Debris flow hazard modelling on medium scale: Valtellina di Tirano, Italy

Debris flow hazard modelling at medium (regional) scale has been subject of various studies in recent years. In this study, hazard zonation was carried out, incorporating information about debris flow initiation probability (spatial and temporal), and the delimitation of the potential runout areas. Debris flow hazard zonation was carried out in the area of the Consortium of Mountain Municipalities of Valtellina di Tirano (Central Alps, Italy). The complexity of the phenomenon, the scale of the study, the variability of local conditioning factors, and the lacking data limited the use of process-based models for the runout zone delimitation. Firstly, a map of hazard initiation probabilities was prepared for the study area, based on the available susceptibility zoning information, and the analysis of two sets of aerial photographs for the temporal probability estimation. Afterwards, the hazard initiation map was used as one of the inputs for an empirical GIS-based model (Flow-R), developed at the University of Lausanne (Switzerland). An estimation of the debris flow magnitude was neglected as the main aim of the analysis was to prepare a debris flow hazard map at medium scale. A digital elevation model, with a 10 m resolution, was used together with landuse, geology and debris flow hazard initiation maps as inputs of the Flow-R model to restrict potential areas within each hazard initiation probability class to locations where debris flows are most likely to initiate. Afterwards, runout areas were calculated using multiple flow direction and energy based algorithms. Maximum probable runout zones were calibrated using documented past events and aerial photographs. Finally, two debris flow hazard maps were prepared. The first simply delimits five hazard zones, while the second incorporates the information about debris flow spreading direction probabilities, showing areas more likely to be affected by future debris flows. Limitations of the modelling arise mainly from the models applied and analysis scale, which are neglecting local controlling factors of debris flow hazard. The presented approach of debris flow hazard analysis, associating automatic detection of the source areas and a simple assessment of the debris flow spreading, provided results for consequent hazard and risk studies. However, for the validation and transferability of the parameters and results to other study areas, more testing is needed.

Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale: some case studies

Every year, debris flows cause huge damage in mountainous areas. Due to population pressure in hazardous zones, the socio-economic impact is much higher than in the past. Therefore, the development of indicative susceptibility hazard maps is of primary importance, particularly in developing countries. However, the complexity of the phenomenon and the variability of local controlling factors limit the use of processbased models for a first assessment. A debris flow model has been developed for regional susceptibility assessments using digital elevation model (DEM) with a GIS-based approach.. The automatic identification of source areas and the estimation of debris flow spreading, based on GIS tools, provide a substantial basis for a preliminary susceptibility assessment at a regional scale. One of the main advantages of this model is its workability. In fact, everything is open to the user, from the data choice to the selection of the algorithms and their parameters. The Flow-R model was tested in three different contexts: two in Switzerland and one in Pakistan, for indicative susceptibility hazard mapping. It was shown that the quality of the DEM is the most important parameter to obtain reliable results for propagation, but also to identify the potential debris flows sources.

Artificial neural network study of whole-cell bacterial bioreporter response determined using fluorescence flow cytometry.

Genetically engineered bioreporters are an excellent complement to traditional methods of chemical analysis. The application of fluorescence flow cytometry to detection of bioreporter response enables rapid and efficient characterization of bacterial bioreporter population response on a single-cell basis. In the present study, intrapopulation response variability was used to obtain higher analytical sensitivity and precision. We have analyzed flow cytometric data for an arsenic-sensitive bacterial bioreporter using an artificial neural network-based adaptive clustering approach (a single-layer perceptron model). Results for this approach are far superior to other methods that we have applied to this fluorescent bioreporter (e.g., the arsenic detection limit is 0.01 microM, substantially lower than for other detection methods/algorithms). The approach is highly efficient computationally and can be implemented on a real-time basis, thus having potential for future development of high-throughput screening applications.

Comparativecost-effectivenessanalyses of cardiacmagneticresonanceimaging versus invasive coronary angiography combined with fractional flow reserve testing to diagnose ischemia in coronary artery disease

Introduction: According to guidelines, patients with coronary artery disease (CAD) should undergo revascularization if myocardial ischemia is present. While coronary angiography (CXA) allows the morphological assessment of CAD, the fractional flow reserve (FFR) has proved to be a complementary invasive test to assess the functional significance of CAD, i.e. to detect ischemia. Perfusion Cardiac Magnetic Resonance (CMR) has turned out to be a robust non-invasive technique to assess myocardial ischemia. The objective: is to compare the cost-effectiveness ratio - defined as the costs per patient correctly diagnosed - of two algorithms used to diagnose hemodynamically significant CAD in relation to the pretest likelihood of CAD: 1) aCMRto assess ischemia before referring positive patients to CXA (CMR + CXA), 2) a CXA in all patients combined with a FFR test in patients with angiographically positive stenoses (CXA + FFR). Methods: The costs, evaluated from the health care system perspective in the Swiss, German, the United Kingdom (UK) and the United States (US) contexts, included public prices of the different tests considered as outpatient procedures, complications' costs and costs induced by diagnosis errors (false negative). The effectiveness criterion wasthe ability to accurately identify apatient with significantCAD.Test performancesused in the model were based on the clinical literature. Using a mathematical model, we compared the cost-effectiveness ratio for both algorithms for hypothetical patient cohorts with different pretest likelihood of CAD. Results: The cost-effectiveness ratio decreased hyperbolically with increasing pretest likelihood of CAD for both strategies. CMR + CXA and CXA + FFR were equally costeffective at a pretest likelihood of CAD of 62% in Switzerland, 67% in Germany, 83% in the UK and 84% in the US with costs of CHF 5'794, Euros 1'472, £ 2'685 and $ 2'126 per patient correctly diagnosed. Below these thresholds, CMR + CXA showed lower costs per patient correctly diagnosed than CXA + FFR. Implications for the health care system/professionals/patients/society These results facilitate decision making for the clinical use of new generations of imaging procedures to detect ischemia. They show to what extent the cost-effectiveness to diagnose CAD depends on the prevalence of the disease.

S-system parameter estimation for noisy metabolic profiles using newton-flow analysis.

Biochemical systems are commonly modelled by systems of ordinary differential equations (ODEs). A particular class of such models called S-systems have recently gained popularity in biochemical system modelling. The parameters of an S-system are usually estimated from time-course profiles. However, finding these estimates is a difficult computational problem. Moreover, although several methods have been recently proposed to solve this problem for ideal profiles, relatively little progress has been reported for noisy profiles. We describe a special feature of a Newton-flow optimisation problem associated with S-system parameter estimation. This enables us to significantly reduce the search space, and also lends itself to parameter estimation for noisy data. We illustrate the applicability of our method by applying it to noisy time-course data synthetically produced from previously published 4- and 30-dimensional S-systems. In addition, we propose an extension of our method that allows the detection of network topologies for small S-systems. We introduce a new method for estimating S-system parameters from time-course profiles. We show that the performance of this method compares favorably with competing methods for ideal profiles, and that it also allows the determination of parameters for noisy profiles.

Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale

The development of susceptibility maps for debris flows is of primary importance due to population pressure in hazardous zones. However, hazard assessment by processbased modelling at a regional scale is difficult due to the complex nature of the phenomenon, the variability of local controlling factors, and the uncertainty in modelling parameters. A regional assessment must consider a simplified approach that is not highly parameter dependant and that can provide zonation with minimum data requirements. A distributed empirical model has thus been developed for regional susceptibility assessments using essentially a digital elevation model (DEM). The model is called Flow-R for Flow path assessment of gravitational hazards at a Regional scale (available free of charge under www.flow-r.org) and has been successfully applied to different case studies in various countries with variable data quality. It provides a substantial basis for a preliminary susceptibility assessment at a regional scale. The model was also found relevant to assess other natural hazards such as rockfall, snow avalanches and floods. The model allows for automatic source area delineation, given user criteria, and for the assessment of the propagation extent based on various spreading algorithms and simple frictional laws.We developed a new spreading algorithm, an improved version of Holmgren's direction algorithm, that is less sensitive to small variations of the DEM and that is avoiding over-channelization, and so produces more realistic extents. The choices of the datasets and the algorithms are open to the user, which makes it compliant for various applications and dataset availability. Amongst the possible datasets, the DEM is the only one that is really needed for both the source area delineation and the propagation assessment; its quality is of major importance for the results accuracy. We consider a 10m DEM resolution as a good compromise between processing time and quality of results. However, valuable results have still been obtained on the basis of lower quality DEMs with 25m resolution.

Debris flow modeling for susceptibility mapping at regional to national scale in Norway

Debris flows and related landslide processes occur in many regions all over Norway and pose a significant hazard to inhabited areas. Within the framework of the development of a national debris flows susceptibility map, we are working on a modeling approach suitable for Norway with a nationwide coverage. The discrimination of source areas is based on an index approach, which includes topographic parameters and hydrological settings. For the runout modeling, we use the Flow-R model (IGAR, University of Lausanne), which is based on combined probabilistic and energetic algorithms for the assessment of the spreading of the flow and maximum runout distances. First results for different test areas have shown that runout distances can be modeled reliably. For the selection of source areas, however, additional factors have to be considered, such as the lithological and quaternary geological setting, in order to accommodate the strong variation in debris flow activity in the different geological, geomorphological and climate regions of Norway.

Performance of noninvasive ventilation algorithms on ICU ventilators during pressure support: a clinical study.

To evaluate the impact of noninvasive ventilation (NIV) algorithms available on intensive care unit ventilators on the incidence of patient-ventilator asynchrony in patients receiving NIV for acute respiratory failure. Prospective multicenter randomized cross-over study. Intensive care units in three university hospitals. Patients consecutively admitted to the ICU and treated by NIV with an ICU ventilator were included. Airway pressure, flow and surface diaphragmatic electromyography were recorded continuously during two 30-min periods, with the NIV (NIV+) or without the NIV algorithm (NIV0). Asynchrony events, the asynchrony index (AI) and a specific asynchrony index influenced by leaks (AIleaks) were determined from tracing analysis. Sixty-five patients were included. With and without the NIV algorithm, respectively, auto-triggering was present in 14 (22%) and 10 (15%) patients, ineffective breaths in 15 (23%) and 5 (8%) (p = 0.004), late cycling in 11 (17%) and 5 (8%) (p = 0.003), premature cycling in 22 (34%) and 21 (32%), and double triggering in 3 (5%) and 6 (9%). The mean number of asynchronies influenced by leaks was significantly reduced by the NIV algorithm (p < 0.05). A significant correlation was found between the magnitude of leaks and AIleaks when the NIV algorithm was not activated (p = 0.03). The global AI remained unchanged, mainly because on some ventilators with the NIV algorithm premature cycling occurs. In acute respiratory failure, NIV algorithms provided by ICU ventilators can reduce the incidence of asynchronies because of leaks, thus confirming bench test results, but some of these algorithms can generate premature cycling.

PulseCath iVAC 3LTM hemodynamic performance for simple assisted flow.

The PulseCath iVAC 3L? left ventricular assist device is an option to treat transitory left heart failure or dysfunction post-cardiac surgery. Assisted blood flow should reach up to 3 l/min. In the present in vitro model exact pump flow, depending on various frequencies and afterload was examined. Optimal flow was achieved with inflation/deflation frequencies of about 70-80/min. The maximal flow rate was achieved at about 2.5 l/min with a minimal afterload of 22 mmHg. Handling of the device was easy due to the connection to a standard intra-aortic balloon pump console. With increasing afterload (up to a simulated mean systemic pressure of 66 mmHg) flow rate and cardiac support are in some extent limited.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Quantification of aortic flow by phase-contrast magnetic resonance in patients with bicuspid aortic valve.

AIMS: Bicuspid aortic valve (BAV) causes complex flow patterns in the ascending aorta (AAo), which may compromise the accuracy of flow measurement by phase-contrast magnetic resonance (PC-MR). Therefore, we aimed to assess and compare the accuracy of forward flow measurement in the AAo, where complex flow is more dominant in BAV patients, with flow quantification in the left ventricular outflow tract (LVOT) and the aortic valve orifice (AV), where complex flow is less important, in BAV patients and controls. METHODS AND RESULTS: Flow was measured by PC-MR in 22 BAV patients and 20 controls at the following positions: (i) LVOT, (ii) AV, and (iii) AAo, and compared with the left ventricular stroke volume (LVSV). The correlation between the LVSV and the forward flow in the LVOT, the AV, and the AAo was good in BAV patients (r = 0.97/0.96/0.93; P < 0.01) and controls (r = 0.96/0.93/0.93; P < 0.01). However, in relation with the LVSV, the forward flow in the AAo was mildly underestimated in controls and much more in BAV patients [median (inter-quartile range): 9% (4%/15%) vs. 22% (8%/30%); P < 0.01]. This was not the case in the LVOT and the AV. The severity of flow underestimation in the AAo was associated with flow eccentricity. CONCLUSION: Flow measurement in the AAo leads to an underestimation of the forward flow in BAV patients. Measurement in the LVOT or the AV, where complex flow is less prominent, is an alternative means for quantifying the systolic forward flow in BAV patients.

Atrial natriuretic peptides: reproducibility of renal effects and response of liver blood flow.

To assess the variability of the response to exogenous atrial natriuretic peptide (ANP), it was infused at the rate of 1 microgram/min for 2 h in 6 salt-loaded normal volunteers under controlled conditions on 2 occasions at an interval of 1 week. The effect on solute excretion and the haemodynamic and endocrine actions were highly reproducible. The constant ANP infusion caused a delayed and prolonged excretion of sodium, chloride and calcium, no change in potassium or phosphate excretion or in glomerular filtration rate but a marked decrease in renal plasma flow. Blood pressure, heart rate and the plasma levels of angiotensin II, aldosterone, arginine vasopressin and plasma renin activity were unaltered. The effect of a 2-h infusion of ANP 0.5 microgram/min or its vehicle on apparent hepatic blood flow (HBF) was also studied in 14 normal volunteers by measuring the indocyanine green clearance. A 21% decrease in HBF was observed in subjects who received the ANP infusion (p less than 0.01 vs vehicle). Thus, ANP infused at a dose that did not lower blood pressure decreased both renal and liver blood flow in normotensive volunteers. The renal and endocrine responses to ANP were reproducible over a 1-week interval.

Gene flow among wild and domesticated almond species: insights from chloroplast and nuclear markers

Hybridization has played a central role in the evolutionary history of domesticated plants. Notably, several breeding programs relying on gene introgression from the wild compartment have been performed in fruit tree species within the genus Prunus but few studies investigated spontaneous gene flow among wild and domesticated Prunus species. Consequently, a comprehensive understanding of genetic relationships and levels of gene flow between domesticated and wild Prunus species is needed. Combining nuclear and chloroplastic microsatellites, we investigated the gene flow and hybridization among two key almond tree species, the cultivated Prunus dulcis and one of the most widespread wild relative Prunus orientalis in the Fertile Crescent. We detected high genetic diversity levels in both species along with substantial and symmetric gene flow between the domesticated P. dulcis and the wild P. orientalis. These results were discussed in light of the cultivated species diversity, by outlining the frequent spontaneous genetic contributions of wild species to the domesticated compartment. In addition, crop-to-wild gene flow suggests that ad hoc transgene containment strategies would be required if genetically modified cultivars were introduced in the northwestern Mediterranean.