Effects of Obesity and Weight Loss Following Bariatric Surgery on Brain Function, Structural Integrity and Metabolism

Obesity is one of the key challenges to health care system worldwide and its prevalence is estimated to rise to pandemic proportions. Numerous adverse health effects follow with increasing body weight, including increased risk of hypertension, diabetes, hypercholesterolemia, musculoskeletal pain and cancer. Current evidence suggests that obesity is associated with altered cerebral reward circuit functioning and decreased inhibitory control over appetitive food cues. Furthermore, obesity causes adverse shifts in metabolism and loss of structural integrity within the brain. Prior cross-sectional studies do not allow delineating which of these cerebral changes are recoverable after weight loss. We compared morbidly obese subjects with healthy controls to unravel brain changes associated with obesity. Bariatric surgery was used as an intervention to study which cerebral changes are recoverable after weight loss. In Study I we employed functional magnetic resonance imaging (fMRI) to detect the brain basis of volitional appetite control and its alterations in obesity. In Studies II-III we used diffusion tensor imaging (DTI) and voxel-based morphometry (VBM) to quantify the effects of obesity and the effects of weight loss on structural integrity of the brain. In study IV we used positron emission tomography (PET) with [18F]-FDG in fasting state and during euglycemic hyperinsulinemia to quantify effects of obesity and weight loss on brain glucose uptake. The fMRI experiment revealed that a fronto-parietal network is involved in volitional appetite control. Obese subjects had lower medial frontal and dorsal striatal brain activity during cognitive appetite control and increased functional connectivity within the appetite control circuit. Obese subjects had initially lower grey matter and white matter densities than healthy controls in VBM analysis and loss of integrity in white matter tracts as measured by DTI. They also had initially elevated glucose metabolism under insulin stimulation but not in fasting state. After the weight loss following bariatric surgery, obese individuals’ brain volumes recovered and the insulin-induced increase in glucose metabolism was attenuated. In conclusion, obesity is associated with altered brain function, coupled with loss of structural integrity and elevated glucose metabolism, which are likely signs of adverse health effects to the brain. These changes are reversed by weight loss after bariatric surgery, implicating that weight loss has a causal role on these adverse cerebral changes. Altogether these findings suggest that weight loss also promotes brain health.Key words: brain, obesity, bariatric surgery, appetite control, structural magnetic resonance

A structural basis for the inhibition of collagen-stimulated platelet function by quercetin and structurally related flavonoids

Background and purpose: Molecular mechanisms underlying the links between dietary intake of flavonoids and reduced cardiovascular disease risk are only partially understood. Key events in the pathogenesis of cardiovascular disease, particularly thrombosis, are inhibited by these polyphenolic compounds via mechanisms such as inhibition of platelet activation and associated signal transduction, attenuation of generation of reactive oxygen species, enhancement of nitric oxide production and binding to thromboxane A2 receptors. In vivo, effects of flavonoids are mediated by their metabolites, but the effects and modes of action of these compounds are not well-characterized. A good understanding of flavonoid structure–activity relationships with regard to platelet function is also lacking. Experimental approach: Inhibitory potencies of structurally distinct flavonoids (quercetin, apigenin and catechin) and plasma metabolites (tamarixetin, quercetin-3′-sulphate and quercetin-3-glucuronide) for collagen-stimulated platelet aggregation and 5-hydroxytryptamine secretion were measured in human platelets. Tyrosine phosphorylation of total protein, Syk and PLCγ2 (immunoprecipitation and Western blot analyses), and Fyn kinase activity were also measured in platelets. Internalization of flavonoids and metabolites in a megakaryocytic cell line (MEG-01 cells) was studied by fluorescence confocal microscopy. Key results: The inhibitory mechanisms of these compounds included blocking Fyn kinase activity and the tyrosine phosphorylation of Syk and PLCγ2 following internalization. Principal functional groups attributed to potent inhibition were a planar, C-4 carbonyl substituted and C-3 hydroxylated C ring in addition to a B ring catechol moiety. Conclusions and implications: The structure–activity relationship for flavonoids on platelet function presented here may be exploited to design selective inhibitors of cell signalling.

Neurofuzzy network model construction using Bézier-Bernstein polynomial functions

Neurofuzzy modelling systems combine fuzzy logic with quantitative artificial neural networks via a concept of fuzzification by using a fuzzy membership function usually based on B-splines and algebraic operators for inference, etc. The paper introduces a neurofuzzy model construction algorithm using Bezier-Bernstein polynomial functions as basis functions. The new network maintains most of the properties of the B-spline expansion based neurofuzzy system, such as the non-negativity of the basis functions, and unity of support but with the additional advantages of structural parsimony and Delaunay input space partitioning, avoiding the inherent computational problems of lattice networks. This new modelling network is based on the idea that an input vector can be mapped into barycentric co-ordinates with respect to a set of predetermined knots as vertices of a polygon (a set of tiled Delaunay triangles) over the input space. The network is expressed as the Bezier-Bernstein polynomial function of barycentric co-ordinates of the input vector. An inverse de Casteljau procedure using backpropagation is developed to obtain the input vector's barycentric co-ordinates that form the basis functions. Extension of the Bezier-Bernstein neurofuzzy algorithm to n-dimensional inputs is discussed followed by numerical examples to demonstrate the effectiveness of this new data based modelling approach.

A general basis for quarter-power scaling in animals

It has been known for decades that the metabolic rate of animals scales with body mass with an exponent that is almost always <1, >2/3, and often very close to 3/4. The 3/4 exponent emerges naturally from two models of resource distribution networks, radial explosion and hierarchically branched, which incorporate a minimum of specific details. Both models show that the exponent is 2/3 if velocity of flow remains constant, but can attain a maximum value of 3/4 if velocity scales with its maximum exponent, 1/12. Quarterpower scaling can arise even when there is no underlying fractality. The canonical “fourth dimension” in biological scaling relations can result from matching the velocity of flow through the network to the linear dimension of the terminal “service volume” where resources are consumed. These models have broad applicability for the optimal design of biological and engineered systems where energy, materials, or information are distributed from a single source.

Using Social Network Research in HRM: Scratching the surface of a fundamental basis of HRM

This article is the guest editors' introduction to a special issue on using Social Network Research in the field of Human Resource Management. The goals of the special issue are: (1) to draw attention to the points of integration between the two fields, (2) to showcase research that applies social network perspectives and methodology to issues relevant to HRM and (3) to identify common challenges where future collaborative efforts could contribute to advancements in both fields.

Percolation in a network with long-range connections: Implications for cytoskeletal structure and function

Cell shape, signaling, and integrity depend on cytoskeletal organization. In this study we describe the cytoskeleton as a simple network of filamentary proteins (links) anchored by complex protein structures (nodes). The structure of this network is regulated by a distance-dependent probability of link formation as P = p/d(s), where p regulates the network density and s controls how fast the probability for link formation decays with node distance (d). It was previously shown that the regulation of the link lengths is crucial for the mechanical behavior of the cells. Here we examined the ability of the two-dimensional network to percolate (i.e. to have end-to-end connectivity), and found that the percolation threshold depends strongly on s. The system undergoes a transition around s = 2. The percolation threshold of networks with s < 2 decreases with increasing system size L, while the percolation threshold for networks with s > 2 converges to a finite value. We speculate that s < 2 may represent a condition in which cells can accommodate deformation while still preserving their mechanical integrity. Additionally, we measured the length distribution of F-actin filaments from publicly available images of a variety of cell types. In agreement with model predictions, cells originating from more deformable tissues show longer F-actin cytoskeletal filaments. (C) 2008 Elsevier B.V. All rights reserved.

Comparative study between RBF and radial-PPS neural networks

The study of function approximation is motivated by the human limitation and inability to register and manipulate with exact precision the behavior variations of the physical nature of a phenomenon. These variations are referred to as signals or signal functions. Many real world problem can be formulated as function approximation problems and from the viewpoint of artificial neural networks these can be seen as the problem of searching for a mapping that establishes a relationship from an input space to an output space through a process of network learning. Several paradigms of artificial neural networks (ANN) exist. Here we will be investigated a comparative of the ANN study of RBF with radial Polynomial Power of Sigmoids (PPS) in function approximation problems. Radial PPS are functions generated by linear combination of powers of sigmoids functions. The main objective of this paper is to show the advantages of the use of the radial PPS functions in relationship traditional RBF, through adaptive training and ridge regression techniques.

Glia-Pinealocyte Network: The Paracrine Modulation of Melatonin Synthesis by Tumor Necrosis Factor (TNF)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Designing a modified Hopfield network to solve an Economic Dispatch problem with nonlinear cost function

Economic Dispatch (ED) problems have recently been solved by artificial neural networks approaches. In most of these dispatch models, the cost function must be linear or quadratic. Therefore, functions that have several minimum points represent a problem to the simulation since these approaches have not accepted nonlinear cost function. Another drawback pointed out in the literature is that some of these neural approaches fail to converge efficiently towards feasible equilibrium points. This paper discusses the application of a modified Hopfield architecture for solving ED problems defined by nonlinear cost function. The internal parameters of the neural network adopted here are computed using the valid-subspace technique, which guarantees convergence to equilibrium points that represent a solution for the ED problem. Simulation results and a comparative analysis involving a 3-bus test system are presented to illustrate efficiency of the proposed approach.

Activation function study for wavelet network

The main purpose of this paper is to investigate theoretically and experimentally the use of family of Polynomial Powers of the Sigmoid (PPS) Function Networks applied in speech signal representation and function approximation. This paper carries out practical investigations in terms of approximation fitness (LSE), time consuming (CPU Time), computational complexity (FLOP) and representation power (Number of Activation Function) for different PPS activation functions. We expected that different activation functions can provide performance variations and further investigations will guide us towards a class of mappings associating the best activation function to solve a class of problems under certain criteria.

An automatic methodology for obtaining optimum shape factors for the radial point interpolation method

In this letter, a methodology is proposed for automatically (and locally) obtaining the shape factor c for the Gaussian basis functions, for each support domain, in order to increase numerical precision and mainly to avoid matrix inversion impossibilities. The concept of calibration function is introduced, which is used for obtaining c. The methodology developed was applied for a 2-D numerical experiment, which results are compared to analytical solution. This comparison revels that the results associated to the developed methodology are very close to the analytical solution for the entire bandwidth of the excitation pulse. The proposed methodology is called in this work Local Shape Factor Calibration Method (LSFCM).

Análise de pathway e network em tecido adiposo in vivo e o papel da vitamina D na adipogênese in vitro

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Glia-Pinealocyte Network: The Paracrine Modulation of Melatonin Synthesis by Tumor Necrosis Factor (TNF)

The pineal gland, a circumventricular organ, plays an integrative role in defense responses. The injury-induced suppression of the pineal gland hormone, melatonin, which is triggered by darkness, allows the mounting of innate immune responses. We have previously shown that cultured pineal glands, which express toll-like receptor 4 (TLR4) and tumor necrosis factor receptor 1 (TNFR1), produce TNF when challenged with lipopolysaccharide (LPS). Here our aim was to evaluate which cells present in the pineal gland, astrocytes, microglia or pinealocytes produced TNF, in order to understand the interaction between pineal activity, melatonin production and immune function. Cultured pineal glands or pinealocytes were stimulated with LPS. TNF content was measured using an enzyme-linked immunosorbent assay. TLR4 and TNFR1 expression were analyzed by confocal microscopy. Microglial morphology was analyzed by immunohistochemistry. In the present study, we show that although the main cell types of the pineal gland (pinealocytes, astrocytes and microglia) express TLR4, the production of TNF induced by LPS is mediated by microglia. This effect is due to activation of the nuclear factor kappa B (NF-kB) pathway. In addition, we observed that LPS activates microglia and modulates the expression of TNFR1 in pinealocytes. As TNF has been shown to amplify and prolong inflammatory responses, its production by pineal microglia suggests a glia-pinealocyte network that regulates melatonin output. The current study demonstrates the molecular and cellular basis for understanding how melatonin synthesis is regulated during an innate immune response, thus our results reinforce the role of the pineal gland as sensor of immune status.

Computational methods for the analysis of protein structure and function

The vast majority of known proteins have not yet been experimentally characterized and little is known about their function. The design and implementation of computational tools can provide insight into the function of proteins based on their sequence, their structure, their evolutionary history and their association with other proteins. Knowledge of the three-dimensional (3D) structure of a protein can lead to a deep understanding of its mode of action and interaction, but currently the structures of <1% of sequences have been experimentally solved. For this reason, it became urgent to develop new methods that are able to computationally extract relevant information from protein sequence and structure. The starting point of my work has been the study of the properties of contacts between protein residues, since they constrain protein folding and characterize different protein structures. Prediction of residue contacts in proteins is an interesting problem whose solution may be useful in protein folding recognition and de novo design. The prediction of these contacts requires the study of the protein inter-residue distances related to the specific type of amino acid pair that are encoded in the so-called contact map. An interesting new way of analyzing those structures came out when network studies were introduced, with pivotal papers demonstrating that protein contact networks also exhibit small-world behavior. In order to highlight constraints for the prediction of protein contact maps and for applications in the field of protein structure prediction and/or reconstruction from experimentally determined contact maps, I studied to which extent the characteristic path length and clustering coefficient of the protein contacts network are values that reveal characteristic features of protein contact maps. Provided that residue contacts are known for a protein sequence, the major features of its 3D structure could be deduced by combining this knowledge with correctly predicted motifs of secondary structure. In the second part of my work I focused on a particular protein structural motif, the coiled-coil, known to mediate a variety of fundamental biological interactions. Coiled-coils are found in a variety of structural forms and in a wide range of proteins including, for example, small units such as leucine zippers that drive the dimerization of many transcription factors or more complex structures such as the family of viral proteins responsible for virus-host membrane fusion. The coiled-coil structural motif is estimated to account for 5-10% of the protein sequences in the various genomes. Given their biological importance, in my work I introduced a Hidden Markov Model (HMM) that exploits the evolutionary information derived from multiple sequence alignments, to predict coiled-coil regions and to discriminate coiled-coil sequences. The results indicate that the new HMM outperforms all the existing programs and can be adopted for the coiled-coil prediction and for large-scale genome annotation. Genome annotation is a key issue in modern computational biology, being the starting point towards the understanding of the complex processes involved in biological networks. The rapid growth in the number of protein sequences and structures available poses new fundamental problems that still deserve an interpretation. Nevertheless, these data are at the basis of the design of new strategies for tackling problems such as the prediction of protein structure and function. Experimental determination of the functions of all these proteins would be a hugely time-consuming and costly task and, in most instances, has not been carried out. As an example, currently, approximately only 20% of annotated proteins in the Homo sapiens genome have been experimentally characterized. A commonly adopted procedure for annotating protein sequences relies on the "inheritance through homology" based on the notion that similar sequences share similar functions and structures. This procedure consists in the assignment of sequences to a specific group of functionally related sequences which had been grouped through clustering techniques. The clustering procedure is based on suitable similarity rules, since predicting protein structure and function from sequence largely depends on the value of sequence identity. However, additional levels of complexity are due to multi-domain proteins, to proteins that share common domains but that do not necessarily share the same function, to the finding that different combinations of shared domains can lead to different biological roles. In the last part of this study I developed and validate a system that contributes to sequence annotation by taking advantage of a validated transfer through inheritance procedure of the molecular functions and of the structural templates. After a cross-genome comparison with the BLAST program, clusters were built on the basis of two stringent constraints on sequence identity and coverage of the alignment. The adopted measure explicity answers to the problem of multi-domain proteins annotation and allows a fine grain division of the whole set of proteomes used, that ensures cluster homogeneity in terms of sequence length. A high level of coverage of structure templates on the length of protein sequences within clusters ensures that multi-domain proteins when present can be templates for sequences of similar length. This annotation procedure includes the possibility of reliably transferring statistically validated functions and structures to sequences considering information available in the present data bases of molecular functions and structures.

Bestimmung der spektralen Empfindlichkeit eines Bewegungsdetektors auf der Basis von Zapfenerregungen beim Menschen

Die Detektion von Bewegung stellt eine der fundamentalsten Fähigkeiten der visuellen Wahrnehmung dar. Um zu klären, ob das System zur Bewegungswahrnehmung Eingang nur durch einen Zapfentyp erhält, oder ob eine Kombination von verschiedenen Zapfentypen vorliegt, wurde eine rotierende zwei-armige archimedische Spiralscheibe verwendet (reale Bewegung), bei der sich Spirale und Hintergrund farblich unterschieden. Durch Veränderung der Intensität farbiger Leuchtstoffröhren konnte eine Beleuchtungssituation geschaffen werden, bei der die (radiale) Bewegung der Spirale nicht mehr wahrgenommen werden konnte, obwohl Spirale und Hintergrund farblich verschieden waren. Die Bestimmung der Zapfenerregungen im 3-D Rezeptorraum ließ einen Beitrag sowohl des L– als auch des M-Zapfens bei normalsichtigen Trichromaten (dominiert durch L), jedoch einen alleinigen Beitrag des M-Zapfens bei Protanopen erkennen. Die Ermittlung der spektralen Empfindlichkeit basierend auf einer Vektor Analyse im 3D-Rezeptorraum zeigte schließlich, dass dem neuronalen Bewegungsdetektor ein additiver Beitrag des L- und M-Zapfens, in Übereinstimmung mit der Hellempfindlichkeitsfunktion (Vλ), zugrunde liegt. Als Ergebnis schreiben wir die Detektion von Objektbewegung einem farbenblinden Mechanismus zu. Es ist sehr wahrscheinlich, dass der Magnozelluläre-Kanal das neuronale Substrat dieses Bewegungsdetektors repräsentiert.