Orientador: Ronei Jesus Poppi / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Química / Made available in DSpace on 2018-08-22T08:18:21Z (GMT). No. of bitstreams: 1 Avila_ThiagoCarvalhode_M.pdf: 7831860 bytes, checksum: 010f2295e00f097a9ecfaf3f498a7069 (MD5) Previous issue date: 2013 / Resumo: Este trabalho visou o uso de Espectroscopia Raman e de Quimiometria para monitoramento e controle da fermentação de glicose por Saccharomyces cerevisiae. Na primeira etapa, foi utilizada calibração multivariada baseada no método dos Mínimos Quadrados Parciais (PLS) para quantificação de glicose, etanol, glicerol, ácido acético e células. Os modelos foram desenvolvidos baseados nos valores de concentração obtidos pelos métodos de referência, cromatografia líquida de alta eficiência ¿ HPLC e espectrofotometria UV/Vis. Tanto na etapa de calibração quanto na de validação, a otimização foi realizada com eliminação de amostras anômalas, baseada nos valores de leverage, resíduos e escores. Na segunda etapa, cartas de controle multivariadas foram usadas para identificação de falhas em bateladas durante o processo de fermentação. Foram construídos modelos MPCA (Análise de Componentes Principais Multimodo) a partir de bateladas NOC (Condições Normais de Operação). As cartas de controle multivariadas foram aplicadas em dois modos de desdobramento dos dados obtidos durante o monitoramento, um preservando a direção das bateladas e outro a direção do tempo. As falhas estudadas foram temperatura, mudança no substrato e contaminação do sistema. No modo de desdobramento por bateladas, a carta de controle Q foi eficiente para detecção das falhas estudas, fato comprovado pela classificação correta de três bateladas NOC como dentro de controle. No entanto, a carta de controle T2 não foi capaz de identificar as falhas estudadas corretamente como fora de controle. O modo de desdobramento pelo tempo também apresentou classificações corretas das falhas estudadas / Abstract: This work aims the use of Raman Spectroscopy and Chemometrics in the monitoring and control in the fermentation of the glucose by Saccharomyces cerevisiae. In the first step, it was applied the multivariate calibration based on Partial Least Squares (PLS) for the quantification of glucose, ethanol, glycerol, acetic acid and cells. The developed of calibration models was performed against the concentration values obtained by the reference methods, High Performance Liquid Chromatography and UV/Vis spectrophotometer. The optimization of the calibration and validation steps, the elimination of outliers was performed based on the values of leverage, residues and scores. In the second step, multivariate control charts were used for identification of batch-fault during the fermentation process. Multi-way Principal Component Analysis (MPCA) models were developed from batch NOC (Normal Operation Conditions). The multivariate control charts were based on two modes of unfolding the multi-way data, obtained during monitoring, one preserving the direction of the batch and another the direction of time. The fault studied were temperature, changes in the substrate and contamination of the system. In unfolding batch mode, the chart Q was effective for detection of the faults studied, proven by the correctly classification of 3 NOC batches as in control. However, the chart T2 failed to identify faults studied. The unfolding in time mode, also presented correct classifications of the faults studied / Mestrado / Quimica Analitica / Mestre em Química

Seasonal allergic rhinitis (SAR) is a disease caused by allergens from both environmental and genetic factors. Some researchers have studied the SAR based on traditional genetic methodologies. As technology develops, a new technique called single-cell RNA sequencing (scRNA-seq) is developed, which can generate high-dimension data. We apply two machine learning (ML) algorithms, random forest (RF) and partial least squares discriminant analysis (PLS-DA), for cell source classification and gene selection based on the SAR scRNA-seq time-series data from three allergic patients and four healthy controls denoised by single-cell variational inference (scVI). We additionally propose a new fitting method consisting of bootstrap and cubic smoothing splines to fit the averaged gene expressions per cell from different populations. To sum up, we find that both RF and PLS-DA could provide high classification accuracy, and RF is more preferable, considering its stable performance and strong gene-selection ability. Based on our analysis, there are 10 genes having discriminatory power to classify cells of allergic patients and healthy controls at any timepoints. Although there is no literature founded to show the direct connections between such 10 genes and SAR, the potential associations are indirectly confirmed by some studies. It shows a possibility that we can alarm allergic patients before a disease outbreak based on their genetic information. Meanwhile, our experiment results indicate that ML algorithms may discover something between genes and SAR compared with traditional techniques, which needs to be analyzed in genetics in the future.

Insurance has been considered as a useful tool for farmers to mitigate income volatility. However, there remain concerns that insurance may distort crop production decisions. Positive mathematical programming (PMP) models of farmers’ cropping decisions can be applied to study the effect of agricultural business risk management (BRM) policies on farmers’ decisions on land use and their incomes. Before being used to examine agricultural producer responses to policy changes under the expected utility framework, the models must first be calibrated to obtain the values of the risk aversion coefficient and the cost function parameters. In chapter 2, three calibration approaches are compared for disentangling the risk parameter from the parameters of the cost function. Then, in chapter 3, to investigate the impacts on production incentives of changes in Canada’s AgriStability program, farm management models are calibrated for farms with different cost structures for three different Alberta regions. Results indicate that farmers’ observed attitudes towards risk vary with cost structure. After joining the program, all farmers alter their land allocations to some extent. The introduction of a reference margin limit (RML) in the AgriStability program under Growing Forward 2 (2013-2018), which was retained in the replacement legislation until 2020, has the most negative impact on farmers with the lowest costs. The removal of RML significantly increases the benefits to low-cost farmers. Traditional insurance products provide financial support to farmers. However, for fruit farmers, the products’ quality can be greatly affected by the weather conditions during the stage of fruit development and ripening, which may lead to quality downgrade and a significant loss in revenue with little impacts on yields. Hence, chapters 4 and 5 investigate the conceptual feasibility of using weather-indexed insurance (WII) to hedge against non-catastrophic, but quality-impacting weather conditions to complement existing traditional insurance. Prospect theory is applied to analyze a farmer’s demand for WII. The theoretical model demonstrates that an increase in the volatility of total revenue and the revenue proportion from blueberries increases the possibility of farmers’ participation in WII. On the other hand, the increase in the value loss aversion coefficient and WII’s basis risk leads to less demand for WII. To design a WII product for blueberry growers to hedge against quality risk, a quality index must be constructed and the relationship between key weather conditions, such as cumulative maximum temperature and cumulative excess rainfall, and the quality index should be quantified. The results from a partial least squares structural equation modeling (PLS-SEM) show that the above goals are achievable. Further, rainfall and temperature can be modelled via a time-series model and statistical distributions, respectively, to provide reasonable estimates for calculating insurance premia. / Graduate / 2022-08-05

Mass spectrometry has been used extensively in recent years as a valuable tool in the study of proteomics. However, the data thus produced exhibits hyper-dimensionality. Reducing the dimensionality of the data often requires the imposition of many assumptions which can be harmful to subsequent analysis. The IP algorithm is a dimension reduction algorithm, similar in purpose to latent variable analysis. It is based on the principle of maximum entropy and therefore imposes a minimum number of assumptions on the data. Partial Least Squares (PLS) is an algorithm commonly used with proteomics data from mass spectrometry in order to reduce the dimension of the data. The IP algorithm and a PLS algorithm were applied to proteomics data from mass spectrometry to reduce the dimension of the data. The data came from three groups of patients, those with no tumors, malignant or benign tumors. Reduced data sets were produced from the IP algorithm and the PLS algorithm. Logistic regression models were constructed using predictor variables extracted from these data sets. The response was threefold and indicated which tumor classifications each patient belonged. Misclassification rates were determined for the IP algorithm and the PLS algorithm. The rates correct classification associated with the IP algorithm were equal or better than those rates associated with the PLS algorithm.

It is estimated that about 35-40% of adults in the U.S. suffer from insufficient sleep. Chronic sleep deprivation has become a prevalent phenomenon because of contemporary lifestyle and work-related factors. Sleep deprivation can reduce the capabilities and efficiency of attentional performance by impairing perception, increasing effort to maintain concentration, as well as introducing vision disturbance. Thus, it is important to understand the neural mechanisms behind how chronic sleep deprivation impairs sustained attention. In recent years, more attention has been paid to the study of the integration between anatomically distributed and functionally connected brain regions. Functional connectivity has been widely used to characterize brain functional integration, which measures the statistical dependency between neurophysiological events of the human brain. Further, evidence from recent studies has shown the non-stationary nature of brain functional connectivity, which may reveal more information about the human brain. Thus, the objective of this thesis is to investigate the effects of chronic sleep deprivation on sustained attention from the perspective of dynamic functional connectivity. A modified spatial cueing paradigm was used to assess human sustained attention in rested wakefulness and chronic sleep deprivation conditions. Partial least squares approach was applied to distinguish brain functional connectivity for the experimental conditions. With the integration of a sliding-window approach, dynamic patterns of brain functional connectivity were identified in two experimental conditions. The brain was modeled as a series of dynamic functional networks in each experimental condition. Graph theoretic analysis was performed to investigate the dynamic properties of brain functional networks, using network measures of clustering coefficient and characteristics path length. In the chronic sleep deprivation condition, a compensation mechanism between highly clustered organization and ineffective adaptability of brain functional networks was observed. Specifically, a highly clustered organization of brain functional networks was illustrated with a large clustering coefficient. This organization suggested that brain utilizes more connections to maintain attention in the chronic sleep deprivation condition. A smaller impact of clustering coefficient variation on characteristics path lengths indicated an ineffective adaptability of brain functional networks in the chronic sleep deprivation condition. In the rested wakefulness condition, brain functional networks showed the small-world topology in general, with the average small-world topology index larger than one. Small-world topology was identified as an optimal network structure with the balance between local information processing and global integration. Given the fluctuating values of the index over time, small-world brain networks were observed in most cases, indicating an effective adaptability of the human brain to maintain the dominance of small-world networks in the rested wakefulness condition. On the contrary, given that the average small-world topology index was smaller than one, brain functional networks generally exhibited random network structure. From the perspective of dynamic functional networks, even though there were few cases showing small-world brain networks, brain functional networks failed to maintain the dominance of small-world topology in the chronic sleep deprivation condition. In conclusion, to the best of our knowledge this thesis was the first to investigate the effects of chronic sleep deprivation on sustained attention from the perspective of dynamic brain functional connectivity. A compensation mechanism between highly clustered organization and ineffective adaptability of brain functional networks was observed in the chronic sleep deprivation condition. Furthermore, chronic sleep deprivation impaired sustained attention by reducing the effectiveness of brain functional networks' adaptability, resulting in the disrupted dominance of small-world brain networks.

evaluate disruption risks in the supply chain of petroleum production. This methodology is developed to formalise and facilitate the systematic integration and implementation of various models; such as analytical hierarchy process (AHP) and partial least squares structural equation model (PLS-SEM) and various statistical tests. The methodology is validated with the case of Nigeria’s oil industry. The study revealed the need to provide a responsive approach to managing the influence of geopolitical risk factors affecting supply chain in the petroleum production industry. However, the exploration and production risk, and geopolitical risk were identified as concomitant risk factors that impact performance in Nigeria’s oil industry. The research findings show that behavioural-based mechanisms successfully predict the ability of the petroleum industry to manage supply chain risks. The significant implication for this study is that the current theoretical debate on the supply chain risk management creates the understanding of agency theory as a governing mechanism for supply chain risk in the Nigerian oil industry. The systematic approach results provide an insight and objective information for decisions-making in resolving disruption risk to the petroleum supply chain in Nigeria. Furthermore, this study highlights to stakeholders on how to develop supply chain risk management strategies for mitigating and building resilience in the supply chain in the Nigerian oil industry. The developed systematic method is associated with supply chain risk management and performance measure. The approach facilitates an effective way for the stakeholders to plan according to their risk mitigation strategies. This will consistently help the stakeholders to evaluate supply chain risk and respond to disruptions in supply chain. This capability will allow for efficient management of supply chain and provide the organization with quicker response to customer needs, continuity of supply, lower costs of operations and improve return on investment in the Nigeria oil industry. Therefore, the methodology applied provide a new way for implementing good practice for managing disruption risk in supply chain. Further, the systematic approach provides a simplistic modelling process for disruption risk evaluation for researchers and oil industry professionals. This approach would develop a holistic procedure for monitoring and controlling disruption risk in supply chains practices in Nigeria.

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京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24041号 / 情博第797号 / 新制||情||135(附属図書館) / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 加納 学, 教授 下平 英寿, 教授 石井 信 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

More than 350,000 anterior cruciate ligament (ACL) injuries occur every year in the United States. A torn ACL is typically replaced with an allograft or autograft tendon (patellar, quadriceps or hamstring), with the choice of tissue generally dictated by surgeon preference. Despite the number of ACL reconstructions performed every year, the process of ligamentization, transformation of a tendon graft to a healthy functional ligament, is poorly understood. Previous research studies have relied on mechanical, biochemical and histological studies. However, these methods are destructive. Clinically, magnetic resonance imaging (MRI) is the most common method of graft evaluation, but it lacks adequate resolution and molecular specificity. There is a need for objective methodology to study the ligament repair process that would ideally be non- or minimally invasive. Development of such a method could lead to a better understanding of the effects of therapeutic interventions and rehabilitation protocols in animal models of ligamentization, and ultimately, in clinical studies. Fourier transform infrared (FT-IR) spectroscopy is a technique sensitive to molecular structure and composition in tissues. FT-IR fiber optic probes combined with arthroscopy could prove to be an important tool where minimally invasive tissue assessment is required, such as assessment of graft composition during the ligamentization process. Spectroscopic methods have been used to differentiate normal and diseased connective tissues, but have not been applied to investigate ligamentization, or to investigate differences in tendons and ligaments. In the proposed studies, we hypothesize that infrared spectroscopy can provide molecular information about the compositional differences between tendons and ligaments, which can serve as a foundation to non-destructively monitor the tissue transformation that occurs during ligamentization. / Bioengineering