Neural signals of “Intensity” but not “Wanting” or “Liking” of rewards may be trait markers for depression

We have shown previously that particpants “at risk” of depression have decreased neural processing of reward suggesting this might be a neural biomarker for depression. However, how the neural signal related to subjective experiences of reward (wanting, liking, intensity) might differ as trait markers for depression, is as yet unknown. Using SPM8 parametric modulation analysis the neural signal related to the subjective report of wanting, liking and intensity was compared between 25 young people with a biological parent with depression (FH) and 25 age/gender matched controls. In a second study the neural signal related to the subjective report of wanting, liking and intensity was compared between 13 unmedicated recovered depressed (RD) patients and 14 healthy age/gender matched controls. The analysis revealed differences in the neural signal for wanting, liking and intensity ratings in the ventral striatum, dmPFC and caudate respectively in the RD group compared to controls . Despite no differences in the FH groups neural signal for wanting and liking there was a difference in the neural signal for intensity ratings in the dACC and anterior insula compared to controls. These results suggest that the neural substrates tracking the intensity but not the wanting or liking for rewards and punishers might be a trait marker for depression.

Contrasting phylogenetic signals and evolutionary rates in floral traits of Neotropical lianas

The diversity of floral forms has long been considered a prime example of radiation through natural selection. However, little is still known about the evolution of floral traits, a critical piece of evidence for the understanding of the processes that may have driven flower evolution. We studied the pattern of evolution of quantitative floral traits in a group of Neotropical lianas (Bignonieae, Bignoniaceae) and used a time-calibrated phylogeny as basis to: (1) test for phylogenetic signal in 16 continuous floral traits; (2) evaluate the rate of evolution in those traits; and (3) reconstruct the ancestral state of the individual traits. Variation in floral traits among extant species of Bignonieae was highly explained by their phylogenetic history. However, opposite signals were found in floral traits associated with the attraction of pollinators (calyx and corolla) and pollen transfer (androecium and gynoecium), suggesting a differential role of selection in different floral whorls. Phylogenetic independent contrasts indicate that traits evolved at different rates, whereas ancestral character state reconstructions indicate that the ancestral size of most flower traits was larger than the mean observed sizes of the same traits in extant species. The implications of these patterns for the reproductive biology of Bignonieae are discussed. (C) 2011 The Linnean Society of London, Biological Journal of the Linnean Society, 2011, 102, 378-390.

Cladistic analysis of continuous modularized traits provides phylogenetic signals in Homo evolution

Evolutionary novelties in the skeleton are usually expressed as changes in the timing of growth of features intrinsically integrated at different hierarchical levels of development(1). As a consequence, most of the shape- traits observed across species do vary quantitatively rather than qualitatively(2), in a multivariate space(3) and in a modularized way(4,5). Because most phylogenetic analyses normally use discrete, hypothetically independent characters(6), previous attempts have disregarded the phylogenetic signals potentially enclosed in the shape of morphological structures. When analysing low taxonomic levels, where most variation is quantitative in nature, solving basic requirements like the choice of characters and the capacity of using continuous, integrated traits is of crucial importance in recovering wider phylogenetic information. This is particularly relevant when analysing extinct lineages, where available data are limited to fossilized structures. Here we show that when continuous, multivariant and modularized characters are treated as such, cladistic analysis successfully solves relationships among main Homo taxa. Our attempt is based on a combination of cladistics, evolutionary- development- derived selection of characters, and geometric morphometrics methods. In contrast with previous cladistic analyses of hominid phylogeny, our method accounts for the quantitative nature of the traits, and respects their morphological integration patterns. Because complex phenotypes are observable across different taxonomic groups and are potentially informative about phylogenetic relationships, future analyses should point strongly to the incorporation of these types of trait.

Inhibitory Signals Mediated by Programmed Death-1 Are Involved With T-Cell Function in Chronic Periodontitis

Background: Inhibitory signals mediated via molecules such as programmed death-1 (PD-1) play a critical role in downmodulating immune responses and maintaining peripheral tolerance. We investigated the involvement of cytokines and PD-1 engagement in mediating the T-cell unresponsiveness to bacterial and ubiquitous antigens in periodontal diseases. Methods: Gingival and peripheral blood samples from healthy individuals and patients with chronic periodontitis were collected and used for the subsequent assays. Leukocytes in the lesion site and blood were evaluated using flow cytometry. The production of interferon-gamma, interleukin-10, and transforming growth factor-P proteins was evaluated by enzyme-linked immunosorbent assay (ELISA), and the presence of PD-1+cells in the inflamed gingiva was confirmed by immunofluorescence confocal microscopy for CD4 and PD-1 colocalization. Results: T cells from patients with chronic periodontitis proliferated poorly in response to Aggregatibacter actinomycetem comitans (previously Actinobacillus actinomycetemcomitans) antigen. T-cell unresponsiveness was not associated with imbalanced cytokine production. However, T cells from patients with chronic periodontitis expressed significantly higher levels of PD-1 either upon isolation or after culture with antigens. Moreover, PD-1 blocking did not result in significant T-cell proliferation in cells cultured with phytohemagglutinin or bacterial antigens. The blockade of PD-1 resulted in the increased production of IFN-gamma. In addition, CD4+ and CD8+ T cells expressing PD-1 accumulated in lesions with chronic periodontitis. Conclusion: These data show that PD-1 engagement could be involved in the modulation of IFN-gamma production by T cells in patients with chronic periodontitis. J Periodontol 2009,80:1833-1844.

Multiple Pathways Analysis of Brain Functional Networks from EEG Signals: An Application to Real Data

In the present study, we propose a theoretical graph procedure to investigate multiple pathways in brain functional networks. By taking into account all the possible paths consisting of h links between the nodes pairs of the network, we measured the global network redundancy R (h) as the number of parallel paths and the global network permeability P (h) as the probability to get connected. We used this procedure to investigate the structural and dynamical changes in the cortical networks estimated from a dataset of high-resolution EEG signals in a group of spinal cord injured (SCI) patients during the attempt of foot movement. In the light of a statistical contrast with a healthy population, the permeability index P (h) of the SCI networks increased significantly (P < 0.01) in the Theta frequency band (3-6 Hz) for distances h ranging from 2 to 4. On the contrary, no significant differences were found between the two populations for the redundancy index R (h) . The most significant changes in the brain functional network of SCI patients occurred mainly in the lower spectral contents. These changes were related to an improved propagation of communication between the closest cortical areas rather than to a different level of redundancy. This evidence strengthens the hypothesis of the need for a higher functional interaction among the closest ROIs as a mechanism to compensate the lack of feedback from the peripheral nerves to the sensomotor areas.

The Arabidopsis bZIP Gene AtbZIP63 Is a Sensitive Integrator of Transient Abscisic Acid and Glucose Signals

Glucose modulates plant metabolism, growth, and development. In Arabidopsis (Arabidopsis thaliana), Hexokinase1 (HXK1) is a glucose sensor that may trigger abscisic acid (ABA) synthesis and sensitivity to mediate glucose-induced inhibition of seedling development. Here, we show that the intensity of short-term responses to glucose can vary with ABA activity. We report that the transient (2 h/4 h) repression by 2% glucose of AtbZIP63, a gene encoding a basic-leucine zipper (bZIP) transcription factor partially involved in the Snf1-related kinase KIN10-induced responses to energy limitation, is independent of HXK1 and is not mediated by changes in ABA levels. However, high-concentration (6%) glucose-mediated repression appears to be modulated by ABA, since full repression of AtbZIP63 requires a functional ABA biosynthetic pathway. Furthermore, the combination of glucose and ABA was able to trigger a synergistic repression of AtbZIP63 and its homologue AtbZIP3, revealing a shared regulatory feature consisting of the modulation of glucose sensitivity by ABA. The synergistic regulation of AtbZIP63 was not reproduced by an AtbZIP63 promoter-5`-untranslated region:beta-glucuronidase fusion, thus suggesting possible posttranscriptional control. A transcriptional inhibition assay with cordycepin provided further evidence for the regulation of mRNA decay in response to glucose plus ABA. Overall, these results indicate that AtbZIP63 is an important node of the glucose-ABA interaction network. The mechanisms by which AtbZIP63 may participate in the fine-tuning of ABA-mediated abiotic stress responses according to sugar availability (i.e., energy status) are discussed.

Mitochondrial ATP-sensitive K(+) channels as redox signals to liver mitochondria in response to hypertriglyceridemia

We have recently demonstrated that hypertriglyceridemic (HTG) mice present both elevated body metabolic rates and mild mitochondrial uncoupling in the liver owing to stimulated activity of the ATP-sensitive potassium channel (mitoK(ATP)). Because lipid excess normally leads to cell redox imbalance, we examined the hepatic oxidative status in this model. Cell redox imbalance was evidenced by increased total levels of carbonylated proteins, malondialdehydes, and GSSG/GSH ratios in HTG livers compared to wild type. In addition, the activities of the extramitochondrial enzymes NADPH oxidase and xanthine oxidase were elevated in HTG livers. In contrast, Mn-superoxide dismutase activity and content, a mitochondrial matrix marker, were significantly decreased in HTG livers. isolated HTG liver mitochondria presented lower rates of H(2)O(2) production, which were reversed by mitoK(ATP) antagonists. In vivo antioxidant treatment with N-acetylcysteine decreased both mitoKATP activity and metabolic rates in HTG mice. These data indicate that high levels of triglycerides increase reactive oxygen generation by extramitochondrial enzymes that promote MitoK(ATP) activation. The mild uncoupling mediated by mitoK(ATP) increases metabolic rates and protects mitochondria against oxidative damage. Therefore, a biological role for mitoK(ATP) is a redox sensor is shown here for the first time in an in vivo model of systemic and cellular lipid excess, (C) 2009 Elsevier Inc. All rights reserved.

Metabotropic glutamate receptors transduce signals for neurite outgrowth after binding of the prion protein to laminin gamma 1 chain

The prion protein (PrP(C)) is highly expressed in the nervous system, and its abnormal conformer is associated with prion diseases. PrP(C) is anchored to cell membranes by glycosylphosphatidylinositol, and transmembrane proteins are likely required for PrP(C)-mediated intracellular signaling. Binding of laminin (Ln) to PrP(C) modulates neuronal plasticity and memory. We addressed signaling pathways triggered by PrP(C)-Ln interaction in order to identify transmembrane proteins involved in the transduction of PrP(C)-Ln signals. The Ln gamma 1-chain peptide, which contains the Ln binding site for PrP(C), induced neuritogenesis through activation of phospholipase C (PLC), Ca(2+) mobilization from intracellular stores, and protein kinase C and extracellular signal-regulated kinase (ERK1/2) activation in primary cultures of neurons from wild-type, but not PrP(C)-null mice. Phage display, coimmunoprecipitation, and colocalization experiments showed that group I metabotropic glutamate receptors (mGluR1/5) associate with PrP(C). Expression of either mGluR1 or mGluR5 in HEK293 cells reconstituted the signaling pathways mediated by PrP(C)-Ln gamma 1 peptide interaction. Specific inhibitors of these receptors impaired PrP(C)-Ln gamma 1 peptide-induced signaling and neuritogenesis. These data show that group I mGluRs are involved in the transduction of cellular signals triggered by PrP(C)-Ln, and they support the notion that PrP(C) participates in the assembly of multiprotein complexes with physiological functions on neurons.-Beraldo, F. H., Arantes, C. P., Santos, T. G., Machado, C. F., Roffe, M., Hajj, G. N., Lee, K. S., Magalhaes, A. C., Caetano, F. A., Mancini, G. L., Lopes, M. H., Americo, T. A., Magdesian, M. H., Ferguson, S. S. G., Linden, R., Prado, M. A. M., Martins, V. R. Metabotropic glutamate receptors trans-duce signals for neurite outgrowth after binding of the prion protein to laminin gamma 1 chain. FASEB J. 25, 265-279 (2011). www.fasebj.org

Solving the Vehicle Routing Problem with Genetic ALgorithm and Simulated Annealing

This Thesis Work will concentrate on a very interesting problem, the Vehicle Routing Problem (VRP). In this problem, customers or cities have to be visited and packages have to be transported to each of them, starting from a basis point on the map. The goal is to solve the transportation problem, to be able to deliver the packages-on time for the customers,-enough package for each Customer,-using the available resources- and – of course - to be so effective as it is possible.Although this problem seems to be very easy to solve with a small number of cities or customers, it is not. In this problem the algorithm have to face with several constraints, for example opening hours, package delivery times, truck capacities, etc. This makes this problem a so called Multi Constraint Optimization Problem (MCOP). What’s more, this problem is intractable with current amount of computational power which is available for most of us. As the number of customers grow, the calculations to be done grows exponential fast, because all constraints have to be solved for each customers and it should not be forgotten that the goal is to find a solution, what is best enough, before the time for the calculation is up. This problem is introduced in the first chapter: form its basics, the Traveling Salesman Problem, using some theoretical and mathematical background it is shown, why is it so hard to optimize this problem, and although it is so hard, and there is no best algorithm known for huge number of customers, why is it a worth to deal with it. Just think about a huge transportation company with ten thousands of trucks, millions of customers: how much money could be saved if we would know the optimal path for all our packages.Although there is no best algorithm is known for this kind of optimization problems, we are trying to give an acceptable solution for it in the second and third chapter, where two algorithms are described: the Genetic Algorithm and the Simulated Annealing. Both of them are based on obtaining the processes of nature and material science. These algorithms will hardly ever be able to find the best solution for the problem, but they are able to give a very good solution in special cases within acceptable calculation time.In these chapters (2nd and 3rd) the Genetic Algorithm and Simulated Annealing is described in details, from their basis in the “real world” through their terminology and finally the basic implementation of them. The work will put a stress on the limits of these algorithms, their advantages and disadvantages, and also the comparison of them to each other.Finally, after all of these theories are shown, a simulation will be executed on an artificial environment of the VRP, with both Simulated Annealing and Genetic Algorithm. They will both solve the same problem in the same environment and are going to be compared to each other. The environment and the implementation are also described here, so as the test results obtained.Finally the possible improvements of these algorithms are discussed, and the work will try to answer the “big” question, “Which algorithm is better?”, if this question even exists.

Nonstationary feature extraction techniques for automatic classification of impact acoustic signals

Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.

The Optimal trigger speed of vehicle activated signs

The thesis aims to elaborate on the optimum trigger speed for Vehicle Activated Signs (VAS) and to study the effectiveness of VAS trigger speed on drivers’ behaviour. Vehicle activated signs (VAS) are speed warning signs that are activated by individual vehicle when the driver exceeds a speed threshold. The threshold, which triggers the VAS, is commonly based on a driver speed, and accordingly, is called a trigger speed. At present, the trigger speed activating the VAS is usually set to a constant value and does not consider the fact that an optimal trigger speed might exist. The optimal trigger speed significantly impacts driver behaviour. In order to be able to fulfil the aims of this thesis, systematic vehicle speed data were collected from field experiments that utilized Doppler radar. Further calibration methods for the radar used in the experiment have been developed and evaluated to provide accurate data for the experiment. The calibration method was bidirectional; consisting of data cleaning and data reconstruction. The data cleaning calibration had a superior performance than the calibration based on the reconstructed data. To study the effectiveness of trigger speed on driver behaviour, the collected data were analysed by both descriptive and inferential statistics. Both descriptive and inferential statistics showed that the change in trigger speed had an effect on vehicle mean speed and on vehicle standard deviation of the mean speed. When the trigger speed was set near the speed limit, the standard deviation was high. Therefore, the choice of trigger speed cannot be based solely on the speed limit at the proposed VAS location. The optimal trigger speeds for VAS were not considered in previous studies. As well, the relationship between the trigger value and its consequences under different conditions were not clearly stated. The finding from this thesis is that the optimal trigger speed should be primarily based on lowering the standard deviation rather than lowering the mean speed of vehicles. Furthermore, the optimal trigger speed should be set near the 85th percentile speed, with the goal of lowering the standard deviation.

Speed prediction for triggering vehicle activated signs

Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.

A data driven approach for automating vehicle activated signs

Vehicle activated signs (VAS) display a warning message when drivers exceed a particular threshold. VAS are often installed on local roads to display a warning message depending on the speed of the approaching vehicles. VAS are usually powered by electricity; however, battery and solar powered VAS are also commonplace. This thesis investigated devel-opment of an automatic trigger speed of vehicle activated signs in order to influence driver behaviour, the effect of which has been measured in terms of reduced mean speed and low standard deviation. A comprehen-sive understanding of the effectiveness of the trigger speed of the VAS on driver behaviour was established by systematically collecting data. Specif-ically, data on time of day, speed, length and direction of the vehicle have been collected for the purpose, using Doppler radar installed at the road. A data driven calibration method for the radar used in the experiment has also been developed and evaluated. Results indicate that trigger speed of the VAS had variable effect on driv-ers’ speed at different sites and at different times of the day. It is evident that the optimal trigger speed should be set near the 85th percentile speed, to be able to lower the standard deviation. In the case of battery and solar powered VAS, trigger speeds between the 50th and 85th per-centile offered the best compromise between safety and power consump-tion. Results also indicate that different classes of vehicles report differ-ences in mean speed and standard deviation; on a highway, the mean speed of cars differs slightly from the mean speed of trucks, whereas a significant difference was observed between the classes of vehicles on lo-cal roads. A differential trigger speed was therefore investigated for the sake of completion. A data driven approach using Random forest was found to be appropriate in predicting trigger speeds respective to types of vehicles and traffic conditions. The fact that the predicted trigger speed was found to be consistently around the 85th percentile speed justifies the choice of the automatic model.