479 research outputs found

    Progress towards an improved particle flow algorithm at CMS with machine learning

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    The particle-flow (PF) algorithm, which infers particles based on tracks and calorimeter clusters, is of central importance to event reconstruction in the CMS experiment at the CERN LHC, and has been a focus of development in light of planned Phase-2 running conditions with an increased pileup and detector granularity. In recent years, the machine learned particle-flow (MLPF) algorithm, a graph neural network that performs PF reconstruction, has been explored in CMS, with the possible advantages of directly optimizing for the physical quantities of interest, being highly reconfigurable to new conditions, and being a natural fit for deployment to heterogeneous accelerators. We discuss progress in CMS towards an improved implementation of the MLPF reconstruction, now optimized using generator/simulation-level particle information as the target for the first time. This paves the way to potentially improving the detector response in terms of physical quantities of interest. We describe the simulation-based training target, progress and studies on event-based loss terms, details on the model hyperparameter tuning, as well as physics validation with respect to the current PF algorithm in terms of high-level physical quantities such as the jet and missing transverse momentum resolutions. We find that the MLPF algorithm, trained on a generator/simulator level particle information for the first time, results in broadly compatible particle and jet reconstruction performance with the baseline PF, setting the stage for improving the physics performance by additional training statistics and model tuning.Comment: 7 pages, 4 Figures, 1 Tabl

    Razlika u infestaciji krpeljima: Hyalomma dromedarii i Rhipicephalus sanguineus sensu lato na jugu AlŇĺira

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    The aim of this study was to identify the species and parasitic indices of ticks. A flock of 57 individuals (sheep, goats, cattle and dogs) in southern Algeria was studied from March 2019 to February 2020 during monthly visits. A total of 2544 ticks were collected by examining the entire body of the animal. Two tick species were identified: Hyalomma dromedarii (2430 ticks) and Rhipicephalus sanguineus sensu lato (114 ticks). The first species had annual activity and consisted only of adults, while the second species had spring and summer activity and contained both adults and nymphs. At the annual level, the prevalence of infestation, abundance, and intensity were 38.60%, 44.63 and 115.64 ticks per animal, respectively. At the monthly level there were species specific fluctuations, with a peak in activity in September.Ova je studija provedena na jugu AlŇĺira u svrhu identifikacije vrste i parazitskih indeksa krpelja, a prouńćavano je stado u vremenskom razdoblju od oŇĺujka 2019. do veljańće 2020. uz uńćestalu posjetu jednom mjeseńćno. Ispitivanjem cijelog tijela Ňĺivotinje prikupljena su ukupno 2544 krpelja ispitivanjem cijelog tijela Ňĺivotinje. Identificirane su dvije vrste krpelja: Hyalomma dromedarii (2430 krpelja) i Rhipicephalus sanguineus sensu lato (114 krpelja). Prva vrsta bila je aktivna cijelu godinu i sadrŇĺavala je samo odrasle jedinke, dok je druga vrsta bila aktivna u proljeńáe i jesen i sadrŇĺala je i odrasle i nimfe. Pojavnost infestacije, brojnost i intenzitet bili su 38,60 %, 44,63, odnosno 115,64 krpelja godiŇ°nje po Ňĺivotinji. Na mjeseńćnoj razini te ovisno o vrsti Ňĺivotinje, bilo je fluktuacija, s vrhuncem aktivnosti u rujnu

    Scalable neural network models and terascale datasets for particle-flow reconstruction

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    We study scalable machine learning models for full event reconstruction in high-energy electron-positron collisions based on a highly granular detector simulation. Particle-flow (PF) reconstruction can be formulated as a supervised learning task using tracks and calorimeter clusters or hits. We compare a graph neural network and kernel-based transformer and demonstrate that both avoid quadratic memory allocation and computational cost while achieving realistic PF reconstruction. We show that hyperparameter tuning on a supercomputer significantly improves the physics performance of the models. We also demonstrate that the resulting model is highly portable across hardware processors, supporting Nvidia, AMD, and Intel Habana cards. Finally, we demonstrate that the model can be trained on highly granular inputs consisting of tracks and calorimeter hits, resulting in a competitive physics performance with the baseline. Datasets and software to reproduce the studies are published following the findable, accessible, interoperable, and reusable (FAIR) principles.Comment: 19 pages, 7 figure

    M (M: Cu, Co, Cr or Fe) nanoparticles-loaded metal-organic framework MIL-101(Cr) material by sonication process: Catalytic activity and antibacterial properties

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    The current study deals with the preparation and development of nanomaterials based on iron, copper, chromium or cobalt to study their antibacterial and catalytic properties. To achieve this, the different metals were dispersed in the material MIL-101(Cr) by an ultrasonic-assisted method and then treated by chemical reduction in order to produce corresponding metal nanoparticles (MNPs). The obtained nanocatalysts MIL-101(Cr)/MNPs were characterized by various techniques such as XRD, XPS, SEM, TEM, FTIR; TGA, XRF, Adsorption-desoprtion of nitrogen at 77 K and UV‚Äďvis DR. The results showed that the nanocatalysts consist of a mixture of metal phases and oxides. All the prepared nanocatalysts were evaluated based on their performance in reducing the methylene blue (MB) dye in the presence of NaBH4 as reducing agent, for selection of the optimal catalyst. The best catalytic activity was obtained by the MIL-101 (Cr)/CuNPs nanocatalyst in which 6 min was sufficient to reduce the MB dye and the recorded rate constant kapp was 0.503 min‚ąí1. The performance of this catalyst was evaluated by varying the effects of three important parameters such as catalyst loading and the concentration of NaBH4 and MB dye. The study of the effects of these three parameters on the reduction process reveals that more than 99% of MB dye was reduced using 0.6 mM of MB dye, 6.8 mM of NaBH4 and 3 mg of nanocatalyst. The kinetic study shows that the reduction of MB dye by the MOF-101(Cr)/CuNPs nanocatalyst follows pseudo-first order kinetics. In addition, the MIL-101(Cr)/CoNPs and MIL-101(Cr)/CuNPs samples demonstrated efficacy at inhibiting bacterial and fungal growth. Hence, it is concluded through this work that the nature, size and concentration of nanoparticles present in the MOF matrix are the key parameters that can influence the catalytic and antibacterial properties of these MNP-loaded MIL-101(Cr) systems

    Fluorogenic derivatization of aryl halides based on the formation of biphenyl by Suzuki coupling reaction with phenylboronic acid.

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    The fluorogenic derivatization method for aryl halide was developed for the first time. This method was based on the formation of fluorescent biphenyl structure by Suzuki coupling reaction between aryl halides and non-fluorescent phenylboronic acid (PBA). We measured the fluorescence spectra of the products obtained by the reaction of p-substituted aryl bromides (i.e., 4-bromobenzonitrile, 4-bromoanisole, 4-bromobenzoic acid ethyl ester and 4-bromotoluene) with PBA in the presence of palladium (II) acetate as a catalyst. The significant fluorescence at excitation maximum wavelength of 275-290 nm and emission maximum wavelength of 315-350 nm was detected in all the tested aryl bromides. This result demonstrated that non-fluorescent aryl bromides could be converted to the fluorescent biphenyl derivatives by the coupling reaction with non-fluorescent PBA. We tried to determine these aryl bromides by HPLC-fluorescence detection with pre-column derivatization. The aryl bromide derivatives were detected on the chromatogram within 30 min without any interfering peak derived from the reagent blank. The detection limits (S/N=3) for aryl bromides were 13-157 fmol/injection

    Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

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    BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

    Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study