Optimization of Removal Efficiency and Minimum Contact Time for Cadmium and Zinc Removal onto Iron-modified Zeolite in a Two-stage Batch Sorption Reactor

In highly congested industrial sites where significant volumes of effluents have to be treated in the minimum contact time, the application of a multi-stage batch reactor is suggested. To achieve better balance between capacity utilization and cost efficiency in design optimization, a two-stage batch reactor is usually the optimal solution. Thus, in this paper, a two-stage batch sorption design approach was applied to the experimental data of cadmium and zinc uptake onto iron-modified zeolite. The optimization approach involves the application of the Vermeulen’s approximation model and mass balance equation to kinetic data. A design analysis method was developed to optimize the removal efficiency and minimum total contact time by combining the time required in the two-stages, in order to achieve the maximum percentage of cadmium and zinc removal using a fixed mass of zeolite. The benefits and limitations of the two-stage design approach have been investigated and discussed. This work is licensed under a Creative Commons Attribution 4.0 International License

Design of Fixed Bed Column for Lead Removal on Natural Zeolite Based on Batch Studies

This paper presents the prediction of breakthrough curves for the fixed bed column based on batch studies. Batch equilibrium studies of lead removal on natural zeolite clinoptilolite have been performed. The obtained experimental data have been tested according to the Langmuir and the Freundlich isotherm, and their parameters have been calculated. These parameters and the Mass Transfer Model have been used to predict theoretical breakthrough curves. Theoretical breakthrough curves have been compared with the experimental ones and good agreement has been observed. This indicates that the Mass Transfer Model is applicable for prediction of breakthrough curves from batch studies. The overall mass transfer coefficient has been calculated from column experiments. This value allows for calculation of the height of the mass transfer zone as a very important parameter necessary for column design

Glycosylation of immunoglobulin G is regulated by a large network of genes pleiotropic with inflammatory diseases

Effector functions of immunoglobulin G (IgG) are regulated by the composition of a glycan moiety, thus affecting activity of the immune system. Aberrant glycosylation of IgG has been observed in many diseases, but little is understood about the underlying mechanisms. We performed a genome-wide association study of IgG N-glycosylation (N = 8090) and, using a data-driven network approach, suggested how associated loci form a functional network. We confirmed in vitro that knockdown of IKZF1 decreases the expression of fucosyltransferase FUT8, resulting in increased levels of fucosylated glycans, and suggest that RUNX1 and RUNX3, together with SMARCB1, regulate expression of glycosyltransferase MGAT3. We also show that variants affecting the expression of genes involved in the regulation of glycoenzymes colocalize with variants affecting risk for inflammatory diseases. This study provides new evidence that variation in key transcription factors coupled with regulatory variation in glycogenes modifies IgG glycosylation and has influence on inflammatory diseases.Molecular Epidemiolog

Choosing proper normalization is essential for discovery of sparse glycan biomarkers

Rapid progress in high-throughput glycomics analysis enables the researchers to conduct large sample studies. Typically, the between-subject differences in total abundance of raw glycomics data are very large, and it is necessary to reduce the differences, making measurements comparable across samples. Essentially there are two ways to approach this issue: row-wise and column-wise normalization. In glycomics, the differences per subject are usually forced to be exactly zero, by scaling each sample having the sum of all glycan intensities equal to 100%. This total area (row-wise) normalization (TA) results in so-called compositional data, rendering many standard multivariate statistical methods inappropriate or inapplicable. Ignoring the compositional nature of the data, moreover, may lead to spurious results. Alternatively, a log-transformation to the raw data can be performed prior to column-wise normalization and implementing standard statistical tools. Until now, there is no clear consensus on the appropriate normalization method applied to glycomics data. Nor, systematic investigation of impact of TA on downstream analysis is available to justify the choice of TA. Our motivation lies in efficient variable selection to identify glycan biomarkers with regard to accurate prediction as well as intepretability of the model chosen. Via extensive simulations we investigate how different normalization methods affect the performance of variable selection, and compare their performance. We also address the effect of various types of measurement error in glycans: additive, multiplicative and two-component error. We show that when sample-wise differences are not large row-wise normalization (like TA) can have deleterious effects on variable selection and prediction

Choosing proper normalization is essential for discovery of sparse glycan biomarkers

Rapid progress in high-throughput glycomics analysis enables the researchers to conduct large sample studies. Typically, the between-subject differences in total abundance of raw glycomics data are very large, and it is necessary to reduce the differences, making measurements comparable across samples. Essentially there are two ways to approach this issue: row-wise and column-wise normalization. In glycomics, the differences per subject are usually forced to be exactly zero, by scaling each sample having the sum of all glycan intensities equal to 100%. This total area (row-wise) normalization (TA) results in so-called compositional data, rendering many standard multivariate statistical methods inappropriate or inapplicable. Ignoring the compositional nature of the data, moreover, may lead to spurious results. Alternatively, a log-transformation to the raw data can be performed prior to column-wise normalization and implementing standard statistical tools. Until now, there is no clear consensus on the appropriate normalization method applied to glycomics data. Nor is systematic investigation of impact of TA on downstream analysis available to justify the choice of TA. Our motivation lies in efficient variable selection to identify glycan biomarkers with regard to accurate prediction as well as interpretability of the model chosen.Viaextensive simulations we investigate how different normalization methods affect the performance of variable selection, and compare their performance. We also address the effect of various types of measurement error in glycans: additive, multiplicative and two-component error. We show that when sample-wise differences are not large row-wise normalization (like TA) can have deleterious effects on variable selection and prediction

American Gut: an open platform for citizen science microbiome research

Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the United States, United Kingdom, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes compared to environmental samples; demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations; uncover new molecules and kinds of molecular communities in the human stool metabolome; and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education