Alternative methods for detecting soybean seeds genetically modified for resistance to herbicide glyphosate

The objective of this study was to verify application of two methodologies: substrate moistened with herbicide solution (SM) and immersion of seeds in herbicide solution (IH) for detecting soybean seeds genetically modified. For this, non-transgenic and transgenic soybean seeds, harvested in the 2008/2009 crop seasons were used. The treatments with substrate moistened were: SM1) 0.03% herbicide solution, at 25 ºC, with evaluation in the sixth day (hs -0.03% -25 ºC, 6th d); SM2) HS -0.03% -35 ºC, 5th d; SM3) HS -0.03% - 40 ºC, 5th d; and SM4) hs -0.06% -5 ºC, 5th d. In the methodology of immersion of seeds the following treatments were performed: IH1) seed immersion in a 0.6% herbicide solution, at 25 ºC, for 1 h, (si -0.06% -25 ºC, 1 h; IH2) si -0.06% - 35 ºC, 30 min.; IH3) si -0.06% -40 ºC, 30 min.; IH4) si -0.12% -35 ºC, 30 min.; and IH5) si -0.12% -40 ºC, 30 min. Bioassays allow detecting soybean seeds tolerant to glyphosate herbicide within five days. The seeds of non-genetically modified and genetically modified soybean cultivars may be easily distinguished through the treatments SM2 and SM4 of the moistened substrate methodology; and treatments IH3, IH4, and IH5 of seed immersion methodology. Both methodologies are easily feasible, practical, and applicable in seed analysis laboratories, once do not require special equipments.

Pre, intra and post-ischemic hypothermic neuroprotection in temporary focal cerebral ischemia in rats: morphometric analysis

Objective: To evaluate the neuroprotection of mild hypothermia, applied in different moments, in temporary focal cerebral ischemia in rats. Methods: Rats was divided into Control (C), Sham (S), Ischemic-control(IC), Pre-ischemic Hypothermia (IH1), Intra-ischemic Hypothermia (IH2), and Post-ischemic Hypothermia (IH3) groups. Morphometry was performed using the KS400 software (Carl Zeiss (R)) in coronal sections stained by Luxol Fast Blue. Ischemic areas and volumes were obtained. Results: Statistically, blue areas showed difference for C vs. IC, IC vs. IH1 and IC vs. IH2 (p=0.0001; p=0.01; p=0.03), and no difference between C vs. S, IC vs. IH3 and IH vs. IH2 (p=0.39; p=0.85; p=0.63). Red areas showed difference between C vs. IC, IC vs. IH1 and IC vs. IH2 (p=0.0001; p=0.009; p=0.03), and no difference between C vs. S, IC vs. IH3 and IH1 vs. IH2 (p=0.48; p=0.27; p=0.68). Average ischemic areas and ischemic volumes showed difference between IC vs. IH1 and IC vs. IH2 (p=0.0001 and p=0.0011), and no difference between IC vs. IH3 and IH1 vs. IH2 (p=0.57; p=0.79). Conclusion: Pre-ischemic and intra-ischemic hypothermia were shown to be similarly neuroprotective, but this was not true for post-ischemic hypothermia.

Life cycle assessment of biofuels produced by the new integrated hydropyrolysis-hydroconversion (IH2) process

Biofuels are alternative fuels that have the promise of reducing reliance on imported fossil fuels and decreasing emission of greenhouse gases from energy consumption. This thesis analyses the environmental impacts focusing on the greenhouse gas (GHG) emissions associated with the production and delivery of biofuel using the new Integrated Hydropyrolysis and Hydroconversion (IH2) process. The IH2 process is an innovative process for the conversion of woody biomass into hydrocarbon liquid transportation fuels in the range of gasoline and diesel. A cradle-to-grave life cycle assessment (LCA) was used to calculate the greenhouse gas emissions associated with diverse feedstocks production systems and delivery to the IH2 facility plus producing and using these new renewable liquid fuels. The biomass feedstocks analyzed include algae (microalgae), bagasse from a sugar cane-producing locations such as Brazil or extreme southern US, corn stover from Midwest US locations, and forest feedstocks from a northern Wisconsin location. The life cycle greenhouse gas (GHG) emissions savings of 58%–98% were calculated for IH2 gasoline and diesel production and combustion use in vehicles compared to fossil fuels. The range of savings is due to different biomass feedstocks and transportation modes and distances. Different scenarios were conducted to understand the uncertainties in certain input data to the LCA model, particularly in the feedstock production section, the IH2 biofuel production section, and transportation sections.