Combination of diffusion tensor and functional magnetic resonance imaging during recovery from the vegetative state

Background: The rate of recovery from the vegetative state (VS) is low. Currently, little is known of the mechanisms and cerebral changes that accompany those relatively rare cases of good recovery. Here, we combined functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) to study the evolution of one VS patient at one month post-ictus and again twelve months later when he had recovered consciousness. Methods fMRI was used to investigate cortical responses to passive language stimulation as well as task-induced deactivations related to the default-mode network. DTI was used to assess the integrity of the global white matter and the arcuate fasciculus. We also performed a neuropsychological assessment at the time of the second MRI examination in order to characterize the profile of cognitive deficits. Results: fMRI analysis revealed anatomically appropriate activation to speech in both the first and the second scans but a reduced pattern of task-induced deactivations in the first scan. In the second scan, following the recovery of consciousness, this pattern became more similar to that classically described for the default-mode network. DTI analysis revealed relative preservation of the arcuate fasciculus and of the global normal-appearing white matter at both time points. The neuropsychological assessment revealed recovery of receptive linguistic functioning by 12-months post-ictus. Conclusions: These results suggest that the combination of different structural and functional imaging modalities may provide a powerful means for assessing the mechanisms involved in the recovery from the VS.

Anticipating linear stochastic differential equations driven by a Lévy process

In this paper we study the existence of a unique solution for linear stochastic differential equations driven by a Lévy process, where the initial condition and the coefficients are random and not necessarily adapted to the underlying filtration. Towards this end, we extend the method based on Girsanov transformations on Wiener space and developped by Buckdahn [7] to the canonical Lévy space, which is introduced in [25].

Hierarchical linear models in Psychiatry: A bibliometric study

Development of research methods requires a systematic review of their status. This study focuses on the use of Hierarchical Linear Modeling methods in psychiatric research. Evaluation includes 207 documents published until 2007, included and indexed in the ISI Web of Knowledge databases; analyses focuses on the 194 articles in the sample. Bibliometric methods are used to describe the publications patterns. Results indicate a growing interest in applying the models and an establishment of methods after 2000. Both Lotka"s and Bradford"s distributions are adjusted to the data.

Source separation techniques applied to linear prediction

The prediction filters are well known models for signal estimation, in communications, control and many others areas. The classical method for deriving linear prediction coding (LPC) filters is often based on the minimization of a mean square error (MSE). Consequently, second order statistics are only required, but the estimation is only optimal if the residue is independent and identically distributed (iid) Gaussian. In this paper, we derive the ML estimate of the prediction filter. Relationships with robust estimation of auto-regressive (AR) processes, with blind deconvolution and with source separation based on mutual information minimization are then detailed. The algorithm, based on the minimization of a high-order statistics criterion, uses on-line estimation of the residue statistics. Experimental results emphasize on the interest of this approach.

Exploring Non-linear Transformations for an Entropybased Voice Activity Detector

In this paper we explore the use of non-linear transformations in order to improve the performance of an entropy based voice activity detector (VAD). The idea of using a non-linear transformation comes from some previous work done in speech linear prediction (LPC) field based in source separation techniques, where the score function was added into the classical equations in order to take into account the real distribution of the signal. We explore the possibility of estimating the entropy of frames after calculating its score function, instead of using original frames. We observe that if signal is clean, estimated entropy is essentially the same; but if signal is noisy transformed frames (with score function) are able to give different entropy if the frame is voiced against unvoiced ones. Experimental results show that this fact permits to detect voice activity under high noise, where simple entropy method fails.

Rotational Doppler effect in magnetic resonance

We compute the shift in the frequency of the spin resonance in a solid that rotates in the field of a circularly polarized electromagnetic wave. Electron-spin resonance, nuclear magnetic resonance, and ferromagnetic resonance are considered. We show that contrary to the case of the rotating LC circuit, the shift in the frequency of the spin resonance has strong dependence on the symmetry of the receiver. The shift due to rotation occurs only when rotational symmetry is broken by the anisotropy of the gyromagnetic tensor, by the shape of the body or by magnetocrystalline anisotropy. General expressions for the resonance frequency and power absorption are derived and implications for experiment are discussed.

Rapid analysis of multiclass antibiotic residues and some of their metabolites in hospital, urban wastewater and river water by ultra-highperformance liquid chromatography coupled to quadrupole-linear ion trap tandem mass spectrometry

The present work describes the development of a fast and robust analytical method for the determination of 53 antibiotic residues, covering various chemical groups and some of their metabolites, in environmental matrices that are considered important sources of antibiotic pollution, namely hospital and urban wastewaters, as well as in river waters. The method is based on automated off-line solid phase extraction (SPE) followed by ultra-high-performance liquid chromatography coupled to quadrupole linear ion trap tandem mass spectrometry (UHPLC–QqLIT). For unequivocal identification and confirmation, and in order to fulfill EU guidelines, two selected reaction monitoring (SRM) transitions per compound are monitored (the most intense one is used for quantification and the second one for confirmation). Quantification of target antibiotics is performed by the internal standard approach, using one isotopically labeled compound for each chemical group, in order to correct matrix effects. The main advantages of the method are automation and speed-up of sample preparation, by the reduction of extraction volumes for all matrices, the fast separation of a wide spectrum of antibiotics by using ultra-high-performance liquid chromatography, its sensitivity (limits of detection in the low ng/L range) and selectivity (due to the use of tandem mass spectrometry) The inclusion of β-lactam antibiotics (penicillins and cephalosporins), which are compounds difficult to analyze in multi-residue methods due to their instability in water matrices, and some antibiotics metabolites are other important benefits of the method developed. As part of the validation procedure, the method developed was applied to the analysis of antibiotics residues in hospital, urban influent and effluent wastewaters as well as in river water samples

Nanoantenna enhanced emission of lightharvesting complex 2: the role of resonance, polarization, and radiative and non-radiative rates

Nanoantennae show potential for photosynthesis research for two reasons; first by spatially confining light for experiments which require high spatial resolution, and second by enhancing the photon emission of single light-harvesting complexes. For effective use of nanoantennae a detailed understanding of the interaction between the nanoantenna and the light-harvesting complex is required. Here we report how the excitation and emission of multiple purple bacterial LH2s (light-harvesting complex 2) are controlled by single gold nanorod antennae. LH2 complexes were chemically attached to such antennae, and the antenna length was systematically varied to tune the resonance with respect to the LH2 absorption and emission. There are three main findings. (i) The polarization of the LH2 emission is fully controlled by the resonant nanoantenna. (ii) The largest fluorescence enhancement, of 23 times, is reached for excitation with light at λ = 850 nm, polarized along the long antenna-axis of the resonant antenna. The excitation enhancement is found to be 6 times, while the emission efficiency is increased 3.6 times. (iii) The fluorescence lifetime of LH2 depends strongly on the antenna length, with shortest lifetimes of [similar]40 ps for the resonant antenna. The lifetime shortening arises from an 11 times resonant enhancement of the radiative rate, together with a 2–3 times increase of the non-radiative rate, compared to the off-resonant antenna. The observed length dependence of radiative and non-radiative rate enhancement is in good agreement with simulations. Overall this work gives a complete picture of how the excitation and emission of multi-pigment light-harvesting complexes are influenced by a dipole nanoantenna.

Classifier combination for in vivo magnetic resonance spectra of brain tumours

Peer-reviewed

Classifier combination for in vivo magnetic resonance spectra of brain tumours

In this paper we present a multi-stage classifier for magnetic resonance spectra of human brain tumours which is being developed as part of a decision support system for radiologists. The basic idea is to decompose a complex classification scheme into a sequence of classifiers, each specialising in different classes of tumours and trying to reproducepart of the WHO classification hierarchy. Each stage uses a particular set of classification features, which are selected using a combination of classical statistical analysis, splitting performance and previous knowledge.Classifiers with different behaviour are combined using a simple voting scheme in order to extract different error patterns: LDA, decision trees and the k-NN classifier. A special label named "unknown¿ is used when the outcomes of the different classifiers disagree. Cascading is alsoused to incorporate class distances computed using LDA into decision trees. Both cascading and voting are effective tools to improve classification accuracy. Experiments also show that it is possible to extract useful information from the classification process itself in order to helpusers (clinicians and radiologists) to make more accurate predictions and reduce the number of possible classification mistakes.

Readout electronics for low dark count pixel detectors based on geiger mode avalanche photodiodes fabricated in conventional CMOS technologies for future linear colliders

The high sensitivity and excellent timing accuracy of Geiger mode avalanche photodiodes makes them ideal sensors as pixel detectors for particle tracking in high energy physics experiments to be performed in future linear colliders. Nevertheless, it is well known that these sensors suffer from dark counts and afterpulsing noise, which induce false hits (indistinguishable from event detection) as well as an increase of the necessary area of the readout system. In this work, we present a comparison between APDs fabricated in a high voltage 0.35 µm and a high integration 0.13 µm commercially available CMOS technologies that has been performed to determine which of them best fits the particle collider requirements. In addition, a readout circuit that allows low noise operation is introduced. Experimental characterization of the proposed pixel is also presented in this work.