Analytical Study of Light Propagation in Highly Nonlinear Media

The present dissertation is devoted to the construction of exact and approximate analytical solutions of the problem of light propagation in highly nonlinear media. It is demonstrated that for many experimental conditions, the problem can be studied under the geometrical optics approximation with a sufficient accuracy. Based on the renormalization group symmetry analysis, exact analytical solutions of the eikonal equations with a higher order refractive index are constructed. A new analytical approach to the construction of approximate solutions is suggested. Based on it, approximate solutions for various boundary conditions, nonlinear refractive indices and dimensions are constructed. Exact analytical expressions for the nonlinear self-focusing positions are deduced. On the basis of the obtained solutions a general rule for the single filament intensity is derived; it is demonstrated that the scaling law (the functional dependence of the self-focusing position on the peak beam intensity) is defined by a form of the nonlinear refractive index but not the beam shape at the boundary. Comparisons of the obtained solutions with results of experiments and numerical simulations are discussed.

Understanding Subsystems in Biology through Dimensionality Reduction, Graph Partitioning and Analytical Modeling

Biological systems exhibit rich and complex behavior through the orchestrated interplay of a large array of components. It is hypothesized that separable subsystems with some degree of functional autonomy exist; deciphering their independent behavior and functionality would greatly facilitate understanding the system as a whole. Discovering and analyzing such subsystems are hence pivotal problems in the quest to gain a quantitative understanding of complex biological systems. In this work, using approaches from machine learning, physics and graph theory, methods for the identification and analysis of such subsystems were developed. A novel methodology, based on a recent machine learning algorithm known as non-negative matrix factorization (NMF), was developed to discover such subsystems in a set of large-scale gene expression data. This set of subsystems was then used to predict functional relationships between genes, and this approach was shown to score significantly higher than conventional methods when benchmarking them against existing databases. Moreover, a mathematical treatment was developed to treat simple network subsystems based only on their topology (independent of particular parameter values). Application to a problem of experimental interest demonstrated the need for extentions to the conventional model to fully explain the experimental data. Finally, the notion of a subsystem was evaluated from a topological perspective. A number of different protein networks were examined to analyze their topological properties with respect to separability, seeking to find separable subsystems. These networks were shown to exhibit separability in a nonintuitive fashion, while the separable subsystems were of strong biological significance. It was demonstrated that the separability property found was not due to incomplete or biased data, but is likely to reflect biological structure.

La experiencia de los receptores en el trasplante de órganos y tejidos

El trasplante de órganos y/o tejidos es considerado como una opción terapéutica viable para el tratamiento tanto de enfermedades crónicas o en estadios terminales, como de afectaciones no vitales, pero que generen una disminución en la calidad de vida percibida por el paciente. Este procedimiento, de carácter multidimensional, está compuesto por 3 actores principales: el donante, el órgano/tejido, y el receptor. Si bien un porcentaje significativo de investigaciones y planes de intervención han girado en torno a la dimensión biológica del trasplante, y a la promoción de la donación; el interés por la experiencia psicosocial y la calidad de vida de los receptores en este proceso ha aumentado durante la última década. En relación con esto, la presente monografía se plantea como objetivo general la exploración de la experiencia y los significados construidos por los pacientes trasplantados, a través de una revisión sistemática de la literatura sobre esta temática. Para ello, se plantearon unos objetivos específicos derivados del general, se seleccionaron términos o palabras claves por cada uno de estos, y se realizó una búsqueda en 5 bases de datos para revistas indexadas: Ebsco Host (Academic Search; y Psychology and Behavioral Sciences Collection); Proquest; Pubmed; y Science Direct. A partir de los resultados, se establece que si bien la vivencia de los receptores ha comenzado a ser investigada, aún es necesaria una mayor exploración sobre la experiencia de estos pacientes; exploración que carecería de objetivo si no se hiciera a través de las narrativas o testimonios de los mismos receptores

Rattlesnake hemoglobins: Functional properties and tetrameric stability

The present work analyzed the tetrameric stability of the hemoglobins from the rattlesnake C. durissus terrificus using analytical gel filtration chromatography, SAXS and osmotic stress. We show that the dissociation mechanism proposed for L. miliaris hemoglobin does not apply for these hemoglobins, which constitute stable tetramers even at low concentrations.

Regionalization of health in Brazil: an analytical perspective

This article aims to contribute to the debate on the SUS regionalization policy and the establishment of health regions in Brazil. Understanding them require to recognize the dichotomy between public health and individual health - which marks the history of Brazilian public health - and identify the different rationalities that lead this process. Such rationalities allow not only to consider the legacy of municipalization in the current regionalization process, as well as to establish links between the two fields of fundamental knowledge to the debate, epidemiology and geography. Clinical epidemiology, privileging individual health, gives basis to a healthcare model that prioritizes the optimization of resources. The recognition of health in its broader concept, in the social epidemiology, bases an attention model aimed at social determinants. With geography, functional regions can be formulated, based on Christaller's theory, or lablachianas regions which recognize the social loco / regional structure, allowing intervention in determining or conditioning the way of illness and death of populations.

Bioactive and toxic compounds in different foodstuffs: analytical techniques and sensory analysis

Foods that provide medical and health benefits or have a role in disease risk prevention are termed functional foods. The functionality of functional foods is derived from bioactive compounds that are extranutritional constituents present in small quantities in food. Bioactive components include a range of chemical compounds with varying structures such as carotenoids, flavonoids, plant sterols, omega-3 fatty acids (n-3), allyl and diallyl sulfides, indoles (benzopyrroles), and phenolic acids. The increasing consumer interest in natural bioactive compounds has brought about a rise in demand for these kinds of compounds and, in parallel, an increasing number of scientific studies have this type of substance as main topic. The principal aim of this PhD research project was the study of different bioactive and toxic compounds in several natural matrices. To achieve this goal, chromatographic, spectroscopic and sensorial analysis were performed. This manuscript reports the main results obtained in the six activities briefly summarized as follows: • SECTION I: the influence of conventional packaging on lipid oxidation of pasta was evaluated in egg spaghetti. • SECTION II: the effect of the storage at different temperatures of virgin olive oil was monitored by peroxide value, fatty acid activity, OSI test and sensory analysis. • SECTION III: the glucosinolate and phenolic content of 37 rocket salad accessions were evaluated, comparing Eruca sativa and Diplotaxis tenuifolia species. Sensory analysis and the influence of the phenolic and glucosinolate composition on sensory attributes of rocket salads has been also studied. • SECTION IV: ten buckwheat honeys were characterised on the basis of their pollen, physicochemical, phenolic and volatile composition. • SECTION V: the polyphenolic fraction, anthocyanins and other polar compounds, the antioxidant capacity and the anty-hyperlipemic action of the aqueous extract of Hibiscus sabdariffa were achieved. • SECTION VI: the optimization of a normal phase high pressure liquid chromatography–fluorescence detection method for the quantitation of flavanols and procyanidins in cocoa powder and chocolate samples was performed.

Development of instrumental and sensory analytical methods of food obtained by traditional and emerging technologies

The consumer demand for natural, minimally processed, fresh like and functional food has lead to an increasing interest in emerging technologies. The aim of this PhD project was to study three innovative food processing technologies currently used in the food sector. Ultrasound-assisted freezing, vacuum impregnation and pulsed electric field have been investigated through laboratory scale systems and semi-industrial pilot plants. Furthermore, analytical and sensory techniques have been developed to evaluate the quality of food and vegetable matrix obtained by traditional and emerging processes. Ultrasound was found to be a valuable technique to improve the freezing process of potatoes, anticipating the beginning of the nucleation process, mainly when applied during the supercooling phase. A study of the effects of pulsed electric fields on phenol and enzymatic profile of melon juice has been realized and the statistical treatment of data was carried out through a response surface method. Next, flavour enrichment of apple sticks has been realized applying different techniques, as atmospheric, vacuum, ultrasound technologies and their combinations. The second section of the thesis deals with the development of analytical methods for the discrimination and quantification of phenol compounds in vegetable matrix, as chestnut bark extracts and olive mill waste water. The management of waste disposal in mill sector has been approached with the aim of reducing the amount of waste, and at the same time recovering valuable by-products, to be used in different industrial sectors. Finally, the sensory analysis of boiled potatoes has been carried out through the development of a quantitative descriptive procedure for the study of Italian and Mexican potato varieties. An update on flavour development in fresh and cooked potatoes has been realized and a sensory glossary, including general and specific definitions related to organic products, used in the European project Ecropolis, has been drafted.

Milk and dairy products: evaluation of bioactive components by analytical techniques

Milk and dairy products are important source of bioactive compounds useful to satisfy the nutritional and physiological needs of any newborns of mammalian species and useful to guarantee adequate growth and development of infants as well as provide a complete nourishment of adults. Physico-chemical, nutritional and organoleptic properties of the main constituents and the “minor” components have a crucial role in the quality of milk and milk products. Although in the past decades dietary milk fat was often regarded as harmful for the human health, recent researches suggest that milk contains specific fatty acids with nutritional and physiological health benefits. For these reasons, a major attention is given to the quantity and quality of total fat intake. In the recent years, as a result of the new concept of multifunctional agriculture and the changing behaviours about diet, consumer demands in favor of high-quality, security and safety dairy products are increased. Moreover, milk proteins and milk-derived bioactive peptides are recognized to have a high nutritive value, several health-promoting functional activities and excellent technological properties. Accordingly, growing interest in the development of functional dairy products and preparation of infant formulae for babies who cannot be breast-fed, has been give in order to meet the specific consumer’s requests. This manuscript presents the main results obtained during my PhD research aimed to evaluate the main bioactive lipids and proteins in milk and dairy products using innovative analytical techniques. The experimental section of this manuscript is divided in two sections where are reported the main results obtained during my research activities on dairy products and human milks in order to characterize their bioactive compounds for functional food applications.

New analytical LC-mass spectrometry methodologies for the quali-quantitative determination of natural substances and drugs in complex matrices

This thesis reports an integrated analytical and physicochemical approach for the study of natural substances and new drugs based on mass spectrometry techniques combined with liquid chromatography. In particular, Chapter 1 concerns the study of Berberine a natural substance with pharmacological activity for the treatment of hepatobiliary and intestinal diseases. The first part focused on the relationships between physicochemical properties, pharmacokinetics and metabolism of Berberine and its metabolites. For this purpose a sensitive HPLC-ES-MS/MS method have been developed, validated and used to determine these compounds during their physicochemical properties studies and plasma levels of berberine and its metabolites including berberrubine(M1), demethylenberberine(M3), and jatrorrhizine(M4) in humans. Data show that M1, could have an efficient intestinal absorption by passive diffusion due to a keto-enol tautomerism confirmed by NMR studies and its higher plasma concentration. In the second part of Chapter 1, a comparison between M1 and BBR in vivo biodistribution in rat has been studied. In Chapter 2 a new HPLC-ES-MS/MS method for the simultaneous determination and quantification of glucosinolates, as glucoraphanin, glucoerucin and sinigrin, and isothiocyanates, as sulforaphane and erucin, has developed and validated. This method has been used for the analysis of functional foods enriched with vegetable extracts. Chapter 3 focused on a physicochemical study of the interaction between the bile acid sequestrants used in the treatment of hypercholesterolemia including colesevelam and cholestyramine with obeticolic acid (OCA), potent agonist of nuclear receptor farnesoid X (FXR). In particular, a new experimental model for the determination of equilibrium binding isotherm was developed. Chapter 4 focused on methodological aspects of new hard ionization coupled with liquid chromatography (Direct-EI-UHPLC-MS) not yet commercially available and potentially useful for qualitative analysis and for “transparent” molecules to soft ionization techniques. This method was applied to the analysis of several steroid derivatives.

The functional significance of EEG microstates-Associations with modalities of thinking

The momentary, global functional state of the brain is reflected by its electric field configuration. Cluster analytical approaches consistently extracted four head-surface brain electric field configurations that optimally explain the variance of their changes across time in spontaneous EEG recordings. These four configurations are referred to as EEG microstate classes A, B, C, and D and have been associated with verbal/phonological, visual, attention reorientation, and subjective interoceptive-autonomic processing, respectively. The present study tested these associations via an intra-individual and inter-individual analysis approach. The intra-individual approach tested the effect of task-induced increased modality-specific processing on EEG microstate parameters. The inter-individual approach tested the effect of personal modality-specific parameters on EEG microstate parameters. We obtained multichannel EEG from 61 healthy, right-handed, male students during four eyes-closed conditions: object-visualization, spatial-visualization, verbalization (6 runs each), and resting (7 runs). After each run, we assessed participants' degrees of object-visual, spatial-visual, and verbal thinking using subjective reports. Before and after the recording, we assessed modality-specific cognitive abilities and styles using nine cognitive tests and two questionnaires. The EEG of all participants, conditions, and runs was clustered into four classes of EEG microstates (A, B, C, and D). RMANOVAs, ANOVAs and post-hoc paired t-tests compared microstate parameters between conditions. TANOVAs compared microstate class topographies between conditions. Differences were localized using eLORETA. Pearson correlations assessed interrelationships between personal modality-specific parameters and EEG microstate parameters during no-task resting. As hypothesized, verbal as opposed to visual conditions consistently affected the duration, occurrence, and coverage of microstate classes A and B. Contrary to associations suggested by previous reports, parameters were increased for class A during visualization, and class B during verbalization. In line with previous reports, microstate D parameters were increased during no-task resting compared to the three internal, goal-directed tasks. Topographic differences between conditions concerned particular sub-regions of components of the metabolic default mode network. Modality-specific personal parameters did not consistently correlate with microstate parameters except verbal cognitive style which correlated negatively with microstate class A duration and positively with class C occurrence. This is the first study that aimed to induce EEG microstate class parameter changes based on their hypothesized functional significance. Beyond, the associations of microstate classes A and B with visual and verbal processing, respectively and microstate class D with interoceptive-autonomic processing, our results suggest that a finely-tuned interplay between all four EEG microstate classes is necessary for the continuous formation of visual and verbal thoughts, as well as interoceptive-autonomic processing. Our results point to the possibility that the EEG microstate classes may represent the head-surface measured activity of intra-cortical sources primarily exhibiting inhibitory functions. However, additional studies are needed to verify and elaborate on this hypothesis.

Amygdala response to self-critical stimuli and symptom improvement in psychotherapy for depression

Background: Cognitive–behavioural therapy is efficacious in the treatment of major depressive disorder but response rates are still far from satisfactory. Aims: To better understand brain responses to individualised emotional stimuli and their association with outcome, to enhance treatment. Method: Functional magnetic resonance imaging data were collected prior to individual psychotherapy. Differences in brain activity during passive viewing of individualised self-critical material in 23 unmedicated out-patients with depression and 28 healthy controls were assessed. The associations between brain activity, cognitive and emotional change, and outcome were analysed in 21 patients. Results: Patients showed enhanced activity in the amygdala and ventral striatum compared with the control group. Non-response to therapy was associated with enhanced activity in the right amygdala compared with those who responded, and activity in this region was negatively associated with outcome. Emotional but not cognitive changes mediated this association. Conclusions: Amygdala hyperactivity may lessen symptom improvement in psychotherapy for depression through attenuating emotional skill acquisition.

Prefrontal Thinning Affects Functional Connectivity and Regional Homogeneity of the Anterior Cingulate Cortex in Depression

Major depressive disorder (MDD) is associated with structural and functional alterations in the prefrontal cortex (PFC) and anterior cingulate cortex (ACC). Enhanced ACC activity at rest (measured using various imaging methodologies) is found in treatment-responsive patients and is hypothesized to bolster treatment response by fostering adaptive rumination. However, whether structural changes influence functional coupling between fronto-cingulate regions and ACC regional homogeneity (ReHo) and whether these functional changes are related to levels of adaptive rumination and treatment response is still unclear. Cortical thickness and ReHo maps were calculated in 21 unmedicated depressed patients and 35 healthy controls. Regions with reduced cortical thickness defined the seeds for the subsequent functional connectivity (FC) analyses. Patients completed the Response Style Questionnaire, which provided a measure of adaptive rumination associated with better response to psychotherapy. Compared with controls, depressed patients showed thinning of the right anterior PFC, increased prefrontal connectivity with the supragenual ACC (suACC), and higher ReHo in the suACC. The suACC clusters of increased ReHo and FC spatially overlapped. In depressed patients, suACC ReHo scores positively correlated with PFC thickness and with FC strength. Moreover, stronger fronto-cingulate connectivity was related to higher levels of adaptive rumination. Greater suACC ReHo and connectivity with the right anterior PFC seem to foster adaptive forms of self-referential processing associated with better response to psychotherapy, whereas prefrontal thinning impairs the ability of depressed patients to engage the suACC during a major depressive episode. Bolstering the function of the suACC may represent a potential target for treatment.

Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50–100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing.

HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity

The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the ‘traditional’ set of linear methods, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality. This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified-easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox. Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab® environment (The Mathworks, Inc), which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis.

HERMES: towards an integrated toolbox to characterize functional and effective brain connectivity

The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the ?traditional? set of linear methods, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality. This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified, easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox. Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab® environment (The Mathworks, Inc), which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis.