SwimmerNET: Underwater 2D Swimmer Pose Estimation Exploiting Fully Convolutional Neural Networks

Professional swimming coaches make use of videos to evaluate their athletes' performances. Specifically, the videos are manually analyzed in order to observe the movements of all parts of the swimmer's body during the exercise and to give indications for improving swimming technique. This operation is time-consuming, laborious and error prone. In recent years, alternative technologies have been introduced in the literature, but they still have severe limitations that make their correct and effective use impossible. In fact, the currently available techniques based on image analysis only apply to certain swimming styles; moreover, they are strongly influenced by disturbing elements (i.e., the presence of bubbles, splashes and reflections), resulting in poor measurement accuracy. The use of wearable sensors (accelerometers or photoplethysmographic sensors) or optical markers, although they can guarantee high reliability and accuracy, disturb the performance of the athletes, who tend to dislike these solutions. In this work we introduce swimmerNET, a new marker-less 2D swimmer pose estimation approach based on the combined use of computer vision algorithms and fully convolutional neural networks. By using a single 8 Mpixel wide-angle camera, the proposed system is able to estimate the pose of a swimmer during exercise while guaranteeing adequate measurement accuracy. The method has been successfully tested on several athletes (i.e., different physical characteristics and different swimming technique), obtaining an average error and a standard deviation (worst case scenario for the dataset analyzed) of approximately 1 mm and 10 mm, respectively

Hydrogel Microparticles for Fluorescence Detection of miRNA in Mix-Read Bioassay

Herein we describe the development of a mix-read bioassay based on a three-dimensional (3D) poly ethylene glycol-(PEG)-hydrogel microparticles for the detection of oligonucleotides in complex media. The key steps of hydrogels synthesis and molecular recognition in a 3D polymer network are elucidated. The design of the DNA probes and their density in polymer network were opportunely optimized. Furthermore, the diffusion into the polymer was tuned adjusting the polymer concentration and consequently the characteristic mesh size. Upon parameters optimization, 3D-PEG-hydrogels were synthetized in a microfluidic system and provided with fluorescent probe. Target detection occurred by double strand displacement assay associated to fluorescence depletion within the hydrogel microparticle. Proposed 3D-PEG-hydrogel microparticles were designed for miR-143-3p detection. Results showed 3D-hydrogel microparticles with working range comprise between 10-6-10-12 M, had limit of detection of 30 pM and good specificity. Moreover, due to the anti-fouling properties of PEG-hydrogel, the target detection occurred in human serum with performance comparable to that in buffer. Due to the approach versatility, such design could be easily adapted to other short oligonucleotides detection

An Optimized Workflow for the Discovery of New Antimicrobial Compounds Targeting Bacterial RNA Polymerase Complex Formation

Bacterial resistance represents a major health problem worldwide and there is an urgent need to develop first-in-class compounds directed against new therapeutic targets. We previously developed a drug-discovery platform to identify new antimicrobials able to disrupt the protein-protein interaction between the beta' subunit and the sigma(70) initiation factor of bacterial RNA polymerase, which is essential for transcription. As a follow-up to such work, we have improved the discovery strategy to make it less time-consuming and more cost-effective. This involves three sequential assays, easily scalable to a high-throughput format, and a subsequent in-depth characterization only limited to hits that passed the three tests. This optimized workflow, applied to the screening of 5360 small molecules from three synthetic and natural compound libraries, led to the identification of six compounds interfering with the beta'-sigma(70) interaction, and thus was capable of inhibiting promoter-specific RNA transcription and bacterial growth. Upon supplementation with a permeability adjuvant, the two most potent transcription-inhibiting compounds displayed a strong antibacterial activity against Escherichia coli with minimum inhibitory concentration (MIC) values among the lowest (0.87-1.56 mu M) thus far reported for beta'-sigma PPI inhibitors. The newly identified hit compounds share structural feature similarities with those of a pharmacophore model previously developed from known inhibitors

Brief Communication Co-seismic displacement on October 26 and 30, 2016 (M_w 5.9 and 6.5) – earthquakes in central Italy from the analysis of discrete GNSS network

<p><strong>Abstract.</strong> On October 26<sup>th</sup> 2016, immediately north of the epicentral area affected by the M<sub>w</sub> 6.0, August 24<sup>th</sup> earthquake, a strong earthquake (<b><i>M</i></b><sub>w</sub> = 5.9), with a focal mechanism showing W-dipping normal faulting, occurred at the boundary between Marche and Umbria regions (central Apennines, Italy). Four days later (on October 30<sup>th</sup>), the main-shock (<b><i>M</i></b><sub>w</sub> = 6.5) of the whole seismic sequence occurred in the same area. The central Apennines are characterized by northeast-verging thrust-propagation folds, involving Mesozoic- Tertiary sedimentary successions. During the 2016 sequence, coseismic deformation has been recorded at the rear of the Sibillini Thrust which separates the main mountain chain from the Marche-Abruzzi foothills (Fig. 1). This contractional structure has been partly dissected and/or inverted by NNW-SSE trending Quaternary normal and oblique-slip faults. The major event (October 30) induced extensive geological effects at the surface and structural damages in the broader epicentral area up to a distance of 30 km. According to the report of the Istituto Nazionale di Geofisica e Vulcanologia (SUMMARY REPORT ON THE 30 OCTOBER, 2016 EARTHQUAKE IN CENTRAL ITALY Mw 6.5, Gruppo di Lavoro INGV sul Terremoto in centro Italia 10 November 2016), the hypocenter of major event was located at 42.8322° N, 13.1107° E at a depth of 9.2 km (Figs. 1 and 2). Following the August seismic events, we installed five new geodetic points located on both sides of the principal fracture zone and carried out two campaigns of GNSS measurements, the first one at the end of September (30-09/02-10, 2016), the second one early November (11/13-11, 2016) that covered the period of the October events. <br><br> In this brief communication, we provide the results of our geodetic campaigns that registered the co- seismic displacement occurred in the period between doy (day of year) 2016/274 and doy 2016/318, therefore documenting the two latter major shocks. We also compare our results with the available surface deformation field of the broader area obtained on the basis of the DInSAR technique and particularly the elaboration realized by CNR-IREA of Sentinel-1 radar imaging of Copernicus European Program of 26/10–1/11 (<a href=" http://www.irea.cnr.it/index.php?option=com_k2&view=item&id=761:nuovi-risultati-sul-terremoto- del-30-ottobre-2016-ottenuti-dai-radar-dei-satelliti-sentinel-1"target="_blank">http://www.irea.cnr.it/index.php?option=com_k2&view=item&id=761:nuovi-risultati-sul-terremoto- del-30-ottobre-2016-ottenuti-dai-radar-dei-satelliti-sentinel-1</a>). The comparison shows an overall good fit. It’s worth to note that these earthquakes occurred in a sector of the Central Apennines characterized by high geodetic strain-rates (e.g., D’Agostino 2014), where several continuous GNSS stations are operating.</p&gt

Standalone vertex finding in the ATLAS muon spectrometer

A dedicated reconstruction algorithm to find decay vertices in the ATLAS muon spectrometer is presented. The algorithm searches the region just upstream of or inside the muon spectrometer volume for multi-particle vertices that originate from the decay of particles with long decay paths. The performance of the algorithm is evaluated using both a sample of simulated Higgs boson events, in which the Higgs boson decays to long-lived neutral particles that in turn decay to bbar b final states, and pp collision data at √s = 7 TeV collected with the ATLAS detector at the LHC during 2011

Measurements of Higgs boson production and couplings in diboson final states with the ATLAS detector at the LHC

Measurements are presented of production properties and couplings of the recently discovered Higgs boson using the decays into boson pairs, H →γ γ, H → Z Z∗ →4l and H →W W∗ →lνlν. The results are based on the complete pp collision data sample recorded by the ATLAS experiment at the CERN Large Hadron Collider at centre-of-mass energies of √s = 7 TeV and √s = 8 TeV, corresponding to an integrated luminosity of about 25 fb−1. Evidence for Higgs boson production through vector-boson fusion is reported. Results of combined fits probing Higgs boson couplings to fermions and bosons, as well as anomalous contributions to loop-induced production and decay modes, are presented. All measurements are consistent with expectations for the Standard Model Higgs boson

Hunt for new phenomena using large jet multiplicities and missing transverse momentum with ATLAS in 4.7 fb−1 of √s=7 TeV proton-proton collisions

Results are presented of a search for new particles decaying to large numbers of jets in association with missing transverse momentum, using 4.7 fb−1 of pp collision data at s√=7TeV collected by the ATLAS experiment at the Large Hadron Collider in 2011. The event selection requires missing transverse momentum, no isolated electrons or muons, and from ≥6 to ≥9 jets. No evidence is found for physics beyond the Standard Model. The results are interpreted in the context of a MSUGRA/CMSSM supersymmetric model, where, for large universal scalar mass m 0, gluino masses smaller than 840 GeV are excluded at the 95% confidence level, extending previously published limits. Within a simplified model containing only a gluino octet and a neutralino, gluino masses smaller than 870 GeV are similarly excluded for neutralino masses below 100 GeV

Brain Derived Neurotrophic Factor (BDNF) Expression Is Regulated by MicroRNAs miR-26a and miR-26b Allele-Specific Binding

Brain-derived neurotrophic factor (BDNF) is a neurotrophin that plays an essential role in neuronal development and plasticity. MicroRNA (miRNAs) are small non-coding RNAs of about 22-nucleotides in length regulating gene expression at post-transcriptional level. In this study we explore the role of miRNAs as post-transcriptional inhibitors of BDNF and the effect of 3′UTR sequence variations on miRNAs binding capacity. Using an in silico approach we identified a group of miRNAs putatively regulating BDNF expression and binding to BDNF 3′UTR polymorphic sequences. Luciferase assays demonstrated that these miRNAs (miR-26a1/2 and miR-26b) downregulates BDNF expression and that the presence of the variant alleles of two single nucleotide polymorphisms (rs11030100 and rs11030099) mapping in BDNF 3′UTR specifically abrogates miRNAs targeting. Furthermore we found a high linkage disequilibrium rate between rs11030100, rs11030099 and the non-synonymous coding variant rs6265 (Val66Met), which modulates BDNF mRNA localization and protein intracellular trafficking. Such observation led to hypothesize that miR-26s mediated regulation could extend to rs6265 leading to an allelic imbalance with potentially functional effects, such as peptide's localization and activity-dependent secretion. Since rs6265 has been previously implicated in various neuropsychiatric disorders, we evaluated the distribution of rs11030100, rs11030099 and rs6265 both in a control and schizophrenic group, but no significant difference in allele frequencies emerged. In conclusion, in the present study we identified two novel miRNAs regulating BDNF expression and the first BDNF 3′UTR functional variants altering miRNAs-BDNF binding