Smart dual thermal network

Conventional district heating (DH) systems enable demand aggregation at district level and can provide high centralized heat generation performance values. However, thermal Renewable Energy Sources (RES) deployment at building level still remains low, and exploitation suboptimal, as it is limited by the instantaneous thermal load and storage capacity availability of each building. Buildings play the role of consumers that request a variable amount of heat over time and the thermal network the role of unidirectional heat supplier, without any smart interaction. The FP7 project A2PBEER has developed an innovative Smart Dual Thermal Network concept based on RES and Combined Heat and Power (CHP) as generation technologies, that enables transforming existing suboptimal DH systems, into integrated thermal networks with optimized performance and building level RES system production exploitation. It is based on an innovative Smart Dual Building Thermal Substation concept, which allows a bidirectional heat exchange of the buildings with the thermal network, and to aggregate district level distributed production and storage capacity (Virtual District Plant). With this approach buildings become prosumers maximizing decentralized RES production exploitation, as any possible local heat production surplus on any building of the district, will be delivered to the network to be used by other buildings. Additionally, this thermal network allows the delivery of the energy necessary to meet the heating and cooling demand of the buildings through a single hot water distribution network. In this way, it is possible to upgrade conventional DH systems to district heating and cooling systems, without the construction of a district cooling plant and a dedicated cooling distribution network. Cooling is produced at building level through sorption technologies using locally deployed solar collectors and the thermal network as energy sources. Finally, the district typologies and climatic conditions that maximize the potential of this thermal network concept have been identified.The research activities leading to the described developments and results, were funded by the FP7 project A2PBEER, under grant agreement No 906090. Special thanks to Olof Hallström and ClimateWell AB for making the TRNSYS model of the innovative sorption system and developing the component level simulation work

Knowledge-based gene expression classification via matrix factorization

Motivation: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks. Results: In this study we focus on unsupervised matrix factorization techniques and apply ICA and sparse NMF to microarray datasets. The latter monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that these tools are able to identify relevant signatures in the deduced component matrices and extract informative sets of marker genes from these gene expression profiles. The methods rely on the joint discriminative power of a set of marker genes rather than on single marker genes. With these sets of marker genes, corroborated by leave-one-out or random forest cross-validation, the datasets could easily be classified into related diagnostic categories. The latter correspond to either monocytes versus macrophages or healthy vs Niemann Pick C disease patients.Siemens AG, MunichDFG (Graduate College 638)DAAD (PPP Luso - Alem˜a and PPP Hispano - Alemanas

Dietary Fat Patterns and Outcomes in Acute Pancreatitis in Spain

Background/Objective: Evidence from basic and clinical studies suggests that unsaturated fatty acids (UFAs) might be relevant mediators of the development of complications in acute pancreatitis (AP). Objective: The aim of this study was to analyze outcomes in patients with AP from regions in Spain with different patterns of dietary fat intake. Materials and Methods: A retrospective analysis was performed with data from 1,655 patients with AP from a Spanish prospective cohort study and regional nutritional data from a Spanish cross-sectional study. Nutritional data considered in the study concern the total lipid consumption, detailing total saturated fatty acids, UFAs and monounsaturated fatty acids (MUFAs) consumption derived from regional data and not from the patient prospective cohort. Two multivariable analysis models were used: (1) a model with the Charlson comorbidity index, sex, alcoholic etiology, and recurrent AP; (2) a model that included these variables plus obesity. Results: In multivariable analysis, patients from regions with high UFA intake had a significantly increased frequency of local complications, persistent organ failure (POF), mortality, and moderate-to-severe disease in the model without obesity and a higher frequency of POF in the model with obesity. Patients from regions with high MUFA intake had significantly more local complications and moderate-to-severe disease; this significance remained for moderate-to-severe disease when obesity was added to the model. Conclusions: Differences in dietary fat patterns could be associated with different outcomes in AP, and dietary fat patterns may be a pre-morbid factor that determines the severity of AP. UFAs, and particulary MUFAs, may influence the pathogenesis of the severity of AP

Treatment of Asthma Exacerbations with the Human-Powered Nebuliser: A Randomised Parallel-Group Clinical Trial

Aims: The aim of this study was to compare a low-cost, human-powered nebuliser compressor with an electric nebuliser compressor for the treatment of mild to moderate asthma exacerbations in adults and children. Methods: This was a non-blinded, parallel-group, equivalence study, with 110 subjects between 6 and 65 years of age, conducted in the emergency department of a district hospital in Ilopango, El Salvador. Participants were assigned by random allocation to receive a 2.5-mg dose of salbutamol from the experimental human-powered nebuliser or the electric nebuliser control. All assigned participants completed treatment and were included in analysis. The study was not blinded as this was clinically unfeasible; however, data analysis was blinded. Results: The mean improvement in peak flow of the experimental and control groups was 37.5 (95% confidence interval (CI) 26.7–48.2) l/min and 38.7 (95% CI, 26.1–51.3) l/min, respectively, with a mean difference of 1.3 (95% CI, −15.1 to 17.7) l/min. The mean improvement in percent-expected peak flow for the experimental and control groups was 12.3% (95% CI, 9.1–15.5%) and 13.8% (95% CI, 9.8–17.9%), respectively, with a mean difference of 1.5% (95% CI, −3.6 to 6.6%). Conclusions: The human-powered nebuliser compressor is equivalent to a standard nebuliser compressor for the treatment of mild-to-moderate asthma. (Funded by the Opus Dean’s Fund, Marquette University College of Engineering; ClinicalTrials.gov NCT01795742.

Hemophagocytic syndrome in patients from SLE Registry from the Spanish Society of Rheumatology (RELESSER)

Poster presentationInstituto de Salud Carlos III; PI11/0285

North Tropical Atlantic influence on western Amazon fire season variability

The prevailing wet climate in the western Amazon is not favorable to the natural occurrence of fires. Nevertheless, the current process of clearing of humid forests for agriculture and cattle ranching has increased the vulnerability of the region to the spread of fires. Using meteorological stations precipitation and the Moderate Resolution Spectroradiometer (MODIS) Active-Fires (AF) during 2000-2009, we show that fire anomalies vary closely with July-August-September (JAS) precipitation variability as measured by the Standardized Precipitation Index (SPI). The precipitation variability is, in turn, greatly determined by sea surface temperature (SST) anomalies in the North Tropical Atlantic (NTA). We develop a linear regression model to relate local fire activity to an index of the NTA-SST. By using seasonal forecasts of SST from a coupled model, we are able to predict anomalous JAS fire activity as early as April. We applied the method to predict the severe 2010 JAS season, which indicated strongly positive seasonal fire anomalies within the 95% prediction confidence intervals in most western Amazon. The spatial distribution of predicted SPI was also in accordance with observed precipitation anomalies. This three months lead time precipitation and fire prediction product in the western Amazon could help local decision makers to establish an early warning systems or other appropriate course of action before the fire season begins

Fast 2D/3D object representation with growing neural gas

This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction

Preliminary experience with the smooth muscle troponin-like protein, calponin, as a novel biomarker for diagnosing acute aortic dissection

Aims The early diagnosis of acute aortic dissection (AD) remains challenging. We sought to determine the utility of the troponin-like protein of smooth muscle, calponin, as a diagnostic biomarker of acute AD. Methods and results Immunoassays against calponin (acidic, basic, and neutral isoforms) were developed and the levels were compared in a convenience sample of 59 patients with radiographically proven AD [34 males, age 59+15 (SD) years] vs. 158 patients suspected of having AD at presentation (116 males, age 63+15 years) but whose final diagnosis was not AD. Basic calponin, which is the most specific and abundant in smooth muscle, and acidic calponin, respectively, showed greater than two-fold and three-fold elevations in patients with acute AD. Diagnostic performance as determined by receiver-operating characteristics curve analysis showed that both acidic and basic calponin have the potential to detect AD in the first 24 h [respective areas under the curve (AUCs) 0.63 and 0.58], with superior performance of basic calponin (when compared with acidic) in the initial 6 h (respective AUCs 0.63 and 0.67). Conclusion Circulating calponin levels were elevated in acute AD compared with controls. These biomarkers have the potential for use as an early diagnostic biomarker for acute AD. Keywords Aortic dissection ? Biomarke