Modeling and optimization of extracellular polysaccharides production by Enterobacter A47

Polysaccharides are gaining increasing attention as potential environmental friendly and sustainable building blocks in many fields of the (bio)chemical industry. The microbial production of polysaccharides is envisioned as a promising path, since higher biomass growth rates are possible and therefore higher productivities may be achieved compared to vegetable or animal polysaccharides sources. This Ph.D. thesis focuses on the modeling and optimization of a particular microbial polysaccharide, namely the production of extracellular polysaccharides (EPS) by the bacterial strain Enterobacter A47. Enterobacter A47 was found to be a metabolically versatile organism in terms of its adaptability to complex media, notably capable of achieving high growth rates in media containing glycerol byproduct from the biodiesel industry. However, the industrial implementation of this production process is still hampered due to a largely unoptimized process. Kinetic rates from the bioreactor operation are heavily dependent on operational parameters such as temperature, pH, stirring and aeration rate. The increase of culture broth viscosity is a common feature of this culture and has a major impact on the overall performance. This fact complicates the mathematical modeling of the process, limiting the possibility to understand, control and optimize productivity. In order to tackle this difficulty, data-driven mathematical methodologies such as Artificial Neural Networks can be employed to incorporate additional process data to complement the known mathematical description of the fermentation kinetics. In this Ph.D. thesis, we have adopted such an hybrid modeling framework that enabled the incorporation of temperature, pH and viscosity effects on the fermentation kinetics in order to improve the dynamical modeling and optimization of the process. A model-based optimization method was implemented that enabled to design bioreactor optimal control strategies in the sense of EPS productivity maximization. It is also critical to understand EPS synthesis at the level of the bacterial metabolism, since the production of EPS is a tightly regulated process. Methods of pathway analysis provide a means to unravel the fundamental pathways and their controls in bioprocesses. In the present Ph.D. thesis, a novel methodology called Principal Elementary Mode Analysis (PEMA) was developed and implemented that enabled to identify which cellular fluxes are activated under different conditions of temperature and pH. It is shown that differences in these two parameters affect the chemical composition of EPS, hence they are critical for the regulation of the product synthesis. In future studies, the knowledge provided by PEMA could foster the development of metabolically meaningful control strategies that target the EPS sugar content and oder product quality parameters.

An assessment of chronic kidney diseases

Kidney renal failure means that one’s kidney have unexpectedlystoppedfunctioning,i.e.,oncechronicdiseaseis exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapiddeteriorationoftherenalfunction,butisoftenreversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis. The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow onetoconsiderincomplete,unknown,and evencontradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively.

An evaluation of parchments' degradation a hybrid approach

Parchment stands for a multifaceted material made from animal skin, which has been used for centuries as a writing support or as bookbinding. Due to the historic value of objects made of parchment, understanding their degradation and their condition is of utmost importance to archives, libraries and museums, i.e., the assessment of parchment degradation is mandatory, although it is hard to do with traditional methodologies and tools for problem solving. Hence, in this work we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate Parchment Degradation and the respective Degree-of-Confidence that one has on such a happening.

Sistema de apoio à decisão clínica: quality of life system

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Estimativa dos parâmetros de compactação de solos usando técnicas de data mining

Dissertação de mestrado integrado em Engenharia Civil

Optimização do processo de recolha de resíduos: desenvolvimento de ferramentas de investigação operacional para o problema de orientação de equipas com multi-restrições

Tese de Doutoramento em Engenharia Industrial e de Sistemas.

Advanced mapping of environmental data: Geostatistics, Machine Learning and Bayesian Maximum Entropy

This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more.

Sleep EEG changes after middle cerebral artery infarcts in mice: different effects of striatal and cortical lesions.

STUDY OBJECTIVES: Hemispheric stroke in humans is associated with sleep-wake disturbances and sleep electroencephalogram (EEG) changes. The correlation between these changes and stroke extent remains unclear. In the absence of experimental data, we assessed sleep EEG changes after focal cerebral ischemia of different extensions in mice. DESIGN: Following electrode implantation and baseline sleep-wake EEG recordings, mice were submitted to sham surgery (control group), 30 minutes of intraluminal middle cerebral artery (MCA) occlusion (striatal stroke), or distal MCA electrocoagulation (cortical stroke). One and 12 days after stroke, sleep-wake EEG recordings were repeated. The EEG recorded from the healthy hemisphere was analyzed visually and automatically (fast Fourier analysis) according to established criteria. MEASUREMENTS AND RESULTS: Striatal stroke induced an increase in non-rapid eye movement (NREM) sleep and a reduction of rapid eye movement sleep. These changes were detectable both during the light and the dark phase at day 1 and persisted until day 12 after stroke. Cortical stroke induced a less-marked increase in NREM sleep, which was present only at day 1 and during the dark phase. In cortical stroke, the increase in NREM sleep was associated in the wake EEG power spectra, with an increase in the theta and a reduction in the beta activity. CONCLUSION: Cortical and striatal stroke lead to different sleep-wake EEG changes in mice, which probably reflect variable effects on sleep-promoting and wakefulness-maintaining neuronal networks.

Diffusion spectrum imaging shows the structural basis of functional cerebellar circuits in the human cerebellum in vivo.

BACKGROUND: The cerebellum is a complex structure that can be affected by several congenital and acquired diseases leading to alteration of its function and neuronal circuits. Identifying the structural bases of cerebellar neuronal networks in humans in vivo may provide biomarkers for diagnosis and management of cerebellar diseases. OBJECTIVES: To define the anatomy of intrinsic and extrinsic cerebellar circuits using high-angular resolution diffusion spectrum imaging (DSI). METHODS: We acquired high-resolution structural MRI and DSI of the cerebellum in four healthy female subjects at 3T. DSI tractography based on a streamline algorithm was performed to identify the circuits connecting the cerebellar cortex with the deep cerebellar nuclei, selected brainstem nuclei, and the thalamus. RESULTS: Using in-vivo DSI in humans we were able to demonstrate the structure of the following cerebellar neuronal circuits: (1) connections of the inferior olivary nucleus with the cerebellar cortex, and with the deep cerebellar nuclei (2) connections between the cerebellar cortex and the deep cerebellar nuclei, (3) connections of the deep cerebellar nuclei conveyed in the superior (SCP), middle (MCP) and inferior (ICP) cerebellar peduncles, (4) complex intersections of fibers in the SCP, MCP and ICP, and (5) connections between the deep cerebellar nuclei and the red nucleus and the thalamus. CONCLUSION: For the first time, we show that DSI tractography in humans in vivo is capable of revealing the structural bases of complex cerebellar networks. DSI thus appears to be a promising imaging method for characterizing anatomical disruptions that occur in cerebellar diseases, and for monitoring response to therapeutic interventions.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Clustering and hot spot detection in socio-economic spatio-temporal data

Distribution of socio-economic features in urban space is an important source of information for land and transportation planning. The metropolization phenomenon has changed the distribution of types of professions in space and has given birth to different spatial patterns that the urban planner must know in order to plan a sustainable city. Such distributions can be discovered by statistical and learning algorithms through different methods. In this paper, an unsupervised classification method and a cluster detection method are discussed and applied to analyze the socio-economic structure of Switzerland. The unsupervised classification method, based on Ward's classification and self-organized maps, is used to classify the municipalities of the country and allows to reduce a highly-dimensional input information to interpret the socio-economic landscape. The cluster detection method, the spatial scan statistics, is used in a more specific manner in order to detect hot spots of certain types of service activities. The method is applied to the distribution services in the agglomeration of Lausanne. Results show the emergence of new centralities and can be analyzed in both transportation and social terms.

Redox dysregulation in schizophrenia : implication for oligodendrocyte maturation and structural connectivity

Schizophrenia, which results from an interaction between gene and environmental factors, is a psychiatric disorder characterized by reality distortion. The clinical symptoms, which are generally diagnosed in late adolescence or early adulthood, partly derive from altered brain connectivity especially in prefrontal cortex. Disruption of neuronal networks implies oligodendrocyte and myelin abnormalities in schizophrenia pathophysiology. The mechanisms of these impairments are still unclear. Converging evidences indicate a role of redox dysregulation, generated by an imbalance between pro-oxidants and antioxidant defense mechanisms, in the development of schizophrenia pathophysiology. In particular, genetic and biochemical data indicate impaired synthesis of glutathione, the main cellular antioxidant and redox regulator. As oligodendrocyte maturation is dependent on redox state, we evaluated whether abnormal redox control could contribute to oligodendrocyte and myelin impairments in schizophrenia. We found that glutathione in prefrontal cortex of early psychosis patients and control subjects positively correlated with white matter integrity. We then further explored the interplay between glutathione and myelin using a translational approach. Our data showed that in mice with genetically impaired glutathione synthesis, oligodendrocyte late maturation as well as myelination was delayed in the anterior cingulate cortex. Specifically, oligodendrocyte number and myelin levels were lowered at peripubertal age, coincident in time with the peak of myelin- related gene expression during normal brain development. These data suggest that early adolescence is a vulnerable developmental period during which an adequate redox control is required for oligodendrocyte maturation and active myelination process. Consistently, oxidative stress mediated by psychosocial stress also delayed myelination in peripubertal mice. At cellular levels, impaired glutathione synthesis altered oligodendrocyte development at several levels. Using oligodendrocyte progenitor cells cultures, our data showed that glutathione deficiency was associated with (i) cell cycle arrest and a reduction in oligodendrocyte proliferation, and (ii) an impairment in oligodendrocyte maturation. Abnormal oligodendrocyte proliferation was mediated by upregulation of Fyn kinase activity. Consistently, under oxidative stress conditions, we observed abnormal regulation of Fyn kinase in fibroblasts of patients deficient in glutathione synthesis. Together, our data support that a redox dysregulation due to glutathione deficit could underlie myelination impairment in schizophrenia, possibly mediated by dysregulated Fyn pathway. Better characterization of Fyn mechanisms would pave the way towards new drug targets. -- La schizophrénie est une maladie psychiatrique qui se définit par une distorsion de la perception de la réalité. Les symptômes cliniques sont généralement diagnostiqués durant l'adolescence ou au début de l'âge adulte et proviennent de troubles de la connectivité, principalement au niveau du cortex préfrontal. Les dysfonctionnements des réseaux neuronaux impliquent des anomalies au niveau des oligodendrocytes et de la myéline dans la pathophysiologie de la schizophrénie. Les mécanismes responsables des ces altérations restent encore mal compris. Dans le développement de la schizophrénie, des évidences mettent en avant un rôle de la dérégulation rédox, traduit par un déséquilibre entre facteurs pro-oxydants et défenses antioxydantes. Des données génétiques et biochimiques indiquent notamment un défaut de la synthèse du glutathion, le principal antioxydant et rédox régulateur des cellules. Etant donné que la maturation des oligodendrocytes est dépendante de l'état rédox, nous avons regardé si une dérégulation rédox contribue aux anomalies de la myéline dans le cadre de la schizophrénie. Dans le cortex préfrontal des sujets contrôles et des patients en phase précoce de psychose, nous avons montré que le glutathion était positivement associé à l'intégrité de matière blanche. Afin d'explorer plus en détail la relation entre le glutathion et la myéline, nous avons mené une étude translationnelle. Nos résultats ont montré que des souris ayant un déficit de la synthèse du glutathion présentaient un retard dans les processus de maturation des oligodendrocytes et de la myélinisation dans le cortex cingulaire antérieure. Plus précisément, le nombre d'oligodendrocytes et le taux de myéline étaient uniquement diminués durant la période péripubertaire. Cette même période correspond au pic de l'expression des gènes en lien avec la myéline. Ces données soulignent le fait que l'adolescence est une période du développement particulièrement sensible durant laquelle un contrôle adéquat de l'état rédox est nécessaire aux processus de maturation des oligodendrocytes et de myélinisation. Ceci est en accord avec la diminution de myéline observée suite à un stress oxydatif généré par un stress psychosocial. Au niveau cellulaire, un déficit du glutathion affecte le développement des oligodendrocytes à différents stades. En effet, dans des cultures de progéniteurs d'oligodendrocytes, nos résultats montrent qu'une réduction du taux de glutathion était associée à (i) un arrêt du cycle cellulaire ainsi qu'une diminution de la prolifération des oligodendrocytes, et à (ii) des dysfonctionnements de la maturation des oligodendrocytes. Par ailleurs, au niveau moléculaire, les perturbations de la prolifération étaient générées par une augmentation de l'activité de la kinase Fyn. Ceci est en accord avec la dérégulation de Fyn observée dans les fibroblastes de patients ayant une déficience en synthèse du glutathion en condition de stress oxydatif. Les résultats de cette thèse soulignent qu'une dérégulation rédox induite par un déficit en glutathion peut contribuer aux anomalies des oligodendrocytes et de la myéline via le dysfonctionnement des voies de signalisation Fyn. Une recherche plus avancée de l'implication de Fyn dans la maladie pourrait ouvrir la voie à de nouvelles cibles thérapeutiques.

Using self organizing maps on compositional data

Self-organizing maps (Kohonen 1997) is a type of artificial neural network developedto explore patterns in high-dimensional multivariate data. The conventional versionof the algorithm involves the use of Euclidean metric in the process of adaptation ofthe model vectors, thus rendering in theory a whole methodology incompatible withnon-Euclidean geometries.In this contribution we explore the two main aspects of the problem:1. Whether the conventional approach using Euclidean metric can shed valid resultswith compositional data.2. If a modification of the conventional approach replacing vectorial sum and scalarmultiplication by the canonical operators in the simplex (i.e. perturbation andpowering) can converge to an adequate solution.Preliminary tests showed that both methodologies can be used on compositional data.However, the modified version of the algorithm performs poorer than the conventionalversion, in particular, when the data is pathological. Moreover, the conventional ap-proach converges faster to a solution, when data is \well-behaved".Key words: Self Organizing Map; Artificial Neural networks; Compositional data

GeoKernels: Modeling of Spatial Data on GeoManifolds

This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental data modeling on natural manifolds, such as complex topographies of the mountainous regions, where environmental processes are highly influenced by the relief. These relations, possibly regionalized and nonlinear, can be modeled from data with machine learning using the digital elevation models in semi-supervised kernel methods. The range of the tools and methodological issues discussed in the study includes feature selection and semisupervised Support Vector algorithms. The real case study devoted to data-driven modeling of meteorological fields illustrates the discussed approach.