Learning about Distributed Systems

This paper presents the "state of the art" about distributed systems and applications and it's focused on teaching about these systems. It presents different platforms where to run distributed applications and describes some development toolkits whose can be used to develop prototypes, practices and distributed applications. It also presents some existing distributed algorithms useful for class practices, and some tools to help managing distributed environments. Finally, the paper presents some teaching experiences with different approaches on how to teach about distributed systems.

Evaluation of MPI collectives for HPC applications on distributed virtualized environments

Through this study, we will measure how the collective MPI operations behaves in virtual and physical clusters, and its impact on the application performance. As we stated before, we will use as a test case the Weather Research and Forecasting simulations.

A distributed environment for virtual and/or real experiments for underwater robots

This paper presents the distributed environment for virtual and/or real experiments for underwater robots (DEVRE). This environment is composed of a set of processes running on a local area network composed of three sites: 1) the onboard AUV computer; 2) a surface computer used as human-machine interface (HMI); and 3) a computer used for simulating the vehicle dynamics and representing the virtual world. The HMI can be transparently linked to the real sensors and actuators dealing with a real mission. It can also be linked with virtual sensors and virtual actuators, dealing with a virtual mission. The aim of DEVRE is to assist engineers during the software development and testing in the lab prior to real experiments

Selection on a eumelanic ornament is stronger in the tropics than in temperate zones in the worldwide-distributed barn owl.

Spatial variation in the pattern of natural selection can promote local adaptation and genetic differentiation between populations. Because heritable melanin-based ornaments can signal resistance to environmentally mediated elevation in glucocorticoids, to oxidative stress and parasites, populations may vary in the mean degree of melanic coloration if selection on these phenotypic aspects varies geographically. Within a population of Swiss barn owls (Tyto alba), the size of eumelanic spots is positively associated with survival, immunity and resistance to stress, but it is yet unknown whether Tyto species that face stressful environments evolved towards a darker eumelanic plumage. Because selection regimes vary along environmental gradients, we examined whether melanin-based traits vary clinally and are expressed to a larger extent in the tropics where parasites are more abundant than in temperate zones. To this end, we considered 39 barn owl species distributed worldwide. Barn owl species living in the tropics displayed larger eumelanic spots than those found in temperate zones. This was, however, verified in the northern hemisphere only. Parasites being particularly abundant in the tropics, they may promote the evolution of darker eumelanic ornaments.

Reliable and persistent storage for CoDeS a distributed collaborative system

This research is aimed to find a solution for a distributed storage system adapted for CoDeS. By studying how DSSs work and how they are implemented, we can conclude how we can implement a DSS compatible with CoDeS requirements.

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.

Nanomotors: new strategies of (bio)sensing based on motion

Nanomotors are nanoscale devices capable of converting energy into movement and forces. Among them, self-propelled nanomotors offer considerable promise for developing new and novel bioanalytical and biosensing strategies based on the direct isolation of target biomolecules or changes in their movement in the presence of target analytes. The mainachievements of this project consists on the development of receptor-functionalized nanomotors that offer direct and rapid target detection, isolation and transport from raw biological samples without preparatory and washing steps. For example, microtube engines functionalized with aptamer, antibody, lectin and enzymes receptors were used for the direct isolation of analytes of biomedical interest, including proteins and whole cells, among others. A target protein was also isolated from a complex sample by using an antigen-functionalized microengine navigating into the reservoirs of a lab-on-a-chip device. The new nanomotorbased target biomarkers detection strategy not only offers highly sensitive, rapid, simple and low cost alternative for the isolation and transport of target molecules, but also represents a new dimension of analytical information based on motion. The recognition events can be easily visualized by optical microscope (without any sophisticated analytical instrument) to reveal the target presence and concentration. The use of artificial nanomachines has shown not only to be useful for (bio)recognition and (bio)transport but also for detection of environmental contamination and remediation. In this context, micromotors modified with superhydrophobic layer demonstrated that effectively interacted, captured, transported and removed oil droplets from oil contaminated samples. Finally, a unique micromotor-based strategy for water-quality testing, that mimics live-fish water-quality testing, based on changes in the propulsion behavior of artificial biocatalytic microswimmers in the presence of aquatic pollutants was also developed. The attractive features of the new micromachine-based target isolation and signal transduction protocols developed in this project offer numerous potential applications in biomedical diagnostics, environmental monitoring, and forensic analysis.

Understanding the two-component sensing System of virulent bacteria

Report for the scientific sojourn carried out at the l’ Institute for Computational Molecular Science of the Temple University, United States, from 2010 to 2012. Two-component systems (TCS) are used by pathogenic bacteria to sense the environment within a host and activate mechanisms related to virulence and antimicrobial resistance. A prototypical example is the PhoQ/PhoP system, which is the major regulator of virulence in Salmonella. Hence, PhoQ is an attractive target for the design of new antibiotics against foodborne diseases. Inhibition of the PhoQ-mediated bacterial virulence does not result in growth inhibition, presenting less selective pressure for the generation of antibiotic resistance. Moreover, PhoQ is a histidine kinase (HK) and it is absent in animals. Nevertheless, the design of satisfactory HK inhibitors has been proven to be a challenge. To compete with the intracellular ATP concentrations, the affinity of a HK inhibidor must be in the micromolar-nanomolar range, whereas the current lead compounds have at best millimolar affinities. Moreover, the drug selectivity depends on the conformation of a highly variable loop, referred to as the “ATP-lid, which is difficult to study by X-Ray crystallography due to its flexibility. I have investigated the binding of different HK inhibitors to PhoQ. In particular, all-atom molecular dynamics simulations have been combined with enhanced sampling techniques in order to provide structural and dynamic information of the conformation of the ATP-lid. Transient interactions between these drugs and the ATP-lid have been identified and the free energy of the different binding modes has been estimated. The results obtained pinpoint the importance of protein flexibility in the HK-inhibitor binding, and constitute a first step in developing more potent and selective drugs. The computational resources of the hosting institution as well as the experience of the members of the group in drug binding and free energy methods have been crucial to carry out this work.

Distributed all-atom simulations of intrinsically unstructured proteins

The Computational Biophysics Group at the Universitat Pompeu Fabra (GRIB-UPF) hosts two unique computational resources dedicated to the execution of large scale molecular dynamics (MD) simulations: (a) the ACMD molecular-dynamics software, used on standard personal computers with graphical processing units (GPUs); and (b) the GPUGRID. net computing network, supported by users distributed worldwide that volunteer GPUs for biomedical research. We leveraged these resources and developed studies, protocols and open-source software to elucidate energetics and pathways of a number of biomolecular systems, with a special focus on flexible proteins with many degrees of freedom. First, we characterized ion permeation through the bactericidal model protein Gramicidin A conducting one of the largest studies to date with the steered MD biasing methodology. Next, we addressed an open problem in structural biology, the determination of drug-protein association kinetics; we reconstructed the binding free energy, association, and dissaciociation rates of a drug like model system through a spatial decomposition and a Makov-chain analysis. The work was published in the Proceedings of the National Academy of Sciences and become one of the few landmark papers elucidating a ligand-binding pathway. Furthermore, we investigated the unstructured Kinase Inducible Domain (KID), a 28-peptide central to signalling and transcriptional response; the kinetics of this challenging system was modelled with a Markovian approach in collaboration with Frank Noe’s group at the Freie University of Berlin. The impact of the funding includes three peer-reviewed publication on high-impact journals; three more papers under review; four MD analysis components, released as open-source software; MD protocols; didactic material, and code for the hosting group.

Sodium sensing in neurons with a dendrimer-based nanoprobe.

Ion imaging is a powerful methodology to assess fundamental biological processes in live cells. The limited efficiency of some ion-sensing probes and their fast leakage from cells are important restrictions to this approach. In this study, we present a novel strategy based on the use of dendrimer nanoparticles to obtain better intracellular retention of fluorescent probes and perform prolonged fluorescence imaging of intracellular ion dynamics. A new sodium-sensitive nanoprobe was generated by encapsulating a sodium dye in a PAMAM dendrimer nanocontainer. This nanoprobe is very stable and has high sodium sensitivity and selectivity. When loaded in neurons in live brain tissue, it homogenously fills the entire cell volume, including small processes, and stays for long durations, with no detectable alterations of cell functional properties. We demonstrate the suitability of this new sodium nanosensor for monitoring physiological sodium responses such as those occurring during neuronal activity.

Induction of innate immune responses through Nalp3 inflammasome sensing of asbestos and silica

The inhalation of airborne pollutants such as asbestos or silica is linked to inflammation of the lung, fibrosis and lung cancer. How the presence of pathogenic dust is recognised, and how chronic inflammatory diseases are triggered are poorly understood. We will se show that asbestos and silica are sensed by the Nalp3 inflammasome, whose subsequent activation leads to IL-1b secretion. Inflammasome activation is triggered by reactive oxygen species, which are generated by a NADPH oxidase upon particle phagocytosis. In a model of asbestos inhalation, Nalp3_/_ mice showed diminished recruitment of inflammatory cells to the lungs, paralleled by lower cytokine production. Our findings implicate the Nalp3 inflammasome in particulate matter-related pulmonary diseases and support its role as a major proinflammatory ''danger" receptor.

Selective regulation of acid-sensing ion channel 1 by serine proteases.

Acid-sensing ion channels (ASICs) are neuronal Na(+) channels that belong to the epithelial Na(+) channel/degenerin family. ASICs are transiently activated by a rapid drop in extracellular pH. Conditions of low extracellular pH, such as ischemia and inflammation in which ASICs are thought to be active, are accompanied by increased protease activity. We show here that serine proteases modulate the function of ASIC1a and ASIC1b but not of ASIC2a and ASIC3. We show that protease exposure shifts the pH dependence of ASIC1a activation and steady-state inactivation to more acidic pH. As a consequence, protease exposure leads to a decrease in current response if ASIC1a is activated by a pH drop from pH 7.4. If, however, acidification occurs from a basal pH of approximately 7, protease-exposed ASIC1a shows higher activity than untreated ASIC1a. We provide evidence that this bi-directional regulation of ASIC1a function also occurs in neurons. Thus, we have identified a mechanism that modulates ASIC function and may allow ASIC1a to adapt its gating to situations of persistent extracellular acidification.

Innate immune activation through Nalp3 inflammasome sensing of asbestos and silica

The inhalation of airborne pollutants, such as asbestos or silica, is linked to inflammation of the lung, fibrosis, and lung cancer. How the presence of pathogenic dust is recognized and how chronic inflammatory diseases are triggered are poorly understood. Here, we show that asbestos and silica are sensed by the Nalp3 inflammasome, whose subsequent activation leads to interleukin-1beta secretion. Inflammasome activation is triggered by reactive oxygen species, which are generated by a NADPH oxidase upon particle phagocytosis. (NADPH is the reduced form of nicotinamide adenine dinucleotide phosphate.) In a model of asbestos inhalation, Nalp3-/- mice showed diminished recruitment of inflammatory cells to the lungs, paralleled by lower cytokine production. Our findings implicate the Nalp3 inflammasome in particulate matter-related pulmonary diseases and support its role as a major proinflammatory "danger" receptor