Measuring of heat capacity

A simply device was developed and tested. It is designed to measure the heat capacity of square plates with size ～30 × 30 mm, at room temperature. This device consumes permanent electric power. The heat losses to the surroundings are taken into account. A mathematical model, of this heat capacity meter, is described in the paper. The heat capacity of a sample is calculated as a difference between measured heat capacity (of heater and sample) and heat capacity of the heater only. The optimal size of a test body, and reference one, is so as the contact surface of the heater.     

Incidence of Listeria monocytogenes in beef,pork, raw-dried and raw-smoked sausages in Bulgaria

For a 5-years period (2002–2007) 786 samples (505 samples of beef, pork, minced beef, minced pork and 281 samples of raw-dry and raw-smoked sausages) were analyzed. From beef and pork 39 strains Listeria monocytogenes (7.7%), three strains Listeria ivanovii (0.6%), 23 strains Listeria innocua (4.6%) and four strains Listeria welshimeri (0.8%) were obtained. L. monocytogenes were isolated in 28 samples (10.0%), L. ivanovii, L. innocua and L. welshimeri – in 0.7%, 4.3% and 0.7%, respectively, from investigated raw-dried and raw-smoked sausages.     

Accuracy of breeding values in small genotyped populations using different sources of external information—A simulation study

Genetically linked small and large dairy cattle populations were simulated to test the effect of different sources of information from foreign populations on the accuracy of predicting breeding values for young animals in a small population. A large dairy cattle population (P_L) with >20 generations was simulated, and a small subpopulation (P_S) with 3 generations was formed as a related population, including phenotypes and genomic information. Predicted breeding values for young animals in the small population were calculated using BLUP and single-step genomic BLUP (ssGBLUP) in 4 different scenarios: (S1) 3,166 phenotypes, 22,855 pedigree animals, and 1,000 to 6,000 genotypes for P_S; (S2) S1 plus genomic estimated breeding value (GEBV) for 4,475 sires from P_L as external information; (S3) S1 plus 221,580 phenotypes, 402,829 pedigree animals, and 53,558 genotypes for P_L; and (S4) single nucleotide polymorphism (SNP) effects calculated based on P_L data. The ability to predict true breeding value was assessed in the youngest third of the genotyped animals in the small population. When data only from the small population were used and 1,000 animals were genotyped, the accuracy of GEBV was only 1 point greater than the estimated breeding value accuracy (0.32 vs. 0.31). Adding external GEBV for sires from P_L did not considerably increase accuracy (0.33 vs. 0.32 in S1). Combining phenotypes, pedigree, and genotypes for P_S and P_L was beneficial for predicting accuracy of GEBV in the small population, and the prediction accuracy of GEBV in this scenario was 0.38 compared with 0.31 from estimated breeding values. When SNP effects from P_L were used to predict GEBV for young genotyped animals from P_S, accuracy was greatest (0.56). With 6,000 genotyped animal in P_S, accuracy was greatest (0.61) with the combined populations. In a small population with few genotypes, the highest accuracy of evaluation may be obtained by using SNP effects derived from a related large population.     

Optimum design of machine systems in R & D performing laboratoriesПpoeктиpoвaниe мaщиннЫч cиcтeм в тayЧнo-иccлeдoвaтeльcкич лaвopтpии

Research and Development in machine systems is often being carried out by mathematical programming. The purpose of this paper is to review modern work in mechanisms design and to familiarize the reader with the problem of design optimization with multiple objectives, as applied to machine systems. A new optimization method called selective synthesis is discussed. It is characterized by two basic steps: (a) generating a fixed set of design alternatives, and (b) decision making (optimization) in cascade. Two additional steps, namely evaluation of unclear preferences and replacing the original decision problem with new one, are applied when necessary. An example problem is resolved, proving the potential application of selective synthesis on the practice of R & D performing laboratories.     

The binding of asparagine, glutamine and homoserine to human erythrocytes containing hemoglobins S and CS and to soluble hemoglobins S and CS

Tritium labeled asparagine binds to oxyhemoglobin S and to a mixture of hemoglobins C and S in the molar ratio of 3.38:1 and 8.2:1 respectively. From the dialysis equilibrium studies it appears that labeled asparagine does not bind to oxy- or deoxy- hemoglobin A nor to deoxyhemoglobin S. The constant for equilibrium association of asparagine for oxyhemoglobin S is 7.38 x 10(7) M(-1) and for oxyhemoglobin CS 4.8 X 10(4) M(-1) at 23 degrees C. Tritium labeled asparagine is bound to oxyhemoglobin S and CS sufficiently strongly to prevent dissociation under the conditions of gel electrophoresis at pH 9.50. The protein with and without bound asparagine, glutamine or homoserine, is indistinguishable in molecular net charge and size by the criteria of quantitative polyacrylamide gel electrophoresis (PAGE). Also there were no significant differences in mobility between hemoglobin S and hemoglobin C in the presence and absence of asparagine, glutamine and homoserine as detectable in agar coated cellulose acetate electrophoresis at pH 6.3.