Atualmente, a demanda e a necessidade de metodologias analíticas e bioanalíticas que promovam análise rápida e seletiva tem impulsionado o constante desenvolvimento de sorventes para preparo de amostra e fases estacionárias para cromatografia líquida de alta eficiência (HPLC). Desta maneira, esta tese tem por objetivo desenvolver materiais utilizando precursores de sílica com aplicação em preparo de amostra e suportes cromatográficos para cromatografia líquida de ultra alta eficiência (UHPLC). Foram sintetizadas e caracterizadas duas fases extratoras as quais tiveram sua aplicabilidade demonstrada na extração de fármacos em fluidos biológicos empregando a técnica de extração em fase sólida (SPE). A primeira metodologia desenvolvida foi a síntese de partículas de sílica empregando precursores de baixo custo (silicato de sódio) pelo método sil-gel. Após a funcionalização com grupamento C18, e caracterização da fase extratora, o material sintetizado foi aplicado em SPE off-line para a determinação de fluoxetina e seu metabólito, norfluoxetina, em plasma humano por HPLC-UV. O método desenvolvido foi validado e aplicado em amostra de plasma de pacientes sob tratamento de fluoxetina. Além disso, o sorvente desenvolvido apresentou resultados similares quando comparado com fases extratoras comerciais. O segundo método desenvolvido utilizou a técnica de impressão molecular (MIP) a qual tem se tornado uma fase extratora atrativa devido a sua maior seletividade em relação as fases típicas empregadas em SPE. Ao mesmo tempo, o uso de SPE online com MIP (MISPE) é uma alternativa atraente no preparo de amostra, pois é simples, diminui o tempo total da análise, necessita de pequena quantidade de amostra, e pode ser automatizado. Assim, MIP amino-funcionalizado pelo processo sol-gel utilizando ibuprofeno como template foi sintetizado. Para comparação, o polímero não impresso (NIP) foi preparado nas mesmas condições, sem a presença do template. As análises por cromatografia líquida foram realizadas utilizando uma coluna de extração MIP e uma coluna analítica em fase reversa configuradas para operar no modo SPE online por column switching. O método desenvolvido, MISPE-HPLC-UV, foi validado e aplicado na extração seletiva de cinco anti-inflamatórios não esteroidais (NSAIDs) em urina humana com um tempo total de análise de 22 minutos. Além dos materiais voltados para preparo de amostra, foi avaliado a síntese e caracterização de suporte cromatográfico baseado em sílica com diâmetro de partícula abaixo de 2 µm com aplicabilidade em análises rápidas em cromatografia (UHPLC). Nesta vertente, partículas de sílica esférica não porosa (0,9 µm) foram sintetizadas pelo método sol-gel com DPR abaixo de 10 %. As partículas foram funcionalizadas com o grupamento C18 (ODS) e submetidas posteriormente ao processo de endcapping (TMS) para operar em modo reverso. Métodos físico-químicos de caracterização foram utilizados para determinar a morfologia, área superficial e quantidade de carbono na superfície do material. Adicionalmente, as técnicas de infravermelho e ressonância magnética nuclear forneceram informações sobre a estrutura da sílica e das ligações após a funcionalização. A fase estacionária foi empacotada em uma coluna (40 mm x 0,25 mm) e a sua aplicabilidade foi avaliada em cromatografia líquida capilar (cLC). Apesar de não alcançar a eficiência ótima da separação cromatográfica devido a limitação instrumental, a fase estacionária sub 1 µm apresenta potencial para separações rápidas sem perda de eficiência. / Currently, the demand and need for bioanalytical and analytical methodologies that promotes rapid and selective analysis has stimulated the development of sorbents for sample preparation and stationary phases for high performance liquid chromatography (HPLC). Therefore, this thesis aims to develop materials using silica precursors with application in sample preparation and chromatographic supports for ultra high performance liquid chromatography (UHPLC). Thus, in this study two sorbents were synthesized and characterized and their applicability was demonstrated in the extraction of drugs in biological fluids employing the solid phase extraction technique (SPE). The first method developed was the synthesis of silica particles employing low cost precursors (sodium silicate) using the sil-gel method. After functionalization with C18 group and characterization of the extraction phase, the synthesized material was applied to SPE off-line for the determination of fluoxetine and its metabolite, norfluoxetine, in human plasma by HPLC-UV. The method was validated and applied to plasma samples from patients treated with fluoxetine. Furthermore, the labmade sorbent presented similar results when compared with commercial ones. The second method developed used the molecular imprinting technique (MIP) that has become an attractive sorbent due to its greater selectivity over the typical phases employed in SPE. At the same time, the use of online SPE with MIP (MISPE) is an attractive alternative in sample preparation because it is simple, reduces the total time of analysis, needs little amount of sample and can be automated. Thus, MIP amino-functionalized by the sol-gel method using ibuprofen as template was synthesized. For comparison, the non-imprinted polymer (NIP) was prepared under the same conditions without the presence of template. LC analysis was performed using a MIP extraction column and a reverse phase analytical column in order to perform column switching on-line sample separation. The developed method, MISPE-HPLC-UV, was validated and applied to the selective extraction of five anti-inflammatory drugs (NSAIDs) in human urine with a total analysis time of 22 minutes. Moreover it was evaluated the synthesis and characterization of chromatographic support based on silica with particle diameter under 2 µm with applicability in fast LC analysis (UHPLC). In this instance, spherical particles of non-porous silica (0.9 µm) were synthesized by sol-gel method with RSD below 10 %. The particles were functionalized with the C18 group and submitted to the endcapping process with TMS to operate in reverse phase mode. Physico-chemical methods were used to determine the morphology, surface area and amount of carbon in the silica surface. Additionally, the infrared and nuclear magnetic resonance techniques provided information about the structure of silica and bonds after the functionalization step. The stationary phase was packed into a column (40 mm x 0.25 mm), and its applicability was evaluated in capillary liquid chromatography (cLC). Although optimum chromatographic efficiency was not achieved separation due to instrumental limitations, the sub 1 µm stationary phase has potential for rapid separations without loss of efficiency.

Language sample analysis is accepted as the gold standard in child language assessment. Unfortunately it is often viewed as too time consuming for the practicing clinician. Over the last 15 years a great deal of research has been invested in the automated analysis of child language samples to make the process more time efficient. One step in the analysis process may be precoding the sample, as is used in the Systematic Analysis of Language Transcripts (SALT) software. However, a claim has been made (MacWhinney, 2008) that such precoding in fact leads to lower accuracy because of manual coding errors. No data on this issue have been published. The current research measured the accuracy of language samples analyzed with and without SALT precoding. This study also compared the accuracy of current software to an older version called GramCats (Channell & Johnson 1999). The results presented support the use of precoding schemes such as SALT and suggest that the accuracy of automated analysis has improved over time.

El proyecto tiene por objetivo reducir el costo de Setup en un proceso de extrusión por soplado de una empresa dedicada a la fabricación de lámina termocontraible, enfocándose en la problemática del proceso de desarrollo de muestras. Dicha problemática radica en la baja efectividad de este proceso, el cual es clave en el desarrollo de las operaciones de la organización porque de no ser satisfactorio no se concretan las ventas y se genera insatisfacción en el cliente. Además, acarrea sobrecostos como parte del Setup de producción, ya que mientras este proceso no arroje resultados positivos, la producción no iniciará y se empleará tiempo que bien se puede utilizar para producción de otros articulos. Esta falta de efectividad se produce por diversos motivos entre los que se destaca el pobre manejo del “know how”, de manera que constantemente se tiene que repetir el proceso hasta conseguir resultados. En ese sentido, se plantea la implementación de un sistema de Gestión del Conocimiento en este proceso, el cual organiza y transforma la información para generar conocimiento mediante el aprendizaje y tiene la ventaja de integrar el conocimiento implícito del trabajador, producto de años de experiencia, y transformarlo en explicito para toda la organización. Por ello, la propuesta busca reducir el tiempo de Setup al menos en un 70.31%, reduciendo en 487.71 horas el Setup anual, lo cual ahorraría 20367.81 soles anuales en costo de Setup. / The project aims to reduce the cost of Setup in a blow extrusion process of a company dedicated to the manufacture of heat shrink sheet, focusing on the problem of the sample development process. This problem lies in the low effectiveness of this process, which is key in the development of the operations of the organization because if not satisfactory, sales are not realized and customer dissatisfaction is generated. In addition, it costs overruns as part of the production setup, as long as this process does not yield positive results, production will not start and time will be used that can well be used for the production of other items. This lack of effectiveness occurs for a number of reasons, including poor management of the know-how, so that the process has to be repeated until results are achieved. In this sense, the implementation of a Knowledge Management system in this process is proposed, which organizes and transforms information to generate knowledge through learning and has the advantage of integrating the implicit knowledge of the worker , product of years of experience, and transform it into explicit for the whole organization. Therefore, the proposal seeks to reduce Setup time by at least 70.31%, reducing the annual Setup by 487.71 hours, which would save 20367.81 annual suns in Setup cost. / Tesis

Phospholipids (PLs) are a component of cell membranes, biological fluids and tissues. These compounds are problematic for the bioanalytical chemist, especially when PLs are not the analytes of interest. PL interference with bioanalysis highly impacts reverse-phase chromatographic methods coupled with mass spectrometric detection. Phospholipids are strongly retained on hydrophobic columns, and can cause significant ionization suppression in the mass spectrometer, as they out-compete analyte molecules for ionization. Strategies for improving analyte detection in the presence of PLs are reviewed, including in-analysis modifications and sample preparation strategies. Removal of interfering PLs prior to analysis seems to be most effective at moderating the matrix effects from these endogenous cellular components, and has the potential to simplify chromatography and improve column lifetime. Products targeted at PL removal for sample pre-treatment, as well as products that combine multiple modes of sample preparation (i.e. Hybrid SPE), show significant promise in mediating the effect on PL interference in bioanalysis.

The goal of this research was to aid in the fight against the heroin and opioid epidemic by developing new methodology for heroin provenance determination and forensic sample comparison. Over 400 illicit heroin powder samples were analyzed using quadrupole and high-resolution inductively-coupled plasma mass spectrometry (Q-ICP-MS and HR-ICP-MS) in order to measure and identify elemental contaminants useful for associating heroin samples of common origin and differentiating heroin of different geographic origins. Additionally, 198 heroin samples were analyzed by multi-collector ICP-MS (MC-ICP-MS) to measure radiogenic strontium isotope ratios (87Sr/86Sr) with high-precision for heroin provenance determination, for the first time. Supervised discriminant analysis models were constructed to predict heroin origin using elemental composition. The model was able to correctly associate 88% of the samples to their region of origin. When 87Sr/86Sr data were combined with Q-ICP-MS elemental data, the correct association of heroin samples improved to ≥90% for all groups with an average of 93% correct classification. For forensic sample comparisons, quantitative elemental data (11 elements measured) from 120 samples, 30 from each of the four regions, were compared in order to assess the rate of discrimination (5400 total comparisons). Using a match criterion of ±3 standard deviations about the mean, only 14 of the 5400 possible comparison pairs were not discriminated resulting in a discrimination rate of 99.7%. For determining the rate of correct associations, 3 replicates of 24 duplicate samples were prepared and analyzed on separate days. Only 1 of the 24 correct pairs were not associated for a correct association rate of 95.8%. New methods for provenance determination and sample comparison are expected to be incredibly useful to intelligence agencies and law enforcement working to reduce the proliferation of heroin.

The purpose of this study was to develop behavior by the pigeon illustrative of S+ and S- rules and to examine if behavior consistent with a concept rule interpretation developed. In order to examine this possibility six groups of pigeons (N=31) were provided different histories of autoshaping. Histories involved the identity of the color of the lighted center key and one side key of three horizontally mounted pigeon keys. Center key onset was followed three seconds later by onset of either outer key. Outer key onset was followed six seconds later by food presentation (explicitly paired) or 45 seconds later, during the inter-trial interval (explicitly unpaired). The foregoing reinforcement conditions were factored into two stimulus conditions, one where the center and side keys were lit by the same hue (identity) and one where the center and side key were lit by different hues (nonidentity). Two groups received identity stimulus sequences with explicitly unpaired food presentation. Two groups were exposed to nonidentity stimulus sequences with explicitly unpaired food presentation. One group received sessions combining exposure to both the explicitly paired identity and explicitly unpaired nonidentity trials. One group received no pretraining. Following pretraining, all birds were placed in a simultaneous matching-to-sample task utilizing the same hues used during pretraining. After reaching criterion on matching-to-sample, on a random ratio 3 schedule of reinforcement, birds were exposed to transfer tests, with a novel hue, to assess for S+ rules, S- rules, and a concept rule. The results indicated that there were no significant differences among groups in terms of their acquisition of matching-to-sample or in terms of their performance on transfer tests. All birds' responding during transfer conditions provided evidence of S+ rules, but neither demonstration of S- rules nor concept rule performance was evidenced. During autoshaping, birds in the identity, explicitly paired groups responded primarily to the center key, suggesting that the stimulus on the outer key was not a salient stimulus for the identity discrimination. For birds in the nonidentity, explicitly unpaired groups neither the center nor outer key controlled responding. For the group combining identity, explicitly paired and nonidentity, explicitly unpaired trials, the birds failed to form a discrimination between types of trials.

<p>The purpose of this paper is to investigate the predictive power of the variables advanced notice of dismissal (layoffs) and vacancies for the unemployment rate. Based on the Box Jenkins Methodology, the paper makes use of Granger causality and out-of-sample tests to compare the forecast performance of a naïve reference model and the two models extended to include either lagged values of layoffs or vacancies. It is shown that layoffs make up a significant leading variable, exhibiting particularly strong predictive power at forecast horizons of 2-6 months. It is also shown that the predictive power of vacancies is more ambiguous. Vacancies constitute a valuable explanatory variable for the unemployment rate, but does not possess the same leading, predictive qualities as layoffs.</p>

The purpose of this paper is to investigate the predictive power of the variables advanced notice of dismissal (layoffs) and vacancies for the unemployment rate. Based on the Box Jenkins Methodology, the paper makes use of Granger causality and out-of-sample tests to compare the forecast performance of a naïve reference model and the two models extended to include either lagged values of layoffs or vacancies. It is shown that layoffs make up a significant leading variable, exhibiting particularly strong predictive power at forecast horizons of 2-6 months. It is also shown that the predictive power of vacancies is more ambiguous. Vacancies constitute a valuable explanatory variable for the unemployment rate, but does not possess the same leading, predictive qualities as layoffs.

Biological sample analysis is a costly and time-consuming process. It involves highly trained technicians operating large and expensive instruments in a temperature and dust controlled environment. In the world of rising healthcare cost, the drive towards a more cost-effective solution calls for a point-of-care device that performs accurate analyses of human blood samples. To achieve this goal, today's bulky laboratory instruments need to be scaled down and integrated on a single microchip of only a few square centimeters or millimeters in size. Dielectrophoresis (DEP), a phenomenon where small particles such as human blood cells are manipulated by non-uniform electric fields, stands to feature prominently in the point-of-care device. An original device that enhances DEP effect through novel geometry of the electrodes is presented. When activated with two inverting sinusoidal waveforms, the novel-shaped electrodes generate horizontal bands of increasing electric fields on the surface of the microchip. With these bands of electric fields, particles can be manipulated to form a straight horizontal line at a predictable location. Experimental results showing the collection, separation, and transportation of mammalian cells are presented. A strategy for simultaneous processing of two or more types of particles is also demonstrated. With capabilities for an accurate position control and an increased throughput by parallel processing, the novel microchip device delivers substantial improvements over the existing DEP designs. The research presented here explores the effects of novel electrode geometries in cell manipulations and contributes to the overall progress of an automated blood analysis system.

Machine learning has enjoyed astounding practical success in a wide range of applications in recent years-practical success that often hurries ahead of our theoretical understanding. The standard framework for machine learning theory assumes full supervision, that is, training data consists of correctly labeled iid examples from the same task that the learned classifier is supposed to be applied to. However, many practical applications successfully make use of the sheer abundance of data that is currently produced. Such data may not be labeled or may be collected from various sources. The focus of this thesis is to provide theoretical analysis of machine learning regimes where the learner is given such (possibly large amounts) of non-perfect training data. In particular, we investigate the benefits and limitations of learning with unlabeled data in semi-supervised learning and active learning as well as benefits and limitations of learning from data that has been generated by a task that is different from the target task (domain adaptation learning). For all three settings, we propose Probabilistic Lipschitzness to model the relatedness between the labels and the underlying domain space, and we discuss our suggested notion by comparing it to other common data assumptions.