Relatedness with plant species in native community influences ecological consequences of range expansions

Global warming is enabling many plant species to expand their range to higher latitudes and altitudes, where they may suffer less from natural aboveground and belowground enemies. Reduced control by natural enemies can enable climate warming‐induced range expanders to gain an advantage in competition with natives and become disproportionally abundant in their new range. However, so far studies have only examined individual growth of range expanders, which have common congeneric plant species in their new range. Thus it is not known how general is this reduced effect of above‐ and belowground enemies and how it operates in communities, where multiple plant species also interact with each other. Here we show that range‐expanding plant species with and without congenerics in the invaded habitats differ in their ecological interactions in the new range. In a community‐level experiment, range‐expanding plant species, both with and without congenerics, suppressed the growth of a herbivore. However, only range expanders without congenerics reduced biomass production of the native plant species. In the present study, range expanders without congenerics allocated more biomass aboveground compared to native plant species, which can explain their competitive advantage. Competitive interaction and also biomass allocation of native plants and their congeneric range expanders were similar. Our results highlight that information about species phylogenetic relatedness with native flora can be crucial for improving predictions about the consequences of climate warming‐induced range expansions.     

AGATHE: A tool for personalized rehabilitation of cognitive functions based on simulated activities of daily living

Every year, tens of thousands of people fall victim to one of invalidating neurological pathologies. Acquired brain injury leads to cognitive impairment and heavy loss of autonomy. Rehabilitation interventions are needed to enable people to recover capacity and return to Instrumental Activities of Daily Living (iADL), such as grocery shopping. Unfortunately, the resources made available in cognitive rehabilitation are insufficient for the growing needs of victims of brain damage. The goal of the AGATHE project is to develop a tool that will provide therapists with an innovative means of dealing with cognitive rehabilitation and offer patients customized rehabilitation sessions, on the basis of simulated iADL. AGATHE fits into accessibility prospects of the tool (for everyone, everywhere and anywhere) and reduction of the cost of rehabilitation. AGATHE allows the strengthening and diversification of skills and expertise of the project's clinical and research partners as well as the opening of a new application field to the technology of the industrial partners. Initial tests of the AGATHE tool have been performed among therapists and patients after brain injury in order to validate usability issues. Perspective of efficacy trials has been identified and development projects are explored.     

GPU-BSM: A GPU-Based Tool to Map Bisulfite-Treated Reads

Cytosine DNA methylation is an epigenetic mark implicated in several biological processes. Bisulfite treatment of DNA is acknowledged as the gold standard technique to study methylation. This technique introduces changes in the genomic DNA by converting cytosines to uracils while 5-methylcytosines remain nonreactive. During PCR amplification 5-methylcytosines are amplified as cytosine, whereas uracils and thymines as thymine. To detect the methylation levels, reads treated with the bisulfite must be aligned against a reference genome. Mapping these reads to a reference genome represents a significant computational challenge mainly due to the increased search space and the loss of information introduced by the treatment. To deal with this computational challenge we devised GPU-BSM, a tool based on modern Graphics Processing Units. Graphics Processing Units are hardware accelerators that are increasingly being used successfully to accelerate general-purpose scientific applications. GPU-BSM is a tool able to map bisulfite-treated reads from whole genome bisulfite sequencing and reduced representation bisulfite sequencing, and to estimate methylation levels, with the goal of detecting methylation. Due to the massive parallelization obtained by exploiting graphics cards, GPU-BSM aligns bisulfite-treated reads faster than other cutting-edge solutions, while outperforming most of them in terms of unique mapped reads.     

Reliability of Trachoma Clinical Grading—Assessing Grading of Marginal Cases

Background

Clinical examination of trachoma is used to justify intervention in trachoma-endemic regions. Currently, field graders are certified by determining their concordance with experienced graders using the kappa statistic. Unfortunately, trachoma grading can be highly variable and there are cases where even expert graders disagree (borderline/marginal cases). Prior work has shown that inclusion of borderline cases tends to reduce apparent agreement, as measured by kappa. Here, we confirm those results and assess performance of trainees on these borderline cases by calculating their reliability error, a measure derived from the decomposition of the Brier score.

Methods and Findings

We trained 18 field graders using 200 conjunctival photographs from a community-randomized trial in Niger and assessed inter-grader agreement using kappa as well as reliability error. Three experienced graders scored each case for the presence or absence of trachomatous inflammation - follicular (TF) and trachomatous inflammation - intense (TI). A consensus grade for each case was defined as the one given by a majority of experienced graders. We classified cases into a unanimous subset if all 3 experienced graders gave the same grade. For both TF and TI grades, the mean kappa for trainees was higher on the unanimous subset; inclusion of borderline cases reduced apparent agreement by 15.7% for TF and 12.4% for TI. When we assessed the breakdown of the reliability error, we found that our trainees tended to over-call TF grades and under-call TI grades, especially in borderline cases.

Conclusions

The kappa statistic is widely used for certifying trachoma field graders. Exclusion of borderline cases, which even experienced graders disagree on, increases apparent agreement with the kappa statistic. Graders may agree less when exposed to the full spectrum of disease. Reliability error allows for the assessment of these borderline cases and can be used to refine an individual trainee''s grading.     

Gastro-Esophageal Reflux Disease Symptoms and Demographic Factors as a Pre-Screening Tool for Barrett’s Esophagus

Background

Barrett’s esophagus (BE) occurs as consequence of reflux and is a risk factor for esophageal adenocarcinoma. The current “gold-standard” for diagnosing BE is endoscopy which remains prohibitively expensive and impractical as a population screening tool. We aimed to develop a pre-screening tool to aid decision making for diagnostic referrals.

Methodology/Principal Findings

A prospective (training) cohort of 1603 patients attending for endoscopy was used for identification of risk factors to develop a risk prediction model. Factors associated with BE in the univariate analysis were selected to develop prediction models that were validated in an independent, external cohort of 477 non-BE patients referred for endoscopy with symptoms of reflux or dyspepsia. Two prediction models were developed separately for columnar lined epithelium (CLE) of any length and using a stricter definition of intestinal metaplasia (IM) with segments ≥2 cm with areas under the ROC curves (AUC) of 0.72 (95%CI: 0.67–0.77) and 0.81 (95%CI: 0.76–0.86), respectively. The two prediction models included demographics (age, sex), symptoms (heartburn, acid reflux, chest pain, abdominal pain) and medication for “stomach” symptoms. These two models were validated in the independent cohort with AUCs of 0.61 (95%CI: 0.54–0.68) and 0.64 (95%CI: 0.52–0.77) for CLE and IM≥2 cm, respectively.

Conclusions

We have identified and validated two prediction models for CLE and IM≥2 cm. Both models have fair prediction accuracies and can select out around 20% of individuals unlikely to benefit from investigation for Barrett’s esophagus. Such prediction models have the potential to generate useful cost-savings for BE screening among the symptomatic population.     

Detecting network cyber-attacks using an integrated statistical approach

Cluster Computing - Anomaly detection in the Internet of Things (IoT) is imperative to improve its reliability and safety. Detecting denial of service (DOS) and distributed DOS (DDOS) is one of the...