Effect of diet composition and ration size on key enzyme activities of glycolysis-gluconeogenesis, pentose phosphate pathway and amino acid metabolism in liver of Sparus aurata

The effects of diet composition and ration size on the activities of key enzymes involved in intermediary metabolism were studied in the liver of gilthead sea bream (Sparus aurata). Highcarbohydrate, low-protein diets stimulated 6-phosphofructo 1-kinase (EC 2.7.1.11), pyruvate kinase (EC 2.7.1.40), glucose-6-phosphate dehydrogenase (EC 1.1.1.49) and 6-phosphogluconate dehydrogenase (EC 1.1.1.44) enzyme activities, while they decreased alanine aminotransferase (EC 2.6.1.2) activity. A high degree of correlation was found between food ration size and the activity of the enzymes 6-phosphofructo 1-kinase, pyruvate kinase, glucose-6-phosphate dehydrogenase (positive correlations) and fructose-1,6-bisphosphatase (EC 3.1.3.11) (negative correlation). These correlations matched well with the high correlation also found between ration size and growth rate in starved fish refed for 22 d. Limited feeding (5 g/kg body weight) for 22 d decreased the activities of the key enzymes for glycolysis and lipogenesis, and alanine aminotransferase activity. The findings presented here indicate a high level of metabolic adaptation to both diet type and ration size. In particular, adaptation of enzyme activities to the consumption of a diet with a high carbohydrate level suggests that a carnivorous fish like Sparus aurata can tolerate partial replacement of protein by carbohydrate in the commercial diets supplied in culture. The relationship between enzyme activities, ration size and fish growth indicates that the enzymes quickly respond to dietary manipulations of cultured fish.

Shout and Act: an Algorithm for Digital Objects Preservation Inspired from Rescue Robots

We adapt the Shout and Act algorithm to Digital Objects Preservation where agents explore file systems looking for digital objects to be preserved (victims). When they find something they “shout” so that agent mates can hear it. The louder the shout, the urgent or most important the finding is. Louder shouts can also refer to closeness. We perform several experiments to show that this system works very scalably, showing that heterogeneous teams of agents outperform homogeneous ones over a wide range of tasks complexity. The target at-risk documents are MS Office documents (including an RTF file) with Excel content or in Excel format. Thus, an interesting conclusion from the experiments is that fewer heterogeneous (varying skills) agents can equal the performance of many homogeneous (combined super-skilled) agents, implying significant performance increases with lower overall cost growth. Our results impact the design of Digital Objects Preservation teams: a properly designed combination of heterogeneous teams is cheaper and more scalable when confronted with uncertain maps of digital objects that need to be preserved. A cost pyramid is proposed for engineers to use for modeling the most effective agent combinations

An energy efficient vertical handover decision algorithm

As wireless communications evolve towards heterogeneousnetworks, mobile terminals have been enabled tohandover seamlessly from one network to another. At the sametime, the continuous increase in the terminal power consumptionhas resulted in an ever-decreasing battery lifetime. To that end,the network selection is expected to play a key role on howto minimize the energy consumption, and thus to extend theterminal lifetime. Hitherto, terminals select the network thatprovides the highest received power. However, it has been provedthat this solution does not provide the highest energy efficiency.Thus, this paper proposes an energy efficient vertical handoveralgorithm that selects the most energy efficient network thatminimizes the uplink power consumption. The performance of theproposed algorithm is evaluated through extensive simulationsand it is shown to achieve high energy efficiency gains comparedto the conventional approach.

A concurrent region growing algorithm guided by circumscribed contours

Image segmentation of natural scenes constitutes a major problem in machine vision. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. This approach begins by detecting the main contours of the scene which are later used to guide a concurrent set of growing processes. A previous analysis of the seed pixels permits adjustment of the homogeneity criterion to the region's characteristics during the growing process. Since the high variability of regions representing outdoor scenes makes the classical homogeneity criteria useless, a new homogeneity criterion based on clustering analysis and convex hull construction is proposed. Experimental results have proven the reliability of the proposed approach

Proposal of a parallel architecture for a motion detection algorithm

This paper proposes a parallel architecture for estimation of the motion of an underwater robot. It is well known that image processing requires a huge amount of computation, mainly at low-level processing where the algorithms are dealing with a great number of data. In a motion estimation algorithm, correspondences between two images have to be solved at the low level. In the underwater imaging, normalised correlation can be a solution in the presence of non-uniform illumination. Due to its regular processing scheme, parallel implementation of the correspondence problem can be an adequate approach to reduce the computation time. Taking into consideration the complexity of the normalised correlation criteria, a new approach using parallel organisation of every processor from the architecture is proposed

Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar

This paper proposes a pose-based algorithm to solve the full SLAM problem for an autonomous underwater vehicle (AUV), navigating in an unknown and possibly unstructured environment. The technique incorporate probabilistic scan matching with range scans gathered from a mechanical scanning imaging sonar (MSIS) and the robot dead-reckoning displacements estimated from a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method utilizes two extended Kalman filters (EKF). The first, estimates the local path travelled by the robot while grabbing the scan as well as its uncertainty and provides position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augment state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach

Fast calculation of the CAC convolution algorithm using the multinomial distribution function

The authors focus on one of the methods for connection acceptance control (CAC) in an ATM network: the convolution approach. With the aim of reducing the cost in terms of calculation and storage requirements, they propose the use of the multinomial distribution function. This permits direct computation of the associated probabilities of the instantaneous bandwidth requirements. This in turn makes possible a simple deconvolution process. Moreover, under certain conditions additional improvements may be achieved

A Label space reduction algorithm for P2MP LSPs using asymmetric tunnels

The aim of traffic engineering is to optimise network resource utilization. Although several works on minimizing network resource utilization have been published, few works have focused on LSR label space. This paper proposes an algorithm that uses MPLS label stack features in order to reduce the number of labels used in LSPs forwarding. Some tunnelling methods and their MPLS implementation drawbacks are also discussed. The algorithm described sets up the NHLFE tables in each LSR, creating asymmetric tunnels when possible. Experimental results show that the algorithm achieves a large reduction factor in the label space. The work presented here applies for both types of connections: P2MP and P2P

Speculative parallelization of multipath radiosity algorithm

In computer graphics, global illumination algorithms take into account not only the light that comes directly from the sources, but also the light interreflections. This kind of algorithms produce very realistic images, but at a high computational cost, especially when dealing with complex environments. Parallel computation has been successfully applied to such algorithms in order to make it possible to compute highly-realistic images in a reasonable time. We introduce here a speculation-based parallel solution for a global illumination algorithm in the context of radiosity, in which we have taken advantage of the hierarchical nature of such an algorithm

Role of phosphatidylinositol 3-kinase pathway (PI3K/Akt) in humans

The signaling pathway of phosphatidylinositol 3-kinase (PI3K) is critical in many aspects of growth and cell survival. The path of PI3K is stimulated physiologically as a result of many growth factors and regulatory factors. Several genetic alterations such as amplification, mutation and chromosomal arrangements may compromise the PI3K pathway, generating permanent activation in different cancer types have found evidence of these deleterious genetic modifications. Abnormal activation of the PI3K pathway results in alteration of the control mechanisms of growth and cell survival, which favors the competitive growth, and frequently metastatic capacity, greater resistance to treatment. The aim of this paper is to review matters relating to the operation of the PI3K/Akt signaling pathway and its role in the process of carcinogenesis in humans.

A Monte Carlo-Based Fiber Tracking Algorithm using Diffusion Tensor MRI

Diffusion tensor magnetic resonance imaging, which measures directional information of water diffusion in the brain, has emerged as a powerful tool for human brain studies. In this paper, we introduce a new Monte Carlo-based fiber tracking approach to estimate brain connectivity. One of the main characteristics of this approach is that all parameters of the algorithm are automatically determined at each point using the entropy of the eigenvalues of the diffusion tensor. Experimental results show the good performance of the proposed approach

Characterization of cytotoxic ribonucleases: from the internalization pathway to the importance of dimeric structures

En aquesta tesi s'ha caracteritzat la ruta d'internalització de l'onconasa, una RNasa citotòxica. Els resultats indiquen que l'onconasa entra a les cèl·lules per la via dependent de clatrina i del complex AP-2. Seguidament es dirigeix als endosomes de reciclatge i es a través d'aquesta ruta que la proteïna exerceix la citotoxicitat. Per altra banda, els resultats d'aquest treball demostren que PE5, una variant citotòxica de la ribonucleasa pancreàtica humana (HP-RNasa), interacciona amb la importina  mitjançant diferents residus que tot i que no són seqüencials, es troben propers en l'estructura tridimensional d'aquesta proteïna. PM8 és una HP-RNasa amb estructura cristal·logràfica dimèrica constituïda per intercanvi de dominis N-terminals. En aquesta tesi s'han establert les condicions per estabilitzar aquest dimer en solució i també es proposa un mecanisme per la dimerització.

A role for hippocampal PSA-NCAM and NMDA-NR2B receptor function in flavonoid-induced spatial memory improvements in young rats

The increase in incidence and prevalence of neurodegenerative diseases highlights the need for a more comprehensive understanding of how food components may affect neural systems. In particular, flavonoids have been recognized as promising agents capable of influencing different aspects of synaptic plasticity resulting in improvements in memory and learning in both animals and humans. Our previous studies highlight the efficacy of flavonoids in reversing memory impairments in aged rats, yet little is known about the effects of these compounds in healthy animals, particularly with respect to the molecular mechanisms by which flavonoids might alter the underlying synaptic modifications responsible for behavioral changes. We demonstrate that a 3-week intervention with two dietary doses of flavonoids (Dose I: 8.7 mg/day and Dose II: 17.4 mg/day) facilitates spatial memory acquisition and consolidation (24 recall) (p < 0.05) in young healthy rats. We show for the first time that these behavioral improvements are linked to increased levels in the polysialylated form of the neural adhesion molecule (PSA-NCAM) in the dentate gyrus (DG) of the hippocampus, which is known to be required for the establishment of durable memories. We observed parallel increases in hippocampal NMDA receptors containing the NR2B subunit for both 8.7 mg/day (p < 0.05) and 17.4 mg/day (p < 0.001) doses, suggesting an enhancement of glutamate signaling following flavonoid intervention. This is further strengthened by the simultaneous modulation of hippocampal ERK/CREB/BDNF signaling and the activation of the Akt/mTOR/Arc pathway, which are crucial in inducing changes in the strength of hippocampal synaptic connections that underlie learning. Collectively, the present data supports a new role for PSA-NCAM and NMDA-NR2B receptor on flavonoid-induced improvements in learning and memory, contributing further to the growing body of evidence suggesting beneficial effects of flavonoids in cognition and brain health.

Signal Processing and patternrecognition algorithm for monitoringParkinson’s disease.

This masters thesis describes the development of signal processing and patternrecognition in monitoring Parkison’s disease. It involves the development of a signalprocess algorithm and passing it into a pattern recogniton algorithm also. Thesealgorithms are used to determine , predict and make a conclusion on the study ofparkison’s disease. We get to understand the nature of how the parkinson’s disease isin humans.

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.