Correlation of detector dog alerts to cocaine decomposition products found in illicit forensic specimens

The volatile chemicals which comprise the odor of the illicit drug cocaine have been analyzed by adsorption onto activated charcoal followed by solvent elution and GC/MS analysis. A series of field tests have been performed to determine the dominant odor compound to which dogs alert. All of our data to date indicate that the dominant odor is due to the presence of methyl benzoate which is associated with the cocaine, rather than the cocaine itself. When methyl benzoate and cocaine are spiked onto U.S. currency, the threshold level of methyl benzoate required for a canine to signal an alert is typically 1-10 $\mu$g. Humans have been shown to have a sensitivity similar to dogs for methyl benzoate but with poorer selectivity/reliability. The dominant decomposition pathway for cocaine has been evaluated at elevated temperatures (up to 280$\sp\circ$C). Benzoic acid, but no detectable methyl benzoate, is formed. Solvent extraction and SFE were used to study the recovery of cocaine from U.S. currency. The amount of cocaine which could be recovered was found to decrease with time. ^

Identification and characterization of interfering products in fire debris analysis

The purpose of this research was to study interfering products in fire debris analysis, including their identification and characterization. Different substrates were classified, burned, extracted and analyzed in order to identify all the interfering products that they may release. It has been shown that these products come from three different sources: substrate background products, pyrolysis products and possibly combustion products. Different parameters in the creation of these products were evaluated such as the extinguishment process as well as the weathering of the sample prior to the analysis. It has been shown that the presence of these products is not always constant and thus, makes it difficult to extrapolate data to similar cases. Furthermore, some of these products are similar to the ones found in ignitable liquids. Finally, it shows one more time how important it is to collect and analyze control samples in fire debris analysis. ^

Titanium dioxide photocatalytic degradation of aliphatic ethers and their primary oxidation products

Two studies were performed to obtain fundamental mechanistic information on the TiO2 catalyzed degradation of organic substrates irradiated at 350 nm in dilute aqueous solutions under oxygenated conditions: (a) The photodecomposition of methyl tert-butyl ether (MTBE) and its intermediate products from β-oxidation, 2-methoxy-2-methylpropanol and 2-methoxy-2-methylpropanol. (b) The photodecomposition of two haloethers, bis-(2-chloroethyl) ether, and bis-(2-chloroisopropyl) ether. Controls were carried out throughout the two studies in the absence of light, and without the semiconductor in order to evaluate the role of photolysis. ^ The syntheses of proposed intermediate products, 2-methoxy-2-methylpropanol, 2-methoxy-2-methylpropanal, 2-methoxy-2-methylpropanoic acid, 2-chloroethyl formate, and 1-chloro-2-propyl acetate, were performed. The formation of these products in the titanium dioxide photocatalytic oxidation of the substrates of interest was also confirmed. TiO2 photocatalysis is a very effective method for the mineralization of aliphatic ethers and their primary oxidation products. ^

Bioinformatic analysis for exploring relationships between genes and gene products

To carry out their specific roles in the cell, genes and gene products often work together in groups, forming many relationships among themselves and with other molecules. Such relationships include physical protein-protein interaction relationships, regulatory relationships, metabolic relationships, genetic relationships, and much more. With advances in science and technology, some high throughput technologies have been developed to simultaneously detect tens of thousands of pairwise protein-protein interactions and protein-DNA interactions. However, the data generated by high throughput methods are prone to noise. Furthermore, the technology itself has its limitations, and cannot detect all kinds of relationships between genes and their products. Thus there is a pressing need to investigate all kinds of relationships and their roles in a living system using bioinformatic approaches, and is a central challenge in Computational Biology and Systems Biology. This dissertation focuses on exploring relationships between genes and gene products using bioinformatic approaches. Specifically, we consider problems related to regulatory relationships, protein-protein interactions, and semantic relationships between genes. A regulatory element is an important pattern or "signal", often located in the promoter of a gene, which is used in the process of turning a gene "on" or "off". Predicting regulatory elements is a key step in exploring the regulatory relationships between genes and gene products. In this dissertation, we consider the problem of improving the prediction of regulatory elements by using comparative genomics data. With regard to protein-protein interactions, we have developed bioinformatics techniques to estimate support for the data on these interactions. While protein-protein interactions and regulatory relationships can be detected by high throughput biological techniques, there is another type of relationship called semantic relationship that cannot be detected by a single technique, but can be inferred using multiple sources of biological data. The contributions of this thesis involved the development and application of a set of bioinformatic approaches that address the challenges mentioned above. These included (i) an EM-based algorithm that improves the prediction of regulatory elements using comparative genomics data, (ii) an approach for estimating the support of protein-protein interaction data, with application to functional annotation of genes, (iii) a novel method for inferring functional network of genes, and (iv) techniques for clustering genes using multi-source data.

Assessment of the occurrence and potential effects of pharmaceuticals and personal care products in South Florida waters and sediments

A LLE-GC-MS method was developed to detect PPCPs in surface water samples from Big Cypress National Park, Everglades National Park and Biscayne National Park in South Florida. The most frequently found PPCPs were caffeine, DEET and triclosan with detected maximum concentration of 169 ng/L, 27.9 ng/L and 10.9 ng/L, respectively. The detection frequencies of hormones were less than PPCPs. Detected maximal concentrations of estrone, 17β-estradiol, coprostan-3-ol, coprostane and coprostan-3-one were 5.98 ng/L, 3.34 ng/L, 16.5 ng/L, 13.5 ng/L and 6.79 ng/L, respectively. An ASE-SPE-GC-MS method was developed and applied to the analysis of the sediment and soil area where reclaimed water was used for irrigation. Most analytes were below detection limits, even though some of analytes were detected in the reclaimed water at relatively high concentrations corroborating the fact that PPCPs do not significantly partition to mineral phases. An online SPE-HPLC-APPI-MS/MS method and an online SPE-HPLC-HESI-MS/MS method were developed to analyze reclaimed water and drinking water samples. In the reclaimed water study, reclaimed water samples were collected from the sprinkler for a year-long period at Florida International University Biscayne Bay Campus, where reclaimed water was reused for irrigation. Analysis results showed that several analytes were continuously detected in all reclaimed water samples. Coprostanol, bisphenol A and DEET's maximum concentration exceeded 10 μg/L (ppb). The four most frequently detected compounds were diphenhydramine (100%), DEET (98%), atenolol (98%) and carbamazepine (96%). In the study of drinking water, 54 tap water samples were collected from the Miami-Dade area. The maximum concentrations of salicylic acid, ibuprofen and DEET were 521 ng/L, 301 ng/L and 290 ng/L, respectively. The three most frequently detected compounds were DEET (93%), carbamazepine (43%) and salicylic acid (37%), respectively. Because the source of drinking water in Miami-Dade County is the relatively pristine Biscayne aquifer, these findings suggest the presence of wastewater intrusions into the delivery system or the onset of direct influence of surface waters into the shallow aquifer.

Development, validation and uncertainty analysis of quantitative structure and activity relationship models for Log P of disinfection by -products

Hydrophobicity as measured by Log P is an important molecular property related to toxicity and carcinogenicity. With increasing public health concerns for the effects of Disinfection By-Products (DBPs), there are considerable benefits in developing Quantitative Structure and Activity Relationship (QSAR) models capable of accurately predicting Log P. In this research, Log P values of 173 DBP compounds in 6 functional classes were used to develop QSAR models, by applying 3 molecular descriptors, namely, Energy of the Lowest Unoccupied Molecular Orbital (ELUMO), Number of Chlorine (NCl) and Number of Carbon (NC) by Multiple Linear Regression (MLR) analysis. The QSAR models developed were validated based on the Organization for Economic Co-operation and Development (OECD) principles. The model Applicability Domain (AD) and mechanistic interpretation were explored. Considering the very complex nature of DBPs, the established QSAR models performed very well with respect to goodness-of-fit, robustness and predictability. The predicted values of Log P of DBPs by the QSAR models were found to be significant with a correlation coefficient R2 from 81% to 98%. The Leverage Approach by Williams Plot was applied to detect and remove outliers, consequently increasing R 2 by approximately 2% to 13% for different DBP classes. The developed QSAR models were statistically validated for their predictive power by the Leave-One-Out (LOO) and Leave-Many-Out (LMO) cross validation methods. Finally, Monte Carlo simulation was used to assess the variations and inherent uncertainties in the QSAR models of Log P and determine the most influential parameters in connection with Log P prediction. The developed QSAR models in this dissertation will have a broad applicability domain because the research data set covered six out of eight common DBP classes, including halogenated alkane, halogenated alkene, halogenated aromatic, halogenated aldehyde, halogenated ketone, and halogenated carboxylic acid, which have been brought to the attention of regulatory agencies in recent years. Furthermore, the QSAR models are suitable to be used for prediction of similar DBP compounds within the same applicability domain. The selection and integration of various methodologies developed in this research may also benefit future research in similar fields.

Chlorine contribution to quantitative structure and activity relationship models of disinfection by -products' quantum chemical descriptors and toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: (1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (E LUMO) via QSAR modelling and analysis; (2) to validate the models by using internal and external cross-validation techniques; (3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl ) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: (1) Linear or Multi-linear Regression (MLR); (2) Partial Least Squares (PLS); and (3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: (1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; (2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; (3) E LUMO are shown to correlate highly with the NCl for several classes of DBPs; and (4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.

Antibacterial Activity of Extracellular Products from Cyanobacteria.

Cyanobacteria are photosynthetic prokaryotes that can be found in freshwater and marine environments as well as in soil. These organisms produce a variety of different biologically active compounds exhibiting anti-bacterial, anti-fungal and anti-cancer properties among others. In this study, cyanobacterial isolates were screened for their ability to produce extracellular antibacterial products. Cyanobacteria were isolated from fresh water and soil samples collected in the Pembroke Pines, FL area. Twenty- seven strains of cyanobacteria were isolated belonging to the following genera: Limnothrix, Nostoc, Fischerella, Anabaena, Pseudoanabaena, Lyngbya, Leptolyngbya, Tychonema, and Calothrix. Individual strains were grown in liquid culture in laboratory conditions. Following 14-day cultivation, the culture liquid was filtered and tested for activity against the following bacteria: Escherichia coli, Bacillus megatarium, Staphylococcus aureus, and Micrococcus luteus. Among all genera of cyanobacterial strains tested, Fischerella exhibited the greatest inhibitory activity. An attempt was made to isolate the active compound from the culture liquid of the active strains. Lipophilic extracts from culture liquid were obtained from three selected Fischerella strains. The extracts proved to have varying levels of activity against the tested bacteria. Inhibitory activity from all three Fischerella strains was detected against B. megatarium and M luteus. The only strain that was active against S. aureus was Fischerella sp. 114-12 while none of the extracts showed activity against E. coli. This kind of screening has potential pharmaceutical and agricultural benefits, including possible discovery of novel antibiotics.

The Effect of Natural Products and Small Gtpases in Adipogenesis

Insulin signaling is one of the main initiators of adipogenesis, the conversion from pre-adipocyte to adipocyte or lipid droplet. Rab proteins are the master regulator of intracellular trafficking and endosome fusion in endocytosis, making them potential regulators of insulin signaling in adipogenesis. Pre-adipocytes 3T3-Ll cells expressing several Rab5 constructs were used to examine the effect of dehydroleucodine (DhL ), a sesquiterpene lactone isolated from aerial parts of Artemisia douglasiana Besser. The results obtained identify Rab5 deactivation as a key step for adipogenesis by forming signaling endosomes. The addition of DhL significantly inhibited the lipid droplet accumulation in a dose-dependent manner and dramatically attenuated the synthesis of adipogenic transcriptional factors, C/EBPa and PPARy. Activation of AMPKa, Erk and Akt during adipocytic differentiation was not inhibited by treatment with DhL. This data suggest that DhL has an important role in Rab5 dependent adipogenesis by regulating several transcriptional factors including PP ARy expression, which is known to play an essential role during fat formation.

The non-timber forest products sector in nepal : policy issues in plant conservation and utilization

The non-timber forest products (NTFPs) sector in Nepal is being promoted with the concept of sustainable management as articulated by the Convention on Biological Diversity. To promote and regulate this sector, Nepal adopted the Herbs and NTFP Development Policy in 2004. The goal of this thesis was to assess the effectiveness of this policy along with other forestry and natural resource policies in Nepal concerning the conservation and sustainable use of NTFPs. I conducted open-ended semi-structured interviews with 28 key informants in summer 2006 in Nepal where I also collected relevant documents and publications. I did qualitative analysis of data obtained from interviews and document review. The research found many important issues that need to be addressed to promote the NTFP sector as envisioned by the Government of Nepal. The findings of this research will help to further implement the policy and promote the NTFP sector through sustainable management practices.

Chlorine Contribution to Quantitative Structure and Activity Relationship Models of Disinfection By-Products' Quantum Chemical Descriptors and Toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: 1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (ELUMO) via QSAR modelling and analysis; 2) to validate the models by using internal and external cross-validation techniques; 3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: 1) Linear or Multi-linear Regression (MLR); 2) Partial Least Squares (PLS); and 3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: 1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; 2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; 3) ELUMO are shown to correlate highly with the NCl for several classes of DBPs; and 4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.