Mathematical Models of E-Antigen Mediated Immune Tolerance and Activation following Prenatal HBV Infection

We develop mathematical models for the role of hepatitis B e-antigen in creating immunological tolerance during hepatitis B virus infection and propose mechanisms for hepatitis B e-antigen clearance, subsequent emergence of a potent cellular immune response, and the effect of these on liver damage. We investigate the dynamics of virus-immune cells interactions, and derive parameter regimes that allow for viral persistence. We modify the model to account for mechanisms responsible for hepatitis B e-antigen loss, such as seroconversion and virus mutations that lead to emergence of cellular immune response to the mutant virus. Our models demonstrate that either seroconversion or mutations can induce immune activation and that instantaneous loss of e-antigen by either mechanism is associated with least liver damage and is therefore more beneficial for disease outcomes

Phytoplankton composition in the coastal Magnetic Island lagoon, Western Pacific Ocean (Australia)

1 - Coastal lagoons have traditionally been considered as transitional systems between continental and marine domains. The phytoplankton plays a key role in these aquatic environments, forming the base of the food web and having a substantial function in nutrient dynamics and in the carbon biogeochemical cycle.2 - Due to their short life cycle, planktonic algae respond quickly to environmental changes and they are thus a valuable indicator of water quality. It is essential to investigate the development of phytoplankton populations to understand the biological functioning and to detect changes in aquatic systems.3 - Phytoplankton studies in the Australian estuaries and lagoons are relatively scarce. This study has provided a broad perspective and preliminary information on taxonomic structure of phytoplankton guilds for the Magnetic Island Lagoon (Queensland, Australia). This work may provide valuable information of interest to later ecological studies.4 - In the whole sampling a total of 143 taxa were identified. In terms of species richness, diatoms (Bacillariophyceae, Coscinodiscophyceae, Fragilariophyceae) and dinoflagellates (Dinophyceae) were the most important groups. In taxonomic terms, diatoms were the major contributor to the phytoplankton composition (~ 70%) whereas Dinophyceae were moderately abundant (~23%). Diatoms are a very important component in estuarine and shallow coastal wetlands and they are increasingly being utilized as indicators of environmental change

Quantification of total T-cell receptor diversity by flow cytometry and spectratyping

BACKGROUND: T-cell receptor diversity correlates with immune competency and is of particular interest in patients undergoing immune reconstitution. Spectratyping generates data about T-cell receptor CDR3 length distribution for each BV gene but is technically complex. Flow cytometry can also be used to generate data about T-cell receptor BV gene usage, but its utility has not been compared to or tested in combination with spectratyping. RESULTS: Using flow cytometry and spectratype data, we have defined a divergence metric that quantifies the deviation from normal of T-cell receptor repertoire. We have shown that the sample size is a sensitive parameter in the predicted flow divergence values, but not in the spectratype divergence values. We have derived two ways to correct for the measurement bias using mathematical and statistical approaches and have predicted a lower bound in the number of lymphocytes needed when using the divergence as a substitute for diversity. CONCLUSIONS: Using both flow cytometry and spectratyping of T-cells, we have defined the divergence measure as an indirect measure of T-cell receptor diversity. We have shown the dependence of the divergence measure on the sample size before it can be used to make predictions regarding the diversity of the T-cell receptor repertoire

Keratin hydrolysates obtained from sheep wool from the leather industry

This work presents experiments in making biopolymer-based materials with smart functionalities from renewable resources in the leather industry, applicable in various areas. Keratin hydrolysates were obtained by alkaline hydrolysis in the presence of NaOH and CaO. The physico-chemical characterization, DLS and FT-IR analysis of keratin hydrolysates highlighted the rich content in protein and total nitrogen. The recovery of wool by-products from the leather industry leads to less waste and helps prevent environmental pollution

Mapping adaptation of barley to droughted environments

Identifying barley genomic regions influencing the response of yield and its components to water deficits will aid in our understanding of the genetics of drought tolerance and the development of more drought tolerant cultivars. We assembled a population of 192 genotypes that represented landraces, old, and contemporary cultivars sampling key regions around the Mediterranean basin and the rest of Europe. The population was genotyped with a stratified set of 50 genomic and EST derived molecular markers, 49 of which were Simple Sequence Repeats (SSRs), which revealed an underlying population sub-structure that corresponded closely to the geographic regions in which the genotypes were grown. A more dense whole genome scan was generated by using Diversity Array Technology (DArT®) to generate 1130 biallelic markers for the population. The population was grown at two contrasting sites in each of seven Mediterranean countries for harvest 2004 and 2005 and grain yield data collected. Mean yield levels ranged from 0.3 to 6.2 t/ha, with highly significant genetic variation in low-yielding environments. Associations of yield with barley genomic regions were then detected by combining the DArT marker data with the yield data in mixed model analyses for the individual trials, followed by multiple regression of yield on markers to identify a multi-locus subset of significant markers/QTLs. QTLs exhibiting a pre-defined consistency across environments were detected in bins 4, 6, 6 and 7 on barley chromosomes 3H, 4H, 5H and 7H respectivel