Towards an Energy-Aware Framework for Application Development and Execution in Heterogeneous Parallel Architectures

The Transparent heterogeneous hardware Architecture deployment for eNergy Gain in Operation (TANGO) project’s goal is to characterise factors which affect power consumption in software development and operation for Heterogeneous Parallel Hardware (HPA) environments. Its main contribution is the combination of requirements engineering and design modelling for self-adaptive software systems, with power consumption awareness in relation to these environments. The energy efficiency and application quality factors are integrated into the application lifecycle (design, implementation and operation). To support this, the key novelty of the project is a reference architecture and its implementation. Moreover, a programming model with built-in support for various hardware architectures including heterogeneous clusters, heterogeneous chips and programmable logic devices is provided. This leads to a new cross-layer programming approach for heterogeneous parallel hardware architectures featuring software and hardware modelling. Application power consumption and performance, data location and time-criticality optimization, as well as security and dependability requirements on the target hardware architecture are supported by the architecture

Preoperative oral antibiotic prophylaxis reduces Pseudomonas aeruginosa surgical site infections after elective colorectal surgery: a multicenter prospective cohort study

BACKGROUND: Healthcare-associated infections caused by Pseudomonas aeruginosa are associated with poor outcomes. However, the role of P. aeruginosa in surgical site infections after colorectal surgery has not been evaluated. The aim of this study was to determine the predictive factors and outcomes of surgical site infections caused by P. aeruginosa after colorectal surgery, with special emphasis on the role of preoperative oral antibiotic prophylaxis. METHODS: We conducted an observational, multicenter, prospective cohort study of all patients undergoing elective colorectal surgery at 10 Spanish hospitals (2011-2014). A logistic regression model was used to identify predictive factors for P. aeruginosa surgical site infections. RESULTS: Out of 3701 patients, 669 (18.1%) developed surgical site infections, and 62 (9.3%) of these were due to P. aeruginosa. The following factors were found to differentiate between P. aeruginosa surgical site infections and those caused by other microorganisms: American Society of Anesthesiologists' score III-IV (67.7% vs 45.5%, p = 0.001, odds ratio (OR) 2.5, 95% confidence interval (95% CI) 1.44-4.39), National Nosocomial Infections Surveillance risk index 1-2 (74.2% vs 44.2%, p < 0.001, OR 3.6, 95% CI 2.01-6.56), duration of surgery ≥75thpercentile (61.3% vs 41.4%, p = 0.003, OR 2.2, 95% CI 1.31-3.83) and oral antibiotic prophylaxis (17.7% vs 33.6%, p = 0.01, OR 0.4, 95% CI 0.21-0.83). Patients with P. aeruginosa surgical site infections were administered antibiotic treatment for a longer duration (median 17 days [interquartile range (IQR) 10-24] vs 13d [IQR 8-20], p = 0.015, OR 1.1, 95% CI 1.00-1.12), had a higher treatment failure rate (30.6% vs 20.8%, p = 0.07, OR 1.7, 95% CI 0.96-2.99), and longer hospitalization (median 22 days [IQR 15-42] vs 19d [IQR 12-28], p = 0.02, OR 1.1, 95% CI 1.00-1.17) than those with surgical site infections due to other microorganisms. Independent predictive factors associated with P. aeruginosa surgical site infections were the National Nosocomial Infections Surveillance risk index 1-2 (OR 2.3, 95% CI 1.03-5.40) and the use of oral antibiotic prophylaxis (OR 0.4, 95% CI 0.23-0.90). CONCLUSIONS: We observed that surgical site infections due to P. aeruginosa are associated with a higher National Nosocomial Infections Surveillance risk index, poor outcomes, and lack of preoperative oral antibiotic prophylaxis. These findings can aid in establishing specific preventive measures and appropriate empirical antibiotic treatment

Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data

© 2016. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. Ioannis Kioutsioukis, et al, ‘Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data’, Atmospheric Chemistry and Physics, Vol 16(24): 15629-15652, published 20 December 2016, the version of record is available at doi:10.5194/acp-16-15629-2016 Published by Copernicus Publications on behalf of the European Geosciences Union.Simulations from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions) as well as those intrinsic to the model (e.g. physical parameterization, chemical mechanism). Multi-model ensembles can improve the forecast skill, provided that certain mathematical conditions are fulfilled. In this work, four ensemble methods were applied to two different datasets, and their performance was compared for ozone (O3), nitrogen dioxide (NO2) and particulate matter (PM10). Apart from the unconditional ensemble average, the approach behind the other three methods relies on adding optimum weights to members or constraining the ensemble to those members that meet certain conditions in time or frequency domain. The two different datasets were created for the first and second phase of the Air Quality Model Evaluation International Initiative (AQMEII). The methods are evaluated against ground level observations collected from the EMEP (European Monitoring and Evaluation Programme) and AirBase databases. The goal of the study is to quantify to what extent we can extract predictable signals from an ensemble with superior skill over the single models and the ensemble mean. Verification statistics show that the deterministic models simulate better O3 than NO2 and PM10, linked to different levels of complexity in the represented processes. The unconditional ensemble mean achieves higher skill compared to each station's best deterministic model at no more than 60 % of the sites, indicating a combination of members with unbalanced skill difference and error dependence for the rest. The promotion of the right amount of accuracy and diversity within the ensemble results in an average additional skill of up to 31 % compared to using the full ensemble in an unconditional way. The skill improvements were higher for O3 and lower for PM10, associated with the extent of potential changes in the joint distribution of accuracy and diversity in the ensembles. The skill enhancement was superior using the weighting scheme, but the training period required to acquire representative weights was longer compared to the sub-selecting schemes. Further development of the method is discussed in the conclusion.Peer reviewedFinal Published versio

Assessment of the MACC reanalysis and its influence as chemical boundary conditions for regional air quality modeling in AQMEII-2

The Air Quality Model Evaluation International Initiative (AQMEII) has now reached its second phase which is dedicated to the evaluation of online coupled chemistry-meteorology models. Sixteen modeling groups from Europe and five from North America have run regional air quality models to simulate the year 2010 over one European and one North American domain. The MACC re-analysis has been used as chemical initial (IC) and boundary conditions (BC) by all participating regional models in AQMEII-2. The aim of the present work is to evaluate the MACC re-analysis along with the participating regional models against a set of ground-based measurements (O3, CO, NO, NO2, SO2, SO42−) and vertical profiles (O3 and CO). Results indicate different degrees of agreement between the measurements and the MACC re-analysis, with an overall better performance over the North American domain. The influence of BC on regional air quality simulations is analyzed in a qualitative way by contrasting model performance for the MACC re-analysis with that for the regional models. This approach complements more quantitative approaches documented in the literature that often have involved sensitivity simulations but typically were limited to only one or only a few regional scale models. Results suggest an important influence of the BC on ozone for which the underestimation in winter in the MACC re-analysis is mimicked by the regional models. For CO, it is found that background concentrations near the domain boundaries are rather close to observations while those over the interior of the two continents are underpredicted by both MACC and the regional models over Europe but only by MACC over North America. This indicates that emission differences between the MACC re-analysis and the regional models can have a profound impact on model performance and points to the need for harmonization of inputs in future linked global/regional modeling studies

Morphological and molecular analysis of natural hybrids between the diploid Centaurea aspera L. and the tetraploid C. seridis L. (Compositae)

[EN] Polyploidy and hybridisation are the basis of the evolution of Centaurea (Compositae). At the El Saler dune field (eastern Spain), the diploid Centaurea aspera ssp. stenophylla and the tetraploid C. seridis ssp. maritima form a polyploid complex in which C. x subdecurrens individuals occur. This polyploid complex was analysed morphologically and genetically, using random amplified polymorphic DNA (RAPD) and tubulin-based polymorphism (TBP) markers. Flow cytometry showed that the hybrids are triploid, which is a rare finding in Centaurea. Morphologically, in contrast to leaf characters, flowering characters clearly discriminated the three taxa. The genetic analyses confirm that C. x subdecurrens is a result of the hybridisation between Centaurea aspera ssp. stenophylla and C. seridis ssp. maritima, and suggest that backcrossing events and gene flow are very rare or absent. Although the hybrids likely represent true F1 offspring, they displayed some genetic diversity that is probably due to the combination of alleles. Genetic diversity was higher in diploid than in tetraploid individuals. This fact, and the high degree of sterility of the triploid hybrids, may reflect a cytotype minority exclusion effect. This may cause spatial segregation, which effectively takes place in the study area. Dune disturbance may lead to an overlapping of the parents' distribution areas, facilitating hybridisation.This work is posthumously dedicated to Antonio Samo Lumbreras, to whom we are very grateful for all his help. This study was sponsored by the Valencian Government (Research Project GVPRE/2008/130) and the Universitat Politecnica de Valencia (Research Project Ref. 3241).Ferriol Molina, M.; Garmendia, A.; Ruiz, J.; Merle Farinós, HB.; Boira Tortajada, H. (2012). Morphological and molecular analysis of natural hybrids between the diploid Centaurea aspera L. and the tetraploid C. seridis L. (Compositae). Plant Biosystems. 146(1):86-100. https://doi.org/10.1080/11263504.2012.727878S86100146

Factors related to the development of health-promoting community activities in Spanish primary healthcare: two case-control studies.

Objective Spanish primary healthcare teams have the responsibility of performing health-promoting community activities (CAs), although such activities are not widespread. Our aim was to identify the factors related to participation in those activities. Design Two case–control studies. setting Performed in primary care of ve Spanish regions. subjects In the rst study, cases were teams that performed health-promoting CAs and controls were those that did not. In the second study (on case teams from the rst study), cases were professionals who developed these activities and controls were those who did not. Main outcome measures Team, professional and community characteristics collected through questionnaires (team managers/professionals) and from secondary sources. results The rst study examined 203 teams (103 cases, 100 controls). Adjusted factors associated with performing CAs were percentage of nurses (OR 1.07, 95% CI 1.01 to 1.14), community socioeconomic status (higher vs lower OR 2.16, 95% CI 1.18 to 3.95) and performing undergraduate training (OR 0.44, 95% CI 0.21 to 0.93). In the second study, 597 professionals responded (254 cases, 343 controls). Adjusted factors were professional classi cation (physicians do fewer activities than nurses and social workers do more), training in CAs (OR 1.9, 95% CI 1.2 to 3.1), team support (OR 2.9, 95% CI 1.5 to 5.7), seniority (OR 1.06, 95% CI 1.03 to 1.09), nursing tutor (OR 2.0, 95% CI 1.1 to 3.5), motivation (OR 3.7, 95% CI 1.8 to 7.5), collaboration with non-governmental organisations (OR 1.9, 95% CI 1.2 to 3.1) and participation in neighbourhood activities (OR 3.1, 95% CI 1.9 to 5.1). Conclusions Professional personal characteristics, such as social sensitivity, profession, to feel team support or motivation, have in uence in performing health-promoting CAs. In contrast to the opinion expressed by many professionals, workload is not related to performance of health-promoting CAs

Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data

Simulations from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions) as well as those intrinsic to the model (e.g. physical parameterization, chemical mechanism). Multi-model ensembles can improve the forecast skill, provided that certain mathematical conditions are fulfilled. In this work, four ensemble methods were applied to two different datasets, and their performance was compared for ozone (O$_{3}$), nitrogen dioxide (NO$_{2}$) and particulate matter (PM$_{10}$). Apart from the unconditional ensemble average, the approach behind the other three methods relies on adding optimum weights to members or constraining the ensemble to those members that meet certain conditions in time or frequency domain. The two different datasets were created for the first and second phase of the Air Quality Model Evaluation International Initiative (AQMEII). The methods are evaluated against ground level observations collected from the EMEP (European Monitoring and Evaluation Programme) and AirBase databases. The goal of the study is to quantify to what extent we can extract predictable signals from an ensemble with superior skill over the single models and the ensemble mean. Verification statistics show that the deterministic models simulate better O$_{3}$ than NO$_{2}$ and PM$_{10}$, linked to different levels of complexity in the represented processes. The unconditional ensemble mean achieves higher skill compared to each station’s best deterministic model at no more than 60% of the sites, indicating a combination of members with unbalanced skill difference and error dependence for the rest. The promotion of the right amount of accuracy and diversity within the ensemble results in an average additional skill of up to 31% compared to using the full ensemble in an unconditional way. The skill improvements were higher for O$_{3}$ and lower for PM$_{10}$, associated with the extent of potential changes in the joint distribution of accuracy and diversity in the ensembles. The skill enhancement was superior using the weighting scheme, but the training period required to acquire representative weights was longer compared to the sub-selecting schemes. Further development of the method is discussed in the conclusion

Near-ground Effect of Height on Pollen Exposure

The effect of height on pollen concentration is not well documented and little is known about the near-ground vertical profile of airborne pollen. This is important as most measuring stations are on roofs, but patient exposure is at ground level. Our study used a big data approach to estimate the near-ground vertical profile of pollen concentrations based on a global study of paired stations located at different heights. We analyzed paired sampling stations located at different heights between 1.5 and 50m above ground level (AGL). This provided pollen data from 59 Hirst-type volumetric traps from 25 different areas, mainly in Europe, but also covering North America and Australia, resulting in about 2,000,000 daily pollen concentrations analyzed. The daily ratio of the amounts of pollen from different heights per location was used, and the values of the lower station were divided by the higher station. The lower station of paired traps recorded more pollen than the higher trap. However, while the effect of height on pollen concentration was clear, it was also limited (average ratio 1.3, range 0.7–2.2). The standard deviation of the pollen ratio was highly variable when the lower station was located close to the ground level (below 10m AGL). We show that pollen concentrations measured at >10m are representative for background near-ground levels