Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment

[EN] Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in Xe-136. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a Th-228 calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analysesThis study used computing resources from Artemisa, co-funded by the European Union through the 2014-2020 FEDER Operative Programme of the Comunitat Valenciana, project DIFEDER/2018/048. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. The NEXT collaboration acknowledges support from the following agencies and institutions: Xunta de Galicia (Centro singularde investigacion de Galicia accreditation 2019-2022), by European Union ERDF, and by the "Maria de Maeztu" Units of Excellence program MDM-2016-0692 and the Spanish Research State Agency"; the European Research Council (ERC) under the Advanced Grant 339787-NEXT; the European Union's Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under the Grant Agreements No. 674896, 690575 and 740055; the Ministerio de Economia y Competitividad and the Ministerio de Ciencia, Innovacion y Universidades of Spain under grants FIS2014-53371-C04, RTI2018-095979, the Severo Ochoa Program grants SEV-20140398 and CEX2018-000867-S; the GVA of Spain under grants PROMETEO/2016/120 and SEJI/2017/011; the Portuguese FCT under project PTDC/FIS-NUC/2525/2014 and under projects UID/FIS/04559/2020 to fund the activities of LIBPhys-UC; the U.S. Department of Energy under contracts number DE-AC02-07CH11359 (Fermi National Accelerator Laboratory), DE-FG02-13ER42020 (Texas A&M) and DE-SC0019223/DE SC0019054 (University of Texas at Arlington); and the University of Texas at Arlington. DGD acknowledges Ramon y Cajal program (Spain) under contract number RYC-2015 18820. JMA acknowledges support from Fundacion Bancaria "la Caixa" (ID 100010434), grant code LCF/BQ/PI19/11690012. We also warmly acknowledge the Laboratori Nazionali del Gran Sasso (LNGS) and the Dark Side collaboration for their help with TPB coating of various parts of the NEXT-White TPC. Finally, we are grateful to the Laboratorio Subterraneo de Canfranc for hosting and supporting the NEXT experiment.Kekic, M.; Adams, C.; Woodruff, K.; Renner, J.; Church, E.; Del Tutto, M.; Hernando Morata, JA.... (2021). Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment. Journal of High Energy Physics (Online). (1):1-22. https://doi.org/10.1007/JHEP01(2021)189S1221NEXT collaboration, The Next White (NEW) Detector, 2018 JINST 13 P12010 [arXiv:1804.02409] [INSPIRE].NEXT collaboration, Energy calibration of the NEXT-White detector with 1% resolution near Qββ of 136Xe, JHEP 10 (2019) 230 [arXiv:1905.13110] [INSPIRE].NEXT collaboration, Demonstration of the event identification capabilities of the NEXT-White detector, JHEP 10 (2019) 052 [arXiv:1905.13141] [INSPIRE].NEXT collaboration, Radiogenic Backgrounds in the NEXT Double Beta Decay Experiment, JHEP 10 (2019) 051 [arXiv:1905.13625] [INSPIRE].G. Carleo et al., Machine learning and the physical sciences, Rev. Mod. Phys. 91 (2019) 045002 [arXiv:1903.10563] [INSPIRE].A. Aurisano et al., A Convolutional Neural Network Neutrino Event Classifier, 2016 JINST 11 P09001 [arXiv:1604.01444] [INSPIRE].MicroBooNE collaboration, Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber, 2017 JINST 12 P03011 [arXiv:1611.05531] [INSPIRE].MicroBooNE collaboration, Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber, Phys. Rev. D 99 (2019) 092001 [arXiv:1808.07269] [INSPIRE].N. Choma et al., Graph Neural Networks for IceCube Signal Classification, in proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, U.S.A., 17–20 December 2018, pp. 386–391 [arXiv:1809.06166] [INSPIRE].E. Racah et al., Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks, in proceedings of the 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, CA, U.S.A., 18–20 December 2016, pp. 892–897 [arXiv:1601.07621] [INSPIRE].EXO collaboration, Deep Neural Networks for Energy and Position Reconstruction in EXO-200, 2018 JINST 13 P08023 [arXiv:1804.09641] [INSPIRE].H. Qiao, C. Lu, X. Chen, K. Han, X. Ji and S. Wang, Signal-background discrimination with convolutional neural networks in the PandaX-III experiment using MC simulation, Sci. China Phys. Mech. Astron. 61 (2018) 101007 [arXiv:1802.03489] [INSPIRE].P. Ai, D. Wang, G. Huang and X. Sun, Three-dimensional convolutional neural networks for neutrinoless double-beta decay signal/background discrimination in high-pressure gaseous Time Projection Chamber, 2018 JINST 13 P08015 [arXiv:1803.01482] [INSPIRE].NEXT collaboration, Background rejection in NEXT using deep neural networks, 2017 JINST 12 T01004 [arXiv:1609.06202] [INSPIRE].NEXT collaboration, Sensitivity of NEXT-100 to Neutrinoless Double Beta Decay, JHEP 05 (2016) 159 [arXiv:1511.09246] [INSPIRE].D. Nygren, High-pressure xenon gas electroluminescent TPC for 0-ν ββ-decay search, Nucl. Instrum. Meth. A 603 (2009) 337 [INSPIRE].NEXT collaboration, Calibration of the NEXT-White detector using 83mKr decays, 2018 JINST 13 P10014 [arXiv:1804.01780] [INSPIRE].J. Martín-Albo, The NEXT experiment for neutrinoless double beta decay searches, Ph.D. Thesis, University of Valencia, Valencia Spain (2015) [INSPIRE].GEANT4 collaboration, GEANT4 — a simulation toolkit, Nucl. Instrum. Meth. A 506 (2003) 250 [INSPIRE].A. Krizhevsky, I. Sutskever and G.E. Hinton, Imagenet classification with deep convolutional neural networks, Commun. ACM 60 (2017) 84.N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, J. Mach. Learn. Res. 15 (2014) 1929.S. Ioffe and C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv:1502.03167 [INSPIRE].C. Guo, G. Pleiss, Y. Sun and K.Q. Weinberger, On calibration of modern neural networks, arXiv:1706.04599.K. He, X. Zhang, S. Ren and J. Sun, Deep Residual Learning for Image Recognition, arXiv:1512.03385 [INSPIRE].K. He, X. Zhang, S. Ren and J. Sun, Identity mappings in deep residual networks, arXiv:1603.05027.X. Li, S. Chen, X. Hu and J. Yang, Understanding the Disharmony Between Dropout and Batch Normalization by Variance Shift, in proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, U.S.A., 15–20 June 2019, pp. 2677–2685.J. Deng, W. Dong, R. Socher, L. Li, K. Li and L. Fei-Fei, ImageNet: A large-scale hierarchical image database, in proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, U.S.A., 20–25 June 2009, pp. 248–255.B. Graham and L. van der Maaten, Submanifold sparse convolutional networks, arXiv:1706.01307.L. Dominé and K. Terao, Scalable deep convolutional neural networks for sparse, locally dense liquid argon time projection chamber data, Phys. Rev. D 102 (2020) 012005 [arXiv:1903.05663] [INSPIRE].C. Shorten and T.M. Khoshgoftaar, A survey on image data augmentation for deep learning, J. Big Data 6 (2019) 60.G.J. Székely and M.L. Rizzo, Testing for equal distributions in high dimension, InterStat 5 (2004) 1.G. Székely and M.L. Rizzo, Energy statistics: A class of statistics based on distances, J. Stat. Plann. Infer. 8 (2013) 1249.R.A. Fisher, The Design of Experiments, Oliver and Boyd (1935).NEXT collaboration, Sensitivity of a tonne-scale NEXT detector for neutrinoless double beta decay searches, arXiv:2005.06467 [INSPIRE].NEXT collaboration, Initial results of NEXT-DEMO, a large-scale prototype of the NEXT-100 experiment, 2013 JINST 8 P04002 [arXiv:1211.4838] [INSPIRE].NEXT collaboration, Operation and first results of the NEXT-DEMO prototype using a silicon photomultiplier tracking array, 2013 JINST 8 P09011 [arXiv:1306.0471] [INSPIRE]

Matrix metalloproteinase-9, -10, and tissue inhibitor of matrix metalloproteinases-1 blood levels as biomarkers of severity and mortality in sepsis

INTRODUCTION: Matrix metalloproteinases (MMPs) play a role in infectious diseases through extracellular matrix (ECM) degradation, which favors the migration of immune cells from the bloodstream to sites of inflammation. Although higher levels of MMP-9 and tissue inhibitor of matrix metalloproteinases-1 (TIMP-1) have been found in small series of patients with sepsis, MMP-10 levels have not been studied in this setting. The objective of this study was to determine the predictive value of MMP-9, MMP-10, and TIMP-1 on clinical severity and mortality in a large series of patients with severe sepsis. METHODS: This was a multicenter, observational, and prospective study carried out in six Spanish Intensive Care Units. We included 192 (125 surviving and 67 nonsurviving) patients with severe sepsis and 50 age- and sex-matched healthy controls in the study. Serum levels of MMP-9, MMP-10, TIMP-1, tumor necrosis factor (TNF)-alpha, and interleukin (IL)-10 were measured in patients with severe sepsis at the time of diagnosis and in healthy controls. RESULTS: Sepsis patients had higher levels of MMP-10 and TIMP-1, higher MMP-10/TIMP-1 ratios, and lower MMP-9/TIMP-1 ratios than did healthy controls (P < 0.001). An association was found between MMP-9, MMP-10, TIMP-1, and MMP-9/TIMP-1 ratios and parameters of sepsis severity, assessed by the SOFA score, the APACHE-II score, lactic acid, platelet count, and markers of coagulopathy. Nonsurviving sepsis patients had lower levels of MMP-9 (P = 0.037), higher levels of TIMP-1 (P < 0.001), lower MMP-9/TIMP-1 ratio (P = 0.003), higher levels of IL-10 (P < 0.001), and lower TNF-alpha/IL-10 ratio than did surviving patients. An association was found between MMP-9, MMP-10, and TIMP-1 levels, and TNF-alpha and IL-10 levels. The risk of death in sepsis patients with TIMP-1 values greater than 531 ng/ml was 80% higher than that in patients with lower values (RR = 1.80; 95% CI = 1.13 to 2.87;P = 0.01; sensitivity = 0.73; specificity = 0.45). CONCLUSIONS: The novel findings of our study on patients with severe sepsis (to our knowledge, the largest series reporting data about MMP levels in sepsis) are that reduced MMP-9/TIMP-1 ratios and increased MMP-10 levels may be of great pathophysiologic significance in terms of severity and mortality, and that TIMP-1 levels may represent a biomarker to predict the clinical outcome of patients with sepsis

Characteristics of emergency medicine residency programs in Colombia

Introduction: Emergency medicine (EM) is in different stages of development around the world. Colombia has made significant strides in EM development in the last two decades and recognized it as a medical specialty in 2005. The country now has seven EM residency programs: three in the capital city of Bogotá, two in Medellin, one in Manizales, and one in Cali. The seven residency programs are in different stages of maturity, with the oldest founded 20 years ago and two founded in the last two years. The objective of this study was to characterize these seven residency programs. Methods: We conducted semi-structured interviews with faculty and residents from all the existing programs in 2013-2016. Topics included program characteristics and curricula. Results: Colombian EM residencies are three-year programs, with the exception of one four-year program. Programs accept 3-10 applicants yearly Only one program has free tuition and the rest charge tuition. The number of EM faculty ranges from 2-15. EM rotation requirements range from 11-33% of total clinical time. One program does not have a pediatric rotation. The other programs require 1-2 months of pediatrics or pediatric EM. Critical care requirements range from 4-7 months. Other common rotations include anesthesia, general surgery, internal medicine, obstetrics, gynecology, orthopedics, ophthalmology, radiology, toxicology, psychiatry, neurology, cardiology, pulmonology, and trauma. All programs offer 4-6 hours of protected didactic time each week. Some programs require Advanced Cardiac Life Support, Pediatric Advanced Life Support and Advanced Trauma Life Support, with some programs providing these trainings in-house or subsidizing the cost. Most programs require one research project for graduation. Resident evaluations consist of written tests and oral exams several times per year. Point-of-care ultrasound training is provided in four of the seven programs. Conclusion: As emergency medicine continues to develop in Colombia, more residency programs are expected to emerge. Faculty development and sustainability of academic pursuits will be critically important. In the long term, the specialty will need to move toward certifying board exams and professional development through a national EM organization to promote standardization across programs. © 2017 Patiño et al

Dependence of polytetrafluoroethylene reflectance on thickness at visible and ultraviolet wavelengths in air

[EN] Polytetrafluoroethylene (PTFE) is an excellent diffuse reflector widely used in light collection systems for particle physics experiments. However, the reflectance of PTFE is a function of its thickness. In this work, we investigate this dependence in air for light of wavelengths 260 nm and 450 nm using two complementary methods. We find that PTFE reflectance for thicknesses from 5 mm to 10 mm ranges from 92.5% to 94.5% at 450 nm, and from 90.0% to 92.0% at 260 nm We also see that the reflectance of PIFE of a given thickness can vary by as much as 2.7% within the same piece of material. Finally, we show that placing a specular reflector behind the PTFE can recover the loss of reflectance in the visible without introducing a specular component in the reflectance.The NEXT Collaboration acknowledges support from the following agencies and institutions: the European Research Council (ERC) under the Advanced Grant 339787-NEXT; the European Union's Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under the Grant Agreements No. 674896, 690575 and 740055; the Ministerio de Economia y Competitividad and the Ministerio de Ciencia, Innovacion y Universidades of Spain under grants FIS2014-53371-C04, RTI2018-095979, the Severo Ochoa Program grants SEV-2014-0398 and CEX2018-000867-S, and the Maria de Maeztu Program MDM-2016-0692; the Generalitat Valenciana under grants PROMETEO/2016/120 and SEJI/2017/011; the Portuguese FCT under project PTDC/FIS-NUC/2525/2014 and under projects UID/04559/2020 to fund the activities of LIBPhys-UC; the U.S. Department of Energy under contracts No. DE-AC02-06CH11357 (Argonne National Laboratory), DE-AC0207CH11359 (Fermi National Accelerator Laboratory), DE-FG02-13ER42020 (Texas A&M) and DE-SC0019223/DE-SC0019054 (University of Texas at Arlington); and the University of Texas at Arlington (USA). DGD acknowledges Ramon y Cajal program (Spain) under contract number RYC2015-18820. JM-A acknowledges support from Fundacion Bancaria "la Caixa" (ID 100010434), grant code LCF/BQ/PI19/11690012. Finally, we thank Brendon Bullard, Paolo Giromini and Neeraj Tata for helpful discussions and assistance with preliminary measurements.Ghosh, S.; Haefner, J.; Martín-Albo, J.; Guenette, R.; Li, X.; Loya Villalpando, A.; Burch, C.... (2020). Dependence of polytetrafluoroethylene reflectance on thickness at visible and ultraviolet wavelengths in air. Journal of Instrumentation. 15(11):1-17. https://doi.org/10.1088/1748-0221/15/11/P11031S1171511Auger, M., Auty, D. J., Barbeau, P. S., Bartoszek, L., Baussan, E., Beauchamp, E., … Cleveland, B. (2012). The EXO-200 detector, part I: detector design and construction. Journal of Instrumentation, 7(05), P05010-P05010. doi:10.1088/1748-0221/7/05/p05010Martín-Albo, J., Muñoz Vidal, J., Ferrario, P., Nebot-Guinot, M., Gómez-Cadenas, J. J., … Cárcel, S. (2016). Sensitivity of NEXT-100 to neutrinoless double beta decay. Journal of High Energy Physics, 2016(5). doi:10.1007/jhep05(2016)159Rogers, L., Clark, R. A., Jones, B. J. P., McDonald, A. D., Nygren, D. R., Psihas, F., … Azevedo, C. D. . (2018). High voltage insulation and gas absorption of polymers in high pressure argon and xenon gases. Journal of Instrumentation, 13(10), P10002-P10002. doi:10.1088/1748-0221/13/10/p10002Silva, C., Pinto da Cunha, J., Pereira, A., Chepel, V., Lopes, M. I., Solovov, V., & Neves, F. (2010). Reflectance of polytetrafluoroethylene for xenon scintillation light. Journal of Applied Physics, 107(6), 064902. doi:10.1063/1.3318681Haefner, J., Neff, A., Arthurs, M., Batista, E., Morton, D., Okunawo, M., … Lorenzon, W. (2017). Reflectance dependence of polytetrafluoroethylene on thickness for xenon scintillation light. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 856, 86-91. doi:10.1016/j.nima.2017.01.057Kravitz, S., Smith, R. J., Hagaman, L., Bernard, E. P., McKinsey, D. N., Rudd, L., … Sakai, M. (2020). Measurements of angle-resolved reflectivity of PTFE in liquid xenon with IBEX. The European Physical Journal C, 80(3). doi:10.1140/epjc/s10052-020-7800-6Geis, C., Grignon, C., Oberlack, U., García, D. R., & Weitzel, Q. (2017). Optical response of highly reflective film used in the water Cherenkov muon veto of the XENON1T dark matter experiment. Journal of Instrumentation, 12(06), P06017-P06017. doi:10.1088/1748-0221/12/06/p06017Allison, J., Amako, K., Apostolakis, J., Arce, P., Asai, M., Aso, T., … Barrand, G. (2016). Recent developments in Geant4. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 835, 186-225. doi:10.1016/j.nima.2016.06.12

Low-diffusion Xe-He gas mixtures for rare-event detection: electroluminescence yield

[EN] High pressure xenon Time Projection Chambers (TPC) based on secondary scintillation (electroluminescence) signal amplification are being proposed for rare event detection such as directional dark matter, double electron capture and double beta decay detection. The discrimination of the rare event through the topological signature of primary ionisation trails is a major asset for this type of TPC when compared to single liquid or double-phase TPCs, limited mainly by the high electron diffusion in pure xenon. Helium admixtures with xenon can be an attractive solution to reduce the electron diffu- sion significantly, improving the discrimination efficiency of these optical TPCs. We have measured the electroluminescence (EL) yield of Xe-He mixtures, in the range of 0 to 30% He and demonstrated the small impact on the EL yield of the addition of helium to pure xenon. For a typical reduced electric field of 2.5 kV/cm/bar in the EL region, the EL yield is lowered by similar to 2%, 3%, 6% and 10% for 10%, 15%, 20% and 30% of helium concentration, respectively. This decrease is less than what has been obtained from the most recent simulation framework in the literature. The impact of the addition of helium on EL statistical fluctuations is negligible, within the experimental uncertainties. The present results are an important benchmark for the simulation tools to be applied to future optical TPCs based on Xe-He mixtures.The NEXT Collaboration acknowledges support from the following agencies and institutions: the European Research Council (ERC) under the Advanced Grant 339787-NEXT; the European Union's Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under the Marie Sklodowska-Curie Grant Agreements No. 674896, 690575 and 740055; the Ministerio de Economa y Competitividad of Spain under grants FIS2014-53371-C04, RTI2018-095979, the Severo Ochoa Program SEV-2014-0398 and the Mara de Maetzu Program MDM-2016-0692; the GVA of Spain under grants PROMETEO/2016/120 and SEJI/2017/011; the Portuguese FCT under project PTDC/FIS-NUC/2525/2014, under project UID/FIS/04559/2013 to fund the activities of LIBPhys, and under grants PD/BD/105921/2014, SFRH/BPD/109180/2015; the U.S. Department of Energy under contracts number DEAC02-06CH11357 (Argonne National Laboratory), DE-AC0207CH11359 (Fermi National Accelerator Laboratory), DE-FG02-13ER42020 (Texas A& M) and DE-SC0019223/DESC0019054 (University of Texas at Arlington); and the University of Texas at Arlington. DGD acknowledges Ramon y Cajal program (Spain) under contract number RYC-2015-18820. We also warmly acknowledge the Laboratori Nazionali del Gran Sasso (LNGS) and the Dark Side collaboration for their help with TPB coating of various parts of the NEXT-White TPC. Finally, we are grateful to the Laboratorio Subterraneo de Canfranc for hosting and supporting the NEXT experiment.Fernandes, A.; Henriques, C.; Mano, R.; González-Díaz, D.; Azevedo, C.; Silva, P.; Gómez-Cadenas, J.... (2020). Low-diffusion Xe-He gas mixtures for rare-event detection: electroluminescence yield. Journal of High Energy Physics (Online). (4):1-18. https://doi.org/10.1007/JHEP04(2020)034S1184D.R. Nygren, Columnar recombination: a tool for nuclear recoil directional sensitivity in a xenon-based direct detection WIMP search, J. Phys. Conf. Ser.460 (2013) 012006 [INSPIRE].G. Mohlabeng et al., Dark matter directionality revisited with a high pressure xenon gas detector, JHEP07 (2015) 092 [arXiv:1503.03937] [INSPIRE].N.S. Phan, R.J. Lauer, E.R. Lee, D. Loomba, J.A.J. Matthews and E.H. Miller, GEM-based TPC with CCD Imaging for Directional Dark Matter Detection, Astropart. Phys.84 (2016) 82 [arXiv:1510.02170] [INSPIRE].J. Martin-Albo et al., Sensitivity of NEXT-100 to neutrinoless double beta decay, JHEP05 (2016) 159 [arXiv:1511.09246] [INSPIRE].K. Nakamura et al., AXEL — a high pressure xenon gas TPC for neutrinoless double beta decay search, Nucl. Instrum. Meth.A 845 (2017) 394 [INSPIRE].D. Yu. Akimov, A.A. Burenkov, V.F. Kuzichev, V.L. Morgunov and V.N. Solovev, Low background experiments with high pressure gas scintillation proportional detector, physics/9704021 [INSPIRE].Yu. M. Gavrilyuk et al., A technique for searching for the 2K capture in124Xe with a copper proportional counter, Phys. Atom. Nucl.78 (2015) 1563 [INSPIRE].Yu. M. Gavrilyuk et al., Results of In-Depth Analysis of Data Obtained in the Experimental Search for 2K (2ν)-Capture in78Kr, Phys. Part. Nucl.49 (2018) 540 [INSPIRE].C.A.N. Conde and A.J.P.L. Policarpo, A Gas Proportional Scintillation Counter, Nucl. Instrum. Meth.53 (1967) 7.A.J.P.L. Policarpo, M.A.F. Alves and C.A.N. Conde, The Argon-Nitrogen Proportional Scintillation Counter, Nucl. Instrum. Meth.55 (1967) 105.J.M.F. dos Santos et al., Development of portable gas proportional scintillation counters for x-ray spectrometry, X-Ray Spectrom.30 (2001) 373.NEXT collaboration, Accurate γ and MeV-electron track reconstruction with an ultra-low diffusion Xenon/TMA TPC at 10 atm, Nucl. Instrum. Meth.A 804 (2015) 8 [arXiv:1504.03678] [INSPIRE].NEXT collaboration, Characterisation of NEXT-DEMO using xenon KαX-rays, 2014 JINST9 P10007 [arXiv:1407.3966] [INSPIRE].NEXT collaboration, Energy calibration of the NEXT-White detector with 1% resolution near Qββof136Xe, JHEP10 (2019) 230 [arXiv:1905.13110] [INSPIRE].R. Lüscher et al., Search for beta beta decay in Xe-136: New results from the Gotthard experiment, Phys. Lett.B 434 (1998) 407 [INSPIRE].NEXT collaboration, First proof of topological signature in the high pressure xenon gas TPC with electroluminescence amplification for the NEXT experiment, JHEP01 (2016) 104 [arXiv:1507.05902] [INSPIRE].NEXT collaboration, Background rejection in NEXT using deep neural networks, 2017 JINST12 T01004 [arXiv:1609.06202] [INSPIRE].NEXT collaboration, The Next White (NEW) Detector, 2018 JINST13 P12010 [arXiv:1804.02409] [INSPIRE].H. Qiao et al., Signal-background discrimination with convolutional neural networks in the PandaX-III experiment using MC simulation, Sci. China Phys. Mech. Astron.61 (2018) 101007 [arXiv:1802.03489] [INSPIRE].NEXT collaboration, Secondary scintillation yield of xenon with sub-percent levels of CO2additive for rare-event detection, Phys. Lett.B 773 (2017) 663 [arXiv:1704.01623] [INSPIRE].C.M.B. Monteiro et al., Secondary Scintillation Yield in Pure Xenon, 2007 JINST2 P05001 [physics/0702142] [INSPIRE].C.M.B. Monteiro, J.A.M. Lopes, J.F. C.A. Veloso and J.M.F. dos Santos, Secondary scintillation yield in pure argon, Phys. Lett.B 668 (2008) 167 [INSPIRE].C.A.B. Oliveira et al., A simulation toolkit for electroluminescence assessment in rare event experiments, Phys. Lett.B 703 (2011) 217 [arXiv:1103.6237] [INSPIRE].E.D.C. Freitas et al., Secondary scintillation yield in high-pressure xenon gas for neutrinoless double beta decay (0νββ) search, Phys. Lett.B 684 (2010) 205 [INSPIRE].C.M.B. Monteiro et al., Secondary scintillation yield from gaseous micropattern electron multipliers in direct dark matter detection, Phys. Lett.B 677 (2009) 133 [INSPIRE].C.M.B. Monteiro, L.M.P. Fernandes, J.F. C.A. Veloso, C.A.B. Oliveira and J.M.F. dos Santos, Secondary scintillation yield from GEM and THGEM gaseous electron multipliers for direct dark matter search, Phys. Lett.B 714 (2012) 18 [INSPIRE].C. Balan et al., MicrOMEGAs operation in high pressure xenon: Charge and scintillation readout, 2011 JINST6 P02006 [arXiv:1009.2960] [INSPIRE].C.M.B. Monteiro, L.M.P. Fernandes, J.F. C.A. Veloso and J.M.F. dos Santos, Secondary scintillation readout from GEM and THGEM with a large area avalanche photodiode, 2012 JINST7 P06012 [INSPIRE].C.D.R. Azevedo et al., An homeopathic cure to pure Xenon large diffusion, 2016 JINST11 C02007 [arXiv:1511.07189] [INSPIRE].C.D.R. Azevedo et al., Microscopic simulation of xenon-based optical TPCs in the presence of molecular additives, Nucl. Intrum. Meth.A 877 (2018) 157 [arXiv:1705.09481] [INSPIRE].NEXT collaboration, Electroluminescence TPCs at the Thermal Diffusion Limit, JHEP01 (2019) 027 [arXiv:1806.05891] [INSPIRE].R.C. Lanza et al., Gas scintillators for imaging of low energy isotopes, IEEE Trans. Nucl. Sci.34 (1987) 406.R. Felkai et al., Helium-Xenon mixtures to improve the topological signature in high pressure gas xenon TPCs, Nucl. Intrum. Meth.A 905 (2018) 82 [arXiv:1710.05600] [INSPIRE].NEXT collaboration, Electron Drift and Longitudinal Diffusion in High Pressure Xenon-Helium Gas Mixtures, 2019 JINST14 P08009 [arXiv:1902.05544] [INSPIRE].J.A.M. Lopes et al., A xenon gas proportional scintillation counter with a UV-sensitive large-area avalanche photodiode, IEEE Trans. Nucl. Sci.48 (2001) 312.C.M.B. Monteiro et al., An argon gas proportional scintillation counter with UV avalanche photodiode scintillation readout, IEEE Trans. Nucl. Sci.48 (2001) 1081.Advanced Photonix, Inc., 1240 Avenida Acaso, Camarillo, CA 93012, U.S.A. .L.M.P. Fernandes et al., Characterization of large area avalanche photodiodes in X-ray and VUV-light detection, 2007 JINST2 P08005 [physics/0702130] [INSPIRE].L.M.P. Fernandes, E.D.C. Freitas, M. Ball, J.J. Gomez-Cadenas, C.M.B. Monteiro, N. Yahlali et al., Primary and secondary scintillation measurements in a xenon Gas Proportional Scintillation Counter, 2010 JINST5 P09006 [Erratum ibid.5 (2010) A12001] [arXiv:1009.2719] [INSPIRE].C.A.B. Oliveira, M. Sorel, J. Martin-Albo, J.J. Gomez-Cadenas, A.L. Ferreira and J.F. C.A. Veloso, Energy Resolution studies for NEXT, 2011 JINST6 P05007 [arXiv:1105.2954] [INSPIRE].D.F. Anderson et al., A large area, gas scintillation proportional counter, Nucl. Instrum. Meth.163 (1979) 125.T.Z. Kowalski et al., Fano factor implications from gas scintillation proportional counter measurements, Nucl. Instrum. Meth.A 279 (1989) 567.T. Doke, Basic properties of high pressure xenon gas as detector medium, in Proceedings of the XeSAT, Tokyo Japan (2005), pg. 92.S.J.C. do Carmo et al., Experimental Study of the ω-Values and Fano Factors of Gaseous Xenon and Ar-Xe Mixtures for X-Rays, IEEE Trans. Nucl. Sci.55 (2008) 2637.A. Buzulutskov, E. Shemyakina, A. Bondar, A. Dolgov, E. Frolov, V. Nosov et al., Revealing neutral bremsstrahlung in two-phase argon electroluminescence, Astropart. Phys.103 (2018) 29 [arXiv:1803.05329] [INSPIRE]

Measurement of the Xe 136 two-neutrino double -decay half-life via direct background subtraction in NEXT

[EN] We report a measurement of the half-life of the 136Xe two-neutrino double-ß decay performed with a novel direct-background-subtraction technique. The analysis relies on the data collected with the NEXT-White detector operated with 136Xe-enriched and 136Xe-depleted xenon, as well as on the topology of double-electron tracks. With a fiducial mass of only 3.5 kg of Xe, a half-life of 2.34+0.80(stat)+0.30(sys)×1021 yr is derived from ¿0.46 ¿0.17 the background-subtracted energy spectrum. The presented technique demonstrates the feasibility of unique background-model-independent neutrinoless double-ß-decay searches.The NEXT Collaboration acknowledges support from the following agencies and institutions: the European Research Council (ERC) under Grant No. 951281-BOLD; the European Union's Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under Grant No. 957202-HIDDEN; the MCIN/AEI/10.13039/501100011033 of Spain and ERDF "A way of making Europe" under Grant No. RTI2018-095979, the Severo Ochoa Program Grant No. CEX2018-000867-S, and the Maria de Maeztu Program Grant No. MDM-2016-0692; the Generalitat Valenciana of Spain under Grants No. PROMETEO/2021/087 and No. CIDEGENT/2019/049; the Portuguese FCT under Project No. UID/FIS/04559/2020 to fund the activities of LIBPhys-UC; the Pazy Foundation (Israel) under Grants No. 877040 and No. 877041; the U.S. Department of Energy under Contracts No. DE-AC02-06CH11357 (Argonne National Laboratory), No. DE-AC02-07CH11359 (Fermi National Accelerator Laboratory), No. DE-FG02-13ER42020 (Texas A&M), No. DE-SC0019054 (Texas Arlington), and No. DE-SC0019223 (Arlington, TX); the U.S. National Science Foundation under Grant No. CHE 2004111; and the Robert A. Welch Foundation under Grant No. Y-203120200401. D.G.D. acknowledges support from the Ramon y Cajal program (Spain) under Contract No. RYC-2015-18820. Finally, we are grateful to the Laboratorio Subterraneo de Canfranc for hosting and supporting the NEXT experiment.Novella, P.; Sorel, M.; Usón, A.; Adams, C.; Almazán, H.; Álvarez-Puerta, V.; Aparicio, B.... (2022). Measurement of the Xe 136 two-neutrino double -decay half-life via direct background subtraction in NEXT. Physical Review C (Online). 105(5):055501-1-055501-8. https://doi.org/10.1103/PhysRevC.105.055501055501-1055501-8105

Radiogenic backgrounds in the NEXT double beta decay experiment

[EN] Natural radioactivity represents one of the main backgrounds in the search for neutrinoless double beta decay. Within the NEXT physics program, the radioactivity- induced backgrounds are measured with the NEXT-White detector. Data from 37.9 days of low-background operations at the Laboratorio Subterraneo de Canfranc with xenon depleted in Xe-136 are analyzed to derive a total background rate of (0.84 +/- 0.02) mHz above 1000 keV. The comparison of data samples with and without the use of the radon abatement system demonstrates that the contribution of airborne-Rn is negligible. A radiogenic background model is built upon the extensive radiopurity screening campaign conducted by the NEXT collaboration. A spectral fit to this model yields the specific contributions of Co-60, K-40, Bi-214 and Tl-208 to the total background rate, as well as their location in the detector volumes. The results are used to evaluate the impact of the radiogenic backgrounds in the double beta decay analyses, after the application of topological cuts that reduce the total rate to (0.25 +/- 0.01) mHz. Based on the best-fit background model, the NEXT-White median sensitivity to the two-neutrino double beta decay is found to be 3.5 sigma after 1 year of data taking. The background measurement in a Q(beta beta)+/- 100 keV energy window validates the best-fit background model also for the neutrinoless double beta decay search with NEXT-100. Only one event is found, while the model expectation is (0.75 +/- 0.12) events.The NEXT collaboration acknowledges support from the following agencies and institutions: the European Research Council (ERC) under the Advanced Grant 339787-NEXT; the European Union's Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under the Marie Sklodowska-Curie Grant Agreements No. 674896, 690575 and 740055; the Ministerio de Economia y Competitividad and the Ministerio de Ciencia, Innovacion y Universidades of Spain under grants FIS2014-53371-C04, RTI2018-095979, the Severo Ochoa Program SEV-2014-0398 and the Maria de Maetzu Program MDM-2016-0692; the GVA of Spain under grants PROMETEO/2016/120 and SEJI/2017/011; the Portuguese FCT under project PTDC/FIS-NUC/2525/2014, under project UID/FIS/04559/2013 to fund the activities of LIBPhys, and under grants PD/BD/105921/2014, SFRH/BPD/109180/2015 and SFRH/BPD/76842/2011; the U.S. Department of Energy under contracts number DE-AC02-06CH11357 (Argonne National Laboratory), DE-AC02-07CH11359 (Fermi National Accelerator Laboratory), DE-FG02-13ER42020 (Texas A&M) and DE-SC0019223/DE-SC0019054 (University of Texas at Arlington); and the University of Texas at Arlington. DGD acknowledges Ramon y Cajal program (Spain) under contract number RYC-2015-18820. We also warmly acknowledge the Laboratori Nazionali del Gran Sasso (LNGS) and the Dark Side collaboration for their help with TPB coating of various parts of the NEXT-White TPC. Finally, we are grateful to the Laboratorio Subterraneo de Canfranc for hosting and supporting the NEXT experiment.Novella, P.; Palmeiro, B.; Sorel, M.; Usón, A.; Ferrario, P.; Gómez-Cadenas, JJ.; Adams, C.... (2019). Radiogenic backgrounds in the NEXT double beta decay experiment. Journal of High Energy Physics (Online). (10):1-26. https://doi.org/10.1007/JHEP10(2019)051S12610KamLAND-Zen collaboration, Search for Majorana Neutrinos near the Inverted Mass Hierarchy Region with KamLAND-Zen, Phys. Rev. Lett.117 (2016) 082503 [arXiv:1605.02889] [INSPIRE].GERDA collaboration, Improved Limit on Neutrinoless Double-β Decay of76Ge from GERDA Phase II, Phys. Rev. Lett.120 (2018) 132503 [arXiv:1803.11100] [INSPIRE].NEXT collaboration, NEXT-100 Technical Design Report (TDR): Executive Summary, 2012JINST7 T06001 [arXiv:1202.0721] [INSPIRE].M. Redshaw, E. Wingfield, J. McDaniel and E.G. Myers, Mass and double-beta-decay Q value of Xe-136, Phys. Rev. Lett.98 (2007) 053003 [INSPIRE].EXO-200 collaboration, Improved measurement of the 2νββ half-life of136Xe with the EXO-200 detector, Phys. Rev.C 89 (2014) 015502 [arXiv:1306.6106] [INSPIRE].KamLAND-Zen collaboration, Measurement of the double-β decay half-life of136Xe with the KamLAND-Zen experiment, Phys. Rev.C 85 (2012) 045504 [arXiv:1201.4664] [INSPIRE].NEXT collaboration, Initial results on energy resolution of the NEXT-White detector, 2018JINST13 P10020 [arXiv:1808.01804] [INSPIRE].NEXT collaboration, Energy Calibration of the NEXT-White Detector with 1% Resolution Near Qββof136Xe, arXiv:1905.13110 [INSPIRE].NEXT collaboration, Near-Intrinsic Energy Resolution for 30 to 662 keV Gamma Rays in a High Pressure Xenon Electroluminescent TPC, Nucl. Instrum. Meth.A 708 (2013) 101 [arXiv:1211.4474] [INSPIRE].NEXT collaboration, Characterisation of NEXT-DEMO using xenon KαX-rays, 2014JINST9 P10007 [arXiv:1407.3966] [INSPIRE].NEXT collaboration, First proof of topological signature in the high pressure xenon gas TPC with electroluminescence amplification for the NEXT experiment, JHEP01 (2016) 104 [arXiv:1507.05902] [INSPIRE].NEXT collaboration, Demonstration of the event identification capabilities of the NEXT-White detector, arXiv:1905.13141 [INSPIRE].A.D. McDonald et al., Demonstration of Single Barium Ion Sensitivity for Neutrinoless Double Beta Decay using Single Molecule Fluorescence Imaging, Phys. Rev. Lett.120 (2018) 132504 [arXiv:1711.04782] [INSPIRE].P. Thapa et al., Barium Chemosensors with Dry-Phase Fluorescence for Neutrinoless Double Beta Decay, arXiv:1904.05901 [INSPIRE].NEXT collaboration, Ionization and scintillation response of high-pressure xenon gas to alpha particles, 2013 JINST8 P05025 [arXiv:1211.4508] [INSPIRE].NEXT collaboration, Initial results of NEXT-DEMO, a large-scale prototype of the NEXT-100 experiment, 2013 JINST8 P04002 [arXiv:1211.4838] [INSPIRE].NEXT collaboration, Operation and first results of the NEXT-DEMO prototype using a silicon photomultiplier tracking array, 2013 JINST8 P09011 [arXiv:1306.0471] [INSPIRE].NEXT collaboration, Description and commissioning of NEXT-MM prototype: first results from operation in a Xenon-Trimethylamine gas mixture, 2014 JINST9 P03010 [arXiv:1311.3242] [INSPIRE].NEXT collaboration, Ionization and scintillation of nuclear recoils in gaseous xenon, Nucl. Instrum. Meth.A 793 (2015) 62 [arXiv:1409.2853] [INSPIRE].NEXT collaboration, An improved measurement of electron-ion recombination in high-pressure xenon gas, 2015 JINST10 P03025 [arXiv:1412.3573] [INSPIRE].NEXT collaboration, Accurate γ and MeV-electron track reconstruction with an ultra-low diffusion Xenon/TMA TPC at 10 atm, Nucl. Instrum. Meth.A 804 (2015) 8 [arXiv:1504.03678] [INSPIRE].NEXT collaboration, The Next White (NEW) Detector, 2018 JINST13 P12010 [arXiv:1804.02409] [INSPIRE].NEXT collaboration, Sensitivity of NEXT-100 to Neutrinoless Double Beta Decay, JHEP05 (2016) 159 [arXiv:1511.09246] [INSPIRE].V. Alvarez et al., Radiopurity control in the NEXT-100 double beta decay experiment: procedures and initial measurements, 2013 JINST8 T01002 [arXiv:1211.3961] [INSPIRE].NEXT collaboration, Radiopurity assessment of the tracking readout for the NEXT double beta decay experiment, 2015 JINST10 P05006 [arXiv:1411.1433] [INSPIRE].NEXT collaboration, Radiopurity assessment of the energy readout for the NEXT double beta decay experiment, 2017 JINST12 T08003 [arXiv:1706.06012] [INSPIRE].NEXT collaboration, Measurement of radon-induced backgrounds in the NEXT double beta decay experiment, JHEP10 (2018) 112 [arXiv:1804.00471] [INSPIRE].NEXT collaboration, Electron drift properties in high pressure gaseous xenon, 2018 JINST13 P07013 [arXiv:1804.01680] [INSPIRE].NEXT collaboration, Calibration of the NEXT-White detector using83m Kr decays, 2018JINST13 P10014 [arXiv:1804.01780] [INSPIRE].NEXT collaboration, Background rejection in NEXT using deep neural networks, 2017JINST12 T01004 [arXiv:1609.06202] [INSPIRE].NEXT collaboration, Application and performance of an ML-EM algorithm in NEXT, 2017JINST12 P08009 [arXiv:1705.10270] [INSPIRE]

Las transformaciones de la estructura de la población activa en los núcleos rurales del entorno de Cáceres

El presente artículo se planteó en un primer momento con el objetivo único de analizar las transformaciones generadas por la ciudad y capital de Cáceres sobre su entorno más inmediato. En este sentido, se trataba de continuar una serie de trabajos ya realizados por algunos miembros del Departamento anteriormente y por nosotros mismos, en especial del Dr. Campesino Fernández y del Dr. Barrientos Alfageme . En segundo lugar, se pretendía estudiar, dentro del área de influencia de Cáceres, las interacciones con su entorno más próximo. Comprobar hasta qué punto una ciudad puede organizar el espacio rural que le rodea. Y ver, en definitiva, los factores y causas que determinan toda la serie de transformaciones acaecidas en la última década fundamentalmente por el progreso y mayor grado de interacción. Sin embargo, tras los resultados de las primeras informaciones recogidas, ya se dejaron entrever una serie de profundas transformaciones, que tienen su reflejo más claro en la estructura de la poblaci6n activa y en las modificaciones de este espacio rural. Será en estos aspectos en los que hagamos mayor hincapié, aun sin olvidar otras cuestiones, también de interés, pero que sólo enunciaremos o plantearemos por la brevedad que exigen estos artículos.This article was raised in a first moment with the sole objective of analysing the changes generated by the city and capital of Cáceres on their immediate environment. In this sense, it was a continuation of a series of work already done by some members of the Department previously and for ourselves, in particular Dr. Peasant Fernández and Dr. Barrientos Alfageme . In the second place, it was intended to study, within the area of influence of Cáceres, interactions with their environment. The extent to which a city can organize the rural space that surrounds it. And see, in the final analysis, the causes and factors that determine the entire series of transformations in the last decade, mainly by the progress and greater degree of interaction. However, after the results of the first information collected, already hinted a series of profound transformations, which are reflected in the structure of the active population and changes in this rural area. It will be in these aspects in which we make greater emphasis, even without neglecting other issues, also of interest, but that only enunciaremos or raise by the brevity that require these items

Characteristics of Emergency Medicine Residency Programs in Colombia

Introduction: Emergency medicine (EM) is in different stages of development around the world. Colombia has made significant strides in EM development in the last two decades and recognized it as a medical specialty in 2005. The country now has seven EM residency programs: three in the capital city of Bogotá, two in Medellin, one in Manizales, and one in Cali. The seven residency programs are in different stages of maturity, with the oldest founded 20 years ago and two founded in the last two years. The objective of this study was to characterize these seven residency programs. Methods: We conducted semi-structured interviews with faculty and residents from all the existing programs in 2013–2016. Topics included program characteristics and curricula. Results: Colombian EM residencies are three-year programs, with the exception of one four-year program. Programs accept 3–10 applicants yearly. Only one program has free tuition and the rest charge tuition. The number of EM faculty ranges from 2–15. EM rotation requirements range from 11–33% of total clinical time. One program does not have a pediatric rotation. The other programs require 1–2 months of pediatrics or pediatric EM. Critical care requirements range from 4–7 months. Other common rotations include anesthesia, general surgery, internal medicine, obstetrics, gynecology, orthopedics, ophthalmology, radiology, toxicology, psychiatry, neurology, cardiology, pulmonology, and trauma. All programs offer 4–6 hours of protected didactic time each week. Some programs require Advanced Cardiac Life Support, Pediatric Advanced Life Support and Advanced Trauma Life Support, with some programs providing these trainings in-house or subsidizing the cost. Most programs require one research project for graduation. Resident evaluations consist of written tests and oral exams several times per year. Point-of-care ultrasound training is provided in four of the seven programs. Conclusion: As emergency medicine continues to develop in Colombia, more residency programs are expected to emerge. Faculty development and sustainability of academic pursuits will be critically important. In the long term, the specialty will need to move toward certifying board exams and professional development through a national EM organization to promote standardization across programs

Association between serum tissue inhibitor of matrix metalloproteinase-1 levels and mortality in patients with severe brain trauma injury

OBJECTIVE: Matrix metalloproteinases (MMPs) and tissue inhibitors of matrix metalloproteinases (TIMPs) play a role in neuroinflammation after brain trauma injury (TBI). Previous studies with small sample size have reported higher circulating MMP-2 and MMP-9 levels in patients with TBI, but no association between those levels and mortality. Thus, the aim of this study was to determine whether serum TIMP-1 and MMP-9 levels are associated with mortality in patients with severe TBI. METHODS: This was a multicenter, observational and prospective study carried out in six Spanish Intensive Care Units. Patients with severe TBI defined as Glasgow Coma Scale (GCS) lower than 9 were included, while those with Injury Severity Score (ISS) in non-cranial aspects higher than 9 were excluded. Serum levels of TIMP-1, MMP-9 and tumor necrosis factor (TNF)-alpha, and plasma levels of tissue factor (TF) and plasminogen activator inhibitor (PAI)-1 plasma were measured in 100 patients with severe TBI at admission. Endpoint was 30-day mortality. RESULTS: Non-surviving TBI patients (n = 27) showed higher serum TIMP-1 levels than survivor ones (n = 73). We did not find differences in MMP-9 serum levels. Logistic regression analysis showed that serum TIMP-1 levels were associated 30-day mortality (OR = 1.01; 95% CI = 1.001-1.013; P = 0.03). Survival analysis showed that patients with serum TIMP-1 higher than 220 ng/mL presented increased 30-day mortality than patients with lower levels (Chi-square = 5.50; P = 0.02). The area under the curve (AUC) for TIMP-1 as predictor of 30-day mortality was 0.73 (95% CI = 0.624-0.844; P<0.001). An association between TIMP-1 levels and APACHE-II score, TNF- alpha and TF was found. CONCLUSIONS: The most relevant and new findings of our study, the largest series reporting data on TIMP-1 and MMP-9 levels in patients with severe TBI, were that serum TIMP-1 levels were associated with TBI mortality and could be used as a prognostic biomarker of mortality in TBI patients