Pipeline for macro- and microarray analyses

The pipeline for macro- and microarray analyses (PMmA) is a set of scripts with a web interface developed to analyze DNA array data generated by array image quantification software. PMmA is designed for use with single- or double-color array data and to work as a pipeline in five classes (data format, normalization, data analysis, clustering, and array maps). It can also be used as a plugin in the BioArray Software Environment, an open-source database for array analysis, or used in a local version of the web service. All scripts in PMmA were developed in the PERL programming language and statistical analysis functions were implemented in the R statistical language. Consequently, our package is a platform-independent software. Our algorithms can correctly select almost 90% of the differentially expressed genes, showing a superior performance compared to other methods of analysis. The pipeline software has been applied to 1536 expressed sequence tags macroarray public data of sugarcane exposed to cold for 3 to 48 h. PMmA identified thirty cold-responsive genes previously unidentified in this public dataset. Fourteen genes were up-regulated, two had a variable expression and the other fourteen were down-regulated in the treatments. These new findings certainly were a consequence of using a superior statistical analysis approach, since the original study did not take into account the dependence of data variability on the average signal intensity of each gene. The web interface, supplementary information, and the package source code are available, free, to non-commercial users at http://ipe.cbmeg.unicamp.br/pub/PMmA.

Relationship between Brazilian airline pilot errors and time of day

Flight safety is one of the most important and frequently discussed issues in aviation. Recent accident inquiries have raised questions as to how the work of flight crews is organized and the extent to which these conditions may have been contributing factors to accidents. Fatigue is based on physiologic limitations, which are reflected in performance deficits. The purpose of the present study was to provide an analysis of the periods of the day in which pilots working for a commercial airline presented major errors. Errors made by 515 captains and 472 copilots were analyzed using data from flight operation quality assurance systems. To analyze the times of day (shifts) during which incidents occurred, we divided the light-dark cycle (24:00) in four periods: morning, afternoon, night, and early morning. The differences of risk during the day were reported as the ratio of morning to afternoon, morning to night and morning to early morning error rates. For the purposes of this research, level 3 events alone were taken into account, since these were the most serious in which company operational limits were exceeded or when established procedures were not followed. According to airline flight schedules, 35% of flights take place in the morning period, 32% in the afternoon, 26% at night, and 7% in the early morning. Data showed that the risk of errors increased by almost 50% in the early morning relative to the morning period (ratio of 1:1.46). For the period of the afternoon, the ratio was 1:1.04 and for the night a ratio of 1:1.05 was found. These results showed that the period of the early morning represented a greater risk of attention problems and fatigue.

Physicochemical analyses indicated to the quality control of royal jelly with honey

Royal jelly (RJ) is used as a revitalizing tonic. In order to avoid rejection to its acid taste, it is added to honey. There are regulations for honey and for royal jelly separately but not for the mixture. The objective of this work is, therefore, to verify if the same methods used for pure honey quality control can be used for honey mixed with royal jelly and also the presence of RJ through 10-HDA determination. The methods used were: moisture, reducing sugars, apparent sucrose, ash, hydroxymethylfurfural, insoluble solids, diastase activity, acidity and 10-HDA. Samples were prepared by adding 0-100% of RJ in honey. The results showed that the ash method was the only suitable one to all the samples. The acidity analysis (direct titration) was suitable to 0-30%RJ samples; the reducing sugar analysis was suitable to 0-20% RJ samples. Concerning moisture analysis the refractometric method is suitable to 0-10% RJ and the Infra Red method is suggested to be used for samples with more than 10% RJ. The methods for diastase activity, HMF, apparent sucrose and insoluble solids were inadequate for all samples with RJ. The presence of RJ in the samples was confirmed by the 10-HDA analyses.

Processing, physicochemical, and sensory analyses of ostrich meat hamburger

The objective of this study was to assess the potential utilization of ostrich meat trimming in hamburger preparation, as well as its physicochemical and sensory characterization. Using ostrich meat trimmings from the legs and neck, four different formulations were prepared with varied amounts of bacon and textured soybean protein. Physical analysis of yield, shrinkage percentage, and water retention capacity and chemical analysis of proximate composition, cholesterol levels, and calories were performed. The formulations underwent sensory analysis by 52 potential ostrich meat consumers, who evaluated tenderness, juiciness, flavor, and purchase intent. The formulations containing textured soybean protein showed the highest yield, lowest shrinkage percentage, and highest water retention capacity. Lipid content varied from 0.58 to 4.99%; protein from 17.08 to 21.37%; ash from 3.00 to 3.62%; moisture from 73.87 to 76.27%; cholesterol from 22.54 to 32.11 mg.100 g-1; and calorie from 87.22 to 163.42 kcal.100 g-1. All formulations showed low cholesterol and calorie levels, even that containing 10% bacon and 3.5% textured soybean protein, which achieved the best scores and acceptance by the panelists.

Non-parametric stability analyses of dough properties in wheat

AbstractLiterature has unveiled that a paper has not been published yet on using non-parametric stability statistics (NPSSs) for evaluating genotypic stability in dough properties of wheat. Accordingly, the effects of genotype (G), environment (E) and GE interaction (GEI) on alveograph parameters, i.e. dough baking strength (W) and its tenacity (P)/extensibility (L), of 18 wheat (T. aestivum L.) genotypes were studied under irrigated field conditions in an 8-year trial (2006-2014) in central Turkey. Furthermore, genotypic stability for W and P/L was determined using 8 NPSSs viz. RM-Rank mean, RSD-Rank’s standard deviation, RS-Rank Sum, TOP-Ranking, Si(1), Si(2), Si(3) and Si(6) rank statistics. The ANOVA revealed that W and P/L were primarily controlled by E, although G and GEI also had significant effects. Among the 8 NPSSs, only RM, RS and TOP statistics were suitable for detecting the genotypes with high stable and bread making quality (e.g. G1 and G17). In conclusion, using RM, RS and TOP statistics is advisable to select for dough quality in wheat under multi-environment trials (METs).