Classification and Pay Plans FY2014, June 2013

This document Classifications and Pay Plans is produced by the State of Iowa Executive Branch, Department of Administrative Services. Informational document about the pay plan codes and classification codes, how to use them.

Characterization and classification of matrix effects in biological samples analyses.

An exhaustive classification of matrix effects occurring when a sample preparation is performed prior to liquid-chromatography coupled to mass spectrometry (LC-MS) analyses was proposed. A total of eight different situations were identified allowing the recognition of the matrix effect typology via the calculation of four recovery values. A set of 198 compounds was used to evaluate matrix effects after solid phase extraction (SPE) from plasma or urine samples prior to LC-ESI-MS analysis. Matrix effect identification was achieved for all compounds and classified through an organization chart. Only 17% of the tested compounds did not present significant matrix effects.

Stochastic models and numerical algorithms for a class of regulatory gene networks.

Regulatory gene networks contain generic modules, like those involving feedback loops, which are essential for the regulation of many biological functions (Guido et al. in Nature 439:856-860, 2006). We consider a class of self-regulated genes which are the building blocks of many regulatory gene networks, and study the steady-state distribution of the associated Gillespie algorithm by providing efficient numerical algorithms. We also study a regulatory gene network of interest in gene therapy, using mean-field models with time delays. Convergence of the related time-nonhomogeneous Markov chain is established for a class of linear catalytic networks with feedback loops.

Typologies textuelles et partitions musicales : dissimilarités, classification et autocorrélation

Axée dans un premier temps sur le formalisme et les méthodes, cette thèse est construite sur trois concepts formalisés: une table de contingence, une matrice de dissimilarités euclidiennes et une matrice d'échange. À partir de ces derniers, plusieurs méthodes d'Analyse des données ou d'apprentissage automatique sont exprimées et développées: l'analyse factorielle des correspondances (AFC), vue comme un cas particulier du multidimensional scaling; la classification supervisée, ou non, combinée aux transformations de Schoenberg; et les indices d'autocorrélation et d'autocorrélation croisée, adaptés à des analyses multivariées et permettant de considérer diverses familles de voisinages. Ces méthodes débouchent dans un second temps sur une pratique de l'analyse exploratoire de différentes données textuelles et musicales. Pour les données textuelles, on s'intéresse à la classification automatique en types de discours de propositions énoncées, en se basant sur les catégories morphosyntaxiques (CMS) qu'elles contiennent. Bien que le lien statistique entre les CMS et les types de discours soit confirmé, les résultats de la classification obtenus avec la méthode K- means, combinée à une transformation de Schoenberg, ainsi qu'avec une variante floue de l'algorithme K-means, sont plus difficiles à interpréter. On traite aussi de la classification supervisée multi-étiquette en actes de dialogue de tours de parole, en se basant à nouveau sur les CMS qu'ils contiennent, mais aussi sur les lemmes et le sens des verbes. Les résultats obtenus par l'intermédiaire de l'analyse discriminante combinée à une transformation de Schoenberg sont prometteurs. Finalement, on examine l'autocorrélation textuelle, sous l'angle des similarités entre diverses positions d'un texte, pensé comme une séquence d'unités. En particulier, le phénomène d'alternance de la longueur des mots dans un texte est observé pour des voisinages d'empan variable. On étudie aussi les similarités en fonction de l'apparition, ou non, de certaines parties du discours, ainsi que les similarités sémantiques des diverses positions d'un texte. Concernant les données musicales, on propose une représentation d'une partition musicale sous forme d'une table de contingence. On commence par utiliser l'AFC et l'indice d'autocorrélation pour découvrir les structures existant dans chaque partition. Ensuite, on opère le même type d'approche sur les différentes voix d'une partition, grâce à l'analyse des correspondances multiples, dans une variante floue, et à l'indice d'autocorrélation croisée. Qu'il s'agisse de la partition complète ou des différentes voix qu'elle contient, des structures répétées sont effectivement détectées, à condition qu'elles ne soient pas transposées. Finalement, on propose de classer automatiquement vingt partitions de quatre compositeurs différents, chacune représentée par une table de contingence, par l'intermédiaire d'un indice mesurant la similarité de deux configurations. Les résultats ainsi obtenus permettent de regrouper avec succès la plupart des oeuvres selon leur compositeur.

Allergic reactions following contrast material administration: nomenclature, classification, and mechanisms

In forensic pathology routine, fatal cases of contrast agent exposure can be occasionally encountered. In such situations, beyond the difficulties inherent in establishing the cause of death due to nonspecific or absent autopsy and histology findings as well as limited laboratory investigations, pathologists may face other problems in formulating exhaustive, complete reports, and conclusions that are scientifically accurate. Indeed, terminology concerning adverse drug reactions and allergy nomenclature is confusing. Some terms, still utilized in forensic and radiological reports, are outdated and should be avoided. Additionally, not all forensic pathologists master contrast material classification and pathogenesis of contrast agent reactions. We present a review of the literature covering allergic reactions to contrast material exposure in order to update used terminology, explain the pathophysiology, and list currently available laboratory investigations for diagnosis in the forensic setting.

Tire traces: discrimination and classification of pyrolysis-GC/MS profiles

Tire traces can be observed on several crime scenes as vehicles are often used by criminals. The tread abrasion on the road, while braking or skidding, leads to the production of small rubber particles which can be collected for comparison purposes. This research focused on the statistical comparison of Py-GC/MS profiles of tire traces and tire treads. The optimisation of the analytical method was carried out using experimental designs. The aim was to determine the best pyrolysis parameters regarding the repeatability of the results. Thus, the pyrolysis factor effect could also be calculated. The pyrolysis temperature was found to be five time more important than time. Finally, a pyrolysis at 650 °C during 15 s was selected. Ten tires of different manufacturers and models were used for this study. Several samples were collected on each tire, and several replicates were carried out to study the variability within each tire (intravariability). More than eighty compounds were integrated for each analysis and the variability study showed that more than 75% presented a relative standard deviation (RSD) below 5% for the ten tires, thus supporting a low intravariability. The variability between the ten tires (intervariability) presented higher values and the ten most variant compounds had a RSD value above 13%, supporting their high potential of discrimination between the tires tested. Principal Component Analysis (PCA) was able to fully discriminate the ten tires with the help of the first three principal components. The ten tires were finally used to perform braking tests on a racetrack with a vehicle equipped with an anti-lock braking system. The resulting tire traces were adequately collected using sheets of white gelatine. As for tires, the intravariability for the traces was found to be lower than the intervariability. Clustering methods were carried out and the Ward's method based on the squared Euclidean distance was able to correctly group all of the tire traces replicates in the same cluster than the replicates of their corresponding tire. Blind tests on traces were performed and were correctly assigned to their tire source. These results support the hypothesis that the tested tires, of different manufacturers and models, can be discriminated by a statistical comparison of their chemical profiles. The traces were found to be not differentiable from their source but differentiable from all the other tires present in the subset. The results are promising and will be extended on a larger sample set.

Treatment implications of the emerging molecular classification system for melanoma.

Melanoma is an aggressive disease with few standard treatment options. The conventional classification system for this disease is based on histological growth patterns, with division into four subtypes: superficial spreading, lentigo maligna, nodular, and acral lentiginous. Major limitations of this classification system are absence of prognostic importance and little correlation with treatment outcomes. Recent preclinical and clinical findings support the notion that melanoma is not one malignant disorder but rather a family of distinct molecular diseases. Incorporation of genetic signatures into the conventional histopathological classification of melanoma has great implications for development of new and effective treatments. Genes of the mitogen-associated protein kinase (MAPK) pathway harbour alterations sometimes identified in people with melanoma. The mutation Val600Glu in the BRAF oncogene (designated BRAF(V600E)) has been associated with sensitivity in vitro and in vivo to agents that inhibit BRAF(V600E) or MEK (a kinase in the MAPK pathway). Melanomas arising from mucosal, acral, chronically sun-damaged surfaces sometimes have oncogenic mutations in KIT, against which several inhibitors have shown clinical efficacy. Some uveal melanomas have activating mutations in GNAQ and GNA11, rendering them potentially susceptible to MEK inhibition. These findings suggest that prospective genotyping of patients with melanoma should be used increasingly as we work to develop new and effective treatments for this disease.

What's so special about conversion disorder? A problem and a proposal for diagnostic classification.

Conversion disorder presents a problem for the revisions of DSM-IV and ICD-10, for reasons that are informative about the difficulties of psychiatric classification more generally. Giving up criteria based on psychological aetiology may be a painful sacrifice but it is still the right thing to do.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Classification of current anticancer immunotherapies.

During the past decades, anticancer immunotherapy has evolved from a promising therapeutic option to a robust clinical reality. Many immunotherapeutic regimens are now approved by the US Food and Drug Administration and the European Medicines Agency for use in cancer patients, and many others are being investigated as standalone therapeutic interventions or combined with conventional treatments in clinical studies. Immunotherapies may be subdivided into "passive" and "active" based on their ability to engage the host immune system against cancer. Since the anticancer activity of most passive immunotherapeutics (including tumor-targeting monoclonal antibodies) also relies on the host immune system, this classification does not properly reflect the complexity of the drug-host-tumor interaction. Alternatively, anticancer immunotherapeutics can be classified according to their antigen specificity. While some immunotherapies specifically target one (or a few) defined tumor-associated antigen(s), others operate in a relatively non-specific manner and boost natural or therapy-elicited anticancer immune responses of unknown and often broad specificity. Here, we propose a critical, integrated classification of anticancer immunotherapies and discuss the clinical relevance of these approaches.