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      Geographically Sourcing Cocaine’s Origin – Delineation of the Nineteen Major Coca Growing Regions in South America

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          Abstract

          Previously, geo-sourcing to five major coca growing regions within South America was accomplished. However, the expansion of coca cultivation throughout South America made sub-regional origin determinations increasingly difficult. The former methodology was recently enhanced with additional stable isotope analyses ( 2H and 18O) to fully characterize cocaine due to the varying environmental conditions in which the coca was grown. An improved data analysis method was implemented with the combination of machine learning and multivariate statistical analysis methods to provide further partitioning between growing regions. Here, we show how the combination of trace cocaine alkaloids, stable isotopes, and multivariate statistical analyses can be used to classify illicit cocaine as originating from one of 19 growing regions within South America. The data obtained through this approach can be used to describe current coca cultivation and production trends, highlight trafficking routes, as well as identify new coca growing regions.

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          Most cited references12

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          Comparison of the predicted and observed secondary structure of T4 phage lysozyme.

          Predictions of the secondary structure of T4 phage lysozyme, made by a number of investigators on the basis of the amino acid sequence, are compared with the structure of the protein determined experimentally by X-ray crystallography. Within the amino terminal half of the molecule the locations of helices predicted by a number of methods agree moderately well with the observed structure, however within the carboxyl half of the molecule the overall agreement is poor. For eleven different helix predictions, the coefficients giving the correlation between prediction and observation range from 0.14 to 0.42. The accuracy of the predictions for both beta-sheet regions and for turns are generally lower than for the helices, and in a number of instances the agreement between prediction and observation is no better than would be expected for a random selection of residues. The structural predictions for T4 phage lysozyme are much less successful than was the case for adenylate kinase (Schulz et al. (1974) Nature 250, 140-142). No one method of prediction is clearly superior to all others, and although empirical predictions based on larger numbers of known protein structure tend to be more accurate than those based on a limited sample, the improvement in accuracy is not dramatic, suggesting that the accuracy of current empirical predictive methods will not be substantially increased simply by the inclusion of more data from additional protein structure determinations.
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            K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space

            Background Kernel-based classification and regression methods have been successfully applied to modelling a wide variety of biological data. The Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) method offers unique properties facilitating separate modelling of predictive variation and structured noise in the feature space. While providing prediction results similar to other kernel-based methods, K-OPLS features enhanced interpretational capabilities; allowing detection of unanticipated systematic variation in the data such as instrumental drift, batch variability or unexpected biological variation. Results We demonstrate an implementation of the K-OPLS algorithm for MATLAB and R, licensed under the GNU GPL and available at . The package includes essential functionality and documentation for model evaluation (using cross-validation), training and prediction of future samples. Incorporated is also a set of diagnostic tools and plot functions to simplify the visualisation of data, e.g. for detecting trends or for identification of outlying samples. The utility of the software package is demonstrated by means of a metabolic profiling data set from a biological study of hybrid aspen. Conclusion The properties of the K-OPLS method are well suited for analysis of biological data, which in conjunction with the availability of the outlined open-source package provides a comprehensive solution for kernel-based analysis in bioinformatics applications.
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              Illicit Production of Cocaine.

              The predominant methods currently used for illicit production of cocaine are described. For illicit natural cocaine (i.e., from coca leaf), this includes production of coca paste from coca leaf via both the solvent and acid extraction techniques, purification of coca paste to cocaine base, and conversion of cocaine base to cocaine hydrochloride. For illicit synthetic cocaine (i.e., synthesized from precursor chemicals), the classic five-step synthetic route used in all clandestine laboratories seized to date is summarized. The origins of the most common alkaloidal impurities and processing/synthetic by-products typically identified in illicit natural, illicit synthetic, and pharmaceutical cocaine are discussed. Forensic differentiation of exhibits arising from the various production methods are addressed both in terms of overall product purity and the presence/absence of these impurities and by-products.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                23 March 2016
                2016
                : 6
                : 23520
                Affiliations
                [1 ]U.S. Drug Enforcement Administration, Special Testing and Research Laboratory , Dulles, VA 20166 USA
                [2 ]National Geospatial Intelligence Agency , Springfield, VA 20150 USA
                [3 ]U.S. Drug Enforcement Administration, Intelligence Division , Arlington, VA 22202 USA
                Author notes
                Article
                srep23520
                10.1038/srep23520
                4804289
                27006288
                bbd6f3ec-cc6d-4b57-bad9-672514587bb7
                Copyright © 2016, Macmillan Publishers Limited

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 09 October 2015
                : 26 February 2016
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