Statistical Delay Bound for WirelessHART Networks

In this paper we provide a performance analysis framework for wireless industrial networks by deriving a service curve and a bound on the delay violation probability. For this purpose we use the (min,x) stochastic network calculus as well as a recently presented recursive formula for an end-to-end delay bound of wireless heterogeneous networks. The derived results are mapped to WirelessHART networks used in process automation and were validated via simulations. In addition to WirelessHART, our results can be applied to any wireless network whose physical layer conforms the IEEE 802.15.4 standard, while its MAC protocol incorporates TDMA and channel hopping, like e.g. ISA100.11a or TSCH-based networks. The provided delay analysis is especially useful during the network design phase, offering further research potential towards optimal routing and power management in QoS-constrained wireless industrial networks.Comment: Accepted at PE-WASUN 201

Combined use of weather forecasting and satellite remote sensing information for fire risk, fire and fire impact monitoring

The restoration of fire-affected forest areas needs to be combined with their future protection from renewed catastrophic fires, such as those that occurred in Greece during the 2007 summer season. The present work demonstrates that the use of various sources of satellite data in conjunction with weather forecast information is capable of providing valuable information for the characterization of fire danger with the purpose of protecting the Greek national forest areas. This study shows that favourable meteorological conditions have contributed to the fire outbreak during the days of the unusually damaging fires in Peloponnese as well as Euboia (modern Greek: Evia) at the end of August 2007. During those days, Greece was located between an extended high pressure system in Central Europe and a low pressure system in the Middle East. Their combination resulted in strong north-northeasterly winds in the Aegean Sea. As a consequence, strong winds were also observed in the regions of Evia and Peloponnese, especially in mountainous areas. The analysis of satellite images showing smoke emitted from the fires corroborates the results from the weather forecasts. A further analysis using the Fraction of Absorbed Photosyntetically Active Radiation (FAPAR) as an indicator of active vegetation shows the extent of the destruction caused by the fire. The position of the burned areas coincides with that of the active fires detected in the earlier satellite image. Using the annual maximum FAPAR as an indicator of regional vegetation density, it was found that only regions with relatively high FAPAR were burned

How can patient registries facilitate guideline-based healthcare? A retrospective analysis of the CEDATA-GPGE registry for pediatric inflammatory bowel disease

Background: Early diagnosis is mandatory for the medical care of children and adolescents with pediatric-onset inflammatory bowel disease (PIBD). International guidelines ('Porto criteria') of the European Society for Pediatric Gastroenterology, Hepatology and Nutrition recommend medical diagnostic procedures in PIBD. Since 2004, German and Austrian pediatric gastroenterologists document diagnostic and treatment data in the patient registry CEDATA-GPGE on a voluntary basis. The aim of this retrospective study was to analyze whether the registry CEDATA-GPGE reflects the Porto criteria and to what extent diagnostic measures of PIBD according to the Porto criteria are documented. Methods: Data of CEDATA-GPGE were analyzed for the period January 2014 to December 2018. Variables representing the Porto criteria for initial diagnostic were identified and categorized. The average of the number of measures documented in each category was calculated for the diagnoses CD, UC, and IBD-U. Differences between the diagnoses were tested by Chi-square test. Data on possible differences between data documented in the registry and diagnostic procedures that were actually performed were obtained via a sample survey. Results: There were 547 patients included in the analysis. The median age of patients with incident CD (n = 289) was 13.6 years (IQR: 11.2-15.2), of patients with UC (n = 212) 13.1 years (IQR: 10.4-14.8) and of patients with IBD-U (n = 46) 12.2 years (IQR: 8.6-14.7). The variables identified in the registry fully reflect the recommendations by the Porto criteria. Only the disease activity indices PUCAI and PCDAI were not directly provided by participants but calculated from obtained data. The category 'Case history' were documented for the largest part (78.0%), the category 'Imaging of the small bowel' were documented least frequently (39.1%). In patients with CD, the categories 'Imaging of the small bowel' (χ2 = 20.7, Cramer-V = 0.2, p < 0.001) and 'Puberty stage' (χ2 = 9.8, Cramer-V = 0.1, p < 0.05) were documented more often than in patients with UC and IBD-U. Conclusion: The registry fully reproduces the guideline's recommendations for the initial diagnosis of PIBD. The proportion of documented diagnostic examinations varied within the diagnostic categories and between the diagnoses. Despite technological innovations, time and personnel capacities at participating centers and study center are necessary to ensure reliable data entry and to enable researchers to derive important insights into guideline-based care

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Excitation of local magnetic moments by tunnelling electrons

The advent of milli-kelvin scanning tunneling microscopes (STM) with inbuilt magnetic fields has opened access to the study of magnetic phenomena with atomic resolution at surfaces. In the case of single atoms adsorbed on a surface, the existence of different magnetic energy levels localized on the adsorbate is due to the breaking of the rotational invariance of the adsorbate spin by the interaction with its environment, leading to energy terms in the meV range. These structures were revealed by STM experiments in IBM Almaden in the early 2000's for atomic adsorbates on CuN surfaces. The experiments consisted in the study of the changes in conductance caused by inelastic tunnelling of electrons (IETS, Inelastic Electron Tunnelling Spectroscopy). Manganese and Iron adatoms were shown to have different magnetic anisotropies induced by the substrate. More experiments by other groups followed up, showing that magnetic excitations could be detected in a variety of systems: e.g. complex organic molecules showed that their magnetic anistropy was dependent on the molecular environment, piles of magnetic molecules showed that they interact via intermolecular exchange interaction, spin waves were excited on ferromagnetic surfaces and in Mn chains, and magnetic impurities have been analyzed on semiconductors. These experiments brought up some intriguing questions: the efficiency of magnetic excitations was very high, the excitations could or could not involve spin flip of the exciting electron and singular-like behavior was sometimes found at the excitation thresholds. These facts called for extended theoretical analysis; perturbation theories, sudden-approximation approaches and a strong coupling scheme successfully explained most of the magnetic inelastic processes. In addition, many-body approaches were also used to decipher the interplay between inelasComment: Review article to appear in Progress of Surface Scienc

Shared component modelling as an alternative to assess geographical variations in medical practice: gender inequalities in hospital admissions for chronic diseases

<p>Abstract</p> <p>Background</p> <p>Small area analysis is the most prevalent methodological approach in the study of unwarranted and systematic variation in medical practice at geographical level. Several of its limitations drive researchers to use disease mapping methods -deemed as a valuable alternative. This work aims at exploring these techniques using - as a case of study- the gender differences in rates of hospitalization in elderly patients with chronic diseases.</p> <p>Methods</p> <p>Design and study setting: An empirical study of 538,358 hospitalizations affecting individuals aged over 75, who were admitted due to a chronic condition in 2006, were used to compare Small Area Analysis (SAVA), the Besag-York-Mollie (BYM) modelling and the Shared Component Modelling (SCM). Main endpoint: Gender spatial variation was measured, as follows: SAVA estimated gender-specific utilization ratio; BYM estimated the fraction of variance attributable to spatial correlation in each gender; and, SCM estimated the fraction of variance shared by the two genders, and those specific for each one.</p> <p>Results</p> <p>Hospitalization rates due to chronic diseases in the elderly were higher in men (median per area 21.4 per 100 inhabitants, interquartile range: 17.6 to 25.0) than in women (median per area 13.7 per 100, interquartile range: 10.8 to 16.6). Whereas Utilization Ratios showed a similar geographical pattern of variation in both genders, BYM found a high fraction of variation attributable to spatial correlation in both men (71%, CI95%: 50 to 94) and women (62%, CI95%: 45 to 77). In turn, SCM showed that the geographical admission pattern was mainly shared, with just 6% (CI95%: 4 to 8) of variation specific to the women component.</p> <p>Conclusions</p> <p>Whereas SAVA and BYM focused on the magnitude of variation and on allocating where variability cannot be due to chance, SCM signalled discrepant areas where latent factors would differently affect men and women.</p

Ice-sheet collapse and sea-level rise at the Bølling warming 14,600 years ago

Past sea-level records provide invaluable information about the response of ice sheets to climate forcing. Some such records suggest that the last deglaciation was punctuated by a dramatic period of sea-level rise, of about 20 metres, in less than 500 years. Controversy about the amplitude and timing of this meltwater pulse (MWP-1A) has, however, led to uncertainty about the source of the melt water and its temporal and causal relationships with the abrupt climate changes of the deglaciation. Here we show that MWP-1A started no earlier than 14,650 years ago and ended before 14,310 years ago, making it coeval with the Bolling warming. Our results, based on corals drilled offshore from Tahiti during Integrated Ocean Drilling Project Expedition 310, reveal that the increase in sea level at Tahiti was between 12 and 22 metres, with a most probable value between 14 and 18 metres, establishing a significant meltwater contribution from the Southern Hemisphere. This implies that the rate of eustatic sea-level rise exceeded 40 millimetres per year during MWP-1A

Machine learning-based prediction of in-hospital death for patients with takotsubo syndrome: The InterTAK-ML model

AIMS Takotsubo syndrome (TTS) is associated with a substantial rate of adverse events. We sought to design a machine learning (ML)-based model to predict the risk of in-hospital death and to perform a clustering of TTS patients to identify different risk profiles. METHODS AND RESULTS A ridge logistic regression-based ML model for predicting in-hospital death was developed on 3482 TTS patients from the International Takotsubo (InterTAK) Registry, randomly split in a train and an internal validation cohort (75% and 25% of the sample size, respectively) and evaluated in an external validation cohort (1037 patients). Thirty-one clinically relevant variables were included in the prediction model. Model performance represented the primary endpoint and was assessed according to area under the curve (AUC), sensitivity and specificity. As secondary endpoint, a K-medoids clustering algorithm was designed to stratify patients into phenotypic groups based on the 10 most relevant features emerging from the main model. The overall incidence of in-hospital death was 5.2%. The InterTAK-ML model showed an AUC of 0.89 (0.85-0.92), a sensitivity of 0.85 (0.78-0.95) and a specificity of 0.76 (0.74-0.79) in the internal validation cohort and an AUC of 0.82 (0.73-0.91), a sensitivity of 0.74 (0.61-0.87) and a specificity of 0.79 (0.77-0.81) in the external cohort for in-hospital death prediction. By exploiting the 10 variables showing the highest feature importance, TTS patients were clustered into six groups associated with different risks of in-hospital death (28.8% vs. 15.5% vs. 5.4% vs. 1.0.8% vs. 0.5%) which were consistent also in the external cohort. CONCLUSION A ML-based approach for the identification of TTS patients at risk of adverse short-term prognosis is feasible and effective. The InterTAK-ML model showed unprecedented discriminative capability for the prediction of in-hospital death

Metabolic investigation of host/pathogen interaction using MS2-infected Escherichia coli

<p>Abstract</p> <p>Background</p> <p>RNA viruses are responsible for a variety of illnesses among people, including but not limited to the common cold, the flu, HIV, and ebola. Developing new drugs and new strategies for treating diseases caused by these viruses can be an expensive and time-consuming process. Mathematical modeling may be used to elucidate host-pathogen interactions and highlight potential targets for drug development, as well providing the basis for optimizing patient treatment strategies. The purpose of this work was to determine whether a genome-scale modeling approach could be used to understand how metabolism is impacted by the host-pathogen interaction during a viral infection. <it>Escherichia coli</it>/MS2 was used as the host-pathogen model system as MS2 is easy to work with, harmless to humans, but shares many features with eukaryotic viruses. In addition, the genome-scale metabolic model of <it>E. coli </it>is the most comprehensive model at this time.</p> <p>Results</p> <p>Employing a metabolic modeling strategy known as "flux balance analysis" coupled with experimental studies, we were able to predict how viral infection would alter bacterial metabolism. Based on our simulations, we predicted that cell growth and biosynthesis of the cell wall would be halted. Furthermore, we predicted a substantial increase in metabolic activity of the pentose phosphate pathway as a means to enhance viral biosynthesis, while a break down in the citric acid cycle was predicted. Also, no changes were predicted in the glycolytic pathway.</p> <p>Conclusions</p> <p>Through our approach, we have developed a technique of modeling virus-infected host metabolism and have investigated the metabolic effects of viral infection. These studies may provide insight into how to design better drugs. They also illustrate the potential of extending such metabolic analysis to higher order organisms, including humans.</p

Impact of stoichiometry representation on simulation of genotype-phenotype relationships in metabolic networks.

<div><p>Genome-scale metabolic networks provide a comprehensive structural framework for modeling genotype-phenotype relationships through flux simulations. The solution space for the metabolic flux state of the cell is typically very large and optimization-based approaches are often necessary for predicting the active metabolic state under specific environmental conditions. The objective function to be used in such optimization algorithms is directly linked with the biological hypothesis underlying the model and therefore it is one of the most relevant parameters for successful modeling. Although linear combination of selected fluxes is widely used for formulating metabolic objective functions, we show that the resulting optimization problem is sensitive towards stoichiometry representation of the metabolic network. This undesirable sensitivity leads to different simulation results when using numerically different but biochemically equivalent stoichiometry representations and thereby makes biological interpretation intrinsically subjective and ambiguous. We hereby propose a new method, Minimization of Metabolites Balance (MiMBl), which decouples the artifacts of stoichiometry representation from the formulation of the desired objective functions, by casting objective functions using metabolite turnovers rather than fluxes. By simulating perturbed metabolic networks, we demonstrate that the use of stoichiometry representation independent algorithms is fundamental for unambiguously linking modeling results with biological interpretation. For example, MiMBl allowed us to expand the scope of metabolic modeling in elucidating the mechanistic basis of several genetic interactions in <em>Saccharomyces cerevisiae</em>.</p> </div