Fungal contamination of air in 10 gymnasiums with swimming pools was monitored. Fifty air samples of 200 L each were collected, using a Millipore air tester, from the area surrounding the pool, in training studios, in showers and changing rooms for both sexes, and also, outside premises, since these are the places regarded as reference. Simultaneously, environmental parameters -temperature and humidity -were also monitored. Some 25 different species of fungi were identified. The six most commonly isolated genera were the following: Cladosporium sp.
Gene regulatory networks inference from gene expression data is an important problem in systems biology field, in which the main goal is to comprehend the global molecular mechanisms underlying diseases for the development of medical treatments and drugs. This problem involves the estimation of the gene dependencies and the regulatory functions governing these interactions to provide a model that explains the dataset (usually obtained from gene expression data) on which the estimation relies. However, such problem is considered an open problem, since it is difficult to obtain a satisfactory estimation of the dependencies given a very limited number of samples subject to experimental noises. Several gene networks inference methods exist in the literature, including those based on genetic algorithms, which codify whole networks as possible solutions (chromosomes). Given the huge search space of possible networks, genetic algorithms are suitable for the task, even though it is still hard to achieve good networks that explain the data by codifying whole networks as solutions. The objective of this work is the proposal of a method based on genetic algorithms to infer gene networks, whose main idea consists in applying one genetic algorithm for each gene independently, instead of applying a unique genetic algorithm to determine the whole network as usually done in the literature. Besides, the method involves the application of a network inference method to generate the initial populations to serve as more promising starting points for the genetic algorithms than random populations. To guide the genetic algorithms, we propose the use of Akaike information criterion (AIC) as fitness function. Results obtained from inference of artificial Boolean networks show that AIC correlates very well with popular topological similarity metrics even in cases with small number of samples. Besides, the benefit of applying one genetic algorithm per gene starting from initial populations defined by a network inference technique is evident according to the results. Comparative analysis involving a recently proposed genetic algorithm method for the same purpose is presented, showing that our method achieves superior performance.
Fungal contamination of the floor in 10 gyms with swimming pools was monitored. One hundred and twenty swab samples were collected: 60 before and 60 after cleaning operations. The samples were taken near the pool and jacuzzi, in surrounding the pool access stairs, in training studios and in male and female showers and changing rooms. Simultaneously, environmental parameterstemperature and relative humidity -were also monitored. Thirty-seven different species of fungi were identified. Among those species, Fusarium sp. was the operations. Twelve different species of yeasts were identified. The most identified genera before cleaning was Cryptococcus (40,6%), and after cleaning was Candida (49,3%). The difference between before and after cleaning operations was statistically significant (p<0,05) for fungi in pool access stairs and jacuzzi, and for yeasts in male showers and changing rooms. Taking into account the average values of fungal contamination the decreased due to cleaning procedures only occurred in male shower and changing rooms and only in yeasts count. The fungal contamination showed no significant relationship (p>0,05) with temperature and relative humidity.
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