Summary The Cancer Genome Atlas (TCGA) project has analyzed mRNA expression, miRNA expression, promoter methylation, and DNA copy number in 489 high-grade serous ovarian adenocarcinomas (HGS-OvCa) and the DNA sequences of exons from coding genes in 316 of these tumors. These results show that HGS-OvCa is characterized by TP53 mutations in almost all tumors (96%); low prevalence but statistically recurrent somatic mutations in 9 additional genes including NF1, BRCA1, BRCA2, RB1, and CDK12; 113 significant focal DNA copy number aberrations; and promoter methylation events involving 168 genes. Analyses delineated four ovarian cancer transcriptional subtypes, three miRNA subtypes, four promoter methylation subtypes, a transcriptional signature associated with survival duration and shed new light on the impact on survival of tumors with BRCA1/2 and CCNE1 aberrations. Pathway analyses suggested that homologous recombination is defective in about half of tumors, and that Notch and FOXM1 signaling are involved in serous ovarian cancer pathophysiology.

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Background-Antidepressant medications offer an effective treatment for depression, yet nearly 50% of patients either do not respond or have side-effects rendering them unable to continue the course of treatment. Mechanistic studies might help advance the pharmacology of depression by identifying pathways through which treatments exert their effects. Toward this goal, we aimed to identify the effects of antidepressant treatment on neural connectivity, the relationship with symptom improvement, and to test whether these effects were reproducible across two studies.Methods-We completed two double-blind, placebo-controlled trials of SNRI antidepressant medications with MRI scans obtained before and after treatment. One was a 10-week trial of duloxetine (30-120 mg daily; mean 92•1 mg/day [SD 30•00]) and the other was a 12-week trial of desvenlafaxine (50-100 mg daily; 93•6 mg/day [16•47]). Participants consisted of adults with persistent depressive disorder. Adjusting for sex and age, we examined the effect of treatment on whole-brain functional connectivity. We also examined correlations between change in functional connectivity and improvement in symptoms of depression (24-item Hamilton Depression Rating Scale) and pain symptom severity (Symptom Checklist-90-Revised).

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BackgroundIncreasing evidence has revealed important roles for complex glycans as mediators of normal and pathological processes. Glycosaminoglycans are a class of glycans that bind and regulate the function of a wide array of proteins at the cell-extracellular matrix interface. The specific sequence and chemical organization of these polymers likely define function; however, identification of the structure-function relationships of glycosaminoglycans has been met with challenges associated with the unique level of complexity and the nontemplate-driven biosynthesis of these biopolymers.Methodology/Principal FindingsTo address these challenges, we have devised a computational approach to predict fine structure and patterns of domain organization of the specific glycosaminoglycan, heparan sulfate (HS). Using chemical composition data obtained after complete and partial digestion of mixtures of HS chains with specific degradative enzymes, the computational analysis produces populations of theoretical HS chains with structures that meet both biosynthesis and enzyme degradation rules. The model performs these operations through a modular format consisting of input/output sections and three routines called chainmaker, chainbreaker, and chainsorter. We applied this methodology to analyze HS preparations isolated from pulmonary fibroblasts and epithelial cells. Significant differences in the general organization of these two HS preparations were observed, with HS from epithelial cells having a greater frequency of highly sulfated domains. Epithelial HS also showed a higher density of specific HS domains that have been associated with inhibition of neutrophil elastase. Experimental analysis of elastase inhibition was consistent with the model predictions and demonstrated that HS from epithelial cells had greater inhibitory activity than HS from fibroblasts.Conclusions/SignificanceThis model establishes the conceptual framework for a new class of computational tools to use to assess patterns of domain organization within glycosaminoglycans. These tools will provide a means to consider high-level chain organization in deciphering the structure-function relationships of polysaccharides in biology.

tal health difficulties among victims of bullying. Animal models may provide useful insights, because they allow for a better control of the bullying experience and offer an opportunity to explore biological mechanisms in more depth. For example, an experiment on mice demonstrated the role of brain-derived neurotrophic factor in the mesolimbic dopamine pathway to explain social aversion among mice exposed to repeated aggression 10 . Tackling bullying behaviors could not only reduce children's and adolescents' mental health symptoms but also prevent psychiatric and socio-economic difficulties in adulthood. Anti-bullying programs show promise in controlling bullying behaviors 11 . However, the chances of eradicating bullying completely are minimal and we need to acknowledge that, despite such programs, a considerable proportion of young people will not escape this form of abuse. Intervention efforts should therefore also focus on limiting distress among young victims and possibly, by the same token, preventing longlasting difficulties in later life. A new innovative strategy could aim at preventing children from becoming the targets of bullying in the first place. Such a public health approach might be a more effective way to reduce the bullying-related burden.

Brain predicted age difference, or BrainPAD, compares chronological age to an age estimate derived by applying machine learning (ML) to MRI brain data. BrainPAD studies in youth have been relatively limited, often using only a single MRI modality or a single ML algorithm. Here, we use multimodal MRI with a stacked ensemble ML approach that iteratively applies several ML algorithms (AutoML). Eligible participants in the Healthy Brain Network (N = 489) were split into training and test sets. Morphometry estimates, white matter connectomes, or both were entered into AutoML to develop BrainPAD models. The best model was then applied to a held‐out evaluation dataset, and associations with psychometrics were estimated. Models using morphometry and connectomes together had a mean absolute error of 1.18 years, outperforming models using a single MRI modality. Lower BrainPAD values were associated with more symptoms on the CBCL (pcorr = .012) and lower functioning on the Children's Global Assessment Scale (pcorr = .012). Higher BrainPAD values were associated with better performance on the Flanker task (pcorr = .008). Brain age prediction was more accurate using ComBat‐harmonized brain data (MAE = 0.26). Associations with psychometric measures remained consistent after ComBat harmonization, though only the association with CGAS reached statistical significance in the reduced sample. Our findings suggest that BrainPAD scores derived from unharmonized multimodal MRI data using an ensemble ML approach may offer a clinically relevant indicator of psychiatric and cognitive functioning in youth.

Suicide is the second leading cause of death among undergraduate students, with an annual rate of 7.5 per 100,000. Suicidal behavior (SB) is complex and heterogeneous, which might be explained by there being multiple etiologies of SB. Data-driven identification of distinct at-risk subgroups among undergraduates would bolster this argument. We conducted a latent class analysis (LCA) on survey data from a large convenience sample of undergraduates to identify subgroups, and validated the resulting latent class model on a sample of graduate students. Data were collected through the Interactive Screening Program deployed by the American Foundation for Suicide Prevention. LCA identified 6 subgroups from the undergraduate sample (N=5654). In the group with the most students reporting current suicidal thoughts (N=623, 66% suicidal), 22.5% reported a prior suicide attempt, and 97.6% endorsed moderately severe or worse depressive symptoms. Notably, LCA identified a second at-risk group (N=662, 27% suicidal), in which only 1.5% of respondents noted moderately severe or worse depressive symptoms. When graduate students (N=1138) were classified using the model, a similar frequency distribution of groups was found. Finding multiple replicable groups at-risk for suicidal behavior, each with a distinct prevalence of risk factors, including a group of students who would not be classified as high risk with depression-based screening, is consistent with previous studies that identified multiple potential etiologies of SB.