Quantitative analysis of crystal/grain sizes and their distributions in 2D and 3D

We review methods to estimate the average crystal (grain) size and the crystal (grain) size distribution in solid rocks. Average grain sizes often provide the base for stress estimates or rheological calculations requiring the quantification of grain sizes in a rock's microstructure. The primary data for grain size data are either 1D (i.e. line intercept methods), 2D (area analysis) or 3D (e.g., computed tomography, serial sectioning). These data have been used for different data treatments over the years, whereas several studies assume a certain probability function (e.g., logarithm, square root) to calculate statistical parameters as the mean, median, mode or the skewness of a crystal size distribution. The finally calculated average grain sizes have to be compatible between the different grain size estimation approaches in order to be properly applied, for example, in paleo-piezometers or grain size sensitive flow laws. Such compatibility is tested for different data treatments using one- and two-dimensional measurements. We propose an empirical conversion matrix for different datasets. These conversion factors provide the option to make different datasets compatible with each other, although the primary calculations were obtained in different ways. In order to present an average grain size, we propose to use the area-weighted and volume-weighted mean in the case of unimodal grain size distributions, respectively, for 2D and 3D measurements. The shape of the crystal size distribution is important for studies of nucleation and growth of minerals. The shape of the crystal size distribution of garnet populations is compared between different 2D and 3D measurements, which are serial sectioning and computed tomography. The comparison of different direct measured 3D data; stereological data and direct presented 20 data show the problems of the quality of the smallest grain sizes and the overestimation of small grain sizes in stereological tools, depending on the type of CSD. (C) 2011 Published by Elsevier Ltd.

Diagnostic Performances of 18F-FDG Positron Emission Tomography in Large Vessels Vasculitis: Meta-analysis

Toperform a meta-analysis of FDG-PET performances in the diagnosis of largevessels vasculitis (Giant Cell Arteritis (GCA) associated or not withPolymyalgia Rheumatica(PMR), Takayasu). Materials and methods : The MEDLINE,Cochrane Library, Embase were searched for relevant original articlesdescribing FDG-PET for vasculitis assessment, using MesH terms ("GiantCell Arteritis or Vasculitis" AND "PET"). Criteria for inclusionwere:(1)FDG-PET for diagnosis of vasculitis(2)American College of Rheumatologycriteria as reference standard(3)control group. After data extraction, analyseswere performed using a random-effects model. Results : Of 184 citations(database search and references screening),70 articles were reviewed of which12 eligible studies were extracted (sensitivity range from 32% to 97%). 7studies fulfilled all inclusion criteria. Owing to overlapping population, 1study was excluded. Statistical heterogeneity justified the random-effectsmodel. Pooled 6 studies analysis(116 vasculitis,224 controls) showed a 81%sensitivity (95%CI:70-89%);a 89% specificity (95%CI:77-95%);a 85%PPV(95%CI:63-95%); a 90% NPV(95%CI:79-95%);a 7.1 positive LR(95%CI:3.4-14.9); a0.2 negative LR(95%CI:0.14-0.35) and 90.1 DOR(95%CI: 18.6-437). Conclusion :FDG-PET has good diagnostic performances in the detection of large vesselsvasculitis. Its promising role could be extended to follow up patients undertreatment, but further studies are needed to confirm this possibility.

Risk factors for survival after lung metastasectomy in colorectal cancer patients: a systematic review and meta-analysis.

BACKGROUND: Resection of lung metastases (LM) from colorectal cancer (CRC) is increasingly performed with a curative intent. It is currently not possible to identify those CRC patients who may benefit the most from this surgical strategy. The aim of this study was to perform a systematic review of risk factors for survival after lung metastasectomy for CRC. METHODS: We performed a meta-analysis of series published between 2000 and 2011, which focused on surgical management of LM from CRC and included more than 40 patients each. Pooled hazard ratios (HR) were calculated by using random effects model for parameters considered as potential prognostic factors. RESULTS: Twenty-five studies including a total of 2925 patients were considered in this analysis. Four parameters were associated with poor survival: (1) a short disease-free interval between primary tumor resection and development of LM (HR 1.59, 95 % confidence interval [CI] 1.27-1.98); (2) multiple LM (HR 2.04, 95 % CI 1.72-2.41); (3) positive hilar and/or mediastinal lymph nodes (HR 1.65, 95 % CI 1.35-2.02); and (4) elevated prethoracotomy carcinoembryonic antigen (HR 1.91, 95 % CI 1.57-2.32). By comparison, a history of resected liver metastases (HR 1.22, 95 % CI 0.91-1.64) did not achieve statistical significance. CONCLUSIONS: Clinical variables associated with prolonged survival after surgery for LM in CRC patients include prolonged disease-free interval between primary tumor and metastatic spread, normal prethoracotomy carcinoembryonic antigen, absence of thoracic node involvement, and a single pulmonary lesion.

A limited area model intercomparison on the "Montserrat-2000" flash-flood event using statistical and deterministic methods

In the scope of the European project Hydroptimet, INTERREG IIIB-MEDOCC programme, limited area model (LAM) intercomparison of intense events that produced many damages to people and territory is performed. As the comparison is limited to single case studies, the work is not meant to provide a measure of the different models' skill, but to identify the key model factors useful to give a good forecast on such a kind of meteorological phenomena. This work focuses on the Spanish flash-flood event, also known as "Montserrat-2000" event. The study is performed using forecast data from seven operational LAMs, placed at partners' disposal via the Hydroptimet ftp site, and observed data from Catalonia rain gauge network. To improve the event analysis, satellite rainfall estimates have been also considered. For statistical evaluation of quantitative precipitation forecasts (QPFs), several non-parametric skill scores based on contingency tables have been used. Furthermore, for each model run it has been possible to identify Catalonia regions affected by misses and false alarms using contingency table elements. Moreover, the standard "eyeball" analysis of forecast and observed precipitation fields has been supported by the use of a state-of-the-art diagnostic method, the contiguous rain area (CRA) analysis. This method allows to quantify the spatial shift forecast error and to identify the error sources that affected each model forecasts. High-resolution modelling and domain size seem to have a key role for providing a skillful forecast. Further work is needed to support this statement, including verification using a wider observational data set.

A statistical methodology for the comparison of blue gel pen inks analyzed by laser desorption/ionization mass spectrometry

A statistical methodology for the objective comparison of LDI-MS mass spectra of blue gel pen inks was evaluated. Thirty-three blue gel pen inks previously studied by RAMAN were analyzed directly on the paper using both positive and negative mode. The obtained mass spectra were first compared using relative areas of selected peaks using the Pearson correlation coefficient and the Euclidean distance. Intra-variability among results from one ink and inter-variability between results from different inks were compared in order to choose a differentiation threshold minimizing the rate of false negative (i.e. avoiding false differentiation of the inks). This yielded a discriminating power of up to 77% for analysis made in the negative mode. The whole mass spectra were then compared using the same methodology, allowing for a better DP in the negative mode of 92% using the Pearson correlation on standardized data. The positive mode results generally yielded a lower differential power (DP) than the negative mode due to a higher intra-variability compared to the inter-variability in the mass spectra of the ink samples.

Numerical analysis of wave-induced fluid flow effects on seismic data: Application to monitoring of CO2 storage at the Sleipner Field

In this work we analyze how patchy distributions of CO2 and brine within sand reservoirs may lead to significant attenuation and velocity dispersion effects, which in turn may have a profound impact on surface seismic data. The ultimate goal of this paper is to contribute to the understanding of these processes within the framework of the seismic monitoring of CO2 sequestration, a key strategy to mitigate global warming. We first carry out a Monte Carlo analysis to study the statistical behavior of attenuation and velocity dispersion of compressional waves traveling through rocks with properties similar to those at the Utsira Sand, Sleipner field, containing quasi-fractal patchy distributions of CO2 and brine. These results show that the mean patch size and CO2 saturation play key roles in the observed wave-induced fluid flow effects. The latter can be remarkably important when CO2 concentrations are low and mean patch sizes are relatively large. To analyze these effects on the corresponding surface seismic data, we perform numerical simulations of wave propagation considering reservoir models and CO2 accumulation patterns similar to the CO2 injection site in the Sleipner field. These numerical experiments suggest that wave-induced fluid flow effects may produce changes in the reservoir's seismic response, modifying significantly the main seismic attributes usually employed in the characterization of these environments. Consequently, the determination of the nature of the fluid distributions as well as the proper modeling of the seismic data constitute important aspects that should not be ignored in the seismic monitoring of CO2 sequestration problems.

Tension-band wiring of olecranon fractures - Biomechanical analysis of different fixation techniques

Tension-band wiring is a recognised standard treatment for fixation of olecranon fractures. The classical operation technique is well known and widespread among the orthopaedic surgeons. Nevertheless complications like K-wire migration or skin perforation and difficult technical as well as anatomical prerequisites require better-adapted operation fixation methods. In older female patients a cut through of the Kirschner wires with concomitant secondary displacement was observed. We intent to develop a new, better adapted operation technique for olecranon fractures in the old patients, in order to decrease complications and follow-up procedures. In this study we compare two different K-wire positions: 10 models of the classical AO tension-banding to 10 models with adapted K-wire insertion. In this group the K-wire passes from the tip of the olecranon to the posterior cortical of the distal fragment of the ulna. We tested maximal failure load, maximal opening angle as well as maximal work to achieve maximal force. In either technique we were able to determine different variables: a maximal failure load of more than 600N (p = 0.94) for both fixation methods and a maximal opening angle for both techniques of about 10° (p = 0.86). To achieve the maximal force our modified technique required a slightly increased work (p = 0.16). In this study no statistical significant differences between the two fixation techniques was shown. This leads to the conclusion that the modified version is comparable to the classical operation technique considering the stability, but due to the adaption of the angle in the modified procedure, less lesions of neurovascular structures on the volar side can be expected. To support our findings cadaver studies are needed for further investigations.

Configurational statistical model for the damaged structure of silicon oxide after ion implantation

A configurational model for silicon oxide damaged after a high-dose ion implantation of a nonreactive species is presented. Based on statistics of silicon-centered tetrahedra, the model takes into account not only the closest environment of a given silicon atom, but also the second neighborhood, so it is specified whether the oxygen attached to one given silicon is bridging two tetrahedra or not. The frequencies and intensities of infrared vibrational bands have been calculated by averaging over the distributions and these results are in agreement with the ones obtained from infrared experimental spectra. Likewise, the chemical shifts obtained from x-ray photoelectron spectroscopy (XPS) analysis are similar to the reported values for the charge-transfer model of SiOx compounds.

Asymptotic analysis of the Gunn effect with realistic boundary conditions

A general asymptotic analysis of the Gunn effect in n-type GaAs under general boundary conditions for metal-semiconductor contacts is presented. Depending on the parameter values in the boundary condition of the injecting contact, different types of waves mediate the Gunn effect. The periodic current oscillation typical of the Gunn effect may be caused by moving charge-monopole accumulation or depletion layers, or by low- or high-field charge-dipole solitary waves. A new instability caused by multiple shedding of (low-field) dipole waves is found. In all cases the shape of the current oscillation is described in detail: we show the direct relationship between its major features (maxima, minima, plateaus, etc.) and several critical currents (which depend on the values of the contact parameters). Our results open the possibility of measuring contact parameters from the analysis of the shape of the current oscillation.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Spectral analysis of the luteinizing hormone in the blood samples

Generally, medicine books are concentrated almost exclusively in explaining methodology that analyzes fixed measures, measures done in a certain moment, nevertheless the evolution of the measurement and correct interpretation of the missed values are very important and sometimes can give the key information of the results obtained. Thus, the analysis of the temporary series and spectral analysis or analysis of the time series in the dominion of frequencies can be regarded as an appropriate tool for this kind of studies.In this work the frequency of the pulsating secretion of luteinizing hormone LH (thatregulates the fertile life of women) were analyzed in order to determine the existence of the significant frequencies obtained by analysis of Fourier. Detection of the frequencies, with which the pulsating secretion of the LH takes place, is a quite difficult question due topresence of the random errors in measures and samplings, i.e. that pulsating secretions of small amplitude are not detected and disregarded. In physiology it is accepted that cyclical patterns in the secretion of the LH exist and in the results of this research confirm this pattern and determine its frequency presented in the corresponded periodograms to each of studied cycle. The obtained results can be used as key pattern for future sampling frequencies in order to ¿catch¿ the significant picks of the luteinizing hormone and reflect on time forproductivity treatment of women.

Statistical mechanics in the extended Gaussian ensemble

The extended Gaussian ensemble (EGE) is introduced as a generalization of the canonical ensemble. This ensemble is a further extension of the Gaussian ensemble introduced by Hetherington [J. Low Temp. Phys. 66, 145 (1987)]. The statistical mechanical formalism is derived both from the analysis of the system attached to a finite reservoir and from the maximum statistical entropy principle. The probability of each microstate depends on two parameters ß and ¿ which allow one to fix, independently, the mean energy of the system and the energy fluctuations, respectively. We establish the Legendre transform structure for the generalized thermodynamic potential and propose a stability criterion. We also compare the EGE probability distribution with the q-exponential distribution. As an example, an application to a system with few independent spins is presented.

Measurement Errors Should Always Be Incorporated in Phylogenetic Comparative Analysis

The evolution of continuous traits is the central component of comparative analyses in phylogenetics, and the comparison of alternative models of trait evolution has greatly improved our understanding of the mechanisms driving phenotypic differentiation. Several factors influence the comparison of models, and we explore the effects of random errors in trait measurement on the accuracy of model selection. We simulate trait data under a Brownian motion model (BM) and introduce different magnitudes of random measurement error. We then evaluate the resulting statistical support for this model against two alternative models: Ornstein-Uhlenbeck (OU) and accelerating/decelerating rates (ACDC). Our analyses show that even small measurement errors (10%) consistently bias model selection towards erroneous rejection of BM in favour of more parameter-rich models (most frequently the OU model). Fortunately, methods that explicitly incorporate measurement errors in phylogenetic analyses considerably improve the accuracy of model selection. Our results call for caution in interpreting the results of model selection in comparative analyses, especially when complex models garner only modest additional support. Importantly, as measurement errors occur in most trait data sets, we suggest that estimation of measurement errors should always be performed during comparative analysis to reduce chances of misidentification of evolutionary processes.