We present the design of the trigger system for the KLOE experiment at the Frascati f-factory DAFNE: The detector consists of a large-volume drift chamber and a calorimeter both immersed in a 0:52 Tsolenoidal field. The trigger, structured with a first- and a second-decision level, is based on the multiplicity of energy deposits in the calorimeter and of hits in the drift chamber. The selection criteria are described and the efficiency for detecting f decays is evaluated using data

RNA molecules are able to bind proteins, DNA and other small or long RNAs using information at primary, secondary or tertiary structure level. Recent techniques that use cross-linking and immunoprecipitation of RNAs can detect these interactions and, if followed by high-throughput sequencing, molecules can be analysed to find recurrent elements shared by interactors, such as sequence and/or structure motifs. Many tools are able to find sequence motifs from lists of target RNAs, while others focus on structure using different approaches to find specific interaction elements. In this work, we make a systematic analysis of RBP–RNA and RNA–RNA datasets to better characterize the interaction landscape with information about multi-motifs on the same RNAs. To achieve this goal, we updated our BEAM algorithm to combine both sequence and structure information to create pairs of patterns that model motifs of interaction. This algorithm was applied to several RNA binding proteins and ncRNAs interactors, confirming already known motifs and discovering new ones. This landscape analysis on interaction variability reflects the diversity of target recognition and underlines that often both primary and secondary structure are involved in molecular recognition.

MotivationRNA structural motif finding is a relevant problem that becomes computationally hard when working on high-throughput data (e.g. eCLIP, PAR-CLIP), often represented by thousands of RNA molecules. Currently, the BEAM server is the only web tool capable to handle tens of thousands of RNA in input with a motif discovery procedure that is only limited by the current secondary structure prediction accuracies.ResultsThe recently developed method BEAM (BEAr Motifs finder) can analyze tens of thousands of RNA molecules and identify RNA secondary structure motifs associated to a measure of their statistical significance. BEAM is extremely fast thanks to the BEAR encoding that transforms each RNA secondary structure in a string of characters. BEAM also exploits the evolutionary knowledge contained in a substitution matrix of secondary structure elements, extracted from the RFAM database of families of homologous RNAs. The BEAM web server has been designed to streamline data pre-processing by automatically handling folding and encoding of RNA sequences, giving users a choice for the preferred folding program. The server provides an intuitive and informative results page with the list of secondary structure motifs identified, the logo of each motif, its significance, graphic representation and information about its position in the RNA molecules sharing it.Availability and implementationThe web server is freely available at http://beam.uniroma2.it/ and it is implemented in NodeJS and Python with all major browsers supported.Supplementary information Supplementary data are available at Bioinformatics online.

The interaction between RNA and RNA-binding proteins (RBPs) has a key role in the regulation of gene expression, in RNA stability, and in many other biological processes. RBPs accomplish these functions by binding target RNA molecules through specific sequence and structure motifs. The identification of these binding motifs is therefore fundamental to improve our knowledge of the cellular processes and how they are regulated. Here, we present BRIO (BEAM RNA Interaction mOtifs), a new web server designed for the identification of sequence and structure RNA-binding motifs in one or more RNA molecules of interest. BRIO enables the user to scan over 2508 sequence motifs and 2296 secondary structure motifs identified in Homo sapiens and Mus musculus, in three different types of experiments (PAR-CLIP, eCLIP, HITS). The motifs are associated with the binding of 186 RBPs and 69 protein domains. The web server is freely available at http://brio.bio.uniroma2.it.

Structural characterization of RNAs is a dynamic field, offering many modelling possibilities. RNA secondary structure models are usually characterized by an encoding that depicts structural information of the molecule through string representations or graphs. In this work, we provide a generalization of the BEAR encoding (a context-aware structural encoding we previously developed) by expanding the set of alignments used for the construction of substitution matrices and then applying it to secondary structure encodings ranging from fine-grained to more coarse-grained representations. We also introduce a re-interpretation of the Shannon Information applied on RNA alignments, proposing a new scoring metric, the Relative Information Gain (RIG). The RIG score is available for any position in an alignment, showing how different levels of detail encoded in the RNA representation can contribute differently to convey structural information. The approaches presented in this study can be used alongside state-of-the-art tools to synergistically gain insights into the structural elements that RNAs and RNA families are composed of. This additional information could potentially contribute to their improvement or increase the degree of confidence in the secondary structure of families and any set of aligned RNAs.

No abstract

RPCs are strong candidates for the outer regions of the LHCb muon detector. We have tested single-gap and dual-gap detectors built with low-resistivity phenolic plates (ρ = 9 · 10 9 Ωcm) and operated in avalanche mode. Measurements have been performed over a wide range of beam intensities and on the GIF at CERN. The results are presented and discussed, with special emphasis on the detection efficiency.

Obesity is associated with a higher risk of developing many cancer types including acute promyelocytic leukaemia (APL), a subset of acute myeloid leukemias (AML) characterized by expression of the PML-RARα oncogene. The molecular mechanisms linking obesity and APL development are not known. To model clinical observations, we established a mouse model of diet-induced obesity using transgenic mice constitutively expressing PML-RARΑ α in the hematopoietic system (PML-RARα KI mice) fed either standard (SD) or high-fat (HFD) diets. HFD-fed PML-RARα KI mice developed leukaemia with reduced latency and increased penetrance, as compared to SD-fed mice. HFD leads to accumulation of DNA damage in hematopoietic stem cells (HSCs), but, surprisingly, this was not associated with mutational load gain, as shown by whole genome/exome sequencing of pre-leukemic and leukemic cells. Importantly, very few of the observed mutations were predicted to act as cancer drivers, suggesting the relevance of nongenetic mechanisms. HFD led to an expansion of hematopoietic progenitor cells with a concomitant reduction in long-term hematopoietic stem cells, and in the presence of PML-RARα this was also accompanied by an enhancement of in vitro and in vivo self-renewal. Interestingly, Linoleic Acid (LA), abundant in HFD, recapitulates the effect of HFD on the self-renewal of PML-RARα HPCs by activating the peroxisome proliferator-activated receptor delta (PPARδ), a central regulator of fatty acid metabolism involved in the promotion of cancer progression. Our findings have implications for dietary or pharmacological interventions aimed at counteracting the cancer-promoting effect of obesity.