How Mexico's Dairy Industry Has Evolved Under the NAFTA - Implications for U.S. Dairy Exporters and U.S. Investors in Mexico's Dairy-Food Businesses

This Discussion Paper shows that the demand for imported dairy products will continue to be strong in Mexico, especially after the 2001-2002 recession ends. However, Mexico's dairy markets have matured under the NAFTA. As part of the maturity, a larger number of strong domestic firms have emerged and powerful European multi-nationals have increased dairy product sales in Mexico. In addition, the expansion of U.S. exports of fluid milk, yogurt, dried whey, and lactose to Mexico will be slow in near future because U.S. market shares of imports of these products are already large. Thus, Mexico's dairy markets no longer represent "low hanging fruit" (if they ever did) for U.S. dairy exporters and direct investorsNAFTA, Maturing Mexican Dairy Markets, U.S. Dairy Exports to Mexico, Demand and Price Analysis, Food Consumption/Nutrition/Food Safety, International Development, International Relations/Trade,

Global analysis of SNPs, proteins and protein-protein interactions: approaches for the prioritisation of candidate disease genes.

PhDUnderstanding the etiology of complex disease remains a challenge in biology. In recent years there has been an explosion in biological data, this study investigates machine learning and network analysis methods as tools to aid candidate disease gene prioritisation, specifically relating to hypertension and cardiovascular disease. This thesis comprises four sets of analyses: Firstly, non synonymous single nucleotide polymorphisms (nsSNPs) were analysed in terms of sequence and structure based properties using a classifier to provide a model for predicting deleterious nsSNPs. The degree of sequence conservation at the nsSNP position was found to be the single best attribute but other sequence and structural attributes in combination were also useful. Predictions for nsSNPs within Ensembl have been made publicly available. Secondly, predicting protein function for proteins with an absence of experimental data or lack of clear similarity to a sequence of known function was addressed. Protein domain attributes based on physicochemical and predicted structural characteristics of the sequence were used as input to classifiers for predicting membership of large and diverse protein superfamiles from the SCOP database. An enrichment method was investigated that involved adding domains to the training dataset that are currently absent from SCOP. This analysis resulted in improved classifier accuracy, optimised classifiers achieved 66.3% for single domain proteins and 55.6% when including domains from multi domain proteins. The domains from superfamilies with low sequence similarity, share global sequence properties enabling applications to be developed which compliment profile methods for detecting distant sequence relationships. Thirdly, a topological analysis of the human protein interactome was performed. The results were combined with functional annotation and sequence based properties to build models for predicting hypertension associated proteins. The study found that predicted hypertension related proteins are not generally associated with network hubs and do not exhibit high clustering coefficients. Despite this, they tend to be closer and better connected to other hypertension proteins on the interaction network than would be expected by chance. Classifiers that combined PPI network, amino acid sequence and functional properties produced a range of precision and recall scores according to the applied 3 weights. Finally, interactome properties of proteins implicated in cardiovascular disease and cancer were studied. The analysis quantified the influential (central) nature of each protein and defined characteristics of functional modules and pathways in which the disease proteins reside. Such proteins were found to be enriched 2 fold within proteins that are influential (p<0.05) in the interactome. Additionally, they cluster in large, complex, highly connected communities, acting as interfaces between multiple processes more often than expected. An approach to prioritising disease candidates based on this analysis was proposed. Each analyses can provide some new insights into the effort to identify novel disease related proteins for cardiovascular disease

Regional and Individual Variations in the Function of the Human Eccrine Sweat Gland

Derived values for sodium concentration of the precursor fluid, free water clearance and counts of the number of active sweat glands were determined on the forehead, forearm and back of 14 subjects. Maximal sweat rate per gland and maximal free water clearance per gland were also calculated. The sodium concentration of the precursor fluid averaged 140 mEq/L. The large variations among our subjects in the maximal sweat rate (SR max) per m2 and maximal free water clearance (FWC max) per m2 depended mainly on differences in the functional capacity of individual sweat glands rather than in differences in population. However, regional variations in SR max per m2 and FWC max per m2 in each subject depended largely on differences in the population of active sweat glands. A significant correlation was found between secretory (SR max per gland) and reabsorptive capacity (FWC max per gland)

Modeling Rare Interactions in Time Series Data Through Qualitative Change: Application to Outcome Prediction in Intensive Care Units

Many areas of research are characterised by the deluge of large-scale highly-dimensional time-series data. However, using the data available for prediction and decision making is hampered by the current lag in our ability to uncover and quantify true interactions that explain the outcomes.We are interested in areas such as intensive care medicine, which are characterised by i) continuous monitoring of multivariate variables and non-uniform sampling of data streams, ii) the outcomes are generally governed by interactions between a small set of rare events, iii) these interactions are not necessarily definable by specific values (or value ranges) of a given group of variables, but rather, by the deviations of these values from the normal state recorded over time, iv) the need to explain the predictions made by the model. Here, while numerous data mining models have been formulated for outcome prediction, they are unable to explain their predictions. We present a model for uncovering interactions with the highest likelihood of generating the outcomes seen from highly-dimensional time series data. Interactions among variables are represented by a relational graph structure, which relies on qualitative abstractions to overcome non-uniform sampling and to capture the semantics of the interactions corresponding to the changes and deviations from normality of variables of interest over time. Using the assumption that similar templates of small interactions are responsible for the outcomes (as prevalent in the medical domains), we reformulate the discovery task to retrieve the most-likely templates from the data.Comment: 8 pages, 3 figures. Accepted for publication in the European Conference of Artificial Intelligence (ECAI 2020

Evaluation of bridge decks using non-destructive evaluation (NDE) at near highway speeds for effective asset management-implementation for routine inspection (Phase III)

This project focused on implementing the 3DOBS technology (developed under Phase I and Phase II) for successful detection, quantification, and visualization of concrete bridge deck distress features at near-highway speeds for routine MDOT inspections. The integration and further re-defining of the 3DOBS methods into MDOT practices was accomplished by assessing 11 bridge decks with an average size of 10,350 square feet. Distress features were categorized according to the Bridge Element Inspection Manual and compared to traditional (visual) element level inspection results. The Great Lakes Engineering Group, LLC worked with the research team to inspect, interpret, report results, and advise on current condition state reporting requirements. The project team also trained MDOT bridge inspectors in the use of the remote sensing equipment, data collection, data processing, and reporting through multiple different training sessions. A cost comparison between 3DOBS and traditional inspection methods was conducted, with 3DOBS costing an average of $92 per bridge, as compared to$39 for traditional methods. For producing standard element level condition state tables, 3DOBS cost more than a traditional inspector, but is still estimated to be less than $100 for an average bridge in this study