Cellular distribution of glucose and monocarboxylate transporters in human brain white matter and multiple sclerosis lesions.

To ensure efficient energy supply to the high demanding brain, nutrients are transported into brain cells via specific glucose (GLUT) and monocarboxylate transporters (MCT). Mitochondrial dysfunction and altered glucose metabolism are thought to play an important role in the progression of neurodegenerative diseases, including multiple sclerosis (MS). Here, we investigated the cellular localization of key GLUT and MCT proteins in human brain tissue of non-neurological controls and MS patients. We show that in control brain tissue GLUT and MCT proteins were abundantly expressed in a variety of central nervous system cells, particularly in microglia and endothelial cells. In active MS lesions, GLUTs and MCTs were highly expressed in infiltrating leukocytes and reactive astrocytes. Astrocytes manifest increased MCT1 staining and maintain GLUT expression in inactive lesions, whereas demyelinated axons exhibit significantly reduced GLUT3 and MCT2 immunoreactivity in inactive lesions. Finally, we demonstrated that the co-transcription factor peroxisome proliferator-activated receptor gamma co-activator 1-alpha (PGC-1α), an important protein involved in energy metabolism, is highly expressed in reactive astrocytes in active MS lesions. Overexpression of PGC-1α in astrocyte-like cells resulted in increased production of several GLUT and MCT proteins. In conclusion, we provide for the first time a comprehensive overview of key nutrient transporters in white matter brain samples. Moreover, our data demonstrate an altered expression of these nutrient transporters in MS brain tissue, including a marked reduction of axonal GLUT3 and MCT2 expression in chronic lesions, which may impede efficient nutrient supply to the hypoxic demyelinated axons thereby contributing to the ongoing neurodegeneration in MS. GLIA 2014;62:1125-1141.

Sweet bird that shunn'st the noise of folly : [extr. de "L'Allegro, il penseroso ed il moderato"] / Haendel, comp. ; Mme Melba, S ; avec orchestre et flûte. O lovely night / Ronald, comp. ; Mme Melba, S ; avec orchestre

Comprend : Sweet bird that shunn'st the noise of folly / Haendel, comp. ; Mme Melba, S ; avec orchestre et flûte ; O lovely night / Ronald, comp. ; Mme Melba, S ; avec orchestre

Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians

The human brainstem is a densely packed, complex but highly organised structure. It not only serves as a conduit for long projecting axons conveying motor and sensory information, but also is the location of multiple primary nuclei that control or modulate a vast array of functions, including homeostasis, consciousness, locomotion, and reflexive and emotive behaviours. Despite its importance, both in understanding normal brain function as well as neurodegenerative processes, it remains a sparsely studied structure in the neuroimaging literature. In part, this is due to the difficulties in imaging the internal architecture of the brainstem in vivo in a reliable and repeatable fashion. A modified multivariate mixture of Gaussians (mmMoG) was applied to the problem of multichannel tissue segmentation. By using quantitative magnetisation transfer and proton density maps acquired at 3 T with 0.8 mm isotropic resolution, tissue probability maps for four distinct tissue classes within the human brainstem were created. These were compared against an ex vivo fixated human brain, imaged at 0.5 mm, with excellent anatomical correspondence. These probability maps were used within SPM8 to create accurate individual subject segmentations, which were then used for further quantitative analysis. As an example, brainstem asymmetries were assessed across 34 right-handed individuals using voxel based morphometry (VBM) and tensor based morphometry (TBM), demonstrating highly significant differences within localised regions that corresponded to motor and vocalisation networks. This method may have important implications for future research into MRI biomarkers of pre-clinical neurodegenerative diseases such as Parkinson's disease.

Fusion of data sets in multivariate linear regression with errors-in-variables

We consider the application of normal theory methods to the estimation and testing of a general type of multivariate regressionmodels with errors--in--variables, in the case where various data setsare merged into a single analysis and the observable variables deviatepossibly from normality. The various samples to be merged can differ on the set of observable variables available. We show that there is a convenient way to parameterize the model so that, despite the possiblenon--normality of the data, normal--theory methods yield correct inferencesfor the parameters of interest and for the goodness--of--fit test. Thetheory described encompasses both the functional and structural modelcases, and can be implemented using standard software for structuralequations models, such as LISREL, EQS, LISCOMP, among others. An illustration with Monte Carlo data is presented.

Descrição de Hephialtes mourei sp. nov. e notas em Hephialtes Thomson e Braderochus mundus (White) (Coleoptera, Cerambycidae, Prioninae)

Hephialtes mourei sp. nov. é descrita do Brasil (Minas Gerais, Rio de Janeiro, São Paulo e Paraná). A chave para espécies de Braderochus e a ilustração do holótipo de B. mundus (White, 1853) fornecidas por Bleuzen (1994) são discutidas.

Individual Classification of Mild Cognitive Impairment Subtypes by Support Vector Machine Analysis of White Matter DTI.

BACKGROUND AND PURPOSE: MCI was recently subdivided into sd-aMCI, sd-fMCI, and md-aMCI. The current investigation aimed to discriminate between MCI subtypes by using DTI. MATERIALS AND METHODS: Sixty-six prospective participants were included: 18 with sd-aMCI, 13 with sd-fMCI, and 35 with md-aMCI. Statistics included group comparisons using TBSS and individual classification using SVMs. RESULTS: The group-level analysis revealed a decrease in FA in md-aMCI versus sd-aMCI in an extensive bilateral, right-dominant network, and a more pronounced reduction of FA in md-aMCI compared with sd-fMCI in right inferior fronto-occipital fasciculus and inferior longitudinal fasciculus. The comparison between sd-fMCI and sd-aMCI, as well as the analysis of the other diffusion parameters, yielded no significant group differences. The individual-level SVM analysis provided discrimination between the MCI subtypes with accuracies around 97%. The major limitation is the relatively small number of cases of MCI. CONCLUSIONS: Our data show that, at the group level, the md-aMCI subgroup has the most pronounced damage in white matter integrity. Individually, SVM analysis of white matter FA provided highly accurate classification of MCI subtypes.

Asymptotic robustness in multi-sample analysis of multivariate linear relations

Standard methods for the analysis of linear latent variable models oftenrely on the assumption that the vector of observed variables is normallydistributed. This normality assumption (NA) plays a crucial role inassessingoptimality of estimates, in computing standard errors, and in designinganasymptotic chi-square goodness-of-fit test. The asymptotic validity of NAinferences when the data deviates from normality has been calledasymptoticrobustness. In the present paper we extend previous work on asymptoticrobustnessto a general context of multi-sample analysis of linear latent variablemodels,with a latent component of the model allowed to be fixed across(hypothetical)sample replications, and with the asymptotic covariance matrix of thesamplemoments not necessarily finite. We will show that, under certainconditions,the matrix $\Gamma$ of asymptotic variances of the analyzed samplemomentscan be substituted by a matrix $\Omega$ that is a function only of thecross-product moments of the observed variables. The main advantage of thisis thatinferences based on $\Omega$ are readily available in standard softwareforcovariance structure analysis, and do not require to compute samplefourth-order moments. An illustration with simulated data in the context ofregressionwith errors in variables will be presented.

Trabecular bone score is associated with volumetric bone density and microarchitecture as assessed by central QCT and HRpQCT in Chinese American and white women.

Although high-resolution peripheral quantitative computed tomography (HRpQCT) and central quantitative computed tomography (QCT) studies have shown bone structural differences between Chinese American (CH) and white (WH) women, these techniques are not readily available in the clinical setting. The trabecular bone score (TBS) estimates trabecular microarchitecture from dual-energy X-ray absorptiometry spine images. We assessed TBS in CH and WH women and investigated whether TBS is associated with QCT and HRpQCT indices. Areal bone mineral density (aBMD) by dual-energy X-ray absorptiometry, lumbar spine (LS) TBS, QCT of the LS and hip, and HRpQCT of the radius and tibia were performed in 71 pre- (37 WH and 34 CH) and 44 postmenopausal (21 WH and 23 CH) women. TBS did not differ by race in either pre- or postmenopausal women. In the entire cohort, TBS positively correlated with LS trabecular volumetric bone mineral density (vBMD) (r = 0.664), femoral neck integral (r = 0.651), trabecular (r = 0.641) and cortical vBMD (r = 0.346), and cortical thickness (C/I; r = 0.540) by QCT (p < 0.001 for all). TBS also correlated with integral (r = 0.643), trabecular (r = 0.574) and cortical vBMD (r = 0.491), and C/I (r = 0.541) at the total hip (p < 0.001 for all). The combination of TBS and LS aBMD predicted more of the variance in QCT measures than aBMD alone. TBS was associated with all HRpQCT indices (r = 0.20-0.52) except radial cortical thickness and tibial trabecular thickness. Significant associations between TBS and measures of HRpQCT and QCT in WH and CH pre- and postmenopausal women demonstrated here suggest that TBS may be a useful adjunct to aBMD for assessing bone quality.

Noise and aggregation of information in large markets

We study a novel class of noisy rational expectations equilibria in markets with largenumber of agents. We show that, as long as noise increases with the number of agents inthe economy, the limiting competitive equilibrium is well-defined and leads to non-trivialinformation acquisition, perfect information aggregation, and partially revealing prices,even if per capita noise tends to zero. We find that in such equilibrium risk sharing and price revelation play dierent roles than in the standard limiting economy in which per capita noise is not negligible. We apply our model to study information sales by a monopolist, information acquisition in multi-asset markets, and derivatives trading. Thelimiting equilibria are shown to be perfectly competitive, even when a strategic solutionconcept is used.

Severe stroke: patient profile and predictors of favorable outcome.

Summary.  Background:  Severe stroke carries high rates of mortality and morbidity. The aims of this study were to determine the characteristics of patients who initially presented with severe ischemic stroke, and to identify acute and subacute predictors of favorable clinical outcome in these patients. Methods:  An observational cohort study, Acute Stroke Registry and Analysis of Lausanne (ASTRAL), was analyzed, and all patients presenting with severe stroke - defined as a National Institute of Health Stroke Scale score of ≥ 20 on admission - were compared with all other patients. In a multivariate analysis, associations with demographic, clinical, pathophysiologic, metabolic and neuroimaging factors were determined. Furthermore, we analyzed predictors of favorable outcome (modified Rankin scale score of ≤ 3 at 3 months) in the subgroup of severe stroke patients. Results:  Of 1915 consecutive patients, 243 (12.7%) presented with severe stroke. This was significantly associated with cardio-embolic stroke mechanism (odds ratio [OR] 1.74, 95% confidence interval [CI] 1.19-2.54), unknown stroke onset (OR 2.35, 95% CI 1.14-4.83), more neuroimaging signs of early ischemia (mostly computed tomography; OR 2.65, 95% CI 1.79-3.92), arterial occlusions on acute imaging (OR 27.01, 95% CI 11.5-62.9), fewer chronic radiologic infarcts (OR 0.43, 95% CI 0.26-0.72), lower hemoglobin concentration (OR 0.97, 95% CI 0.96-0.99), and higher white cell count (OR 1.05, 95% CI 1.00-1.11). In the 68 (28%) patients with favorable outcomes despite presenting with severe stroke, this was predicted by lower age (OR 0.94, 95% CI 0.92-0.97), preceding cerebrovascular events (OR 3.00, 95% CI 1.01-8.97), hypolipemic pretreatment (OR 3.82, 95% CI 1.34-10.90), lower acute temperature (OR 0.43, 95% CI 0.23-0.78), lower subacute glucose concentration (OR 0.74, 95% CI 0.56-0.97), and spontaneous or treatment-induced recanalization (OR 4.51, 95% CI 1.96-10.41). Conclusions:  Severe stroke presentation is predicted by multiple clinical, radiologic and metabolic variables, several of which are modifiable. Predictors in the 28% of patients with favorable outcome despite presenting with severe stroke include hypolipemic pretreatment, lower acute temperature, lower glucose levels at 24 h, and arterial recanalization.

Combined analysis of grey matter voxel-based morphometry and white matter tract-based spatial statistics in late-life bipolar disorder.

Background: Previous magnetic resonance imaging (MRI) studies in young patients with bipolar disorder indicated the presence of grey matter concentration changes as well as microstructural alterations in white matter in various neocortical areas and the corpus callosum. Whether these structural changes are also present in elderly patients with bipolar disorder with long-lasting clinical evolution remains unclear. Methods: We performed a prospective MRI study of consecutive elderly, euthymic patients with bipolar disorder and healthy, elderly controls. We conducted a voxel-based morphometry (VBM) analysis and a tract-based spatial statistics (TBSS) analysis to assess fractional anisotropy and longitudinal, radial and mean diffusivity derived by diffusion tensor imaging (DTI). Results: We included 19 patients with bipolar disorder and 47 controls in our study. Fractional anisotropy was the most sensitive DTI marker and decreased significantly in the ventral part of the corpus callosum in patients with bipolar disorder. Longitudinal, radial and mean diffusivity showed no significant between-group differences. Grey matter concentration was reduced in patients with bipolar disorder in the right anterior insula, head of the caudate nucleus, nucleus accumbens, ventral putamen and frontal orbital cortex. Conversely, there was no grey matter concentration or fractional anisotropy increase in any brain region in patients with bipolar disorder compared with controls. Limitations: The major limitation of our study is the small number of patients with bipolar disorder. Conclusion: Our data document the concomitant presence of grey matter concentration decreases in the anterior limbic areas and the reduced fibre tract coherence in the corpus callosum of elderly patients with long-lasting bipolar disorder.

Management of patient dose and image noise in routine pediatric CT abdominal examinations.

The aim was to propose a strategy for finding reasonable compromises between image noise and dose as a function of patient weight. Weighted CT dose index (CTDI(w)) was measured on a multidetector-row CT unit using CTDI test objects of 16, 24 and 32 cm in diameter at 80, 100, 120 and 140 kV. These test objects were then scanned in helical mode using a wide range of tube currents and voltages with a reconstructed slice thickness of 5 mm. For each set of acquisition parameter image noise was measured and the Rose model observer was used to test two strategies for proposing a reasonable compromise between dose and low-contrast detection performance: (1) the use of a unique noise level for all test object diameters, and (2) the use of a unique dose efficacy level defined as the noise reduction per unit dose. Published data were used to define four weight classes and an acquisition protocol was proposed for each class. The protocols have been applied in clinical routine for more than one year. CTDI(vol) values of 6.7, 9.4, 15.9 and 24.5 mGy were proposed for the following weight classes: 2.5-5, 5-15, 15-30 and 30-50 kg with image noise levels in the range of 10-15 HU. The proposed method allows patient dose and image noise to be controlled in such a way that dose reduction does not impair the detection of low-contrast lesions. The proposed values correspond to high- quality images and can be reduced if only high-contrast organs are assessed.

Combining multivariate density forecasts using predictive criteria

This paper combines multivariate density forecasts of output growth, inflationand interest rates from a suite of models. An out-of-sample weighting scheme based onthe predictive likelihood as proposed by Eklund and Karlsson (2005) and Andersson andKarlsson (2007) is used to combine the models. Three classes of models are considered: aBayesian vector autoregression (BVAR), a factor-augmented vector autoregression (FAVAR)and a medium-scale dynamic stochastic general equilibrium (DSGE) model. Using Australiandata, we find that, at short forecast horizons, the Bayesian VAR model is assignedthe most weight, while at intermediate and longer horizons the factor model is preferred.The DSGE model is assigned little weight at all horizons, a result that can be attributedto the DSGE model producing density forecasts that are very wide when compared withthe actual distribution of observations. While a density forecast evaluation exercise revealslittle formal evidence that the optimally combined densities are superior to those from thebest-performing individual model, or a simple equal-weighting scheme, this may be a resultof the short sample available.

Inbreeding in the greater white-toothed shrew, Crocidura russula.

We combined mark-and-recapture studies with genetic techniques of parentage assignment to evaluate the interactions between mating, dispersal, and inbreeding, in a free-ranging population of Crocidura russula. We found a pattern of limited and female-biased dispersal, followed by random mating within individual neighborhoods. This results in significant inbreeding at the population level: mating among relatives occurs more often than random, and F(IT) analyses reveal significant deficits in heterozygotes. However, related mating partners were not less fecund, and inbred offspring had no lower lifetime reproductive output. Power analyses show these negative results to be quite robust. Absence of phenotypic evidence of inbreeding depression might result from a history of purging: local populations are small and undergo disequilibrium gene dynamics. Dispersal is likely caused by local saturation and (re)colonization of empty breeding sites, rather than inbreeding avoidance.