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328 results on '"Bhanderi, A."'

1. Few LGBTQ+ Science and Engineering Instructors Come Out to Students, Despite Potential Benefits

Author

Carly A. Busch, Parth B. Bhanderi, Katelyn M. Cooper, and Sara E. Brownell

Abstract

LGBTQ+ undergraduates have higher attrition from science and engineering (S&E) than straight and cisgender undergraduates and perceive that having LGBTQ+ instructors would benefit them. However, it is unknown how many S&E instructors are LGBTQ+, the extent to which they disclose this information to students, and how disclosure affects "all" students, both LGBTQ+ and non-LGBTQ+. In study I, we surveyed 108 LGBTQ+ S&E instructors across the U.S. to explore the extent to which they reveal their LGBTQ+ identities across professional contexts and why they reveal or conceal their identities to undergraduates. Overall, 75% of instructors came out to at least some colleagues but only 48% came out to any undergraduates. Instructors most commonly chose to conceal LGBTQ+ identities from undergraduates because they perceived their identities to be irrelevant to course content and anticipated negative student reactions. In study II, 666 introductory biology undergraduates were randomly assigned to evaluate one of two identical teaching demonstration videos except the instructor revealed her LGBTQ+ identity in one but not the other. We assessed differences in students' impressions of the instructor across conditions. We found no differences in most ratings of the instructor except participants reported higher rapport with the instructor when she came out.

Published
2024
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7. Association of Insulin Resistance with Hepatitis B and Hepatitis C Infection: A Cross-sectional Study

Author

Shruti Bhanderi and Nivedita Priya

Subjects
blood glucose, homeostasis model assessment, liver disease, viral infection, Medicine
Abstract

Introduction: Hepatitis B and C viruses (HBV and HCV) are the most common causes of chronic viral hepatitis in the United States and worldwide. As several studies have shown that some viral infections promote Insulin Resistance (IR), it becomes imperative to investigate the effect of HBV and HCV on the same. Aim: To investigate the association between IR and Hepatitis C and Hepatitis B infection. Materials and Methods: The present cross-sectional study was conducted in the Department of Paramedical and Health Sciences, Parul Institute of Paramedical and Health Sciences, Faculty of Medicine, Parul University, Vadodara, Gujarat, India, from November 2022 to June 2023. The study comprised 100 consecutive patients, with 55% testing positive for Hepatitis B and 45% for Hepatitis C. Biochemical parameters such as fasting blood sugar, fasting insulin and IR via the Homeostasis Model Assessment (HOMA-IR) were determined. Statistical analysis was performed using International Business Machines (IBM) Statistical Package for Social Sciences (SPSS) software version 26.0. The z-test of proportion was calculated and a Z-score calculator was used for p-value. Results: In the present study, out of 100 consecutive patients, 76 were males and 24 were females. The mean insulin level for HBV patients was found to be high, with values of 28.94±2.22 mU/L in 23.63% of cases, whereas in HCV cases, 68.8% of patients were found to have higher levels, with mean values of 34.52±9.00 mU/L. The mean HOMA-IR values for HBV patients were found to be high, at 4.84±2.32 in 47.27% of cases, whereas in HCV cases, 77.7% of patients were found to have higher values of HOMA-IR, with a mean of 7.94±4.83. Conclusion: Insulin resistance can be a consequence of the inability of insulin to induce the appropriate effect on glucose metabolism and is the principal indication for the development of metabolic syndrome and diabetes mellitus. In the current study, HCV-positive patients showed more IR compared to HBV-positive cases.

Published
2024
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13. Thickness and surface profiling of optically transparent and reflecting samples using lens-less self-referencing digital holographic microscopy

Author

Subhash Utadiya, Vismay Trivedi, Kevin Bhanderi, Mugdha Joglekar, Chaitanya Limberkar, Kireet Patel, Gyanendra Sheoran, Humberto Cabrera, Bahram Javidi, and Arun Anand

Subjects
Digital holographic microscopy, Quantitative phase imaging, Surface profile, Optical thickness, Materials of engineering and construction. Mechanics of materials, TA401-492, Industrial electrochemistry, TP250-261
Abstract

Thickness and surface profiling of transparent/semi-transparent specimens are vital in various applications, including electronics, optics, healthcare, and biotechnology. Surface profiling techniques characterize and analyze surface thickness, morphology, and roughness. Developing easy-to-use, single-shot, wide field-of-view techniques that provide nanometer level surface thickness and profiling is vital for these applications. Digital holography is a state-of-the-art technique that provides the quantitative phase images of transparent objects, from which their thickness profiles could be extracted and used for surface profiling. It has the added advantage of numerical focusing. The present manuscript details the development of a compact wide field of view, self-referencing, lens-less digital holographic microscope for surface profiling of transparent/semi-transparent samples in transmission and reflection mode. The developed microscope requires only a glass plate to generate holograms and can be used to study the dynamics of the surfaces also. It provides a field of view of 3.2mm x 2.5 mm along with a thickness measurement resolution of 2.8 nm and temporal stability of 1.1 nm over a period of 120 s. The developed microscope was tested by measuring the thickness of GeSe semiconductor thin films grown on glass substrates and comparing it with AFM measurements. The microscope was then used to quantify spatially varying thickness profiles of overlapped thin films, junction formed by heterogenous compounds and metal thin films. The microscope was also tested for dynamic studies of surface profiles by thermally loading ink markings on glass slides.

Published
2023
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16. Prevalence of self-reported noncommunicable diseases in grassroot-level health worker of Petlad taluka: A cross-sectional study

Author

Charvi P Mistry and Dinesh J Bhanderi

Subjects
gujarat, health practices of health workers, lifestyle of health workers, ncd in health workers, stepwise approach for ncds, Medicine
Abstract

Context: Noncommunicable diseases (NCDs) kill 41 million people each year globally. The financial burden of NCDs, including lifelong and expensive treatment with loss of income, forces many people into poverty. Healthcare workers (HCWs) are an essential group of the workforce in building a healthier society. They must choose a healthy lifestyle to have better health for themselves. Aim: To estimate the prevalence of self-reported NCDs in grassroot-level HCWs of Petlad taluka along with the study of various healthcare and lifestyle practices among them. Methods and Material: In this cross-sectional study, grassroot-level HCWs of Petlad taluka of Anand district were given a self-administered questionnaire, which included their demographic details, their current health status, and health practices followed by them. Statistical Analysis: The data collected were entered in Microsoft Excel-2019 and analysed by SPSS version 15. Descriptive analysis and univariate analysis were performed. Results: NCDs' prevalence was 10.2%. Hypertension and diabetes were the commonest. Nearly 50% HCWs have their body mass index in a normal range. The majority of them were aware of healthy diet practices and exercise. Conclusions: Our study reported a lower prevalence of NCDs than other studies (50%). Dietary practices appear to be healthy, but a significant proportion of them are overweight. The majority of them prefer to consult at a government hospital. Healthy behaviour and practices should be encouraged and maintained in HCWs.

Published
2023
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17. A method to determine the electric field of liquid argon time projection chambers using a UV laser system and its application in MicroBooNE

Author

Adams, C, Alrashed, M, An, R, Anthony, J, Asaadi, J, Ashkenazi, A, Balasubramanian, S, Baller, B, Barnes, C, Barr, G, Basque, V, Bass, M, Bay, F, Berkman, S, Bhanderi, A, Bhat, A, Bishai, M, Blake, A, Bolton, T, Camilleri, L, Caratelli, D, Terrazas, IC, Carr, R, Fernandez, RC, Cavanna, F, Cerati, G, Chen, Y, Church, E, Cianci, D, Cohen, EO, Conrad, JM, Convery, M, Cooper-Troendle, L, Crespo-Anadón, JI, Tutto, MD, Devitt, D, Diaz, A, Domine, L, Duffy, K, Dytman, S, Eberly, B, Ereditato, A, Sanchez, LE, Evans, JJ, Fitzpatrick, RS, Fleming, BT, Foppiani, N, Franco, D, Furmanski, AP, Garcia-Gamez, D, Gardiner, S, Genty, V, Goeldi, D, Gollapinni, S, Goodwin, O, Gramellini, E, Green, P, Greenlee, H, Grosso, R, Gu, L, Gu, W, Guenette, R, Guzowski, P, Hamilton, P, Hen, O, Hill, C, Horton-Smith, GA, Hourlier, A, Huang, EC, Itay, R, James, C, De Vries, JJ, Ji, X, Jiang, L, Jo, JH, Johnson, RA, Joshi, J, Jwa, YJ, Karagiorgi, G, Ketchum, W, Kirby, B, Kirby, M, Kobilarcik, T, Kreslo, I, Lepetic, I, Li, Y, Lister, A, Littlejohn, BR, Lockwitz, S, Lorca, D, Louis, WC, Luethi, M, Lundberg, B, Luo, X, Marchionni, A, Marcocci, S, Mariani, C, Marshall, J, Martin-Albo, J, and Caicedo, DAM

Subjects
Ionization and excitation processes, Neutrino detectors, Noble liquid detectors, Time projection chambers, physics.ins-det, Nuclear & Particles Physics, Physical Sciences, Engineering
Abstract

Liquid argon time projection chambers (LArTPCs) are now a standard detector technology for making accelerator neutrino measurements, due to their high material density, precise tracking, and calorimetric capabilities. An electric field (E-field) is required in such detectors to drift ionization electrons to the anode where they are collected. The E-field of a TPC is often approximated to be uniform between the anode and the cathode planes. However, significant distortions can appear from effects such as mechanical deformations, electrode failures, or the accumulation of space charge generated by cosmic rays. The latter effect is particularly relevant for detectors placed near the Earth's surface and with large drift distances and long drift time. To determine the E-field in situ, an ultraviolet (UV) laser system is installed in the MicroBooNE experiment at Fermi National Accelerator Laboratory. The purpose of this system is to provide precise measurements of the E-field, and to make it possible to correct for 3D spatial distortions due to E-field non-uniformities. Here we describe the methodology developed for deriving spatial distortions, the drift velocity and the E-field from UV-laser measurements.

Published
2020

18. Calibration of the charge and energy loss per unit length of the MicroBooNE liquid argon time projection chamber using muons and protons

Author

Adams, C, Alrashed, M, An, R, Anthony, J, Asaadi, J, Ashkenazi, A, Balasubramanian, S, Baller, B, Barnes, C, Barr, G, Basque, V, Bass, M, Bay, F, Berkman, S, Bhanderi, A, Bhat, A, Bishai, M, Blake, A, Bolton, T, Camilleri, L, Caratelli, D, Terrazas, I Caro, Carr, R, Fernandez, R Castillo, Cavanna, F, Cerati, G, Chen, Y, Church, E, Cianci, D, Cohen, EO, Conrad, JM, Convery, M, Cooper-Troendle, L, Crespo-Anadón, JI, Del Tutto, M, Devitt, D, Diaz, A, Domine, L, Duffy, K, Dytman, S, Eberly, B, Ereditato, A, Sanchez, L Escudero, Esquivel, J, Evans, JJ, Fitzpatrick, RS, Fleming, BT, Foppiani, N, Franco, D, Furmanski, AP, Garcia-Gamez, D, Gardiner, S, Genty, V, Goeldi, D, Gollapinni, S, Goodwin, O, Gramellini, E, Green, P, Greenlee, H, Grosso, R, Gu, L, Gu, W, Guenette, R, Guzowski, P, Hamilton, P, Hen, O, Hill, C, Horton-Smith, GA, Hourlier, A, Huang, E-C, Itay, R, James, C, de Vries, J Jan, Ji, X, Jiang, L, Jo, JH, Johnson, RA, Joshi, J, Jwa, Y-J, Karagiorgi, G, Ketchum, W, Kirby, B, Kirby, M, Kobilarcik, T, Kreslo, I, Lepetic, I, Li, Y, Lister, A, Littlejohn, BR, Lockwitz, S, Lorca, D, Louis, WC, Luethi, M, Lundberg, B, Luo, X, Marchionni, A, Marcocci, S, Mariani, C, Marshall, J, and Martin-Albo, J

Subjects
Nuclear and Plasma Physics, Synchrotrons and Accelerators, Physical Sciences, Affordable and Clean Energy, Calorimeters, dE/dx detectors, Neutrino detectors, Time projection chambers, physics.ins-det, hep-ex, Engineering, Nuclear & Particles Physics, Physical sciences
Abstract

We describe a method used to calibrate the position- and time-dependent response of the MicroBooNE liquid argon time projection chamber anode wires to ionization particle energy loss. The method makes use of crossing cosmic-ray muons to partially correct anode wire signals for multiple effects as a function of time and position, including cross-connected TPC wires, space charge effects, electron attachment to impurities, diffusion, and recombination. The overall energy scale is then determined using fully-contained beam-induced muons originating and stopping in the active region of the detector. Using this method, we obtain an absolute energy scale uncertainty of 2% in data. We use stopping protons to further refine the relation between the measured charge and the energy loss for highly-ionizing particles. This data-driven detector calibration improves both the measurement of total deposited energy and particle identification based on energy loss per unit length as a function of residual range. As an example, the proton selection efficiency is increased by 2% after detector calibration.

Published
2020

19. Search for heavy neutral leptons decaying into muon-pion pairs in the MicroBooNE detector

Author

Abratenko, P, Alrashed, M, An, R, Anthony, J, Asaadi, J, Ashkenazi, A, Balasubramanian, S, Baller, B, Barnes, C, Barr, G, Basque, V, Berkman, S, Bhanderi, A, Bhat, A, Bishai, M, Blake, A, Bolton, T, Camilleri, L, Caratelli, D, Terrazas, I Caro, Fernandez, R Castillo, Cavanna, F, Cerati, G, Chen, Y, Church, E, Cianci, D, Cohen, EO, Conrad, JM, Convery, M, Cooper-Troendle, L, Crespo-Anadón, JI, Del Tutto, M, Devitt, A, Domine, L, Duffy, K, Dytman, S, Eberly, B, Ereditato, A, Sanchez, L Escudero, Evans, JJ, Fitzpatrick, RS, Fleming, BT, Foppiani, N, Franco, D, Furmanski, AP, Garcia-Gamez, D, Gardiner, S, Genty, V, Goeldi, D, Gollapinni, S, Goodwin, O, Gramellini, E, Green, P, Greenlee, H, Gu, L, Gu, W, Guenette, R, Guzowski, P, Hamilton, P, Hen, O, Hill, C, Horton-Smith, GA, Hourlier, A, Huang, E-C, Itay, R, James, C, de Vries, J Jan, Ji, X, Jiang, L, Jo, JH, Johnson, RA, Joshi, J, Jwa, Y-J, Karagiorgi, G, Ketchum, W, Kirby, B, Kirby, M, Kobilarcik, T, Kreslo, I, LaZur, R, Lepetic, I, Li, Y, Lister, A, Littlejohn, BR, Lockwitz, S, Lorca, D, Louis, WC, Luethi, M, Lundberg, B, Luo, X, Marchionni, A, Marcocci, S, Mariani, C, Marshall, J, Martin-Albo, J, Caicedo, DA Martinez, Mason, K, Mastbaum, A, McConkey, N, and Meddage, V

Subjects
Nuclear and Plasma Physics, Particle and High Energy Physics, Physical Sciences, hep-ex, physics.ins-det, Astronomical and Space Sciences, Atomic, Molecular, Nuclear, Particle and Plasma Physics, Quantum Physics, Nuclear & Particles Physics, Mathematical physics, Astronomical sciences, Particle and high energy physics
Abstract

We present upper limits on the production of heavy neutral leptons (HNLs) decaying to μπ pairs using data collected with the MicroBooNE liquid-argon time projection chamber (TPC) operating at Fermilab. This search is the first of its kind performed in a liquid-argon TPC. We use data collected in 2017 and 2018 corresponding to an exposure of 2.0×1020 protons on target from the Fermilab Booster Neutrino Beam, which produces mainly muon neutrinos with an average energy of ≈800 MeV. HNLs with higher mass are expected to have a longer time of flight to the liquid-argon TPC than Standard Model neutrinos. The data are therefore recorded with a dedicated trigger configured to detect HNL decays that occur after the neutrino spill reaches the detector. We set upper limits at the 90% confidence level on the element |Uμ4|2 of the extended PMNS mixing matrix in the range |Uμ4|2

Published
2020

20. An open-source instrumentation package for intensive soil hydraulic characterization

Author

Concialdi, Paola, Di Prima, Simone, Bhanderi, Harsh M, Stewart, Ryan D, Najm, Majdi R Abou, Gaur, Murari Lal, Angulo-Jaramillo, Rafael, and Lassabatere, Laurent

Subjects
Automatic infiltrometer, Infiltration rate measurements, Soil hydraulic properties, Arduino, Environmental Engineering
Abstract

We present a new open-source and modular instrumentation package composed of up to ten automatic infiltrometers connected to data acquisition systems for automatic recording of multiple infiltration experiments. The infiltrometers are equipped with differential transducers to monitor water level changes in a Mariotte reservoir, and, in turn, to quantify water infiltration rates. The data acquisition systems consist of low-cost components and operate on the open-source microcontroller platform Arduino. The devices were tested both in the laboratory and on different urban and agricultural soils in France and India. More specifically, we tested three procedures to treat the transducers readings, including a filtering algorithm that substantially improved the ability to determine cumulative infiltration from raw data. We combined these three procedures with four methods for estimating the soil parameters from infiltrometer data, showing pros and cons of each scenario. We also demonstrated advantages in using the automatic infiltrometers when infiltration measurements were hindered by: i) linearity in cumulative infiltration curves owing to gravity-driven flow, ii) an imprecise description of the transient state of infiltration, and iii) the occurrence of soil water repellency. The use of the automatic infiltrometers allows the user to obtain more accurate estimates of soil hydraulic parameters, while also reducing the amount of effort needed to run multiple experiments.

Published
2020

21. An open-source instrumentation package for intensive soil hydraulic characterization

Author

Concialdi, P, Di Prima, S, Bhanderi, HM, Stewart, RD, Abou Najm, MR, Lal Gaur, M, Angulo-Jaramillo, R, and Lassabatere, L

Subjects
Automatic infiltrometer, Infiltration rate measurements, Soil hydraulic properties, Arduino, Environmental Engineering
Abstract

We present a new open-source and modular instrumentation package composed of up to ten automatic infiltrometers connected to data acquisition systems for automatic recording of multiple infiltration experiments. The infiltrometers are equipped with differential transducers to monitor water level changes in a Mariotte reservoir, and, in turn, to quantify water infiltration rates. The data acquisition systems consist of low-cost components and operate on the open-source microcontroller platform Arduino. The devices were tested both in the laboratory and on different urban and agricultural soils in France and India. More specifically, we tested three procedures to treat the transducers readings, including a filtering algorithm that substantially improved the ability to determine cumulative infiltration from raw data. We combined these three procedures with four methods for estimating the soil parameters from infiltrometer data, showing pros and cons of each scenario. We also demonstrated advantages in using the automatic infiltrometers when infiltration measurements were hindered by: i) linearity in cumulative infiltration curves owing to gravity-driven flow, ii) an imprecise description of the transient state of infiltration, and iii) the occurrence of soil water repellency. The use of the automatic infiltrometers allows the user to obtain more accurate estimates of soil hydraulic parameters, while also reducing the amount of effort needed to run multiple experiments.

Published
2020

22. Reconstruction and measurement of (100) MeV energy electromagnetic activity from π0 arrow γγ decays in the MicroBooNE LArTPC

Author

Adams, C, Alrashed, M, An, R, Anthony, J, Asaadi, J, Ashkenazi, A, Balasubramanian, S, Baller, B, Barnes, C, Barr, G, Basque, V, Bass, M, Bay, F, Berkman, S, Bhanderi, A, Bhat, A, Bishai, M, Blake, A, Bolton, T, Camilleri, L, Caratelli, D, Terrazas, IC, Carr, R, Fernandez, RC, Cavanna, F, Cerati, G, Chen, Y, Church, E, Cianci, D, Cohen, EO, Conrad, JM, Convery, M, Cooper-Troendle, L, Crespo-Anadón, JI, Tutto, MD, Devitt, D, Diaz, A, Domine, L, Duffy, K, Dytman, S, Eberly, B, Ereditato, A, Sanchez, LE, Esquivel, J, Evans, JJ, Fitzpatrick, RS, Fleming, BT, Foppiani, N, Franco, D, Furmanski, AP, Garcia-Gamez, D, Gardiner, S, Genty, V, Goeldi, D, Gollapinni, S, Goodwin, O, Gramellini, E, Green, P, Greenlee, H, Grosso, R, Gu, L, Gu, W, Guenette, R, Guzowski, P, Hamilton, P, Hen, O, Hill, C, Horton-Smith, GA, Hourlier, A, Huang, EC, Itay, R, James, C, De Vries, JJ, Ji, X, Jiang, L, Jo, JH, Johnson, RA, Joshi, J, Jwa, YJ, Karagiorgi, G, Ketchum, W, Kirby, B, Kirby, M, Kobilarcik, T, Kreslo, I, Lepetic, I, Li, Y, Lister, A, Littlejohn, BR, Lockwitz, S, Lorca, D, Louis, WC, Luethi, M, Lundberg, B, Luo, X, Marchionni, A, Marcocci, S, Mariani, C, Marshall, J, and Martin-Albo, J

Subjects
Noble liquid detectors, Pattern recognition, cluster finding, calibration and fitting methods, Performance of High Energy Physics Detectors, Time projection chambers, hep-ex, physics.ins-det, Pattern recognition, cluster finding, calibration and fitting methods, Nuclear & Particles Physics, Physical Sciences, Engineering
Abstract

We present results on the reconstruction of electromagnetic (EM) activity from photons produced in charged current νμ interactions with final state π0s. We employ a fully-automated reconstruction chain capable of identifying EM showers of (100) MeV energy, relying on a combination of traditional reconstruction techniques together with novel machine-learning approaches. These studies demonstrate good energy resolution, and good agreement between data and simulation, relying on the reconstructed invariant π0 mass and other photon distributions for validation. The reconstruction techniques developed are applied to a selection of νμ + Ar → μ + π0 + X candidate events to demonstrate the potential for calorimetric separation of photons from electrons and reconstruction of π0 kinematics.

Published
2020

31. Novel approach for evaluating detector-related uncertainties in a LArTPC using MicroBooNE data

Author

P. Abratenko, R. An, J. Anthony, L. Arellano, J. Asaadi, A. Ashkenazi, S. Balasubramanian, B. Baller, C. Barnes, G. Barr, V. Basque, L. Bathe-Peters, O. Benevides Rodrigues, S. Berkman, A. Bhanderi, A. Bhat, M. Bishai, A. Blake, T. Bolton, J. Y. Book, L. Camilleri, D. Caratelli, I. Caro Terrazas, F. Cavanna, G. Cerati, Y. Chen, D. Cianci, J. M. Conrad, M. Convery, L. Cooper-Troendle, J. I. Crespo-Anadón, M. Del Tutto, S. R. Dennis, P. Detje, A. Devitt, R. Diurba, R. Dorrill, K. Duffy, S. Dytman, B. Eberly, A. Ereditato, J. J. Evans, R. Fine, G. A. Fiorentini Aguirre, R. S. Fitzpatrick, B. T. Fleming, N. Foppiani, D. Franco, A. P. Furmanski, D. Garcia-Gamez, S. Gardiner, G. Ge, S. Gollapinni, O. Goodwin, E. Gramellini, P. Green, H. Greenlee, W. Gu, R. Guenette, P. Guzowski, L. Hagaman, O. Hen, C. Hilgenberg, G. A. Horton-Smith, A. Hourlier, R. Itay, C. James, X. Ji, L. Jiang, J. H. Jo, R. A. Johnson, Y.-J. Jwa, D. Kalra, N. Kamp, N. Kaneshige, G. Karagiorgi, W. Ketchum, M. Kirby, T. Kobilarcik, I. Kreslo, I. Lepetic, K. Li, Y. Li, K. Lin, B. R. Littlejohn, W. C. Louis, X. Luo, K. Manivannan, C. Mariani, D. Marsden, J. Marshall, D. A. Martinez Caicedo, K. Mason, A. Mastbaum, N. McConkey, V. Meddage, T. Mettler, K. Miller, J. Mills, K. Mistry, A. Mogan, T. Mohayai, J. Moon, M. Mooney, A. F. Moor, C. D. Moore, L. Mora Lepin, J. Mousseau, M. Murphy, D. Naples, A. Navrer-Agasson, M. Nebot-Guinot, R. K. Neely, D. A. Newmark, J. Nowak, M. Nunes, O. Palamara, V. Paolone, A. Papadopoulou, V. Papavassiliou, S. F. Pate, N. Patel, A. Paudel, Z. Pavlovic, E. Piasetzky, I. D. Ponce-Pinto, S. Prince, X. Qian, J. L. Raaf, V. Radeka, A. Rafique, M. Reggiani-Guzzo, L. Ren, L. C. J. Rice, L. Rochester, J. Rodriguez Rondon, M. Rosenberg, M. Ross-Lonergan, G. Scanavini, D. W. Schmitz, A. Schukraft, W. Seligman, M. H. Shaevitz, R. Sharankova, J. Shi, J. Sinclair, A. Smith, E. L. Snider, M. Soderberg, S. Söldner-Rembold, P. Spentzouris, J. Spitz, M. Stancari, J. St. John, T. Strauss, K. Sutton, S. Sword-Fehlberg, A. M. Szelc, W. Tang, K. Terao, C. Thorpe, D. Totani, M. Toups, Y.-T. Tsai, M. A. Uchida, T. Usher, W. Van De Pontseele, B. Viren, M. Weber, H. Wei, Z. Williams, S. Wolbers, T. Wongjirad, M. Wospakrik, K. Wresilo, N. Wright, W. Wu, E. Yandel, T. Yang, G. Yarbrough, L. E. Yates, H. W. Yu, G. P. Zeller, J. Zennamo, C. Zhang, and MicroBooNE Collaboration

Subjects
Astrophysics, QB460-466, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract Primary challenges for current and future precision neutrino experiments using liquid argon time projection chambers (LArTPCs) include understanding detector effects and quantifying the associated systematic uncertainties. This paper presents a novel technique for assessing and propagating LArTPC detector-related systematic uncertainties. The technique makes modifications to simulation waveforms based on a parameterization of observed differences in ionization signals from the TPC between data and simulation, while remaining insensitive to the details of the detector model. The modifications are then used to quantify the systematic differences in low- and high-level reconstructed quantities. This approach could be applied to future LArTPC detectors, such as those used in SBN and DUNE.

Published
2022
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36. Ixazomib plus daratumumab and dexamethasone: Final analysis of a phase 2 study among patients with relapsed/refractory multiple myeloma.

Author

Delimpasi, Sosana, Dimopoulos, Meletios A., Straub, Jan, Symeonidis, Argiris, Pour, Luděk, Hájek, Roman, Touzeau, Cyrille, Bhanderi, Viralkumar K., Berdeja, Jesus G., Pavlíček, Petr, Matous, Jeffrey V., Robak, Pawel J., Suryanarayan, Kaveri, Miller, Alison, Villarreal, Miguel, Cherepanov, Dasha, Srimani, Jaydeep K., Yao, Huilan, Labotka, Richard, and Orlowski, Robert Z.

Published
2024
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37. Calorimetric classification of track-like signatures in liquid argon TPCs using MicroBooNE data

Author

The MicroBooNE collaboration, P. Abratenko, R. An, J. Anthony, J. Asaadi, A. Ashkenazi, S. Balasubramanian, B. Baller, C. Barnes, G. Barr, V. Basque, L. Bathe-Peters, O. Benevides Rodrigues, S. Berkman, A. Bhanderi, A. Bhat, M. Bishai, A. Blake, T. Bolton, L. Camilleri, D. Caratelli, I. Caro Terrazas, R. Castillo Fernandez, F. Cavanna, G. Cerati, Y. Chen, E. Church, D. Cianci, J. M. Conrad, M. Convery, L. Cooper-Troendle, J. I. Crespo-Anadón, M. Del Tutto, S. R. Dennis, D. Devitt, R. Diurba, R. Dorrill, K. Duffy, S. Dytman, B. Eberly, A. Ereditato, J. J. Evans, R. Fine, G. A. Fiorentini Aguirre, R. S. Fitzpatrick, B. T. Fleming, N. Foppiani, D. Franco, A. P. Furmanski, D. Garcia-Gamez, S. Gardiner, G. Ge, S. Gollapinni, O. Goodwin, E. Gramellini, P. Green, H. Greenlee, W. Gu, R. Guenette, P. Guzowski, L. Hagaman, E. Hall, P. Hamilton, O. Hen, G. A. Horton-Smith, A. Hourlier, R. Itay, C. James, X. Ji, L. Jiang, J. H. Jo, R. A. Johnson, Y.-J. Jwa, N. Kamp, N. Kaneshige, G. Karagiorgi, W. Ketchum, M. Kirby, T. Kobilarcik, I. Kreslo, R. LaZur, I. Lepetic, K. Li, Y. Li, K. Lin, B. R. Littlejohn, W. C. Louis, X. Luo, K. Manivannan, C. Mariani, D. Marsden, J. Marshall, D. A. Martinez Caicedo, K. Mason, A. Mastbaum, N. McConkey, V. Meddage, T. Mettler, K. Miller, J. Mills, K. Mistry, A. Mogan, T. Mohayai, J. Moon, M. Mooney, A. F. Moor, C. D. Moore, L. Mora Lepin, J. Mousseau, M. Murphy, D. Naples, A. Navrer-Agasson, R. K. Neely, J. Nowak, M. Nunes, O. Palamara, V. Paolone, A. Papadopoulou, V. Papavassiliou, S. F. Pate, A. Paudel, Z. Pavlovic, E. Piasetzky, I. D. Ponce-Pinto, S. Prince, X. Qian, J. L. Raaf, V. Radeka, A. Rafique, M. Reggiani-Guzzo, L. Ren, L. C. J. Rice, L. Rochester, J. Rodriguez Rondon, H. E. Rogers, M. Rosenberg, M. Ross-Lonergan, G. Scanavini, D. W. Schmitz, A. Schukraft, W. Seligman, M. H. Shaevitz, R. Sharankova, J. Sinclair, A. Smith, E. L. Snider, M. Soderberg, S. Söldner-Rembold, P. Spentzouris, J. Spitz, M. Stancari, J. St. John, T. Strauss, K. Sutton, S. Sword-Fehlberg, A. M. Szelc, N. Tagg, W. Tang, K. Terao, C. Thorpe, D. Totani, M. Toups, Y.-T. Tsai, M. A. Uchida, T. Usher, W. Van De Pontseele, B. Viren, M. Weber, H. Wei, Z. Williams, S. Wolbers, T. Wongjirad, M. Wospakrik, N. Wright, W. Wu, E. Yandel, T. Yang, G. Yarbrough, L. E. Yates, G. P. Zeller, J. Zennamo, and C. Zhang

Subjects
Other experiments, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract The MicroBooNE liquid argon time projection chamber located at Fermilab is a neutrino experiment dedicated to the study of short-baseline oscillations, the measurements of neutrino cross sections in liquid argon, and to the research and development of this novel detector technology. Accurate and precise measurements of calorimetry are essential to the event reconstruction and are achieved by leveraging the TPC to measure deposited energy per unit length along the particle trajectory, with mm resolution. We describe the non-uniform calorimetric reconstruction performance in the detector, showing dependence on the angle of the particle trajectory. Such non-uniform reconstruction directly affects the performance of the particle identification algorithms which infer particle type from calorimetric measurements. This work presents a new particle identification method which accounts for and effectively addresses such non-uniformity. The newly developed method shows improved performance compared to previous algorithms, illustrated by a 93.7% proton selection efficiency and a 10% muon mis-identification rate, with a fairly loose selection of tracks performed on beam data. The performance is further demonstrated by identifying exclusive final states in ν μ CC interactions. While developed using MicroBooNE data and simulation, this method is easily applicable to future LArTPC experiments, such as SBND, ICARUS, and DUNE.

Published
2021
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40. Comparative analysis of intradermal tuberculin test and γ-interferon assay for diagnosis of bovine tuberculosis

Author

S S VYAVAHARE, M K JHALA, and B B BHANDERI

Subjects
Bovine tuberculosis, ELISPOT, IFN-γ, Tuberculin test, Animal culture, SF1-1100
Abstract

The present study was carried out in and around Anand district of Gujarat with the aim to assess the sensitivity and specificity of IFN-γ assay as compared to intradermal tuberculin test for diagnosis of bovine tuberculosis during the period 2011-12. The prevalence of bovine TB in the cattle was 26.19% by TST and 38.49% by IFN- γ assay. Breed wise, highest prevalence of bovine TB was found in Gir (38.96%), followed by Triple cross (23.95%) and Kankrej (16.45%) by TST; while by IFN- γ assay, highest prevalence was observed in Triple cross (42.70%). Age wise, highest prevalence of bovine TB was found in adults (37.90%) followed by calves (26.66%) and heifers (11.22%) by TST; while by IFN-γ assay, highest prevalence was observed in calves (66.66%) followed by adults (44.35%) and heifers (22.44%). Sex wise, more prevalence of bovine TB was found in males (56.25%) than in females (21.81%) by TST; while by IFN-γ assay, more prevalence was observed in females (39.25%) than in males (31.25%). Sensitivity and specificity of TST in detecting bovine TB were 27.27% and 57.52% respectively, compared to IFN-γ assay. ELISPOT assay showed 34.78% animals were found positive for bovine TB. IFN-γ assay showed better sensitivity in detecting bovine TB at younger age compared to TST, and hence can be useful in timely removal of the infected animals from the herd.

Published
2022
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41. Novel approach for evaluating detector-related uncertainties in a LArTPC using MicroBooNE data

Author

Abratenko, P., An, R., Anthony, J., Arellano, L., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnes, C., Barr, G., Basque, V., Bathe-Peters, L., Benevides Rodrigues, O., Berkman, S., Bhanderi, A., Bhat, A., Bishai, M., Blake, A., Bolton, T., Book, J. Y., Camilleri, L., Caratelli, D., Caro Terrazas, I., Cavanna, F., Cerati, G., Chen, Y., Cianci, D., Conrad, J. M., Convery, M., Cooper-Troendle, L., Crespo-Anadón, J. I., Del Tutto, M., Dennis, S. R., Detje, P., Devitt, A., Diurba, R., Dorrill, R., Duffy, K., Dytman, S., Eberly, B., Ereditato, A., Evans, J. J., Fine, R., Fiorentini Aguirre, G. A., Fitzpatrick, R. S., Fleming, B. T., Foppiani, N., Franco, D., Furmanski, A. P., Garcia-Gamez, D., Gardiner, S., Ge, G., Gollapinni, S., Goodwin, O., Gramellini, E., Green, P., Greenlee, H., Gu, W., Guenette, R., Guzowski, P., Hagaman, L., Hen, O., Hilgenberg, C., Horton-Smith, G. A., Hourlier, A., Itay, R., James, C., Ji, X., Jiang, L., Jo, J. H., Johnson, R. A., Jwa, Y.-J., Kalra, D., Kamp, N., Kaneshige, N., Karagiorgi, G., Ketchum, W., Kirby, M., Kobilarcik, T., Kreslo, I., Lepetic, I., Li, K., Li, Y., Lin, K., Littlejohn, B. R., Louis, W. C., Luo, X., Manivannan, K., Mariani, C., Marsden, D., Marshall, J., Caicedo, D. A. Martinez, Mason, K., Mastbaum, A., McConkey, N., Meddage, V., Mettler, T., Miller, K., Mills, J., Mistry, K., Mogan, A., Mohayai, T., Moon, J., Mooney, M., Moor, A. F., Moore, C. D., Mora Lepin, L., Mousseau, J., Murphy, M., Naples, D., Navrer-Agasson, A., Nebot-Guinot, M., Neely, R. K., Newmark, D. A., Nowak, J., Nunes, M., Palamara, O., Paolone, V., Papadopoulou, A., Papavassiliou, V., Pate, S. F., Patel, N., Paudel, A., Pavlovic, Z., Piasetzky, E., Ponce-Pinto, I. D., Prince, S., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rice, L. C. J., Rochester, L., Rodriguez Rondon, J., Rosenberg, M., Ross-Lonergan, M., Scanavini, G., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sharankova, R., Shi, J., Sinclair, J., Smith, A., Snider, E. L., Soderberg, M., Söldner-Rembold, S., Spentzouris, P., Spitz, J., Stancari, M., St. John, J., Strauss, T., Sutton, K., Sword-Fehlberg, S., Szelc, A. M., Tang, W., Terao, K., Thorpe, C., Totani, D., Toups, M., Tsai, Y.-T., Uchida, M. A., Usher, T., Van De Pontseele, W., Viren, B., Weber, M., Wei, H., Williams, Z., Wolbers, S., Wongjirad, T., Wospakrik, M., Wresilo, K., Wright, N., Wu, W., Yandel, E., Yang, T., Yarbrough, G., Yates, L. E., Yu, H. W., Zeller, G. P., Zennamo, J., and Zhang, C.

Published
2022
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42. A Novel Computed Tomographic Angiography Tortuosity Index to Predict Successful Sentinel Cerebral Embolic Protection Delivery for Transcatheter Aortic Valve Replacement

Author

Denny Wang, BS, Craig Basman, MD, Sahar Mahani, MD, Arber Kodra, MD, Luigi Pirelli, MD, Priti Mehla, MD, Nirav Patel, MD, Jacob Scheinerman, MD, Nirmay Bhanderi, and Chad Kliger, MD

Subjects
Computed tomographic angiography, Embolic protection, Modeling, Tortuosity, Transcatheter aortic valve replacement, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Background: Percutaneous transradial placement of the Sentinel cerebral embolic protection device (CEPD) (Boston Scientific) is indicated during transcatheter aortic valve replacement to capture embolic material in patients without excessive tortuosity of the right subclavian/innominate arteries. We aimed to generate a quantitative tortuosity index (TI) from the preoperative computed tomographic angiography (CTA) as an objective measure of tortuosity to determine suitability for CEPD placement. Methods: Eighty-one patients considered for CEPD were included in this study. A centerline of the right subclavian/innominate arteries was generated from preoperative CTA scans. Three-dimensional Cartesian coordinates of landmarks along the centerline were used to calculate curvature. Tortuosity was derived as a change in angulation along each vessel segment. Peak and average TI values were calculated. Results: Sixty-seven patients had CEPD placement attempted. Unsuccessful CEPD placement occurred in 3 of 67 (4.4%) patients. The peak tortuosity for the successful, unsuccessful, and visually tortuous (not attempted) cohorts were 49.66 ± 11.96°/cm, 113.92 ± 5.70°/cm, and 70.44 ± 17.01°/cm, respectively. The peak and average TI of the successful cohort follows a normal distribution. A proposed TI cutoff for safe CEPD was peak tortuosity of 74°/cm and average tortuosity of 30°/cm, 2 standard deviations above the peak and average TI of the successful cohort. All unsuccessful CEPD patients fell outside the boundaries. Half of the visually tortuous patients were within the boundaries but did not have CEPD attempted. Conclusions: A novel TI based on preoperative CTA can assist in selecting patients for transradial CEPD. Our proposed quantitative tool may help to appropriately include and exclude patients for CEPD placement.

Published
2022
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43. Smartphone use and its addiction among adolescents in the age group of 16–19 years

Author

Dinesh J Bhanderi, Yogita P Pandya, and Deepak B Sharma

Subjects
addiction, adolescents, smartphone, Public aspects of medicine, RA1-1270
Abstract

Background: Smartphone use is escalating among adolescents, thereby increasing the risk of its addiction among them. Objective: The objective of this study was to estimate the prevalence of smartphone use and its addiction among adolescents in 16–19 years of age group. Materials and Methods: An observational cross-sectional study was conducted among 496 students in the age group of 16–19 years. Relevant information was collected using a self-administered questionnaire and the Smartphone Addiction (SA) Scale. Chi-square test and logistic regression were applied to study the association between independent and dependent variables. Results: Smartphone use was found to be 83.9%. It was associated with age, area of residence, discipline, use of hands-free kit, and parents' education and income. The smartphone addiction rate was reported to be 37%. It was found to be associated with age, area of residence, place of education, duration of smartphone use, daily hours of use, perception that cellphone use is harmful to health, and parents' education and income. Conclusion: A high rate of SA among adolescents warrants effective strategies at local, state, and national level to address this growing health problem in this population.

Published
2021
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44. Nonconventional driving force for selective oxidative C–C coupling reaction due to concurrent and curious formation of Ag0

Author

Khushboo Bhanderi, Prasanna S. Ghalsasi, and Katsuya Inoue

Subjects
Medicine, Science
Abstract

Abstract Is it possible to ‘explore’ metal’s intrinsic property—a cohesive interaction—which naturally transform M0 into an aggregate or a particle or film for driving oxidative C–C bond formation? With this intention, reduction of [Ag(NH3)2]+ to Ag0 with concurrent oxidation of different phenols/naphthols to biphenyls was undertaken. The work is originated during careful observation of an undergraduate experiment—Tollens’ test—where silver mirror film deposition takes place on the walls of borosilicate glass test tube. When the same reaction was carried out in polypropylene (plastic-Eppendorf) tube, we observed aggregation of Ag0 leading to floating Ag-particles but not silver film deposition. This prompted us to carry out challenging cross-coupling reaction by ONLY changing the surface of the reaction flask from glass to plastic to silicones. To our surprise, we observed good selective oxidative homo-coupling on Teflon surface while cross-coupling in Eppendorf vial. Thus, we propose that the formation of biphenyl is driven by the macroscopic growth of Ag0 into [Ag-particle] orchestrated by Ag…Ag cohesive interaction. To validate results, experiments were also performed on gram scale. More importantly, oxidation of β-naphthol carried out in quartz (chiral) tube which yielded slight enantioselective excess of BINOL. Details are discussed.

Published
2021
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46. Emerging Geospatial Technologies in Environmental Research, Education, and Outreach

Author

Sergio Bernardes, Margueritte Madden, Ashurst Walker, Andrew Knight, Nicholas Neel, Akshay Mendki, Dhaval Bhanderi, Andrew Guest, Shannon Healy, and Thomas Jordan

Subjects
environmental education, geovisualization, augmented reality, virtual reality, uas, photogrammetry, Geography. Anthropology. Recreation
Abstract

Drawing on the historical importance of visual interpretation for image understanding and knowledge discovery, emerging technologies in geovisualization are incorporated into research, education and outreach at the Center for Geospatial Research (CGR) in the Department of Geography at the University of Georgia (UGA), USA. This study aimed to develop the 3D Immersion and Geovisualization (3DIG) system consisting of uncrewed aerial systems (UAS) for data acquisition, augmented and virtual reality headsets and mobile devices, an augmented reality digital sandbox, and a video wall. We were working together integrated data products from the UAS imagery, including digital image mosaics and 3D models, and readily available gaming engine software to create augmented and virtual reality immersive visualizations. The use of 3DIG in research is demonstrated in a case study documenting the seasonal growth of vegetables in small gardens with a time series of 3D crop models generated from UAS imagery and Structure from Motion photogrammetry. Demonstrations of 3DIG in geography and geology courses, as well as public events, also indicate the benefits of emerging geospatial technologies for creating active learning environments and fostering participatory community engagement.

Published
2020
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47. Calorimetric classification of track-like signatures in liquid argon TPCs using MicroBooNE data

Author

Abratenko, P., An, R., Anthony, J., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnes, C., Barr, G., Basque, V., Bathe-Peters, L., Benevides Rodrigues, O., Berkman, S., Bhanderi, A., Bhat, A., Bishai, M., Blake, A., Bolton, T., Camilleri, L., Caratelli, D., Caro Terrazas, I., Castillo Fernandez, R., Cavanna, F., Cerati, G., Chen, Y., Church, E., Cianci, D., Conrad, J. M., Convery, M., Cooper-Troendle, L., Crespo-Anadón, J. I., Del Tutto, M., Dennis, S. R., Devitt, D., Diurba, R., Dorrill, R., Duffy, K., Dytman, S., Eberly, B., Ereditato, A., Evans, J. J., Fine, R., Fiorentini Aguirre, G. A., Fitzpatrick, R. S., Fleming, B. T., Foppiani, N., Franco, D., Furmanski, A. P., Garcia-Gamez, D., Gardiner, S., Ge, G., Gollapinni, S., Goodwin, O., Gramellini, E., Green, P., Greenlee, H., Gu, W., Guenette, R., Guzowski, P., Hagaman, L., Hall, E., Hamilton, P., Hen, O., Horton-Smith, G. A., Hourlier, A., Itay, R., James, C., Ji, X., Jiang, L., Jo, J. H., Johnson, R. A., Jwa, Y.-J., Kamp, N., Kaneshige, N., Karagiorgi, G., Ketchum, W., Kirby, M., Kobilarcik, T., Kreslo, I., LaZur, R., Lepetic, I., Li, K., Li, Y., Lin, K., Littlejohn, B. R., Louis, W. C., Luo, X., Manivannan, K., Mariani, C., Marsden, D., Marshall, J., Martinez Caicedo, D. A., Mason, K., Mastbaum, A., McConkey, N., Meddage, V., Mettler, T., Miller, K., Mills, J., Mistry, K., Mogan, A., Mohayai, T., Moon, J., Mooney, M., Moor, A. F., Moore, C. D., Mora Lepin, L., Mousseau, J., Murphy, M., Naples, D., Navrer-Agasson, A., Neely, R. K., Nowak, J., Nunes, M., Palamara, O., Paolone, V., Papadopoulou, A., Papavassiliou, V., Pate, S. F., Paudel, A., Pavlovic, Z., Piasetzky, E., Ponce-Pinto, I. D., Prince, S., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rice, L. C. J., Rochester, L., Rodriguez Rondon, J., Rogers, H. E., Rosenberg, M., Ross-Lonergan, M., Scanavini, G., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sharankova, R., Sinclair, J., Smith, A., Snider, E. L., Soderberg, M., Söldner-Rembold, S., Spentzouris, P., Spitz, J., Stancari, M., St. John, J., Strauss, T., Sutton, K., Sword-Fehlberg, S., Szelc, A. M., Tagg, N., Tang, W., Terao, K., Thorpe, C., Totani, D., Toups, M., Tsai, Y.-T., Uchida, M. A., Usher, T., Van De Pontseele, W., Viren, B., Weber, M., Wei, H., Williams, Z., Wolbers, S., Wongjirad, T., Wospakrik, M., Wright, N., Wu, W., Yandel, E., Yang, T., Yarbrough, G., Yates, L. E., Zeller, G. P., Zennamo, J., and Zhang, C.

Published
2021
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49. Cosmic Ray Background Removal With Deep Neural Networks in SBND

Author

R. Acciarri, C. Adams, C. Andreopoulos, J. Asaadi, M. Babicz, C. Backhouse, W. Badgett, L. Bagby, D. Barker, V. Basque, M. C. Q. Bazetto, M. Betancourt, A. Bhanderi, A. Bhat, C. Bonifazi, D. Brailsford, A. G. Brandt, T. Brooks, M. F. Carneiro, Y. Chen, H. Chen, G. Chisnall, J. I. Crespo-Anadón, E. Cristaldo, C. Cuesta, I. L. de Icaza Astiz, A. De Roeck, G. de Sá Pereira, M. Del Tutto, V. Di Benedetto, A. Ereditato, J. J. Evans, A. C. Ezeribe, R. S. Fitzpatrick, B. T. Fleming, W. Foreman, D. Franco, I. Furic, A. P. Furmanski, S. Gao, D. Garcia-Gamez, H. Frandini, G. Ge, I. Gil-Botella, S. Gollapinni, O. Goodwin, P. Green, W. C. Griffith, R. Guenette, P. Guzowski, T. Ham, J. Henzerling, A. Holin, B. Howard, R. S. Jones, D. Kalra, G. Karagiorgi, L. Kashur, W. Ketchum, M. J. Kim, V. A. Kudryavtsev, J. Larkin, H. Lay, I. Lepetic, B. R. Littlejohn, W. C. Louis, A. A. Machado, M. Malek, D. Mardsen, C. Mariani, F. Marinho, A. Mastbaum, K. Mavrokoridis, N. McConkey, V. Meddage, D. P. Méndez, T. Mettler, K. Mistry, A. Mogan, J. Molina, M. Mooney, L. Mora, C. A. Moura, J. Mousseau, A. Navrer-Agasson, F. J. Nicolas-Arnaldos, J. A. Nowak, O. Palamara, V. Pandey, J. Pater, L. Paulucci, V. L. Pimentel, F. Psihas, G. Putnam, X. Qian, E. Raguzin, H. Ray, M. Reggiani-Guzzo, D. Rivera, M. Roda, M. Ross-Lonergan, G. Scanavini, A. Scarff, D. W. Schmitz, A. Schukraft, E. Segreto, M. Soares Nunes, M. Soderberg, S. Söldner-Rembold, J. Spitz, N. J. C. Spooner, M. Stancari, G. V. Stenico, A. Szelc, W. Tang, J. Tena Vidal, D. Torretta, M. Toups, C. Touramanis, M. Tripathi, S. Tufanli, E. Tyley, G. A. Valdiviesso, E. Worcester, M. Worcester, G. Yarbrough, J. Yu, B. Zamorano, J. Zennamo, and A. Zglam

Subjects
deep learning, neutrino physics, SBN program, SBND, UNet, liquid Ar detectors, Electronic computers. Computer science, QA75.5-76.95
Abstract

In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons, and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded. In practice, this means that data from surface liquid argon time projection chambers will be dominated by cosmic particles, both as a source of event triggers and as the majority of the particle count in true neutrino-triggered events. In this work, we demonstrate a novel application of deep learning techniques to remove these background particles by applying deep learning on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, on a pixel-by-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.

Published
2021
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50. Critical care usage after major gastrointestinal and liver surgery: a prospective, multicentre observational study

Author

McLean, K.A., Glasbey, J.C., Borakati, A., Brooks, T.M., Chang, H.M., Choi, S.M., Goodson, R., Nielsen, M., Pronin, S., Salloum, N.L., Sewart, E., Vanniasegaram, D., Drake, T.M., Gillies, M.A., Harrison, E.M., Chapman, S.J., Khatri, C., Kong, C.Y., Claireaux, H.A., Bath, M.F., Mohan, M., McNamee, L., Kelly, M., Mitchell, H., Fitzgerald, J.E., Bhangu, A., Nepogodiev, D., Antoniou, I., Dean, R., Davies, N., Trecarten, S., Henderson, I., Holmes, C., Wylie, J., Shuttleworth, R.H., Jindal, A., Hughes, F., Gouda, P., Fleck, R., Hanrahan, M., Karunakaran, P., Chen, J.H., Sykes, M.C., Sethi, R.K., Suresh, S., Patel, P., Patel, M., Varma, R.K., Mushtaq, J., Gundogan, B., Bolton, W., Khan, T., Burke, J., Morley, R., Favero, N., Adams, R., Thirumal, V., Kennedy, E.D., Ong, K.K., Tan, Y.H., Gabriel, J., Bakhsh, A., Low, J.Y.L., Yener, A., Paraoan, V., Preece, R., Tilston, T.W., Cumber, E., Dean, S., Ross, T., McCance, E., Amin, H., Satterthwaite, L., Clement, K.D., Gratton, R., Mills, E.D., Chiu, S.M., Hung, G., Rafiq, N.M., Hayes, J.D.B., Robertson, K.L., Dynes, K., Huang, H.C., Assadullah, S., Duncumb, J.W., Moon, R.D.C., Poo, S.X., Mehta, J.K., Joshi, K.R., Callan, R., Norris, J.M., Chilvers, N.J., Keevil, H., Jull, P., Mallick, S., Elf, D., Carr, L., Player, C., Barton, E.C., Martin, A.L., Ratu, S.G., Roberts, E.J., Phan, P.N., Dyal, A.R., Rogers, J.E., Henson, A.D., Reid, N.B., Burke, D., Culleton, G., Lynne, S., Mansoor, S., Brennan, C., Blessed, R., Holloway, C., Hill, A., Goldsmith, T., Mackin, S., Kim, S., Woin, E., Brent, G., Coffin, J., Ziff, O., Momoh, Z., Debenham, R., Ahmed, M., Yong, C.S., Wan, J.C., Copley, H.C., Raut, P., Chaudhry, F.I., Nixon, G., Dorman, C., Tan, R., Kanabar, S., Canning, N., Dolaghan, M., Bell, N., McMenamin, M., Chhabra, A., Duke, K., Turner, L., Patel, T., Chew, L.S., Mirza, M., Lunawat, S., Oremule, B., Ward, N., Khan, M., Tan, E.T., Maclennan, D., McGregor, R.J., Chisholm, E.G., Griffin, E.J., Bell, L., Hughes, B.A., Davies, J., Haq, H., Ahmed, H., Ungcharoen, N., Whacha, C., Thethi, R., Markham, R.M., Lee, A.H.Y., Batt, E., Bullock, N.P., Francescon, C.T., Davies, J.E., Shafiq, N.M., Zhao, J., Vivekanantham, S., Barai, I., Allen, J.L.Y., Marshall, D.C., McIntyre, C.J., Wilson, H.C.P., Ashton, A.J., Lek, C., Behar, N., Davis-Hall, M., Seneviratne, N., Esteve, L., Sirakaya, M., Ali, S., Pope, S., Ahn, J.S., Craig-McQuaide, A., Gatfield, W.A., Leong, S., Demetri, A.M., Kerr, A.L., Rees, C., Loveday, J., Liu, S., Wijesekera, M., Maru, D., Attalla, M., Smith, N., Brown, D., Sritharan, P., Shah, A., Charavanamuttu, V., Heppenstall-Harris, G., Ng, K., Raghvani, T., Rajan, N., Hulley, K., Moody, N., Williams, M., Cotton, A., Sharifpour, M., Lwin, K.N., Bright, M., Chitnis, A.R., Abdelhadi, M., Semana, A.D., Morgan, F., Reid, R., Dickson, J., Anderson, L., McMullan, R., Ahern, N., Asmadi, A., Anderson, L.B., Boon Xuan, J. Lua, Crozier, L., McAleer, S., Lees, D.M., Adebayo, A.A., Das, M., Amphlett, A.H., Al-Robeye, A., Valli, A., Khangura, J., Winarski, A., Ali, A., Woodward, H., Gouldthrope, C., Turner, M., Sasapu, K., Tonkins, M., Wild, J.R.L., Robinson, M., Hardie, J., Heminway, R., Narramore, R., Ramjeeawon, N., Hibberd, A., Winslow, F., Ho, W., Chong, B.F., Lim, K., Ho, S., Crewdson, J.A., Singagireson, S., Kalra, N., Koumpa, F., Jhala, H., Soon, W.C., Karia, M., Rasiah, M.G., Xylas, D., Gilbert, H., Sundar-Singh, M., Wills, J., Akhtar, S., Patel, S., Hu, L., Brathwaite-Shirley, C., Nayee, H., Amin, O., Rangan, T., Turner, E.J.H., McCrann, C., Shepherd, R., Patel, N., Prest-Smith, J., Auyoung, E., Murtaza, A., Coates, A., Prys-Jones, O., King, M., Gaffney, S., Dewdney, C.J., Nehikhare, I., Lavery, J., Bassett, J., Davies, K., Ahmad, K., Collins, A., Acres, M., Egerton, C., Cheng, K., Chen, X., Chan, N., Sheldon, A., Khan, S., Empey, J., Ingram, E., Malik, A., Johnstone, M., Goodier, R., Shah, J.P., Giles, J.E., Sanders, J.A., McLure, S.W., Pal, S., Rangedara, A., Baker, A.N., Asbjoernsen, C.A., Girling, C., Gray, L., Gauntlett, L., Joyner, C., Qureshi, S., Mogan, Y.P., Ng, J.C.K., Kumar, A.N., Park, J.H., Tan, D., Choo, K.P., Raman, K.P., Buakuma, P., Xiao, C., Govinden, S., Thompson, O.D., Charalambos, M.A., Brown, E., Karsan, R.B., Dogra, T., Bullman, L.M., Dawson, P.M., Frank, A.L., Abid, H., Tung, L., Qureshi, U., Tahmina, A., Matthews, B.W., Harris, R.T., O'Connor, A., Mazan, K., Iqbal, S., Stanger, S.A., Thompson, J.D., Sullivan, J.A.L., Uppal, E., MacAskill, A., Bamgbose, F.A., Neophytou, C., Carroll, A.F., Rookes, C.W., Datta, U., Dhutia, A.J., Rashid, S., Ahmed, N., Lo, T., Bhanderi, S., Blore, C.D., Ahmed, S., Shaheen, H., Abburu, S., Majid, S., Abbas, Z., Talukdar, S.S., Burney, L.J., Patel, J.B., Al-Obaedi, O., Roberts, A.W., Mahboob, S., Singh, B., Sheth, S., Karia, P., Prabhudesai, A., Kow, K., Koysombat, K., Wang, S., Morrison, P., Maheswaran, Y., Keane, P., Copley, P.C., Brewster, O., Xu, G.X., Harries, P., Wall, C., Al-Mousawi, A., Bonsu, S., Cunha, P., Ward, T., Paul, J., Nadanakumaran, K., Tayeh, S., Holyoak, H., Remedios, J., Theodoropoulou, K., Luhishi, A., Jacob, L., Long, F., Atayi, A., Sarwar, S., Parker, O., Harvey, J., Ross, H., Rampal, R., Thomas, G., Vanmali, P., McGowan, C., Stein, J., Robertson, V., Carthew, L., Teng, V., Fong, J., Street, A.N., Thakker, C.E., O'Reilly, D., Bravo, M., Pizzolato, A., Khokhar, H.A., Ryan, M., Cheskes, L., Carr, R., Salih, A.E., Bassiony, S., Yuen, R., Chrastek, D., Rosen O'Sullivan, H., Amajuoyi, A., Wang, A., Sitta, O., Wye, J., Qamar, M.A., Major, C., Kaushal, A., Morgan, C., Petrarca, M., Allot, R., Verma, K., Dutt, S., Chilima, C.P., Peroos, S., Kosasih, S.R., Chin, H., Ashken, L., Pearse, R.J., O'Loughlin, R.A., Menon, A., Singh, K., Norton, J., Sagar, R., Jathanna, N., Rothwell, L., Watson, N., Harding, F., Dube, P., Khalid, H., Punjabi, N., Sagmeister, M., Gill, P., Shahid, S., Hudson-Phillips, S., George, D., Ashwood, J., Lewis, T., Dhar, M., Sangal, P., Rhema, I.A., Kotecha, D., Afzal, Z., Syeed, J.A., Prakash, E., Jalota, P., Herron, J., Kimani, L., Delport, A., Shukla, A., Agarwal, V., Parthiban, S., Thakur, H., Cymes, W., Rinkoff, S., Turnbull, J.A., Hayat, M., Darr, S., Khan, U., Lim, J., Higgins, A., Lakshmipathy, G., Forte, B., Canning, E., Jaitley, A., Lamont, J., Toner, E., Ghaffar, A., McDowell, M., Salmon, D., O'Carroll, O., Khan, A., Kelly, M.E., Clesham, K., Palmer, C., Lyons, R., Bell, A., Chin, R., Waldron, R.M., Trimble, A., Cox, S.E., Ashfaq, U., Campbell, J., Holliday, R.B.S., McCabe, G., Morris, F., Priestland, R., Vernon, O.K., Ledsam, A., Vaughan, R., Lim, D., Bakewell, Z.R., Hughes, R.K., Koshy, R.M., Jackson, H.R., Narayan, P., Cardwell, A.E., Jubainville, C.L., Arif, T., Elliott, L.E., Gupta, V., Bhaskaran, G., Odeleye, A., Ahmed, F., Shah, R., Pickard, J., Suleman, Y.N., North, A.S., McClymont, L.F., Hussain, N., Ibrahim, I., Ng, G.S., Wong, V., Lim, A.E., Harris, L.N., Tharmachandirar, T., Mittapalli, D., Patel, V., Lakhani, M., Bazeer, H.Z., Narwani, V., Sandhu, K.K., Wingfield, L.R., Gentry, S., Adjei, H., Bhatti, M., Braganza, L., Barnes, J., Mistry, S., Chillarge, G., Stokes, S., Cleere, J., Wadanamby, S., Bucko, A.M., Meek, J., Boxall, N., Heywood, E.G., Wiltshire, J.J., Toh, C., Ward, A.E., Shurovi, B.N., Horth, D., Patel, B.Y., Ali, B., Spencer, T., Axelson, T., Kretzmer, L., Chhina, C., Anandarajah, C., Fautz, T., Horst, C., Thevathasan, A.A., Ng, J.Q., Hirst, F., Brewer, C.F., Logan, A.E., Lockey, J.W., Forrest, P.R., Keelty, N., Wood, A.D., Springford, L.R., Avery, P., Schulz, T.M., Bemand, T.P., Howells, L., Collier, H., Khajuria, A., Tharakan, R.G., Parsons, S., Buchan, A.M., McGalliard, R.J., Mason, J.D., Cundy, O.J., Li, N., Redgrave, N.A., Watson, R.P., Pezas, T.P., Dennis, Y.F., Segall, E., Hameed, M., Lynch, A.S., Chamberlain, M., Peck, F.S., Neo, Y.N., Russell, G., Elseedawy, M., Lee, S., Foster, N.L., Soo, Y.H., Puan, L., Dennis, R., Goradia, H., Qureshi, A., Osman, S., Reeves, T., Dinsmore, L., Marsden, M., Lu, Q., Pitts-Tucker, T., Dunn, C.E., Walford, R.A., Heathcote, E., Martin, R., Pericleous, A., Brzyska, K., Reid, K.G., Williams, M.R., Wetherall, N., McAleer, E., Thomas, D., Kiff, R., Milne, S., Holmes, M.J.V., Bartlett, J., Lucas de Carvalho, J., Bloomfield, T., Tongo, F., Bremner, R.H., Yong, N., Atraszkiewicz, B.A., Mehdi, A., Tahir, M., Sherliker, G.X.J., Tear, A.K., Pandey, A., Broyd, A., Omer, H.M., Raphael, M., Chaudhry, W.W., Shahidi, S., Jawad, A.S., Gill, C.K., Fisher, I. Hindle, Adeleja, I., Clark, I.J., Aidoo-Micah, G.E., Stather, P.W., Salam, G.J., Glover, T.E., Deas, G., Sim, N.K., Obute, R.D., Wynell-Mayow, W.M., Sait, M.S., Mitha, N., de Bernier, G.L., Siddiqui, M., Shaunak, R., Wali, A., Cuthbert, G., Bhudia, R., Webb, E., Shah, S., Ansari, N., Perera, M., Kelly, N., McAllister, R., Stanley, G.H., Keane, C.P., Shatkar, V., Maxwell-Armstrong, C., Henderson, L.A., Maple, N., Manson, R., Adams, R.D., Semple, E., Mills, M., Daoub, A., Marsh, A., Ramnarine, A., Hartley, J., Malaj, M., Jewell, P.D., Whatling, E.A., Hitchen, N., Chen, M., Goh, B., Fern, J., Rogers, S., Derbyshire, L., Robertson, D.T., Abuhussein, N., Deekonda, P., Abid, A., Harrison, P.L.M., Aildasani, L., Turley, H., Sherif, M.A., Pandey, G., Filby, J.J., Johnston, A., Burke, E., Mohamud, M., Gohil, K., Tsui, A.Y., Singh, R., Lim, S.J., O'Sullivan, K., McKelvey, L.L., O'Neill, S., Roberts, H.F., Brown, F.S., Cao, Y., Buckle, R.T., Liew, Y., Sii, S., Ventre, C.M., Graham, C.J., Filipescu, T., Yousif, A., Dawar, R., Wright, A., Peters, M., Varley, R., Owczarek, S., Hartley, S., Khattak, M., Iqbal, A., Ali, M., Durrani, B., Narang, Y., Bethell, G.S., Horne, L., Pinto, R., Nicholls, K., Kisyov, I., Torrance, H.D., English, W., Lakhani, S.M., Ashraf, S.F., Venn, M., Elangovan, V., Kazmi, Z., Brecher, J., Sukumar, S., Mastan, A., Mortimer, A., Parker, J., Boyle, J., Elkawafi, M., Beckett, J., Mohite, A., Narain, A., Mazumdar, E., Sreh, A., Hague, A., Weinberg, D., Fletcher, L., Steel, M., Shufflebotham, H., Masood, M., Sinha, Y., Jenvey, C., Kitt, H., Slade, R., Craig, A.R., Deall, C., Reakes, T., Chervenkoff, J., Strange, E., O'Bryan, M., Murkin, C., Joshi, D., Bergara, T., Naqib, S., Wylam, D., Scotcher, S.E., Hewitt, C.M., Stoddart, M.T., Kerai, A., Trist, A.J., Cole, S.J., Knight, C.L., Stevens, S., Cooper, G.E., Ingham, R., Dobson, J., O'Kane, A., Moradzadeh, J., Duffy, A., Henderson, C., Ashraf, S., McLaughin, C., Hoskins, T.C., Reehal, R.S., Bookless, L.R., McLean, R.C., Stone, E.J., Wright, E.V., Abdikadir, H.R., Roberts, C., Spence, O., Srikantharajah, M., Ruiz, E.M., Matthews, J.H., Gardner, E., Hester, E., Naran, P., Simpson, R., Minhas, M., Cornish, E., Semnani, S.A., Rojoa, D., Radotra, A., Eraifej, J., Eparh, K., Smith, D.N.E., Mistry, B.D., Hickling, S.L., Din, W., Liu, C., Mithrakumar, P., Mirdavoudi, V., Rashid, M., Mcgenity, C., Hussain, O., Kadicheeni, M., Gardner, H., Anim-Addo, N., Pearce, J., Aslanyan, A., Ntala, C., Sorah, T., Parkin, J., Alizadeh, M., White, A., Edozie, F., Johnston, J., Kahar, A., Navayogaarajah, V., Patel, B., Carter, D., Khonsari, P., Burgess, A., Kong, C., Ponweera, A., Cody, A., Tan, Y., Ng, A.Y.L., Croall, A., Allan, C., Ng, S., Raghuvir, V., Telfer, R., Greenhalgh, A.D., McKerr, C.N., Edison, M.A., Patel, B.A., Dear, K., Hardy, M.R., Williams, P., Hassan, S., Sajjad, U., O'Neill, E.M., Lopes, S., Healy, L., Jamal, N., Tan, S., Lazenby, D., Husnoo, S.B., Beecroft, S., Sarvanandan, T., Weston, C., Bassam, N., Rabinthiran, S., Hayat, U., Ng, L., Varma, D., Sukkari, M., Mian, A., Omar, A., Kim, J.W., Sellathurai, J., Mahmood, J., O'Connell, C., Bose, R., Heneghan, H., Lalor, P., Matheson, J., Doherty, C., Cullen, C., Cooper, D., Angelov, S., Drislane, C., Smith, A.C.D., Kreibich, A., Palkhi, E., Durr, A., Lotfallah, A., Gold, D., Mckean, E., Dhanji, A., Anilkumar, A., Thacoor, A., Siddiqui, Z.H., Lim, S., Piquet, A., Anderson, S.M., McCormack, D.R., Gulati, J., Ibrahim, A., Murray, S.E., Walsh, S.L., McGrath, A., Ziprin, P., Chua, E.Y., Lou, C.N., Bloomer, J., Paine, H.R., Osei-Kuffour, D., White, C.J., Szczap, A., Gokani, S., Patel, K., Malys, M.K., Reed, A., Torlot, G.E., Cumber, E.M., Charania, A., Ahmad, S., Varma, N., Cheema, H., Austreng, L., Petra, H., Chaudhary, M., Zegeye, M.I., Cheung, F., Coffey, D., Heer, R.S., Singh, S., Seager, E., Cumming, S., Suresh, R.S., Verma, S., Ptacek, I.B., Gwozdz, A.M., Yang, T., Khetarpal, A.A., Shumon, S., Fung, T.M.P., Leung, W., Kwang, P., Chew, L., Loke, W., Curran, A., Chan, C., McGarrigle, C., Mohan, K., Cullen, S., Wong, E., Toale, C., Collins, D., Keane, N., Traynor, B.P., Shanahan, D., Yan, A., Jafree, D.J., Topham, C., Mitrasinovic, S., Omara, S., Bingham, G., Lykoudis, P.M., Miranda, B.H., Whitehurst, K., Kumaran, G., Devabalan, Y., Aziz, H., Shoa, M., Dindyal, S., Yates, J.A., Bernstein, I., Rattan, G., Coulson, R., Stezaker, S., Isaac, A., Salem, M., McBride, A., McFarlane, H., Yow, L., MacDonald, J., Bartlett, R.D., Turaga, S., White, U., Liew, W., Yim, N., Ang, A., Simpson, A., McAuley, D., Craig, E., Murphy, L., Shepherd, P., Kee, J.Y., Abdulmajid, A., Chung, A., Warwick, H.L., Livesey, A., Holton, P., Theodoreson, M.D., Jenkin, S.L., Turner, J., Entwisle, J.H., Marchal, S.T., O'Connor, S., Blege, H.K., Aithie, J.M., Sabine, L.M., Stewart, G.E., Jackson, S., Kishore, A., Lankage, C.M., Acquaah, F., Joyce, H.L., McKevitt, K.L., Coffey, C.J., Fawaz, A.S., Dolbec, K.S., O'Sullivan, D.A., Geraghty, J.M., Lim, E., Bolton, L., FitzPatrick, D., Robinson, C., Ramtoola, T., Collinson, S., Grundy, L., McEnhill, P.M., Harbhajan Singh, G.S., Loughran, D., Golding, D.M., Keeling, R.E., Williams, R.P., Whitham, R.D.J., Yoganathan, S., Nachiappan, R., Egan, R.J., Owasil, R., Kwan, M.L., He, A., Goh, R.W., Bhome, R., Wilson, H., Teoh, P.J., Raji, K., Jayakody, N., Matthams, J., Chong, J., Luk, C.Y., Greig, R.J., Trail, M., Charalambous, G., Rocke, A.S., Gardiner, N., Bulley, F., Warren, N., Brennan, E., Fergurson, P., Wilson, R., Whittingham, H., Brown, E.J., Khanijau, R., Gandhi, K., Morris, S., Boulton, A.J., Chandan, N., Barthorpe, A.E., Maamari, R., Sandhu, S., McCann, M., Higgs, L., Balian, V., Reeder, C., Diaper, C., Sale, T., Ali, H., Archer, C.H., Clarke, A.K., Heskin, J., Hurst, P.C., Farmer, J.D., O'Flynn, L.D., Doan, L., Shuker, B.A., Stott, G.D., Vithanage, N.A., Hoban, K.A., Nesargikar, P.N., Kennedy, H.R., Grossart, C.M., Tan, E.S.M., Roy, C.S.D., Sim, P., Leslie, K.E., Sim, D., Abul, M.H., Cody, N., Tay, A.Y., Woon, E., Sng, S., Mah, J., Robson, J., Shakweh, E., Wing, V.C., Mills, H., Li, M.M., Barrow, T.R., Balaji, S., Jordan, H.E.M., Phillips, C., Naveed, H., Hirani, S., Tai, A., Ratnakumaran, R., Sahathevan, A., Shafi, A.M.A., Seedat, M., Weaver, R., Batho, A., Punj, R., Selvachandran, H., Bhatt, N., Botchey, S., Khonat, Z., Brennan, K., Morrison, C.J., Devlin, E., Linton, A., Galloway, E., McGarvie, S., Ramsay, N., McRobbie, H.D., Whewell, H., Dean, W., Nelaj, S., Eragat, M., Mishra, A., Kane, T., Zuhair, M., Wells, M., Wilkinson, D., Woodcock, N., Sun, E., Aziz, N., and Ghaffar, M. K. Abd

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2019
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