Petroleum contamination impact on macrobenthic communities under the influence of an oil refinery: Integrating chemical and biological multivariate data

Petroleum contamination impact on macrobenthic communities in the northeast portion of Todos os Santos Bay was assessed combining in multivariate analyses, chemical parameters such as aliphatic and polycyclic aromatic hydrocarbon indices and concentration ratios with benthic ecological parameters. Sediment samples were taken in August 2000 with a 0.05 m(2) van Veen grab at 28 sampling locations. The predominance of n-alkanes with more than 24 carbons, together with CPI values close to one, and the fact that most of the stations showed UCM/resolved aliphatic hydrocarbons ratios (UCM:R) higher than two, indicated a high degree of anthropogenic contribution, the presence of terrestrial plant detritus, petroleum products and evidence of chronic oil pollution. The indices used to determine the origin of PAH indicated the occurrence of a petrogenic contribution. A pyrolytic contribution constituted mainly by fossil fuel combustion derived PAH was also observed. The results of the stepwise multiple regression analysis performed with chemical data and benthic ecological descriptors demonstrated that not only total PAH concentrations but also specific concentration ratios or indices such as >= C24:< C24, An/178 and Fl/Fl + Py, are determining the structure of benthic communities within the study area. According to the BIO-ENV results petroleum related variables seemed to have a main influence on macrofauna community structure. The PCA ordination performed with the chemical data resulted in the formation of three groups of stations. The decrease in macrofauna density, number of species and diversity from groups III to I seemed to be related to the occurrence of high aliphatic hydrocarbon and PAH concentrations associated with fine sediments. Our results showed that macrobenthic communities in the northeast portion of Todos os Santos Bay are subjected to the impact of chronic oil pollution as was reflected by the reduction in the number of species and diversity. These results emphasise the importance to combine in multivariate approaches not only total hydrocarbon concentrations but also indices, isomer pair ratios and specific compound concentrations with biological data to improve the assessment of anthropogenic impact on marine ecosystems. (c) 2008 Elsevier Ltd. All rights reserved.

Assessing the gain of biological data integration in gene networks inference

Background: A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included biological data, and the possibility of discovering new relationships between genes when observed the expression data. Although several works in data integration have increased the performance of the network inference methods, the real contribution of adding each type of biological information in the obtained improvement is not clear. Methods: We propose a methodology to include biological information into an inference algorithm in order to assess its prediction gain by using biological information and expression profile together. We also evaluated and compared the gain of adding four types of biological information: (a) protein-protein interaction, (b) Rosetta stone fusion proteins, (c) KEGG and (d) KEGG+GO. Results and conclusions: This work presents a first comparison of the gain in the use of prior biological information in the inference of GNs by considering the eukaryote (P. falciparum) organism. Our results indicates that information based on direct interaction can produce a higher improvement in the gain than data about a less specific relationship as GO or KEGG. Also, as expected, the results show that the use of biological information is a very important approach for the improvement of the inference. We also compared the gain in the inference of the global network and only the hubs. The results indicates that the use of biological information can improve the identification of the most connected proteins.

Trichogramma pretiosum parasitism and dispersal capacity: a basis for developing biological control programs for soybean caterpillars

In order to succeed in biological control programs, not only is it crucial to understand the number of natural enemies to be released but also on how many sites per area this releasing must be performed. These variables might differ deeply among egg parasitoid species and crops worked. Therefore, these trials were carried out to evaluate the parasitism (%) in eggs of Anticarsia gemmatalis and Pseudoplusia includens after the release of different densities of the egg parasitoid Trichogramma pretiosum. Field dispersal was also studied, in order to determine appropriate recommendations for the release of this parasitoid in soybean fields. The regression analysis between parasitism (%) and densities of the parasitoid indicated a quadratic effect for both A. gemmatalis and P. includens. The maximum parasitism within 24 h after the release was reached with densities of 25.6 and 51.2 parasitoids per host egg, respectively, for the two pests. Parasitism of T. pretiosum in eggs of P. includens decreased linearly as the distance of the pest eggs from the parasitoid release sites increased. For P. includens, the mean radius of T. pretiosum action and the area of parasitoid dispersal in the soybean crop were 8.01 m and 85.18 m(2), respectively. We conclude that for a successful biological control program of lepidopteran pests using T. pretiosum in soybean fields, a density of 25.6 parasitoids per host egg, divided into 117 sites per hectare, should be used.

Job satisfaction and discrepancies between social and biological timing

The discrepancies between social and biological timing are reflected in shift workers' well-being. The aim of this study was to verify the association between job satisfaction and chronotype among day and night nursing personnel. Several variables, including seniority at the hospital and, in the same shift, sleep duration, quality of sleep, sleepiness and willingness to change sleep timing were also analyzed. Chronotype was calculated by using the morningness-eveningness questionnaire. We studied 514 nursing professionals from a public university hospital. Among the day workers, the higher the morningness, the more the workers were satisfied with their job. In contrast, among night workers, job satisfaction was associated with sleep quality and seniority at the hospital but not with chronotype. Our results suggest that an agreement between work schedule and chronotype may help to increase job satisfaction among diurnal workers.

Sleep length, working hours and socio-demographic variables are associated with time attending evening classes among working college students

It is the aim of the present study to assess factors associated with time spent in class among working college students. Eighty-two working students from 21 to 26 years old participated in this study. They were enrolled in an evening course of the University of Sao Paulo, Brazil. Participants answered a questionnaire on living and working conditions. During seven consecutive days, they wore an actigraph, filled out daily activity diaries (including time spent in classes) and the Karolinska Sleepiness Scale every three hours from waking until bedtime. Linear regression analyses were performed in order to assess the variables associated with time spent in classes. The results showed that gender, sleep length, excessive sleepiness, alcoholic beverage consumption (during workdays) and working hours were associated factors with time spent in class. Thus, those who spent less time in class were males, slept longer hours, reported excessive sleepiness on Saturdays, worked longer hours, and reported alcohol consumption. The combined effects of long work hours (>40 h/week) and reduced sleep length may affect lifestyles and academic performance. Future studies should aim to look at adverse health effects induced by reduced sleep duration, even among working students who spent more time attending evening classes.

Degree of multicollinearity and variables involved in linear dependence in additive-dominant models

The objective of this work was to assess the degree of multicollinearity and to identify the variables involved in linear dependence relations in additive-dominant models. Data of birth weight (n=141,567), yearling weight (n=58,124), and scrotal circumference (n=20,371) of Montana Tropical composite cattle were used. Diagnosis of multicollinearity was based on the variance inflation factor (VIF) and on the evaluation of the condition indexes and eigenvalues from the correlation matrix among explanatory variables. The first model studied (RM) included the fixed effect of dam age class at calving and the covariates associated to the direct and maternal additive and non-additive effects. The second model (R) included all the effects of the RM model except the maternal additive effects. Multicollinearity was detected in both models for all traits considered, with VIF values of 1.03 - 70.20 for RM and 1.03 - 60.70 for R. Collinearity increased with the increase of variables in the model and the decrease in the number of observations, and it was classified as weak, with condition index values between 10.00 and 26.77. In general, the variables associated with additive and non-additive effects were involved in multicollinearity, partially due to the natural connection between these covariables as fractions of the biological types in breed composition.