Neural network models for biological waste-gas treatment systems

This paper outlines the procedure for developing artificial neural network (ANN) based models for three bioreactor configurations used for waste-gas treatment. The three bioreactor configurations chosen for this modelling work were: biofilter (BF), continuous stirred tank bioreactor (CSTB) and monolith bioreactor (MB). Using styrene as the model pollutant, this paper also serves as a general database of information pertaining to the bioreactor operation and important factors affecting gas-phase styrene removal in these biological systems. Biological waste-gas treatment systems are considered to be both advantageous and economically effective in treating a stream of polluted air containing low to moderate concentrations of the target contaminant, over a rather wide range of gas-flow rates. The bioreactors were inoculated with the fungus Sporothrix variecibatus, and their performances were evaluated at different empty bed residence times (EBRT), and at different inlet styrene concentrations (C(i)). The experimental data from these bioreactors were modelled to predict the bioreactors performance in terms of their removal efficiency (RE, %), by adequate training and testing of a three-layered back propagation neural network (input layer-hidden layer-output layer). Two models (BIOF1 and BIOF2) were developed for the BF with different combinations of easily measurable BF parameters as the inputs, that is concentration (gm(-3)), unit flow (h(-1)) and pressure drop (cm of H(2)O). The model developed for the CSTB used two inputs (concentration and unit flow), while the model for the MB had three inputs (concentration, G/L (gas/liquid) ratio, and pressure drop). Sensitivity analysis in the form of absolute average sensitivity (AAS) was performed for all the developed ANN models to ascertain the importance of the different input parameters, and to assess their direct effect on the bioreactors performance. The performance of the models was estimated by the regression coefficient values (R(2)) for the test data set. The results obtained from this modelling work can be useful for obtaining important relationships between different bioreactor parameters and for estimating their safe operating regimes.     

Genome-wide screen for differential DNA methylation associated with neural cell differentiation in mouse

Cellular differentiation involves widespread epigenetic reprogramming, including modulation of DNA methylation patterns. Using Differential Methylation Hybridization (DMH) in combination with a custom DMH array containing 51,243 features covering more than 16,000 murine genes, we carried out a genome-wide screen for cell- and tissue-specific differentially methylated regions (tDMRs) in undifferentiated embryonic stem cells (ESCs), in in-vitro induced neural stem cells (NSCs) and 8 differentiated embryonic and adult tissues. Unsupervised clustering of the generated data showed distinct cell- and tissue-specific DNA methylation profiles, revealing 202 significant tDMRs (p<0.005) between ESCs and NSCs and a further 380 tDMRs (p<0.05) between NSCs/ESCs and embryonic brain tissue. We validated these tDMRs using direct bisulfite sequencing (DBS) and methylated DNA immunoprecipitation on chip (MeDIP-chip). Gene ontology (GO) analysis of the genes associated with these tDMRs showed significant (absolute Z score>1.96) enrichment for genes involved in neural differentiation, including, for example, Jag1 and Tcf4. Our results provide robust evidence for the relevance of DNA methylation in early neural development and identify novel marker candidates for neural cell differentiation.     

Characterization of dendritic cells subpopulations in skin and afferent lymph in the swine model

Transcutaneous delivery of vaccines to specific skin dendritic cells (DC) subsets is foreseen as a promising strategy to induce strong and specific types of immune responses such as tolerance, cytotoxicity or humoral immunity. Because of striking histological similarities between human and pig skin, pig is recognized as the most suitable model to study the cutaneous delivery of medicine. Therefore improving the knowledge on swine skin DC subsets would be highly valuable to the skin vaccine field. In this study, we showed that pig skin DC comprise the classical epidermal langerhans cells (LC) and dermal DC (DDC) that could be divided in 3 subsets according to their phenotypes: (1) the CD163(neg)/CD172a(neg), (2) the CD163(high)CD172a(pos) and (3) the CD163(low)CD172a(pos) DDC. These subtypes have the capacity to migrate from skin to lymph node since we detected them in pseudo-afferent lymph. Extensive phenotyping with a set of markers suggested that the CD163(high) DDC resemble the antibody response-inducing human skin DC/macrophages whereas the CD163(neg)CD172(low) DDC share properties with the CD8(+) T cell response-inducing murine skin CD103(pos) DC. This work, by showing similarities between human, mouse and swine skin DC, establishes pig as a model of choice for the development of transcutaneous immunisation strategies targeting DC.     

Interleukin-19: a constituent of the regulome that controls antigen presenting cells in the lungs and airway responses to microbial products

Background

Interleukin (IL)-19 has been reported to enhance chronic inflammatory diseases such as asthma but the in vivo mechanism is incompletely understood. Because IL-19 is produced by and regulates cells of the monocyte lineage, our studies focused on in vivo responses of CD11c positive (CD11c+) alveolar macrophages and lung dendritic cells.

Methodology/Principal Findings

IL-19-deficient (IL-19-/-) mice were studied at baseline (naïve) and following intranasal challenge with microbial products, or recombinant cytokines. Naïve IL-19-/- mixed background mice had a decreased percentage of CD11c+ cells in the bronchoalveolar-lavage (BAL) due to the deficiency in IL-19 and a trait inherited from the 129-mouse strain. BAL CD11c+ cells from fully backcrossed IL-19-/- BALB/c or C57BL/6 mice expressed significantly less Major Histocompatibility Complex class II (MHCII) in response to intranasal administration of lipopolysaccharide, Aspergillus antigen, or IL-13, a pro-allergic cytokine. Neurogenic-locus-notch-homolog-protein-2 (Notch2) expression by lung monocytes, the precursors of BAL CD11c+ cells, was dysregulated: extracellular Notch2 was significantly decreased, transmembrane/intracellular Notch2 was significantly increased in IL-19-/- mice relative to wild type. Instillation of recombinant IL-19 increased extracellular Notch2 expression and dendritic cells cultured from bone marrow cells in the presence of IL-19 showed upregulated extracellular Notch2. The CD205 positive subset among the CD11c+ cells was 3-5-fold decreased in the airways and lungs of naïve IL-19-/- mice relative to wild type. Airway inflammation and histological changes in the lungs were ameliorated in IL-19-/- mice challenged with Aspergillus antigen that induces T lymphocyte-dependent allergic inflammation but not in IL-19-/- mice challenged with lipopolysaccharide or IL-13.

Conclusions/Significance

Because MHCII is the molecular platform that displays peptides to T lymphocytes and Notch2 determines cell fate decisions, our studies suggest that endogenous IL-19 is a constituent of the regulome that controls both processes in vivo.     

A computational method for prediction of excretory proteins and application to identification of gastric cancer markers in urine

A novel computational method for prediction of proteins excreted into urine is presented. The method is based on the identification of a list of distinguishing features between proteins found in the urine of healthy people and proteins deemed not to be urine excretory. These features are used to train a classifier to distinguish the two classes of proteins. When used in conjunction with information of which proteins are differentially expressed in diseased tissues of a specific type versus control tissues, this method can be used to predict potential urine markers for the disease. Here we report the detailed algorithm of this method and an application to identification of urine markers for gastric cancer. The performance of the trained classifier on 163 proteins was experimentally validated using antibody arrays, achieving >80% true positive rate. By applying the classifier on differentially expressed genes in gastric cancer vs normal gastric tissues, it was found that endothelial lipase (EL) was substantially suppressed in the urine samples of 21 gastric cancer patients versus 21 healthy individuals. Overall, we have demonstrated that our predictor for urine excretory proteins is highly effective and could potentially serve as a powerful tool in searches for disease biomarkers in urine in general.     

Defining smallness for gestational age in the early years of the Danish Medical Birth Registry

Background

Being born small for gestational age (SGA) is associated with decreased insulin sensitivity and increased blood pressure in childhood, but the association with clinical disease in early adulthood is less certain. The Danish Medical Birth Registry has registered all births in Denmark since 1973, but due to variable data quality, data is most often used only from 1981 onwards, and birth registers in other countries may have similar problems for the early years. We wanted to examine whether the data can be used for identification of children born SGA and used in future research.

Methodology/Principal Findings

All persons born between 1974 and 1996 were identified in the Danish Medical Birth Registry (n = 1.704.890). Immigrants and children without data on gestational age and birth weight were excluded, and a total of 1.348.106 children were included in the analysis. The difference between the different variables used in the history of the registry were examined, and the quality of data in the birth registry from 1974-1981 was examined and compared to subsequent years.Data on birth weight and gestational age in the early years of the registry is inconsistent, and the identification of children born SGA is inaccurate, with 49% false-positives. The biggest source of error is due to the rough and inaccurate intervals used for gestational age. By using –3 standard deviations as a cut-off for the identification of children born SGA, the number of false-positives was reduced to 9%, while the amount of false-negatives were increased.

Conclusion

Choosing –3 standard deviations for identifying children born SGA is a viable, though not optimal solution for identifying children born SGA. Overall the data in the registry is of sufficient quality to be used in further medical research.