A global historical ozone data set and prominent features of stratospheric variability prior to 1979

We present a vertically resolved zonal mean monthly mean global ozone data set spanning the period 1901 to 2007, called HISTOZ.1.0. It is based on a new approach that combines information from an ensemble of chemistry climate model (CCM) simulations with historical total column ozone information. The CCM simulations incorporate important external drivers of stratospheric chemistry and dynamics (in particular solar and volcanic effects, greenhouse gases and ozone depleting substances, sea surface temperatures, and the quasi-biennial oscillation). The historical total column ozone observations include ground-based measurements from the 1920s onward and satellite observations from 1970 to 1976. An off-line data assimilation approach is used to combine model simulations, observations, and information on the observation error. The period starting in 1979 was used for validation with existing ozone data sets and therefore only ground-based measurements were assimilated. Results demonstrate considerable skill from the CCM simulations alone. Assimilating observations provides additional skill for total column ozone. With respect to the vertical ozone distribution, assimilating observations increases on average the correlation with a reference data set, but does not decrease the mean squared error. Analyses of HISTOZ.1.0 with respect to the effects of El Niño–Southern Oscillation (ENSO) and of the 11 yr solar cycle on stratospheric ozone from 1934 to 1979 qualitatively confirm previous studies that focussed on the post-1979 period. The ENSO signature exhibits a much clearer imprint of a change in strength of the Brewer–Dobson circulation compared to the post-1979 period. The imprint of the 11 yr solar cycle is slightly weaker in the earlier period. Furthermore, the total column ozone increase from the 1950s to around 1970 at northern mid-latitudes is briefly discussed. Indications for contributions of a tropospheric ozone increase, greenhouse gases, and changes in atmospheric circulation are found. Finally, the paper points at several possible future improvements of HISTOZ.1.0.

A 950 yr temperature reconstruction from Duckhole Lake, southern Tasmania, Australia

A lack of quantitative high resolution paleoclimate data from the Southern Hemisphere limits the ability to examine current trends within the context of long-term natural climate variability. This study presents a temperature reconstruction for southern Tasmania based on analyses of a sediment core from Duckhole Lake (43.365°S, 146.875°E). The relationship between non-destructive whole core scanning reflectance spectroscopy measurements in the visible spectrum (380–730 nm) and the instrumental temperature record (ad 1911–2000) was used to develop a calibration-in-time reflectance spectroscopy-based temperature model. Results showed that a trough in reflectance from 650 to 700 nm, which represents chlorophyll and its derivatives, was significantly correlated to annual mean temperature. A calibration model was developed (R = 0.56, p auto < 0.05, root mean squared error of prediction (RMSEP) = 0.21°C, five-year filtered data, calibration period 1911–2000) and applied down-core to reconstruct annual mean temperatures in southern Tasmania over the last c. 950 years. This indicated that temperatures were initially cool c. ad 1050, but steadily increased until the late ad 1100s. After a brief cool period in the ad 1200s, temperatures again increased. Temperatures steadily decreased during the ad 1600s and remained relatively stable until the start of the 20th century when they rapidly decreased, before increasing from ad 1960s onwards. Comparisons with high resolution temperature records from western Tasmania, New Zealand and South America revealed some similarities, but also highlighted differences in temperature variability across the mid-latitudes of the Southern Hemisphere. These are likely due to a combination of factors including the spatial variability in climate between and within regions, and differences between records that document seasonal (i.e. warm season/late summer) versus annual temperature variability. This highlights the need for further records from the mid-latitudes of the Southern Hemisphere in order to constrain past natural spatial and seasonal/annual temperature variability in the region, and to accurately identify and attribute changes to natural variability and/or anthropogenic activities.

Quantitative high-resolution warm season rainfall recorded in varved sediments of Lake Oeschinen, northern Swiss Alps: calibration and validation AD 1901–2008

High-resolution, well-calibrated records of lake sediments are critically important for quantitative climate reconstructions, but they remain a methodological and analytical challenge. While several comprehensive paleotemperature reconstructions have been developed across Europe, only a few quantitative high-resolution studies exist for precipitation. Here we present a calibration and verification study of lithoclastic sediment proxies from proglacial Lake Oeschinen (46°30′N, 7°44′E, 1,580 m a.s.l., north–west Swiss Alps) that are sensitive to rainfall for the period AD 1901–2008. We collected two sediment cores, one in 2007 and another in 2011. The sediments are characterized by two facies: (A) mm-laminated clastic varves and (B) turbidites. The annual character of the laminae couplets was confirmed by radiometric dating (210Pb, 137Cs) and independent flood-layer chronomarkers. Individual varves consist of a dark sand-size spring-summer layer enriched in siliciclastic minerals and a lighter clay-size calcite-rich winter layer. Three subtypes of varves are distinguished: Type I with a 1–1.5 mm fining upward sequence; Type II with a distinct fine-sand base up to 3 mm thick; and Type III containing multiple internal microlaminae caused by individual summer rainstorm deposits. Delta-fan surface samples and sediment trap data fingerprint different sediment source areas and transport processes from the watershed and confirm the instant response of sediment flux to rainfall and erosion. Based on a highly accurate, precise and reproducible chronology, we demonstrate that sediment accumulation (varve thickness) is a quantitative predictor for cumulative boreal alpine spring (May–June) and spring/summer (May–August) rainfall (rMJ = 0.71, rMJJA = 0.60, p < 0.01). Bootstrap-based verification of the calibration model reveals a root mean squared error of prediction (RMSEPMJ = 32.7 mm, RMSEPMJJA = 57.8 mm) which is on the order of 10–13 % of mean MJ and MJJA cumulative precipitation, respectively. These results highlight the potential of the Lake Oeschinen sediments for high-resolution reconstructions of past rainfall conditions in the northern Swiss Alps, central and eastern France and south-west Germany.

Image Space Adaptive Rendering

In this thesis, we develop an adaptive framework for Monte Carlo rendering, and more specifically for Monte Carlo Path Tracing (MCPT) and its derivatives. MCPT is attractive because it can handle a wide variety of light transport effects, such as depth of field, motion blur, indirect illumination, participating media, and others, in an elegant and unified framework. However, MCPT is a sampling-based approach, and is only guaranteed to converge in the limit, as the sampling rate grows to infinity. At finite sampling rates, MCPT renderings are often plagued by noise artifacts that can be visually distracting. The adaptive framework developed in this thesis leverages two core strategies to address noise artifacts in renderings: adaptive sampling and adaptive reconstruction. Adaptive sampling consists in increasing the sampling rate on a per pixel basis, to ensure that each pixel value is below a predefined error threshold. Adaptive reconstruction leverages the available samples on a per pixel basis, in an attempt to have an optimal trade-off between minimizing the residual noise artifacts and preserving the edges in the image. In our framework, we greedily minimize the relative Mean Squared Error (rMSE) of the rendering by iterating over sampling and reconstruction steps. Given an initial set of samples, the reconstruction step aims at producing the rendering with the lowest rMSE on a per pixel basis, and the next sampling step then further reduces the rMSE by distributing additional samples according to the magnitude of the residual rMSE of the reconstruction. This iterative approach tightly couples the adaptive sampling and adaptive reconstruction strategies, by ensuring that we only sample densely regions of the image where adaptive reconstruction cannot properly resolve the noise. In a first implementation of our framework, we demonstrate the usefulness of our greedy error minimization using a simple reconstruction scheme leveraging a filterbank of isotropic Gaussian filters. In a second implementation, we integrate a powerful edge aware filter that can adapt to the anisotropy of the image. Finally, in a third implementation, we leverage auxiliary feature buffers that encode scene information (such as surface normals, position, or texture), to improve the robustness of the reconstruction in the presence of strong noise.

A chrysophyte-based quantitative reconstruction of winter severity from varved lake sediments in NE Poland during the past millennium and its relationship to natural climate variability

Chrysophyte cysts are recognized as powerful proxies of cold-season temperatures. In this paper we use the relationship between chrysophyte assemblages and the number of days below 4 °C (DB4 °C) in the epilimnion of a lake in northern Poland to develop a transfer function and to reconstruct winter severity in Poland for the last millennium. DB4 °C is a climate variable related to the length of the winter. Multivariate ordination techniques were used to study the distribution of chrysophytes from sediment traps of 37 low-land lakes distributed along a variety of environmental and climatic gradients in northern Poland. Of all the environmental variables measured, stepwise variable selection and individual Redundancy analyses (RDA) identified DB4 °C as the most important variable for chrysophytes, explaining a portion of variance independent of variables related to water chemistry (conductivity, chlorides, K, sulfates), which were also important. A quantitative transfer function was created to estimate DB4 °C from sedimentary assemblages using partial least square regression (PLS). The two-component model (PLS-2) had a coefficient of determination of View the MathML sourceRcross2 = 0.58, with root mean squared error of prediction (RMSEP, based on leave-one-out) of 3.41 days. The resulting transfer function was applied to an annually-varved sediment core from Lake Żabińskie, providing a new sub-decadal quantitative reconstruction of DB4 °C with high chronological accuracy for the period AD 1000–2010. During Medieval Times (AD 1180–1440) winters were generally shorter (warmer) except for a decade with very long and severe winters around AD 1260–1270 (following the AD 1258 volcanic eruption). The 16th and 17th centuries and the beginning of the 19th century experienced very long severe winters. Comparison with other European cold-season reconstructions and atmospheric indices for this region indicates that large parts of the winter variability (reconstructed DB4 °C) is due to the interplay between the oscillations of the zonal flow controlled by the North Atlantic Oscillation (NAO) and the influence of continental anticyclonic systems (Siberian High, East Atlantic/Western Russia pattern). Differences with other European records are attributed to geographic climatological differences between Poland and Western Europe (Low Countries, Alps). Striking correspondence between the combined volcanic and solar forcing and the DB4 °C reconstruction prior to the 20th century suggests that winter climate in Poland responds mostly to natural forced variability (volcanic and solar) and the influence of unforced variability is low.

Biomechanical Role of Bone Anisotropy Estimated on Clinical CT Scans by Image Registration

Image-based modeling is a popular approach to perform patient-specific biomechanical simulations. Accurate modeling is critical for orthopedic application to evaluate implant design and surgical planning. It has been shown that bone strength can be estimated from the bone mineral density (BMD) and trabecular bone architecture. However, these findings cannot be directly and fully transferred to patient-specific modeling since only BMD can be derived from clinical CT. Therefore, the objective of this study was to propose a method to predict the trabecular bone structure using a µCT atlas and an image registration technique. The approach has been evaluated on femurs and patellae under physiological loading. The displacement and ultimate force for femurs loaded in stance position were predicted with an error of 2.5% and 3.7%, respectively, while predictions obtained with an isotropic material resulted in errors of 7.3% and 6.9%. Similar results were obtained for the patella, where the strain predicted using the registration approach resulted in an improved mean squared error compared to the isotropic model. We conclude that the registration of anisotropic information from of a single template bone enables more accurate patient-specific simulations from clinical image datasets than isotropic model.

Modern diatom, cladocera, chironomid, and chrysophyte cyst assemblages as quantitative indicators for the reconstruction of past environmental conditions in the Alps. II. Nutrients

Surface sediments from 68 small lakes in the Alps and 9 well-dated sediment core samples that cover a gradient of total phosphorus (TP) concentrations of 6 to 520 μg TP l-1 were studied for diatom, chrysophyte cyst, cladocera, and chironomid assemblages. Inference models for mean circulation log10 TP were developed for diatoms, chironomids, and benthic cladocera using weighted-averaging partial least squares. After screening for outliers, the final transfer functions have coefficients of determination (r2, as assessed by cross-validation, of 0.79 (diatoms), 0.68 (chironomids), and 0.49 (benthic cladocera). Planktonic cladocera and chrysophytes show very weak relationships to TP and no TP inference models were developed for these biota. Diatoms showed the best relationship with TP, whereas the other biota all have large secondary gradients, suggesting that variables other than TP have a strong influence on their composition and abundance. Comparison with other diatom – TP inference models shows that our model has high predictive power and a low root mean squared error of prediction, as assessed by cross-validation.

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().

Single fluoroscopic image based 2D/3D reconstruction of a scaled, patient-specific vertebral model

Purpose Accurate three-dimensional (3D) models of lumbar vertebrae can enable image-based 3D kinematic analysis. The common approach to derive 3D models is by direct segmentation of CT or MRI datasets. However, these have the disadvantages that they are expensive, timeconsuming and/or induce high-radiation doses to the patient. In this study, we present a technique to automatically reconstruct a scaled 3D lumbar vertebral model from a single two-dimensional (2D) lateral fluoroscopic image. Methods Our technique is based on a hybrid 2D/3D deformable registration strategy combining a landmark-to-ray registration with a statistical shape model-based 2D/3D reconstruction scheme. Fig. 1 shows different stages of the reconstruction process. Four cadaveric lumbar spine segments (total twelve lumbar vertebrae) were used to validate the technique. To evaluate the reconstruction accuracy, the surface models reconstructed from the lateral fluoroscopic images were compared to the associated ground truth data derived from a 3D CT-scan reconstruction technique. For each case, a surface-based matching was first used to recover the scale and the rigid transformation between the reconstructed surface model Results Our technique could successfully reconstruct 3D surface models of all twelve vertebrae. After recovering the scale and the rigid transformation between the reconstructed surface models and the ground truth models, the average error of the 2D/3D surface model reconstruction over the twelve lumbar vertebrae was found to be 1.0 mm. The errors of reconstructing surface models of all twelve vertebrae are shown in Fig. 2. It was found that the mean errors of the reconstructed surface models in comparison to their associated ground truths after iterative scaled rigid registrations ranged from 0.7 mm to 1.3 mm and the rootmean squared (RMS) errors ranged from 1.0 mm to 1.7 mm. The average mean reconstruction error was found to be 1.0 mm. Conclusion An accurate, scaled 3D reconstruction of the lumbar vertebra can be obtained from a single lateral fluoroscopic image using a statistical shape model based 2D/3D reconstruction technique. Future work will focus on applying the reconstructed model for 3D kinematic analysis of lumbar vertebrae, an extension of our previously-reported imagebased kinematic analysis. The developed method also has potential applications in surgical planning and navigation.

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.

Kernel density correlation based non-rigid point set matching for statistical model-based 2D/3D reconstruction

This paper presents a kernel density correlation based nonrigid point set matching method and shows its application in statistical model based 2D/3D reconstruction of a scaled, patient-specific model from an un-calibrated x-ray radiograph. In this method, both the reference point set and the floating point set are first represented using kernel density estimates. A correlation measure between these two kernel density estimates is then optimized to find a displacement field such that the floating point set is moved to the reference point set. Regularizations based on the overall deformation energy and the motion smoothness energy are used to constraint the displacement field for a robust point set matching. Incorporating this non-rigid point set matching method into a statistical model based 2D/3D reconstruction framework, we can reconstruct a scaled, patient-specific model from noisy edge points that are extracted directly from the x-ray radiograph by an edge detector. Our experiment conducted on datasets of two patients and six cadavers demonstrates a mean reconstruction error of 1.9 mm

A Portable Image Overlay Projection Device for Computer-Aided Open Liver Surgery

Image overlay projection is a form of augmented reality that allows surgeons to view underlying anatomical structures directly on the patient surface. It improves intuitiveness of computer-aided surgery by removing the need for sight diversion between the patient and a display screen and has been reported to assist in 3-D understanding of anatomical structures and the identification of target and critical structures. Challenges in the development of image overlay technologies for surgery remain in the projection setup. Calibration, patient registration, view direction, and projection obstruction remain unsolved limitations to image overlay techniques. In this paper, we propose a novel, portable, and handheld-navigated image overlay device based on miniature laser projection technology that allows images of 3-D patient-specific models to be projected directly onto the organ surface intraoperatively without the need for intrusive hardware around the surgical site. The device can be integrated into a navigation system, thereby exploiting existing patient registration and model generation solutions. The position of the device is tracked by the navigation system’s position sensor and used to project geometrically correct images from any position within the workspace of the navigation system. The projector was calibrated using modified camera calibration techniques and images for projection are rendered using a virtual camera defined by the projectors extrinsic parameters. Verification of the device’s projection accuracy concluded a mean projection error of 1.3 mm. Visibility testing of the projection performed on pig liver tissue found the device suitable for the display of anatomical structures on the organ surface. The feasibility of use within the surgical workflow was assessed during open liver surgery. We show that the device could be quickly and unobtrusively deployed within the sterile environment.

Validation of a modified AVHRR aerosol optical depth retrieval algorithm over Central Europe

The Advanced Very High Resolution Radiometer (AVHRR) carried on board the National Oceanic and Atmospheric Administration (NOAA) and the Meteorological Operational Satellite (MetOp) polar orbiting satellites is the only instrument offering more than 25 years of satellite data to analyse aerosols on a daily basis. The present study assessed a modified AVHRR aerosol optical depth τa retrieval over land for Europe. The algorithm might also be applied to other parts of the world with similar surface characteristics like Europe, only the aerosol properties would have to be adapted to a new region. The initial approach used a relationship between Sun photometer measurements from the Aerosol Robotic Network (AERONET) and the satellite data to post-process the retrieved τa. Herein a quasi-stand-alone procedure, which is more suitable for the pre-AERONET era, is presented. In addition, the estimation of surface reflectance, the aerosol model, and other processing steps have been adapted. The method's cross-platform applicability was tested by validating τa from NOAA-17 and NOAA-18 AVHRR at 15 AERONET sites in Central Europe (40.5° N–50° N, 0° E–17° E) from August 2005 to December 2007. Furthermore, the accuracy of the AVHRR retrieval was related to products from two newer instruments, the Medium Resolution Imaging Spectrometer (MERIS) on board the Environmental Satellite (ENVISAT) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Aqua/Terra. Considering the linear correlation coefficient R, the AVHRR results were similar to those of MERIS with even lower root mean square error RMSE. Not surprisingly, MODIS, with its high spectral coverage, gave the highest R and lowest RMSE. Regarding monthly averaged τa, the results were ambiguous. Focusing on small-scale structures, R was reduced for all sensors, whereas the RMSE solely for MERIS substantially increased. Regarding larger areas like Central Europe, the error statistics were similar to the individual match-ups. This was mainly explained with sampling issues. With the successful validation of AVHRR we are now able to concentrate on our large data archive dating back to 1985. This is a unique opportunity for both climate and air pollution studies over land surfaces.

Effect of pulsatility on the mathematical modeling of rotary blood pumps

In this study, the effect of time derivatives of flow rate and rotational speed was investigated on the mathematical modeling of a rotary blood pump (RBP). The basic model estimates the pressure head of the pump as a dependent variable using measured flow and speed as predictive variables. Performance of the model was evaluated by adding time derivative terms for flow and speed. First, to create a realistic working condition, the Levitronix CentriMag RBP was implanted in a sheep. All parameters from the model were physically measured and digitally acquired over a wide range of conditions, including pulsatile speed. Second, a statistical analysis of the different variables (flow, speed, and their time derivatives) based on multiple regression analysis was performed to determine the significant variables for pressure head estimation. Finally, different mathematical models were used to show the effect of time derivative terms on the performance of the models. In order to evaluate how well the estimated pressure head using different models fits the measured pressure head, root mean square error and correlation coefficient were used. The results indicate that inclusion of time derivatives of flow and speed can improve model accuracy, but only minimally.

Atrial remodeling, autonomic tone, and lifetime training hours in nonelite athletes

Endurance athletes have an increased risk of developing atrial fibrillation (AF) at 40 to 50 years of age. Signal-averaged P-wave analysis has been used for identifying patients at risk for AF. We evaluated the impact of lifetime training hours on signal-averaged P-wave duration and modifying factors. Nonelite men athletes scheduled to participate in the 2010 Grand Prix of Bern, a 10-mile race, were invited. Four hundred ninety-two marathon and nonmarathon runners applied for participation, 70 were randomly selected, and 60 entered the final analysis. Subjects were stratified according to their lifetime training hours (average endurance and strength training hours per week × 52 × training years) in low (<1,500 hours), medium (1,500 to 4,500 hours), and high (>4,500 hours) training groups. Mean age was 42 ± 7 years. From low to high training groups signal-averaged P-wave duration increased from 131 ± 6 to 142 ± 13 ms (p = 0.026), and left atrial volume increased from 24.8 ± 4.6 to 33.1 ± 6.2 ml/m(2) (p = 0.001). Parasympathetic tone expressed as root of the mean squared differences of successive normal-to-normal intervals increased from 34 ± 13 to 47 ± 16 ms (p = 0.002), and premature atrial contractions increased from 6.1 ± 7.4 to 10.8 ± 7.7 per 24 hours (p = 0.026). Left ventricular mass increased from 100.7 ± 9.0 to 117.1 ± 18.2 g/m(2) (p = 0.002). Left ventricular systolic and diastolic function and blood pressure at rest were normal in all athletes and showed no differences among training groups. Four athletes (6.7%) had a history of paroxysmal AF, as did 1 athlete in the medium training group and 3 athletes in the high training group (p = 0.252). In conclusion, in nonelite men athletes lifetime training hours are associated with prolongation of signal-averaged P-wave duration and an increase in left atrial volume. The altered left atrial substrate may facilitate occurrence of AF. Increased vagal tone and atrial ectopy may serve as modifying and triggering factors.