A laranja é uma das frutas mais produzidas e consumidas no mundo, sendo o Brasil o maior produtor e exportador do seu suco concentrado. Entretanto, pragas e doenças comprometem consideravelmente sua produção. Atualmente, a doença mais preocupante é o Greening, também conhecida mundialmente como Huanglongbing (HLB). A doença não possui cura, apresenta longa fase assintomática e não possui um método eficiente de controle. Além disso, não existem métodos de diagnóstico aplicáveis em larga escala. Neste trabalho são propostas as técnicas fotônicas de fluorescência induzida por laser e de infravermelho por transformada de Fourier para o diagnóstico do HLB. Para a realização das medidas, foram coletadas folhas de árvores saudáveis, doentes com HLB e doentes com a clorose variegada dos citros, sendo esta incluída nos estudos para verificar a capacidade de diferenciação entre as doenças. Foram coletadas quatro classes de folhas nessas plantas: sadia, HLB-sintomática, HLB-assintomática e CVC-sintomática. As folhas foram medidas em laboratório e seus espectros foram pré-processados para indução de um classificador via regressão por mínimos quadrados parciais. Além das folhas, foram medidas amostras dos seguintes metabólitos primários e secundários para entendimento espectral: amido, glicose, sacarose, hesperidina, naringina e umbeliferona. Taxas de acerto de superiores a 89% foram obtidas na classificação das folhas nas técnicas de fluorescência e infravermelho, sendo superior às taxas dos métodos de manejo empregados atualmente no campo. A fluorescência induzida por laser apresenta um grande potencial para uso em campo devido a possibilidade de miniaturização de seus componentes. Os espectros dos metabólitos secundários apresentam fortes indícios de que a alteração de suas concentrações podem contribuir na detecção de doenças pelas técnicas fotônicas. / Sweet orange is one of the most produced and consumed fruit in the world, and Brazil is the largest producer and exporter of this fruit. However, pests and diseases significantly reduce the worldwide production. Currently, the most destructive disease in the field is called greening, also known as huanglongbing (HLB). There is no control for HLB. In addition, the disease presents a long asymptomatic phase. Furthermore, no diagnostic methods are available to use in large scale. In this study are proposed fluorescence and infrared spectroscopy for the HLB diagnosis. For the measurements were collected leaves from healthy, HLB- and citrus variegated chlorosis-infected plants, being the last one to comparison between the diseases. It were collected four classes of leaves: healthy, HLB-asymptomatic, HLB-symptomatic and CVC-symptomatic. The leaves were measured and their spectra were pre-processed for the induction of classifier via partial least squares regression. In addition, samples of plant metabolites were measured for leaves spectral interpretation: starch, glucose, sucrose, hesperidin, naringin and umbelliferone. Success rates above 89% were obtained through both photonic techniques, higher compared to the sucess rates of the actual management methods. The metabolites spectra have shown strong evidence that their concentrations changes could contribute to the diagnosis of the diseases by photonic techniques. Particularly, the fluorescence spectroscopy seems interesting because it has a great potential for field application due to the existence of portable photonic devices.

A laranja é uma das frutas mais produzidas e consumidas no mundo, sendo o Brasil o maior produtor e exportador do seu suco concentrado. Entretanto, pragas e doenças comprometem consideravelmente sua produção. Atualmente, a doença mais preocupante é o Greening, também conhecida mundialmente como Huanglongbing (HLB). A doença não possui cura, apresenta longa fase assintomática e não possui um método eficiente de controle. Além disso, não existem métodos de diagnóstico aplicáveis em larga escala. Neste trabalho são propostas as técnicas fotônicas de fluorescência induzida por laser e de infravermelho por transformada de Fourier para o diagnóstico do HLB. Para a realização das medidas, foram coletadas folhas de árvores saudáveis, doentes com HLB e doentes com a clorose variegada dos citros, sendo esta incluída nos estudos para verificar a capacidade de diferenciação entre as doenças. Foram coletadas quatro classes de folhas nessas plantas: sadia, HLB-sintomática, HLB-assintomática e CVC-sintomática. As folhas foram medidas em laboratório e seus espectros foram pré-processados para indução de um classificador via regressão por mínimos quadrados parciais. Além das folhas, foram medidas amostras dos seguintes metabólitos primários e secundários para entendimento espectral: amido, glicose, sacarose, hesperidina, naringina e umbeliferona. Taxas de acerto de superiores a 89% foram obtidas na classificação das folhas nas técnicas de fluorescência e infravermelho, sendo superior às taxas dos métodos de manejo empregados atualmente no campo. A fluorescência induzida por laser apresenta um grande potencial para uso em campo devido a possibilidade de miniaturização de seus componentes. Os espectros dos metabólitos secundários apresentam fortes indícios de que a alteração de suas concentrações podem contribuir na detecção de doenças pelas técnicas fotônicas. / Sweet orange is one of the most produced and consumed fruit in the world, and Brazil is the largest producer and exporter of this fruit. However, pests and diseases significantly reduce the worldwide production. Currently, the most destructive disease in the field is called greening, also known as huanglongbing (HLB). There is no control for HLB. In addition, the disease presents a long asymptomatic phase. Furthermore, no diagnostic methods are available to use in large scale. In this study are proposed fluorescence and infrared spectroscopy for the HLB diagnosis. For the measurements were collected leaves from healthy, HLB- and citrus variegated chlorosis-infected plants, being the last one to comparison between the diseases. It were collected four classes of leaves: healthy, HLB-asymptomatic, HLB-symptomatic and CVC-symptomatic. The leaves were measured and their spectra were pre-processed for the induction of classifier via partial least squares regression. In addition, samples of plant metabolites were measured for leaves spectral interpretation: starch, glucose, sucrose, hesperidin, naringin and umbelliferone. Success rates above 89% were obtained through both photonic techniques, higher compared to the sucess rates of the actual management methods. The metabolites spectra have shown strong evidence that their concentrations changes could contribute to the diagnosis of the diseases by photonic techniques. Particularly, the fluorescence spectroscopy seems interesting because it has a great potential for field application due to the existence of portable photonic devices.

This study aims to predict the binding affinity of the PCP-like compounds by means of molecular hydrophobicity. Molecular hydrophobicity is an important property which affects the binding affinity of molecules. The values of molecular hydrophobicity of molecules are obtained on three-dimensional coordinate system. Our aim is to reduce the number of points on the hydrophobicity surface of the molecules. This is modeled by using self organizing maps (SOM) and k-means clustering. The feature sets obtained from SOM and k-means clustering are used in order to predict binding affinity of molecules individually. Support vector regression and partial least squares regression are used for prediction.

The objective of this research was to develop fluorescence-based tools that are suitable for performing rapid, accurate and direct characterization of natural organic matter (NOM) and colloidal/particulate substances present in natural water. Most available characterization methods are neither suitable for characterizing all the major NOM fractions such as protein-, humic acid-, fulvic acid- and polysaccharide-like substances as well as colloidal/particulate matter present in natural water nor are they suitable for rapid analyses. The individual and combined contributions of these NOM fractions and colloidal/particulate matter present in natural water contribute to membrane fouling, disinfection by-products formation and undesirable biological growth in drinking water treatment processes and distribution systems. The novel techniques developed in this research therefore, provide an avenue for improved understanding of these negative effects and proactive implementation of control and/or optimization strategies. The fluorescence excitation-emission matrix (EEM) method was used for characterization of NOM and colloidal/particulate matter present in water. Unlike most NOM and colloidal/particulate matter characterization techniques, this method can provide fast and consistent analyses with high instrumental sensitivity. The feasibility of using this method for monitoring NOM at very low concentration levels was also demonstrated with an emphasis on optimizing the instrument parameters necessary to obtain reproducible fluorescence signals. Partial least squares regression (PLS) was used to develop calibration models by correlating the fluorescence EEM intensities of water samples that contained surrogate NOM fractions with their corresponding dissolved organic carbon (DOC) concentrations. These fluorescence-based calibration models were found to be suitable for identifying/monitoring the extent of the relative changes that occur in different NOM fractions and the interactions between polysaccharide- and protein-like NOM in water treatment processes and distribution systems. Principal component analysis (PCA) of fluorescence EEMs was identified as a viable tool for monitoring the performance of biological filtration as a pre-treatment step, as well as ultrafiltration (UF) and nanofiltration (NF) membrane systems. The principal components (PCs) extracted in this approach were related to the major membrane foulant groups such as humic substances (HS), protein-like and colloidal/particulate matter in natural water. The PC score plots generated using the fluorescence EEMs obtained after just one hour of UF or NF operation could be related to high fouling events likely caused by elevated levels of colloidal/particulate-like material in the biofilter effluents. This fluorescence EEM-based PCA approach was sensitive enough to be used at low organic carbon levels present in NF permeate and has potential as an early detection method to identify high fouling events, allowing appropriate operational countermeasures to be taken. This fluorescence EEM-based PCA approach was also used to extract information relevant to reversible and irreversible membrane fouling behaviour in a bench-scale flat sheet cross flow UF process consisting of cycles of permeation and back-washing. PC score-based analysis revealed that colloidal/particulate matter mostly contributed to reversible fouling, while HS and protein-like matter were largely responsible for irreversible fouling. This method therefore has potential for monitoring modes of membrane fouling in drinking water treatment applications. The above approach was further improved by utilizing the evolution of the PC scores over the filtration time and relating these to membrane fouling by the use of PC scores??? balanced-based differential equations. Using these equations the proposed fluorescence-based modeling approach was capable of forecasting UF fouling behaviours with good accuracy based solely on fluorescence data obtained at time = 15 min from the initiation of the filtration process. In addition, this approach was tested experimentally as a basis for optimization by modifying the UF back-washing times with the objective of minimizing energy consumption and maximizing water production. Preliminary optimization results demonstrated the potential of this approach to reduce power consumption by significant percentages. This approach was also useful for identifying the fouling components of the NOM that were contributing to reversible and irreversible membrane fouling. Grand River water (Southwestern Ontario, Canada) was used as the natural water source for developing the techniques presented in this thesis. Future research focusing on testing these methods for monitoring of membrane fouling and treatment processes in large-scale drinking water treatment facilities that experience different sources of raw water would be useful for identifying the limitation of these techniques and areas for improvements.

Made available in DSpace on 2016-08-19T12:56:41Z (GMT). No. of bitstreams: 1 Dissertacao Marcelo Jose.pdf: 1590491 bytes, checksum: 7be3e83649dd910e0afe9a5a25de4e73 (MD5) Previous issue date: 2013-10-22 / The ethanol has continuously gained interests in many countries including Brazil due to the PROÁLCOOL program. The experimental determination of properties of ethanol and other fuels through official methods is very time consuming as well as tedious process. The estimation of these properties with the help of computational tools can be very useful. In the present work, the methods of partial least squares regression (PLS) and artificial neural network multilayer (ANN) were used to estimate one of the most important properties of fuel ethanol, density, using official quality parameters for ethanol, collected from LAPQAP/UFMA laboratory corresponding to 12 years (period: 2002-2013) of analyzes. A careful analysis of the data was performed to obtain a set of variables and data that best represents satisfactory performance of the two models. The estimates of both approaches were compared and validated. The predictive ability of the network obtained was very good for the parameters studied, consistent with the accuracy of the experimental measurements. The low mean square error, the randomness, the zero mean and the constant variance, obtained for the residues, indicated the suitability of the models, suggesting their use to estimate (predict) the density of ethanol. Results indicated that the model ANN was adequate, and the value of NMSE (normalized mean square error) of 0.0012, less than the PLS model of 0.2221. The result achieved is less than the range of measurement uncertainty of the equipment responsible for testing the density proving that the model used has satisfactory performance. / O etanol tem alcançado crescente interesse em muitos países, principalmente, no Brasil devido ao programa PROÁLCOOL. A determinação experimental das propriedades deste biocombustível e de outros combustíveis por meio de métodos oficiais é muito demorada, bem como é considerado um tedioso processo. A estimativa dessas propriedades com a ajuda de ferramentas computacionais pode ser de grande utilidade. No presente trabalho, os métodos de regressão por mínimos quadrados parciais (PLS) e redes neurais artificiais de múltiplas camadas (RNA) foram usados para estimar uma das mais importantes propriedades do etanol combustível, massa específica, utilizando parâmetros de qualidade oficiais de etanol, oriundos de análises realizadas no laboratório LAPQAP/UFMA, durante 12 anos (período: 2002-2013). Inicialmente, uma análise cuidadosa dos dados foi realizada a fim de selecionar um conjunto de variáveis e dados que melhor representasse um desempenho satisfatório dos dois modelos estudados. As estimativas de ambas as abordagens foram comparadas e validadas. A capacidade preditiva da rede neural obtida foi considerada muito boa para os parâmetros estudados, e compatível com a precisão das medidas experimentais. O baixo erro quadrático médio, a aleatoriedade, a média nula e a variância constante, obtida para os resíduos, evidenciaram a adequabilidade dos modelos usados, sugerindo a utilização destes modelos para estimar (predizer) a massa específica do etanol. Resultados indicaram que o modelo de RNA foi adequado, sendo o valor de NMSE (erro quadrático médio normalizado) de 0,0012, valor este, muito inferior ao modelo de PLS de 0,2221. Este resultado alcançado é inferior aos valores da faixa de incerteza de medição do equipamento responsável pelo ensaio experimental da massa específica, comprovando que o modelo utilizado possui desempenho considerado muito bom.

Due to the invasive nature of quantification techniques, baseline pigment data for coral-dwelling zooxanthellae are not known. In an attempt to develop a model for non-invasive estimation of zooxanthellae pigment concentrations from corals, field samples were taken from Porites rus and P. lutea in Apra Harbor, Guam. In-situ reflectance spectra (R400-R800) from 22 coral colonies were collected. “Coral truthing” was accomplished by extracting corresponding tissue core samples. Subsequent analysis to quantify the concentrations of 6 zooxanthellae pigments (µg cm-2) was performed using HPLC. Trials of multiple linear regressions were attempted (EJ Hochberg) and found inappropriate, despite previous success. The multivariate calibration technique partial least squares regression (PLS-R) is an excellent tool in the case of co-linear variables. Thus, PLS-R was attempted for chlorophyll c2 and peridinin after demonstration of co-linearity. This may be an appropriate approach for development of bio-optical models to estimate zooxanthellae pigment concentrations. Further, the dinoflagellate diagnostic pigment peridinin may be of great value for reef-scale remote sensing of changes in coral status in the future.

Background: Joint stability has been experimentally and clinically linked to mechanisms of knee injury and joint degeneration. The only dynamic, and perhaps most important, regulators of knee joint stability are contributions from muscular contractions. In participants with unstable knees, such as anterior cruciate ligament (ACL) injured, a range of neuromuscular adaptations has been observed including quadriceps weakness and increased co-activation of adjacent musculature. This co-activation is seen as a compensation strategy to increase joint stability. In fact, despite increased co-activation, instability persists and it remains unknown whether observed adaptations are the result of injury induced quadriceps weakness or the mechanical instability itself. Furthermore, there exists conflicting evidence on how and which of the neuromuscular adaptations actually improve and/or reduce knee joint stability. Purpose: The overall aim of this thesis is therefore to elucidate the role of injury and muscle weakness on muscular contributions to knee joint stability by addressing two main objectives: (1) to further our understanding of individual muscle contribution to internal knee joint moments; and (2) to investigate neuromuscular adaptations, and their effects on knee joint moments, caused by either ACL injury and experimental voluntary quadriceps inhibition (induced by pain). Methods: The relationship between individual muscle activation and internal net joint moments was quantified using partial least squares regression models. To limit the biomechanical contributions to force production, surface electromyography (EMG) and kinetic data was elicited during a weight-bearing isometric force matching task. A cross-sectional study design determined differences in individual EMG-moment relationships between ACL deficient and healthy controls (CON) groups. A crossover placebo controlled study design determined these differences in healthy participants with and without induced quadriceps muscle pain. Injections of hypertonic saline (5.8%) to the vastus medialis induced muscle pain. Isotonic saline (0.9%) acted as control. Effect of muscle pain on muscle synergies recruited for the force matching task, lunging and squatting tasks was also evaluated. Synergies were extracted using a concatenated non-negative matrix factorization framework. Results/Discussion: In CON, significant relationships of the rectus femoris and tensor fascia latae to knee extension and hip flexion; hamstrings to hip extension and knee flexion; and gastrocnemius and hamstrings to knee rotation were identified. Vastii activation was independent of moment generation, suggesting mono-articular vastii activate to produce compressive forces, essentially bracing the knee, so that bi-articular muscles crossing the hip can generate moments for the purpose of sagittal plane movement. Hip ab/adductor muscles modulate frontal plane moments, while hamstrings and gastrocnemius support the knee against externally applied rotational moments. Compared to CON, ACL had 1) stronger relationships between rectus femoris and knee extension, semitendinosus and knee flexion, and gastrocnemius and knee flexion moments; and 2) weaker relationships between biceps femoris and knee flexion, gastrocnemius and external knee rotation, and gluteus medius and hip abduction moments. Since the knee injury mechanism, is associated with shallow knee flexion angles, valgus alignment and rotation, adaptations after ACL injury are suggested to improve sagittal plane stability, but reduce frontal and rotational plane stability. During muscle pain, EMG-moment relationships of 1) semitendinosus and knee flexor moments were stronger compared to no pain, while 2) rectus femoris and tensor fascia latae to knee extension moments and 3) semitendinosus and lateral gastrocnemius to knee internal rotation moments were reduced. Results support the theory that adaptations to quadriceps pain reduces knee extensor demand to protect the joint and prevent further pain; however, changes in non-painful muscles reduce rotational plane stability. Individual muscle synergies were identified for each moment type: flexion and extension moments were respectively accompanied by dominant hamstring and quadriceps muscle synergies while co-activation was observed in muscle synergies associated with abduction and rotational moments. Effect of muscle pain was not evident on muscle synergies recruited for the force matching task. This may be due to low loading demands and/or a subject-specific redistribution of muscle activation. Similarly, muscle pain did not affect synergy composition in lunging and squatting tasks. Rather, activation of the extensor dominant muscle synergy and knee joint dynamics were reduced, supporting the notion that adaptive response to pain is to reduce the load and risk of further pain and/or injury. Conclusion: This thesis evaluated the interrelationship between muscle activation and internal joint moments and the effect of ACL injury and muscle pain on this relationship. Findings indicate muscle activation is not always dependent on its anatomical orientation as previous works suggest, but rather on its role in maintaining knee joint stability especially in the frontal and transverse loading planes. In tasks that are dominated by sagittal plane loads, hamstring and quadriceps will differentially activate. However, when the knee is required to resist externally applied rotational and abduction loads, strategies of global co-activation were identified. Contributions from muscles crossing the knee for supporting against knee adduction loads were not apparent. Alternatively hip abductors were deemed more important regulators of knee abduction loads. Both muscle pain and ACL groups demonstrated changes in muscle activation that reduced rotational stability. Since frontal plane EMG-moment changes were not present during muscle pain, reduced relationships between hip muscles and abduction moments may be chronic adaptions by ACL that facilitate instability. Findings provide valuable insight into the roles muscles play in maintaining knee joint stability. Rehabilitative/ preventative exercise interventions should focus on neuromuscular training during tasks that elicit rotational and frontal loads (i.e. side cuts, pivoting maneuvers) as well as maintaining hamstring balance, hip abductor and plantarflexor muscle strength in populations with knee pathologies and quadriceps muscle weakness.

No description available.

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

In the past 30 years zero-valent iron (ZVI) has become an increasingly popular reducing agent technology for remediating environmental contaminants prone to chemical degradation. Azo dyes and chlorinated organic compounds (COCs) are two classes of such contaminants, both of which include toxic compounds with known carcinogenic potential. ZVI has been successfully applied to the surfaces of permeable reactive barriers, as well as grown into nanoscale particles (nZVI) and applied in-situ to chemically reduce these contaminants into more environmentally benign compounds. However, the reactivity of ZVI and nZVI in these technologies is limited by their finite supply of electrons for facilitating chemical reduction, and the tendency of nZVI particles to homo-aggregate in solution and form colloids with reduced surface area to volume ratio, and thus reduced reactivity. The goal of this project was to combine reactive nanoparticle and membrane technologies to create an electro-catalytic permeable reactive barrier that overcomes the weaknesses of nZVI for the enhanced electrochemical filtration of azo dyes in solution. Specifically, nZVI was successfully grown and stabilized in a network of functionalized carbon nanotubes (CNTs) and deposited into an electrically conductive thin film on the surface of a polymeric microfiltration support membrane. Under a cathodic applied voltage, this thin film facilitated the direct reduction of the methyl orange (MO) azo dye in solution, and regenerated nZVI reactivity for enhanced electro-catalytic operation. The electro-catalytic performance of these nZVI-CNT membrane surface composites to remove MO was validated, modelled, and optimized in a batch system, as well as tested in a dead-end continuous flow cell system. In the batch experiments, systems with nZVI and a -2 V applied potential demonstrated synergistic enhancement of MO removal, which indicated the regeneration of nZVI reactivity and allowed for the complete removal of 0.25 mM MO batches within 2-3 hours. Partial least squares regression (PLSR) modelling was used to determine the impact of each experimental parameter in the batch system and provided the means for an optimization leading to maximized MO removal. Finally, tests in a continuous system yielded rates of MO removal 1.6 times greater than those of the batch system in a single pass, and demonstrated ~87% molar removal of MO at fluxes of approximately 422 lmh. The work herein lays the foundation for a promising technology that, if further developed, could be applied to remediate azo dyes and COCs in textile industry effluents and groundwater sites respectively. / Thesis / Master of Applied Science (MASc)