to a major simplification of the likelihood equations and (a) permits analysis of models with large numbers of group factors, (b) permits conditional dependence within identified subsets of items, and (c) provides more parsimonious factor solutions than an unrestricted full-information item factor analysis in some cases. Analysis of data obtained from 586 chronically mentally ill patients revealed a clear bifactor structure. (Contains 4 tables.)

The objective of our study is to discern the aberrant functional and structural brain connectivity in mental disorder patients, ultimately to develop quality therapeutic strategies. We developed a bivariate mixed-effects model to ascertain the differences in brain connectivity between healthy individuals and affected patients. The model accounts for subject-specific variability and heteroscedastic errors, enabling it to handle correlations within and across different connectivity measures. Such comprehensive accounting allowed us to identify notable connectivity differences between groups. To validate the performance of our model, we conducted a simulation study. Then, we applied our method to neuroimaging data for late-life depression patients, assessing biomarker identification compared to univariate mixed-effects models. [ABSTRACT FROM AUTHOR]

Background: We determined how the vaginal and penile microbiomes contribute to herpes simplex virus type 2 (HSV-2) serostatus within sexual partnerships.Methods: Microbiomes were characterized in cervicovaginal lavage and penile meatal swab specimens through high-throughput 16s ribosomal RNA gene amplicon sequencing. HSV-2 antibody was detected in serum specimens. We modeled vaginal and penile taxa and covariates contributing to HSV-2 status in women and men using bivariate probit analysis.Results: Among 231 couples, HSV-2 was detected in both partners in 78 couples (33.8%), in the woman only in 52 (22.5%),in the man only in 27 (11.7%), and in neither in 74 (32.0%). Among the women (median age, 22 years) 10.9% had human immunodeficiency virus (HIV), and 21.4% had Bacterial vaginosis. Among men (median age, 26 years), 11.8% had HIV, and 55.0% circumcised. In an analysis with adjustment for sociodemographics and Bacterial vaginosis, enrichment of vaginal Gardnerella vaginalis and Lactobacillus iners was associated with increased likelihood of HSV-2 in both partners. Penile taxa (including Ureaplasma and Aerococcus) were associated with HSV-2 in women.Conclusions: We demonstrate that penile taxa are associated with HSV-2 in female partners, and vaginal taxa are associated with HSV-2 in male partners. Our findings suggest that couples-level joint consideration of genital microbiome and sexually transmitted infection or related outcomes could lead to new avenues for prevention. [ABSTRACT FROM AUTHOR]