Effect of haematocrit on pump and kidney dysfunction after myocardial revascularization

Objective - Kidney dysfunction is a common complication after cardiac surgery. It occurs in 7 to 31% of the patients. The lowest haematocrit after cardiopulmonary bypass surgery (LHCT) has been identified as a risk factor for kidney dysfunction after cardiac surgery. The aim of this study is to determine whether different levels of haematocrit during cardiopulmonary bypass surgery are related to kidney dysfunction.Methods and results-A prospective study was conducted on consecutive adult patients undergoing myocardial revascularization. Preoperative renal function was assessed by baseline serum creatinine level (CrPre). Peak postoperative creatinine (CrPost) was defined as the highest daily in-hospital postoperative value. Peak fractional change in creatinine (% Delta Cr) was defined as the difference between the CrPre and CrePost represented as a percentage of the preoperative value. The LHTC was defined as the lowest recorded haematocrit prior to weaning from the initial pump run. A category variable was created for haematocrit based on the distribution of values. The category variable had the following cut-off points: less than 23%, 23.1 to 28% and greater than 28.1 %. Lowest haematocrit (26.62 +/- 4.15%), CPB (74.71 +/- 24.90 min), CrPre (1.23 +/- 0.37 mg/dl) and highest CrPost (1.52 +/- 0.47 mg/dl) data varied in near-normal fashion. Statistical significance has been observed in the < 23% lowest haematocrit group (CrIPOD and Cr5POD; P = 0.006) and the 23.1 28% lowest haematocrit level group (CrPre and Cr2POD; P = 0.047). CrPre and Cr5POD did not differ between groups (P > 0.05). The multiple linear regression model confirmed that the determinants for higher %Delta Cr were age, body surface area and preoperative serum creatinine level.Conclusion - The LHTC was not identified as a risk factor for kidney dysfunction after myocardial revascularization.

An adaptive view of caste differentiation in the neotropical wasp Polybia (Trichothorax) sericea Olivier (Hymenoptera: Vespidae)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

An adaptive system based on artificial neural networks for NPK fluid fertilizer application

The application process of fluid fertilizers through variable rates implemented by classical techniques with feedback and conventional equipments can be inefficient or unstable. This paper proposes an open-loop control system based on artificial neural network of the type multilayer perceptron for the identification and control of the fertilizer flow rate. The network training is made by the algorithm of Levenberg-Marquardt with training data obtained from measurements. Preliminary results indicate a fast, stable and low cost control system for precision fanning. Copyright (C) 2000 IFAC.

A fast electric load forecasting using adaptive neural networks

This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.

Evolution of extrafloral nectaries: adaptive process and selective regime changes from forest to savanna

Much effort has been devoted to understanding the function of extrafloral nectaries (EFNs) for antplantherbivore interactions. However, the pattern of evolution of such structures throughout the history of plant lineages remains unexplored. In this study, we used empirical knowledge on plant defences mediated by ants as a theoretical framework to test specific hypotheses about the adaptive role of EFNs during plant evolution. Emphasis was given to different processes (neutral or adaptive) and factors (habitat change and trade-offs with new trichomes) that may have affected the evolution of antplant associations. We measured seven EFN quantitative traits in all 105 species included in a well-supported phylogeny of the tribe Bignonieae (Bignoniaceae) and collected field data on antEFN interactions in 32 species. We identified a positive association between ant visitation (a surrogate of ant guarding) and the abundance of EFNs in vegetative plant parts and rejected the hypothesis of phylogenetic conservatism of EFNs, with most traits presenting K-values < 1. Modelling the evolution of EFN traits using maximum likelihood approaches further suggested adaptive evolution, with static-optimum models showing a better fit than purely drift models. In addition, the abundance of EFNs was associated with habitat shifts (with a decrease in the abundance of EFNs from forest to savannas), and a potential trade-off was detected between the abundance of EFNs and estipitate glandular trichomes (i.e. trichomes with sticky secretion). These evolutionary associations suggest divergent selection between species as well as explains K-values < 1. Experimental studies with multiple lineages of forest and savanna taxa may improve our understanding of the role of nectaries in plants. Overall, our results suggest that the evolution of EFNs was likely associated with the adaptive process which probably played an important role in the diversification of this plant group.

Adaptive T-spline refinement for isogeometric analysis in planar geometries

[EN]We present a new strategy, based on the meccano method [1, 2, 3], to construct a T-spline parameterization of 2D geometries for the application of isogeometric analysis. The proposed method only demands a boundary representation of the geometry as input data. The algorithm obtains, as a result, high quality parametric transformation between 2D objects and the parametric domain, the unit square. The key of the method lies in defining an isomorphic transformation between the parametric and physical T-mesh finding the optimal position of the interior nodes by applying a new T-mesh untangling and smoothing procedure. Bivariate T-spline representation is calculated by imposing the interpolation conditions on points sited both on the interior and on the boundary of the geometry…

Wind ensamble forecasting using an adaptive mass-consistent model

[EN]Ensemble forecasting [1] is a methodology to deal with uncertainties in the numerical wind prediction. In this work we propose to apply ensemble methods to the adaptive wind forecasting model presented in [2]. The wind _eld forecasting is based on a mass-consistent model and a log-linear wind pro_le using as input data the resulting forecast wind from Harmonie [3], a Non-Hydrostatic Dynamic model. The mass-consistent model parameters are estimated by using genetic algorithms [4]. The mesh is generated using the meccano method [5] and adapted to the geometry. The main source of uncertainties in this model is the parameter estimation and the in- trinsic uncertainties of the Harmonie Model…

Adaptive Finite Element Method with a local element parametrization nested meses

[EN]This work presents a novel approach to solve a two dimensional problem by using an adaptive finite element approach. The most common strategy to deal with nested adaptivity is to generate a mesh that represents the geometry and the input parameters correctly, and to refine this mesh locally to obtain the most accurate solution. As opposed to this approach, the authors propose a technique using independent meshes : geometry, input data and the unknowns. Each particular mesh is obtained by a local nested refinement of the same coarse mesh at the parametric space…

Ensemble wind forecasting based on the HARMONIE model and adaptive finite elements in complex orography

[EN]Ensemble forecasting is a methodology to deal with uncertainties in the numerical wind prediction. In this work we propose to apply ensemble methods to the adaptive wind forecasting model presented in. The wind field forecasting is based on a mass-consistent model and a log-linear wind profile using as input data the resulting forecast wind from Harmonie, a Non-Hydrostatic Dynamic model used experimentally at AEMET with promising results. The mass-consistent model parameters are estimated by using genetic algorithms. The mesh is generated using the meccano method and adapted to the geometry…

On-line adaptive visual tracking

Visual tracking is the problem of estimating some variables related to a target given a video sequence depicting the target. Visual tracking is key to the automation of many tasks, such as visual surveillance, robot or vehicle autonomous navigation, automatic video indexing in multimedia databases. Despite many years of research, long term tracking in real world scenarios for generic targets is still unaccomplished. The main contribution of this thesis is the definition of effective algorithms that can foster a general solution to visual tracking by letting the tracker adapt to mutating working conditions. In particular, we propose to adapt two crucial components of visual trackers: the transition model and the appearance model. The less general but widespread case of tracking from a static camera is also considered and a novel change detection algorithm robust to sudden illumination changes is proposed. Based on this, a principled adaptive framework to model the interaction between Bayesian change detection and recursive Bayesian trackers is introduced. Finally, the problem of automatic tracker initialization is considered. In particular, a novel solution for categorization of 3D data is presented. The novel category recognition algorithm is based on a novel 3D descriptors that is shown to achieve state of the art performances in several applications of surface matching.

The interaction of Leishmania major parasites with human myeloid cells and its consequence for adaptive immunity

In der vorliegenden Arbeit fokussierten wir uns auf drei verschiedene Aspekte der Leishmanien-Infektion. Wir charakterisierten den Prozess des Zelltods „Apoptose“ bei Parasiten (1), untersuchten die Eignung von Makrophagen und dendritischen Zellen als Wirtszelle für die Entwicklung der Parasiten (2) und analysierten die Konsequenzen der Infektion für die Entstehung einer adaptiven Immunantwort im humanen System. Von zentraler Bedeutung für dieses Projekt war die Hypothese, dass apoptotische Leishmanien den Autophagie-Mechanismus ihrer Wirtszellen ausnutzen, um eine T-Zell-vermittelte Abtötung der Parasiten zu vermindern.rnWir definierten eine apoptotische Leishmanien-Population, welche durch eine rundliche Morphologie und die Expression von Phosphatidylserin auf der Parasitenoberfläche charakterisiert war. Die apoptotischen Parasiten befanden sich zudem in der SubG1-Phase und wiesen weniger und fragmentierte DNA auf, welche durch TUNEL-Assay nachgewiesen werden konnte. Bei der Interaktion der Parasiten mit humanen Makrophagen und dendritischen Zellen zeigte sich, dass die anti-inflammatorischen Makrophagen anfälliger für Infektionen waren als die pro-inflammatorischen Makrophagen oder die dendritischen Zellen. Interessanterweise wurde in den dendritischen Zellen jedoch die effektivste Umwandlung zur krankheitsauslösenden, amastigoten Lebensform beobachtet. Da sowohl Makrophagen als auch dendritische Zellen zu den antigenpräsentierenden Zellen gehören, könnte dies zur Aktivierung der T-Zellen des adaptiven Immunsystems führen. Tatsächlich konnte während der Leishmanien-Infektion die Proliferation von T-Zellen beobachtet werden. Dabei stellten wir fest, dass es sich bei den proliferierenden T-Zellen um CD3+CD4+ T-Zellen handelte, welche sich überraschenderweise als Leishmanien-spezifische CD45RO+ T-Gedächtniszellen herausstellten. Dies war unerwartet, da ein vorheriger Kontakt der Spender mit Leishmanien als unwahrscheinlich gilt. In Gegenwart von apoptotischen Parasiten konnte eine signifikant schwächere T-Zell-Proliferation in Makrophagen, jedoch nicht in dendritischen Zellen beobachtet werden. Da sich die T-Zell-Proliferation negativ auf das Überleben der Parasiten auswirkt, konnten die niedrigsten Überlebensraten in dendritischen Zellen vorgefunden werden. Innerhalb der Zellen befanden sich die Parasiten in beiden Zelltypen im Phagosom, welches allerdings nur in Makrophagen den Autophagie-Marker LC3 aufwies. Chemische Induktion von Autophagie führte, ebenso wie die Anwesenheit von apoptotischen Parasiten, zu einer stark reduzierten T-Zell-Proliferation und dementsprechend zu einem höheren Überleben der Parasiten.rnZusammenfassend lässt sich aus unseren Daten schließen, dass Apoptose in Einzellern vorkommt. Während der Infektion können sowohl Makrophagen, als auch dendritische Zellen mit Leishmanien infiziert und das adaptive Immunsystem aktivert werden. Die eingeleitete T-Zell-Proliferation nach Infektion von Makrophagen ist in Gegenwart von apoptotischen Parasiten reduziert, weshalb sie im Vergleich zu dendritischen Zellen die geeigneteren Wirtszellen für Leishmanien darstellen. Dafür missbrauchen die Parasiten den Autophagie-Mechanismus der Makrophagen als Fluchtstrategie um das adaptive Immunsystem zu umgehen und somit das Überleben der Gesamtpopulation zu sichern. Diese Ergebnisse erklären den Vorteil von Apoptose in Einzellern und verdeutlichen, dass der Autophagie-Mechanismus als potentielles therapeutisches Ziel für die Behandlung von Leishmaniose dienen kann.rn

Tecniche Cloud Avanzate per Adaptive Hadoop Load Balancing tramite Sahara

Ogni giorno vengono generati grandi moli di dati attraverso sorgenti diverse. Questi dati, chiamati Big Data, sono attualmente oggetto di forte interesse nel settore IT (Information Technology). I processi digitalizzati, le interazioni sui social media, i sensori ed i sistemi mobili, che utilizziamo quotidianamente, sono solo un piccolo sottoinsieme di tutte le fonti che contribuiscono alla produzione di questi dati. Per poter analizzare ed estrarre informazioni da questi grandi volumi di dati, tante sono le tecnologie che sono state sviluppate. Molte di queste sfruttano approcci distribuiti e paralleli. Una delle tecnologie che ha avuto maggior successo nel processamento dei Big Data, e Apache Hadoop. Il Cloud Computing, in particolare le soluzioni che seguono il modello IaaS (Infrastructure as a Service), forniscono un valido strumento all'approvvigionamento di risorse in maniera semplice e veloce. Per questo motivo, in questa proposta, viene utilizzato OpenStack come piattaforma IaaS. Grazie all'integrazione delle tecnologie OpenStack e Hadoop, attraverso Sahara, si riesce a sfruttare le potenzialita offerte da un ambiente cloud per migliorare le prestazioni dell'elaborazione distribuita e parallela. Lo scopo di questo lavoro e ottenere una miglior distribuzione delle risorse utilizzate nel sistema cloud con obiettivi di load balancing. Per raggiungere questi obiettivi, si sono rese necessarie modifiche sia al framework Hadoop che al progetto Sahara.

Urinary metabolomics in Fxr-null mice reveals activated adaptive metabolic pathways upon bile acid challenge

Farnesoid X receptor (FXR) is a nuclear receptor that regulates genes involved in synthesis, metabolism, and transport of bile acids and thus plays a major role in maintaining bile acid homeostasis. In this study, metabolomic responses were investigated in urine of wild-type and Fxr-null mice fed cholic acid, an FXR ligand, using ultra-performance liquid chromatography (UPLC) coupled with electrospray time-of-flight mass spectrometry (TOFMS). Multivariate data analysis between wild-type and Fxr-null mice on a cholic acid diet revealed that the most increased ions were metabolites of p-cresol (4-methylphenol), corticosterone, and cholic acid in Fxr-null mice. The structural identities of the above metabolites were confirmed by chemical synthesis and by comparing retention time (RT) and/or tandem mass fragmentation patterns of the urinary metabolites with the authentic standards. Tauro-3alpha,6,7alpha,12alpha-tetrol (3alpha,6,7alpha,12alpha-tetrahydroxy-5beta-cholestan-26-oyltaurine), one of the most increased metabolites in Fxr-null mice on a CA diet, is a marker for efficient hydroxylation of toxic bile acids possibly through induction of Cyp3a11. A cholestatic model induced by lithocholic acid revealed that enhanced expression of Cyp3a11 is the major defense mechanism to detoxify cholestatic bile acids in Fxr-null mice. These results will be useful for identification of biomarkers for cholestasis and for determination of adaptive molecular mechanisms in cholestasis.

MaxMAC: a Maximally Traffic-Adaptive MAC Protocol for Wireless Sensor Networks

Energy efficiency is a major concern in the design of Wireless Sensor Networks (WSNs) and their communication protocols. As the radio transceiver typically accounts for a major portion of a WSN node’s power consumption, researchers have proposed Energy-Efficient Medium Access (E2-MAC) protocols that switch the radio transceiver off for a major part of the time. Such protocols typically trade off energy-efficiency versus classical quality of service parameters (throughput, latency, reliability). Today’s E2-MAC protocols are able to deliver little amounts of data with a low energy footprint, but introduce severe restrictions with respect to throughput and latency. Regrettably, they yet fail to adapt to varying traffic load at run-time. This paper presents MaxMAC, an E2-MAC protocol that targets at achieving maximal adaptivity with respect to throughput and latency. By adaptively tuning essential parameters at run-time, the protocol reaches the throughput and latency of energy-unconstrained CSMA in high-traffic phases, while still exhibiting a high energy-efficiency in periods of sparse traffic. The paper compares the protocol against a selection of today’s E2-MAC protocols and evaluates its advantages and drawbacks.