Investigation of the CRT performance of a PET scanner based in liquid xenon: a Monte Carlo study

The measurement of the time of flight of the two 511 keV gammas recorded in coincidence in a PET scanner provides an effective way of reducing the random background and therefore increases the scanner sensitivity, provided that the coincidence resolving time (CRT) of the gammas is sufficiently good. Existing commercial systems based in LYSO crystals, such as the GEMINIS of Philips, reach CRT values of 600 ps (FWHM). In this paper we present a Monte Carlo investigation of the CRT performance of a PET scanner exploiting the scintillating properties of liquid xenon. We find that an excellent CRT of 60 70 ps (depending on the PDE of the sensor) can be obtained if the scanner is instrumented with silicon photomultipliers (SiPMs) sensitive to the ultraviolet light emitted by xenon. Alternatively, a CRT of 120 ps can be obtained instrumenting the scanner with (much cheaper) blue-sensitive SiPMs coated with a suitable wavelength shifter. These results show the excellent time of flight capabilities of a PET device based in liquid xenon.The authors acknowledge support from the following agencies and institutions: the European Research Council (ERC) under the Advanced Grant 339787-NEXT, the Ministerio de Economia y Competitividad and FEDER of Spain, the Severo Ochoa Program SEV-2014-0398 and GVA under grant PROMETEO/2016/120; we acknowledge enlightening discussions with J. Varela and C. Lerche.Gómez-Cadenas, JJ.; Benlloch-Rodriguez, JM.; Ferrario, P.; Monrabal, F.; Rodriguez-Samaniego, J.; Toledo Alarcón, JF. (2016). Investigation of the CRT performance of a PET scanner based in liquid xenon: a Monte Carlo study. Journal of Instrumentation. 11(P09011). https://doi.org/10.1088/1748-0221/11/09/P09011S11P0901

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]

New Abundant Microbial Groups in Aquatic Hypersaline Environments

We describe the microbiota of two hypersaline saltern ponds, one of intermediate salinity (19%) and a NaCl saturated crystallizer pond (37%) using pyrosequencing. The analyses of these metagenomes (nearly 784 Mb) reaffirmed the vast dominance of Haloquadratum walsbyi but also revealed novel, abundant and previously unsuspected microbial groups. We describe for the first time, a group of low GC Actinobacteria, related to freshwater Actinobacteria, abundant in low and intermediate salinities. Metagenomic assembly revealed three new abundant microbes: a low-GC euryarchaeon with the lowest GC content described for any euryarchaeon, a high-GC euryarchaeon and a gammaproteobacterium related to Alkalilimnicola and Nitrococcus. Multiple displacement amplification and sequencing of the genome from a single archaeal cell of the new low GC euryarchaeon suggest a photoheterotrophic and polysaccharide-degrading lifestyle and its relatedness to the recently described lineage of Nanohaloarchaea. These discoveries reveal the combined power of an unbiased metagenomic and single cell genomic approach

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]

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

Development and Validation of Hepamet Fibrosis Scoring System-a Simple, Non-invasive Test to Identify Patients With Nonalcoholic Fatty liver Disease With Advanced Fibrosis

BACKGROUND & AIMS: Fibrosis affects prognoses for patients with nonalcoholic fatty liver disease (NAFLD). Several non-invasive scoring systems have aimed to identify patients at risk for advanced fibrosis, but inconclusive results and variations in features of patients (diabetes, obesity and older age) reduce their diagnostic accuracy. We sought to develop a scoring system based on serum markers to identify patients with NAFLD at risk for advanced fibrosis. METHODS: We collected data from 2452 patients with NAFLD at medical centers in Italy, France, Cuba, and China. We developed the Hepamet fibrosis scoring system using demographic, anthropometric, and laboratory test data, collected at time of liver biopsy, from a training cohort of patients from Spain (n=768) and validated the system using patients from Cuba (n=344), Italy (n=288), France (n=830), and China (n=232). Hepamet fibrosis score (HFS) were compared with those of previously developed fibrosis scoring systems (the NAFLD fibrosis score [NFS] and FIB-4). The diagnostic accuracy of the Hepamet fibrosis scoring system was assessed based on area under the receiver operating characteristic (AUROC) curve, sensitivity, specificity, diagnostic odds ratio, and positive and negative predictive values and likelihood ratios. RESULTS: Variables used to determine HFS were patient sex, age, homeostatic model assessment score, presence of diabetes, levels of aspartate aminotransferase, and albumin, and platelet counts; these were independently associated with advanced fibrosis. HFS discriminated between patients with and without advanced fibrosis with an AUROC curve value of 0.85 whereas NFS or FIB-4 did so with AUROC values of 0.80 (P=.0001). In the validation set, cut-off HFS of 0.12 and 0.47 identified patients with and without advanced fibrosis with 97.2% specificity, 74% sensitivity, a 92% negative predictive value, a 76.3% positive predictive value, a 13.22 positive likelihood ratio, and a 0.31 negative likelihood ratio. HFS were not affected by patient age, body mass index, hypertransaminasemia, or diabetes. The Hepamet fibrosis scoring system had the greatest net benefit in identifying patients who should undergo liver biopsy analysis and led to significant improvements in reclassification, reducing the number of patients with undetermined results to 20% from 30% for the FIB-4 and NFS systems (P<.05). CONCLUSIONS: Using clinical and laboratory data from patients with NAFLD, we developed and validated the Hepamet fibrosis scoring system, which identified patients with advanced fibrosis with greater accuracy than the FIB-4 and NFS systems. the Hepamet system provides a greater net benefit for the decision-making process to identify patients who should undergo liver biopsy analysis

The NEXT White (NEW) detector

Conceived to host 5 kg of xenon at a pressure of 15 bar in the fiducial volume, the NEXT-White apparatus is currently the largest high pressure xenon gas TPC using electroluminescent amplification in the world. It is also a 1:2 scale model of the NEXT-100 detector for Xe-136 beta beta 0 nu decay searches, scheduled to start operations in 2019. Both detectors measure the energy of the event using a plane of photomultipliers located behind a transparent cathode. They can also reconstruct the trajectories of charged tracks in the dense gas of the TPC with the help of a plane of silicon photomultipliers located behind the anode. A sophisticated gas system, common to both detectors, allows the high gas purity needed to guarantee a long electron lifetime. NEXT-White has been operating since October 2016 at the Laboratorio Subterraneo de Canfranc (LSC), in Spain. This paper describes the detector and associated infrastructures, as well as the main aspects of its initial operation

Time reconstruction study using tubes of response backprojectors in List Mode algorithms, applied to amonolithic crystals based breast PET

[Otros] The LM-EM algorithm has the advantage to calculate the emission probabilities needed for the reconstruction process on the fly, without the need of a pre-calculated system matrix. The reconstruction time for this algorithm strongly depends on the used backprojector and the available statistics. This algorithm when implemented in systems using monolithic crystals to detect gamma radiation allows one to extensively exploit the virtual pixilation feature, not available for systems based on pixilated crystals. In this work we present a backprojector for LM-EM, the TOR method, which achieves a tradeoff between computational efficiency and image quality. Its temporal subset algorithm optimization (LM-OS) has also been implemented in order to achieve real-time reconstructions. To evaluate the performances of LM-OS algorithm with the TOR method backprojector and only with one iteration on the datasets, studies based on the system spatial resolution, uniformity, and contrast coefficients were carried out and they were compared with those obtained with LM-EM and MLEM algorithms using twelve iteration. Finally, a study on reconstruction time using LM-OS has been performed with breast patients dataProject funded by the Spanish Ministry of Economy and Competitiveness and co-funded with FEDER's funds within the INNPACTO 2011 program. This work was supported by the Spanish Plan Nacional de Investigación Científica, Desarrollo e Innovación Tecnológica (I+D+i) under Grant No. FIS2010-21216-CO2-01 and the Valencian Local Government under Grants PROMETEOII/2013/010 and ISIC 2011/013Moliner, L.; Correcher, C.; González Martínez, AJ.; Conde, P.; Crespo, E.; Hernandez, L.; Rigla, JP.... (2013). Time reconstruction study using tubes of response backprojectors in List Mode algorithms, applied to amonolithic crystals based breast PET. IEEE. 14-18. https://doi.org/10.1109/NSSMIC.2013.6829372S141