The investigation of the combined effects of gamma irradiation and environmental manipulation on mortality and sterility of Rhyzopertha dominica (Fabricius) (Coleoptera: Bostrichidae)

With respect to the limitations use of methyl bromide and phosphine, employing ionizing radiation to control stored product pests has attracted great attention. The aim of this study is the investigation of the combined effects of gamma irradiation as viable alternatives to synthetic insecticides and environmental management on mortality and sterility of Rhyzopertha dominica (Fabricius) in a wheat cultivar (Gascogen). The effect of doses range from 30 to 2000?Gy gamma irradiation in combination with manipulation of temperature (15, 21, 27 and 32?°C) and relative humidity (20%, 50%, 65% and 85%) on 5–10?days old adults of R. dominica in Gascogen cultivar of wheat were explored. The experiments were repeated three times and conducted in The Nuclear Agriculture Research School in Karaj and laboratory of shahid steki silo in shahre-kord. Probit analysis revealed that both temperature and relative humidity had combined effects when used with gamma irradiation. The lowest doses of gamma ray required to kill 25% (14.2?Gy) and 50% (610.8?Gy) of the population (LD₂₅ and LD₅₀) were recorded at 21?°C and 85% relative humidity respectively. The low dose for 99% mortality (LD₉₉; 2386.7?Gy) was recorded for beetles maintained at 21?°C and 50% relative humidity. The effect of temperature (15, 21, 27 and 32?°C) on sterility caused by gamma irradiation was also investigated. The results showed that the F₁ generation emerged only when the beetles were treated with doses of 0–100?Gy at 32?°C and 0–70?Gy at 27?°C. These results indicate that temperature and relative humidity play an important role in the susceptibility of the lesser grain beetle to gamma irradiation. The results suggest that controlling the efficiency of gamma radiation through environmental control allows the use of low doses of gamma radiation that have a less harmful effect on human health, non-target organisms and seed agronomic features.     

Cleveland Adolescent Sleepiness Questionnaire (CASQ): a translation and validation study of the Persian version

Sleep and Biological Rhythms - The purpose of this study was to assess the validity and reliability of the Persian version of the Cleveland Adolescent Sleepiness Questionnaire (CASQ) in Iranian...     

A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status

Background

Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER) status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results.

Methods

To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor.

Results

This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients'' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status.

Conclusions

Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions.     

Imaging taste responses in the fly brain reveals a functional map of taste category and behavior

The sense of taste allows animals to distinguish nutritious and toxic substances and elicits food acceptance or avoidance behaviors. In Drosophila, taste cells that contain the Gr5a receptor are necessary for acceptance behavior, and cells with the Gr66a receptor are necessary for avoidance. To determine the cellular substrates of taste behaviors, we monitored taste cell activity in vivo with the genetically encoded calcium indicator G-CaMP. These studies reveal that Gr5a cells selectively respond to sugars and Gr66a cells to bitter compounds. Flies are attracted to sugars and avoid bitter substances, suggesting that Gr5a cell activity is sufficient to mediate acceptance behavior and that Gr66a cell activation mediates avoidance. As a direct test of this hypothesis, we inducibly activated different taste neurons by expression of an exogenous ligand-gated ion channel and found that cellular activity is sufficient to drive taste behaviors. These studies demonstrate that taste cells are tuned by taste category and are hardwired to taste behaviors.