The kallikrein-kinin system in relation to retinal vessel tone in the streptozotocin-diabetic rat model

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

A Model of Lung Tumor Angiogenesis in a Biomimetic Poly(ethylene glycol)-based Hydrogel System

Tumor angiogenesis is critical to tumor growth and metastasis, yet much is unknown about the role vascular cells play in the tumor microenvironment. A major outstanding challenge associated with studying tumor angiogenesis is that existing preclinical models are limited in their recapitulation of in vivo cellular organization in 3D. This disparity highlights the need for better approaches to study the dynamic interplay of relevant cells and signaling molecules as they are organized in the tumor microenvironment. In this thesis, we combined 3D culture of lung adenocarcinoma cells with adjacent 3D microvascular cell culture in 2-layer cell-adhesive, proteolytically-degradable poly(ethylene glycol) (PEG)-based hydrogels to study tumor angiogenesis and the impacts of neovascularization on tumor cell behavior.

In initial studies, 344SQ cells, a highly metastatic, murine lung adenocarcinoma cell line, were characterized alone in 3D in PEG hydrogels. 344SQ cells formed spheroids in 3D culture and secreted proangiogenic growth factors into the conditioned media that significantly increased with exposure to transforming growth factor beta 1 (TGF-β1), a potent tumor progression-promoting factor. Vascular cells alone in hydrogels formed tubule networks with localized activated TGF-β1. To study cancer cell-vascular cell interactions, the engineered 2-layer tumor angiogenesis model with 344SQ and vascular cell layers was employed. Large, invasive 344SQ clusters developed at the interface between the layers, and were not evident further from the interface or in control hydrogels without vascular cells. A modified model with spatially restricted 344SQ and vascular cell layers confirmed that observed 344SQ cluster morphological changes required close proximity to vascular cells. Additionally, TGF-β1 inhibition blocked endothelial cell-driven 344SQ migration.

Two other lung adenocarcinoma cell lines were also explored in the tumor angiogenesis model: primary tumor-derived metastasis-incompetent, murine 393P cells and primary tumor-derived metastasis-capable human A549 cells. These lung cancer cells also formed spheroids in 3D culture and secreted proangiogenic growth factors into the conditioned media. Epithelial morphogenesis varied for the primary tumor-derived cell lines compared to 344SQ cells, with far less epithelial organization present in A549 spheroids. Additionally, 344SQ cells secreted the highest concentration of two of the three angiogenic growth factors assessed. This finding correlated to 344SQ exhibiting the most pronounced morphological response in the tumor angiogenesis model compared to the 393P and A549 cell lines.

Overall, this dissertation demonstrates the development of a novel 3D tumor angiogenesis model that was used to study vascular cell-cancer cell interactions in lung adenocarcinoma cell lines with varying metastatic capacities. Findings in this thesis have helped to elucidate the role of vascular cells in tumor progression and have identified differences in cancer cell behavior in vitro that correlate to metastatic capacity, thus highlighting the usefulness of this model platform for future discovery of novel tumor angiogenesis and tumor progression-promoting targets.

The kallikrein-kinin system in relation to retinal vessel tone in the streptozotocin-diabetic rat model

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NO_x, SO₂ etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

Temperature regulation of a pilot-scale batch reaction system via explicit model predictive control

In this paper, the temperature of a pilot-scale batch reaction system is modeled towards the design of a controller based on the explicit model predictive control (EMPC) strategy -- Some mathematical models are developed from experimental data to describe the system behavior -- The simplest, yet reliable, model obtained is a (1,1,1)-order ARX polynomial model for which the mentioned EMPC controller has been designed -- The resultant controller has a reduced mathematical complexity and, according to the successful results obtained in simulations, will be used directly on the real control system in a next stage of the entire experimental framework

A university e-learning system to enhance higher education teaching environments: a model-base innovative service-orientated clouds intensive evolution approach.

The International Conference on Advanced Materials, Structures and Mechanical Engineering 2015 (ICAMSME 2015) was held on May 29-31, Incheon, South-Korea. The conference was attended by scientists, scholars, engineers and students from universities, research institutes and industries all around the world to present on going research activities. This proceedings volume assembles papers from various professionals engaged in the fields of materials, structures and mechanical engineering.

Effect of grape seed extract on oxidative, color and sensory stability of pre-cooked, frozen, re-heated beef sausage model system

The processing of meats at the factory level can trigger the onset of lipid oxidation, which can lead to meat quality deterioration. Warmed over flavor is an off-flavor, which is associated with oxidative deterioration in meat. To avoid or delay the auto-oxidation process in meat products, synthetic and natural antioxidants have been successfully used. Grape (Vitis Vinifera) is of special interest due to its high content of phenolic compounds. Grape seed extract sold commercially as a dietary supplement, has the potential to reduce lipid oxidation and WOF in cooked ground beef when added at 1%. The objective of study 1 was to compare the antioxidant activity of natural antioxidants including grape seed extract and some herbs belonging to the Lamiaciae family: rosemary (Rosmarinus Officinalis), sage (Salvia Officinalis) and oregano (Origanum Vulgare) with commercial synthetic antioxidants like BHT, BHA, propyl gallate and ascorbic acid using the ORAC assay. All sample solutions were prepared to contain 1.8 gm sample/10 ml solvent. The highest antioxidant activity was observed for the grape seed extract sample (359.75 µM TE), while the lowest was observed for BHA, propyl gallate and rosemary also showed higher antioxidant potential with ORAC values above 300 μmol TE/g. ORAC values obtained for ascorbic acid and Sage were between 250-300μ mol TE/g while lowest values were obtained for Butylated Hydroxytoluene (28.50 µM TE). Based on the high ORAC values obtained for grape seed extract, we can conclude that byproducts of the wine/grape industry have antioxidant potential comparable to or better than those present in synthetic counterparts. The objective of study 2 was to compare three levels of grape seed extract (GSE) to commonly used antioxidants in a pre-cooked, frozen, stored beef and pork sausage model system. Antioxidants added for comparison with control included grape seed extract (100, 300, 500 ppm), ascorbic acid (AA, 100 ppm of fat) and propyl gallate (PG, 100 ppm of fat). Product was formed into rolls, frozen, sliced into patties, cooked on a flat griddle to 70C, overwrapped in PVC, and then frozen at –18C for 4 months. GSE- and PG-containing samples retained their fresh cooked beef odor and flavor longer (p<0.05) than controls during storage. Rancid odor and flavor scores of GSE-containing samples were lower (p<0.05) than those of controls after 4 months of storage. The L* value of all samples increased (p<0.05) during storage. Thiobarbituric acid reactive substances (TBARS) of the control and AA-containing samples increased (p<0.05); those of GSE-containing samples did not change significantly (p>0.05) over the storage period.

Fruit cell culture as a model system to study cell wall changes during strawberry fruit ripening

Strawberry (Fragaria x ananassa, Duch.) fruit is characterized by its fast ripening and soft texture at the ripen stage, resulting in a short postharvest shelf life and high economic losses. It is generally believed that the disassembly of cell walls, the dissolution of the middle lamella and the reduction of cell turgor are the main factors determining the softening of fleshy fruits. In strawberry, several studies indicate that the solubilisation and depolymerisation of pectins, as well as the depolymerisation of xyloglucans, are the main processes occurring during ripening. Functional analyses of genes encoding pectinases such as polygalacturonase and pectate lyase also point out to the pectin fraction as a key factor involved in textural changes. All these studies have been performed with whole fruits, a complex organ containing different tissues that differ in their cell wall composition and undergo ripening at different rates. Cell cultures derived from fruits have been proposed as model systems for the study of several processes occurring during fruit ripening, such as the production of anthocyanin and its regulation by plant hormones. The main objective of this research was to obtain and characterize strawberry cell cultures to evaluate their potential use as a model for the study of the cell wall disassembly process associate with fruit ripening. Cell cultures were obtained from cortical tissue of strawberry fruits, cv. Chandler, at the stages of unripe-green, white and mature-red. Additionally, a cell culture line derived from strawberry leaves was obtained. All cultures were maintained in solid medium supplemented with 2.5 mg.l-1 2,4-D and incubated in the dark. Cell walls from the different callus lines were extracted and fractionated to obtain CDTA and sodium carbonate soluble pectin fractions, which represent polyuronides located in the middle lamella or the primary cell wall, respectively. The amounts of homogalacturonan in both fractions were estimated by ELISA using LM19 and LM20 antibodies, specific against demethylated and methyl-esterified homogalacturonan, respectively. In the CDTA fraction, the cell line from ripe fruit showed a significant lower amount of demethylated pectins than the rest of lines. By contrast, the content of methylated pectins was similar in green- and red-fruit lines, and lower than in white-fruit and leaf lines. In the sodium carbonate pectin fraction, the line from red fruit also showed the lowest amount of pectins. These preliminary results indicate that cell cultures obtained from fruits at different developmental stages differ in their cell wall composition and these differences resemble to some extent the changes that occur during strawberry softening. Experiments are in progress to further characterize cell wall extracts with monoclonal antibodies against other cell wall epitopes.

Collaborative Networked Organizations as System of Systems: A Model-Based Engineering Approach

Part 6: Engineering and Implementation of Collaborative Networks

DYNAMIC MODEL BASED STATE ESTIMATION IN A HEAVY DUTY DIESEL AFTERTREATMENT SYSTEM FOR ONBOARD DIAGNOSTICS AND CONTROLS

Estimating un-measurable states is an important component for onboard diagnostics (OBD) and control strategy development in diesel exhaust aftertreatment systems. This research focuses on the development of an Extended Kalman Filter (EKF) based state estimator for two of the main components in a diesel engine aftertreatment system: the Diesel Oxidation Catalyst (DOC) and the Selective Catalytic Reduction (SCR) catalyst. One of the key areas of interest is the performance of these estimators when the catalyzed particulate filter (CPF) is being actively regenerated. In this study, model reduction techniques were developed and used to develop reduced order models from the 1D models used to simulate the DOC and SCR. As a result of order reduction, the number of states in the estimator is reduced from 12 to 1 per element for the DOC and 12 to 2 per element for the SCR. The reduced order models were simulated on the experimental data and compared to the high fidelity model and the experimental data. The results show that the effect of eliminating the heat transfer and mass transfer coefficients are not significant on the performance of the reduced order models. This is shown by an insignificant change in the kinetic parameters between the reduced order and 1D model for simulating the experimental data. An EKF based estimator to estimate the internal states of the DOC and SCR was developed. The DOC and SCR estimators were simulated on the experimental data to show that the estimator provides improved estimation of states compared to a reduced order model. The results showed that using the temperature measurement at the DOC outlet improved the estimates of the CO , NO , NO2 and HC concentrations from the DOC. The SCR estimator was used to evaluate the effect of NH3 and NOX sensors on state estimation quality. Three sensor combinations of NOX sensor only, NH3 sensor only and both NOX and NH3 sensors were evaluated. The NOX only configuration had the worst performance, the NH3 sensor only configuration was in the middle and both the NOX and NH3 sensor combination provided the best performance.

A novel latent factor model for recommender system

Matrix factorization (MF) has evolved as one of the better practice to handle sparse data in field of recommender systems. Funk singular value decomposition (SVD) is a variant of MF that exists as state-of-the-art method that enabled winning the Netflix prize competition. The method is widely used with modifications in present day research in field of recommender systems. With the potential of data points to grow at very high velocity, it is prudent to devise newer methods that can handle such data accurately as well as efficiently than Funk-SVD in the context of recommender system. In view of the growing data points, I propose a latent factor model that caters to both accuracy and efficiency by reducing the number of latent features of either users or items making it less complex than Funk-SVD, where latent features of both users and items are equal and often larger. A comprehensive empirical evaluation of accuracy on two publicly available, amazon and ml-100 k datasets reveals the comparable accuracy and lesser complexity of proposed methods than Funk-SVD.

Development of a polyplexes-based miRNA delivery system in a 3D-bioplotted osteoarthritis cellular model.

L'osteoartrite (OA) è una patologia infiammatorio/degenerativa ossea per la quale non sono disponibili terapie causali efficaci ma solo approcci palliativi per la riduzione del dolore cronico. E’ quindi giustificato un investimento per individuare nuove strategie di trattamento. In quest’ottica, lo scopo di questa tesi è stato quello di indagare l’efficacia di polyplexi a base di chitosano o di PEI-g-PEG in un modello cellulare 3D in vitro basato su un hydrogel di Gellan Gum Metacrilato (GGMA) con a bordo condrociti in condizioni simulate di OA. Inizialmente sono state studiate la dimensione e il potenziale-Z di un pool di formulazioni di poliplexi. Quindi se ne è valutata la citocompatibilità utilizzando cellule staminali mesenchimali immortalizzate Y201. Infine, una miscela di GGMA, cellule e polyplexi è stata utilizzata per la stampa 3D di campioni che sono stati coltivati fino a 14 giorni. La condizione OA è stata simulata trattando le cellule con una miscela di citochine implicate nello sviluppo della malattia. Tutte le formulazioni a base di chitosano e due basate su PEI-g-PEG si sono dimostrate citocompatibili e sono hanno veicolato i miRNA nelle cellule (come mostrato dai risultati di analisi in fluorescenza). I risultati delle colorazioni H&E e AlcianBlue hanno confermato che il terreno condizionato ha ben ricreato le condizioni di OA. I polyplexi a base di chitosano e PEI-g-PEG hanno controbilanciato gli effetti delle citochine. Risultati incoraggianti, anche se da approfondire ulteriormente, provengono anche dall’analisi di espressione (RT-PCR) di cinque geni specifici della cartilagine. Concludendo, questo modello ha ben riprodotto le condizioni di OA in vitro; il chitosano ha mostrato di essere un adeguato veicolo per un trattamento a base di miRNA; il PEI-g-PEG si propone come un'alternativa più economica e ragionevolmente affidabile, sebbene il rischio di citotossicità alle concentrazioni più elevate richieda una più esteva validazione sperimentale.