Statistical learning theory for geospatial data. Case study: Aral sea

In recent years there has been an explosive growth in the development of adaptive and data driven methods. One of the efficient and data-driven approaches is based on statistical learning theory (Vapnik 1998). The theory is based on Structural Risk Minimisation (SRM) principle and has a solid statistical background. When applying SRM we are trying not only to reduce training error ? to fit the available data with a model, but also to reduce the complexity of the model and to reduce generalisation error. Many nonlinear learning procedures recently developed in neural networks and statistics can be understood and interpreted in terms of the structural risk minimisation inductive principle. A recent methodology based on SRM is called Support Vector Machines (SVM). At present SLT is still under intensive development and SVM find new areas of application (www.kernel-machines.org). SVM develop robust and non linear data models with excellent generalisation abilities that is very important both for monitoring and forecasting. SVM are extremely good when input space is high dimensional and training data set i not big enough to develop corresponding nonlinear model. Moreover, SVM use only support vectors to derive decision boundaries. It opens a way to sampling optimization, estimation of noise in data, quantification of data redundancy etc. Presentation of SVM for spatially distributed data is given in (Kanevski and Maignan 2004).

An Efficient Nominal Unification Algorithm

Nominal Unification is an extension of first-order unification where terms can contain binders and unification is performed modulo α equivalence. Here we prove that the existence of nominal unifiers can be decided in quadratic time. First, we linearly-reduce nominal unification problems to a sequence of freshness and equalities between atoms, modulo a permutation, using ideas as Paterson and Wegman for first-order unification. Second, we prove that solvability of these reduced problems may be checked in quadràtic time. Finally, we point out how using ideas of Brown and Tarjan for unbalanced merging, we could solve these reduced problems more efficiently

Passive knee-extension test to measure hamstring tightness: influence of gravity correction

CONTEXT: A passive knee-extension test has been shown to be a reliable method of assessing hamstring tightness, but this method does not take into account the potential effect of gravity on the tested leg. OBJECTIVE: To compare an original passive knee-extension test with 2 adapted methods including gravity's effect on the lower leg. DESIGN: Repeated measures. SETTING: Laboratory. PARTICIPANTS: 20 young track and field athletes (16.6 ± 1.6 y, 177.6 ± 9.2 cm, 75.9 ± 24.8 kg). INTERVENTION: Each subject was tested in a randomized order with 3 different methods: In the original one (M1), passive knee angle was measured with a standard force of 68.7 N (7 kg) applied proximal to the lateral malleolus. The second (M2) and third (M3) methods took into account the relative lower-leg weight (measured respectively by handheld dynamometer and anthropometrical table) to individualize the force applied to assess passive knee angle. MAIN OUTCOME MEASURES: Passive knee angles measured with video-analysis software. RESULTS: No difference in mean individualized applied force was found between M2 and M3, so the authors assessed passive knee angle only with M2. The mean knee angle was different between M1 and M2 (68.8 ± 12.4 vs 73.1 ± 10.6, P < .001). Knee angles in M1 and M2 were correlated (r = .93, P < .001). CONCLUSIONS: Differences in knee angle were found between the original passive knee-extension test and a method with gravity correction. M2 is an improved version of the original method (M1) since it minimizes the effect of gravity. Therefore, we recommend using it rather than M1.

Efficient T cell activation requires an optimal dwell-time of interaction between the TCR and the pMHC complex.

Cytotoxic T cell (CTL) activation by antigen requires the specific detection of peptide-major histocompatibility class I (pMHC) molecules on the target-cell surface by the T cell receptor (TCR). We examined the effect of mutations in the antigen-binding site of a Kb-restricted TCR on T cell activation, antigen binding and dissociation from antigen.These parameters were also examined for variants derived from a Kd-restricted peptide that was recognized by a CTL clone. Using these two independent systems, we show that T cell activation can be impaired by mutations that either decrease or increase the binding half-life of the TCR-pMHC interaction. Our data indicate that efficient T cell activation occurs within an optimal dwell-time range of TCR-pMHC interaction. This restricted dwell-time range is consistent with the exclusion of either extremely low or high affinity T cells from the expanded population during immune responses.

Saponins from the Spanish saffron Crocus sativus are efficient adjuvants for protein-based vaccines.

Protein and peptide-based vaccines provide rigorously formulated antigens. However, these purified products are only weakly immunogenic by themselves and therefore require the addition of immunostimulatory components or adjuvants in the vaccine formulation. Various compounds derived from pathogens, minerals or plants, possess pro-inflammatory properties which allow them to act as adjuvants and contribute to the induction of an effective immune response. The results presented here demonstrate the adjuvant properties of novel saponins derived from the Spanish saffron Crocus sativus. In vivo immunization studies and tumor protection experiments unambiguously establish the value of saffron saponins as candidate adjuvants. These saponins were indeed able to increase both humoral and cellular immune responses to protein-based vaccines, ultimately providing a significant degree of protection against tumor challenge when administered in combination with a tumor antigen. This preclinical study provides an in depth immunological characterization of a new saponin as a vaccine adjuvant, and encourages its further development for use in vaccine formulations.

Fission yeast rad51 and dmc1, two efficient DNA recombinases forming helical nucleoprotein filaments.

Homologous recombination is important for the repair of double-strand breaks during meiosis. Eukaryotic cells require two homologs of Escherichia coli RecA protein, Rad51 and Dmc1, for meiotic recombination. To date, it is not clear, at the biochemical level, why two homologs of RecA are necessary during meiosis. To gain insight into this, we purified Schizosaccharomyces pombe Rad51 and Dmc1 to homogeneity. Purified Rad51 and Dmc1 form homo-oligomers, bind single-stranded DNA preferentially, and exhibit DNA-stimulated ATPase activity. Both Rad51 and Dmc1 promote the renaturation of complementary single-stranded DNA. Importantly, Rad51 and Dmc1 proteins catalyze ATP-dependent strand exchange reactions with homologous duplex DNA. Electron microscopy reveals that both S. pombe Rad51 and Dmc1 form nucleoprotein filaments. Rad51 formed helical nucleoprotein filaments on single-stranded DNA, whereas Dmc1 was found in two forms, as helical filaments and also as stacked rings. These results demonstrate that Rad51 and Dmc1 are both efficient recombinases in lower eukaryotes and reveal closer functional and structural similarities between the meiotic recombinase Dmc1 and Rad51. The DNA strand exchange activity of both Rad51 and Dmc1 is most likely critical for proper meiotic DNA double-strand break repair in lower eukaryotes.

An Efficient Segmentation Method for Ultrasound Images based on a Semi-supervised Approach and Patch-based Features

Segmenting ultrasound images is a challenging problemwhere standard unsupervised segmentation methods such asthe well-known Chan-Vese method fail. We propose in thispaper an efficient segmentation method for this class ofimages. Our proposed algorithm is based on asemi-supervised approach (user labels) and the use ofimage patches as data features. We also consider thePearson distance between patches, which has been shown tobe robust w.r.t speckle noise present in ultrasoundimages. Our results on phantom and clinical data show avery high similarity agreement with the ground truthprovided by a medical expert.

A rapid and effective vignetting correction for quantitative microscopy

Images acquired using optical microscopes are inherently subject to vignetting effects due to imperfect illumination and image acquisition. However, such vignetting effects hamper accurate extraction of quantitative information from biological images, leading to less effective image segmentation and increased noise in the measurements. Here, we describe a rapid and effective method for vignetting correction, which generates an estimate for a correction function from the background fluorescence without the need to acquire additional calibration images. We validate the usefulness of this algorithm using artificially distorted images as a gold standard for assessing the accuracy of the applied correction and then demonstrate that this correction method enables the reliable detection of biologically relevant variation in cell populations. A simple user interface called FlattifY was developed and integrated into the image analysis platform YeastQuant to facilitate easy application of vignetting correction to a wide range of images.

Spatio-temporal Brain Dynamics Mediating Post-error Behavioral Adjustments.

Optimal behavior relies on flexible adaptation to environmental requirements, notably based on the detection of errors. The impact of error detection on subsequent behavior typically manifests as a slowing down of RTs following errors. Precisely how errors impact the processing of subsequent stimuli and in turn shape behavior remains unresolved. To address these questions, we used an auditory spatial go/no-go task where continual feedback informed participants of whether they were too slow. We contrasted auditory-evoked potentials to left-lateralized go and right no-go stimuli as a function of performance on the preceding go stimuli, generating a 2 × 2 design with "preceding performance" (fast hit [FH], slow hit [SH]) and stimulus type (go, no-go) as within-subject factors. SH trials yielded SH trials on the following trials more often than did FHs, supporting our assumption that SHs engaged effects similar to errors. Electrophysiologically, auditory-evoked potentials modulated topographically as a function of preceding performance 80-110 msec poststimulus onset and then as a function of stimulus type at 110-140 msec, indicative of changes in the underlying brain networks. Source estimations revealed a stronger activity of prefrontal regions to stimuli after successful than error trials, followed by a stronger response of parietal areas to the no-go than go stimuli. We interpret these results in terms of a shift from a fast automatic to a slow controlled form of inhibitory control induced by the detection of errors, manifesting during low-level integration of task-relevant features of subsequent stimuli, which in turn influences response speed.

Correction for heart rate variability during 3D whole heart MR coronary angiography.

PURPOSE: To evaluate the effect of a real-time adaptive trigger delay on image quality to correct for heart rate variability in 3D whole-heart coronary MR angiography (MRA). MATERIALS AND METHODS: Twelve healthy adults underwent 3D whole-heart coronary MRA with and without the use of an adaptive trigger delay. The moment of minimal coronary artery motion was visually determined on a high temporal resolution MRI. Throughout the scan performed without adaptive trigger delay, trigger delay was kept constant, whereas during the scan performed with adaptive trigger delay, trigger delay was continuously updated after each RR-interval using physiological modeling. Signal-to-noise, contrast-to-noise, vessel length, vessel sharpness, and subjective image quality were compared in a blinded manner. RESULTS: Vessel sharpness improved significantly for the middle segment of the right coronary artery (RCA) with the use of the adaptive trigger delay (52.3 +/- 7.1% versus 48.9 +/- 7.9%, P = 0.026). Subjective image quality was significantly better in the middle segments of the RCA and left anterior descending artery (LAD) when the scan was performed with adaptive trigger delay compared to constant trigger delay. CONCLUSION: Our results demonstrate that the use of an adaptive trigger delay to correct for heart rate variability improves image quality mainly in the middle segments of the RCA and LAD.