Achieving a more realistic assessment of rockfall hazards by coupling three-dimensional process models and field-based tree-ring data

Sound knowledge of the spatial and temporal patterns of rockfalls is fundamental for the management of this very common hazard in mountain environments. Process-based, three-dimensional simulation models are nowadays capable of reproducing the spatial distribution of rockfall occurrences with reasonable accuracy through the simulation of numerous individual trajectories on highly-resolved digital terrain models. At the same time, however, simulation models typically fail to quantify the ‘real’ frequency of rockfalls (in terms of return intervals). The analysis of impact scars on trees, in contrast, yields real rockfall frequencies, but trees may not be present at the location of interest and rare trajectories may not necessarily be captured due to the limited age of forest stands. In this article, we demonstrate that the coupling of modeling with tree-ring techniques may overcome the limitations inherent to both approaches. Based on the analysis of 64 cells (40 m × 40 m) of a rockfall slope located above a 1631-m long road section in the Swiss Alps, we illustrate results from 488 rockfalls detected in 1260 trees. We illustrate that tree impact data cannot only be used (i) to reconstruct the real frequency of rockfalls for individual cells, but that they also serve (ii) the calibration of the rockfall model Rockyfor3D, as well as (iii) the transformation of simulated trajectories into real frequencies. Calibrated simulation results are in good agreement with real rockfall frequencies and exhibit significant differences in rockfall activity between the cells (zones) along the road section. Real frequencies, expressed as rock passages per meter road section, also enable quantification and direct comparison of the hazard potential between the zones. The contribution provides an approach for hazard zoning procedures that complements traditional methods with a quantification of rockfall frequencies in terms of return intervals through a systematic inclusion of impact records in trees.

Trends of mean and extreme temperature indices since 1874 at low-elevation sites in the Southern Alps

We describe the recovery of three daily meteorological records for the southern Alps (Domodossola, Riva del Garda, and Rovereto), all starting in the second half of the nineteenth century. We use these new data, along with additional records, to study regional changes in the mean temperature and extreme indices of heat waves and cold spells frequency and duration over the period 1874–2015. The records are homogenized using subdaily cloud cover observations as a constraint for the statistical model, an approach that has never been applied before in the literature. A case study based on a record of parallel observations between a traditional meteorological window and a modern screen shows that the use of cloud cover can reduce the root-mean-square error of the homogenization by up to 30% in comparison to an unaided statistical correction. We find that mean temperature in the southern Alps has increased by 1.4°C per century over the analyzed period, with larger increases in daily minimum temperatures than maximum temperatures. The number of hot days in summer has more than tripled, and a similar increase is observed in duration of heat waves. Cold days in winter have dropped at a similar rate. These trends are mainly caused by climate change over the last few decades.

Trends in mean and extreme precipitation in the Mount Kenya region from observations and reanalyses

Land and water management in semi-arid regions requires detailed information on precipitation distribution, including extremes, and changes therein. Such information is often lacking. This paper describes statistics of mean and extreme precipitation in a unique data set from the Mount Kenya region, encompassing around 50 stations with at least 30 years of data. We describe the data set, including quality control procedures and statistical break detection. Trends in mean precipitation and extreme indices calculated from these data for individual rainy seasons are compared with corresponding trends in reanalysis products. From 1979 to 2011, mean precipitation decreased at 75% of the stations during the ‘long rains’ (March to May) and increased at 70% of the stations during the ‘short rains’ (October to December). Corresponding trends are found in the number of heavy precipitation days, and maximum of consecutive 5-day precipitation. Conversely, an increase in consecutive dry days within both main rainy seasons is found. However, trends are only statistically significant in very few cases. Reanalysis data sets agree with observations with respect to interannual variability, while correlations are considerably lower for monthly deviations (ratios) from the mean annual cycle. While some products well reproduce the rainfall climatology and some the spatial trend pattern, no product reproduces both.

Evaluation of a Bayesian MCMC Random Effects Inference Methodology for Capture-Mark-Recapture Data

Monte Carlo simulation was used to evaluate properties of a simple Bayesian MCMC analysis of the random effects model for single group Cormack-Jolly-Seber capture-recapture data. The MCMC method is applied to the model via a logit link, so parameters p, S are on a logit scale, where logit(S) is assumed to have, and is generated from, a normal distribution with mean μ and variance σ2 . Marginal prior distributions on logit(p) and μ were independent normal with mean zero and standard deviation 1.75 for logit(p) and 100 for μ ; hence minimally informative. Marginal prior distribution on σ2 was placed on τ2=1/σ2 as a gamma distribution with α=β=0.001 . The study design has 432 points spread over 5 factors: occasions (t) , new releases per occasion (u), p, μ , and σ . At each design point 100 independent trials were completed (hence 43,200 trials in total), each with sample size n=10,000 from the parameter posterior distribution. At 128 of these design points comparisons are made to previously reported results from a method of moments procedure. We looked at properties of point and interval inference on μ , and σ based on the posterior mean, median, and mode and equal-tailed 95% credibility interval. Bayesian inference did very well for the parameter μ , but under the conditions used here, MCMC inference performance for σ was mixed: poor for sparse data (i.e., only 7 occasions) or σ=0 , but good when there were sufficient data and not small σ .

Corrected kriging update formulae for batch-sequential data assimilation

Recently, a lot of effort has been spent in the efficient computation of kriging predictors when observations are assimilated sequentially. In particular, kriging update formulae enabling significant computational savings were derived. Taking advantage of the previous kriging mean and variance computations helps avoiding a costly matrix inversion when adding one observation to the TeX already available ones. In addition to traditional update formulae taking into account a single new observation, Emery (2009) proposed formulae for the batch-sequential case, i.e. when TeX new observations are simultaneously assimilated. However, the kriging variance and covariance formulae given in Emery (2009) for the batch-sequential case are not correct. In this paper, we fix this issue and establish correct expressions for updated kriging variances and covariances when assimilating observations in parallel. An application in sequential conditional simulation finally shows that coupling update and residual substitution approaches may enable significant speed-ups.

MOREMATA: Stata module (Mata) to provide various functions

This package includes various Mata functions. kern(): various kernel functions; kint(): kernel integral functions; kdel0(): canonical bandwidth of kernel; quantile(): quantile function; median(): median; iqrange(): inter-quartile range; ecdf(): cumulative distribution function; relrank(): grade transformation; ranks(): ranks/cumulative frequencies; freq(): compute frequency counts; histogram(): produce histogram data; mgof(): multinomial goodness-of-fit tests; collapse(): summary statistics by subgroups; _collapse(): summary statistics by subgroups; gini(): Gini coefficient; sample(): draw random sample; srswr(): SRS with replacement; srswor(): SRS without replacement; upswr(): UPS with replacement; upswor(): UPS without replacement; bs(): bootstrap estimation; bs2(): bootstrap estimation; bs_report(): report bootstrap results; jk(): jackknife estimation; jk_report(): report jackknife results; subset(): obtain subsets, one at a time; composition(): obtain compositions, one by one; ncompositions(): determine number of compositions; partition(): obtain partitions, one at a time; npartitionss(): determine number of partitions; rsubset(): draw random subset; rcomposition(): draw random composition; colvar(): variance, by column; meancolvar(): mean and variance, by column; variance0(): population variance; meanvariance0(): mean and population variance; mse(): mean squared error; colmse(): mean squared error, by column; sse(): sum of squared errors; colsse(): sum of squared errors, by column; benford(): Benford distribution; cauchy(): cumulative Cauchy-Lorentz dist.; cauchyden(): Cauchy-Lorentz density; cauchytail(): reverse cumulative Cauchy-Lorentz; invcauchy(): inverse cumulative Cauchy-Lorentz; rbinomial(): generate binomial random numbers; cebinomial(): cond. expect. of binomial r.v.; root(): Brent's univariate zero finder; nrroot(): Newton-Raphson zero finder; finvert(): univariate function inverter; integrate_sr(): univariate function integration (Simpson's rule); integrate_38(): univariate function integration (Simpson's 3/8 rule); ipolate(): linear interpolation; polint(): polynomial inter-/extrapolation; plot(): Draw twoway plot; _plot(): Draw twoway plot; panels(): identify nested panel structure; _panels(): identify panel sizes; npanels(): identify number of panels; nunique(): count number of distinct values; nuniqrows(): count number of unique rows; isconstant(): whether matrix is constant; nobs(): number of observations; colrunsum(): running sum of each column; linbin(): linear binning; fastlinbin(): fast linear binning; exactbin(): exact binning; makegrid(): equally spaced grid points; cut(): categorize data vector; posof(): find element in vector; which(): positions of nonzero elements; locate(): search an ordered vector; hunt(): consecutive search; cond(): matrix conditional operator; expand(): duplicate single rows/columns; _expand(): duplicate rows/columns in place; repeat(): duplicate contents as a whole; _repeat(): duplicate contents in place; unorder2(): stable version of unorder(); jumble2(): stable version of jumble(); _jumble2(): stable version of _jumble(); pieces(): break string into pieces; npieces(): count number of pieces; _npieces(): count number of pieces; invtokens(): reverse of tokens(); realofstr(): convert string into real; strexpand(): expand string argument; matlist(): display a (real) matrix; insheet(): read spreadsheet file; infile(): read free-format file; outsheet(): write spreadsheet file; callf(): pass optional args to function; callf_setup(): setup for mm_callf().

Why evolutionary biologists should get seriously involved in ecological monitoring and applied biodiversity assessment programs

While ecological monitoring and biodiversity assessment programs are widely implemented and relatively well developed to survey and monitor the structure and dynamics of populations and communities in many ecosystems, quantitative assessment and monitoring of genetic and phenotypic diversity that is important to understand evolutionary dynamics is only rarely integrated. As a consequence, monitoring programs often fail to detect changes in these key components of biodiversity until after major loss of diversity has occurred. The extensive efforts in ecological monitoring have generated large data sets of unique value to macro-scale and long-term ecological research, but the insights gained from such data sets could be multiplied by the inclusion of evolutionary biological approaches. We argue that the lack of process-based evolutionary thinking in ecological monitoring means a significant loss of opportunity for research and conservation. Assessment of genetic and phenotypic variation within and between species needs to be fully integrated to safeguard biodiversity and the ecological and evolutionary dynamics in natural ecosystems. We illustrate our case with examples from fishes and conclude with examples of ongoing monitoring programs and provide suggestions on how to improve future quantitative diversity surveys.

Quantitative T2 mapping of knee cartilage: differentiation of healthy control cartilage and cartilage repair tissue in the knee with unloading - initial results

Purpose: To prospectively determine on T2 cartilage maps the effect of unloading during a clinical magnetic resonance (MR) examination in the postoperative follow-up of patients after matrix-associated autologous chondrocyte transplantation (MACT) of the knee joint. Materials and Methods: Ethical approval for this study was provided by the local ethics commission, and written informed consent was obtained. Thirty patients (mean age, 35.4 years +/- 10.5) with a mean postoperative follow-up period of 29.1 months +/- 24.4 were enrolled. A multiecho spin-echo T2-weighted sequence was performed at the beginning (early unloading) and end (late unloading) of the MR examination, with an interval of 45 minutes. Mean and zonal region of interest T2 measurements were obtained in control cartilage and cartilage repair tissue. Statistical analysis of variance was performed. Results: The change in T2 values of control cartilage (early unloading, 50.2 msec +/- 8.4; late unloading, 51.3 msec +/- 8.5) was less pronounced than the change in T2 values of cartilage repair tissue (early unloading, 51.8 msec +/- 11.7; late unloading, 56.1 msec +/- 14.4) (P = .024). The difference between control cartilage and cartilage repair tissue was not significant for early unloading (P = .314) but was significant for late unloading (P = .036). Zonal T2 measurements revealed a higher dependency on unloading for the superficial cartilage layer. Conclusion: Our results suggest that T2 relaxation can be used to assess early and late unloading values of articular cartilage in a clinical setting and that the time point of the quantitative T2 measurement affects the differentiation between native and abnormal articular cartilage. (c) RSNA, 2010.

Multiplex assays for biomarker research and clinical application: translational science coming of age

Over the last decade, translational science has come into the focus of academic medicine, and significant intellectual and financial efforts have been made to initiate a multitude of bench-to-bedside projects. The quest for suitable biomarkers that will significantly change clinical practice has become one of the biggest challenges in translational medicine. Quantitative measurement of proteins is a critical step in biomarker discovery. Assessing a large number of potential protein biomarkers in a statistically significant number of samples and controls still constitutes a major technical hurdle. Multiplexed analysis offers significant advantages regarding time, reagent cost, sample requirements and the amount of data that can be generated. The two contemporary approaches in multiplexed and quantitative biomarker validation, antibody-based immunoassays and MS-based multiple (or selected) reaction monitoring, are based on different assay principles and instrument requirements. Both approaches have their own advantages and disadvantages and therefore have complementary roles in the multi-staged biomarker verification and validation process. In this review, we discuss quantitative immunoassay and multiple reaction monitoring/selected reaction monitoring assay principles and development. We also discuss choosing an appropriate platform, judging the performance of assays, obtaining reliable, quantitative results for translational research and clinical applications in the biomarker field.

Satisfaction with follow-up consultations among younger adults treated for cancer: the role of quality of life and psychological variables

Given increased survival rates and treatment-related late effects, follow-up for cancer survivors is increasingly recommended. However, information about adverse events (e.g. possibility of late effects) may be distressing for the cancer survivor and lead to poor clinic attendance. Survivor satisfaction with appointments and the information provided are important. The Monitoring Process Model provides a theoretical framework to understand how survivors cope with threatening information, and consequences for follow-up care. Our aims were to describe satisfaction with routine follow-up and association between monitoring/blunting and satisfaction with care.

The warning-sign hierarchy between quantitative subcortical motor mapping and continuous motor evoked potential monitoring during resection of supratentorial brain tumors

Mapping and monitoring are believed to provide an early warning sign to determine when to stop tumor removal to avoid mechanical damage to the corticospinal tract (CST). The objective of this study was to systematically compare subcortical monopolar stimulation thresholds (1-20 mA) with direct cortical stimulation (DCS)-motor evoked potential (MEP) monitoring signal abnormalities and to correlate both with new postoperative motor deficits. The authors sought to define a mapping threshold and DCS-MEP monitoring signal changes indicating a minimal safe distance from the CST.

Long-term cardiac remodeling and arrhythmias in nonelite marathon runners

Long-term endurance sports are associated with atrial remodeling and atrial arrhythmias. More importantly, high-level endurance training may promote right ventricular (RV) dysfunction and complex ventricular arrhythmias. We investigated the long-term consequences of marathon running on cardiac remodeling as a potential substrate for arrhythmias with a focus on the right heart. We invited runners of the 2010 Grand Prix of Bern, a 10-mile race. Of 873 marathon and nonmarathon runners who applied, 122 (61 women) entered the final analysis. Subjects were stratified according to former marathon participations: control group (nonmarathon runners, n = 34), group 1 (1 marathon to 5 marathons, mean 2.7, n = 46), and group 2 (≥6 marathons, mean 12.8, n = 42). Mean age was 42 ± 7 years. Results were adjusted for gender, age, and lifetime training hours. Right and left atrial sizes increased with marathon participations. In group 2, right and left atrial enlargements were present in 60% and 74% of athletes, respectively. RV and left ventricular (LV) dimensions showed no differences among groups, and RV or LV dilatation was present in only 2.4% or 4.3% of marathon runners, respectively. In multiple linear regression analysis, marathon participation was an independent predictor of right and left atrial sizes but had no effect on RV and LV dimensions and function. Atrial and ventricular ectopic complexes during 24-hour Holter monitoring were low and equally distributed among groups. In conclusion, in nonelite athletes, marathon running was not associated with RV enlargement, dysfunction, or ventricular ectopy. Marathon running promoted biatrial remodeling.

Association between conventional risk factors and different ultrasound-based markers of atherosclerosis at carotid and femoral levels in a middle-aged population

Ultrasound detection of sub-clinical atherosclerosis (ATS) may help identify individuals at high cardiovascular risk. Most studies evaluated intima-media thickness (IMT) at carotid level. We compared the relationships between main cardiovascular risk factors (CVRF) and five indicators of ATS (IMT, mean and maximal plaque thickness, mean and maximal plaque area) at both carotid and femoral levels. Ultrasound was performed on 496 participants aged 45-64 years randomly selected from the general population of the Republic of Seychelles. 73.4 % participants had ≥ 1 plaque (IMT thickening ≥ 1.2 mm) at carotid level and 67.5 % at femoral level. Variance (adjusted R2) contributed by age, sex and CVRF (smoking, LDL-cholesterol, HDL-cholesterol, blood pressure, diabetes) in predicting any of the ATS markers was larger at femoral than carotid level. At both carotid and femoral levels, the association between CVRF and ATS was stronger based on plaque-based markers than IMT. Our findings show that the associations between CVRF and ATS markers were stronger at femoral than carotid level, and with plaque-based markers rather than IMT. Pending comparison of these markers using harder cardiovascular endpoints, our findings suggest that markers based on plaque morphology assessed at femoral artery level might be useful cardiovascular risk predictors.

Harmonised monitoring of antimicrobial resistance in Salmonella and Campylobacter isolates from food animals in the European Union

Many Member States of the European Union (EU) currently monitor antimicrobial resistance in zoonotic agents, including Salmonella and Campylobacter. According to Directive 2003/99/EC, Member States shall ensure that the monitoring provides comparable data on the occurrence of antimicrobial resistance. The European Commission asked the European Food Safety Authority to prepare detailed specifications for harmonised schemes for monitoring antimicrobial resistance. The objective of these specifications is to lay down provisions for a monitoring and reporting scheme for Salmonella in fowl (Gallus gallus), turkeys and pigs, and for Campylobacter jejuni and Campylobacter coli in broiler chickens. The current specifications are considered to be a first step towards a gradual implementation of comprehensive antimicrobial resistance monitoring at the EU level. These specifications propose to test a common set of antimicrobial agents against available cut-off values and a specified concentration range to determine the susceptibility of Salmonella and Campylobacter. Using isolates collected through programmes in which the sampling frame covers all epidemiological units of the national production, the target number of Salmonella isolates to be included in the antimicrobial resistance monitoring per Member State per year is 170 for each study population (i.e., laying hens, broilers, turkeys and slaughter pigs). The target number of Campylobacter isolates to be included in the antimicrobial resistance monitoring per Member State per year is 170 for each study population (i.e., broilers). The results of the antimicrobial resistance monitoring are assessed and reported in the yearly national report on trends and sources of zoonoses, zoonotic agents and antimicrobial resistance.