The effect of environment on development and survival of pupae of the necrophagous fly Ophyra albuquerquei Lopes (Diptera, Muscidae)

The effect of environment on development and survival of pupae of the necrophagous fly Ophyra albuquerquei Lopes (Diptera, Muscidae). Species of Ophyra Robineau-Desvoidy, 1830 are found in decomposing bodies, usually in fresh, bloated and decay stages. Ophyra albuquerquei Lopes, for example, can be found in animal carcasses. The influence of environmental factors has not been evaluated in puparia of O. albuquerquei. Thus, the focus of this work was motivated by the need for models to predict the development of a necrophagous insect as a function of abiotic factors. Colonies of O. albuquerquei were maintained in the laboratory to obtain pupae. On the tenth day of each month 200 pupae, divided equally into 10 glass jars, were exposed to the environment and checked daily for adult emergence of each sample. We concluded that the high survival rate observed suggested that the diets used for rearing the larvae and maintaining the adults were appropriate. Also, the data adjusted to robust generalized linear models and there were no interruptions of O. albuquerquei pupae development within the limits of temperatures studied in southern Rio Grande do Sul, given the high survival presented.

Selecting statistical models to study the relationship between soybean yield and soil physical properties

Statistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive state-space models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soil-plant-atmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil physical properties, Akaike's Information Criterion, which provides a coefficient for the selection of the best model, was used in this study. The data sets were sampled in a Rhodic Acrudox soil, along a spatial transect with 84 points spaced 3 m apart. At each sampling point, soybean samples were collected for yield quantification. At the same site, soil penetration resistance was also measured and soil samples were collected to measure soil bulk density in the 0-0.10 m and 0.10-0.20 m layers. Results showed autocorrelation and a cross correlation structure of soybean yield and soil penetration resistance data. Soil bulk density data, however, were only autocorrelated in the 0-0.10 m layer and not cross correlated with soybean yield. The results showed the higher efficiency of the autoregressive space-state models in relation to the equivalent simple and multiple linear regression models using Akaike's Information Criterion. The resulting values were comparatively lower than the values obtained by the regression models, for all combinations of explanatory variables.

Evaluation of statistical and geostatistical models of digital soil properties mapping in tropical mountain regions

Soil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR) and geostatistical (ordinary kriging and co-kriging). The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap). Continuous covariates and categorical predictors were used and their contributions to the model assessed. Only the environmental covariates elevation, aspect, stream power index (SPI), soil wetness index (SWI), normalized difference vegetation index (NDVI), and b3/b2 band ratio were significantly correlated with soil properties. The predictive models had a mean coefficient of determination of 0.21. Best results were obtained with the geostatistical predictive models, where the highest coefficient of determination 0.43 was associated with sand properties between 60 to 100 cm deep. The use of a sparse data set of soil properties for digital mapping can explain only part of the spatial variation of these properties. The results may be related to the sampling density and the quantity and quality of the environmental covariates and predictive models used.

Field data and prediction models of pesticide spray drift on coffee crop

The objective of this work was to generate drift curves from pesticide applications on coffee plants and to compare them with two European drift-prediction models. The used methodology is based on the ISO 22866 standard. The experimental design was a randomized complete block with ten replicates in a 2x20 split-plot arrangement. The evaluated factors were: two types of nozzles (hollow cone with and without air induction) and 20 parallel distances to the crop line outside of the target area, spaced at 2.5 m. Blotting papers were used as a target and placed in each of the evaluated distances. The spray solution was composed of water+rhodamine B fluorescent tracer at a concentration of 100 mg L-1, for detection by fluorimetry. A spray volume of 400 L ha-1 was applied using a hydropneumatic sprayer. The air-induction nozzle reduces the drift up to 20 m from the treated area. The application with the hollow cone nozzle results in 6.68% maximum drift in the nearest collector of the treated area. The German and Dutch models overestimate the drift at distances closest to the crop, although the Dutch model more closely approximates the drift curves generated by both spray nozzles.

p53 immunostaining is correlated with reduced survival and is not correlated with gene mutations in resected pulmonary large cell carcinomas

Malignancy of pulmonary large cell carcinomas (LCC) increases from classic LCC through LCC with neuroendocrine morphology (LCCNM) to large cell neuroendocrine carcinomas (LCNEC). However, the histological classification has sometimes proved to be difficult. Because the malignancy of LCC is highly dependent on proteins with functions in the cell cycle, DNA repair, and apoptosis, p53 has been targeted as a potentially useful biological marker. p53 mutations in lung cancers have been shown to result in expression and protein expression also occurs in the absence of mutations. To validate the importance of both p53 protein expression (by immunostaining) and p53 gene mutations in lung LCC (by PCR-single strand conformational polymorphism analysis of exons 5, 6, 7, and 8) and to study their relationships with clinical factors and sub-classification we investigated the correlation of p53 abnormalities in 15 patients with LCC (5 classic LCC, 5 LCNEC, and 5 LCCNM) who had undergone resection with curative intent. Of these patients, 5/15 expressed p53 and none had mutant p53 sequences. There was a negative survival correlation with positive p53 immunostaining (P = 0.05). After adjustment for stage, age, gender, chemotherapy, radiotherapy, and histological subtypes by multivariate analysis, p53 expression had an independent impact on survival. The present study indicates that p53 assessment may provide an objective marker for the prognosis of LCC irrespective of morphological variants and suggests that p53 expression is important for outcome prediction in patients with the early stages of LCC. The results reported here should be considered to be initial results because tumors from only 15 patients were studied: 5 each from LCC, LCNEC and LCCNM. This was due to the rarity of these specific diseases.

Epidermal growth factor receptor (EGFR) mutations in lung cancer: preclinical and clinical data

Lung cancer leads cancer-related mortality worldwide. Non-small-cell lung cancer (NSCLC), the most prevalent subtype of this recalcitrant cancer, is usually diagnosed at advanced stages, and available systemic therapies are mostly palliative. The probing of the NSCLC kinome has identified numerous nonoverlapping driver genomic events, including epidermal growth factor receptor (EGFR) gene mutations. This review provides a synopsis of preclinical and clinical data on EGFR mutated NSCLC and EGFR tyrosine kinase inhibitors (TKIs). Classic somatic EGFR kinase domain mutations (such as L858R and exon 19 deletions) make tumors addicted to their signaling cascades and generate a therapeutic window for the use of ATP-mimetic EGFR TKIs. The latter inhibit these kinases and their downstream effectors, and induce apoptosis in preclinical models. The aforementioned EGFR mutations are stout predictors of response and augmentation of progression-free survival when gefitinib, erlotinib, and afatinib are used for patients with advanced NSCLC. The benefits associated with these EGFR TKIs are limited by the mechanisms of tumor resistance, such as the gatekeeper EGFR-T790M mutation, and bypass activation of signaling cascades. Ongoing preclinical efforts for treating resistance have started to translate into patient care (including clinical trials of the covalent EGFR-T790M TKIs AZD9291 and CO-1686) and hold promise to further boost the median survival of patients with EGFR mutated NSCLC.