Evaluating Green Solvents and Techniques in Extraction Methods

Of all analytical techniques, extraction is a huge solvent-consuming process that could adversely impact the environment. Use of petroleum-based solvents for extraction of oilseeds is still a common practice, despite the potential fire hazard and the toxic water pollution. The rising awareness of chemical activities created immense need for sustainable development schemes and strategies that should address the environmental impact without compromising the yield. In the course of developing green extraction techniques, automation, alternative solvents, and selective extractions are the growing trend. This dissertation aligns with that progress by surveying green solvents, comparing their performance during oil extraction, examining a prototype automated extraction system, and studying the efficiency of selective adsorbents. Green solvents are of great interest as they are sourced from renewable feedstock and pose little or no danger to the environment. But their application in analytical chemistry is not widely appreciated. This dissertation aimed to study the extraction efficiency of green solvents during accelerated solvent extraction of soybean oil. Five green solvents, 2-methyltetrahydrofuran (2-MeTHF), alpha-pinene, cyclopentyl methyl ether (CPME), ethyl lactate, and t-butyl methyl ether (TBME), were chosen based on the literature, solubility, and viscosity. Using the GSK solvent-scoring system obtained from literature, the ecological and economic impact of these solvents were roughly identified with respect to n-hexane. As the solubility of analytes can influence the initial part of the extraction, relative solubility of triglycerides (of the major soybean fatty acids such as linoleic acid, oleic acid, palmitic acid, and stearic acid) in the green solvents was theoretically predicted using a computer program. Also, the viscosities of the green solvents at different temperatures was investigated prior to the extraction study. Soybean, the most dominant oilseed in the market with rich protein and oil content, was used as the sample for the extraction study. As the initial analysis indicated that the lower size particles give greater oil recovery, soybean particles of average diameter 513 μm were chosen for the elaborate extraction evaluation. For a small-scale fast extraction of analytes from solid and semisolid samples, accelerated solvent extraction (ASE) is a powerful and sophisticated device. This fully automated extraction system uses very little solvent at elevated temperature and pressure and is able to run several queued experiments at programmed conditions. To rely on the results from ASE of soybean oil using green solvents, the hot-ball model was used as a validating tool. The hot-ball model gives a theoretical extraction profile for an ideal spherical matrix that can be used to evaluate and validate any experimental extraction results. As diffusion plays a major role in the kinetics of extraction, comparing the diffusion coefficient of green solvents was the key approach. Upon assessing the performance of green solvents with respect to percent oil recovery, CPME demonstrated the highest diffusion coefficient and highest % recovery for soybean oil. A remarkable 99% recovery was attained within 30 min, which is 17 times faster than n-hexane. These results suggest CPME as a promising green alternative solvent for soybean oil extraction. The second part of this dissertation examines a new green extraction system. A prototype automated extractor from CEM was investigated in terms of its extraction efficiency. The knowledge obtained from previous ASE extraction studies were used to gauge the capabilities of this instrument, and the hot-ball model was used to validate the results. Adsorbents are a significant part of the post-extraction cleanup process and studying their efficiency could reveal their ability to green the analytical techniques. The mechanism of adsorption is complex, and it varies with each adsorbate-solvent-adsorbent system. The last part of the dissertation aimed to investigate the oil adsorption efficiency of five adsorbents – silica, florisil, activated carbon, alumina and diatomaceous earth – during ASE extractions at different temperatures and concentration. Results showed that activated carbon has remarkable tendency to retain oil, at low temperatures and high adsorbent concentrations

Methods for Clustered Competing Risks Data and Causal Inference using Instrumental Variables for Censored Time-to-event Data

In this dissertation, we propose new methods for analysis of clustered competing risks data (Chapters 1 and 2) and for instrumental variable (IV) analysis of univariate censored time-to-event data and competing risks data (Chapters 3 and 4). In Chapter 1, we propose estimating center effects through cause-specific proportional hazards frailty models that allow correlation among a center’s cause-specific effects. To evaluate center performance, we propose a directly standardized excess cumulative incidence (ECI) measure. We apply our methods to evaluate Organ Procurement Organizations with respect to (i) receipt of a kidney transplant and (ii) death on the wait-list. In Chapter 2, we propose to model the effects of cluster and individual-level covariates directly on the cumulative incidence functions of each risk through a semiparametric mixture component model with cluster-specific random effects. Our model permits joint inference on all competing events and provides estimates of the effects of clustering. We apply our method to multicenter competing risks data. In Chapter 3, we turn our focus to causal inference in the censored time-to-event setting in the presence of unmeasured confounders. We develop weighted IV estimators of the complier average causal effect on the restricted mean survival time. Our method accommodates instrument-outcome confounding and covariate dependent censoring. We establish the asymptotic properties, derive easily implementable variance estimators, and apply our method to compare modalities for end stage renal disease (ESRD) patients using national registry data. In Chapter 4, we develop IV analysis methods for competing risks data. Our method permits simultaneous inference of exposure effects on the absolute risk of all competing events and accommodates exposure dependent censoring. We apply the methods to compare dialytic modalities for ESRD patients with respect to risk of death from (i) cardiovascular diseases and (ii) other causes.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144110/1/shdharma_1.pd

Thermodynamic and Structural Characterization of the Interaction between Wd40 Repeat Containing Protein 5 (Wdr5) and Human Set1 Family Methyltransferases

Translocations and amplifications of the mixed lineage leukemia-1 (MLL1) gene are associated with aggressive myeloid and lymphocytic leukemias in humans. MLL1 is a member of the SET1 family of histone H3 lysine 4 (H3K4) methyltransferases, which are required for transcription of genes involved in hematopoiesis and development. MLL1 associates with a sub-complex containing WDR5, RbBP5, Ash2L, and DPY-30 (WRAD), which together form the MLL1 core complex that is required for sequential mono- and dimethylation of H3K4. We previously demonstrated that WDR5 binds the conserved WDR5 interaction (Win) motif of MLL1 in vitro, an interaction that is required for the H3K4 dimethylation activity of the MLL1 core complex. In this dissertation, we demonstrate that arginine 3765 of the MLL1 Win motif is required to co-immunoprecipitate WRAD from mammalian cells, suggesting that the WDR5-Win motif interaction is important for the assembly of the MLL1 core complex in vivo. We also demonstrate that peptides that mimic human SET1 family Win motif sequences (MLL1-4, SETd1a and SETd1b) inhibit H3K4 dimethylation by the MLL1 core complex with varying degrees of efficiency. We show that the MLL3 Win motif peptide is the best inhibitor of the H3K4 dimethylation activity of MLL1 core complex. To understand the structural basis for these differences, we determined three-dimensional structures of WDR5 bound to six different naturally occurring Win motif sequences (MLL1-4, SETd1a and SETd1b). The structural analysis reveal that binding energy differences result from interactions between non-conserved residues C-terminal to the Win motif and to a lesser extent from subtle variation of residues within the Win motif. Based on the structure-function analysis, we deduce structural rules to facilitate the design of two additional Win motif -based inhibitors (Ac-10-mer and six-residue Win motif peptides) that bind WDR5 with \u3c10 nM affinities. To understand the structural basis for this low nanomolar affinity, we determined X-ray three-dimensional structures of the Ac-10-mer Win motif peptide bound to WDR5. The structures suggest that the presence of additional intramolecular hydrogen bonds might contribute to the increased affinities for WDR5 possibly through the stabilization of the bound 310-helical conformation. We extend this structure-function analysis further to identify other peptidomimetics by characterizing peptides identified in a randomized phage display screen, which are also highly specific inhibitors of MLL1 core complex. Crystal structures of these peptidomimetics reveal novel protein structural features that contribute to increased affinity. We also present preliminary evidence suggesting that the MLL3 Win motif based peptide that has a cell penetrating sequence is readily taken up by mammalian cells. This MLL3 Win motif-based peptide (MLL3-FITC-TAT) is localized to euchromatin regions of cell nuclei, induces nuclear defects and inhibits global levels of H3K4 trimethylation. These results highlight a new class of methylation inhibitors that may be useful for the treatment of MLL1-related malignancies

Numerical modeling of chemical recovery from black liquor char

A complete CFD model for the char burning stage of chemical recovery from black liquor is presented. Chemical recovery from black liquor is characterized by the occurrence of multiple, simultaneous reactions occurring in dynamic flow conditions. Rate equations for the different chemical reactions are incorporated into a multiphase CFD code MFIX. Rate equations for sulfate reduction by carbon, gasification of carbon by CO2 and H2O, and, COS and H2S formation, are incorporated into MFIX, to simulate the chemical kinetics occurring in the char burning stage. Oxidation of carbon and Na 2S by O2 are also included.;Pyrolysis of black liquor char in the presence of N2, gasification in the presence of CO2 and H2O were simulated. Results for pyrolysis and gasification, and variation with respect to temperature are presented. Two different models for sulfate reduction are compared with each other and with published experimental results. Competitive consumption of carbon by sodium sulfate and gasification reactions is studied and the effects of temperature, and heating rates of solids are discussed

Case-Mix Adjustment of Adherence Based Pharmacy Quality Indicator Scores

Medication adherence has been shown to be influenced by demographics, health status and socio-economic status of the patient. Thus, adherence-based measures of pharmacy quality may be influenced by patient-related risk factors outside of the healthcare provider\u27s control. This study examines the performance of a classical logistic regression model containing only patient characteristics and a random-effect model including patient characteristics and a pharmacy-specific effect in predicting medication adherence. These models were used to compute three different risk-adjusted scores on adherence-based pharmacy quality indicators: based on the classical logistic regression model (Method 1), the random effects model (Method 2) and the shrinkage estimators of the random-effects model (Method 3). Finally, we compared the classification as low, medium or high quality pharmacies based on unadjusted and adjusted scores. This retrospective cohort study used the 2007 Mississippi Medicare administrative claims dataset. Patient medication adherence was measured using the proportion of days covered (PDC) measure for seven therapeutic classes of medications. Pharmacy Quality scores on adherence-based measures were computed for all pharmacies serving Medicare beneficiaries in the state. The logistic regression model and the random-effect model displayed good predictive ability (c-statistic\u3e0.7) for all therapeutic classes. The residual intra class correlation coefficient ranged from 0.008 to 0.012 indicating that although pharmacy level factors may have a significant impact, they may not be as important as patient level factors in determining adherence. Higher levels of agreement was observed between pharmacy classification based on unadjusted scores and risk-adjusted scores obtained from Methods 1 and 2 (0.

Exploiting Non-Linearities in GDP Growth for Forecasting and Anticipating Regime Changes

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An Efficient Filtering Approach to Likelihood Approximation for State-Space Representations

We develop a numerical filtering procedure that facilitates efficient likelihood evaluation in applications involving non-linear and non-gaussian state-space models. The procedure approximates necessary integrals using continuous or piecewise-continuous approximations of target densities. Construction is achieved via efficient importance sampling, and approximating densities are adapted to fully incorporate current information. --particle filter,adaption,efficient importance sampling,kernel density approximation