Omega-3 PUFAs and vitamin D co-supplementation as a safe-effective therapeutic approach for core symptoms of autism spectrum disorder: case report and literature review

Introduction: Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders characterized by abnormal development of cognitive, social, and communicative skills. Although ASD aetiology and pathophysiology are still unclear, various nutritional factors have been investigated as potential risk factors for ASD development, including omega-3 polyunsaturated fatty acids (PUFAs) and vitamin D deficiency. In fact, both omega-3 PUFAs and vitamin D are important for brain development and function. Case report: Herein, we report the case of a 23-year-old young adult male with autism who was referred to our Unit due to a 12-month history of cyclic episodes of restlessness, agitation, irritability, oppositional and self-injurious behaviours. Laboratory tests documented a markedly altered omega-6/omega-3 balance, along with a vitamin D deficiency, as assessed by serum levels of 25-hydroxyvitamin D. Omega-3 and vitamin D co-supplementation was therefore started, with remarkable improvements in ASD symptoms throughout a 24-month follow-up period. A brief review of the literature for interventional studies evaluating the efficacy of omega-3 or vitamin D supplementation for the treatment of ASD-related symptoms is also provided. Conclusion: To our knowledge, this is the first case reporting remarkable beneficial effects on ASD symptoms deriving from omega-3 and vitamin D combination therapy. This case report suggests omega-3 and vitamin D co-supplementation as a potential safe-effective therapeutic strategy to treat core symptoms of ASD. However, larger studies are needed to evaluate the real efficacy of such therapeutic approach in a broader sample of ASD patients

La computadora en la enseñanza de un primer curso de álgebra lineal en una Facultad de Ingeniería Química

El propósito de este trabajo es analizar esas cuestiones en el caso de la materia Matemática Básica, dictada en la Facultad de Ingeniería Química (FIQ) de la Universidad Nacional del Litoral (UNL) durante el primer semestre de 1998. Se considera ésta una ocasión especialmente faYorable, ya que recientes reformas realizadas por la UNL han establecido solo dos modalidades para el primer curso de matemática de todas las carreras de esta universidad. En el caso de la FIQ, esto significó el dictado de una asignatura conjunta para los alumnos ingresantes de las seis carreras que se cursan: Ingeniería en Alimentos (IA), Ingeniería Industrial(II), Ingeniería Química (IQ), Licenciatura en Matemática (LMA). Licenciatura en Química (LQ) y Técnico universitario Asistente Gerencial (TUAG). El equipo docente de esta asignatura estuvo integrado por los autores de este trabajo, a cargo de las clases teórico-prácticas y supervisión de las clases de laboratorio, y por los auxiliares de docencia: Jorge D'Elía, Adriana Frausin, Egle Haye, Ana Kanaslúro y Mlarcela Porta; encargados de las prácticas en computadora. El trabajo presenta en el punto 2 una descripción somera del curso. En el ítem 3 se analizan encuestas que recogen la opinión de estudiantes y docentes. como también las principales estadísticas de aprobación del primer cuatrimestre de 1998. En el punto 4 se exponen las conclusiones obtenidas. Finalmente, tres apéndices ilustran el desarrollo del trabajo, incluyendo: una guía de trabajos prácticos y un modelo de informe. en el Apéndice A: resúmenes de las encuestas realizadas, en el Apéndice B y ejemplos de ejercicios que se realizan usando el MATLAB, en el Apéndice C

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

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

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

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

Radiation tolerance of the CMS forward pixel detector

In this paper we present some results on the radiation tolerance of the CMS forward pixel detector. They were obtained from a beam test at Fermilab of a pixel-detector module, which was previously irradiated up to a maximum dose of 45 Mrad of protons at 200 MeV. It is shown that CMS forward pixel detector can tolerate this radiation dose without any major deterioration of its performance. © 2008 Elsevier B.V

Design and construction of the MicroBooNE Cosmic Ray Tagger system

The MicroBooNE detector utilizes a liquid argon time projection chamber (LArTPC) with an 85 t active mass to study neutrino interactions along the Booster Neutrino Beam (BNB) at Fermilab. With a deployment location near ground level, the detector records many cosmic muon tracks in each beam-related detector trigger that can be misidentified as signals of interest. To reduce these cosmogenic backgrounds, we have designed and constructed a TPC-external Cosmic Ray Tagger (CRT). This sub-system was developed by the Laboratory for High Energy Physics (LHEP), Albert Einstein center for fundamental physics, University of Bern. The system utilizes plastic scintillation modules to provide precise time and position information for TPC-traversing particles. Successful matching of TPC tracks and CRT data will allow us to reduce cosmogenic background and better characterize the light collection system and LArTPC data using cosmic muons. In this paper we describe the design and installation of the MicroBooNE CRT system and provide an overview of a series of tests done to verify the proper operation of the system and its components during installation, commissioning, and physics data-taking

The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. We will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data

Ionization Electron Signal Processing in Single Phase LArTPCs II. Data/Simulation Comparison and Performance in MicroBooNE

The single-phase liquid argon time projection chamber (LArTPC) provides a large amount of detailed information in the form of fine-grained drifted ionization charge from particle traces. To fully utilize this information, the deposited charge must be accurately extracted from the raw digitized waveforms via a robust signal processing chain. Enabled by the ultra-low noise levels associated with cryogenic electronics in the MicroBooNE detector, the precise extraction of ionization charge from the induction wire planes in a single-phase LArTPC is qualitatively demonstrated on MicroBooNE data with event display images, and quantitatively demonstrated via waveform-level and track-level metrics. Improved performance of induction plane calorimetry is demonstrated through the agreement of extracted ionization charge measurements across different wire planes for various event topologies. In addition to the comprehensive waveform-level comparison of data and simulation, a calibration of the cryogenic electronics response is presented and solutions to various MicroBooNE-specific TPC issues are discussed. This work presents an important improvement in LArTPC signal processing, the foundation of reconstruction and therefore physics analyses in MicroBooNE.Comment: 54 pages, 36 figures; the first part of this work can be found at arXiv:1802.0870

A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber

We have developed a convolutional neural network (CNN) that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a $\nu_\mu$ charged current neutral pion data samples

Ionization Electron Signal Processing in Single Phase LArTPCs I. Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation

We describe the concept and procedure of drifted-charge extraction developed in the MicroBooNE experiment, a single-phase liquid argon time projection chamber (LArTPC). This technique converts the raw digitized TPC waveform to the number of ionization electrons passing through a wire plane at a given time. A robust recovery of the number of ionization electrons from both induction and collection anode wire planes will augment the 3D reconstruction, and is particularly important for tomographic reconstruction algorithms. A number of building blocks of the overall procedure are described. The performance of the signal processing is quantitatively evaluated by comparing extracted charge with the true charge through a detailed TPC detector simulation taking into account position-dependent induced current inside a single wire region and across multiple wires. Some areas for further improvement of the performance of the charge extraction procedure are also discussed.Comment: 60 pages, 36 figures. The second part of this work can be found at arXiv:1804.0258