Termination of Atrial Fibrillation by Catheter Ablation can be Successfully Predicted from Baseline ECG

Purpose Multiple organization indices (OIs) have been used to predict the outcome of stepwise catheter ablation (step-CA) in long-standing persistent AF (pers-AF), however with limited success. Our study aims at developing innovative OIs from baseline (BL, before ablation) ECG in order to predict the outcome of step-CA. Methods Fourteen consecutive patients (pts) (60±5 y, AF duration 21±9 m) underwent a step-CA consisting in pulmonary veins isolation, left atrial (LA) defragmentation and linear ablations, and right atrial (RA) ablations if non terminated. Chest lead V6 was placed in the back (V6b). After QRST cancellation from chest leads V1 to V6b, two OIs were computed to quantify the harmonic components of ECG atrial activity: 1) phase difference variance (PD) between the AF harmonic components as a measure of AF regularity, and 2) adaptive OI (AOI) evaluating the time evolution of the AF harmonic components. Both indices were compared to classical ones: a spectrum-based OI (SOI) and ECG AF cycle length (AFCL). Results Pers-AF was terminated into sinus rhythm or atrial tachycardia in 10/14 pts during step-CA, 8 during LA (LT), 2 during RA (RT) ablation, and 4 were non terminated (NT). The figure shows that LT was best separated from RT/NT before ablation by AOI computed on lead V1 (A) and PD from lead V6b (B) as compared to SOI and AFCL respectively. Conclusion Our results suggest that adaptive OIs computed before ablation perform better than classical OIs for separating LT from RT/NT pts. These findings are indicative of a higher baseline organization in LT pts that could be used to select candidates for the restoration of sinus rhythm by step-CA.

Domain Adaptation in remote sensing: increasing the portability of land-cover classifiers

Among the types of remote sensing acquisitions, optical images are certainly one of the most widely relied upon data sources for Earth observation. They provide detailed measurements of the electromagnetic radiation reflected or emitted by each pixel in the scene. Through a process termed supervised land-cover classification, this allows to automatically yet accurately distinguish objects at the surface of our planet. In this respect, when producing a land-cover map of the surveyed area, the availability of training examples representative of each thematic class is crucial for the success of the classification procedure. However, in real applications, due to several constraints on the sample collection process, labeled pixels are usually scarce. When analyzing an image for which those key samples are unavailable, a viable solution consists in resorting to the ground truth data of other previously acquired images. This option is attractive but several factors such as atmospheric, ground and acquisition conditions can cause radiometric differences between the images, hindering therefore the transfer of knowledge from one image to another. The goal of this Thesis is to supply remote sensing image analysts with suitable processing techniques to ensure a robust portability of the classification models across different images. The ultimate purpose is to map the land-cover classes over large spatial and temporal extents with minimal ground information. To overcome, or simply quantify, the observed shifts in the statistical distribution of the spectra of the materials, we study four approaches issued from the field of machine learning. First, we propose a strategy to intelligently sample the image of interest to collect the labels only in correspondence of the most useful pixels. This iterative routine is based on a constant evaluation of the pertinence to the new image of the initial training data actually belonging to a different image. Second, an approach to reduce the radiometric differences among the images by projecting the respective pixels in a common new data space is presented. We analyze a kernel-based feature extraction framework suited for such problems, showing that, after this relative normalization, the cross-image generalization abilities of a classifier are highly increased. Third, we test a new data-driven measure of distance between probability distributions to assess the distortions caused by differences in the acquisition geometry affecting series of multi-angle images. Also, we gauge the portability of classification models through the sequences. In both exercises, the efficacy of classic physically- and statistically-based normalization methods is discussed. Finally, we explore a new family of approaches based on sparse representations of the samples to reciprocally convert the data space of two images. The projection function bridging the images allows a synthesis of new pixels with more similar characteristics ultimately facilitating the land-cover mapping across images.

On the problematic of reserving and stochastic loss models

Abstract Traditionally, the common reserving methods used by the non-life actuaries are based on the assumption that future claims are going to behave in the same way as they did in the past. There are two main sources of variability in the processus of development of the claims: the variability of the speed with which the claims are settled and the variability between the severity of the claims from different accident years. High changes in these processes will generate distortions in the estimation of the claims reserves. The main objective of this thesis is to provide an indicator which firstly identifies and quantifies these two influences and secondly to determine which model is adequate for a specific situation. Two stochastic models were analysed and the predictive distributions of the future claims were obtained. The main advantage of the stochastic models is that they provide measures of variability of the reserves estimates. The first model (PDM) combines one conjugate family Dirichlet - Multinomial with the Poisson distribution. The second model (NBDM) improves the first one by combining two conjugate families Poisson -Gamma (for distribution of the ultimate amounts) and Dirichlet Multinomial (for distribution of the incremental claims payments). It was found that the second model allows to find the speed variability in the reporting process and development of the claims severity as function of two above mentioned distributions' parameters. These are the shape parameter of the Gamma distribution and the Dirichlet parameter. Depending on the relation between them we can decide on the adequacy of the claims reserve estimation method. The parameters have been estimated by the Methods of Moments and Maximum Likelihood. The results were tested using chosen simulation data and then using real data originating from the three lines of business: Property/Casualty, General Liability, and Accident Insurance. These data include different developments and specificities. The outcome of the thesis shows that when the Dirichlet parameter is greater than the shape parameter of the Gamma, resulting in a model with positive correlation between the past and future claims payments, suggests the Chain-Ladder method as appropriate for the claims reserve estimation. In terms of claims reserves, if the cumulated payments are high the positive correlation will imply high expectations for the future payments resulting in high claims reserves estimates. The negative correlation appears when the Dirichlet parameter is lower than the shape parameter of the Gamma, meaning low expected future payments for the same high observed cumulated payments. This corresponds to the situation when claims are reported rapidly and fewer claims remain expected subsequently. The extreme case appears in the situation when all claims are reported at the same time leading to expectations for the future payments of zero or equal to the aggregated amount of the ultimate paid claims. For this latter case, the Chain-Ladder is not recommended.

Cognitive behavioural therapy for obesity and binge eating associated to antipsychotic drugs

Background and Aims: Overweight, obesity and binge eating disorder are commonly reported in persons with severe mental disorders. Particularly, antipsychotic drugs (AP) induce weight gain in up to half of the patients. The aim of the present study is to confirm a previous study results on a larger sample of patients, to assess the impact of the interventions on other relevant dimensions of eating and weight related cognitions as well as to assess potential clinical indicators of outcomes such as AP drug, concomitant treatment with lithium or carbamazepine, psychiatric diagnostic, binge eating and severity of cognitive distortions. Method: A controlled study (12-week CBT vs. B N E) was carried out on 99 patients treated with an AP and who have gained weight following this treatment. Binge eating symptomatology, eating and weight-related cognitions, as well as weight and body mass index were assessed before treatment, at 12 weeks and at 24 weeks. Results: The findings confirms usefulness and effectiveness of the proposed CBT program on the treatment of binge symptomatology, cognitive distortions and obesity in patients treated with AP. Reduction of binge symptoms and maladapted cognitions appeared early, whereas the effect on weight appeared later during the follow up observation. No differences on outcomes were found across pharmacotherapy characteristics, diagnostic categories, binge eating nor severity of cognitive distortions. Conclusion: The proposed CBT treatment is useful for patients suffering from weight gain associated with AP treatments indeed when a concomitant treatment with lithium or valproate was given.

Magnetoelectricity in the ferrimagnetic Cu2OSeO3: symmetry analysis and Raman scattering study

sublattices ferrimagnet Cu2OSeO3 with a cubic symmetry and a linear magnetoelectric effect. There is no spectroscopic evidence for structural lattice distortions below T-C=60 K, which are expected due to magnetoelectric coupling. Using symmetry arguments we explain this observation by considering a special type of ferrimagnetic ground state which does not generate a spontaneous electric polarization. Interestingly, Raman scattering shows a strong increase of electric polarization of media through a dynamic magnetoelectric effect as a remarkable enhancement of the scattering intensity below T-C. New lines of purely magnetic origin have been detected in the magnetically ordered state. A part of them are attributed as scattering on exchange magnons. Using this observation and further symmetry considerations we argue for strong Dzyaloshinskii-Moriya interaction existing in the Cu2OSeO3. (c) 2010 American Institute of Physics. [doi:10.1063/1.3455808]

Transcranial Doppler Pulsatility Index: What it is and What it Isn't.

BACKGROUND: Transcranial Doppler (TCD) pulsatility index (PI) has traditionally been interpreted as a descriptor of distal cerebrovascular resistance (CVR). We sought to evaluate the relationship between PI and CVR in situations, where CVR increases (mild hypocapnia) and decreases (plateau waves of intracranial pressure-ICP). METHODS: Recordings from patients with head-injury undergoing monitoring of arterial blood pressure (ABP), ICP, cerebral perfusion pressure (CPP), and TCD assessed cerebral blood flow velocities (FV) were analyzed. The Gosling pulsatility index (PI) was compared between baseline and ICP plateau waves (n = 20 patients) or short term (30-60 min) hypocapnia (n = 31). In addition, a modeling study was conducted with the "spectral" PI (calculated using fundamental harmonic of FV) resulting in a theoretical formula expressing the dependence of PI on balance of cerebrovascular impedances. RESULTS: PI increased significantly (p < 0.001) while CVR decreased (p < 0.001) during plateau waves. During hypocapnia PI and CVR increased (p < 0.001). The modeling formula explained more than 65% of the variability of Gosling PI and 90% of the variability of the "spectral" PI (R = 0.81 and R = 0.95, respectively). CONCLUSION: TCD pulsatility index can be easily and quickly assessed but is usually misinterpreted as a descriptor of CVR. The mathematical model presents a complex relationship between PI and multiple haemodynamic variables.

Validity of the acoustic approximation in full-waveform seismic crosshole tomography

Acoustic waveform inversions are an increasingly popular tool for extracting subsurface information from seismic data. They are computationally much more efficient than elastic inversions. Naturally, an inherent disadvantage is that any elastic effects present in the recorded data are ignored in acoustic inversions. We investigate the extent to which elastic effects influence seismic crosshole data. Our numerical modeling studies reveal that in the presence of high contrast interfaces, at which P-to-S conversions occur, elastic effects can dominate the seismic sections, even for experiments involving pressure sources and pressure receivers. Comparisons of waveform inversion results using a purely acoustic algorithm on synthetic data that is either acoustic or elastic, show that subsurface models comprising small low-to-medium contrast (?30%) structures can be successfully resolved in the acoustic approximation. However, in the presence of extended high-contrast anomalous bodies, P-to-S-conversions may substantially degrade the quality of the tomographic images. In particular, extended low-velocity zones are difficult to image. Likewise, relatively small low-velocity features are unresolved, even when advanced a priori information is included. One option for mitigating elastic effects is data windowing, which suppresses later arriving seismic arrivals, such as shear waves. Our tests of this approach found it to be inappropriate because elastic effects are also included in earlier arriving wavetrains. Furthermore, data windowing removes later arriving P-wave phases that may provide critical constraints on the tomograms. Finally, we investigated the extent to which acoustic inversions of elastic data are useful for time-lapse analyses of high contrast engineered structures, for which accurate reconstruction of the subsurface structure is not as critical as imaging differential changes between sequential experiments. Based on a realistic scenario for monitoring a radioactive waste repository, we demonstrated that acoustic inversions of elastic data yield substantial distortions of the tomograms and also unreliable information on trends in the velocity changes.

RDM1, a novel RNA recognition motif (RRM)-containing protein involved in the cell response to cisplatin in vertebrates.

A variety of cellular proteins has the ability to recognize DNA lesions induced by the anti-cancer drug cisplatin, with diverse consequences on their repair and on the therapeutic effectiveness of this drug. We report a novel gene involved in the cell response to cisplatin in vertebrates. The RDM1 gene (for RAD52 Motif 1) was identified while searching databases for sequences showing similarities to RAD52, a protein involved in homologous recombination and DNA double-strand break repair. Ablation of RDM1 in the chicken B cell line DT40 led to a more than 3-fold increase in sensitivity to cisplatin. However, RDM1-/- cells were not hypersensitive to DNA damages caused by ionizing radiation, UV irradiation, or the alkylating agent methylmethane sulfonate. The RDM1 protein displays a nucleic acid binding domain of the RNA recognition motif (RRM) type. By using gel-shift assays and electron microscopy, we show that purified, recombinant chicken RDM1 protein interacts with single-stranded DNA as well as double-stranded DNA, on which it assembles filament-like structures. Notably, RDM1 recognizes DNA distortions induced by cisplatin-DNA adducts in vitro. Finally, human RDM1 transcripts are abundant in the testis, suggesting a possible role during spermatogenesis.

A Reappraisal of the Allocation Puzzle through the Portfolio Approach

Paradoxically, high-growth, high-investment developing countries tend to experience capital outflows. This paper shows that this allocation puzzle can be explained simply by introducing uninsurable idiosyncratic investment risk in the neoclassical growth model with international trade in bonds, and by taking into account not only TFP catch-up, but also the capital wedge, that is, the distortions on the return to capital. The model fits the two following facts, documented on a sample of 67 countries between 1980 and 2003: (i) TFP growth is positively correlated with capital outflows in a sample including creditor countries; (ii) the long-run level of capital per efficient unit of labor is positively correlated with capital outflows. Consistently, we show that the capital flows predicted by the model are positively correlated with the actual ones in this sample once the capital wedge is accounted for. The fact that Asia dominates global imbalances can be explained by its relatively low capital wedge.

Correcting surface coil excitation inhomogeneities in single-shot SPEN MRI.

Given their high sensitivity and ability to limit the field of view (FOV), surface coils are often used in magnetic resonance spectroscopy (MRS) and imaging (MRI). A major downside of surface coils is their inherent radiofrequency (RF) B1 heterogeneity across the FOV, decreasing with increasing distance from the coil and giving rise to image distortions due to non-uniform spatial responses. A robust way to compensate for B1 inhomogeneities is to employ adiabatic inversion pulses, yet these are not well adapted to all imaging sequences - including to single-shot approaches like echo planar imaging (EPI). Hybrid spatiotemporal encoding (SPEN) sequences relying on frequency-swept pulses provide another ultrafast MRI alternative, that could help solve this problem thanks to their built-in heterogeneous spatial manipulations. This study explores how this intrinsic SPEN-based spatial discrimination, could be used to compensate for the B1 inhomogeneities inherent to surface coils. Experiments carried out in both phantoms and in vivo rat brains demonstrate that, by suitably modulating the amplitude of a SPEN chirp pulse that progressively excites the spins in a direction normal to the coil, it is possible to compensate for the RF transmit inhomogeneities and thus improve sensitivity and image fidelity.

Simulation-based evaluation of susceptibility distortion correction methods in diffusion MRI for connectivity analysis

Connectivity analysis on diffusion MRI data of the whole- brain suffers from distortions caused by the standard echo- planar imaging acquisition strategies. These images show characteristic geometrical deformations and signal destruction that are an important drawback limiting the success of tractography algorithms. Several retrospective correction techniques are readily available. In this work, we use a digital phantom designed for the evaluation of connectivity pipelines. We subject the phantom to a âeurooetheoretically correctâeuro and plausible deformation that resembles the artifact under investigation. We correct data back, with three standard methodologies (namely fieldmap-based, reversed encoding-based, and registration- based). Finally, we rank the methods based on their geometrical accuracy, the dropout compensation, and their impact on the resulting connectivity matrices.