A role for miR-132 in learned safety.

Learned safety is a fear inhibitory mechanism, which regulates fear responses, promotes episodes of safety and generates positive affective states. Despite its potential as experimental model for several psychiatric illnesses, including post-traumatic stress disorder and depression, the molecular mechanisms of learned safety remain poorly understood, We here investigated the molecular mediators of learned safety, focusing on the characterization of miRNA expression in the basolateral amygdala (BLA). Comparing levels of 22 miRNAs in learned safety and learned fear trained mice, six safety-related miRNAs, including three members of the miR-132/-212 family, were identified. A gain-of-function approach based upon in-vivo transfection of a specific miRNA mimic, and miR-132/212 knock-out mice as loss-of-function tool were used in order to determine the relevance of miR-132 for learned safety at the behavioral and the neuronal functional levels. Using a designated bioinformatic approach, PTEN and GAT1 were identified as potential novel miR-132 target genes and further experimentally validated. We here firstly provide evidence for a regulation of amygdala miRNA expression in learned safety and propose miR-132 as signature molecule to be considered in future preclinical and translational approaches testing the transdiagnostic relevance of learned safety as intermediate phenotype in fear and stress-related disorders.

DeepSNP: An End-to-End Deep Neural Network with Attention-Based Localization for Breakpoint Detection in Single-Nucleotide Polymorphism Array Genomic Data.

Clinical decision-making in cancer and other diseases relies on timely and cost-effective genome-wide testing. Classical bioinformatic algorithms, such as Rawcopy, can support genomic analysis by calling genomic breakpoints and copy-number variations (CNVs), but often require manual data curation, which is error prone, time-consuming, and thus substantially increasing costs of genomic testing and hampering timely delivery of test results to the treating physician. We aimed to investigate whether deep learning algorithms can be used to learn from genome-wide single-nucleotide polymorphism array (SNPa) data and improve state-of-the-art algorithms. We developed, applied, and validated a novel deep neural network (DNN), DeepSNP. A manually curated data set of 50 SNPa analyses was used as truth-set. We show that DeepSNP can learn from SNPa data and classify the presence or absence of genomic breakpoints within large genomic windows with high precision and recall. DeepSNP was compared with well-known neural network models as well as with Rawcopy. Moreover, the use of a localization unit indicates the ability to pinpoint genomic breakpoints despite their exact location not being provided while training. DeepSNP results demonstrate the potential of DNN architectures to learn from genomic SNPa data and encourage further adaptation for CNV detection in SNPa and other genomic data types.

A similarity measure for image and volumetric data based on Hermann Weyl's discrepancy.

The paper focuses on similarity measures for translationally misaligned image and volumetric patterns. For measures based on standard concepts such as cross-correlation, L(p)-norm, and mutual information, monotonicity with respect to the extent of misalignment cannot be guaranteed. In this paper, we introduce a novel distance measure based on Hermann Weyl's discrepancy concept that relies on the evaluation of partial sums. In contrast to standard concepts, in this case, monotonicity, positive-definiteness, and a homogenously linear upper bound with respect to the extent of misalignment can be proven. We show that this monotonicity property is not influenced by the image's frequencies or other characteristics, which makes this new similarity measure useful for similarity-based registration, tracking, and segmentation.