Signal propagation in Bayesian networks and its relationship with intrinsically multivariate predictive variables

A set of predictor variables is said to be intrinsically multivariate predictive (IMP) for a target variable if all properly contained subsets of the predictor set are poor predictors of the. target but the full set predicts the target with great accuracy. In a previous article, the main properties of IMP Boolean variables have been analytically described, including the introduction of the IMP score, a metric based on the coefficient of determination (CoD) as a measure of predictiveness with respect to the target variable. It was shown that the IMP score depends on four main properties: logic of connection, predictive power, covariance between predictors and marginal predictor probabilities (biases). This paper extends that work to a broader context, in an attempt to characterize properties of discrete Bayesian networks that contribute to the presence of variables (network nodes) with high IMP scores. We have found that there is a relationship between the IMP score of a node and its territory size, i.e., its position along a pathway with one source: nodes far from the source display larger IMP scores than those closer to the source, and longer pathways display larger maximum IMP scores. This appears to be a consequence of the fact that nodes with small territory have larger probability of having highly covariate predictors, which leads to smaller IMP scores. In addition, a larger number of XOR and NXOR predictive logic relationships has positive influence over the maximum IMP score found in the pathway. This work presents analytical results based on a simple structure network and an analysis involving random networks constructed by computational simulations. Finally, results from a real Bayesian network application are provided. (C) 2012 Elsevier Inc. All rights reserved.

Sleeve Gastrectomy With Transit Bipartition A Potent Intervention for Metabolic Syndrome and Obesity

Objective: To present 5-year results of sleeve gastrectomy (SG) with transit bipartition (TB) as a metabolic intervention for obesity. Background: Recent data suggest that high glycemic index foods may lead to a hormonally hyperactive proximal gut and a hypoactivate distal gut, which are linked to metabolic syndrome. TB was designed to counterbalance these effects. Methods: A total of 1020 obese patients with body mass index (BMI) ranging from 33 to 72 Kg/m(2) underwent SG and TB (SG + TB). TB creates a gastroileal anastomosis in the antrum after the SG; nutrient transit is maintained in the duodenum, avoiding blind loops and minimizing malabsorption. The stomach retains 2 outflow pathways. A lateral enteroanastomosis connects both segments at 80 cm proximal to the cecum. Results: Adequate follow-up data were collected in 59.1% of patients from 4 months to 5 years. The average percent of excess BMI loss was 91%, 94%, 85%, 78%, and 74% in the first, second, third, fourth, and fifth year, respectively. Patients experienced early satiety and major improvement in presurgical comorbidities, including diabetes (86% in remission), following surgery. Two deaths occurred (0.2%). Other surgical complications occurred in 6% of patients. Signs of malabsorption were rare. Conclusions: SG + TB is a simple procedure that results in rapid weight loss and remission or major improvement of comorbidities. Strictly aiming at physiological correction, TB avoids prostheses, narrow anastomoses, excluded segments, and malabsorption. Weight and comorbidities are much improved. Diabetes is improved without duodenal exclusion. TB is an excellent complement to an SG.

Damage detection in beams by using artificial neural networks and dynamical parameters

In this paper is presented a multilayer perceptron neural network combined with the Nelder-Mead Simplex method to detect damage in multiple support beams. The input parameters are based on natural frequencies and modal flexibility. It was considered that only a number of modes were available and that only vertical degrees of freedom were measured. The reliability of the proposed methodology is assessed from the generation of random damages scenarios and the definition of three types of errors, which can be found during the damage identification process. Results show that the methodology can reliably determine the damage scenarios. However, its application to large beams may be limited by the high computational cost of training the neural network.

Association of arterial events with the coexistence of metabolic syndrome and primary antiphospholipid syndrome

Objective Metabolic syndrome (MetS) is highly prevalent in rheumatic diseases and is recognized as a new independent cardiovascular risk factor. This study was undertaken to determine the clinical significance of MetS in patients with primary antiphospholipid syndrome (APS). Methods Seventy-one primary APS patients and 73 age- and sex-matched healthy controls were included. Serum samples were tested for lipid profile, Lp(a), glucose, insulin, thyroid-stimulating hormone, free T4, erythrocyte sedimentation rate, C-reactive protein level, and uric acid. MetS was defined by the International Diabetes Federation criteria, and insulin resistance was established using the homeostasis model assessment index. Results The prevalence of MetS was 33.8%, and further comparison between primary APS patients with and without MetS revealed that the former had a higher frequency of arterial events (79.2% versus 42.6%; P = 0.003), angina (29.2% versus 2.1%; P = 0.002), and positive lupus anticoagulant antibody (95.8% versus 76.6%; P = 0.049). In addition, primary APS patients with MetS, as expected, had a higher prevalence of cardiovascular risk factors. On multivariate analysis, only MetS was independently associated with arterial events in primary APS. Conclusion Coexistence of primary APS and MetS seems to identify a subgroup of patients with higher risk of arterial events, suggesting that MetS may aggravate existing endothelial abnormalities of primary APS.

Metabolic Disorders and Adipose Tissue Insulin Responsiveness in Neonatally STZ-Induced Diabetic Rats Are Improved by Long-Term Melatonin Treatment

Diabetes mellitus is a product of low insulin sensibility and pancreatic beta-cell insufficiency. Rats with streptozotocin-induced diabetes during the neonatal period by the fifth day of age develop the classic diabetic picture of hyperglycemia, hypoinsulinemia, polyuria, and polydipsia aggravated by insulin resistance in adulthood. In this study, we investigated whether the effect of long-term treatment with melatonin can improve insulin resistance and other metabolic disorders in these animals. At the fourth week of age, diabetic animals started an 8-wk treatment with melatonin (1 mg/kg body weight) in the drinking water at night. Animals were then killing, and the sc, epididymal (EP), and retroperitoneal (RP) fat pads were excised, weighed, and processed for adipocyte isolation for morphometric analysis as well as for measuring glucose uptake, oxidation, and incorporation of glucose into lipids. Blood samples were collected for biochemical assays. Melatonin treatment reduced hyperglycemia, polydipsia, and polyphagia as well as improved insulin resistance as demonstrated by constant glucose disappearance rate and homeostasis model of assessment-insulin resistance. However, melatonin treatment was unable to recover body weight deficiency, fat mass, and adipocyte size of diabetic animals. Adiponectin and fructosamine levels were completely recovered by melatonin, whereas neither plasma insulin level nor insulin secretion capacity was improved in diabetic animals. Furthermore, melatonin caused a marked delay in the sexual development, leaving genital structures smaller than those of nontreated diabetic animals. Melatonin treatment improved the responsiveness of adipocytes to insulin in diabetic animals measured by tests of glucose uptake (sc, EP, and RP), glucose oxidation, and incorporation of glucose into lipids (EP and RP), an effect that seems partially related to an increased expression of insulin receptor substrate 1, acetyl-coenzyme A carboxylase and fatty acid synthase. In conclusion, melatonin treatment was capable of ameliorating the metabolic abnormalities in this particular diabetes model, including insulin resistance and promoting a better long-term glycemic control. (Endocrinology 153: 2178-2188, 2012)

Experimental hyperprolinemia induces mild oxidative stress, metabolic changes, and tissue adaptation in rat liver

The present study investigated the effects of chronic hyperprolinemia on oxidative and metabolic status in liver and serum of rats. Wistar rats received daily subcutaneous injections of proline from their 6th to 28th day of life. Twelve hours after the last injection the rats were sacrificed and liver and serum were collected. Results showed that hyperprolinemia induced a significant reduction in total antioxidant potential and thiobarbituric acid-reactive substances. The activities of the antioxidant enzymes catalase and superoxide dismutase were significantly increased after chronic proline administration, while glutathione (GSH) peroxidase activity, dichlorofluorescin oxidation, GSH, sulfhydryl, and carbonyl content remained unaltered. Histological analyses of the liver revealed that proline treatment induced changes of the hepatic microarchitecture and increased the number of inflammatory cells and the glycogen content. Biochemical determination also demonstrated an increase in glycogen concentration, as well as a higher synthesis of glycogen in liver of hyperprolinemic rats. Regarding to hepatic metabolism, it was observed an increase on glucose oxidation and a decrease on lipid synthesis from glucose. However, hepatic lipid content and serum glucose levels were not changed. Proline administration did not alter the aminotransferases activities and serum markers of hepatic injury. Our findings suggest that hyperprolinemia alters the liver homeostasis possibly by induction of a mild degree of oxidative stress and metabolic changes. The hepatic alterations caused by proline probably do not implicate in substantial hepatic tissue damage, but rather demonstrate a process of adaptation of this tissue to oxidative stress. However, the biological significance of these findings requires additional investigation. J. Cell. Biochem. 113: 174183, 2012. (C) 2011 Wiley Periodicals, Inc.

Structure-semantics interplay in complex networks and its effects on the predictability of similarity in texts

The classification of texts has become a major endeavor with so much electronic material available, for it is an essential task in several applications, including search engines and information retrieval. There are different ways to define similarity for grouping similar texts into clusters, as the concept of similarity may depend on the purpose of the task. For instance, in topic extraction similar texts mean those within the same semantic field, whereas in author recognition stylistic features should be considered. In this study, we introduce ways to classify texts employing concepts of complex networks, which may be able to capture syntactic, semantic and even pragmatic features. The interplay between various metrics of the complex networks is analyzed with three applications, namely identification of machine translation (MT) systems, evaluation of quality of machine translated texts and authorship recognition. We shall show that topological features of the networks representing texts can enhance the ability to identify MT systems in particular cases. For evaluating the quality of MT texts, on the other hand, high correlation was obtained with methods capable of capturing the semantics. This was expected because the golden standards used are themselves based on word co-occurrence. Notwithstanding, the Katz similarity, which involves semantic and structure in the comparison of texts, achieved the highest correlation with the NIST measurement, indicating that in some cases the combination of both approaches can improve the ability to quantify quality in MT. In authorship recognition, again the topological features were relevant in some contexts, though for the books and authors analyzed good results were obtained with semantic features as well. Because hybrid approaches encompassing semantic and topological features have not been extensively used, we believe that the methodology proposed here may be useful to enhance text classification considerably, as it combines well-established strategies. (c) 2012 Elsevier B.V. All rights reserved.

Extractive summarization using complex networks and syntactic dependency

The realization that statistical physics methods can be applied to analyze written texts represented as complex networks has led to several developments in natural language processing, including automatic summarization and evaluation of machine translation. Most importantly, so far only a few metrics of complex networks have been used and therefore there is ample opportunity to enhance the statistics-based methods as new measures of network topology and dynamics are created. In this paper, we employ for the first time the metrics betweenness, vulnerability and diversity to analyze written texts in Brazilian Portuguese. Using strategies based on diversity metrics, a better performance in automatic summarization is achieved in comparison to previous work employing complex networks. With an optimized method the Rouge score (an automatic evaluation method used in summarization) was 0.5089, which is the best value ever achieved for an extractive summarizer with statistical methods based on complex networks for Brazilian Portuguese. Furthermore, the diversity metric can detect keywords with high precision, which is why we believe it is suitable to produce good summaries. It is also shown that incorporating linguistic knowledge through a syntactic parser does enhance the performance of the automatic summarizers, as expected, but the increase in the Rouge score is only minor. These results reinforce the suitability of complex network methods for improving automatic summarizers in particular, and treating text in general. (C) 2011 Elsevier B.V. All rights reserved.

Improvement of metabolic parameters and vascular function by metformin in obese non-diabetic rats

Aims: Metformin is an insulin sensitizing agent with beneficial effects in diabetic patients on glycemic levels and in the cardiovascular system. We examined whether the metabolic changes and the vascular dysfunction in monosodium glutamate-induced obese non-diabetic (MSG) rats might be improved by metformin. Main methods: 16 week-old MSG rats were treated with metformin for 15 days and compared with age-matched untreated MSG and non-obese non-diabetic rats (control). Blood pressure, insulin sensitivity, vascular reactivity and prostanoid release in the perfused mesenteric arteriolar bed as well as nitric oxide production and reactive oxygen species generation in isolated mesenteric arteries were analyzed. Key findings: 18-week-old MSG rats displayed higher Lee index, fat accumulation, dyslipidemia, insulin resistance and hyperinsulinemia. Metformin treatment improved these alterations. The norepinephrine-induced response, increased in the mesenteric arteriolar bed from MSG rats, was corrected by metformin. Indomethacin corrected the enhanced contractile response in MSG rats but did not affect metformin effects. The sensitivity to acetylcholine, reduced in MSG rats, was also corrected by metformin. Indomethacin corrected the reduced sensitivity to acetylcholine in MSG rats but did not affect metformin effects. The sensitivity to sodium nitroprusside was increased in preparations from metformin-treated rats. Metformin treatment restored both the reduced PGI2/TXA2 ratio and the increased reactive oxygen species generation in preparations from MSG rats. Significance: Metformin improved the vascular function in MSG rats through reduction in reactive oxygen species generation, modulation of membrane hyperpolarization. correction of the unbalanced prostanoids release and increase in the sensitivity of the smooth muscle to nitric oxide. (c) 2011 Elsevier Inc. All rights reserved.

Cardiometabolic benefits of exercise training in an experimental model of metabolic syndrome and menopause

Objective: The aim of this study was to investigate the cardiometabolic effects of exercise training in ovariectomized hypertensive rats both submitted and not submitted to fructose overload. Methods: Spontaneously hypertensive ovariectomized rats were divided into sedentary and trained (THO) groups submitted to normal chow and sedentary and trained groups submitted to fructose overload (100 g/L in drinking water for 19 wk). Exercise training was performed on a treadmill (8 wk). Arterial pressure (AP) was directly recorded. Cardiovascular autonomic control was evaluated through pharmacological blockade (atropine and propranolol) and in the time and frequency domains by spectral analysis. Results: The THO group presented reduced AP (approximately 16 mm Hg) and enhanced cardiac vagal tonus (approximately 49%) and baroreflex sensitivity (approximately 43%) compared with the sedentary hypertensive ovariectomized group. Exercise training attenuated metabolic impairment, resting tachycardia, cardiac and vascular sympathetic increases, and baroreflex sensitivity decrease induced by fructose overload in hypertensive rats. However, the trained hypertensive ovariectomized group submitted to fructose overload presented higher AP (approximately 32 mm Hg), associated with baroreflex sensitivity (approximately 69%) and parasympathetic dysfunctions compared with the THO group. Conclusions: These data suggest that the metabolic disorders in hypertensive rats after ovarian hormone deprivation could blunt and/or attenuate some exercise training benefits.

Three-feature model to reproduce the topology of citation networks and the effects from authors' visibility on their h-index

Various factors are believed to govern the selection of references in citation networks, but a precise, quantitative determination of their importance has remained elusive. In this paper, we show that three factors can account for the referencing pattern of citation networks for two topics, namely "graphenes" and "complex networks", thus allowing one to reproduce the topological features of the networks built with papers being the nodes and the edges established by citations. The most relevant factor was content similarity, while the other two - in-degree (i.e. citation counts) and age of publication - had varying importance depending on the topic studied. This dependence indicates that additional factors could play a role. Indeed, by intuition one should expect the reputation (or visibility) of authors and/or institutions to affect the referencing pattern, and this is only indirectly considered via the in-degree that should correlate with such reputation. Because information on reputation is not readily available, we simulated its effect on artificial citation networks considering two communities with distinct fitness (visibility) parameters. One community was assumed to have twice the fitness value of the other, which amounts to a double probability for a paper being cited. While the h-index for authors in the community with larger fitness evolved with time with slightly higher values than for the control network (no fitness considered), a drastic effect was noted for the community with smaller fitness. (C) 2012 Elsevier Ltd. All rights reserved.

Constraint-based analysis of gene interactions using restricted boolean networks and time-series data

Abstract Background A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and time-series data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it. Results We applied the proposed algorithm in two data sets. First, we used an artificial dataset obtained from a model for the budding yeast cell cycle. The second data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered. Conclusions The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from time-series data of gene expression, and this inference process can be aided by a priori knowledge available.

Metabolic, hemodynamic and structural adjustments to low intensity exercise training in a metabolic syndrome model

Background The increase in fructose consumption is paralleled by a higher incidence of metabolic syndrome, and consequently, cardiovascular disease mortality. We examined the effects of 8 weeks of low intensity exercise training (LET) on metabolic, hemodynamic, ventricular and vascular morphological changes induced by fructose drinking in male rats. Methods Male Wistar rats were divided into (n = 8 each) control (C), sedentary fructose (F) and ET fructose (FT) groups. Fructose-drinking rats received D-fructose (100 g/l). FT rats were assigned to a treadmill training protocol at low intensity (30% of maximal running speed) during 1 h/day, 5 days/week for 8 weeks. Measurements of triglyceride concentrations, white adipose tissue (WAT) and glycemia were carried out together with insulin tolerance test to evaluate metabolic profile. Arterial pressure (AP) signals were directly recorded. Baroreflex sensitivity (BS) was evaluated by the tachycardic and bradycardic responses. Right atria, left ventricle (LV) and ascending aorta were prepared to morphoquantitative analysis. Results LET reduced WAT (−37.7%), triglyceride levels (−33%), systolic AP (−6%), heart weight/body weight (−20.5%), LV (−36%) and aortic (−76%) collagen fibers, aortic intima-media thickness and circumferential wall tension in FT when compared to F rats. Additionally, FT group presented improve of BS, numerical density of atrial natriuretic peptide granules (+42%) and LV capillaries (+25%), as well as the number of elastic lamellae in aorta compared with F group. Conclusions Our data suggest that LET, a widely recommended practice, seems to be particularly effective for preventing metabolic, hemodynamic and morphological disorders triggered by MS.

Changes in food intake, metabolic parameters and insulin resistance are induced by an isoenergetic, medium-chain fatty acid diet and are associated with modifications in insulin signalling in isolated rat pancreatic islets

LLong-chain fatty acids are capable of inducing alterations in the homoeostasis of glucose-stimulated insulin secretion (GSIS), but the effect of medium-chain fatty acids (MCFA) is poorly elucidated. In the present study, we fed a normoenergetic MCFA diet to male rats from the age of 1 month to the age of 4 months in order to analyse the effect of MCFA on body growth, insulin sensitivity and GSIS. The 45% MCFA substitution of whole fatty acids in the normoenergetic diet impaired whole body growth and resulted in increased body adiposity and hyperinsulinaemia, and reduced insulin-mediated glucose uptake in skeletal muscle. In addition, the isolated pancreatic islets from the MCFA-fed rats showed impaired GSIS and reduced protein kinase Ba (AKT1) protein expression and extracellular signal-related kinase isoforms 1 and 2 (ERK(1/2)) phosphorylation, which were accompanied by increased cellular death. Furthermore, there was a mildly increased cholinergic sensitivity to GSIS. We discuss these findings in further detail, and advocate that they might have a role in the mechanistic pathway leading to the compensatory hyperinsulinaemic status found in this animal model.

Dissecting Phaseolus vulgaris Innate Immune System against Colletotrichum lindemuthianum Infection

Background: The genus Colletotrichum is one of the most economically important plant pathogens, causing anthracnose on a wide range of crops including common beans (Phaseolus vulgaris L.). Crop yield can be dramatically decreased depending on the plant cultivar used and the environmental conditions. This study aimed to identify potential genetic components of the bean immune system to provide environmentally friendly control measures against this fungus. Methodology and Principal Findings: As the common bean is not amenable to reverse genetics to explore functionality and its genome is not fully curated, we used putative Arabidopsis orthologs of bean expressed sequence tag (EST) to perform bioinformatic analysis and experimental validation of gene expression to identify common bean genes regulated during the incompatible interaction with C. lindemuthianum. Similar to model pathosystems, Gene Ontology (GO) analysis indicated that hormone biosynthesis and signaling in common beans seem to be modulated by fungus infection. For instance, cytokinin and ethylene responses were up-regulated and jasmonic acid, gibberellin, and abscisic acid responses were down-regulated, indicating that these hormones may play a central role in this pathosystem. Importantly, we have identified putative bean gene orthologs of Arabidopsis genes involved in the plant immune system. Based on experimental validation of gene expression, we propose that hypersensitive reaction as part of effector-triggered immunity may operate, at least in part, by down-regulating genes, such as FLS2-like and MKK5-like, putative orthologs of the Arabidopsis genes involved in pathogen perception and downstream signaling. Conclusions/Significance: We have identified specific bean genes and uncovered metabolic processes and pathways that may be involved in the immune response against pathogens. Our transcriptome database is a rich resource for mining novel defense-related genes, which enabled us to develop a model of the molecular components of the bean innate immune system regulated upon pathogen attack.