Transcriptional portrait of M. bovis BCG during biofilm production shows genes differentially expressed during intercellular aggregation and substrate attachment.

Mycobacterium tuberculosis and M. smegmatis form drug-tolerant biofilms through dedicated genetic programs. In support of a stepwise process regulating biofilm production in mycobacteria, it was shown elsewhere that lsr2 participates in intercellular aggregation, while groEL1 was required for biofilm maturation in M. smegmatis. Here, by means of RNA-Seq, we monitored the early steps of biofilm production in M. bovis BCG, to distinguish intercellular aggregation from attachment to a surface. Genes encoding for the transcriptional regulators dosR and BCG0114 (Rv0081) were significantly regulated and responded differently to intercellular aggregation and surface attachment. Moreover, a M. tuberculosis H37Rv deletion mutant in the Rv3134c-dosS-dosR regulon, formed less biofilm than wild type M. tuberculosis, a phenotype reverted upon reintroduction of this operon into the mutant. Combining RT-qPCR with microbiological assays (colony and surface pellicle morphologies, biofilm quantification, Ziehl-Neelsen staining, growth curve and replication of planktonic cells), we found that BCG0642c affected biofilm production and replication of planktonic BCG, whereas ethR affected only phenotypes linked to planktonic cells despite its downregulation at the intercellular aggregation step. Our results provide evidence for a stage-dependent expression of genes that contribute to biofilm production in slow-growing mycobacteria

The mediating role of the entrepreneurial ecosystem in the entrepreneurial personality and green entrepreneurship : the case of Peruvian's university students.

Nowadays, the value of innovation, green behavior, and entrepreneurship have grown, which is essential for a country's sustainable development. However, there has been no in-depth study of what the ecosystem should be to promote entrepreneurship and, thus, care for the environment. For this reason, this study aims to explore the entrepreneurial ecosystem's effect on Peruvian university students' entrepreneurial personality and green entrepreneurship. This study uses a duly validated instrument that includes the three variables divided by dimensions and was applied to a population of university students in Peru. Using a simple random sampling technique, the data were collected from 384 students of Peruvian universities. This study used the Smart-PLS to examine the reliability of the data and the correlation of the dimensions and items of the variables. In conclusion, providing entrepreneurship tools can help students develop desirable personality traits to generate sustainable businesses. The job of universities is to improve education for sustainable development. This means that students should learn the skills and knowledge they need to use environmental practices in their businesses.Edwerson William Pacori Paricahua (Universidad nacional de Juliaca), Jorge Martín Cruz Padilla (Universidad Norbert Wiener), Soraya del Pilar Carranco (Madrid Universidad Central del Ecuador), Jose Omar García Tarazona (Universidad Nacional de educación), Sonia Alejandrina Sotelo Muñoz (Universidad científica del Sur), Jesus Enrique Reyes Acevedo (Universidad Nacional Autónoma de Alto Amazonas), Jose Daniel Sanchez Fernandez (Universidad Católica de Santa María), Isaac Merino Quispe (Universidad Nacional Jose Maria Arguedas), José Luis Arias-Gonzáles (Pontificia Universidad Católica del Perú), Roxana Yolanda Castillo-Acobo (Universidad Nacional de San Agustín), Milagros del Rosario Cáceres-Chávez (Camosun College)Includes bibliographical references

Implementación de algoritmos de inteligencia artificial para la identificación de pacientes diabéticos utilizando los niveles de lípidos en sangre

In recent years the leading cause of death in Mexico is linked to multifactorial diseases, of which diabetes ranks second, only below heart disease, both related to high cholesterol levels and triglycerides in blood. Objective: Classify patients with diabetes using artificial intelligence algorithms previously trained with total cholesterol, HDL, LDL and triglyceride levels. Materials and methods: Descriptors related to blood lipids belong to the Centro Médico Siglo XXI, composed of a sample of 1019. They are considered: Total Cholesterol Levels, HDL, LDH and Triglycerides. The proposed methodology consists of two main stages: training of artificial intelligence algorithms, in which black box models are developed to look for the relationship of the determinants mentioned and the suffering of diabetes in the subjects (presence = 1, absence = 0), and a second stage for the validation of the algorithms, using as a metric the sensitivity and specificity of the algorithms by means of the ROC curve and the area under the curve (AUC). Results: Logistic regression models, decision trees and support vector machine, acquire a value of 0.613 to 0.727 of AUC, being statistically significant for the automatic detection of diabetic patients. Conclusions: The implementation of Artificial Intelligence algorithms, allow the identification of patients with diabetes using blood lipid metrics, for a computer-aided diagnosis.En los últimos años la principal causa de muerte en México está relacionada con enfermedades multifactoriales, de las cuales, la diabetes ocupa el segundo lugar, solo por debajo de enfermedades de corazón, ambas relacionadas con altos niveles de colesterol y triglicéridos en sangre. Objetivo: Clasificar pacientes con diabetes utilizando algoritmos de inteligencia artificial entrenados previamente con los niveles de colesterol total, HDL, LDH y triglicéridos. Materiales y métodos: Los descriptores relacionados con los lípidos en sangre pertenecen el Centro Médico Siglo XXI, compuesta por una muestra de 1019. Se consideran: Niveles de colesterol total, HDL, LDH y triglicéridos. La metodología propuesta consiste en dos etapas principales: entrenamiento de algoritmos de inteligencia artificial, en la cual se desarrollan modelos de caja negra para buscar la relación de los determinantes mencionados y el padecimiento de diabetes en los sujetos (padecimiento = 1, ausencia = 0), y una segunda etapa para la validación de los algoritmos, utilizando como métrica la sensitividad y especificidad de los mismos mediante la curva ROC y el área bajo la curva (AUC). Resultados: los modelos de regresión logística, árboles de decisión y máquina de soporte vectorial, adquieren un valor de 0.613 hasta 0.727 de AUC, siendo estadísticamente significativos para la detección automática de pacientes diabéticos. Conclusiones: La implementación de algoritmos de Inteligencia artificial, permiten la identificación de pacientes con diabetes utilizando las métricas de lípidos en sangre, para un diagnóstico asistido por computadora

Early Tracheostomy for Managing ICU Capacity During the COVID-19 Outbreak: A Propensity-Matched Cohort Study

10 p.Background: During the first wave of the COVID-19 pandemic, shortages of ventilators and ICU beds overwhelmed health care systems. Whether early tracheostomy reduces the duration of mechanical ventilation and ICU stay is controversial. Research question: Can failure-free day outcomes focused on ICU resources help to decide the optimal timing of tracheostomy in overburdened health care systems during viral epidemics? Study design and methods: This retrospective cohort study included consecutive patients with COVID-19 pneumonia who had undergone tracheostomy in 15 Spanish ICUs during the surge, when ICU occupancy modified clinician criteria to perform tracheostomy in Patients with COVID-19. We compared ventilator-free days at 28 and 60 days and ICU- and hospital bed-free days at 28 and 60 days in propensity score-matched cohorts who underwent tracheostomy at different timings (≤ 7 days, 8-10 days, and 11-14 days after intubation). Results: Of 1,939 patients admitted with COVID-19 pneumonia, 682 (35.2%) underwent tracheostomy, 382 (56%) within 14 days. Earlier tracheostomy was associated with more ventilator-free days at 28 days (≤ 7 days vs > 7 days [116 patients included in the analysis]: median, 9 days [interquartile range (IQR), 0-15 days] vs 3 days [IQR, 0-7 days]; difference between groups, 4.5 days; 95% CI, 2.3-6.7 days; 8-10 days vs > 10 days [222 patients analyzed]: 6 days [IQR, 0-10 days] vs 0 days [IQR, 0-6 days]; difference, 3.1 days; 95% CI, 1.7-4.5 days; 11-14 days vs > 14 days [318 patients analyzed]: 4 days [IQR, 0-9 days] vs 0 days [IQR, 0-2 days]; difference, 3 days; 95% CI, 2.1-3.9 days). Except hospital bed-free days at 28 days, all other end points were better with early tracheostomy. Interpretation: Optimal timing of tracheostomy may improve patient outcomes and may alleviate ICU capacity strain during the COVID-19 pandemic without increasing mortality. Tracheostomy within the first work on a ventilator in particular may improve ICU availability

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

Measurement of the polarisation of W bosons produced with large transverse momentum in pp collisions at sqrt(s) = 7 TeV with the ATLAS experiment

This paper describes an analysis of the angular distribution of W->enu and W->munu decays, using data from pp collisions at sqrt(s) = 7 TeV recorded with the ATLAS detector at the LHC in 2010, corresponding to an integrated luminosity of about 35 pb^-1. Using the decay lepton transverse momentum and the missing transverse energy, the W decay angular distribution projected onto the transverse plane is obtained and analysed in terms of helicity fractions f0, fL and fR over two ranges of W transverse momentum (ptw): 35 < ptw < 50 GeV and ptw > 50 GeV. Good agreement is found with theoretical predictions. For ptw > 50 GeV, the values of f0 and fL-fR, averaged over charge and lepton flavour, are measured to be : f0 = 0.127 +/- 0.030 +/- 0.108 and fL-fR = 0.252 +/- 0.017 +/- 0.030, where the first uncertainties are statistical, and the second include all systematic effects.Comment: 19 pages plus author list (34 pages total), 9 figures, 11 tables, revised author list, matches European Journal of Physics C versio

Observation of a new chi_b state in radiative transitions to Upsilon(1S) and Upsilon(2S) at ATLAS

The chi_b(nP) quarkonium states are produced in proton-proton collisions at the Large Hadron Collider (LHC) at sqrt(s) = 7 TeV and recorded by the ATLAS detector. Using a data sample corresponding to an integrated luminosity of 4.4 fb^-1, these states are reconstructed through their radiative decays to Upsilon(1S,2S) with Upsilon->mu+mu-. In addition to the mass peaks corresponding to the decay modes chi_b(1P,2P)->Upsilon(1S)gamma, a new structure centered at a mass of 10.530+/-0.005 (stat.)+/-0.009 (syst.) GeV is also observed, in both the Upsilon(1S)gamma and Upsilon(2S)gamma decay modes. This is interpreted as the chi_b(3P) system.Comment: 5 pages plus author list (18 pages total), 2 figures, 1 table, corrected author list, matches final version in Physical Review Letter

Search for displaced vertices arising from decays of new heavy particles in 7 TeV pp collisions at ATLAS

We present the results of a search for new, heavy particles that decay at a significant distance from their production point into a final state containing charged hadrons in association with a high-momentum muon. The search is conducted in a pp-collision data sample with a center-of-mass energy of 7 TeV and an integrated luminosity of 33 pb^-1 collected in 2010 by the ATLAS detector operating at the Large Hadron Collider. Production of such particles is expected in various scenarios of physics beyond the standard model. We observe no signal and place limits on the production cross-section of supersymmetric particles in an R-parity-violating scenario as a function of the neutralino lifetime. Limits are presented for different squark and neutralino masses, enabling extension of the limits to a variety of other models.Comment: 8 pages plus author list (20 pages total), 8 figures, 1 table, final version to appear in Physics Letters

Measurement of the inclusive isolated prompt photon cross-section in pp collisions at sqrt(s)= 7 TeV using 35 pb-1 of ATLAS data

A measurement of the differential cross-section for the inclusive production of isolated prompt photons in pp collisions at a center-of-mass energy sqrt(s) = 7 TeV is presented. The measurement covers the pseudorapidity ranges |eta|<1.37 and 1.52<=|eta|<2.37 in the transverse energy range 45<=E_T<400GeV. The results are based on an integrated luminosity of 35 pb-1, collected with the ATLAS detector at the LHC. The yields of the signal photons are measured using a data-driven technique, based on the observed distribution of the hadronic energy in a narrow cone around the photon candidate and the photon selection criteria. The results are compared with next-to-leading order perturbative QCD calculations and found to be in good agreement over four orders of magnitude in cross-section.Comment: 7 pages plus author list (18 pages total), 2 figures, 4 tables, final version published in Physics Letters

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