Emission scenario of non-CO2 gases from energy activities and other sources in China

This paper gives a quantitative analysis on the non-CO2 emissions related to energy demand, energy activities and land use change of six scenarios with different development pattern in 2030 and 2050 based on IPAC emission model. The various mitigation technologies and policies are assessed to understand the corresponding non-CO2 emission reduction effect. The research shows that the future non-CO2 emissions of China will grow along with increasing energy demand, in which thermal power and transportation will be the major emission and mitigation sectors. During the cause of future social and economic development, the control and mitigation of non-CO2 emissions is a problem as challenging and pressing as that of CO2 emissions.This study indicates that the energy efficiency improvement, renewable energy, advanced nuclear power generation, fuel cell, coal-fired combined cycle, clean coal and motor vehicle emission control technologies will contribute to non-CO2 emissions control and mitigation.     

Molecular modeling of glycosyltransferases involved in the biosynthesis of blood group A,blood group B,Forssman, and iGb3 antigens and their interaction with substrates

A terminal alpha1-3 linked Gal or GalNAc sugar residue is the common structure found in several oligosaccharide antigens, such as blood groups A and B, the xeno-antigen, the Forssman antigen, and the isogloboside 3 (iGb3) glycolipid. The enzymes involved in the addition of this residue display strong amino acid sequence similarities, suggesting a common fold. From a recently solved crystal structure of the bovine alpha3-galactosyltransferase complexed with UDP, homology modeling methods were used to build the four other enzymes of this family in their locked conformation. Nucleotide-sugars, the Mn2+ ion, and oligosaccharide acceptors were docked in the models. Nine different amino acid regions are involved in the substrate binding sites. After geometry optimization of the complexes and analysis of the predicted structures, the basis of the specificities can be rationalized. In the nucleotide-sugar binding site, the specificity between Gal or GalNAc transferase activity is due to the relative size of two clue amino acids. In the acceptor site, the presence of up to three tryptophan residues define the complexity of the oligosaccharide that can be specifically recognized. The modeling study helps in rationalizing the crystallographic data obtained in this family and provides insights on the basis of substrate and donor recognition.     

Dealing with Uncertainty in Spatially Explicit Population Models

It has been argued that spatially explicit population models (SEPMs) cannot provide reliable guidance for conservation biology because of the difficulty of obtaining direct estimates for their demographic and dispersal parameters and because of error propagation. We argue that appropriate model calibration procedures can access additional sources of information, compensating the lack of direct parameter estimates. Our objective is to show how model calibration using population-level data can facilitate the construction of SEPMs that produce reliable predictions for conservation even when direct parameter estimates are inadequate. We constructed a spatially explicit and individual-based population model for the dynamics of brown bears (Ursus arctos) after a reintroduction program in Austria. To calibrate the model we developed a procedure that compared the simulated population dynamics with distinct features of the known population dynamics (=patterns). This procedure detected model parameterizations that did not reproduce the known dynamics. Global sensitivity analysis of the uncalibrated model revealed high uncertainty in most model predictions due to large parameter uncertainties (coefficients of variation CV 0.8). However, the calibrated model yielded predictions with considerably reduced uncertainty (CV 0.2). A pattern or a combination of various patterns that embed information on the entire model dynamics can reduce the uncertainty in model predictions, and the application of different patterns with high information content yields the same model predictions. In contrast, a pattern that does not embed information on the entire population dynamics (e.g., bear observations taken from sub-areas of the study area) does not reduce uncertainty in model predictions. Because population-level data for defining (multiple) patterns are often available, our approach could be applied widely.     

Slow and fast pulses in 1-D cultures of excitatory neurons

We analyze the characteristics of front propagation in activity of 1-D neuronal cultures by numerical simulations, using only excitatory dynamics. Experimental results in 1-D cultures of hippocampal neurons from rats have shown the spontaneous generation of a slow, low amplitude pulse that precedes a high amplitude, fast pulse that propagates through all the system. Notably, this transition appears both with and without the presence of functioning inhibitory synapses. In accordance with previous work, we demonstrate that purely excitatory integrate and fire neurons with depression in the synapses suffice to produce fast and uniform pulses but cannot explain the appearance of slow, weak pulses. We propose to explain the slow pulses by increasing the complexity of the neuron model in a purely excitatory network with connectivity as close to the experiments as possible. This approach allows us to show that spike frequency adaptation is a fundamental ingredient for the initiation process of the pulse. The introduction of a slow variable that mimics the presence of the slow K ⁺ channels in the soma and produces spike frequency adaptation increases strongly the persistence of the transient activity before the emergence of the fast pulse up to temporal and spatial scales comparable with the experiments. Finally, we demonstrate that proper levels of additive white noisy currents generate such pulses spontaneously, fully reproducing the experimental results. Electronic supplementary material   The online version of this article (doi:) contains supplementary material, which is available to authorized users.

Genetic diversity and gene flow in a Caribbean tree <Emphasis Type="Italic">Pterocarpus officinalis</Emphasis> Jacq.: a study based on chloroplast and nuclear microsatellites

We analysed the molecular diversity of Pterocarpus officinalis, a tree species distributed in Caribbean islands, South and Central America to quantify the genetic variation within island, to assess the pattern of differentiation and infer levels of gene flow; with the overall goal of defining a strategy of conservation. Two hundred two individuals of 9 populations were analysed using three chloroplast and six nuclear microsatellite markers. The observed heterozygosity varied markedly among the populations for nuclear (H _Onuc = 0.20–0.50) and chloroplast microsatellites (H _cp = 0.22–0.68). The continental population from French Guyana showed a higher value of H _Onuc than island populations, and the differences were significant in some cases. The fixation index F _IS ranged from −0.043 to 0.368; a significant heterozygote deficit was detected in 7 populations. The heterozygosity excess method suggested that two populations in Guadeloupe have undergone a recent bottleneck. Global and pairwise F _ST were high for both nuclear (F _STnuc = 0.29) and chloroplast microsatellites (F _STcp = 0.58). The neighbour-joining tree based on both markers, presented a differentiation pattern that can be explained by the seed dispersal by flotation and marine stream. The comparison of Bayesian approach and the method based on allelic frequency demonstrate a very limited number of migrants between populations.     

Dynamic modeling of Listeria monocytogenes growth in pasteurized milk

AIMS: The development and validation of a dynamic model for predicting Listeria monocytogenes growth in pasteurized milk stored at both static and dynamic temperature conditions. METHODS AND RESULTS: Growth of inoculated L. monocytogenes in a commercial pasteurized whole milk product was monitored at various isothermal conditions from 1.5 to 16 degrees C. The kinetic parameters of the pathogen were modelled as a function of temperature using a square root type model, which was further validated using data from 92 published growth curves from eight different milk products. Compared to four published models for L. monocytogenes growth, the model developed in this study performed better, with a per cent discrepancy and bias of 49.1 and -1.01%, respectively. The performance of the model in predicting growth at dynamic temperature conditions was evaluated at four different fluctuating temperature scenarios with periodic temperature changes from -2 to 16 degrees C. The prediction of growth at dynamic storage temperature was based on the square root model in conjunction with the differential equations of the Baranyi and Roberts model, which were numerically integrated with respect to time. The per cent relative errors between the observed and the predicted growth of L. monocytogenes were less than 10% for all temperature scenarios tested. CONCLUSIONS: Available models from experiments conducted in laboratory media may result in significant overestimation of L. monocytogenes growth in pasteurized milk because they do not take into account factors such as milk composition (e.g. natural antimicrobial compounds present in milk) and the interactions of the pathogen with the natural microflora. The product-targeted model developed in the present study showed a high performance in predicting growth of L. monocytogenes in pasteurized milk under both static and dynamic temperature conditions. SIGNIFICANCE AND IMPACT OF THE STUDY: Temperature fluctuations often occur during the transportation and storage of pasteurized milk. A high performance, dynamic model for the growth of L. monocytogenes can be a useful tool for effective management and optimization of product safety and can lead to more realistic estimations of pasteurized-milk related safety risks.     

Evaluation of environmental conditions for production of bacteriocin-like substance by <Emphasis Type="Italic">Bacillus</Emphasis> sp. strain P34

The influence of temperature, pH and media on bacteriocin production by Bacillus sp. P34 was investigated. The effect of temperature and initial pH was evaluated by factorial design and response surface methodology (RSM). Statistical analysis of results showed that, in the range studied, the two variables have a significant effect on bacteriocin production. Response-surface data showed maximum antimicrobial activity production at initial pH values between 6.0 and 8.0 and temperatures between 25 and 37 °C. No relationship between bacterial growth and bacteriocin production was observed. RSM proved to be a powerful tool in optimizing the production of antimicrobial activity by Bacillus sp. P34. When different media were tested, maximum bacteriocin production was observed in soybean protein-based medium, but antimicrobial activity was not achieved by cultivation on fish meal, feather meal, whey and grape waste.