DOM Model Research Articles

Role of light in methylmercury photodegradation: From irradiation to absorption in the presence of organic ligands

Photochemical degradation acts as the principal sink for methylmercury (MeHg) in surface water, which is regulated by light and solution matrix (especially the presence of dissolved organic matter, DOM). The spectral composition of light irradiation and the light absorption properties of reaction media (often exerted by DOM) are important in MeHg photodegradation, which has not yet been clearly resolved. Aiming to understand the role of light in MeHg photodegradation from the perspectives of both light irradiation and absorption, we investigated the photodegradation of MeHg under different simulated sunlight sources, with and without DOM model compounds of different molecular structures. The results show that the photodegradation of MeHg under different sunlight irradiation yields distinct first-order date constant, mainly due to the slight difference in UVB composition. The degradation of MeHg without DOM under a light source with high intensity in the UV region and no MeHg degradation under the UV-filtered light even in the presence of DOM showed the importance of UV lights in MeHg photodegradation. The use of ultrapure water as a reaction medium may be subject to MeHg loss through vessel adsorption, not just photolysis. Additionally, this work found that the type and position of coexisting substituents on aromatic thiols play a critical role in improving the photodegradation of MeHg, following amino > hydroxyl > carboxyl, para > meta > ortho. Based on the characterization of ultraviolet-visible absorption spectra and our previous work, it was concluded that the presence of DOM could induce red-shift in light absorption and reduce the electronic transition energy of the CHg bond, facilitating MeHg photodegradation. The structures of DOM affect the light absorption properties, which are related to MeHg photodegradation.

Characterization of Coagulant-Induced Membrane Fouling by Multi-Spectral Fusion: Dom Properties and Model Prediction Based on Machine Learning

Characterization of Coagulant-Induced Membrane Fouling by Multi-Spectral Fusion: Dom Properties and Model Prediction Based on Machine Learning

Characterization of Coagulant-Induced Membrane Fouling by Spectroscopic Parameters: DOM Properties, Model Prediction, and Sensitivity Analysis

Characterization of Coagulant-Induced Membrane Fouling by Spectroscopic Parameters: DOM Properties, Model Prediction, and Sensitivity Analysis

Factors affecting genomic selection revealed by empirical evidence in maize

Genomic selection (GS) as a promising molecular breeding strategy has been widely implemented and evaluated for plant breeding, because it has remarkable superiority in enhancing genetic gain, reducing breeding time and expenditure, and accelerating the breeding process. In this study the factors affecting prediction accuracy (rMG) in GS were evaluated systematically, using six agronomic traits (plant height, ear height, ear length, ear diameter, grain yield per plant and hundred-kernel weight) evaluated in one natural and two biparental populations. The factors examined included marker density, population size, heritability, statistical model, population relationships and the ratio of population size between the training and testing sets, the last being revealed by resampling individuals in different proportions from a population. Prediction accuracy continuously increased as marker density and population size increased and was positively correlated with heritability; rMG showed a slight gain when the training set increased to three times as large as the testing set. Low predictive performance between unrelated populations could be attributed to different allele frequencies, and predictive ability and prediction accuracy could be improved by including more related lines in the training population. Among the seven statistical models examined, including ridge regression best linear unbiased prediction (RR-BLUP), genomic BLUP (GBLUP), BayesA, BayesB, BayesC, Bayesian least absolute shrinkage and selection operator (Bayesian LASSO), and reproducing kernel Hilbert space (RKHS), the RKHS and additive-dominance model (Add + Dom model) showed credible ability for capturing non-additive effects, particularly for complex traits with low heritability. Empirical evidence generated in this study for GS-relevant factors will help plant breeders to develop GS-assisted breeding strategies for more efficient development of varieties.

Probing the DOM-mediated photodegradation of methylmercury by using organic ligands with different molecular structures as the DOM model

Photodegradation is the main depletion pathway for methylmercury (MeHg) in surface water. The formation of MeHg-dissolved organic matter (DOM) complexes has been found to be a key step in MeHg photodegradation. However, the major functional groups involved in the DOM-mediated process have yet to be clearly resolved. In this work, we systematically investigated the effects of DOM molecular structures on MeHg photodegradation by using a variety of organic ligands with different functional groups (e.g., thiosalicylate, thiophenol, and thioaniline). The results showed that thiol and phenyl groups may be the major functional groups governing DOM-mediated MeHg photodegradation, with photodegradation rates also dependent on the type (carboxyl, hydroxyl, and amino group) and position (ortho-, meta-, and para-) of other chemical substituents. The addition of “non-photochemically active” thiol ligands (e.g., mercaptoethanol and dithiothreitol) and high concentrations of Cl− can significantly inhibit the o-thiosalicylate-induced MeHg photodegradation, indicating that complexation of MeHg with these ligands is necessary for MeHg photodegradation. Sparging with O2 had a negligible effect on MeHg photodegradation, while sparging with N2 significantly enhanced MeHg photodegradation. This finding suggests that MeHg photodegradation may be a reductive process, which was further supported by identification of the degradation products of MeHg. A possible protonolysis mechanism of MeHg photodegradation in the presence of o-thiosalicylate was then proposed based on the findings of this study.

A comprehensive CFD based model for domestic biomass heating systems

This paper presents the efforts toward a reliable baseline computational fluid dynamics (CFD) model for domestic biomass heating systems with a special emphasis on CO and CO2 emissions.The model accounts for the different phases of biomass thermochemical conversion steps (drying, pyrolysis, gasification, gaseous combustion and char combustion).The conversion of biomass is described using a bed model approach which is considered to be an arrangement of three layers: drying layer, pyrolysis layer and char layer. The fuel release conditions are provided by these layers via a set of mixture fractions.Standard k-ε model is employed for turbulence modelling. Turbulence – chemistry interactions are described by a pre-assumed PDF (Probability Density Function) – mixture fraction method. Radiation is accounted for by the DOM model (Discrete Ordinates Model).The model is developed in the framework of Code_Saturne (open source software). It is applied to analyze the design of a commercial wood log stove with a nominal thermal capacity of 10 kWt. Experiments have been also performed to provide data for model input and validation.The simulation results revealed a considerable potential for stove geometry optimization and emissions reduction. The CFD results can be taken as a guideline for an improved design with regards to emissions and efficiency.

Methane production and diurnal variation measured in dairy cows and predicted from fermentation pattern and nutrient or carbon flow

Many feeding trials have been conducted to quantify enteric methane (CH4) production in ruminants. Although a relationship between diet composition, rumen fermentation and CH4 production is generally accepted, the efforts to quantify this relationship within the same experiment remain scarce. In the present study, a data set was compiled from the results of three intensive respiration chamber trials with lactating rumen and intestinal fistulated Holstein cows, including measurements of rumen and intestinal digestion, rumen fermentation parameters and CH4 production. Two approaches were used to calculate CH4 from observations: (1) a rumen organic matter (OM) balance was derived from OM intake and duodenal organic matter flow (DOM) distinguishing various nutrients and (2) a rumen carbon balance was derived from carbon intake and duodenal carbon flow (DCARB). Duodenal flow was corrected for endogenous matter, and contribution of fermentation in the large intestine was accounted for. Hydrogen (H2) arising from fermentation was calculated using the fermentation pattern measured in rumen fluid. CH4 was calculated from H2 production corrected for H2 use with biohydrogenation of fatty acids. The DOM model overestimated CH4/kg dry matter intake (DMI) by 6.1% (R2=0.36) and the DCARB model underestimated CH4/kg DMI by 0.4% (R2=0.43). A stepwise regression of the difference between measured and calculated daily CH4 production was conducted to examine explanations for the deviance. Dietary carbohydrate composition and rumen carbohydrate digestion were the main sources of inaccuracies for both models. Furthermore, differences were related to rumen ammonia concentration with the DOM model and to rumen pH and dietary fat with the DCARB model. Adding these parameters to the models and performing a multiple regression against observed daily CH4 production resulted in R2 of 0.66 and 0.72 for DOM and DCARB models, respectively. The diurnal pattern of CH4 production followed that of rumen volatile fatty acid (VFA) concentration and the CH4 to CO2 production ratio, but was inverse to rumen pH and the rumen hydrogen balance calculated from 4×(acetate+butyrate)/2×(propionate+valerate). In conclusion, the amount of feed fermented was the most important factor determining variations in CH4 production between animals, diets and during the day. Interactions between feed components, VFA absorption rates and variation between animals seemed to be factors that were complicating the accurate prediction of CH4. Using a ruminal carbon balance appeared to predict CH4 production just as well as calculations based on rumen digestion of individual nutrients.

New progress in community assembly: community phylogenetic struc-ture combining evolution and ecology

Community assembly has long been an important issue in community ecology. The study of community phylogenetic structure, which applies phylogeny to community ecology studies, has provided an effective way to disentangle the most important ecological processes that drive community assembly. Study- ing the phylogenetic structure of a community involves firstly the construction of a supertree representing the species pool of the community, then a calculation of phylogenetic distances between all species within the community, and finally an inference of phylogenetic structure (e.g., clustering, overdispersion) obtained by statistically testing whether the obtained phylogenetic distances are different from those expected under ran- dom model, hence revealing key ecological processes involved in community assembly (e.g., habitat filter- ing, competition exclusion). Community phylogenetic structure is different when studied at different taxo- nomic, spatial or temporal scales. At small spatial scales, community phylogenetic pattern tends to change from clustering to overdispersion with decreasing taxonomical scale or increasing tree age class, while the pattern tends to be tighter clustering at larger spatial scales. Phylogenetic information also indicates the in-

Selective reductions in subpopulations of GABAergic neurons in a developmental rat model of epilepsy

In the rat, early postnatal development is a critical period for neuronal migration, differentiation and network formation, requiring appropriate and timely glutamate and γ-aminobutyric acid (GABA) signaling. Insults that affect either of these systems may result in increased excitatory activity, potentially leading to changes in neuronal proliferation and/or connectivity. We have previously shown that postnatal administration of low doses of domoic acid (DOM) can produce many of the behavioral and morphological changes found in current animal models of temporal lobe epilepsy (TLE), as well as the human condition. Using immunohistochemical techniques, we sought to characterize alterations in specific hippocampal GABAergic subpopulations at various locations along the septo-temporal axis in the DOM model. Results show decreased levels of the GABA-synthesizing enzyme glutamic acid decarboxylase (GAD) in the ventral hilus and region- and sex-specific reductions in parvalbumin (PV)-containing immunoreactivity, but no alterations in somatostatin (SST) expression. These regional and sex-dependent changes in specific subpopulations of GABAergic interneurons may contribute to seizure development in this slowly progressing developmental model of TLE, and highlight how even subtle intervention may alter the interplay between glutamate and GABA systems during critical developmental stages.

Logic-based XML data integration: a semi-materializing approach

We first describe the approach to XML data integration in the XPathLog/LoPiX project that uses a warehouse strategy. We show that the DOM model and the XML Query Data Model are not suitable for this task since the integrated database is not necessarily a tree, but a set of overlapping (original and integrated) trees. The problem is solved by using a node-labeled graph-based data model, called XTreeGraph, for the internal XML database that represents multiple, overlapping XML trees, or tree views. In the second part, we return to the standard XML data model—by still keeping the overlapping tree idea by “simulating” it: The data is internally represented by XML where the “overlayed” resulting tree is represented by XLink elements that refer to the original sources. By using a logical, transparent data model for XLinks as investigated in [May, in: 11th WWW Conference, 2002], all queries behave as stated against the XTreeGraph. The use of links for partial materialization also turns the approach from a warehouse approach into a mixed approach that combines the advantages of the warehouse approach and of the virtual approach. The approach is again illustrated by using XPathLog as data integration language.

Estimates of parental-dominance and full-sib permanent environment variances in laying hens

AbstractEstimates of variance components for five egg traits on 26265 laying hens were obtained by restricted maximum likelihood (REML) using several models. In the DOMFS model, the effects included hatch group, additive genetic, full-sib, parental dominance and inbreeding depression. In the DOM model, the full-sib effect was eliminated. In the FS model, the parental dominance effect was eliminated. In the ADD model, both the full-sib and the dominance effects were eliminated. In the DOMFS model, the estimates of the full-sib variance were generally higher for egg production traits and lower for egg characteristics than those of the parental dominance variance. However, this model has partially failed in separating these two components. When the full-sib effect was removed from the model, almost all of its estimated variance moved to the estimated parental dominance variance. When the parental dominance effect was removed from the model, almost all of its estimated variance moved to the estimated full-sib variance. The estimates obtained with REML and the DOM model were compared with those obtained by method R and tilde-hat methodologies. The d2 (ratio of dominance variance to total variance) differed by up to 86% for method R and up to 225% for tilde-hat. The h2 differed by up to 26 and 28%, respectively. For data sets that are too large to be analysed with REML, method R is a feasible alternative. A model for estimation of dominance variance should also include the full-sib or a similar effect, provided the data set is large. Similarly, to analyse egg production traits, the model should include at least the full-sib effect.