MP2RAGE Multiple Sclerosis Magnetic Resonance Imaging at 3 T.

OBJECTIVES: Lesion detection and characterization in multiple sclerosis (MS) are an essential part of its clinical diagnosis and an important research field. In this pilot study, we applied the recently introduced two inversion-contrast magnetization-prepared rapid gradient echo sequence (MP2RAGE) to patients with early-stage MS.¦MATERIALS AND METHODS: The MP2RAGE is a 3-dimensional (3D) magnetization-prepared rapid gradient echo derivative providing homogeneous T1 weighting and simultaneous T1 mapping. The MP2RAGE performance was compared with that of 2 clinical routine sequences (2D fluid-attenuated inversion recovery [FLAIR] and 3D magnetization-prepared rapid gradient echo [MP-RAGE]) and 2 state-of-the art clinical research sequences (the 3D FLAIR-SPACE [sampling perfection with application-optimized contrasts by using different flip-angle evolutions], a fluid-attenuated variable flip-angle fast spin echo technique, and the 3D double-inversion recovery SPACE). A cohort of 10 early-stage female MS patients (age, 31.6 ± 4.7 years; disease duration, 3.8 ± 1.9 years; median expanded disability status scale score, 1.75) and 10 age- and gender-matched controls were enrolled after approval of the local institutional review board was obtained. Multiple sclerosis lesions were identified and assigned to brain locations and tissue types by two experienced physicians in all 5 contrasts. Subsequently, lesions were manually delineated for comparison and statistical analysis of lesion count, volume and quantitative measures.¦RESULTS AND CONCLUSIONS: The results show that the 3D T1-weighted high-resolution MP2RAGE contrast provides a sensitive means for MS lesion assessment. The additional quantitative T1 relaxation time maps obtained with the MP2RAGE provide further potential diagnostic and prognostic information that could help (a) to better discriminate lesion subtypes and (b) to stage and predict the activity and the evolution of MS. Results also indicate that the T2-weighted double-inversion recovery and FLAIR-SPACE contrasts are attractive complements to the MP2RAGE for lesion detection.

One naive T cell, multiple fates in CD8+ T cell differentiation.

The mechanism by which the immune system produces effector and memory T cells is largely unclear. To allow a large-scale assessment of the development of single naive T cells into different subsets, we have developed a technology that introduces unique genetic tags (barcodes) into naive T cells. By comparing the barcodes present in antigen-specific effector and memory T cell populations in systemic and local infection models, at different anatomical sites, and for TCR-pMHC interactions of different avidities, we demonstrate that under all conditions tested, individual naive T cells yield both effector and memory CD8+ T cell progeny. This indicates that effector and memory fate decisions are not determined by the nature of the priming antigen-presenting cell or the time of T cell priming. Instead, for both low and high avidity T cells, individual naive T cells have multiple fates and can differentiate into effector and memory T cell subsets.

Improved multiple-locus variable-number tandem-repeat assay for Staphylococcus aureus genotyping, providing a highly informative technique together with strong phylogenetic value.

We describe an improved multiple-locus variable-number tandem-repeat (VNTR) analysis (MLVA) scheme for genotyping Staphylococcus aureus. We compare its performance to those of multilocus sequence typing (MLST) and spa typing in a survey of 309 strains. This collection includes 87 epidemic methicillin-resistant S. aureus (MRSA) strains of the Harmony collection, 75 clinical strains representing the major MLST clonal complexes (CCs) (50 methicillin-sensitive S. aureus [MSSA] and 25 MRSA), 135 nasal carriage strains (133 MSSA and 2 MRSA), and 13 published S. aureus genome sequences. The results show excellent concordance between the techniques' results and demonstrate that the discriminatory power of MLVA is higher than those of both MLST and spa typing. Two hundred forty-two genotypes are discriminated with 14 VNTR loci (diversity index, 0.9965; 95% confidence interval, 0.9947 to 0.9984). Using a cutoff value of 45%, 21 clusters are observed, corresponding to the CCs previously defined by MLST. The variability of the different tandem repeats allows epidemiological studies, as well as follow-up of the evolution of CCs and the identification of potential ancestors. The 14 loci can conveniently be analyzed in two steps, based upon a first-line simplified assay comprising a subset of 10 loci (panel 1) and a second subset of 4 loci (panel 2) that provides higher resolution when needed. In conclusion, the MLVA scheme proposed here, in combination with available on-line genotyping databases (including http://mlva.u-psud.fr/), multiplexing, and automatic sizing, can provide a basis for almost-real-time large-scale population monitoring of S. aureus.

Exploring the proposed DSM-5 diagnostic threshold and severity scale for the alcohol use disorder

Aims :¦Several studies have questioned the validity of separating the diagnosis of alcohol abuse from that of alcohol dependence, and the DSM-5 task force has proposed combining the criteria from these two diagnoses to assess a single category of alcohol use disorders (AUD). Furthermore, the DSM-5 task force has proposed including a new 2-symptom threshold and a severity scale based on symptom counts for the AUD diagnosis. The current study aimed to examine these modifications in a large population-based sample.¦Method :¦Data stemmed from an adult sample (N=2588 ; mean age 51.3 years (s.d.: 0.2), 44.9% female) of current and lifetime drinkers from the PsyCoLaus study, conducted in the Lausanne area in Switzerland. AUDs and validating variables were assessed using a semi-structured diagnostic interview for the assessment of alcohol¦and other major psychiatric disorders. First, the adequacy of the proposed 2- symptom threshold was tested by comparing threshold models at each possible cutoff and a linear model, in relation to different validating variables. The model with the smallest Akaike Criterion Information (AIC) value was established as the best¦model for each validating variable. Second, models with varying subsets of individual AUD symptoms were created to assess the associations between each symptom and the validating variables. The subset of symptoms with the smallest AIC value was established as the best subset for each validator.¦Results :¦1) For the majority of validating variables, the linear model was found to be the best fitting model. 2) Among the various subsets of symptoms, the symptoms most frequently associated with the validating variables were : a) drinking despite having knowledge of a physical or psychological problem, b) having had a persistent desire or unsuccessful efforts to cut down or control drinking and c) craving. The¦least frequent symptoms were : d) drinking in larger amounts or over a longer period than was intended, e) spending a great deal of time in obtaining, using or recovering from alcohol use and f) failing to fulfill major role obligations.¦Conclusions :¦The proposed DSM-5 2-symptom threshold did not receive support in our data. Instead, a linear AUD diagnosis was supported with individuals receiving an increasingly severe AUD diagnosis. Moreover, certain symptoms were more frequently associated with the validating variables, which suggests that these¦symptoms should be considered as more severe.

Inversion of multiple intersecting high-resolution crosshole GPR profiles for hydrological characterization at the Boise Hydrogeophysical Research Site

The integration of geophysical data into the subsurface characterization problem has been shown in many cases to significantly improve hydrological knowledge by providing information at spatial scales and locations that is unattainable using conventional hydrological measurement techniques. In particular, crosshole ground-penetrating radar (GPR) tomography has shown much promise in hydrology because of its ability to provide highly detailed images of subsurface radar wave velocity, which is strongly linked to soil water content. Here, we develop and demonstrate a procedure for inverting together multiple crosshole GPR data sets in order to characterize the spatial distribution of radar wave velocity below the water table at the Boise Hydrogeophysical Research Site (BHRS) near Boise, Idaho, USA. Specifically, we jointly invert 31 intersecting crosshole GPR profiles to obtain a highly resolved and consistent radar velocity model along the various profile directions. The model is found to be strongly correlated with complementary neutron porosity-log data and is further corroborated by larger-scale structural information at the BHRS. This work is an important prerequisite to using crosshole GPR data together with existing hydrological measurements for improved groundwater flow and contaminant transport modeling.

A pipeline approach with spatial information for segmenting multiple sclerosis lesions on brain magnetic resonance imaging

Background: Conventional magnetic resonance imaging (MRI) techniques are highly sensitive to detect multiple sclerosis (MS) plaques, enabling a quantitative assessment of inflammatory activity and lesion load. In quantitative analyses of focal lesions, manual or semi-automated segmentations have been widely used to compute the total number of lesions and the total lesion volume. These techniques, however, are both challenging and time-consuming, being also prone to intra-observer and inter-observer variability.Aim: To develop an automated approach to segment brain tissues and MS lesions from brain MRI images. The goal is to reduce the user interaction and to provide an objective tool that eliminates the inter- and intra-observer variability.Methods: Based on the recent methods developed by Souplet et al. and de Boer et al., we propose a novel pipeline which includes the following steps: bias correction, skull stripping, atlas registration, tissue classification, and lesion segmentation. After the initial pre-processing steps, a MRI scan is automatically segmented into 4 classes: white matter (WM), grey matter (GM), cerebrospinal fluid (CSF) and partial volume. An expectation maximisation method which fits a multivariate Gaussian mixture model to T1-w, T2-w and PD-w images is used for this purpose. Based on the obtained tissue masks and using the estimated GM mean and variance, we apply an intensity threshold to the FLAIR image, which provides the lesion segmentation. With the aim of improving this initial result, spatial information coming from the neighbouring tissue labels is used to refine the final lesion segmentation.Results:The experimental evaluation was performed using real data sets of 1.5T and the corresponding ground truth annotations provided by expert radiologists. The following values were obtained: 64% of true positive (TP) fraction, 80% of false positive (FP) fraction, and an average surface distance of 7.89 mm. The results of our approach were quantitatively compared to our implementations of the works of Souplet et al. and de Boer et al., obtaining higher TP and lower FP values.Conclusion: Promising MS lesion segmentation results have been obtained in terms of TP. However, the high number of FP which is still a well-known problem of all the automated MS lesion segmentation approaches has to be improved in order to use them for the standard clinical practice. Our future work will focus on tackling this issue.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Learning about Bayesian networks for forensic interpretation : An example based on the "the problem of multiple propositions"

Both, Bayesian networks and probabilistic evaluation are gaining more and more widespread use within many professional branches, including forensic science. Notwithstanding, they constitute subtle topics with definitional details that require careful study. While many sophisticated developments of probabilistic approaches to evaluation of forensic findings may readily be found in published literature, there remains a gap with respect to writings that focus on foundational aspects and on how these may be acquired by interested scientists new to these topics. This paper takes this as a starting point to report on the learning about Bayesian networks for likelihood ratio based, probabilistic inference procedures in a class of master students in forensic science. The presentation uses an example that relies on a casework scenario drawn from published literature, involving a questioned signature. A complicating aspect of that case study - proposed to students in a teaching scenario - is due to the need of considering multiple competing propositions, which is an outset that may not readily be approached within a likelihood ratio based framework without drawing attention to some additional technical details. Using generic Bayesian networks fragments from existing literature on the topic, course participants were able to track the probabilistic underpinnings of the proposed scenario correctly both in terms of likelihood ratios and of posterior probabilities. In addition, further study of the example by students allowed them to derive an alternative Bayesian network structure with a computational output that is equivalent to existing probabilistic solutions. This practical experience underlines the potential of Bayesian networks to support and clarify foundational principles of probabilistic procedures for forensic evaluation.

Imaging the time-integrated cerebral metabolic activity with subcellular resolution through nanometer-scale detection of biosynthetic products deriving from (13)C-glucose.

Glucose is the primary source of energy for the brain but also an important source of building blocks for proteins, lipids, and nucleic acids. Little is known about the use of glucose for biosynthesis in tissues at the cellular level. We demonstrate that local cerebral metabolic activity can be mapped in mouse brain tissue by quantitatively imaging the biosynthetic products deriving from [U-(13)C]glucose metabolism using a combination of in situ electron microscopy and secondary ion mass-spectroscopy (NanoSIMS). Images of the (13)C-label incorporated into cerebral ultrastructure with ca. 100nm resolution allowed us to determine the timescale on which the metabolic products of glucose are incorporated into different cells, their sub-compartments and organelles. These were mapped in astrocytes and neurons in the different layers of the motor cortex. We see evidence for high metabolic activity in neurons via the nucleus (13)C enrichment. We observe that in all the major cell compartments, such as e.g. nucleus and Golgi apparatus, neurons incorporate substantially higher concentrations of (13)C-label than astrocytes.