Metagenomics reveals a core macrolide resistome related to microbiota in chronic respiratory disease

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordRationale: Long-term antibiotic use for managing chronic respiratory disease is increasing however the role of the airway resistome and its relationship to host microbiomes remains unknown Objective: To evaluate airway resistomes, and, relate them to host and environmental microbiomes using ultra-deep metagenomic shotgun sequencing Methods: Airway specimens from n=85 individuals with and without chronic respiratory disease (severe asthma, COPD and bronchiectasis) were subjected to metagenomic sequencing to an average depth exceeding twenty million reads. Respiratory and device-associated microbiomes were evaluated based on taxonomical classification and functional annotation including the Comprehensive Antibiotic Resistance Database (CARD) to determine airway resistomes. Co-occurrence networks of gene-microbe association were constructed to determine potential microbial sources of the airway resistome. Paired patient-inhaler metagenomes were compared (n=31) to assess for the presence of airway-environment overlap in microbiomes and/or resistomes. Results: Airway metagenomes exhibit taxonomic and metabolic diversity and distinct antimicrobial resistance patterns. A ‘core’ airway resistome dominated by macrolide but with high prevalence of β-lactam, fluoroquinolone and tetracycline resistance genes exist, and, is independent of disease status or antibiotic exposure. Streptococcus and Actinomyces are key potential microbial reservoirs of macrolide resistance including the ermX, ermF and msrD genes. Significant patient-inhaler overlap in airway microbiomes and their resistomes is identified where the latter may be a proxy for airway microbiome assessment in chronic respiratory disease. Conclusion: Metag

Constraints on the χ_(c1) versus χ_(c2) polarizations in proton-proton collisions at √s = 8 TeV

The polarizations of promptly produced χ_(c1) and χ_(c2) mesons are studied using data collected by the CMS experiment at the LHC, in proton-proton collisions at √s=8  TeV. The χ_c states are reconstructed via their radiative decays χ_c → J/ψγ, with the photons being measured through conversions to e⁺e⁻, which allows the two states to be well resolved. The polarizations are measured in the helicity frame, through the analysis of the χ_(c2) to χ_(c1) yield ratio as a function of the polar or azimuthal angle of the positive muon emitted in the J/ψ → μ⁺μ⁻ decay, in three bins of J/ψ transverse momentum. While no differences are seen between the two states in terms of azimuthal decay angle distributions, they are observed to have significantly different polar anisotropies. The measurement favors a scenario where at least one of the two states is strongly polarized along the helicity quantization axis, in agreement with nonrelativistic quantum chromodynamics predictions. This is the first measurement of significantly polarized quarkonia produced at high transverse momentum

Directed cell migration in multi-cue environments

Cell migration plays a critical role in development, angiogenesis, immune response, wound healing and cancer metastasis. During these processes, cells are often directed to migrate towards targets by sensing aligned fibers or gradients in concentration, mechanical properties or electric field. Often times, cells must integrate migrational information from several of these different cues. While the cell migration behavior, signal transduction and cytoskeleton dynamics elicited by individual directional cues has been largely determined, responses to multiple directional cues are much less understood. However, initial work has pointed to several interesting behaviors in multi-cue environments, including competition and cooperation between cues to determine the migrational responses of cells. Much of the work on multi-cue sensing has been driven by the recent development of approaches to systematically and simultaneously control directional cues in vitro coupled with analysis and modeling that quantitatively describe those responses. In this review we present an overview of multi-cue directed migration with an emphasis on how cues compete or cooperate. We outline how multi-cue responses such as cue dominance might change depending on other environmental inputs. Finally, the challenges associated with the design of the environments to control multiple cues and the analysis and modeling of cell migration in multi-cue environments as well as some interesting biological questions associated with migration in complex environments are discussed. Understanding multi-cue migrational responses is critical to the mechanistic description of physiology and pathology, but also to the design of engineered tissues, where cell migration must be orchestrated to form specific tissue structures

Machine Learning based tool for CMS RPC currents quality monitoring

The muon system of the CERN Compact Muon Solenoid (CMS) experiment includes more than a thousand Resistive Plate Chambers (RPC). They are gaseous detectors operated in the hostile environment of the CMS underground cavern on the Large Hadron Collider where pp luminosities of up to $2\times 10^{34}$ $\text{cm}^{-2}\text{s}^{-1}$ are routinely achieved. The CMS RPC system performance is constantly monitored and the detector is regularly maintained to ensure stable operation. The main monitorable characteristics are dark current, efficiency for muon detection, noise rate etc. Herein we describe an automated tool for CMS RPC current monitoring which uses Machine Learning techniques. We further elaborate on the dedicated generalized linear model proposed already and add autoencoder models for self-consistent predictions as well as hybrid models to allow for RPC current predictions in a distant future