EUREGIO MRSA-net Twente/Munsterland - a Dutch-German cross-border network for the prevention and control of infections caused by methicillin-resistant Staphylococcus aureus

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Euregionale MRSA-preventie en bestrijding: een vergelijking van Duitse en Nederlandse richtlijnen

Om te voorkomen dat de MRSA-prevalentie in Nederland stijgt door grensoverschrijdend gezondheidsverkeer met Duitsland dient documentatie van MRSA-richtlijnen zodanig aangepast te worden dat de richtlijnen zowel voor Nederlands als Duits ziekenhuispersoneel toepasbaar en navolgbaar zijn. Een systematische vergelijking van Duitse en Nederlandse MRSA-richtlijnen laat zien dat Duitse richtlijnen de nadruk leggen op het documenteren van standaarden door verwijzingen naar de wet en bewijsvoering. De Nederlandse richtlijnen benadrukken handelingsinstructies door een gebruikersgerichte en risicogeoriënteerde benadering. Mogelijk hangt de niet-optimale naleving van MRSA-richtlijnen samen met de inrichting van het richtlijndocument. Het opstellen van richtlijndocumentatie die aanpasbaar is per land, zou de kwaliteit van het grensoverschrijdende gezondheidsverkeer tussen Nederland en Duitsland kunnen bevorderen. In dit artikel wordt een aanzet gegeven voor een dergelijk adaptief kader

Resolution learning in deep convolutional networks using scale-space theory

Resolution in deep convolutional neural networks (CNNs) is typically bounded by the receptive field size through filter sizes, and subsampling layers or strided convolutions on feature maps. The optimal resolution may vary significantly depending on the dataset. Modern CNNs hard-code their resolution hyper-parameters in the network architecture which makes tuning such hyper-parameters cumbersome. We propose to do away with hard-coded resolution hyper-parameters and aim to learn the appropriate resolution from data. We use scale-space theory to obtain a self-similar parametrization of filters and make use of the N-Jet: a truncated Taylor series to approximate a filter by a learned combination of Gaussian derivative filters. The parameter sigma of the Gaussian basis controls both the amount of detail the filter encodes and the spatial extent of the filter. Since sigma is a continuous parameter, we can optimize it with respect to the loss. The proposed N-Jet layer achieves comparable performance when used in state-of-the art architectures, while learning the correct resolution in each layer automatically. We evaluate our N-Jet layer on both classification and segmentation, and we show that learning sigma is especially beneficial for inputs at multiple sizes

Lessons from spatial and environmental assessment of energy potentials for Anaerobic Digestion production systems applied to the Netherlands

Anaerobic digestion (AD) can play an important role in achieving the renewable energy goals set within the European Union. Within this article the focus is placed on reaching the Dutch local renewable production goal set for the year 2020 with locally available biomass waste flows, avoiding intensive farming and long transport distances of biomass and energy carriers. The bio-energy yields, efficiency and environmental sustainability are analyzed for five municipalities in the northern part of the Netherlands, using three utilization pathways: green gas production, combined heat and power, and waste management. Literature has indicated that there is sufficient bio-energy potential in local waste streams to reach the aforementioned goal. However, the average useful energy finally produced by the AD production pathway is significantly lower, often due to poor quality biomass and difficult harvesting conditions. Furthermore, of the potential bio-energy input in the three utilization pathways considered in this article, on average: 73% can be extracted as green gas; 57% as heat and power; and 44% as green gas in the waste management pathway. This demonstrates that the Dutch renewable production goal cannot be reached. The green gas utilization pathway is preferable for reaching production goals as it retains the highest amount of energy from the feedstock. However, environmental sustainability favors the waste management pathway as it has a higher overall efficiency, and lower emissions and environmental impacts. The main lessons drawn from the aforementioned are twofold: there is a substantial gap between bio-energy potential and net energy gain; there is also a gap between top-down regulation and actual emission reduction and sustainability. Therefore, a full life cycle-based understanding of the absolute energy and environmental impact of biogas production and utilization pathways is required to help governments to develop optimal policies serving a broad set of sustainable objectives. Well-founded ideas and decisions are needed on how best to utilize the limited biomass availability most effectively and sustainably in the near and far future, as biogas can play a supportive role for integrating other renewable sources into local decentralized energy systems as a flexible and storable energy source. (C) 2016 Elsevier Ltd. All rights reserved

Socio-economic factors, gender and smoking as determinants of COPD in a low-income country of sub-Saharan Africa: FRESH AIR Uganda.

In Uganda, biomass smoke seems to be the largest risk factor for the development of COPD, but socio-economic factors and gender may have a role. Therefore, more in-depth research is needed to understand the risk factors. The aim of this study was to investigate the impact of socio-economic factors and gender differences on the COPD prevalence in Uganda. The population comprised 588 randomly selected participants (>30 years) who previously completed the FRESH AIR Uganda study. In this post hoc analysis, the impact of several socio-economic characteristics, gender and smoking on the prevalence of COPD was assessed using a logistic regression model. The main risk factors associated with COPD were non-Bantu ethnicity (odds ratio (OR) 1.73, 95% confidence interval (CI) 1.06-2.82, P=0.030), biomass fuel use for heating (OR 1.76, 95% CI 1.03-3.00, P=0.038), former smoker (OR 1.87, 95% CI 0.97-3.60, P=0.063) and being unmarried (OR 0.087, 95% CI 0.93-2.95, P=0.087). A substantial difference in the prevalence of COPD was seen between the two ethnic groups: non-Bantu 20% and Bantu 12.9%. Additional analysis between these two groups showed significant differences in socio-economic circumstances: non-Bantu people smoked more (57.7% vs 10.7%), lived in tobacco-growing areas (72% vs 14.8%) and were less educated (28.5% vs 12.9% had no education). With regard to gender, men with COPD were unmarried (OR 3.09, 95% CI 1.25-7.61, P=0.015) and used more biomass fuel for heating (OR 2.15, 95% CI 1.02-4.54, P=0.045), and women with COPD were former smokers (OR 3.35, 95% CI 1.22-9.22, P=0.019). Only a few socio-economic factors (i.e., smoking, biomass fuel use for heating, marital status and non-Bantu ethnicity) have been found to be associated with COPD. This applied for gender differences as well (i.e., for men, marital status and biomass fuel for heating, and for women being a former smoker). More research is needed to clarify the complexity of the different risk factors