On the Thermoelectric Effect of Interface Imperfections

Ordinary thermocouples use the well-known Seebeck effect to measure the temperature at the junction of two different conductors. The electromotive force generated by the heat depends on the difference between the respective thermoelectric powers of the contacting metals and the junction temperature. Figure 1 shows the schematic diagram of the thermoelectric measurement as most often used in nondestructive materials characterization. One of the reference electrodes is heated by electrical means to a preset temperature of 100 – 300 °C, pretty much like the tip of a temperature-stabilized soldering iron, and connected to the inverting (−) input of the differential amplifier driving the indicator. The other electrode is left cold at essentially room temperature and connected to the non-inverting (+) input. The measurement is done quickly in a few seconds to assure (i) that the hot reference electrode is not cooled down perceivably by the specimen and (ii) that the rest of the specimen beyond the close vicinity of the contact point is not warmed up perceivably. Ideally, regardless of the temperature difference between the junctions, only thermocouples made of different materials, i.e., materials of different thermoelectric power, will generate thermoelectric signal. This unique feature makes the simple thermoelectric tester one of the most sensitive material discriminators used in nondestructive inspection

Optimised Motion Tracking for Positron Emission Tomography Studies of Brain Function in Awake Rats

Positron emission tomography (PET) is a non-invasive molecular imaging technique using positron-emitting radioisotopes to study functional processes within the body. High resolution PET scanners designed for imaging rodents and non-human primates are now commonplace in preclinical research. Brain imaging in this context, with motion compensation, can potentially enhance the usefulness of PET by avoiding confounds due to anaesthetic drugs and enabling freely moving animals to be imaged during normal and evoked behaviours. Due to the frequent and rapid motion exhibited by alert, awake animals, optimal motion correction requires frequently sampled pose information and precise synchronisation of these data with events in the PET coincidence data stream. Motion measurements should also be as accurate as possible to avoid degrading the excellent spatial resolution provided by state-of-the-art scanners. Here we describe and validate methods for optimised motion tracking suited to the correction of motion in awake rats. A hardware based synchronisation approach is used to achieve temporal alignment of tracker and scanner data to within 10 ms. We explored the impact of motion tracker synchronisation error, pose sampling rate, rate of motion, and marker size on motion correction accuracy. With accurate synchronisation (<100 ms error), a sampling rate of >20 Hz, and a small head marker suitable for awake animal studies, excellent motion correction results were obtained in phantom studies with a variety of continuous motion patterns, including realistic rat motion (<5% bias in mean concentration). Feasibility of the approach was also demonstrated in an awake rat study. We conclude that motion tracking parameters needed for effective motion correction in preclinical brain imaging of awake rats are achievable in the laboratory setting. This could broaden the scope of animal experiments currently possible with PET

Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model

<p>Abstract</p> <p>Background</p> <p>Genomic selection involves breeding value estimation of selection candidates based on high-density SNP genotypes. To quantify the potential benefit of genomic selection, accuracies of estimated breeding values (EBV) obtained with different methods using pedigree or high-density SNP genotypes were evaluated and compared in a commercial layer chicken breeding line.</p> <p>Methods</p> <p>The following traits were analyzed: egg production, egg weight, egg color, shell strength, age at sexual maturity, body weight, albumen height, and yolk weight. Predictions appropriate for early or late selection were compared. A total of 2,708 birds were genotyped for 23,356 segregating SNP, including 1,563 females with records. Phenotypes on relatives without genotypes were incorporated in the analysis (in total 13,049 production records).</p> <p>The data were analyzed with a Reduced Animal Model using a relationship matrix based on pedigree data or on marker genotypes and with a Bayesian method using model averaging. Using a validation set that consisted of individuals from the generation following training, these methods were compared by correlating EBV with phenotypes corrected for fixed effects, selecting the top 30 individuals based on EBV and evaluating their mean phenotype, and by regressing phenotypes on EBV.</p> <p>Results</p> <p>Using high-density SNP genotypes increased accuracies of EBV up to two-fold for selection at an early age and by up to 88% for selection at a later age. Accuracy increases at an early age can be mostly attributed to improved estimates of parental EBV for shell quality and egg production, while for other egg quality traits it is mostly due to improved estimates of Mendelian sampling effects. A relatively small number of markers was sufficient to explain most of the genetic variation for egg weight and body weight.</p

Persistence of accuracy of genomic estimated breeding values over generations in layer chickens

<p>Abstract</p> <p>Background</p> <p>The predictive ability of genomic estimated breeding values (GEBV) originates both from associations between high-density markers and QTL (Quantitative Trait Loci) and from pedigree information. Thus, GEBV are expected to provide more persistent accuracy over successive generations than breeding values estimated using pedigree-based methods. The objective of this study was to evaluate the accuracy of GEBV in a closed population of layer chickens and to quantify their persistence over five successive generations using marker or pedigree information.</p> <p>Methods</p> <p>The training data consisted of 16 traits and 777 genotyped animals from two generations of a brown-egg layer breeding line, 295 of which had individual phenotype records, while others had phenotypes on 2,738 non-genotyped relatives, or similar data accumulated over up to five generations. Validation data included phenotyped and genotyped birds from five subsequent generations (on average 306 birds/generation). Birds were genotyped for 23,356 segregating SNP. Animal models using genomic or pedigree relationship matrices and Bayesian model averaging methods were used for training analyses. Accuracy was evaluated as the correlation between EBV and phenotype in validation divided by the square root of trait heritability.</p> <p>Results</p> <p>Pedigree relationships in outbred populations are reduced by 50% at each meiosis, therefore accuracy is expected to decrease by the square root of 0.5 every generation, as observed for pedigree-based EBV (Estimated Breeding Values). In contrast the GEBV accuracy was more persistent, although the drop in accuracy was substantial in the first generation. Traits that were considered to be influenced by fewer QTL and to have a higher heritability maintained a higher GEBV accuracy over generations. In conclusion, GEBV capture information beyond pedigree relationships, but retraining every generation is recommended for genomic selection in closed breeding populations.</p

Influences of Excluded Volume of Molecules on Signaling Processes on Biomembrane

We investigate the influences of the excluded volume of molecules on biochemical reaction processes on 2-dimensional surfaces using a model of signal transduction processes on biomembranes. We perform simulations of the 2-dimensional cell-based model, which describes the reactions and diffusion of the receptors, signaling proteins, target proteins, and crowders on the cell membrane. The signaling proteins are activated by receptors, and these activated signaling proteins activate target proteins that bind autonomously from the cytoplasm to the membrane, and unbind from the membrane if activated. If the target proteins bind frequently, the volume fraction of molecules on the membrane becomes so large that the excluded volume of the molecules for the reaction and diffusion dynamics cannot be negligible. We find that such excluded volume effects of the molecules induce non-trivial variations of the signal flow, defined as the activation frequency of target proteins, as follows. With an increase in the binding rate of target proteins, the signal flow varies by i) monotonically increasing; ii) increasing then decreasing in a bell-shaped curve; or iii) increasing, decreasing, then increasing in an S-shaped curve. We further demonstrate that the excluded volume of molecules influences the hierarchical molecular distributions throughout the reaction processes. In particular, when the system exhibits a large signal flow, the signaling proteins tend to surround the receptors to form receptor-signaling protein clusters, and the target proteins tend to become distributed around such clusters. To explain these phenomena, we analyze the stochastic model of the local motions of molecules around the receptor.Comment: 31 pages, 10 figure

Location of chlorogenic acid biosynthesis pathway and polyphenol oxidase genes in a new interspecific anchored linkage map of eggplant

© Gramazio et al.; licensee BioMed Central. 2014. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated

Molecular control of sucrose utilization in Escherichia coli W, an efficient sucrose-utilizing strain

Sucrose is an industrially important carbon source for microbial fermentation. Sucrose utilization in Escherichia coli, however, is poorly understood, and most industrial strains cannot utilize sucrose. The roles of the chromosomally encoded sucrose catabolism (csc) genes in E. coli W were examined by knockout and overexpression experiments. At low sucrose concentrations, the csc genes are repressed and cells cannot grow. Removal of either the repressor protein (cscR) or the fructokinase (cscK) gene facilitated derepression. Furthermore, combinatorial knockout of cscR and cscK conferred an improved growth rate on low sucrose. The invertase (cscA) and sucrose transporter (cscB) genes are essential for sucrose catabolism in E. coli W, demonstrating that no other genes can provide sucrose transport or inversion activities. However, cscK is not essential for sucrose utilization. Fructose is excreted into the medium by the cscK-knockout strain in the presence of high sucrose, whereas at low sucrose (when carbon availability is limiting), fructose is utilized by the cell. Overexpression of cscA, cscAK, or cscAB could complement the W Delta cscRKAB knockout mutant or confer growth on a K-12 strain which could not naturally utilize sucrose. However, phenotypic stability and relatively good growth rates were observed in the K-12 strain only when overexpressing cscAB, and full growth rate complementation in W Delta cscRKA Balso required cscAB. Our understanding of sucrose utilization can be used to improve E. coli Wand engineer sucrose utilization in strains which do not naturally utilize sucrose, allowing substitution of sucrose for other, less desirable carbon sources in industrial fermentations

Search for the Decays B^0 -> D^{(*)+} D^{(*)-}

Using the CLEO-II data set we have searched for the Cabibbo-suppressed decays B^0 -> D^{(*)+} D^{(*)-}. For the decay B^0 -> D^{*+} D^{*-}, we observe one candidate signal event, with an expected background of 0.022 +/- 0.011 events. This yield corresponds to a branching fraction of Br(B^0 -> D^{*+} D^{*-}) = (5.3^{+7.1}_{-3.7}(stat) +/- 1.0(syst)) x 10^{-4} and an upper limit of Br(B^0 -> D^{*+} D^{*-}) D^{*\pm} D^\mp and B^0 -> D^+ D^-, no significant excess of signal above the expected background level is seen, and we calculate the 90% CL upper limits on the branching fractions to be Br(B^0 -> D^{*\pm} D^\mp) D^+ D^-) < 1.2 x 10^{-3}.Comment: 12 page postscript file also available through http://w4.lns.cornell.edu/public/CLNS, submitted to Physical Review Letter