Anti-SAA polyclonal antibody

The objective of this study was to evaluate the serum protein pattern and selected acute phase proteins in dairy cows with chronic diarrhoea associated with seropositivity to Mycobacterium avium subsp. paratuberculosis (MAP). Forty-four dairy cows with chronic diarrhoea and MAP-seropositive and 19 clinically healthy MAP-seronegative cows were included in the study. The concentrations of total protein (TP), protein fractions and selected acute phase proteins - serum amyloid A (SAA), haptoglobin (Hp) and C-reactive protein (CRP) were measured in blood serum. In cows with diarrhoea the mean values of TP, albumin and the albumin/globulin ratio were significantly lower (P < 0.001), the relative concentrations of alpha(1)-, beta(1)- and gamma-globulins were significantly (P < 0.001) and alpha(2)- and beta(2)-globulins were non-significantly higher. The electrophoretic pattern of serum proteins showed beta-gamma bridging in 32 from 44 diseased cows. The concentrations of SAA and Hp were non-significantly higher and CRP significantly lower (P < 0.001) in cows with diarrhoea. Presented results indicate a marked effect of chronic diarrhoea in MAP-seropositive cows on the protein metabolism suggesting possible diagnostic significance of some biomarkers from the protein profile in the evaluation of the severity of the disease and changes caused by this protein-losing enteropathy.

Stochastic scheduling has received much attention from both industry and academia. Existing works usually focus on random job processing times. However, the uncertainty existing in job release times may largely impact the performance as well. This work investigates a stochastic parallel machine scheduling problem, where job release times and processing times are uncertain. The problem consists of a two-stage decision-making process: (i) assigning jobs to machines on the first stage before the realisation of uncertain parameters (job release times and processing times) and (ii) scheduling jobs on the second stage given the job-to-machine assignment and the realisation of uncertain parameters. The objective is to minimise the total cost, including the setup cost on machines (induced by job-to-machine assignment) and the expected penalty cost of jobs' earliness and tardiness. A two-stage stochastic program is proposed, and the sample average approximation (SAA) method is applied. A scenario-reduction-based decomposition approach is further developed to improve the computational efficiency. Numerical results show that the scenario-reduction-based decomposition approach performs better than the SAA, in terms of solution quality and computation time.