Ethanol reversal of tolerance to the respiratory depressant effects of morphine

Opioids are the most common drugs associated with unintentional drug overdose. Death results from respiratory depression. Prolonged use of opioids results in the development of tolerance but the degree of tolerance is thought to vary between different effects of the drugs. Many opioid addicts regularly consume alcohol (ethanol), and post-mortem analyses of opioid overdose deaths have revealed an inverse correlation between blood morphine and ethanol levels. In the present study, we determined whether ethanol reduced tolerance to the respiratory depressant effects of opioids. Mice were treated with opioids (morphine, methadone, or buprenorphine) for up to 6 days. Respiration was measured in freely moving animals breathing 5% CO(2) in air in plethysmograph chambers. Antinociception (analgesia) was measured as the latency to remove the tail from a thermal stimulus. Opioid tolerance was assessed by measuring the response to a challenge dose of morphine (10 mg/kg i.p.). Tolerance developed to the respiratory depressant effect of morphine but at a slower rate than tolerance to its antinociceptive effect. A low dose of ethanol (0.3 mg/kg) alone did not depress respiration but in prolonged morphine-treated animals respiratory depression was observed when ethanol was co-administered with the morphine challenge. Ethanol did not alter the brain levels of morphine. In contrast, in methadone- or buprenorphine-treated animals no respiratory depression was observed when ethanol was co-administered along with the morphine challenge. As heroin is converted to morphine in man, selective reversal of morphine tolerance by ethanol may be a contributory factor in heroin overdose deaths

A pivotal role for starch in the reconfiguration of 14C-partitioning and allocation in Arabidopsis thaliana under short-term abiotic stress.

Plant carbon status is optimized for normal growth but is affected by abiotic stress. Here, we used 14C-labeling to provide the first holistic picture of carbon use changes during short-term osmotic, salinity, and cold stress in Arabidopsis thaliana. This could inform on the early mechanisms plants use to survive adverse environment, which is important for efficient agricultural production. We found that carbon allocation from source to sinks, and partitioning into major metabolite pools in the source leaf, sink leaves and roots showed both conserved and divergent responses to the stresses examined. Carbohydrates changed under all abiotic stresses applied; plants re-partitioned 14C to maintain sugar levels under stress, primarily by reducing 14C into the storage compounds in the source leaf, and decreasing 14C into the pools used for growth processes in the roots. Salinity and cold increased 14C-flux into protein, but as the stress progressed, protein degradation increased to produce amino acids, presumably for osmoprotection. Our work also emphasized that stress regulated the carbon channeled into starch, and its metabolic turnover. These stress-induced changes in starch metabolism and sugar export in the source were partly accompanied by transcriptional alteration in the T6P/SnRK1 regulatory pathway that are normally activated by carbon starvation

Projection methods in conic optimization

There exist efficient algorithms to project a point onto the intersection of a convex cone and an affine subspace. Those conic projections are in turn the work-horse of a range of algorithms in conic optimization, having a variety of applications in science, finance and engineering. This chapter reviews some of these algorithms, emphasizing the so-called regularization algorithms for linear conic optimization, and applications in polynomial optimization. This is a presentation of the material of several recent research articles; we aim here at clarifying the ideas, presenting them in a general framework, and pointing out important techniques

Linkage Mapping of Stem Saccharification Digestibility in Rice

Rice is the staple food of almost half of the world population, and in excess 90% of it is grown and consumed in Asia, but the disposal of rice straw poses a problem for farmers, who often burn it in the fields, causing health and environmental problems. However, with increased focus on the development of sustainable biofuel production, rice straw has been recognized as a potential feedstock for non-food derived biofuel production. Currently, the commercial realization of rice as a biofuel feedstock is constrained by the high cost of industrial saccharification processes needed to release sugar for fermentation. This study is focused on the alteration of lignin content, and cell wall chemotypes and structures, and their effects on the saccharification potential of rice lignocellulosic biomass. A recombinant inbred lines (RILs) population derived from a cross between the lowland rice variety IR1552 and the upland rice variety Azucena with 271 molecular markers for quantitative trait SNP (QTS) analyses was used. After association analysis of 271 markers for saccharification potential, 1 locus and 4 pairs of epistatic loci were found to contribute to the enzymatic digestibility phenotype, and an inverse relationship between reducing sugar and lignin content in these recombinant inbred lines was identified. As a result of QTS analyses, several cell-wall associated candidate genes are proposed that may be useful for marker-assisted breeding and may aid breeders to produce potential high saccharification rice varieties

Search for a Light Sterile Neutrino at Daya Bay

published_or_final_versio

The muon system of the Daya Bay Reactor antineutrino experiment

postprin

Independent measure of the neutrino mixing angle θ13 via neutron capture on hydrogen at Daya Bay

published_or_final_versio

A chromosome conformation capture ordered sequence of the barley genome

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Improved Measurement of Electron Antineutrino Disappearance at Daya Bay

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Temporal trend and climate factors of hemorrhagic fever with renal syndrome epidemic in Shenyang City, China

<p>Abstract</p> <p>Background</p> <p>Hemorrhagic fever with renal syndrome (HFRS) is an important infectious disease caused by different species of hantaviruses. As a rodent-borne disease with a seasonal distribution, external environmental factors including climate factors may play a significant role in its transmission. The city of Shenyang is one of the most seriously endemic areas for HFRS. Here, we characterized the dynamic temporal trend of HFRS, and identified climate-related risk factors and their roles in HFRS transmission in Shenyang, China.</p> <p>Methods</p> <p>The annual and monthly cumulative numbers of HFRS cases from 2004 to 2009 were calculated and plotted to show the annual and seasonal fluctuation in Shenyang. Cross-correlation and autocorrelation analyses were performed to detect the lagged effect of climate factors on HFRS transmission and the autocorrelation of monthly HFRS cases. Principal component analysis was constructed by using climate data from 2004 to 2009 to extract principal components of climate factors to reduce co-linearity. The extracted principal components and autocorrelation terms of monthly HFRS cases were added into a multiple regression model called principal components regression model (PCR) to quantify the relationship between climate factors, autocorrelation terms and transmission of HFRS. The PCR model was compared to a general multiple regression model conducted only with climate factors as independent variables.</p> <p>Results</p> <p>A distinctly declining temporal trend of annual HFRS incidence was identified. HFRS cases were reported every month, and the two peak periods occurred in spring (March to May) and winter (November to January), during which, nearly 75% of the HFRS cases were reported. Three principal components were extracted with a cumulative contribution rate of 86.06%. Component 1 represented MinRH₀, MT₁, RH₁, and MWV₁; component 2 represented RH₂, MaxT₃, and MAP₃; and component 3 represented MaxT₂, MAP₂, and MWV₂. The PCR model was composed of three principal components and two autocorrelation terms. The association between HFRS epidemics and climate factors was better explained in the PCR model (<it>F </it>= 446.452, <it>P </it>< 0.001, adjusted <it>R</it>²= 0.75) than in the general multiple regression model (<it>F </it>= 223.670, <it>P </it>< 0.000, adjusted <it>R</it>²= 0.51).</p> <p>Conclusion</p> <p>The temporal distribution of HFRS in Shenyang varied in different years with a distinctly declining trend. The monthly trends of HFRS were significantly associated with local temperature, relative humidity, precipitation, air pressure, and wind velocity of the different previous months. The model conducted in this study will make HFRS surveillance simpler and the control of HFRS more targeted in Shenyang.</p