A chrysophyte stomatocyst-based reconstruction of cold-season air temperature from Alpine Lake Silvaplana (AD 1500–2003); methods and concepts for quantitative inferences

Relatively little is known about past cold-season temperature variability in high-Alpine regions because of a lack of natural cold-season temperature proxies as well as under-representation of high-altitude sites in meteorological, early-instrumental and documentary data sources. Recent studies have shown that chrysophyte stomatocysts, or simply cysts (sub-fossil algal remains of Chrysophyceae and Synurophyceae), are among the very few natural proxies that can be used to reconstruct cold-season temperatures. This study presents a quantitative, high-resolution (5-year), cold-season (Oct–May) temperature reconstruction based on sub-fossil chrysophyte stomatocysts in the annually laminated (varved) sediments of high-Alpine Lake Silvaplana, SE Switzerland (1,789 m a.s.l.), since AD 1500. We first explore the method used to translate an ecologically meaningful variable based on a biological proxy into a simple climate variable. A transfer function was applied to reconstruct the ‘date of spring mixing’ from cyst assemblages. Next, statistical regression models were tested to convert the reconstructed ‘dates of spring mixing’ into cold-season surface air temperatures with associated errors. The strengths and weaknesses of this approach are thoroughly tested. One much-debated, basic assumption for reconstructions (‘stationarity’), which states that only the environmental variable of interest has influenced cyst assemblages and the influence of confounding variables is negligible over time, is addressed in detail. Our inferences show that past cold-season air-temperature fluctuations were substantial and larger than those of other temperature reconstructions for Europe and the Alpine region. Interestingly, in this study, recent cold-season temperatures only just exceed those of previous, multi-decadal warm phases since AD 1500. These findings highlight the importance of local studies to assess natural climate variability at high altitudes.

Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package

Kriging-based optimization relying on noisy evaluations of complex systems has recently motivated contributions from various research communities. Five strategies have been implemented in the DiceOptim package. The corresponding functions constitute a user-friendly tool for solving expensive noisy optimization problems in a sequential framework, while offering some flexibility for advanced users. Besides, the implementation is done in a unified environment, making this package a useful device for studying the relative performances of existing approaches depending on the experimental setup. An overview of the package structure and interface is provided, as well as a description of the strategies and some insight about the implementation challenges and the proposed solutions. The strategies are compared to some existing optimization packages on analytical test functions and show promising performances.

Statistical model-based segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs.

PURPOSE    Segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs is required to create a three-dimensional model of the hip joint for use in planning and treatment. However, manually extracting the femoral contour is tedious and prone to subjective bias, while automatic segmentation must accommodate poor image quality, anatomical structure overlap, and femur deformity. A new method was developed for femur segmentation in AP pelvic radiographs. METHODS    Using manual annotations on 100 AP pelvic radiographs, a statistical shape model (SSM) and a statistical appearance model (SAM) of the femur contour were constructed. The SSM and SAM were used to segment new AP pelvic radiographs with a three-stage approach. At initialization, the mean SSM model is coarsely registered to the femur in the AP radiograph through a scaled rigid registration. Mahalanobis distance defined on the SAM is employed as the search criteria for each annotated suggested landmark location. Dynamic programming was used to eliminate ambiguities. After all landmarks are assigned, a regularized non-rigid registration method deforms the current mean shape of SSM to produce a new segmentation of proximal femur. The second and third stages are iteratively executed to convergence. RESULTS    A set of 100 clinical AP pelvic radiographs (not used for training) were evaluated. The mean segmentation error was [Formula: see text], requiring [Formula: see text] s per case when implemented with Matlab. The influence of the initialization on segmentation results was tested by six clinicians, demonstrating no significance difference. CONCLUSIONS    A fast, robust and accurate method for femur segmentation in digital AP pelvic radiographs was developed by combining SSM and SAM with dynamic programming. This method can be extended to segmentation of other bony structures such as the pelvis.

Regional differences in symptomatic fever management among paediatricians in Switzerland: the results of a cross-sectional Web-based survey

AIMS In symptomatic fever management, there is often a gap between everyday clinical practice and current evidence. We were interested to see whether the three linguistic regions of Switzerland differ in the management of fever. METHODS A close-ended questionnaire, sent to 900 Swiss paediatricians, was answered by 322 paediatricians. Two hundred and fourteen respondents were active in the German speaking, 78 in the French speaking and 30 in the Italian speaking region. RESULTS Paediatricians from the French and Italian speaking regions identify a lower temperature threshold for initiating a treatment and more frequently reduce it for children with a history of febrile seizures. A reduced general appearance leads more frequently to a lower threshold for treatment in the German speaking than in the French and Italian speaking areas. Among 1.5 and 5-year-old children the preference for the rectal route is more pronounced in the German than in the French speaking region. French speaking respondents more frequently prescribe ibuprofen and an alternating regimen with two drugs than German speaking respondents. Finally, the stated occurrence of exaggerated fear of fever was higher in the German and Italian speaking regions. CONCLUSIONS Switzerland offers the opportunity to compare three different regions with respect to management of febrile children. This inquiry shows regional differences in symptomatic fever management and in the perceived frequency of exaggerated fear of fever. The gap between available evidence and clinical practice is more pronounced in the French and in the Italian speaking regions than in the German speaking region.

Temporal evolution of strut light intensity after implantation of bioresorbable polymeric intracoronary scaffolds in the ABSORB cohort B trial-an application of a new quantitative method based on optical coherence tomography.

BACKGROUND Quantitative light intensity analysis of the strut core by optical coherence tomography (OCT) may enable assessment of changes in the light reflectivity of the bioresorbable polymeric scaffold from polymer to provisional matrix and connective tissues, with full disappearance and integration of the scaffold into the vessel wall. The aim of this report was to describe the methodology and to apply it to serial human OCT images post procedure and at 6, 12, 24 and 36 months in the ABSORB cohort B trial. METHODS AND RESULTS In serial frequency-domain OCT pullbacks, corresponding struts at different time points were identified by 3-dimensional foldout view. The peak and median values of light intensity were measured in the strut core by dedicated software. A total of 303 corresponding struts were serially analyzed at 3 time points. In the sequential analysis, peak light intensity increased gradually in the first 24 months after implantation and reached a plateau (relative difference with respect to baseline [%Dif]: 61.4% at 12 months, 115.0% at 24 months, 110.7% at 36 months), while the median intensity kept increasing at 36 months (%Dif: 14.3% at 12 months, 75.0% at 24 months, 93.1% at 36 months). CONCLUSIONS Quantitative light intensity analysis by OCT was capable of detecting subtle changes in the bioresorbable strut appearance over time, and could be used to monitor the bioresorption and integration process of polylactide struts.

Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment

Conservation and monitoring of forest biodiversity requires reliable information about forest structure and composition at multiple spatial scales. However, detailed data about forest habitat characteristics across large areas are often incomplete due to difficulties associated with field sampling methods. To overcome this limitation we employed a nationally available light detection and ranging (LiDAR) remote sensing dataset to develop variables describing forest landscape structure across a large environmental gradient in Switzerland. Using a model species indicative of structurally rich mountain forests (hazel grouse Bonasa bonasia), we tested the potential of such variables to predict species occurrence and evaluated the additional benefit of LiDAR data when used in combination with traditional, sample plot-based field variables. We calibrated boosted regression trees (BRT) models for both variable sets separately and in combination, and compared the models’ accuracies. While both field-based and LiDAR models performed well, combining the two data sources improved the accuracy of the species’ habitat model. The variables retained from the two datasets held different types of information: field variables mostly quantified food resources and cover in the field and shrub layer, LiDAR variables characterized heterogeneity of vegetation structure which correlated with field variables describing the understory and ground vegetation. When combined with data on forest vegetation composition from field surveys, LiDAR provides valuable complementary information for encompassing species niches more comprehensively. Thus, LiDAR bridges the gap between precise, locally restricted field-data and coarse digital land cover information by reliably identifying habitat structure and quality across large areas.

Associating optical measurements and estimating orbits of geocentric objects through population-based meta-heuristic methods

Currently several thousands of objects are being tracked in the MEO and GEO regions through optical means. The problem faced in this framework is that of Multiple Target Tracking (MTT). In this context both, the correct associations among the observations and the orbits of the objects have to be determined. The complexity of the MTT problem is defined by its dimension S. The number S corresponds to the number of fences involved in the problem. Each fence consists of a set of observations where each observation belongs to a different object. The S ≥ 3 MTT problem is an NP-hard combinatorial optimization problem. There are two general ways to solve this. One way is to seek the optimum solution, this can be achieved by applying a branch-and- bound algorithm. When using these algorithms the problem has to be greatly simplified to keep the computational cost at a reasonable level. Another option is to approximate the solution by using meta-heuristic methods. These methods aim to efficiently explore the different possible combinations so that a reasonable result can be obtained with a reasonable computational effort. To this end several population-based meta-heuristic methods are implemented and tested on simulated optical measurements. With the advent of improved sensors and a heightened interest in the problem of space debris, it is expected that the number of tracked objects will grow by an order of magnitude in the near future. This research aims to provide a method that can treat the correlation and orbit determination problems simultaneously, and is able to efficiently process large data sets with minimal manual intervention.