Project description:Batch effects in microbiome data arise from differential processing of specimens and can lead to spurious findings and obscure true signals. Strategies designed for genomic data to mitigate batch effects usually fail to address the zero-inflated and over-dispersed microbiome data. Most strategies tailored for microbiome data are restricted to association testing or specialized study designs, failing to allow other analytic goals or general designs. Here, we develop the Conditional Quantile Regression (ConQuR) approach to remove microbiome batch effects using a two-part quantile regression model. ConQuR is a comprehensive method that accommodates the complex distributions of microbial read counts by non-parametric modeling, and it generates batch-removed zero-inflated read counts that can be used in and benefit usual subsequent analyses. We apply ConQuR to simulated and real microbiome datasets and demonstrate its advantages in removing batch effects while preserving the signals of interest.
Project description:Pine needle litter in Himalayan forests leads to forest fires, ground water recharge inhibition, soil acidification and contamination, and stops the growth of grass and plants. This study provides a possible solution for pine needle litter problem by converting it to biochar. Pine needle litter lying on the ground for approximately a month was collected from the Himalayan region. The pine needle litter biochars were generated using slow pyrolysis (residence time, 30 min; heating rate, 10 °C/min) at 350, 450, 550, 650, and 750 °C. Finally, pine needle litter biochar prepared at 550 °C (PNBC550) was selected for sorptive removal of aqueous lead both in batch and column studies. The PNBC550 was characterized for proximate and elemental compositions, crystallinity, surface area, morphology, and functional groups. A BET surface area of 230.9 m2/g was obtained for PNBC550. Batch sorption studies were carried out to study (1) the adsorption versus pH studies (at pH 2 to 7), (2) isotherms (at 10, 25, and 35 °C) to evaluate the temperature effect on the sorption efficiency, and (3) kinetics to reveal the effect of time, adsorbent dose, and initial concentration on the reaction rate. Increasing pyrolysis temperature raised lead sorption up to 550 °C. Lead adsorption increased considerably as pH rose from 2 to a maximum adsorption around pH 5 and above. The sorption data were fitted using different isotherm models and kinetic equations. The Langmuir adsorption capacity increased from 22.93 mg/g at 10 °C to 40.43 mg/g at 35 °C, showing that adsorption was endothermic. Fixed-bed studies were conducted at room temperature with an initial lead concentration of 7.85 mg/L and 4.0 g of PNBC550 at initial pH 5.0 and a flow rate of 3 mL/min. Desorption studies conducted under the same experimental conditions found about 90-93% lead recovery. Development of high-efficiency biochars for lead remediation provides a sustainable solution for the Himalayan pine needle litter problem. The biochars also possess the possible potential for aqueous removal of other metal cations.
Project description:AbstractA continuous flow procedure for the synthesis of methyl glycosides (Fischer glycosylation) of various monosaccharides using a heterogenous catalyst has been developed. In-depth analysis of the isomeric composition was undertaken and high consistency with corresponding results observed under microwave heating was obtained. Even in cases where addition of water was needed to achieve homogeneity-a prerequisite for the flow experiments-no detrimental effect on the conversion was found. The scalability was demonstrated on a model case (mannose) and as part of the target-oriented synthesis of d-glycero-d-manno heptose, both performed on multigram scale.Graphical abstract
Project description:In this paper, we evaluated the potential of two synthesized bio-based polyurethane foams, PU1 and PU2, for the removal of diesel and gasoline from water mixtures. We started the investigation with the experiment in batch. The total sorption capacity S (g/g) for the diesel/water system was slightly higher with respect to gasoline/water, with a value of 62 g/g for PU1 and 65 g/g for PU2. We found that the sorption follows a pseudo second-order kinetic model for both the materials. The experimental data showed that the best isotherm models were obtained with Langmuir and Redlich-Peterson models. In addition, to provide an idea of the process scalability for future industrial applications, we tested the sorption capacity of the foams using a continuous-flow of the same oil/water mixtures and we obtained performances even better with respect to the batch test. The regeneration can be performed up to 50 times by centrifuge, without losing efficacy.
Project description:The present investigate was intended for adsorption of heavy metals i.e. Pb, Cu, Cr, Zn, Ni and Cd onto activated charcoal prepared from neem leaf powder (AC-NLP) using batch and column studies. Batch adsorption was performed using different variables like adsorbent dose, temperature and contact duration. Thermodynamic analysis of batch treatment concluded that adsorption is thermodynamically feasible and endothermic. This adsorption followed the Pseudo second-order kinetic model derived from correlation coefficient values of chemical kinetic studies. For column study, interpretation of breakthrough curves and parameters were conducted by varying flow rate, initial concentration and bed height; and reveal that optimum conditions were lower flow rate (5 mL/min) and lower initial concentration (5 mg/L) and higher bed height (20 cm). Comparisons of batch and column study through isotherm models were evaluated and column study is more preferred than batch treatment. Maximum Thomas adsorption capacity was achieved upto 205.6, 185.8, 154.5, 133.3, 120.6, 110.9 mg/g for Pb, Cu, Cd, Zn, Ni and Cr respectively. This removal pattern is elucidated by metal ionic properties. Various adsorbing agents such as acids and bases were utilized for adsorption-desorption of AC-NLP.
Project description:Plasmas under atmospheric pressure offer a high-temperature environment for material synthesis, but electrode ablation compromises purity. Here, we introduce an atmospheric-pressure microwave plasma (AMP) operated without electrodes to overcome the existing limitations in pure material synthesis. The distribution of the electrostatic field intensity inside a waveguide during AMP excitation was examined via electrostatic field simulations. The lateral and radial gas temperature distributions were also studied using optical emission spectroscopy. The AMP exhibited a uniform ultrahigh temperature (9,000 K), a large volume (102-104 cm3), and a response time on the millisecond level. AMP efficiently synthesized silicon nanoparticles, graphene, and graphene@Si-Fe core-shell nanoparticles within tens of milliseconds, ensuring purity and size control. We propose the "heat impulse" metric for evaluating the plasma characteristics (n a, T g, and t) in material synthesis, extended to other high-temperature plasmas. AMP is compact, cost-effective, and easy to assemble, promising for eco-friendly mass production of pure materials.
Project description:Herein, we report the synthesis of magnetic nanoparticle (MNP)-reduced graphene oxide (rGO) and polymethylmethacrylate (PMMA) composite (MNPs/rGO/PMMA) as adsorbent via an in situ fabrication strategy and, in turn, the application for adsorptive removal and recovery of Cr(VI) from tannery wastewater. The composite material was characterized via XRD, FTIR and SEM analyses. Under batch mode experiments, the composite achieved maximum adsorption of the Cr(VI) ion (99.53 ± 1.4%, i.e., 1636.49 mg of Cr(VI)/150 mg of adsorbent) at pH 2, adsorbent dose of 150 mg/10 mL of solution and 30 min of contact time. The adsorption process was endothermic, feasible and spontaneous and followed a pseudo-2nd order kinetic model. The Cr ions were completely desorbed (99.32 ± 2%) from the composite using 30 mL of NaOH solution (2M); hence, the composite exhibited high efficiency for five consecutive cycles without prominent loss in activity. The adsorbent was washed with distilled water and diluted HCl (0.1M), then dried under vacuum at 60 °C for reuse. The XRD analysis confirmed the synthesis and incorporation of magnetic iron oxide at 2θ of 30.38°, 35.5°, 43.22° and 57.36°, respectively, and graphene oxide (GO) at 25.5°. The FTIR analysids revealed that the composite retained the configurations of the individual components, whereas the SEM analysis indicated that the magnetic Fe3O4-NPs (MNPs) dispersed on the surface of the PMMA/rGO sheets. To anticipate the behavior of breakthrough, the Thomas and Yoon-Nelson models were applied to fixed-bed column data, which indicated good agreement with the experimental data. This study evaluates useful reference information for designing a cost-effective and easy-to-use adsorbent for the efficient removal of Cr(VI) from wastewater. Therefore, it can be envisioned as an alternative approach for a variety of unexplored industrial-level operations.
Project description:MotivationSources of variability in experimentally derived data include measurement error in addition to the physical phenomena of interest. This measurement error is a combination of systematic components, originating from the measuring instrument and random measurement errors. Several novel biological technologies, such as mass cytometry and single-cell RNA-seq (scRNA-seq), are plagued with systematic errors that may severely affect statistical analysis if the data are not properly calibrated.ResultsWe propose a novel deep learning approach for removing systematic batch effects. Our method is based on a residual neural network, trained to minimize the Maximum Mean Discrepancy between the multivariate distributions of two replicates, measured in different batches. We apply our method to mass cytometry and scRNA-seq datasets, and demonstrate that it effectively attenuates batch effects.Availability and implementationour codes and data are publicly available at https://github.com/ushaham/BatchEffectRemoval.git.Contactyuval.kluger@yale.edu.Supplementary informationSupplementary data are available at Bioinformatics online.
Project description:Cocultivation of cellulolytic and saccharolytic microbial populations is a promising strategy to improve bioethanol production from the fermentation of recalcitrant cellulosic materials. Earlier studies have demonstrated the effectiveness of cocultivation in enhancing ethanolic fermentation of cellulose in batch fermentation. To further enhance process efficiency, a semicontinuous cyclic fed-batch fermentor configuration was evaluated for its potential in enhancing the efficiency of cellulose fermentation using cocultivation. Cocultures of cellulolytic Clostridium thermocellum LQRI and saccharolytic Thermoanaerobacter pseudethanolicus strain X514 were tested in the semicontinuous fermentor as a model system. Initial cellulose concentration and pH were identified as the key process parameters controlling cellulose fermentation performance in the fixed-volume cyclic fed-batch coculture system. At an initial cellulose concentration of 40 g liter(-1), the concentration of ethanol produced with pH control was 4.5-fold higher than that without pH control. It was also found that efficient cellulosic bioethanol production by cocultivation was sustained in the semicontinuous configuration, with bioethanol production reaching 474 mM in 96 h with an initial cellulose concentration of 80 g liter(-1) and pH controlled at 6.5 to 6.8. These results suggested the advantages of the cyclic fed-batch process for cellulosic bioethanol fermentation by the cocultures.
Project description:Genome-scale, constraint-based models (GEM) and their derivatives are commonly used to model and gain insights into microbial metabolism. Often, however, their accuracy and predictive power are limited and enable only approximate designs. To improve their usefulness for strain and bioprocess design, we studied here their capacity to accurately predict metabolic changes in response to operational conditions in a bioreactor, as well as intracellular, active reactions. We used flux balance analysis (FBA) and dynamic FBA (dFBA) to predict growth dynamics of the model organism Saccharomyces cerevisiae under different industrially relevant conditions. We compared simulations with the latest developed GEM for this organism (Yeast8) and its enzyme-constrained version (ecYeast8) herein described with experimental data and found that ecYeast8 outperforms Yeast8 in all the simulations. EcYeast8 was able to predict well-known traits of yeast metabolism including the onset of the Crabtree effect, the order of substrate consumption during mixed carbon cultivation and production of a target metabolite. We showed how the combination of ecGEM and dFBA links reactor operation and genetic modifications to flux predictions, enabling the prediction of yields and productivities of different strains and (dynamic) production processes. Additionally, we present flux sampling as a tool to analyse flux predictions of ecGEM, of major importance for strain design applications. We showed that constraining protein availability substantially improves accuracy of the description of the metabolic state of the cell under dynamic conditions. This therefore enables more realistic and faithful designs of industrially relevant cell-based processes and, thus, the usefulness of such models.