Transcriptome analysis of the oil-rich seed of the bioenergy crop Jatropha curcas L

<p>Abstract</p> <p>Background</p> <p>To date, oil-rich plants are the main source of biodiesel products. Because concerns have been voiced about the impact of oil-crop cultivation on the price of food commodities, the interest in oil plants not used for food production and amenable to cultivation on non-agricultural land has soared. As a non-food, drought-resistant and oil-rich crop, <it>Jatropha curcas </it>L. fulfils many of the requirements for biofuel production.</p> <p>Results</p> <p>We have generated 13,249 expressed sequence tags (ESTs) from developing and germinating <it>Jatropha </it>seeds. This strategy allowed us to detect most known genes related to lipid synthesis and degradation. We have also identified ESTs coding for proteins that may be involved in the toxicity of <it>Jatropha </it>seeds. Another unexpected finding is the high number of ESTs containing transposable element-related sequences in the developing seed library (800) when contrasted with those found in the germinating seed library (80).</p> <p>Conclusions</p> <p>The sequences generated in this work represent a considerable increase in the number of sequences deposited in public databases. These results can be used to produce genetically improved varieties of <it>Jatropha </it>with increased oil yields, different oil compositions and better agronomic characteristics.</p

Detection and Quantification of Leptospira interrogans in Hamster and Rat Kidney Samples: Immunofluorescent Imprints versus Real-time PCR

A major limitation in the clinical management and experimental research of leptospirosis is the poor performance of the available methods for the direct detection of leptospires. In this study, we compared real-time PCR (qPCR), targeting the lipL32 gene, with the immunofluorescent imprint method (IM) for the detection and quantification of leptospires in kidney samples from the rat and hamster experimental models of leptospirosis. Using a virulent strain of Leptospira interrogans serovar Copenhageni, a chronic infection was established in the rat model, which were euthanized 28 days post-infection, while the hamster model simulated an acute infection and the hamsters were euthanized eight days after inoculation. Leptospires in the kidney samples were detected using culture isolation, qPCR and the IM, and quantified using qPCR and the IM. In both the acute and chronic infection models, the correlation between quantification by qPCR and the IM was found to be positive and statistically significant (P<0.05). Therefore, this study demonstrates that the IM is a viable alternative for not only the detection but also the quantification of leptospires, particularly when the use of qPCR is not feasible

Greenhouse gas emissions, water footprint, and ecological footprint of food purchases according to their degree of processing in Brazilian metropolitan areas: a time-series study from 1987 to 2018

Copyright © 2021 The Author(s). Background The consumption of ultra-processed foods has increased worldwide and has been related to the occurrence of obesity and other non-communicable diseases. However, little is known about the environmental effects of ultra-processed foods. We aimed to assess the temporal trends in greenhouse gas emissions (GHGE), water footprint, and ecological footprint of food purchases in Brazilian metropolitan areas, and how these are affected by the amount of food processing. Methods In this time-series study, we used data from five Brazilian Household Budget Surveys (1987–88, 1995–96, 2002–03, 2008–09, 2017–18) to calculate GHGE, water footprint, and ecological footprint per 1000 kcal of food and beverages purchased. Food items were classified into NOVA food groups: unprocessed or minimally processed foods (G1); processed culinary ingredients (G2); processed foods (G3); and ultra-processed foods (G4). We calculated the proportion each NOVA food group contributes to daily kcal per person. Linear regression was performed to evaluate trends of the environmental impacts across the years. Findings Between 1987–88 and 2017–18, diet-related GHGE increased by 21% (from 1538·6 g CO2 equivalent [CO2e] per 1000 kcal [95% CI 1473·3–1604·0] to 1866·0 g CO2e per 1000 kcal [1788·0–1944·0]; ptrend<0·0001), diet-related water footprint increased by 22% (from 1447·2 L/1000 kcal [95% CI 1400·7–1493·8] to 1769·1 L/1000 kcal [1714·5–1823·7]; ptrend<0·0001), and diet-related ecological footprint increased by 17% (from 9·69 m2/1000 kcal [95% CI 9·33–10·05] to 11·36 m2/1000 kcal [10·91–11·81]; ptrend<0·0001). We found that the change in the environmental indicators over time varied between NOVA food groups. We did not find evidence of a change in the environmental indicators for G1 foods over time. GHGE from G2 foods decreased by 18% (ptrend<0·0001), whereas GHGE from G4 foods increased by 245% (ptrend<0·0001). The water footprint from G2 foods decreased by 17% (ptrend<0·0001) whereas the water footprint from G4 foods increased by 233% (ptrend<0·0001). The ecological footprint from G2 foods decreased by 13% (ptrend<0·0001), whereas the ecological footprint from G3 foods increased by 49% (ptrend<0·0001) and from G4 foods increased by 183% (ptrend<0·0001). We found no significant change in contribution by any other NOVA food groups to any of the three environmental indicators over the study period. Interpretation The environmental effects of the Brazilian diet have increased over the past three decades along with increased effects from ultra-processed foods. This means that dietary patterns in Brazil are becoming potentially more harmful to human and planetary health. Therefore, a shift in the current trend would be needed to enhance sustainable healthy food systems.Science and Technologies Facilities Council—Global Challenges Research Fund

Standards for data acquisition and software‐based analysis of in vivo electroencephalography recordings from animals. A TASK1‐WG5 report of the AES/ILAE Translational Task Force of the ILAE

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139127/1/epi13909.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139127/2/epi13909_am.pd