Avaliação da exposição a fungos e partículas em explorações avícolas e suinícolas: estudo

O projeto “Avaliação da Exposição a Fungos e Partículas em Explorações Avícolas e Suinícolas” contemplou um elevado número de colheitas ambientais e biológicas e respectivo processamento laboratorial, sendo apenas possível a sua concretização graças ao financiamento disponibilizado pela Autoridade para as Condições de Trabalho. Foi realizado um estudo transversal para avaliar a contaminação causada por fungos e partículas em 7 explorações avícolas e 7 explorações suinícolas. No que concerne à monitorização biológica, foram medidos os parâmetros espirométricos, utilizando o espirómetro MK8 Microlab, avaliada a existência de sintomas clínicos associados com a asma e outras doenças alérgicas, através de questionário adaptado European Community Respiratory Health Survey e, ainda, avaliada a sensibilização aos agentes fúngicos (IgE). Foram ainda adicionados dois objetivos ao estudo, designadamente: aferir a existência de três espécies/estirpes potencialmente patogénicas/toxinogénicas com recurso à biologia molecular e avaliar a exposição dos trabalhadores à micotoxina aflatoxina B1 por recurso a indicador biológico de exposição. Foram colhidas 27 amostras de ar de 25 litros nas explorações avícolas e 56 de 50 litros nas explorações suinícolas através do método de impacto. As colheitas de ar e a medição da concentração das partículas foram realizadas no interior e no exterior dos pavilhões, sendo este último considerado como local de referência. Simultaneamente, a temperatura e a humidade relativa também foram registadas. As colheitas das superfícies foram realizadas através da técnica de zaragatoa, tendo sido utilizado um quadrado de metal inoxidável de 10 cm de lado, de acordo com a International Standard ISO 18593 – 2004. As zaragatoas obtidas (20 das explorações avícolas e 48 das explorações suinícolas) foram inoculadas em malte de extract agar (2%) com cloranfenicol (0,05 g/L). Além das colheitas de ar e de superfícies, foram também obtidas colheitas da cama das explorações avícolas (7 novas e 14 usadas) e da cobertura do pavimento das explorações suinícolas (3 novas e 4 usadas) e embaladas em sacos esterilizados. Cada amostra foi diluída e inoculada em placas contendo malte extract agar. Todas as amostras foram incubadas a 27,5ºC durante 5 a 7 dias e obtidos resultados quantitativos (UFC/m3; UFC/m2; UFC/g) e qualitativos com a identificação das espécies fúngicas. Para a aplicação dos métodos de biologia molecular foram realizadas colheitas de ar de 300 litros utilizando o método de impinger com a velocidade de recolha de 300 L/min. A identificação molecular de três espécies potencialmente patogénicas e/ou toxinogénicas (Aspergillus flavus, Aspergillus fumigatus e Stachybotrys chartarum) foram obtidas por PCR em tempo real (PCR TR) utilizando o Rotor-Gene 6000 qPCR Detection System. As medições de partículas foram realizadas por recurso a equipamento de leitura direta (modelo Lighthouse, 2016 IAQ). Este recurso permitiu medir a concentração (mg/m3) de partículas em 5 dimensões distintas (PM 0.5; PM 1.0; PM 2.5; PM 5.0; PM10). Nas explorações avícolas, 28 espécies/géneros de fungos foram isolados no ar, tendo Aspergillus versicolor sido a espécie mais frequente (20.9%), seguida por Scopulariopsis brevicaulis (17.0%) e Penicillium sp. (14.1%). Entre o género Aspergillus, Aspergillus flavus apresentou o maior número de esporos (>2000 UFC/m3). Em relação às superfícies, A. versicolor foi detetada em maior número (>3 × 10−2 UFC/m2). Na cama nova, Penicillium foi o género mais frequente (59,9%), seguido por Alternaria (17,8%), Cladosporium (7,1%) e Aspergillus (5,7%). Na cama usada, Penicillium sp. foi o mais frequente (42,3%), seguido por Scopulariopsis sp. (38,3%), Trichosporon sp. (8,8%) e Aspergillus sp. (5,5%). Em relação à contaminação por partículas, as partículas com maior dimensão foram detectadas em maiores concentrações, designadamente as PM5.0 (partículas com a dimensão de 5.0 bm ou menos) e PM10 (partículas com a dimensão de 10 bm ou menos). Neste setting a prevalência da alteração ventilatória obstrutiva foi superior nos indivíduos com maior tempo de exposição (31,7%) independentemente de serem fumadores (17,1%) ou não fumadores (14,6%). Relativamente à avaliação do IgE específico, foi apenas realizado em trabalhadores das explorações avícolas (14 mulheres e 33 homens), não tendo sido encontrada associação positiva (p<0.05%) entre a contaminação fúngica e a sensibilização a antigénios fúngicos. No caso das explorações suinícolas, Aspergillus versicolor foi a espécie mais frequente (20,9%), seguida por Scopulariopsis brevicaulis (17,0%) e Penicillium sp. (14,1%). No género Aspergillus, A. versicolor apresentou o maior isolamento no ar (>2000 UFC/m3) e a maior prevalência (41,9%), seguida por A. flavus e A. fumigatus (8,1%). Em relação às superfícies analisadas, A. versicolor foi detetada em maior número (>3 ×10−2 UFC/m2). No caso da cobertura do pavimento das explorações suinícolas, o género Thicoderma foi o mais frequente na cobertura nova (28,0%) seguida por A. versicolor e Acremonium sp. (14,0%). O género Mucor foi o mais frequente na cobertura usada (25,1%), seguido por Trichoderma sp. (18,3%) e Acremonium sp. (11,2%). Relativamente às partículas, foram evidenciados também valores mais elevados na dimensão PM5 e, predominantes nas PM10. Neste contexto, apenas 4 participantes (22,2%) apresentaram uma alteração ventilatória obstrutiva. Destes, as obstruções mais graves encontraram-se nos que também apresentavam maior tempo de exposição. A prevalência de asma na amostra de trabalhadores em estudo, pertencentes aos 2 contextos em estudo, foi de 8,75%, tendo-se verificado também uma prevalência elevada de sintomatologia respiratória em profissionais não asmáticos. Em relação à utilização complementar dos métodos convencionais e moleculares, é recomendável que a avaliação da contaminação fúngica nestes settings, e, consequentemente, a exposição profissional a fungos, seja suportada pelas duas metodologias e, ainda, que ocorre exposição ocupacional à micotoxina aflatoxina B1 em ambos os contextos profissionais. Face aos resultados obtidos, é importante salientar que os settings alvo de estudo carecem de uma intervenção integrada em Saúde Ocupacional no âmbito da vigilância ambiental e da vigilância da saúde, com o objetivo de diminuir a exposição aos dois factores de risco estudados (fungos e partículas).

Fungal burden in waste industry: an occupational risk to be solved

High loads of fungi have been reported in different types of waste management plants. This study intends to assess fungal contamination in one waste-sorting plant before and after cleaning procedures in order to analyze their effectiveness. Air samples of 50 L were collected through an impaction method, while surface samples, taken at the same time, were collected by the swabbing method and subject to further macro- and microscopic observations. In addition, we collected air samples of 250 L using the impinger Coriolis μ air sampler (Bertin Technologies) at 300 L/min airflow rate in order to perform real-time quantitative PCR (qPCR) amplification of genes from specific fungal species, namely Aspergillus fumigatus and Aspergillus flavus complexes, as well as Stachybotrys chartarum species. Fungal quantification in the air ranged from 180 to 5,280 CFU m−3 before cleaning and from 220 to 2,460 CFU m−3 after cleaning procedures. Surfaces presented results that ranged from 29 × 104 to 109 × 104 CFU m−2 before cleaning and from 11 × 104 to 89 × 104 CFU m−2 after cleaning. Statistically significant differences regarding fungal load were not detected between before and after cleaning procedures. Toxigenic strains from A. flavus complex and S. chartarum were not detected by qPCR. Conversely, the A. fumigatus species was successfully detected by qPCR and interestingly it was amplified in two samples where no detection by conventional methods was observed. Overall, these results reveal the inefficacy of the cleaning procedures and that it is important to determine fungal burden in order to carry out risk assessment.

The daily and hourly energy consumption and load forecasting using artifitial neural network method: a case study using a set of 93 households in Portugal

It is important to understand and forecast a typical or a particularly household daily consumption in order to design and size suitable renewable energy systems and energy storage. In this research for Short Term Load Forecasting (STLF) it has been used Artificial Neural Networks (ANN) and, despite the consumption unpredictability, it has been shown the possibility to forecast the electricity consumption of a household with certainty. The ANNs are recognized to be a potential methodology for modeling hourly and daily energy consumption and load forecasting. Input variables such as apartment area, numbers of occupants, electrical appliance consumption and Boolean inputs as hourly meter system were considered. Furthermore, the investigation carried out aims to define an ANN architecture and a training algorithm in order to achieve a robust model to be used in forecasting energy consumption in a typical household. It was observed that a feed-forward ANN and the Levenberg-Marquardt algorithm provided a good performance. For this research it was used a database with consumption records, logged in 93 real households, in Lisbon, Portugal, between February 2000 and July 2001, including both weekdays and weekend. The results show that the ANN approach provides a reliable model for forecasting household electric energy consumption and load profile. © 2014 The Author.

Optimal design for active damping in sandwich structures using the Direct MultiSearch method

This paper addresses the problem of optimal positioning of surface bonded piezoelectric patches in sandwich plates with viscoelastic core and laminated face layers. The objective is to maximize a set of modal loss factors for a given frequency range using multiobjective topology optimization. Active damping is introduced through co-located negative velocity feedback control. The multiobjective topology optimization problem is solved using the Direct MultiSearch Method. An application to a simply supported sandwich plate is presented with results for the maximization of the first six modal loss factors. The influence of the finite element mesh is analyzed and the results are, to some extent, compared with those obtained using alternative single objective optimization.

Naturally occurring radioactive materials: a method for assessing the risk of exposure

Naturally Occurring Radioactive Materials (NORM) are materials that are found naturally in the environment and contain radioactive isotopes that can cause negative effects on the health of workers who manipulate them. Present in underground work like mining and tunnel construction in granite zones, these materials are difficult to identify and characterize without appropriate equipment for risk evaluation. The assessing methods were exemplified with a case study applied to the handling and processing of phosphoric rock where one found significant amounts of radioactive isotopes and consequently elevated radon concentrations in enclosed spaces containing these materials. © 2015 Taylor & Francis Group, London.

A comprehensive high-throughput FTIR spectroscopy-based method for evaluating the transfection event: estimating the transfection efficiency and extracting associated metabolic responses

Reporter genes are routinely used in every laboratory for molecular and cellular biology for studying heterologous gene expression and general cellular biological mechanisms, such as transfection processes. Although well characterized and broadly implemented, reporter genes present serious limitations, either by involving time-consuming procedures or by presenting possible side effects on the expression of the heterologous gene or even in the general cellular metabolism. Fourier transform mid-infrared (FT-MIR) spectroscopy was evaluated to simultaneously analyze in a rapid (minutes) and high-throughput mode (using 96-wells microplates), the transfection efficiency, and the effect of the transfection process on the host cell biochemical composition and metabolism. Semi-adherent HEK and adherent AGS cell lines, transfected with the plasmid pVAX-GFP using Lipofectamine, were used as model systems. Good partial least squares (PLS) models were built to estimate the transfection efficiency, either considering each cell line independently (R 2 ≥ 0.92; RMSECV ≤ 2 %) or simultaneously considering both cell lines (R 2 = 0.90; RMSECV = 2 %). Additionally, the effect of the transfection process on the HEK cell biochemical and metabolic features could be evaluated directly from the FT-IR spectra. Due to the high sensitivity of the technique, it was also possible to discriminate the effect of the transfection process from the transfection reagent on KEK cells, e.g., by the analysis of spectral biomarkers and biochemical and metabolic features. The present results are far beyond what any reporter gene assay or other specific probe can offer for these purposes.

Slaughterhouses fungal burden assessment: a contribution for the pursuit of a better assessment strategy

In slaughterhouses, the biological risk is present not only from the direct or indirect contact with animal matter, but also from the exposure to bioaerosols. Fungal contamination was already reported from the floors and walls of slaughterhouses. This study intends to assess fungal contamination by cultural and molecular methods in poultry, swine/bovine and large animal slaughterhouses. Air samples were collected through an impaction method, while surface samples were collected by the swabbing method and subjected to further macro- and micro-scopic observations. In addition, we collected air samples using the impinger method in order to perform real-time quantitative PCR (qPCR) amplification of genes from specific fungal species, namely A. flavus, A. fumigatus and A. ochraceus complexes. Poultry and swine/bovine slaughterhouses presented each two sampling sites that surpass the guideline of 150 CFU/m3. Scopulariopsis candida was the most frequently isolated (59.5%) in poultry slaughterhouse air; Cladosporium sp. (45.7%) in the swine/bovine slaughterhouse; and Penicillium sp. (80.8%) in the large animal slaughterhouse. Molecular tools successfully amplified DNA from the A. fumigatus complex in six sampling sites where the presence of this fungal species was not identified by conventional methods. This study besides suggesting the indicators that are representative of harmful fungal contamination, also indicates a strategy as a protocol to ensure a proper characterization of fungal occupational exposure.

Toroid inductor development for a SIC DC-DC converter up to 150kW, based on finite element method

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia

Multiobjective optimization for node adaptation in the analysis of composite plates using a meshless collocation method

The bending of simply supported composite plates is analyzed using a direct collocation meshless numerical method. In order to optimize node distribution the Direct MultiSearch (DMS) for multi-objective optimization method is applied. In addition, the method optimizes the shape parameter in radial basis functions. The optimization algorithm was able to find good solutions for a large variety of nodes distribution.

Multiobjective optimization of viscoelastic laminated sandwich structures using the Direct MultiSearch method

A multiobjective approach for optimization of passive damping for vibration reduction in sandwich structures is presented in this paper. Constrained optimization is conducted for maximization of modal loss factors and minimization of weight of sandwich beams and plates with elastic laminated constraining layers and a viscoelastic core, with layer thickness and material and laminate layer ply orientation angles as design variables. The problem is solved using the Direct MultiSearch (DMS) solver for derivative-free multiobjective optimization and solutions are compared with alternative ones obtained using genetic algorithms.

Parallel GPU architecture for hyperspectral unmixing based on augmented Lagrangian method

Hyperspectral imaging has become one of the main topics in remote sensing applications, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels over the same area generating large data volumes comprising several GBs per flight. This high spectral resolution can be used for object detection and for discriminate between different objects based on their spectral characteristics. One of the main problems involved in hyperspectral analysis is the presence of mixed pixels, which arise when the spacial resolution of the sensor is not able to separate spectrally distinct materials. Spectral unmixing is one of the most important task for hyperspectral data exploitation. However, the unmixing algorithms can be computationally very expensive, and even high power consuming, which compromises the use in applications under on-board constraints. In recent years, graphics processing units (GPUs) have evolved into highly parallel and programmable systems. Specifically, several hyperspectral imaging algorithms have shown to be able to benefit from this hardware taking advantage of the extremely high floating-point processing performance, compact size, huge memory bandwidth, and relatively low cost of these units, which make them appealing for onboard data processing. In this paper, we propose a parallel implementation of an augmented Lagragian based method for unsupervised hyperspectral linear unmixing on GPUs using CUDA. The method called simplex identification via split augmented Lagrangian (SISAL) aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The efficient implementation of SISAL method presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory.

Parallel hyperspectral compressive sensing method on GPU

Remote hyperspectral sensors collect large amounts of data per flight usually with low spatial resolution. It is known that the bandwidth connection between the satellite/airborne platform and the ground station is reduced, thus a compression onboard method is desirable to reduce the amount of data to be transmitted. This paper presents a parallel implementation of an compressive sensing method, called parallel hyperspectral coded aperture (P-HYCA), for graphics processing units (GPU) using the compute unified device architecture (CUDA). This method takes into account two main properties of hyperspectral dataset, namely the high correlation existing among the spectral bands and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. Experimental results conducted using synthetic and real hyperspectral datasets on two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN, reveal that the use of GPUs can provide real-time compressive sensing performance. The achieved speedup is up to 20 times when compared with the processing time of HYCA running on one core of the Intel i7-2600 CPU (3.4GHz), with 16 Gbyte memory.

Parallel hyperspectral unmixing method via split augmented lagrangian on GPU

One of the main problems of hyperspectral data analysis is the presence of mixed pixels due to the low spatial resolution of such images. Linear spectral unmixing aims at inferring pure spectral signatures and their fractions at each pixel of the scene. The huge data volumes acquired by hyperspectral sensors put stringent requirements on processing and unmixing methods. This letter proposes an efficient implementation of the method called simplex identification via split augmented Lagrangian (SISAL) which exploits the graphics processing unit (GPU) architecture at low level using Compute Unified Device Architecture. SISAL aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The proposed implementation is performed in a pixel-by-pixel fashion using coalesced accesses to memory and exploiting shared memory to store temporary data. Furthermore, the kernels have been optimized to minimize the threads divergence, therefore achieving high GPU occupancy. The experimental results obtained for the simulated and real hyperspectral data sets reveal speedups up to 49 times, which demonstrates that the GPU implementation can significantly accelerate the method's execution over big data sets while maintaining the methods accuracy.

Parallel method for sparse semisupervised hyperspectral unmixing

Parallel hyperspectral unmixing problem is considered in this paper. A semisupervised approach is developed under the linear mixture model, where the abundance's physical constraints are taken into account. The proposed approach relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction methods. Since Libraries are potentially very large and hyperspectral datasets are of high dimensionality a parallel implementation in a pixel-by-pixel fashion is derived to properly exploits the graphics processing units (GPU) architecture at low level, thus taking full advantage of the computational power of GPUs. Experimental results obtained for real hyperspectral datasets reveal significant speedup factors, up to 164 times, with regards to optimized serial implementation.

Parallel hyperspectral unmixing method on GPU

Many Hyperspectral imagery applications require a response in real time or near-real time. To meet this requirement this paper proposes a parallel unmixing method developed for graphics processing units (GPU). This method is based on the vertex component analysis (VCA), which is a geometrical based method highly parallelizable. VCA is a very fast and accurate method that extracts endmember signatures from large hyperspectral datasets without the use of any a priori knowledge about the constituent spectra. Experimental results obtained for simulated and real hyperspectral datasets reveal considerable acceleration factors, up to 24 times.