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We used the bioinformatics database Enrichr to examine the gene ontology associations and driver transcription factors of each module. Using gene co-expression analysis, we were able predict tumor recurrence with high accuracy using a single module which mapped to cell cycle-related processes (AUC of 0.81 ± 0.09 and 0.77 ± 0.10 in external validation using microarray and RNA-seq data, respectively). selleck chemical This module remained predictive when controlling for WHO grade in all cohorts, and was associated with several cancer-associated transcription factors which may serve as novel therapeutic targets for patients with this disease. With the easy accessibility of gene panels in healthcare diagnostics, our results offer a basis for routine molecular testing in meningioma management and propose potential therapeutic targets for future research.Noise-induced hearing loss (NIHL) is a common health concern with significant social, psychological, and cognitive implications. Moderate levels of acoustic overstimulation associated with tinnitus and impaired speech perception cause cochlear synaptopathy, characterized physiologically by reduction in wave I of the suprathreshold auditory brainstem response (ABR) and reduced number of synapses between sensory hair cells and auditory neurons. The unfolded protein response (UPR), an endoplasmic reticulum stress response pathway, has been implicated in the pathogenesis and treatment of NIHL as well as neurodegeneration and synaptic damage in the brain. In this study, we used the small molecule UPR modulator Integrated Stress Response InhiBitor (ISRIB) to treat noise-induced cochlear synaptopathy in a mouse model. Mice pretreated with ISRIB prior to noise-exposure were protected against noise-induced synapse loss. Male, but not female, mice also exhibited ISRIB-mediated protection against noise-induced suprathreshold ABR wave-I amplitude reduction. Female mice had higher baseline wave-I amplitudes but greater sensitivity to noise-induced wave-I reduction. Our results suggest that the UPR is implicated in noise-induced cochlear synaptopathy, and can be targeted for treatment.In order to implement a new bariatric surgery technique, we verify the efficacy of intragastric sleeve to reduce weight gain and subcutaneous adipose tissue (SAT). Animals were divided into two groups G1 (single-port intragastric sleeve) and G2 (sham group). The stomach was surgically reduced by single-port intragastric sutures to fo a gastric sleeve. Animals were submitted to computer tomography (CT) before the surgical procedure and after 18 weeks. Images were analyzed and measurements of the thickness of SAT, depth and width of the longissimus dorsi muscle and the rib eye area were made. Body weight and CT measurements were analyzed using the GLM PROC. The correlation coefficients were calculated among weight, moments and measures. There was a significant difference in weight gain, in which G1 had an average of 42.803 ± 3.206 kg, lower than G2 (45.966 ± 4.767 kg). The mean values for SAT and muscle measurements differed significantly between groups, in which G1 achieved the lowest values. All variables had significant correlations and high magnitude. Intragastric sleeve surgery induced a significant decrease of SAT. The new intragastric sleeve technique is feasible, safe and effective, mainly in reducing fat deposition, making it an important alternative in bariatric surgical treatment.Prospection (mentally simulating future events) generates emotionally-charged mental images that guide social decision-making. Positive and negative social expectancies-imagining new social interactions to be rewarding versus threatening-are core components of social approach and avoidance motivation, respectively. Interindividual differences in such positive and negative future-related cognitions may be underpinned by distinct neuroanatomical substrates. Here, we asked 100 healthy adults to vividly imagine themselves in a novel self-relevant event that was ambiguous with regards to possible social acceptance or rejection. During this task we measured participants' expectancies for social reward (anticipated feelings of social connection) or threat (anticipated feelings of rejection). On a separate day they underwent structural MRI; voxel-based morphometry was used to explore the relation between social reward and threat expectancies and regional grey matter volumes (rGMV). Increased rGMV in key default-network regions involved in prospection, socio-emotional cognition, and subjective valuation, including ventromedial prefrontal cortex, correlated with both higher social reward and lower social threat expectancies. In contrast, social threat expectancies uniquely correlated with rGMV of regions involved in social attention (posterior superior temporal sulcus, pSTS) and interoception (somatosensory cortex). These findings provide novel insight into the neurobiology of future-oriented cognitive-affective processes critical to adaptive social functioning.Transfer learning, which transfers patterns learned on a source dataset to a related target dataset for constructing prediction models, has been shown effective in many applications. In this paper, we investigate whether transfer learning can be used to improve the performance of anti-cancer drug response prediction models. Previous transfer learning studies for drug response prediction focused on building models to predict the response of tumor cells to a specific drug treatment. We target the more challenging task of building general prediction models that can make predictions for both new tumor cells and new drugs. Uniquely, we investigate the power of transfer learning for three drug response prediction applications including drug repurposing, precision oncology, and new drug development, through different data partition schemes in cross-validation. We extend the classic transfer learning framework through ensemble and demonstrate its general utility with three representative prediction algorithms including a gradient boosting model and two deep neural networks. The ensemble transfer learning framework is tested on benchmark in vitro drug screening datasets. The results demonstrate that our framework broadly improves the prediction performance in all three drug response prediction applications with all three prediction algorithms.