Comparison of Methods for Detection of Listeria on Wooden Shelves used for Cheese Aging: Challenges Associated with Sampling Porous Surfaces

This thesis examined the efficacy of various sampling and detection methods used for environmental monitoring of Listeria species on wooden surfaces used for cheese aging. Government agencies including the Food and Drug Administration (FDA) and United States Department of Agriculture (USDA) recommend enrichment methods coupled with use of environmental sponges and swabs. Our study compared efficacy of sponge swabs manufactured by 3M™ and World Bioproducts. There is a lack of research validating the best performing swab type and enrichment method combination that is sensitive when used on rough, porous surfaces. The sensitivity of these environmental sampling tools and methods are critical considerations to effectively monitor the presence of Listeria species on wooden boards used during aging of artisan cheese. Seasoned spruce wooden shelves, cut into 100cm2 replicates, were spot inoculated with varying concentrations of Listeria species inocula, the Listeria species strains consisted of two L. monocytogenes strains and a Green Florescence Protein (GFP) expressing strain of L. innocua. The inoculated wooden surface was swabbed with three environmental sampling sponge/swab formats (World Bioproducts© EZ ReachTM environmental swabs (WBEZ) with HiCap (WBHC) and Dey-Engley (WBDE) neutralizing broths; and 3MTM environmental swabs (3MTM) with Dey-Engley neutralizing broth). Enumeration methods were used to determine the low target limits of detection. Once the low target concentrations were identified, five enrichment methods consisting of 3MTM Listeria Environmental Plate, FDA, Dual Enrichment, modified USDA, and modified FDA were challenged against low concentrations of Listeria species inocula (0.01 cfu/cm2, 0.1 cfu/cm2, 1 cfu/cm2) and the three environmental sponge swab formats. Performance of the swab formats was assessed by collection of naturally contaminated environmental samples (n=405) from dairy farm environments, swabbing where wooden surfaces existed, and analyzed using the most effective enrichment methods found from previous experiments. Lastly, the wooden surfaces and sponge swabs were observed under a Florescent Microscope using GFP L. innocua to visually determine how each sponge material of the 3M™ and World Bioproducts recovered the inocula. When wood surfaces were inoculated at high concentration levels of Listeria spp., all swab formats performed equally for detecting Listeria. Success of positive recovery at low concentrations was variable, where enrichment methods and swabs were not dependent on each other. The swab format that worked best for detecting low levels of Listeria species was the WBDE sponge swab. The WBDE swab also performed the best in dairy farm environmental sampling. The m-USDA enrichment method was found to be most effective in recovery and repair of low and potentially injured Listeria spp. Wooden surfaces are rough and porous and should be taken into consideration when creating an environmental sampling plan for these food contact surfaces. All swabs and methods performed with only slight variation, but the variation could be significant when monitoring wooden shelves with low level contamination of Listeria species. Artisan cheesemakers who use wooden shelves during the aging of their cheese, should ensure use of the most sensitive detection methods

Tomographic Image Reconstruction of Fan-Beam Projections with Equidistant Detectors using Partially Connected Neural Networks

We present a neural network approach for tomographic imaging problem using interpolation methods and fan-beam projections. This approach uses a partially connected neural network especially assembled for solving tomographic\ud reconstruction with no need of training. We extended the calculations to perform reconstruction with interpolation and to allow tomography of fan-beam geometry. The main goal is to aggregate speed while maintaining or improving the quality of the tomographic reconstruction process

Comment on "The pros and cons of continuous glucose monitoring for patients with type 1 diabetes on multiple daily injections of insulin."

n/

Iterative Application of the aiNET Algorithm in the Construction of a Radial Basis Function Neural Network

This paper presents some of the procedures adopted in the construction of a Radial Basis Function Neural Network by iteratively applying the aiNET, an Artificial Immune Systems Algorithm. These procedures have shown to be effective in terms of i) the free determination of centroids inspired by an immune heuristics; and ii) the achievement of appropriate minimal square errors after a number of iterations. Experimental and empirical results are compared aiming at confirming (or not) some hypotheses

Potentialities of Gene Therapy in Pediatric Endocrinology

Gene therapy has become an appealing therapeutic option in many pediatric fields, including endocrinology. Unlike traditional drugs based on molecules that require repeated and frequent burdensome administrations, a single genetic therapeutic intervention may allow durable and curative clinical benefits. Although this highly innovative technology holds a great promise for the treatment of monogenic diseases, its clinical applications in the field of endocrinology have been so far challenging. In this review, we will discuss various ex vivo and in vivo approaches and potential applications of gene addition and gene editing approaches for treating hyperfunctional and hypofunctional endocrine diseases due to intrinsic defects or autoimmune origin. We will focus on the recent advances in gene therapy approaches aimed at treating type 1 diabetes and monogenic forms of endocrinopathies such as growth hormone deficiency, congenital adrenal hyperplasia, diabetes insipidus, IPEX, as well as their trends and future directions

Framework para engenharia e processamento de ontologias utilizando computação quântica

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnoclógico, Programa de Pós-Graduação em Engenharia e Gestão do Conhecimento, Florianópolis, 2010Ontologias são recursos largamente utilizados para a representação de conhecimento em sistemas inteligentes. Ao longo do tempo, novos conhecimentos são adicionados e tais ontologias tendem a se tornar redes de complexidade crescente. Esta tese tem como objetivo trazer para a área da Engenharia Ontológica os benefícios de performance e representação que podem ser alcançados a partir do uso da Computação Quântica, a qual tem se mostrado vantajosa em áreas como a criptografia e buscas em conjuntos não ordenados. A abordagem é proposta a partir de um framework constituído dos seguintes conceitos derivados: superposição de classes, superposição de instâncias, superposição de relações e emaranhamento de classes. É demonstrado o uso de algoritmos quânticos para a superposição de classes e instâncias em ontologias, assim como aplicações sobre emaranhamento de classes. O trabalho também inclui um simulador para Computação Quântica como ferramenta de apoio na construção dos algoritmos, visualização dos circuitos quânticos e testes experimentais. A partir da ideia do armazenamento de estados superpostos por um tempo mais longo, o framework evolui para um modelo de representação de conhecimento em ontologias baseado no paradigma quântico. Sob esta ótica, são discutidas ramificações quanto à semelhança com o pensamento simbólico da mente humana e ainda o questionamento da própria definição de ontologias.Ontologies are resources widely used for representing knowledge in intelligent systems. Through the years, new knowledge has been added and such ontologies tend to become more and more complex networks. This paper is focused on the benefits of performance and representation for the Ontologies Engineering area, which can be obtained from the use of the Quantum Computing concepts. This fact has been considerely advantageous in certain science computing areas, such as encryption and searching in unordered sets. The approach is proposed through a framework that shows the following derived concepts: superposition of classes, entanglement of classes, superposition of instances and superposition of relations. It is demonstrated the use of quantum algorithms for superposition of instances and classes in ontologies, as well as some possible applications in entanglement of classes. The study also includes a Quantum Computing simulator as a helping tool in building algorithms, visualizing quantum circuits and experimental testing. From the idea of storing the quantum states in a superposition for longer periods of time, the framework evolves to a representation model based on the quantum paradigm. Under this perspective, there are some considerations over branches towards the similarity with the human mind symbolic way of thinking and even considerations on the proper concept of ontologies

Processing of spent NiW/Al2O3 catalysts

Spent oxidized (500 ºC, 5 h) commercial NiW/Al2O3 catalysts were processed using two different routes: a) fusion with NaOH (650 ºC, 1 h), the roasted mass was leached in water; b) leaching with HCl or H2SO4 (70 ºC, 1-3 h). HCl was the best leachant. In both routes, soluble tungsten was extracted at pH 1 with Alamine 336 (10 vol.% in kerosene) and stripped with 2 mol L-1 NH4OH (25 ºC, one stage, aqueous/organic ratio = 1 v/v). Tungsten was isolated as ammonium paratungstate at very high yield (> 97.5%). The elements were better separated using the acidic route

Building civic intensity : a cultural center for Rua das Flores, Curitiba, Brazil

Thesis (M. Arch.)--Massachusetts Institute of Technology, Dept. of Architecture, 1990.Includes bibliographical references (p. 69-70).by Paulo Frontino Souza de Matos.M.Arch

Bilateral cavo-ilio-femoral thrombosis in an adolescent with transient anti-phospholipid antibodies and Factor V heterozygous mutation: a case report

We report a case of bilateral cavo-ilio-femoral thrombosis in an adolescent with factor V heterozygous mutation and transient antiphospholipid antibodies secondary Varicella infection

Reconstrução de imagens tomográficas com redes neurais parcialmente conectadas

Orientador: Eduardo Parente RibeiroDissertação (mestrado) - Universidade Federal do ParanáResumo: Este trabalho trata do desenvolvimento de um algoritmo utilizando o conceito de redes neurais para reconstrução de imagens tomográficas. Mostramos que através de uma rede neural parcialmente conectada é possível reconstruir imagens com mais rapidez e mesma qualidade utilizando conceitos de tomografia em relação a algoritmos tradicionais. Senogramas referentes a várias seções de imagens de um determinado objeto podem ser reconstruídos por uma única rede parcialmente conectada. Esta estrutura não precisa ser treinada, sendo baseada na geometria da relação entre o espaço de projeção e o espaço da imagem final.Abstract: This work presents a novel approach for tomographic image reconstruction by using neural networks. The reconstruction is performed by a partially connected neural network, using the same concepts from tomography. The processing time is smaller that traditional reconstruction yielding images with the same quality. Sinograms from different cross-sections of the object can be reconstructed by the same partially connected neural network. This structure doesn't need to be trained because it is based on the geometric relationship among the projection space and final image space