We look at the first derivative of the entanglement and the posit

We look at the first derivative of the entanglement and the position-space information entropy to infer information about a possible quantum phase transition. (C) 2010 American selleck products Institute of Physics. [doi:10.1063/1.3364065]“
“Objective-To estimate the sensitivity, specificity, likelihood ratios, and predictive values of blood beta-hydroxybutyrate (BHB) concentrations in dairy cows immediately prior to surgical correction of left-displaced abomasum (LDA) for determining associations between BHB concentration and removal from the herd <= 30 days after surgery and to evaluate postsurgical risk of removal

for cows with the BHB concentration that had highest sensitivity and specificity for predicting this outcome.

Design-Prospective cohort study.

Animals-136 dairy cows with LDA diagnosed between 5 and 30 days in lactation (ie, days in milk).

Procedures-Blood BHB concentration was measured immediately prior to surgery. All cows underwent surgical correction of LDA while standing. Follow-up information was obtained 30 days after surgery. Receiver operating characteristic Smad2 phosphorylation curves were used to estimate a critical threshold value for BHB concentration that was associated with removal from the herd, and this value was used in Poisson regression to estimate risk ratio for the same outcome.

Results-While controlling for parity in the model, cows with a BHB concentration

<1.2 mmol/L at the time of LDA surgery were 2.5 times as likely (95% confidence interval, 1.3 to 5.0) to be removed from the herd <= 30 days after surgery, compared with cows that had a BHB concentration

>= 1.2 mmol/L.

Conclusions and Clinical Relevance-Results indicated that blood BHB concentration in dairy cows undergoing surgical correction of LDA may potentially be Small molecule library mw a useful prognostic indicator for the likelihood of removal from the herd <= 30 days after surgery. Further research is needed to evaluate other risk factors that may be associated With this outcome.”
“Characterizing infectivity as a function of pathogen dose is integral to microbial risk assessment. Dose-response experiments usually administer doses to subjects at one time. Phenomenological models of the resulting data, such as the exponential and the Beta-Poisson models, ignore dose timing and assume independent risks from each pathogen. Real world exposure to pathogens, however, is a sequence of discrete events where concurrent or prior pathogen arrival affects the capacity of immune effectors to engage and kill newly arriving pathogens. We model immune effector and pathogen interactions during the period before infection becomes established in order to capture the dynamics generating dose timing effects. Model analysis reveals an inverse relationship between the time over which exposures accumulate and the risk of infection.

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