Wavelet-denoising of electroencephalogram and the absolute slope method: a new tool to improve electroencephalographic localization and lateralization

OBJECTIVE: In ictal scalp electroencephalogram (EEG) the presence of artefacts and the wide ranging patterns of discharges are hurdles to good diagnostic accuracy. Quantitative EEG aids the lateralization and/or localization process of epileptiform activity. METHODS: Twelve patients achieving Engel Class I/IIa outcome following temporal lobe surgery (1 year) were selected with approximately 1-3 ictal EEGs analyzed/patient. The EEG signals were denoised with discrete wavelet transform (DWT), followed by computing the normalized absolute slopes and spatial interpolation of scalp topography associated to detection of local maxima. For localization, the region with the highest normalized absolute slopes at the time when epileptiform activities were registered (>2.5 times standard deviation) was designated as the region of onset. For lateralization, the cerebral hemisphere registering the first appearance of normalized absolute slopes >2.5 times the standard deviation was designated as the side of onset. As comparison, all the EEG episodes were reviewed by two neurologists blinded to clinical information to determine the localization and lateralization of seizure onset by visual analysis. RESULTS: 16/25 seizures (64%) were correctly localized by the visual method and 21/25 seizures (84%) by the quantitative EEG method. 12/25 seizures (48%) were correctly lateralized by the visual method and 23/25 seizures (92%) by the quantitative EEG method. The McNemar test showed p=0.15 for localization and p=0.0026 for lateralization when comparing the two methods. CONCLUSIONS: The quantitative EEG method yielded significantly more seizure episodes that were correctly lateralized and there was a trend towards more correctly localized seizures. SIGNIFICANCE: Coupling DWT with the absolute slope method helps clinicians achieve a better EEG diagnostic accuracy.

ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis

Given a reproducing kernel Hilbert space (H,〈.,.〉)(H,〈.,.〉) of real-valued functions and a suitable measure μμ over the source space D⊂RD⊂R, we decompose HH as the sum of a subspace of centered functions for μμ and its orthogonal in HH. This decomposition leads to a special case of ANOVA kernels, for which the functional ANOVA representation of the best predictor can be elegantly derived, either in an interpolation or regularization framework. The proposed kernels appear to be particularly convenient for analyzing the effect of each (group of) variable(s) and computing sensitivity indices without recursivity.

Stacking of discontinuous regional palaeoclimate records: Chironomid-based summer temperatures from the Alpine region

Since multi-site reconstructions are less affected by site-specific climatic effects and artefacts, regional palaeotemperature reconstructions based on a number of sites can provide more robust estimates of centennial- to millennial-scale temperature trends than individual, site-specific records. Furthermore, reconstructions based on multiple records are necessary for developing continuous climate records over time scales longer than covered by individual sequences. Here, we present a procedure for developing such reconstructions based on relatively short (centuries to millennia), discontinuously sampled records as are typically developed when using biotic proxies in lake sediments for temperature reconstruction. The approach includes an altitudinal correction of temperatures, an interpolation of individual records to equal time intervals, a stacking procedure for sections of the interval of interest that have the same records available, as well as a splicing procedure to link the individual stacked records into a continuous reconstruction. Variations in the final, stacked and spliced reconstruction are driven by variations in the individual records, whereas the absolute temperature values are determined by the stacked segment based on the largest number of records. With numerical simulations based on the NGRIP δ18O record, we demonstrate that the interpolation and stacking procedure provides an approximation of a smoothed palaeoclimate record if based on a sufficient number of discontinuously sampled records. Finally, we provide an example of a stacked and spliced palaeotemperature reconstruction 15000–90 calibrated 14C yr BP based on six chironomid records from the northern and central Swiss Alps and eastern France to discuss the potential and limitations of this approach.

Right-handed neutrino production rate at T > 160 GeV

The production rate of right-handed neutrinos from a Standard Model plasma at a temperature above a hundred GeV has previously been evaluated up to NLO in Standard Model couplings (g ~ 2/3) in relativistic (M ~ πT) and non-relativistic regimes (M ≫ πT), and up to LO in an ultrarelativistic regime (M ≲ gT). The last result necessitates an all-orders resummation of the loop expansion, accounting for multiple soft scatterings of the nearly light-like particles participating in 1↔2 reactions. In this paper we suggest how the regimes can be interpolated into a result applicable for any right-handed neutrino mass and at all temperatures above 160GeV. The results can also be used for determining the lepton number washout rate in models containing right-handed neutrinos. Numerical results are given in a tabulated form permitting for their incorporation into leptogenesis codes. We note that due to effects from soft Higgs bosons there is a narrow intermediate regime around M ~g 1/2 T in which our interpolation is phenomenological and a more precise study would be welcome.

No shift to a deeper water uptake depth in response to summer drought of two lowland and sub-alpine C3-grasslands in Switzerland

Temperate C3-grasslands are of high agricultural and ecological importance in Central Europe. Plant growth and consequently grassland yields depend strongly on water supply during the growing season, which is projected to change in the future. We therefore investigated the effect of summer drought on the water uptake of an intensively managed lowland and an extensively managed sub-alpine grassland in Switzerland. Summer drought was simulated by using transparent shelters. Standing above- and belowground biomass was sampled during three growing seasons. Soil and plant xylem waters were analyzed for oxygen (and hydrogen) stable isotope ratios, and the depths of plant water uptake were estimated by two different approaches: (1) linear interpolation method and (2) Bayesian calibrated mixing model. Relative to the control, aboveground biomass was reduced under drought conditions. In contrast to our expectations, lowland grassland plants subjected to summer drought were more likely (43–68 %) to rely on water in the topsoil (0–10 cm), whereas control plants relied less on the topsoil (4–37 %) and shifted to deeper soil layers (20–35 cm) during the drought period (29–48 %). Sub-alpine grassland plants did not differ significantly in uptake depth between drought and control plots during the drought period. Both approaches yielded similar results and showed that the drought treatment in the two grasslands did not induce a shift to deeper uptake depths, but rather continued or shifted water uptake to even more shallower soil depths. These findings illustrate the importance of shallow soil depths for plant performance under drought conditions.

Separation of the Ribbon from globally distributed energetic neutral Atom Flux using the first five Years of Ibex Observations

The Interstellar Boundary Explorer (IBEX) observes the IBEX ribbon, which stretches across much of the sky observed in energetic neutral atoms (ENAs). The ribbon covers a narrow (~20°-50°) region that is believed to be roughly perpendicular to the interstellar magnetic field. Superimposed on the IBEX ribbon is the globally distributed flux that is controlled by the processes and properties of the heliosheath. This is a second study that utilizes a previously developed technique to separate ENA emissions in the ribbon from the globally distributed flux. A transparency mask is applied over the ribbon and regions of high emissions. We then solve for the globally distributed flux using an interpolation scheme. Previously, ribbon separation techniques were applied to the first year of IBEX-Hi data at and above 0.71 keV. Here we extend the separation analysis down to 0.2 keV and to five years of IBEX data enabling first maps of the ribbon and the globally distributed flux across the full sky of ENA emissions. Our analysis shows the broadening of the ribbon peak at energies below 0.71 keV and demonstrates the apparent deformation of the ribbon in the nose and heliotail. We show global asymmetries of the heliosheath, including both deflection of the heliotail and differing widths of the lobes, in context of the direction, draping, and compression of the heliospheric magnetic field. We discuss implications of the ribbon maps for the wide array of concepts that attempt to explain the ribbon's origin. Thus, we present the five-year separation of the IBEX ribbon from the globally distributed flux in preparation for a formal IBEX data release of ribbon and globally distributed flux maps to the heliophysics community.

Modelling the system behaviour of wet snow avalanches using an expert system approach for risk management on high alpine traffic roads

The presented approach describes a model for a rule-based expert system calculating the temporal variability of the release of wet snow avalanches, using the assumption of avalanche triggering without the loading of new snow. The knowledge base of the model is created by using investigations on the system behaviour of wet snow avalanches in the Italian Ortles Alps, and is represented by a fuzzy logic rule-base. Input parameters of the expert system are numerical and linguistic variables, measurable meteorological and topographical factors and observable characteristics of the snow cover. Output of the inference method is the quantified release disposition for wet snow avalanches. Combining topographical parameters and the spatial interpolation of the calculated release disposition a hazard index map is dynamically generated. Furthermore, the spatial and temporal variability of damage potential on roads exposed to wet snow avalanches can be quantified, expressed by the number of persons at risk. The application of the rule base to the available data in the study area generated plausible results. The study demonstrates the potential for the application of expert systems and fuzzy logic in the field of natural hazard monitoring and risk management.

On loop corrections to the dilepton rate

Next-to-leading order analyses of the dilepton production rate from a hot QCD plasma are reviewed. In general, the photon invariant mass is taken to be in the range K2∼(πT)2, permitting thereby for an interpolation between an OPE computation in a hard regime K2≫(πT)2 and an LPM resummed computation in a soft regime 0

hp-DGFEM for second-order mixed elliptic problems polyhedra

We prove exponential rates of convergence of hp-version discontinuous Galerkin (dG) interior penalty finite element methods for second-order elliptic problems with mixed Dirichlet-Neumann boundary conditions in axiparallel polyhedra. The dG discretizations are based on axiparallel, σ-geometric anisotropic meshes of mapped hexahedra and anisotropic polynomial degree distributions of μ-bounded variation. We consider piecewise analytic solutions which belong to a larger analytic class than those for the pure Dirichlet problem considered in [11, 12]. For such solutions, we establish the exponential convergence of a nonconforming dG interpolant given by local L 2 -projections on elements away from corners and edges, and by suitable local low-order quasi-interpolants on elements at corners and edges. Due to the appearance of non-homogeneous, weighted norms in the analytic regularity class, new arguments are introduced to bound the dG consistency errors in elements abutting on Neumann edges. The non-homogeneous norms also entail some crucial modifications of the stability and quasi-optimality proofs, as well as of the analysis for the anisotropic interpolation operators. The exponential convergence bounds for the dG interpolant constructed in this paper generalize the results of [11, 12] for the pure Dirichlet case.

Multimodal Imaging of Cotton Wool Spots in Branch Retinal Vein Occlusion.

PURPOSE: To describe and follow cotton wool spots (CWS) in branch retinal vein occlusion (BRVO) using multimodal imaging. METHODS: In this prospective cohort study including 24 patients with new-onset BRVO, CWS were described and analyzed in color fundus photography (CF), spectral domain optical coherence tomography (SD-OCT), infrared (IR) and fluorescein angiography (FA) every 3 months for 3 years. The CWS area on SD-OCT and CF was evaluated using OCT-Tool-Kit software: CWS were marked in each single OCT B-scan and the software calculated the area by interpolation. RESULTS: 29 central CWS lesions were found. 100% of these CWS were visible on SD-OCT, 100% on FA and 86.2% on IR imaging, but only 65.5% on CF imaging. CWS were visible for 12.4 ± 7.5 months on SD-OCT, for 4.4 ± 3 months and 4.3 ± 3.4 months on CF and on IR, respectively, and for 17.5 ± 7.1 months on FA. The evaluated CWS area on SD-OCT was larger than on CF (0.26 ± 0.17 mm(2) vs. 0.13 ± 0.1 mm(2), p < 0.0001). The CWS area on SD-OCT and surrounding pathology such as intraretinal cysts, avascular zones and intraretinal hemorrhage were predictive for how long CWS remained visible (r(2) = 0.497, p < 0.002). CONCLUSIONS: The lifetime and presentation of CWS in BRVO seem comparable to other diseases. SD-OCT shows a higher sensitivity for detecting CWS compared to CF. The duration of visibility of CWS varies among different image modalities and depends on the surrounding pathology and the CWS size.

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().