A versatile proline/alanine transporter in the unicellular pathogen Leishmania donovani regulates amino acid homoeostasis and osmotic stress responses

Unlike all other organisms, parasitic protozoa of the family Trypanosomatidae maintain a large cellular pool of proline that, together with the alanine pool, serve as alternative carbon sources as well as reservoirs of organic osmolytes. These reflect adaptation to their insect vectors whose haemolymphs are exceptionally rich in the two amino acids. In the present study we identify and characterize a new neutral amino acid transporter, LdAAP24, that translocates proline and alanine across the Leishmania donovani plasma membrane. This transporter fulfils multiple functions: it is the sole supplier for the intracellular pool of proline and contributes to the alanine pool; it is essential for cell volume regulation after osmotic stress; and it regulates the transport and homoeostasis of glutamate and arginine, none of which are its substrates. Notably, we provide evidence that proline and alanine exhibit different roles in the parasitic response to hypotonic shock; alanine affects swelling, whereas proline influences the rate of volume recovery. On the basis of our data we suggest that LdAAP24 plays a key role in parasite adaptation to its varying environments in host and vector, a phenomenon essential for successful parasitism.

Genetic composition, genetic diversity, and small-scale environmental variation matter for the experimental reintroduction of a rare plant

Aims Reintroduction has become an important tool for the management of endangered plant species. We tested the little-explored effects of small-scale environmental variation, genotypic composition (i.e. identity of genotypes), and genotypic diversity on the population survival of the regionally rare clonal plant Ranunculus reptans. For this species of periodically inundated lakeshores genetic differentiation had been reported between populations and between short-flooded and long-flooded microsites within populations.Methods We established 306 experimental test populations at a previously unoccupied lake shore, comprising either monocultures of 32 genotypes, mixtures of genotypes within populations or mixtures of genotypes between populations. In 2000, three years after planting out at the experimental site, a long-lasting flood caused the death of half of the experimental populations. In 2003, an extreme drought resulted in the lowest summer water levels ever measured.Important findings Despite these climatic extremes, 27 of the established populations survived until the end of the experiment in December 2003. The success of experimental populations largely differed between microsites. Moreover, the success of genotype monocultures depended on genotype and source population. Genetic differentiation between microsites played a minor role for the success of reintroduction. After the flood, populations planted with genotypes from different source populations increased in abundance, whereas populations with genotypes from single source populations and genotype monocultures decreased. We conclude that sources for reintroductions need to be selected carefully. Moreover, mixtures of plants from different populations appear to be the best choice for successful reintroduction, at least in unpredictably varying environments.

Varying expectancies and attention bias in phobic and non-phobic individuals.

Phobic individuals display an attention bias to phobia-related information and biased expectancies regarding the likelihood of being faced with such stimuli. Notably, although attention and expectancy biases are core features in phobia and anxiety disorders, these biases have mostly been investigated separately and their causal impact has not been examined. We hypothesized that these biases might be causally related. Spider phobic and low spider fearful control participants performed a visual search task in which they specified whether the deviant animal in a search array was a spider or a bird. Shorter reaction times (RTs) for spiders than for birds in this task reflect an attention bias toward spiders. Participants' expectancies regarding the likelihood of these animals being the deviant in the search array were manipulated by presenting verbal cues. Phobics were characterized by a pronounced and persistent attention bias toward spiders; controls displayed slower RTs for birds than for spiders only when spider cues had been presented. More important, we found RTs for spider detections to be virtually unaffected by the expectancy cues in both groups, whereas RTs for bird detections showed a clear influence of the cues. Our results speak to the possibility that evolution has formed attentional systems that are specific to the detection of phylogenetically salient stimuli such as threatening animals; these systems may not be as penetrable to variations in (experimentally induced) expectancies as those systems that are used for the detection of non-threatening stimuli. In sum, our findings highlight the relation between expectancies and attention engagement in general. However, expectancies may play a greater role in attention engagement in safe environments than in threatening environments.

Service level agreements-driven management of distributed applications in cloud computing environments

Advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing workload conditions, such as number of connected users, application performance might suffer, leading to violations of Service Level Agreements (SLA) and possible inefficient use of hardware resources. Combining dynamic application requirements with the increased use of virtualised computing resources creates a challenging resource Management context for application and cloud-infrastructure owners. In such complex environments, business entities use SLAs as a means for specifying quantitative and qualitative requirements of services. There are several challenges in running distributed enterprise applications in cloud environments, ranging from the instantiation of service VMs in the correct order using an adequate quantity of computing resources, to adapting the number of running services in response to varying external loads, such as number of users. The application owner is interested in finding the optimum amount of computing and network resources to use for ensuring that the performance requirements of all her/his applications are met. She/he is also interested in appropriately scaling the distributed services so that application performance guarantees are maintained even under dynamic workload conditions. Similarly, the infrastructure Providers are interested in optimally provisioning the virtual resources onto the available physical infrastructure so that her/his operational costs are minimized, while maximizing the performance of tenants’ applications. Motivated by the complexities associated with the management and scaling of distributed applications, while satisfying multiple objectives (related to both consumers and providers of cloud resources), this thesis proposes a cloud resource management platform able to dynamically provision and coordinate the various lifecycle actions on both virtual and physical cloud resources using semantically enriched SLAs. The system focuses on dynamic sizing (scaling) of virtual infrastructures composed of virtual machines (VM) bounded application services. We describe several algorithms for adapting the number of VMs allocated to the distributed application in response to changing workload conditions, based on SLA-defined performance guarantees. We also present a framework for dynamic composition of scaling rules for distributed service, which used benchmark-generated application Monitoring traces. We show how these scaling rules can be combined and included into semantic SLAs for controlling allocation of services. We also provide a detailed description of the multi-objective infrastructure resource allocation problem and various approaches to satisfying this problem. We present a resource management system based on a genetic algorithm, which performs allocation of virtual resources, while considering the optimization of multiple criteria. We prove that our approach significantly outperforms reactive VM-scaling algorithms as well as heuristic-based VM-allocation approaches.