Differences in genotype and virulence among four multidrug-resistant <i>Streptococcus pneumoniae</i> isolates belonging to the PMEN1 clone

We report on the comparative genomics and characterization of the virulence phenotypes of four &lt;i&gt;S. pneumoniae&lt;/i&gt; strains that belong to the multidrug resistant clone PMEN1 (Spain&lt;sup&gt;23F&lt;/sup&gt; ST81). Strains SV35-T23 and SV36-T3 were recovered in 1996 from the nasopharynx of patients at an AIDS hospice in New York. Strain SV36-T3 expressed capsule type 3 which is unusual for this clone and represents the product of an in vivo capsular switch event. A third PMEN1 isolate - PN4595-T23 - was recovered in 1996 from the nasopharynx of a child attending day care in Portugal, and a fourth strain - ATCC700669 - was originally isolated from a patient with pneumococcal disease in Spain in 1984. We compared the genomes among four PMEN1 strains and 47 previously sequenced pneumococcal isolates for gene possession differences and allelic variations within core genes. In contrast to the 47 strains - representing a variety of clonal types - the four PMEN1 strains grouped closely together, demonstrating high genomic conservation within this lineage relative to the rest of the species. In the four PMEN1 strains allelic and gene possession differences were clustered into 18 genomic regions including the capsule, the blp bacteriocins, erythromycin resistance, the MM1-2008 prophage and multiple cell wall anchored proteins. In spite of their genomic similarity, the high resolution chinchilla model was able to detect variations in virulence properties of the PMEN1 strains highlighting how small genic or allelic variation can lead to significant changes in pathogenicity and making this set of strains ideal for the identification of novel virulence determinant

Boundary-layer turbulence as a kangaroo process

A nonlocal mixing-length theory of turbulence transport by finite size eddies is developed by means of a novel evaluation of the Reynolds stress. The analysis involves the contruct of a sample path space and a stochastic closure hypothesis. The simplifying property of exhange (strong eddies) is satisfied by an analytical sampling rate model. A nonlinear scaling relation maps the path space onto the semi-infinite boundary layer. The underlying near-wall behavior of fluctuating velocities perfectly agrees with recent direct numerical simulations. The resulting integro-differential equation for the mixing of scalar densities represents fully developed boundary-layer turbulence as a nondiffusive (Kubo-Anderson or kangaroo) type of stochastic process. The model involves a scaling exponent (with → in the diffusion limit). For the (partly analytical) solution for the mean velocity profile, excellent agreement with the experimental data yields 0.58. © 1995 The American Physical Society

Comparative assessment of gasification based coal power plants with various CO2 capture technologies producing electricity and hydrogen

Seven different types of gasification-based coal conversion processes for producing mainly electricity and in some cases hydrogen (H2), with and without carbon dioxide (CO2) capture, were compared on a consistent basis through simulation studies. The flowsheet for each process was developed in a chemical process simulation tool “Aspen Plus”. The pressure swing adsorption (PSA), physical absorption (Selexol), and chemical looping combustion (CLC) technologies were separately analyzed for processes with CO2 capture. The performances of the above three capture technologies were compared with respect to energetic and exergetic efficiencies, and the level of CO2 emission. The effect of air separation unit (ASU) and gas turbine (GT) integration on the power output of all the CO2 capture cases is assessed. Sensitivity analysis was carried out for the CLC process (electricity-only case) to examine the effect of temperature and water-cooling of the air reactor on the overall efficiency of the process. The results show that, when only electricity production in considered, the case using CLC technology has an electrical efficiency 1.3% and 2.3% higher than the PSA and Selexol based cases, respectively. The CLC based process achieves an overall CO2 capture efficiency of 99.9% in contrast to 89.9% for PSA and 93.5% for Selexol based processes. The overall efficiency of the CLC case for combined electricity and H2 production is marginally higher (by 0.3%) than Selexol and lower (by 0.6%) than PSA cases. The integration between the ASU and GT units benefits all three technologies in terms of electrical efficiency. Furthermore, our results suggest that it is favorable to operate the air reactor of the CLC process at higher temperatures with excess air supply in order to achieve higher power efficiency

A mobile phone application for the assessment and management of youth mental health problems in primary care: a randomised controlled trial

<p>Abstract</p> <p>Background</p> <p>Over 75% of mental health problems begin in adolescence and primary care has been identified as the target setting for mental health intervention by the World Health Organisation. The <it>mobiletype </it>program is a mental health assessment and management mobile phone application which monitors mood, stress, coping strategies, activities, eating, sleeping, exercise patterns, and alcohol and cannabis use at least daily, and transmits this information to general practitioners (GPs) via a secure website in summary format for medical review.</p> <p>Methods</p> <p>We conducted a randomised controlled trial in primary care to examine the mental health benefits of the <it>mobiletype </it>program. Patients aged 14 to 24 years were recruited from rural and metropolitan general practices. GPs identified and referred eligible participants (those with mild or more mental health concerns) who were randomly assigned to either the intervention group (where mood, stress, and daily activities were monitored) or the attention comparison group (where only daily activities were monitored). Both groups self-monitored for 2 to 4 weeks and reviewed the monitoring data with their GP. GPs, participants, and researchers were blind to group allocation at randomisation. Participants completed pre-, post-, and 6-week post-test measures of the Depression, Anxiety, Stress Scale and an Emotional Self Awareness (ESA) Scale.</p> <p>Results</p> <p>Of the 163 participants assessed for eligibility, 118 were randomised and 114 participants were included in analyses (intervention group n = 68, comparison group n = 46). Mixed model analyses revealed a significant group by time interaction on ESA with a medium size of effect suggesting that the <it>mobiletype </it>program significantly increases ESA compared to an attention comparison. There was no significant group by time interaction for depression, anxiety, or stress, but a medium to large significant main effect for time for each of these mental health measures. Post-hoc analyses suggested that participation in the RCT lead to enhanced GP mental health care at pre-test and improved mental health outcomes.</p> <p>Conclusions</p> <p>Monitoring mental health symptoms appears to increase ESA and implementing a mental health program in primary care and providing frequent reminders, clinical resources, and support to GPs substantially improved mental health outcomes for the sample as a whole.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov <a href="http://www.clinicaltrials.gov/ct2/show/NCT00794222">NCT00794222</a>.</p

ARTIST: a fully automated artifact rejection algorithm for single-pulse TMS-EEG data

Concurrent single-pulse TMS-EEG (spTMS-EEG) is an emerging noninvasive tool for probing causal brain dynamics in humans. However, in addition to the common artifacts in standard EEG data, spTMS-EEG data suffer from enormous stimulation-induced artifacts, posing significant challenges to the extraction of neural information. Typically, neural signals are analyzed after a manual time-intensive and often subjective process of artifact rejection. Here we describe a fully automated algorithm for spTMS-EEG artifact rejection. A key step of this algorithm is to decompose the spTMS-EEG data into statistically independent components (ICs), and then train a pattern classifier to automatically identify artifact components based on knowledge of the spatio-temporal profile of both neural and artefactual activities. The autocleaned and hand-cleaned data yield qualitatively similar group evoked potential waveforms. The algorithm achieves a 95% IC classification accuracy referenced to expert artifact rejection performance, and does so across a large number of spTMS-EEG data sets (n = 90 stimulation sites), retains high accuracy across stimulation sites/subjects/populations/montages, and outperforms current automated algorithms. Moreover, the algorithm was superior to the artifact rejection performance of relatively novice individuals, who would be the likely users of spTMS-EEG as the technique becomes more broadly disseminated. In summary, our algorithm provides an automated, fast, objective, and accurate method for cleaning spTMS-EEG data, which can increase the utility of TMS-EEG in both clinical and basic neuroscience settings.Wei Wu, Corey J. Keller, Nigel C. Rogasch, Parker Longwell, Emmanuel Shpigel, Camarin E. Rolle, Amit Etki