Genetic diversity and ecological success of Staphylococcus aureus strains colonizing humans

The genetic determinants and phenotypic traits which make a Staphylococcus aureus strain a successful colonizer are largely unknown. The genetic diversity and population structure of 133 S. aureus isolates from healthy, generally risk-free adult carriers were investigated using four different typing methods: multilocus sequence typing (MLST), amplified fragment length polymorphism analysis (AFLP), double-locus sequence typing (DLST), and spa typing were compared. Carriage isolates displayed great genetic diversity which could only be revealed fully by DLST. Results of AFLP and MLST were highly concordant in the delineation of genotypic clusters of closely related isolates, roughly equivalent to clonal complexes. spa typing and DLST provided considerably less phylogenetic information. The resolution of spa typing was similar to that of AFLP and inferior to that of DLST. AFLP proved to be the most universal method, combining a phylogeny-building capacity similar to that of MLST with a much higher resolution. However, it had a lower reproducibility than sequencing-based MLST, DLST, and spa typing. We found two cases of methicillin-resistant S. aureus colonization, both of which were most likely associated with employment at a health service. Of 21 genotypic clusters detected, 2 were most prevalent: cluster 45 and cluster 30 each colonized 24% of the carrier population. The number of bacteria found in nasal samples varied significantly among the clusters, but the most prevalent clusters were not particularly numerous in the nasal samples. We did not find much evidence that genotypic clusters were associated with different carrier characteristics, such as age, sex, medical conditions, or antibiotic use. This may provide empirical support for the idea that genetic clusters in bacteria are maintained in the absence of adaptation to different niches. Alternatively, carrier characteristics other than those evaluated here or factors other than human hosts may exert selective pressure maintaining genotypic clusters.

Wearable Computing und RFID in Produktion und Logistik – Ansätze zur bereichsübergreifenden Nutzung digitaler Informationen

Die voranschreitende Entwicklung von Konzepten und Systemen zur Nutzung digitaler Informationen im industriellen Umfeld eröffnet verschiedenste Möglichkeiten zur Optimierung der Informationsverarbeitung und damit der Prozesseffektivität und -effizienz. Werden die relevanten Daten zu Produkten oder Prozessen jedoch lediglich in digitaler Form zur Verfügung gestellt, fällt ein Eingriff des Menschen in die virtuelle Welt immer schwerer. Auf Grundlage dessen wird am Beispiel der RFIDTechnologie dargestellt, inwiefern digitale Informationen durch die Verwendung von in den Arbeitsablauf integrierten Systemen für den Menschen nutzbar werden. Durch die Entwicklung eines Systems zur papierlosen Produktion und Logistik werden exemplarisch Einsatzszenarien zur Unterstützung des Mitarbeiters in Montageprozessen sowie zur Vermeidung von Fehlern in der Kommissionierung aufgezeigt. Dazu findet neben einer am Kopf getragenen Datenbrille zur Visualisierung der Informationen ein mobiles RFID-Lesegerät Anwendung, mit Hilfe dessen die digitalen Transponderdaten ohne zusätzlichen Aufwand für den Anwender genutzt werden können.

Cloud Computing in the EU Policy Sphere Interoperability, Vertical Integration and the Internal Market

Cloud computing is a new development that is based on the premise that data and applications are stored centrally and can be accessed through the Internet. Thisarticle sets up a broad analysis of how the emergence of clouds relates to European competition law, network regulation and electronic commerce regulation, which we relate to challenges for the further development of cloud services in Europe: interoperability and data portability between clouds; issues relating to vertical integration between clouds and Internet Service Providers; and potential problems for clouds to operate on the European Internal Market. We find that these issues are not adequately addressed across the legal frameworks that we analyse, and argue for further research into how to better facilitate innovative convergent services such as cloud computing through European policy – especially in light of the ambitious digital agenda that the European Commission has set out.

Global Standards: Recent Developments betweend the Poles of Privacy and Cloud Computing

The development of the Internet has made it possible to transfer data ‘around the globe at the click of a mouse’. Especially fresh business models such as cloud computing, the newest driver to illustrate the speed and breadth of the online environment, allow this data to be processed across national borders on a routine basis. A number of factors cause the Internet to blur the lines between public and private space: Firstly, globalization and the outsourcing of economic actors entrain an ever-growing exchange of personal data. Secondly, the security pressure in the name of the legitimate fight against terrorism opens the access to a significant amount of data for an increasing number of public authorities.And finally,the tools of the digital society accompany everyone at each stage of life by leaving permanent individual and borderless traces in both space and time. Therefore, calls from both the public and private sectors for an international legal framework for privacy and data protection have become louder. Companies such as Google and Facebook have also come under continuous pressure from governments and citizens to reform the use of data. Thus, Google was not alone in calling for the creation of ‘global privacystandards’. Efforts are underway to review established privacy foundation documents. There are similar efforts to look at standards in global approaches to privacy and data protection. The last remarkable steps were the Montreux Declaration, in which the privacycommissioners appealed to the United Nations ‘to prepare a binding legal instrument which clearly sets out in detail the rights to data protection and privacy as enforceable human rights’. This appeal was repeated in 2008 at the 30thinternational conference held in Strasbourg, at the 31stconference 2009 in Madrid and in 2010 at the 32ndconference in Jerusalem. In a globalized world, free data flow has become an everyday need. Thus, the aim of global harmonization should be that it doesn’t make any difference for data users or data subjects whether data processing takes place in one or in several countries. Concern has been expressed that data users might seek to avoid privacy controls by moving their operations to countries which have lower standards in their privacy laws or no such laws at all. To control that risk, some countries have implemented special controls into their domestic law. Again, such controls may interfere with the need for free international data flow. A formula has to be found to make sure that privacy at the international level does not prejudice this principle.

Collision Detection: Broad Phase Adaptation from Multi-Core to Multi-GPU Architecture

We present in this paper several contributions on the collision detection optimization centered on hardware performance. We focus on the broad phase which is the first step of the collision detection process and propose three new ways of parallelization of the well-known Sweep and Prune algorithm. We first developed a multi-core model takes into account the number of available cores. Multi-core architecture enables us to distribute geometric computations with use of multi-threading. Critical writing section and threads idling have been minimized by introducing new data structures for each thread. Programming with directives, like OpenMP, appears to be a good compromise for code portability. We then proposed a new GPU-based algorithm also based on the "Sweep and Prune" that has been adapted to multi-GPU architectures. Our technique is based on a spatial subdivision method used to distribute computations among GPUs. Results show that significant speed-up can be obtained by passing from 1 to 4 GPUs in a large-scale environment.

Widespread phenotypic and genetic divergence along altitudinal gradients in animals

Altitudinal gradients offer valuable study systems to investigate how adap- tive genetic diversity is distributed within and between natural populations and which factors promote or prevent adaptive differentiation. The environ- mental clines along altitudinal gradients tend to be steep relative to the dispersal distance of many organisms, providing an opportunity to study the joint effects of divergent natural selection and gene flow. Temperature is one variable showing consistent altitudinal changes, and altitudinal gradi- ents can therefore provide spatial surrogates for some of the changes antici- pated under climate change. Here, we investigate the extent and patterns of adaptive divergence in animal populations along altitudinal gradients by sur- veying the literature for (i) studies on phenotypic variation assessed under common garden or reciprocal transplant designs and (ii) studies looking for signatures of divergent selection at the molecular level. Phenotypic data show that significant between-population differences are common and taxo- nomically widespread, involving traits such as mass, wing size, tolerance to thermal extremes and melanization. Several lines of evidence suggest that some of the observed differences are adaptively relevant, but rigorous tests of local adaptation or the link between specific phenotypes and fitness are sorely lacking. Evidence for a role of altitudinal adaptation also exists for a number of candidate genes, most prominently haemoglobin, and for anony- mous molecular markers. Novel genomic approaches may provide valuable tools for studying adaptive diversity, also in species that are not amenable to experimentation.

Population structure, inbreeding and local adaptation within an endangered riverine specialist: the nase (Chondrostoma nasus)

Chondrostoma nasus is a cyprinid fish with highly specialized, ecologically and geographically distinct, ontogenetic trophic niches. Nase population numbers across their Swiss range have shown massive declines and many localized extinctions. Here we integrate data from different genetic markers with phenotypic and demographic data to survey patterns of neutral and adaptive genetic diversity in all extant (and one extinct) Swiss nase populations, with the aim to delineate intraspecific conservation units (CUs) and to inform future population management strategies. We discovered two major genetically and geographically distinct population groupings. The first population grouping comprises nase inhabiting rivers flowing into Lake Constance; the second comprises nase populations from Rhine drainages below Lake Constance. Within these clusters there is generally limited genetic differentiation among populations. Genomic outlier scans based on 256–377 polymorphic AFLP loci revealed little evidence of local adaptation both within and among population clusters, with the exception of one candidate locus identified in scans involving the inbred Schanzengraben population. However, significant phenotypic differentiation in body shape between certain populations suggests a need for more intensive future studies of local adaptation. Our data strongly suggests that the two major population groups should be treated as distinct CUs, with any supplemental stocking and reintroductions sourced only from within the range of the CU concerned.

Occurrence and Genetic Characteristics of Third-Generation Cephalosporin-Resistant Escherichia coli in Swiss Retail Meat

Prevalence and genetic relatedness were determined for third-generation cephalosporin-resistant Escherichia coli (3GC-R-Ec) detected in Swiss beef, veal, pork, and poultry retail meat. Samples from meat-packing plants (MPPs) processing 70% of the slaughtered animals in Switzerland were purchased at different intervals between April and June 2013 and analyzed. Sixty-nine 3GC-R-Ec isolates were obtained and characterized by microarray, PCR/DNA sequencing, Multi Locus Sequence Typing (MLST), and plasmid replicon typing. Plasmids of selected strains were transformed by electroporation into E. coli TOP10 cells and analyzed by plasmid MLST. The prevalence of 3GC-R-Ec was 73.3% in chicken and 2% in beef meat. No 3GC-R-Ec were found in pork and veal. Overall, the blaCTX-M-1 (79.4%), blaCMY-2 (17.6%), blaCMY-4 (1.5%), and blaSHV-12 (1.5%) β-lactamase genes were detected, as well as other genes conferring resistance to chloramphenicol (cmlA1-like), sulfonamides (sul), tetracycline (tet), and trimethoprim (dfrA). The 3GC-R-Ec from chicken meat often harbored virulence genes associated with avian pathogens. Plasmid incompatibility (Inc) groups IncI1, IncFIB, IncFII, and IncB/O were the most frequent. A high rate of clonality (e.g., ST1304, ST38, and ST93) among isolates from the same MPPs suggests that strains persist at the plant and spread to meat at the carcass-processing stage. Additionally, the presence of the blaCTX-M-1 gene on an IncI1 plasmid sequence type 3 (IncI1/pST3) in genetically diverse strains indicates interstrain spread of an epidemic plasmid. The blaCMY-2 and blaCMY-4 genes were located on IncB/O plasmids. This study represents the first comprehensive assessment of 3GC-R-Ec in meat in Switzerland. It demonstrates the need for monitoring contaminants and for the adaptation of the Hazard Analysis and Critical Control Point concept to avoid the spread of multidrug-resistant bacteria through the food chain.

Simulation of SLA-based VM-scaling algorithms for cloud-distributed applications

Cloud Computing has evolved to become an enabler for delivering access to large scale distributed applications running on managed network-connected computing systems. This makes possible hosting Distributed Enterprise Information Systems (dEISs) in cloud environments, while enforcing strict performance and quality of service requirements, defined using Service Level Agreements (SLAs). {SLAs} define the performance boundaries of distributed applications, and are enforced by a cloud management system (CMS) dynamically allocating the available computing resources to the cloud services. We present two novel VM-scaling algorithms focused on dEIS systems, which optimally detect most appropriate scaling conditions using performance-models of distributed applications derived from constant-workload benchmarks, together with SLA-specified performance constraints. We simulate the VM-scaling algorithms in a cloud simulator and compare against trace-based performance models of dEISs. We compare a total of three SLA-based VM-scaling algorithms (one using prediction mechanisms) based on a real-world application scenario involving a large variable number of users. Our results show that it is beneficial to use autoregressive predictive SLA-driven scaling algorithms in cloud management systems for guaranteeing performance invariants of distributed cloud applications, as opposed to using only reactive SLA-based VM-scaling algorithms.

Service Level Agreements-Driven Management of Distributed Applications in Cloud Computing Environments

Cloud Computing enables provisioning and distribution of highly scalable services in a reliable, on-demand and sustainable manner. However, objectives of managing enterprise distributed applications in cloud environments under Service Level Agreement (SLA) constraints lead to challenges for maintaining optimal resource control. Furthermore, conflicting objectives in management of cloud infrastructure and distributed applications might lead to violations of SLAs and inefficient use of hardware and software resources. This dissertation focusses on how SLAs can be used as an input to the cloud management system, increasing the efficiency of allocating resources, as well as that of infrastructure scaling. First, we present an extended SLA semantic model for modelling complex service-dependencies in distributed applications, and for enabling automated cloud infrastructure management operations. Second, we describe a multi-objective VM allocation algorithm for optimised resource allocation in infrastructure clouds. Third, we describe a method of discovering relations between the performance indicators of services belonging to distributed applications and then using these relations for building scaling rules that a CMS can use for automated management of VMs. Fourth, we introduce two novel VM-scaling algorithms, which optimally scale systems composed of VMs, based on given SLA performance constraints. All presented research works were implemented and tested using enterprise distributed applications.

BitWorker, a Decentralized Distributed Computing System based on BitTorrent

In this paper we present BitWorker, a platform for community distributed computing based on BitTorrent. Any splittable task can be easily specified by a user in a meta-information task file, such that it can be downloaded and performed by other volunteers. Peers find each other using Distributed Hash Tables, download existing results, and compute missing ones. Unlike existing distributed computing schemes relying on centralized coordination point(s), our scheme is totally distributed, therefore, highly robust. We evaluate the performance of BitWorker using mathematical models and real tests, showing processing and robustness gains. BitWorker is available for download and use by the community.

Widespread Signals of Convergent Adaptation to High Altitude in Asia and America

Living at high altitude is one of the most difficult challenges that humans had to cope with during their evolution. Whereas several genomic studies have revealed some of the genetic bases of adaptations in Tibetan, Andean, and Ethiopian populations, relatively little evidence of convergent evolution to altitude in different continents has accumulated. This lack of evidence can be due to truly different evolutionary responses, but it can also be due to the low power of former studies that have mainly focused on populations from a single geographical region or performed separate analyses on multiple pairs of populations to avoid problems linked to shared histories between some populations. We introduce here a hierarchical Bayesian method to detect local adaptation that can deal with complex demographic histories. Our method can identify selection occurring at different scales, as well as convergent adaptation in different regions. We apply our approach to the analysis of a large SNP data set from low- and high-altitude human populations from America and Asia. The simultaneous analysis of these two geographic areas allows us to identify several candidate genome regions for altitudinal selection, and we show that convergent evolution among continents has been quite common. In addition to identifying several genes and biological processes involved in high-altitude adaptation, we identify two specific biological pathways that could have evolved in both continents to counter toxic effects induced by hypoxia.

Mobility and Bandwidth Prediction as a Service in Virtualized LTE Systems

Recently telecommunication industry benefits from infrastructure sharing, one of the most fundamental enablers of cloud computing, leading to emergence of the Mobile Virtual Network Operator (MVNO) concept. The most momentous intents by this approach are the support of on-demand provisioning and elasticity of virtualized mobile network components, based on data traffic load. To realize it, during operation and management procedures, the virtualized services need be triggered in order to scale-up/down or scale-out/in an instance. In this paper we propose an architecture called MOBaaS (Mobility and Bandwidth Availability Prediction as a Service), comprising two algorithms in order to predict user(s) mobility and network link bandwidth availability, that can be implemented in cloud based mobile network structure and can be used as a support service by any other virtualized mobile network services. MOBaaS can provide prediction information in order to generate required triggers for on-demand deploying, provisioning, disposing of virtualized network components. This information can be used for self-adaptation procedures and optimal network function configuration during run-time operation, as well. Through the preliminary experiments with the prototype implementation on the OpenStack platform, we evaluated and confirmed the feasibility and the effectiveness of the prediction algorithms and the proposed architecture.

A study of the time complexity of an Elitist Genetic Algorithm used for associating optical observations of MEO and GEO Space Debris

Currently several thousands of objects are being tracked in the MEO and GEO regions through optical means. The problem faced in this framework is that of Multiple Target Tracking (MTT). In this context both the correct associations among the observations, and the orbits of the objects have to be determined. The complexity of the MTT problem is defined by its dimension S. Where S stands for the number of ’fences’ used in the problem, each fence consists of a set of observations that all originate from dierent targets. For a dimension of S ˃ the MTT problem becomes NP-hard. As of now no algorithm exists that can solve an NP-hard problem in an optimal manner within a reasonable (polynomial) computation time. However, there are algorithms that can approximate the solution with a realistic computational e ort. To this end an Elitist Genetic Algorithm is implemented to approximately solve the S ˃ MTT problem in an e cient manner. Its complexity is studied and it is found that an approximate solution can be obtained in a polynomial time. With the advent of improved sensors and a heightened interest in the problem of space debris, it is expected that the number of tracked objects will grow by an order of magnitude in the near future. This research aims to provide a method that can treat the correlation and orbit determination problems simultaneously, and is able to e ciently process large data sets with minimal manual intervention.

Detection of convergent genome-wide signals of adaptation to tropical forests in humans

Tropical forests are believed to be very harsh environments for human life. It is unclear whether human beings would have ever subsisted in those environments without external resources. It is therefore possible that humans have developed recent biological adaptations in response to specific selective pressures to cope with this challenge. To understand such biological adaptations we analyzed genome-wide SNP data under a Bayesian statistics framework, looking for outlier markers with an overly large extent of differentiation between populations living in a tropical forest, as compared to genetically related populations living outside the forest in Africa and the Americas. The most significant positive selection signals were found in genes related to lipid metabolism, the immune system, body development, and RNA Polymerase III transcription initiation. The results are discussed in the light of putative tropical forest selective pressures, namely food scarcity, high prevalence of pathogens, difficulty to move, and inefficient thermoregulation. Agreement between our results and previous studies on the pygmy phenotype, a putative prototype of forest adaptation, were found, suggesting that a few genetic regions previously described as associated with short stature may be evolving under similar positive selection in Africa and the Americas. In general, convergent evolution was less pervasive than local adaptation in one single continent, suggesting that Africans and Amerindians may have followed different routes to adapt to similar environmental selective pressures.