Cyclic nucleotide specific phosphodiesterases of Leishmania major

Background Leishmania represent a complex of important human pathogens that belong to the systematic order of the kinetoplastida. They are transmitted between their human and mammalian hosts by different bloodsucking sandfly vectors. In their hosts, the Leishmania undergo several differentiation steps, and their coordination and optimization crucially depend on numerous interactions between the parasites and the physiological environment presented by the fly and human hosts. Little is still known about the signalling networks involved in these functions. In an attempt to better understand the role of cyclic nucleotide signalling in Leishmania differentiation and host-parasite interaction, we here present an initial study on the cyclic nucleotide-specific phosphodiesterases of Leishmania major. Results This paper presents the identification of three class I cyclic-nucleotide-specific phosphodiesterases (PDEs) from L. major, PDEs whose catalytic domains exhibit considerable sequence conservation with, among other, all eleven human PDE families. In contrast to other protozoa such as Dictyostelium, or fungi such as Saccharomyces cerevisiae, Candida ssp or Neurospora, no genes for class II PDEs were found in the Leishmania genomes. LmjPDEA contains a class I catalytic domain at the C-terminus of the polypeptide, with no other discernible functional domains elsewhere. LmjPDEB1 and LmjPDEB2 are coded for by closely related, tandemly linked genes on chromosome 15. Both PDEs contain two GAF domains in their N-terminal region, and their almost identical catalytic domains are located at the C-terminus of the polypeptide. LmjPDEA, LmjPDEB1 and LmjPDEB2 were further characterized by functional complementation in a PDE-deficient S. cerevisiae strain. All three enzymes conferred complementation, demonstrating that all three can hydrolyze cAMP. Recombinant LmjPDEB1 and LmjPDEB2 were shown to be cAMP-specific, with Km values in the low micromolar range. Several PDE inhibitors were found to be active against these PDEs in vitro, and to inhibit cell proliferation. Conclusion The genome of L. major contains only PDE genes that are predicted to code for class I PDEs, and none for class II PDEs. This is more similar to what is found in higher eukaryotes than it is to the situation in Dictyostelium or the fungi that concomitantly express class I and class II PDEs. Functional complementation demonstrated that LmjPDEA, LmjPDEB1 and LmjPDEB2 are capable of hydrolyzing cAMP. In vitro studies with recombinant LmjPDEB1 and LmjPDEB2 confirmed this, and they demonstrated that both are completely cAMP-specific. Both enzymes are inhibited by several commercially available PDE inhibitors. The observation that these inhibitors also interfere with cell growth in culture indicates that inhibition of the PDEs is fatal for the cell, suggesting an important role of cAMP signalling for the maintenance of cellular integrity and proliferation.

Optimal game theoretic distributed control methodologies in small scale power systems

This dissertation presents the competitive control methodologies for small-scale power system (SSPS). A SSPS is a collection of sources and loads that shares a common network which can be isolated during terrestrial disturbances. Micro-grids, naval ship electric power systems (NSEPS), aircraft power systems and telecommunication system power systems are typical examples of SSPS. The analysis and development of control systems for small-scale power systems (SSPS) lacks a defined slack bus. In addition, a change of a load or source will influence the real time system parameters of the system. Therefore, the control system should provide the required flexibility, to ensure operation as a single aggregated system. In most of the cases of a SSPS the sources and loads must be equipped with power electronic interfaces which can be modeled as a dynamic controllable quantity. The mathematical formulation of the micro-grid is carried out with the help of game theory, optimal control and fundamental theory of electrical power systems. Then the micro-grid can be viewed as a dynamical multi-objective optimization problem with nonlinear objectives and variables. Basically detailed analysis was done with optimal solutions with regards to start up transient modeling, bus selection modeling and level of communication within the micro-grids. In each approach a detail mathematical model is formed to observe the system response. The differential game theoretic approach was also used for modeling and optimization of startup transients. The startup transient controller was implemented with open loop, PI and feedback control methodologies. Then the hardware implementation was carried out to validate the theoretical results. The proposed game theoretic controller shows higher performances over traditional the PI controller during startup. In addition, the optimal transient surface is necessary while implementing the feedback controller for startup transient. Further, the experimental results are in agreement with the theoretical simulation. The bus selection and team communication was modeled with discrete and continuous game theory models. Although players have multiple choices, this controller is capable of choosing the optimum bus. Next the team communication structures are able to optimize the players’ Nash equilibrium point. All mathematical models are based on the local information of the load or source. As a result, these models are the keys to developing accurate distributed controllers.

DISTRIBUTED APPROACH FOR SOLVING OPTIMAL POWER FLOW PROBLEMS IN THREE-PHASE UNBALANCED DISTRIBUTION NETWORK

The objective of this report is to study distributed (decentralized) three phase optimal power flow (OPF) problem in unbalanced power distribution networks. A full three phase representation of the distribution networks is considered to account for the highly unbalance state of the distribution networks. All distribution network’s series/shunt components, and load types/combinations had been modeled on commercial version of General Algebraic Modeling System (GAMS), the high-level modeling system for mathematical programming and optimization. The OPF problem has been successfully implemented and solved in a centralized approach and distributed approach, where the objective is to minimize the active power losses in the entire system. The study was implemented on the IEEE-37 Node Test Feeder. A detailed discussion of all problem sides and aspects starting from the basics has been provided in this study. Full simulation results have been provided at the end of the report.

PyGirl Generating Whole-System VMs from high-level models using PyPy

Virtual machines emulating hardware devices are generally implemented in low-level languages and using a low-level style for performance reasons. This trend results in largely difficult to understand, difficult to extend and unmaintainable systems. As new general techniques for virtual machines arise, it gets harder to incorporate or test these techniques because of early design and optimization decisions. In this paper we show how such decisions can be postponed to later phases by separating virtual machine implementation issues from the high-level machine-specific model. We construct compact models of whole-system VMs in a high-level language, which exclude all low-level implementation details. We use the pluggable translation toolchain PyPy to translate those models to executables. During the translation process, the toolchain reintroduces the VM implementation and optimization details for specific target platforms. As a case study we implement an executable model of a hardware gaming device. We show that our approach to VM building increases understandability, maintainability and extendability while preserving performance.

Editorial: Well Documented Articles Achieve More Impact

This issue offers for the first time supplementary material to articles. It consists of Excel files providing data the research is based on, calculation sheets, and optimization tools. Moreover, text files with pseudo code of algorithms and data of instances used for testing these algorithms as well as a video file with a demonstration of the use of a system in experiments are supplied for download. This material facilitates the understanding of the published articles and the replication of their findings to the benefit of scientific progress. And last but not least, such well documented articles will achieve much more impact.

Simulationsgestütztes Design eines Spritzgieß-Werkzeugeinsatzes mit kontur-angepasster Kühlung

Moderne generische Fertigungsverfahren für innengekühlte Werkzeuge bieten nahezu beliebige Freiheitsgrade zur Gestaltung konturnaher Kühlkanäle. Daraus resultiert ein erhöhter Anspruch an das Werkzeugengineering und die Optimierung der Kühlleistung. Geeignete Simulationsverfahren (wie z.B. Computational Fluid Dynamics - CFD) unterstützen die optimierte Werkzeugauslegung in idealer Weise. Mit der Erstellung virtueller Teststände können Varianten effizient und kostengünstig verglichen und die Kosten für Prototypen und Nacharbeiten reduziert werden. Im Computermodell des Werkzeugs erlauben Soft-Sensoren an beliebiger Position die Überwachung temperatur-kritischer Stellen sowohl im Fluid- als auch im Solidbereich. Der hier durchgeführte Benchmark vergleicht die Performance eines optimierten Werkzeugeinsatzes mit einer konventionellen Kühlung. Die im virtuellen Prozess vorhergesagte Zykluszeitreduzierung steht in guter Übereinstimmung mit realen Experimenten an den ausgeführten Werkzeugen.

Systemvergleich zwischen magnetisch erregten und piezoerregten Schwingförderern

Energieeffizienz und Leistungsfähigkeit von fördertechnischen Anlagen sind in den vergangenen Jahren immer mehr in den Fokus der Betreiber und Hersteller gerückt. Im Rahmen einer Untersuchung am Fraunhofer-Institut für Materialfluss und Logistik IML wurden diese beiden Aspekte an magnetisch erregten und piezoerregten Schwingförderern untersucht. Ein Systemvergleich stellt dabei die wesentlichen Vor- und Nachteile der jeweiligen Antriebsart dar und unterstützt sowohl bei der Auswahl als auch bei der Optimierung von Transportsystemen im logistischen Umfeld.

Leistungsbewertung und -optimierung in der manuellen Kommissionierung

In der Auftragszusammenstellung spielt der Mensch u. a. aufgrund seiner hohen Flexibilität bezüglich der Aufgabeneinteilung und der Fähigkeit, unterschiedlich dimensionierte Objekte leicht handzuhaben eine wesentliche Rolle. Um die Leistungen der Mitarbeiter individuell beurteilen zu können, ist die Berücksichtigung heterogener Arbeitsinhalte notwendig. Beispielsweise ist anzunehmen, dass Kommissionieraufträge mit relativ schweren Artikeln mehr Zeit in Anspruch nehmen als Aufträge mit leichteren Artikeln. Ebenso nimmt der Kommissionieraufwand zu, wenn mehr Auftragspositionen anzufahren sind und wenn größere Distanzen zurückgelegt werden müssen. Durch die Quantifizierung solcher Leistungseinflussfaktoren auf Mitarbeiterebene können individuelle Leistungsprofile erstellt werden, aus denen ganzheitliche Leistungsbeurteilungen sowie Optimierungen in der Personaleinsatzplanung und Kommissionierauftragssteuerung hervorgehen.

Subacute neural stem cell therapy for traumatic brain injury.

INTRODUCTION: Traumatic brain injury (TBI) frequently results in devastating and prolonged morbidity. Cellular therapy is a burgeoning field of experimental treatment that has shown promise in the management of many diseases, including TBI. Previous work suggests that certain stem and progenitor cell populations migrate to sites of inflammation and improve functional outcome in rodents after neural injury. Unfortunately, recent study has revealed potential limitations of acute and intravenous stem cell therapy. We studied subacute, direct intracerebral neural stem and progenitor cell (NSC) therapy for TBI. MATERIALS AND METHODS: The NSCs were characterized by flow cytometry and placed (400,000 cells in 50 muL 1x phosphate-buffered saline) into and around the direct injury area, using stereotactic guidance, of female Sprague Dawley rats 1 wk after undergoing a controlled cortical impact injury. Immunohistochemistry was used to identify cells located in the brain at 48 h and 2 wk after administration. Motor function was assessed using the neurological severity score, foot fault, rotarod, and beam balance. Cognitive function was assessed using the Morris water maze learning paradigm. Repeated measures analysis of variance with post-hoc analysis were used to determine significance at P < 0.05. RESULTS: Immunohistochemistry analysis revealed that 1.4-1.9% of infused cells remained in the neural tissue at 48 h and 2 wk post placement. Nearly all cells were located along injection tracks at 48 h. At 2 wk some cell dispersion was apparent. Rotarod motor testing revealed significant increases in maximal speed among NSC-treated rats compared with saline controls at d 4 (36.4 versus 27.1 rpm, P < 0.05) and 5 (35.8 versus 28.9 rpm, P < 0.05). All other motor and cognitive evaluations were not significantly different compared to controls. CONCLUSIONS: Placement of NSCs led to the cells incorporating and remaining in the tissues 2 wk after placement. Motor function tests revealed improvements in the ability to run on a rotating rod; however, other motor and cognitive functions were not significantly improved by NSC therapy. Further examination of a dose response and optimization of placement strategy may improve long-term cell survival and maximize functional recovery.

A Food Recognition System for Diabetic Patients based on an Optimized Bag of Features Model

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the Bag of Features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5,000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10,000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.

Adaptive Algorithms for Personalized Diabetes Treatment

Dynamic systems, especially in real-life applications, are often determined by inter-/intra-variability, uncertainties and time-varying components. Physiological systems are probably the most representative example in which population variability, vital signal measurement noise and uncertain dynamics render their explicit representation and optimization a rather difficult task. Systems characterized by such challenges often require the use of adaptive algorithmic solutions able to perform an iterative structural and/or parametrical update process towards optimized behavior. Adaptive optimization presents the advantages of (i) individualization through learning of basic system characteristics, (ii) ability to follow time-varying dynamics and (iii) low computational cost. In this chapter, the use of online adaptive algorithms is investigated in two basic research areas related to diabetes management: (i) real-time glucose regulation and (ii) real-time prediction of hypo-/hyperglycemia. The applicability of these methods is illustrated through the design and development of an adaptive glucose control algorithm based on reinforcement learning and optimal control and an adaptive, personalized early-warning system for the recognition and alarm generation against hypo- and hyperglycemic events.

Functional error modeling for uncertainty quantification in hydrogeology

Approximate models (proxies) can be employed to reduce the computational costs of estimating uncertainty. The price to pay is that the approximations introduced by the proxy model can lead to a biased estimation. To avoid this problem and ensure a reliable uncertainty quantification, we propose to combine functional data analysis and machine learning to build error models that allow us to obtain an accurate prediction of the exact response without solving the exact model for all realizations. We build the relationship between proxy and exact model on a learning set of geostatistical realizations for which both exact and approximate solvers are run. Functional principal components analysis (FPCA) is used to investigate the variability in the two sets of curves and reduce the dimensionality of the problem while maximizing the retained information. Once obtained, the error model can be used to predict the exact response of any realization on the basis of the sole proxy response. This methodology is purpose-oriented as the error model is constructed directly for the quantity of interest, rather than for the state of the system. Also, the dimensionality reduction performed by FPCA allows a diagnostic of the quality of the error model to assess the informativeness of the learning set and the fidelity of the proxy to the exact model. The possibility of obtaining a prediction of the exact response for any newly generated realization suggests that the methodology can be effectively used beyond the context of uncertainty quantification, in particular for Bayesian inference and optimization.

A Tutorial of the Mobile Multimedia Wireless Sensor Network (M3WSN) OMNeT++ Framework

In this work, we will give a detailed tutorial instruction about how to use the Mobile Multi-Media Wireless Sensor Networks (M3WSN) simulation framework. The M3WSN framework has been published as a scientific paper in the 6th International Workshop on OMNeT++ (2013) [1]. M3WSN framework enables the multimedia transmission of real video se- quence. Therefore, a set of multimedia algorithms, protocols, and services can be evaluated by using QoE metrics. Moreover, key video-related information, such as frame types, GoP length and intra-frame dependency can be used for creating new assessment and optimization solutions. To support mobility, M3WSN utilizes different mobility traces to enable the understanding of how the network behaves under mobile situations. This tutorial will cover how to install and configure the M3WSN framework, setting and running the experiments, creating mobility and video traces, and how to evaluate the performance of different protocols. The tutorial will be given in an environment of Ubuntu 12.04 LTS and OMNeT++ 4.2.

Fast magnetic resonance temperature imaging for focused ultrasound thermal therapy

The current standard for temperature sensitive imaging using magnetic resonance (MR) is 2-D, spoiled, fast gradient-echo (fGRE) phase-difference imaging exploiting temperature dependent changes in the proton resonance frequency (PRF). The echo-time (TE) for optimal sensitivity is larger than the typical repetition time (TR) of an fGRE sequence. Since TE must be less than TR in the fGRE sequence, this limits the technique's achievable sensitivity, spatial, and temporal resolution. This adversely affects both accuracy and volume coverage of the measurements. Accurate measurement of the rapid temperature changes associated with pulsed thermal therapies, such as high-intensity focused ultrasound (FUS), at optimal temperature sensitivity requires faster acquisition times than those currently available. ^ Use of fast MR acquisition strategies, such as interleaved echo-planar and spiral imaging, can provide the necessary increase in temporal performance and sensitivity while maintaining adequate signal-to-noise and in-plane spatial resolution. This research explored the adaptation and optimization of several fast MR acquisition methods for thermal monitoring of pulsed FUS thermal therapy. Temperature sensitivity, phase-difference noise and phase-difference to phase-difference-to noise ratio for the different pulse sequences were evaluated under varying imaging parameters in an agar gel phantom to establish optimal sequence parameters for temperature monitoring. The temperature sensitivity coefficient of the gel phantom was measured, allowing quantitative temperature extrapolations. ^ Optimized fast sequences were compared based on the ability to accurately monitor temperature changes at the focus of a high-intensity focused ultrasound unit, volume coverage, and contrast-to-noise ratio in the temperature maps. Operating parameters, which minimize complex phase-difference measurement errors introduced by use of the fast-imaging methods, were established. ^

Mapa bioclimático para las travesías de Mendoza (Argentina) basado en la fenología foliar

El objetivo fue generar un mapa bioclimático de la llanura de Mendoza que reflejara las diferencias climáticas expresadas por la actividad de la vegetación (fenología foliar) a escala regional. Se partió de la imagen digital del índice bioclimático de aridez P/ETP, generada en una etapa anterior a partir de una serie temporal de imágenes de índice verde (IVDN), y se recodificó en clases bioclimáticas. Se evaluó en cada clase la influencia antrópica y edáfica sobre las condiciones climáticas de aridez reflejadas por la vegetación. Se graficó la marcha fenológica anual media para cada bioclima a partir de una reconstrucción del IVDN. Las clases de clima húmedo y subhúmedo son de carácter edáfico debido al riego (oasis). Se proponen las clases: subdesértico (8,4%), árido inferior (15,3%), árido superior (24,2 %), semiárido inferior (25%) y semiárido superior (27,1%). Cada bioclima tiene una expresión vegetativa diferente en condiciones naturales. La marcha fenológica anual muestra que a mayor aridez menor es el contraste entre el IVDN mínimo y máximo, y que el momento de máxima cobertura vegetal varía de enero (semiárido) a abril (subdesértico). Esta propuesta permite extender y optimizar el conocimiento climático de las estaciones meteorológicas a través de toda la llanura mediante la expresión fenológica de la vegetación.