The Effect of Sample and Sample Matrix on DNA Processing: Mechanisms for the Detection and Management of Inhibition in Forensic Samples

The presence of inhibitory substances in biological forensic samples has, and continues to affect the quality of the data generated following DNA typing processes. Although the chemistries used during the procedures have been enhanced to mitigate the effects of these deleterious compounds, some challenges remain. Inhibitors can be components of the samples, the substrate where samples were deposited or chemical(s) associated to the DNA purification step. Therefore, a thorough understanding of the extraction processes and their ability to handle the various types of inhibitory substances can help define the best analytical processing for any given sample. A series of experiments were conducted to establish the inhibition tolerance of quantification and amplification kits using common inhibitory substances in order to determine if current laboratory practices are optimal for identifying potential problems associated with inhibition. DART mass spectrometry was used to determine the amount of inhibitor carryover after sample purification, its correlation to the initial inhibitor input in the sample and the overall effect in the results. Finally, a novel alternative at gathering investigative leads from samples that would otherwise be ineffective for DNA typing due to the large amounts of inhibitory substances and/or environmental degradation was tested. This included generating data associated with microbial peak signatures to identify locations of clandestine human graves. Results demonstrate that the current methods for assessing inhibition are not necessarily accurate, as samples that appear inhibited in the quantification process can yield full DNA profiles, while those that do not indicate inhibition may suffer from lowered amplification efficiency or PCR artifacts. The extraction methods tested were able to remove >90% of the inhibitors from all samples with the exception of phenol, which was present in variable amounts whenever the organic extraction approach was utilized. Although the results attained suggested that most inhibitors produce minimal effect on downstream applications, analysts should practice caution when selecting the best extraction method for particular samples, as casework DNA samples are often present in small quantities and can contain an overwhelming amount of inhibitory substances.

The effect of sample and sample matrix on DNA processing: Mechanisms for the detection and management of inhibition in forensic samples

The presence of inhibitory substances in biological forensic samples has, and continues to affect the quality of the data generated following DNA typing processes. Although the chemistries used during the procedures have been enhanced to mitigate the effects of these deleterious compounds, some challenges remain. Inhibitors can be components of the samples, the substrate where samples were deposited or chemical(s) associated to the DNA purification step. Therefore, a thorough understanding of the extraction processes and their ability to handle the various types of inhibitory substances can help define the best analytical processing for any given sample. A series of experiments were conducted to establish the inhibition tolerance of quantification and amplification kits using common inhibitory substances in order to determine if current laboratory practices are optimal for identifying potential problems associated with inhibition. DART mass spectrometry was used to determine the amount of inhibitor carryover after sample purification, its correlation to the initial inhibitor input in the sample and the overall effect in the results. Finally, a novel alternative at gathering investigative leads from samples that would otherwise be ineffective for DNA typing due to the large amounts of inhibitory substances and/or environmental degradation was tested. This included generating data associated with microbial peak signatures to identify locations of clandestine human graves. Results demonstrate that the current methods for assessing inhibition are not necessarily accurate, as samples that appear inhibited in the quantification process can yield full DNA profiles, while those that do not indicate inhibition may suffer from lowered amplification efficiency or PCR artifacts. The extraction methods tested were able to remove >90% of the inhibitors from all samples with the exception of phenol, which was present in variable amounts whenever the organic extraction approach was utilized. Although the results attained suggested that most inhibitors produce minimal effect on downstream applications, analysts should practice caution when selecting the best extraction method for particular samples, as casework DNA samples are often present in small quantities and can contain an overwhelming amount of inhibitory substances.^

Development and evaluation of a geographic information system based method to estimate flooding susceptibility in an area of Broward County, Florida

The objective of this study was to develop a GIS-based multi-class index overlay model to determine areas susceptible to inland flooding during extreme precipitation events in Broward County, Florida. Data layers used in the method include Airborne Laser Terrain Mapper (ALTM) elevation data, excess precipitation depth determined through performing a Soil Conservation Service (SCS) Curve Number (CN) analysis, and the slope of the terrain. The method includes a calibration procedure that uses "weights and scores" criteria obtained from Hurricane Irene (1999) records, a reported 100-year precipitation event, Doppler radar data and documented flooding locations. Results are displayed in maps of Eastern Broward County depicting types of flooding scenarios for a 100-year, 24-hour storm based on the soil saturation conditions. As expected the results of the multi-class index overlay analysis showed that an increase for the potential of inland flooding could be expected when a higher antecedent moisture condition is experienced. The proposed method proves to have some potential as a predictive tool for flooding susceptibility based on a relatively simple approach.

Development and assessment of a method to estimate meal intake of nursing home residents

The objective was to develop and validate a method for estimating food intake of nursing home residents. The study was conducted with certified nursing assistants (CNAs) at a 180-bed nursing facility. CNAs assisted in the development of the new method by providing feedback on existing estimation methods. Four simulated resident trays were used to estimate both food intake and overall meal intake. Twelve CNAs' intake estimates for 34 simulated food items (n=384 estimates) were compared to weighed values. Eightyfive percent of the 384 intake estimates for the simulated food items were correct; Cohen's kappa was 0.80, p

Customer relationship management: A content analysis of issues and best practices

This dissertation is a study of customer relationship management theory and practice. Customer Relationship Management (CRM) is a business strategy whereby companies build strong relationships with existing and prospective customers with the goal of increasing organizational profitability. It is also a learning process involving managing change in processes, people, and technology. CRM implementation and its ramifications are also not completely understood as evidenced by the high number of failures in CRM implementation in organizations and the resulting disappointments. ^ The goal of this dissertation is to study emerging issues and trends in CRM, including the effect of computer software and the accompanying new management processes on organizations, and the dynamics of the alignment of marketing, sales and services, and all other functions responsible for delivering customers a satisfying experience. ^ In order to understand CRM better a content analysis of more than a hundred articles and documents from academic and industry sources was undertaken using a new methodological twist to the traditional method. An Internet domain name (http://crm.fiu.edu) was created for the purpose of this research by uploading an initial one hundred plus abstracts of articles and documents onto it to form a knowledge database. Once the database was formed a search engine was developed to enable the search of abstracts using relevant CRM keywords to reveal emergent dominant CRM topics. The ultimate aim of this website is to serve as an information hub for CRM research, as well as a search engine where interested parties can enter CRM-relevant keywords or phrases to access abstracts, as well as submit abstracts to enrich the knowledge hub. ^ Research questions were investigated and answered by content analyzing the interpretation and discussion of dominant CRM topics and then amalgamating the findings. This was supported by comparisons within and across individual, paired, and sets-of-three occurrences of CRM keywords in the article abstracts. ^ Results show that there is a lack of holistic thinking and discussion of CRM in both academics and industry which is required to understand how the people, process, and technology in CRM impact each other to affect successful implementation. Industry has to get their heads around CRM and holistically understand how these important dimensions affect each other. Only then will organizational learning occur, and overtime result in superior processes leading to strong profitable customer relationships and a hard to imitate competitive advantage. ^

Development of safety performance functions for empirical Bayes estimation of crash reduction factors

Crash reduction factors (CRFs) are used to estimate the potential number of traffic crashes expected to be prevented from investment in safety improvement projects. The method used to develop CRFs in Florida has been based on the commonly used before-and-after approach. This approach suffers from a widely recognized problem known as regression-to-the-mean (RTM). The Empirical Bayes (EB) method has been introduced as a means to addressing the RTM problem. This method requires the information from both the treatment and reference sites in order to predict the expected number of crashes had the safety improvement projects at the treatment sites not been implemented. The information from the reference sites is estimated from a safety performance function (SPF), which is a mathematical relationship that links crashes to traffic exposure. The objective of this dissertation was to develop the SPFs for different functional classes of the Florida State Highway System. Crash data from years 2001 through 2003 along with traffic and geometric data were used in the SPF model development. SPFs for both rural and urban roadway categories were developed. The modeling data used were based on one-mile segments that contain homogeneous traffic and geometric conditions within each segment. Segments involving intersections were excluded. The scatter plots of data show that the relationships between crashes and traffic exposure are nonlinear, that crashes increase with traffic exposure in an increasing rate. Four regression models, namely, Poisson (PRM), Negative Binomial (NBRM), zero-inflated Poisson (ZIP), and zero-inflated Negative Binomial (ZINB), were fitted to the one-mile segment records for individual roadway categories. The best model was selected for each category based on a combination of the Likelihood Ratio test, the Vuong statistical test, and the Akaike's Information Criterion (AIC). The NBRM model was found to be appropriate for only one category and the ZINB model was found to be more appropriate for six other categories. The overall results show that the Negative Binomial distribution model generally provides a better fit for the data than the Poisson distribution model. In addition, the ZINB model was found to give the best fit when the count data exhibit excess zeros and over-dispersion for most of the roadway categories. While model validation shows that most data points fall within the 95% prediction intervals of the models developed, the Pearson goodness-of-fit measure does not show statistical significance. This is expected as traffic volume is only one of the many factors contributing to the overall crash experience, and that the SPFs are to be applied in conjunction with Accident Modification Factors (AMFs) to further account for the safety impacts of major geometric features before arriving at the final crash prediction. However, with improved traffic and crash data quality, the crash prediction power of SPF models may be further improved.

A methodology to estimate time varying user responses to travel time and travel time reliability in a road pricing environment

Road pricing has emerged as an effective means of managing road traffic demand while simultaneously raising additional revenues to transportation agencies. Research on the factors that govern travel decisions has shown that user preferences may be a function of the demographic characteristics of the individuals and the perceived trip attributes. However, it is not clear what are the actual trip attributes considered in the travel decision- making process, how these attributes are perceived by travelers, and how the set of trip attributes change as a function of the time of the day or from day to day. In this study, operational Intelligent Transportation Systems (ITS) archives are mined and the aggregated preferences for a priced system are extracted at a fine time aggregation level for an extended number of days. The resulting information is related to corresponding time-varying trip attributes such as travel time, travel time reliability, charged toll, and other parameters. The time-varying user preferences and trip attributes are linked together by means of a binary choice model (Logit) with a linear utility function on trip attributes. The trip attributes weights in the utility function are then dynamically estimated for each time of day by means of an adaptive, limited-memory discrete Kalman filter (ALMF). The relationship between traveler choices and travel time is assessed using different rules to capture the logic that best represents the traveler perception and the effect of the real-time information on the observed preferences. The impact of travel time reliability on traveler choices is investigated considering its multiple definitions. It can be concluded based on the results that using the ALMF algorithm allows a robust estimation of time-varying weights in the utility function at fine time aggregation levels. The high correlations among the trip attributes severely constrain the simultaneous estimation of their weights in the utility function. Despite the data limitations, it is found that, the ALMF algorithm can provide stable estimates of the choice parameters for some periods of the day. Finally, it is found that the daily variation of the user sensitivities for different periods of the day resembles a well-defined normal distribution.

Improved Annual Average Daily Traffic (AADT) estimation for local roads using parcel-level travel demand modeling

Annual Average Daily Traffic (AADT) is a critical input to many transportation analyses. By definition, AADT is the average 24-hour volume at a highway location over a full year. Traditionally, AADT is estimated using a mix of permanent and temporary traffic counts. Because field collection of traffic counts is expensive, it is usually done for only the major roads, thus leaving most of the local roads without any AADT information. However, AADTs are needed for local roads for many applications. For example, AADTs are used by state Departments of Transportation (DOTs) to calculate the crash rates of all local roads in order to identify the top five percent of hazardous locations for annual reporting to the U.S. DOT. ^ This dissertation develops a new method for estimating AADTs for local roads using travel demand modeling. A major component of the new method involves a parcel-level trip generation model that estimates the trips generated by each parcel. The model uses the tax parcel data together with the trip generation rates and equations provided by the ITE Trip Generation Report. The generated trips are then distributed to existing traffic count sites using a parcel-level trip distribution gravity model. The all-or-nothing assignment method is then used to assign the trips onto the roadway network to estimate the final AADTs. The entire process was implemented in the Cube demand modeling system with extensive spatial data processing using ArcGIS. ^ To evaluate the performance of the new method, data from several study areas in Broward County in Florida were used. The estimated AADTs were compared with those from two existing methods using actual traffic counts as the ground truths. The results show that the new method performs better than both existing methods. One limitation with the new method is that it relies on Cube which limits the number of zones to 32,000. Accordingly, a study area exceeding this limit must be partitioned into smaller areas. Because AADT estimates for roads near the boundary areas were found to be less accurate, further research could examine the best way to partition a study area to minimize the impact.^

Hydro-physical characterization of media used in agricultural systems to develop the best management practices for operation of an environmentally sustainable agricultural enterprise

Florida is the second leading horticulture state in the United States with a total annual industry sale of over $12 Billion. Due to its competitive nature, agricultural plant production represents an extremely intensive practice with large amounts of water and fertilizer usage. Agrochemical and water management are vital for efficient functioning of any agricultural enterprise, and the subsequent nutrient loading from such agricultural practices has been a concern for environmentalists. A thorough understanding of the agrochemical and the soil amendments used in these agricultural systems is of special interest as contamination of soils can cause surface and groundwater pollution leading to ecosystem toxicity. The presence of fragile ecosystems such as the Everglades, Biscayne Bay and Big Cypress near enterprises that use such agricultural systems makes the whole issue even more imminent. Although significant research has been conducted with soils and soil mix, there is no acceptable method for determining the hydraulic properties of mixtures that have been subjected to organic and inorganic soil amendments. Hydro-physical characterization of such mixtures can facilitate the understanding of water retention and permeation characteristics of the commonly used mix which can further allow modeling of soil water interactions. The objective of this study was to characterize some of the locally and commercially available plant growth mixtures for their hydro-physical properties and develop mathematical models to correlate these acquired basic properties to the hydraulic conductivity of the mixture. The objective was also to model the response patterns of soil amendments present in those mixtures to different water and fertilizer use scenarios using the characterized hydro-physical properties with the help of Everglades-Agro-Hydrology Model. The presence of organic amendments helps the mixtures retain more water while the inorganic amendments tend to adsorb more nutrients due to their high surface area. The results of these types of characterization can provide a scientific basis for understanding the non-point source water pollution from horticulture production systems and assist in the development of the best management practices for the operation of environmentally sustainable agricultural enterprise

Hydro-Physical Characterization of Media Used in Agricultural Systems to Develop the Best Management Practices for operation of an Environmentally Sustainable Agricultural Enterprise

Florida is the second leading horticulture state in the United States with a total annual industry sale of over $12 Billion. Due to its competitive nature, agricultural plant production represents an extremely intensive practice with large amounts of water and fertilizer usage. Agrochemical and water management are vital for efficient functioning of any agricultural enterprise, and the subsequent nutrient loading from such agricultural practices has been a concern for environmentalists. A thorough understanding of the agrochemical and the soil amendments used in these agricultural systems is of special interest as contamination of soils can cause surface and groundwater pollution leading to ecosystem toxicity. The presence of fragile ecosystems such as the Everglades, Biscayne Bay and Big Cypress near enterprises that use such agricultural systems makes the whole issue even more imminent. Although significant research has been conducted with soils and soil mix, there is no acceptable method for determining the hydraulic properties of mixtures that have been subjected to organic and inorganic soil amendments. Hydro-physical characterization of such mixtures can facilitate the understanding of water retention and permeation characteristics of the commonly used mix which can further allow modeling of soil water interactions. The objective of this study was to characterize some of the locally and commercially available plant growth mixtures for their hydro-physical properties and develop mathematical models to correlate these acquired basic properties to the hydraulic conductivity of the mixture. The objective was also to model the response patterns of soil amendments present in those mixtures to different water and fertilizer use scenarios using the characterized hydro-physical properties with the help of Everglades-Agro-Hydrology Model. The presence of organic amendments helps the mixtures retain more water while the inorganic amendments tend to adsorb more nutrients due to their high surface area. The results of these types of characterization can provide a scientific basis for understanding the non-point source water pollution from horticulture production systems and assist in the development of the best management practices for the operation of environmentally sustainable agricultural enterprise