Testing for sexually transmitted infections in general practice: cross-sectional study

Background Primary care is an important provider of sexual health care in England. We sought to explore the extent of testing for chlamydia and HIV in general practice and its relation to associated measures of sexual health in two contrasting geographical settings. Methods We analysed chlamydia and HIV testing data from 64 general practices and one genitourinary medicine (GUM) clinic in Brent (from mid-2003 to mid-2006) and 143 general practices and two GUM clinics in Avon (2004). We examined associations between practice testing status, practice characteristics and hypothesised markers of population need (area level teenage conception rates and Index of Multiple Deprivation, IMD scores). Results No HIV or chlamydia testing was done in 19% (12/64) of general practices in Brent, compared to 2.1% (3/143) in Avon. In Brent, the mean age of general practitioners (GPs) in Brent practices that tested for chlamydia or HIV was lower than in those that had not conducted testing. Practices where no HIV testing was done had slightly higher local teenage conception rates (median 23.5 vs. 17.4/1000 women aged 15-44, p = 0.07) and served more deprived areas (median IMD score 27.1 vs. 21.8, p = 0.05). Mean yearly chlamydia and HIV testing rates, in practices that did test were 33.2 and 0.6 (per 1000 patients aged 15-44 years) in Brent, and 34.1 and 10.3 in Avon, respectively. In Brent practices only 20% of chlamydia tests were conducted in patients aged under 25 years, compared with 39% in Avon. Conclusions There are substantial geographical differences in the intensity of chlamydia and HIV testing in general practice. Interventions to facilitate sexually transmitted infection and HIV testing in general practice are needed to improve access to effective sexual health care. The use of routinely-collected laboratory, practice-level and demographic data for monitoring sexual health service provision and informing service planning should be more widely evaluated.

A deep dig--hindsight on Holocene vegetation composition from ancient environmental DNA

Want a glimpse at past vegetation? Studying pollen and other plant remains, which are preserved for example in lake sediments or mires for thousands of years, allows us to document regional occurrences of plant species over radiocarbon-dated time series. Such vegetation reconstructions derived from optical analyses of fossil samples are inherently incomplete because they only comprise taxa that contribute sufficient amounts of pollen, spores, macrofossil or other evidences. To complement optical analyses for paleoecological inference, molecular markers applied to ancient DNA (aDNA) may help in disclosing information hitherto inaccessible to biologists. Parducci et al. (2013) targeted aDNA from sediment cores of two lakes in the Scandes Mountains with generic primers in a meta-barcoding approach. When compared to palynological records from the same cores, respective taxon lists show remarkable differences in their compositions, but also in quantitative representation and in taxonomic resolution similar to a previous study (Jørgensen et al. 2012). While not free of assumptions that need critical and robust testing, notably the question of possible contamination, this study provides thrilling prospects to improve our knowledge about past vegetation composition, but also other organismic groups, stored as a biological treasure in the ground.

Sediment core fossils in ancient Lake Ohrid: testing for faunal change since the Last Interglacial

Ancient Lake Ohrid is probably of early Pleistocene or Pliocene origin and amongst the few lakes in the world harbouring an outstanding degree of endemic biodiversity. Although there is a long history of evolutionary research in Lake Ohrid, particularly on molluscs, a mollusc fossil record has been missing up to date. For the first time, gastropod and bivalve fossils are reported from the basal, calcareous part of a 2.6 m long sediment succession (core Co1200) from the north-eastern part of Lake Ohrid. Electron spin resonance (ESR) dating of mollusc shells from the same stratigraphic level yielded an age of 130 ± 28 ka. Lithofacies III sediments, i.e. a stratigraphic subdivision comprising the basal succession of core Co1200 between 181.5–263 cm, appeared solid, greyish-white, and consisted almost entirely of silt-sized endogenic calcite (CaCO3>70%) and intact and broken mollusc shells. Here we compare the faunal composition of the thanatocoenosis with recent mollusc associations in Lake Ohrid. A total of 13 mollusc species (9 gastropod and 4 bivalve species) could be identified within Lithofacies III sediments. The value of sediment core fossils for reconstructing palaeoenvironmental settings was evaluated and the agreement between sediment and palaeontological proxies was tested. The study also aims at investigating major faunal changes since the Last Interglacial and searching for signs of extinction events. The combined findings of the ecological study and the sediment characteristics suggest deposition in a shallow water environment during the Last Interglacial. The fossil fauna exclusively included species also found in the present fauna, i.e. no extinction events are evident for this site since the Last Interglacial. The thanatocoenosis showed the highest similarity with recent Intermediate Layer (5–25 m water depth) mollusc assemblages. The demonstrated existence of a mollusc fossil record in Lake Ohrid sediment cores also has great significance for future deep drilling projects. It can be hoped that a more far reaching mollusc fossil record will then be obtained, enabling insight into the early evolutionary history of Lake Ohrid.

Simple measures of climate, soil properties and plant traits predict national-scale grassland soil carbon stocks

Soil carbon (C) storage is a key ecosystem service. Soil C stocks play a vital role in soil fertility and climate regulation, but the factors that control these stocks at regional and national scales are unknown, particularly when their composition and stability are considered. As a result, their mapping relies on either unreliable proxy measures or laborious direct measurements. Using data from an extensive national survey of English grasslands, we show that surface soil (0–7 cm) C stocks in size fractions of varying stability can be predicted at both regional and national scales from plant traits and simple measures of soil and climatic conditions. Soil C stocks in the largest pool, of intermediate particle size (50–250 μm), were best explained by mean annual temperature (MAT), soil pH and soil moisture content. The second largest C pool, highly stable physically and biochemically protected particles (0·45–50 μm), was explained by soil pH and the community abundance-weighted mean (CWM) leaf nitrogen (N) content, with the highest soil C stocks under N-rich vegetation. The C stock in the small active fraction (250–4000 μm) was explained by a wide range of variables: MAT, mean annual precipitation, mean growing season length, soil pH and CWM specific leaf area; stocks were higher under vegetation with thick and/or dense leaves. Testing the models describing these fractions against data from an independent English region indicated moderately strong correlation between predicted and actual values and no systematic bias, with the exception of the active fraction, for which predictions were inaccurate. Synthesis and applications. Validation indicates that readily available climate, soils and plant survey data can be effective in making local- to landscape-scale (1–100 000 km2) soil C stock predictions. Such predictions are a crucial component of effective management strategies to protect C stocks and enhance soil C sequestration.

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.