Prevalence of Vaccine Type Infections in Vaccinated and Non-Vaccinated Young Women: HPV-IMPACT, a Self-Sampling Study.

Background: The human papillomavirus (HPV) vaccination program for young girls aged 11⁻26 years was introduced in Switzerland in 2008. The objective of this study was to evaluate the prevalence of high- and low-risk HPV in a population of undergraduate students using self-sampling for monitoring the HPV vaccination program's effect. Undergraduate women aged between 18⁻31 years, attending the Medical School and University of Applied Sciences in Geneva, were invited to participate in the study. Included women were asked to perform vaginal self-sampling for HPV testing using a dry cotton swab. A total of 409 students participated in the study-aged 18⁻31 years-of which 69% of the participants were vaccinated with Gardasil HPV vaccine and 31% did not received the vaccine. About HPV prevalence, 7.2% of unvaccinated women were HPV 16 or 18 positive, while 1.1% of vaccinated women were infected by HPV 16 or 18 (p < 0.01). Prevalence of HPV 6 and 11 was 8.3% in non-vaccinated women versus 2.1% in vaccinated women (p < 0.02). We observed no cross-protection for the other HPV genotypes of a low- and high-risk strain. Prevalence of HPV 6/11/16/18 was lower in vaccinated women versus unvaccinated women. Continued assessment of HPV vaccine effectiveness in real population is needed

Recent Advances in Graph Partitioning

We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions

On the Benefits of Transparent Compression for Cost-Effective Cloud Data Storage

International audienceInfrastructure-as-a-Service (IaaS) cloud computing has revolutionized the way we think of acquiring computational resources: it allows users to deploy virtual machines (VMs) at large scale and pay only for the resources that were actually used throughout the runtime of the VMs. This new model raises new challenges in the design and development of IaaS middleware: excessive storage costs associated with both user data and VM images might make the cloud less attractive, especially for users that need to manipulate huge data sets and a large number of VM images. Storage costs result not only from storage space utilization, but also from bandwidth consumption: in typical deployments, a large number of data transfers between the VMs and the persistent storage are performed, all under high performance requirements. This paper evaluates the trade-off resulting from transparently applying data compression to conserve storage space and bandwidth at the cost of slight computational overhead. We aim at reducing the storage space and bandwidth needs with minimal impact on data access performance. Our solution builds on BlobSeer, a distributed data management service specifically designed to sustain a high throughput for concurrent accesses to huge data sequences that are distributed at large scale. Extensive experiments demonstrate that our approach achieves large reductions (at least 40%) of bandwidth and storage space utilization, while still attaining high performance levels that even surpass the original (no compression) performance levels in several data-intensive scenarios

Discovery and analysis of topographic features using learning algorithms: A seamount case study

Identifying and cataloging occurrences of particular topographic features are important but time-consuming tasks. Typically, automation is challenging, as simple models do not fully describe the complexities of natural features. We propose a new approach, where a particular class of neural network (the “autoencoder”) is used to assimilate the characteristics of the feature to be cataloged, and then applied to a systematic search for new examples. To demonstrate the feasibility of this method, we construct a network that may be used to find seamounts in global bathymetric data. We show results for two test regions, which compare favorably with results from traditional algorithms

Shear properties of MgO inferred using neural networks

Shear properties of mantle minerals are vital for interpreting seismic shear wave speeds and therefore inferring the composition and dynamics of a planetary interior. Shear wave speed and elastic tensor components, from which the shear modulus can be computed, are usually measured in the laboratory mimicking the Earth's (or a planet's) internal pressure and temperature conditions. A functional form that relates the shear modulus to pressure (and temperature) is fitted to the measurements and used to interpolate within and extrapolate beyond the range covered by the data. Assuming a functional form provides prior information, and the constraints on the predicted shear modulus and its uncertainties might depend largely on the assumed prior rather than the data. In the present study, we propose a data-driven approach in which we train a neural network to learn the relationship between the pressure, temperature and shear modulus from the experimental data without prescribing a functional form a priori. We present an application to MgO, but the same approach works for any other mineral if there are sufficient data to train a neural network. At low pressures, the shear modulus of MgO is well-constrained by the data. However, our results show that different experimental results are inconsistent even at room temperature, seen as multiple peaks and diverging trends in probability density functions predicted by the network. Furthermore, although an explicit finite-strain equation mostly agrees with the likelihood predicted by the neural network, there are regions where it diverges from the range given by the networks. In those regions, it is the prior assumption of the form of the equation that provides constraints on the shear modulus regardless of how the Earth behaves (or data behave). In situations where realistic uncertainties are not reported, one can become overconfident when interpreting seismic models based on those defined equations of state. In contrast, the trained neural network provides a reasonable approximation to experimental data and quantifies the uncertainty from experimental errors, interpolation uncertainty, data sparsity and inconsistencies from different experiments.</p

Improvements for better scaling of locally managed marine areas

To protect and restore ecosystems at the speed and scale required to meet current environmental challenges, a greater understanding of how conservation initiatives spread from existing to new adopters is required. According to the diffusion of innovation theory, positive adopter‐to‐peer communication is a powerful driver of innovation spread, whereas negative communications hinder innovation spread. Aware of this, businesses regularly survey customers and respond accordingly to maximize company growth. Therefore, we used 2 consumer satisfaction research measures commonly used by businesses, importance–performance analysis (IPA), which measures performance on metrics that are most important to customers, and net promoter score (NPS), which measures likely spread through positive referrals, to study satisfaction among adopters of locally managed marine areas (LMMAs) in northeastern Madagascar. Our results identified 4 attributes of LMMAs that adopters viewed as important but rated as worsening over time (funding and livestock provided by a nongovernmental organization, conflict in the village, and connections with others). Adopters considered control over resources and fisheries restrictions important and high performing. Villagers rated their quality of life since adopting LMMAs positively on average, but NPS returned a negative result overall and a strongly negative score for nonleaders. Our findings can be used to improve the design and management of LMMAs, inform pre‐ and postproject impact assessments to minimize negative impacts from conservation initiatives, and increase the spread of conservation initiatives. More broadly, this study presents a novel outlook for increasing the adoption of conservation initiatives by framing adopters of conservation initiatives as akin to customers whose perceptions of conservation initiatives matter inherently and because of their power to influence the spread of conservation initiatives

Perspective of Internet Poker Players on Harm-Reduction Strategies: A Cross-Sectional Study.

Background: Internet gambling may increase rates of gambling harm. This current study aimed to assess Internet poker players' views on various harm-reduction (HR) strategies. It also examined differences in these views according to the games played (poker only vs. poker plus other gambling activities), indebtedness, and problem gambling severity. Methods: Internet poker players (n = 311; 94.2% Male) recruited online between 2012 and 2014 were included in the analyses and completed a survey on indebtedness, problem gambling severity index, and ten statements regarding HR features. Results: Among the whole sample, the most frequently endorsed HR strategy was setting money limits, specialized online help, and peer support forums. People who play poker only (70%) are less prone to endorse the utility of information on excessive gambling and specialized healthcare centers. No differences were found between those people with debt versus those without regarding HR assessment. Participants with severe problem gambling were more skeptical about HR strategies based on information on specialized healthcare centers. Conclusion: Setting money limits, online help, and peer support forums are the most commonly endorsed strategies. Future research is needed to evaluate the effectiveness of online harm reduction strategies

Internal structure of matrix-type multilayer capsules templated on porous vaterite CaCO3 crystals as probed by staining with a fluorescence dye

Multilayer capsules templated on decomposable vaterite CaCO3 crystals are widely used as vehicles for drug delivery. The capsule represents typically not a hollow but matrix-like structure due to polymer diffusion into the porous crystals during multilayer deposition. The capsule formation mechanism is not well-studied but its understanding is crucial to tune capsule structure for a proper drug release performance. This study proposes new approach to noninvasively probe and adjust internal capsule structure. Polymer capsules made of poly(styrene-sulfonate) (PSS) and poly(diallyldimethylammonium chloride) (PDAD) have been stained with fluorescence dye rhodamine 6G. Physical-chemical aspects of intermolecular interactions required to validate the approach and adjust capsule structure are addressed. The capsules consist of a defined shell (typically 0.5–2 µm) and an internal matrix of PSS-PDAD complex (typically 10–40% of a total capsule volume). An increase of ionic strength and polymer deposition time leads to the thickening of the capsule shell and formation of a denser internal matrix, respectively. This is explained by effects of a polymer conformation and limitations in polymer diffusion through the crystal pores. We believe that the design of the capsules with desired internal structure will allow achieving effective encapsulation and controlled/programmed release of bioactives for advanced drug delivery applications