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.

Evaluating atmospheric methane inversion model results for Pallas, northern Finland

A state-of-the-art inverse model, CarbonTracker Data Assimilation Shell (CTDAS), was used to optimize estimates of methane (CH4) surface fluxes using atmospheric observations of CH4 as a constraint. The model consists of the latest version of the TM5 atmospheric chemistry-transport model and an ensemble Kalman filter based data assimilation system. The model was constrained by atmospheric methane surface concentrations, obtained from the World Data Centre for Greenhouse Gases (WDCGG). Prior methane emissions were specified for five sources: biosphere, anthropogenic, fire, termites and ocean, of which bio-sphere and anthropogenic emissions were optimized. Atmospheric CH 4 mole fractions for 2007 from northern Finland calculated from prior and optimized emissions were compared with observations. It was found that the root mean squared errors of the posterior esti - mates were more than halved. Furthermore, inclusion of NOAA observations of CH 4 from weekly discrete air samples collected at Pallas improved agreement between posterior CH 4 mole fraction estimates and continuous observations, and resulted in reducing optimized biosphere emissions and their uncertainties in northern Finland.

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.

Good reproducibility of heart rate variability after orthostatic challenge in patients with a history of acute coronary syndrome

BACKGROUND: Several parameters of heart rate variability (HRV) have been shown to predict the risk of sudden cardiac death (SCD) in cardiac patients. There is consensus that risk prediction is increased when measuring HRV during specific provocations such as orthostatic challenge. For the first time, we provide data on reproducibility of such a test in patients with a history of acute coronary syndrome. METHODS: Sixty male patients (65+/-8years) with a history of acute coronary syndrome on stable medication were included. HRV was measured in supine (5min) and standing (5min) position on 2 occasions separated by two weeks. For risk assessment relevant time-domain [standard deviation of all R-R intervals (SDNN) and root mean squared standard differences between adjacent R-R intervals (RMSSD)], frequency domain [low-frequency power (LF), high-frequency power (HF) and LF/HF power ratio] and short-term fractal scaling component (DF1) were computed. Absolute reproducibility was assessed with the standard errors of the mean (SEM) and 95% limits of random variation, and relative reproducibility by the intraclass correlation coefficient (ICC). RESULTS: We found comparable SEMs and ICCs in supine position and after an orthostatic challenge test. All ICCs were good to excellent (ICCs between 0.636 and 0.869). CONCLUSIONS: Reproducibility of HRV parameters during orthostatic challenge is good and comparable with supine position.

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.

Selection bias and the cross-validation of regression models for prediction

Strategies are compared for the development of a linear regression model with stochastic (multivariate normal) regressor variables and the subsequent assessment of its predictive ability. Bias and mean squared error of four estimators of predictive performance are evaluated in simulated samples of 32 population correlation matrices. Models including all of the available predictors are compared with those obtained using selected subsets. The subset selection procedures investigated include two stopping rules, C$\sb{\rm p}$ and S$\sb{\rm p}$, each combined with an 'all possible subsets' or 'forward selection' of variables. The estimators of performance utilized include parametric (MSEP$\sb{\rm m}$) and non-parametric (PRESS) assessments in the entire sample, and two data splitting estimates restricted to a random or balanced (Snee's DUPLEX) 'validation' half sample. The simulations were performed as a designed experiment, with population correlation matrices representing a broad range of data structures.^ The techniques examined for subset selection do not generally result in improved predictions relative to the full model. Approaches using 'forward selection' result in slightly smaller prediction errors and less biased estimators of predictive accuracy than 'all possible subsets' approaches but no differences are detected between the performances of C$\sb{\rm p}$ and S$\sb{\rm p}$. In every case, prediction errors of models obtained by subset selection in either of the half splits exceed those obtained using all predictors and the entire sample.^ Only the random split estimator is conditionally (on $\\beta$) unbiased, however MSEP$\sb{\rm m}$ is unbiased on average and PRESS is nearly so in unselected (fixed form) models. When subset selection techniques are used, MSEP$\sb{\rm m}$ and PRESS always underestimate prediction errors, by as much as 27 percent (on average) in small samples. Despite their bias, the mean squared errors (MSE) of these estimators are at least 30 percent less than that of the unbiased random split estimator. The DUPLEX split estimator suffers from large MSE as well as bias, and seems of little value within the context of stochastic regressor variables.^ To maximize predictive accuracy while retaining a reliable estimate of that accuracy, it is recommended that the entire sample be used for model development, and a leave-one-out statistic (e.g. PRESS) be used for assessment. ^

THE IMPROVEMENT REGION IN RIDGE REGRESSION

One of the difficulties in the practical application of ridge regression is that, for a given data set, it is unknown whether a selected ridge estimator has smaller squared error than the least squares estimator. The concept of the improvement region is defined, and a technique is developed which obtains approximate confidence intervals for the value of ridge k which produces the maximum reduction in mean squared error. Two simulation experiments were conducted to investigate how accurate these approximate confidence intervals might be. ^

Sistematización de suelos : eficiencia en la ejecución del proyecto en suelos complejos

El manejo sustentable de los recursos naturales relacionados con proyectos de utilización de los recursos hídricos (entre otros), requiere en muchos casos de la modificación del relieve existente. Esto conlleva la necesidad de adecuación de la capa homogénea superior del suelo, operación que suele denominarse "sistematización", la cual facilita una distribución más uniforme de las lluvias y del agua de riego. Esta modificación de la capa superior del suelo es realizada en base a un proyecto, cuya inclinación responda a las pendientes naturales o a las establecidas por el diseñador. En la ejecución del diseño proyectado, en superficies superiores a una hectárea, el movimiento de tierra se realiza con equipos pesados, que no aseguran un alto porcentaje de eficiencia en lo que al movimiento de tierra se refiere, ya que parte del material se pierde en el acarreo, pero muy especialmente, por la compactación desuniforme del mismo, asociada con las texturas complejas del suelo a trabajar. El presente trabajo determinó el índice de precisión en la ejecución del proyecto de sistematización a partir de un índice estadístico internacionalmente aceptado, el "Root Mean Squared Error (RMSE)", comparando los valores altimétricos proyectados y los realmente obtenidos luego de la ejecución del proyecto, en tres parcelas con distinta secuencia de labores y maquinaria utilizadas, pero con el mismo tipo de suelo en el área del eje Pilar - La Plata (Argentina). Los resultados obtenidos, que varían de un RMSE de 4 a 6 cm, permiten concluir, para los sitios y las condiciones estudiadas, que no pueden asegurarse en la sistematización índices de precisión en la ejecución de la obra, inferiores a los 4 cm.

Modelos digitales de elevación generados por captura directa e indirecta de datos fuente : evaluación de su calidad

La información básica sobre el relieve de una cuenca hidrográfica, mediante metodologías analítico-descriptivas, permite a quienes evalúan proyectos relacionados con el uso de los recursos naturales, tales como el manejo integrado de cuencas, estudios sobre impacto ambiental, degradación de suelos, deforestación, conservación de los recursos hídricos, entre otros, contar para su análisis con los parámetros físicos necesarios. Estos procesos mencionados tienen un fuerte componente espacial y el empleo de Sistemas de Información Geográfica (SIG) son de suma utilidad, siendo los Modelos Digitales de Elevación (DEM) y sus derivados un componente relevante de esta base de datos. Los productos derivados de estos modelos, como pendiente, orientación o curvatura, resultarán tan precisos como el DEM usado para derivarlos. Por otra parte, es fundamental maximizar la habilidad del modelo para representar las variaciones del terreno; para ello se debe seleccionar una adecuada resolución (grilla) de acuerdo con los datos disponibles para su generación. En este trabajo se evalúa la calidad altimétrica de seis DEMs generados a partir de dos sistemas diferentes de captura de datos fuente y de distintas resoluciones de grilla. Para determinar la exactitud de los DEMs habitualmente se utiliza un grupo de puntos de control considerados como "verdad de campo" que se comparan con los generados por el modelo en la misma posición geográfica. El área seleccionada para realizar el estudio está ubicada en la localidad de Arrecifes, provincia de Buenos Aires (Argentina) y tiene una superficie de aproximadamente 120 ha. Los resultados obtenidos para los dos algoritmos y para los tres tamaños de grilla analizados presentaron los siguientes resultados: el algoritmo DEM from contourn, un RMSE (Root Mean Squared Error) de ± 0,11 m (para grilla de 1 m), ± 0,11 m (para grilla de 5 m) y de ± 0,15 m (para grilla de 10 m). Para el algoritmo DEM from vector/points, un RMSE de ± 0,09 m (para grilla de 1 m), ± 0,11 m (para grilla de 5 m) y de ± 0,11 m (para grilla de 10 m). Los resultados permiten concluir que el DEM generado a partir de puntos acotados del terreno como datos fuente y con el menor tamaño de grilla es el único que satisface los valores enumerados en la bibliografía, tanto nacional como internacional, lo que lo hace apto para proyectos relacionados con recursos naturales a nivel de ecotopo (predial). El resto de los DEMs generados presentan un RMSE que permite asegurar su aptitud para la evaluación de proyectos relacionados con el uso de los recursos naturales a nivel de unidad de paisaje (conjunto de ecotopos).

Snow-pit measurements on Atka Bay landfast sea ice during ANT-Land 2012/2013 season

Up to now, snow cover on Antarctic sea ice and its impact on radar backscatter, particularly after the onset of freeze/thaw processes, are not well understood. Here we present a combined analysis of in situ observations of snow properties from the landfast sea ice in Atka Bay, Antarctica, and high-resolution TerraSAR-X backscatter data, for the transition from austral spring (November 2012) to summer (January 2013). The physical changes in the seasonal snow cover during that time are reflected in the evolution of TerraSAR-X backscatter. We are able to explain 76-93% of the spatio-temporal variability of the TerraSAR-X backscatter signal with up to four snowpack parameters with a root-mean-squared error of 0.87-1.62 dB, using a simple multiple linear model. Over the complete study, and especially after the onset of early-melt processes and freeze/thaw cycles, the majority of variability in the backscatter is influenced by changes in snow/ice interface temperature, snow depth and top-layer grain size. This suggests it may be possible to retrieve snow physical properties over Antarctic sea ice from X-band SAR backscatter.

X-ray fluorescence scanning of discrete samples on the Schwalbenberg II loess-paleosol sequence, raw data, magnetic susceptibility, organic carbon and U-ratio as grainsize index

The Schwalbenberg II loess-paleosol sequence (LPS) denotes a key site for Marine Isotope Stage (MIS 3) in Western Europe owing to eight succeeding cambisols, which primarily constitute the Ahrgau Subformation. Therefore, this LPS qualifies as a test candidate for the potential of temporal high-resolution geochemical data obtained X-ray fluorescence (XRF) scanning of discrete samplesproviding a fast and non-destructive tool for determining the element composition. The geochemical data is first contextualized to existing proxy data such as magnetic susceptibility (MS) and organic carbon (Corg) and then aggregated to element log ratios characteristic for weathering intensity [LOG (Ca/Sr), LOG (Rb/Sr), LOG (Ba/Sr), LOG (Rb/K)] and dust provenance [LOG (Ti/Zr), LOG (Ti/Al), LOG (Si/Al)]. Generally, an interpretation of rock magnetic particles is challenged in western Europe, where not only magnetic enhancement but also depletion plays a role. Our data indicates leaching and top-soil erosion induced MS depletion at the Schwalbenberg II LPS. Besides weathering, LOG (Ca/Sr) is susceptible for secondary calcification. Thus, also LOG (Rb/Sr) and LOG (Ba/Sr) are shown to be influenced by calcification dynamics. Consequently, LOG (Rb/K) seems to be the most suitable weathering index identifying the Sinzig Soils S1 and S2 as the most pronounced paleosols for this site. Sinzig Soil S3 is enclosed by gelic gleysols and in contrast to S1 and S2 only initially weathered pointing to colder climate conditions. Also the Remagen Soils are characterized by subtle to moderate positive excursions in the weathering indices. Comparing the Schwalbenberg II LPS with the nearby Eifel Lake Sediment Archive (ELSA) and other more distant German, Austrian and Czech LPS while discussing time and climate as limiting factors for pedogenesis, we suggest that the lithologically determined paleosols are in-situ soil formations. The provenance indices document a Zr-enrichment at the transition from the Ahrgau to the Hesbaye Subformation. This is explained by a conceptual model incorporating multiple sediment recycling and sorting effects in eolian and fluvial domains.

Inverse kinematics of a 6 DoF human upper limb using ANFIS and ANN for anticipatory actuation in ADL-based physical Neurorehabilitation

Objective: This research is focused in the creation and validation of a solution to the inverse kinematics problem for a 6 degrees of freedom human upper limb. This system is intended to work within a realtime dysfunctional motion prediction system that allows anticipatory actuation in physical Neurorehabilitation under the assisted-as-needed paradigm. For this purpose, a multilayer perceptron-based and an ANFIS-based solution to the inverse kinematics problem are evaluated. Materials and methods: Both the multilayer perceptron-based and the ANFIS-based inverse kinematics methods have been trained with three-dimensional Cartesian positions corresponding to the end-effector of healthy human upper limbs that execute two different activities of the daily life: "serving water from a jar" and "picking up a bottle". Validation of the proposed methodologies has been performed by a 10 fold cross-validation procedure. Results: Once trained, the systems are able to map 3D positions of the end-effector to the corresponding healthy biomechanical configurations. A high mean correlation coefficient and a low root mean squared error have been found for both the multilayer perceptron and ANFIS-based methods. Conclusions: The obtained results indicate that both systems effectively solve the inverse kinematics problem, but, due to its low computational load, crucial in real-time applications, along with its high performance, a multilayer perceptron-based solution, consisting in 3 input neurons, 1 hidden layer with 3 neurons and 6 output neurons has been considered the most appropriated for the target application.