CheRRI-Accurate classification of the biological relevance of putative RNA-RNA interaction sites.

Background: RNA-RNA interactions are key to a wide range of cellular functions. The detection of potential interactions helps to understand the underlying processes. However, potential interactions identified via in silico or experimental high-throughput methods can lack precision because of a high false-positive rate.

How to do RNA-RNA Interaction Prediction? A Use-Case Driven Handbook Using IntaRNA.

Computational prediction of RNA-RNA interactions (RRI) is a central methodology for the specific investigation of inter-molecular RNA interactions and regulatory effects of non-coding RNAs like eukaryotic microRNAs or prokaryotic small RNAs. Available methods can be classified according to their underlying prediction strategies, each implicating specific capabilities and restrictions often not transparent to the non-expert user. Within this work, we review seven classes of RRI prediction strategies and discuss the advantages and limitations of respective tools, since such knowledge is essential for selecting the right tool in the first place.

Knowledge Representations: Individual Differences in Novel Problem Solving.

The present study investigates how the quality of knowledge representations contributes to rule transfer in a problem-solving context and how working memory capacity (WMC) might contribute to the subsequent failure or success in transferring the relevant information. Participants were trained on individual figural analogy rules and then asked to rate the subjective similarity of the rules to determine how abstract their rule representations were. This rule representation score, along with other measures (WMC and fluid intelligence measures), was used to predict accuracy on a set of novel figural analogy test items, of which half included only the trained rules, and half were comprised of entirely new rules.

Distinct SARS-CoV-2 RNA fragments activate Toll-like receptors 7 and 8 and induce cytokine release from human macrophages and microglia.

Introduction: The pandemic coronavirus disease 19 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is marked by thromboembolic events and an inflammatory response throughout the body, including the brain.

Methods: Employing the machine learning approach BrainDead we systematically screened for SARS-CoV-2 genome-derived single-stranded (ss) RNA fragments with high potential to activate the viral RNA-sensing innate immune receptors Toll-like receptor (TLR)7 and/or TLR8. Analyzing HEK TLR7/8 reporter cells we tested such RNA fragments with respect to their potential to induce activation of human TLR7 and TLR8 and to activate human macrophages, as well as iPSC-derived human microglia, the resident immune cells in the brain.

Substantial interreader agreement for biopsy with reduction in biopsy rate: A multireader diagnostic performance study of ACR TI-RADS.

Purpose: To assess diagnostic performance of ACR TI-RADS in thyroid cancer detection and evaluate interobserver agreement among radiologists with lower interpreting experience.

Methods: Four radiologists retrospectively assessed 295 biopsied thyroid nodules from ultrasound studies performed between 2009 and 2019, blinded to histopathology. Diagnostic performance for cancer detection was determined individually, and interobserver agreement among four readers was evaluated with Fleiss kappa coefficient (ⱪ).