Accuracy of Brain Multimodal Monitoring to Detect Cerebral Hypoperfusion After Traumatic Brain Injury.

OBJECTIVE:: To examine the accuracy of brain multimodal monitoring-consisting of intracranial pressure, brain tissue PO2, and cerebral microdialysis-in detecting cerebral hypoperfusion in patients with severe traumatic brain injury. DESIGN:: Prospective single-center study. PATIENTS:: Patients with severe traumatic brain injury. SETTING:: Medico-surgical ICU, university hospital. INTERVENTION:: Intracranial pressure, brain tissue PO2, and cerebral microdialysis monitoring (right frontal lobe, apparently normal tissue) combined with cerebral blood flow measurements using perfusion CT. MEASUREMENTS AND MAIN RESULTS:: Cerebral blood flow was measured using perfusion CT in tissue area around intracranial monitoring (regional cerebral blood flow) and in bilateral supra-ventricular brain areas (global cerebral blood flow) and was matched to cerebral physiologic variables. The accuracy of intracranial monitoring to predict cerebral hypoperfusion (defined as an oligemic regional cerebral blood flow < 35 mL/100 g/min) was examined using area under the receiver-operating characteristic curves. Thirty perfusion CT scans (median, 27 hr [interquartile range, 20-45] after traumatic brain injury) were performed on 27 patients (age, 39 yr [24-54 yr]; Glasgow Coma Scale, 7 [6-8]; 24/27 [89%] with diffuse injury). Regional cerebral blood flow correlated significantly with global cerebral blood flow (Pearson r = 0.70, p < 0.01). Compared with normal regional cerebral blood flow (n = 16), low regional cerebral blood flow (n = 14) measurements had a higher proportion of samples with intracranial pressure more than 20 mm Hg (13% vs 30%), brain tissue PO2 less than 20 mm Hg (9% vs 20%), cerebral microdialysis glucose less than 1 mmol/L (22% vs 57%), and lactate/pyruvate ratio more than 40 (4% vs 14%; all p < 0.05). Compared with intracranial pressure monitoring alone (area under the receiver-operating characteristic curve, 0.74 [95% CI, 0.61-0.87]), monitoring intracranial pressure + brain tissue PO2 (area under the receiver-operating characteristic curve, 0.84 [0.74-0.93]) or intracranial pressure + brain tissue PO2+ cerebral microdialysis (area under the receiver-operating characteristic curve, 0.88 [0.79-0.96]) was significantly more accurate in predicting low regional cerebral blood flow (both p < 0.05). CONCLUSION:: Brain multimodal monitoring-including intracranial pressure, brain tissue PO2, and cerebral microdialysis-is more accurate than intracranial pressure monitoring alone in detecting cerebral hypoperfusion at the bedside in patients with severe traumatic brain injury and predominantly diffuse injury.

Spatial modelling of biodiversity at the community level

1. Statistical modelling is often used to relate sparse biological survey data to remotely derived environmental predictors, thereby providing a basis for predictively mapping biodiversity across an entire region of interest. The most popular strategy for such modelling has been to model distributions of individual species one at a time. Spatial modelling of biodiversity at the community level may, however, confer significant benefits for applications involving very large numbers of species, particularly if many of these species are recorded infrequently. 2. Community-level modelling combines data from multiple species and produces information on spatial pattern in the distribution of biodiversity at a collective community level instead of, or in addition to, the level of individual species. Spatial outputs from community-level modelling include predictive mapping of community types (groups of locations with similar species composition), species groups (groups of species with similar distributions), axes or gradients of compositional variation, levels of compositional dissimilarity between pairs of locations, and various macro-ecological properties (e.g. species richness). 3. Three broad modelling strategies can be used to generate these outputs: (i) 'assemble first, predict later', in which biological survey data are first classified, ordinated or aggregated to produce community-level entities or attributes that are then modelled in relation to environmental predictors; (ii) 'predict first, assemble later', in which individual species are modelled one at a time as a function of environmental variables, to produce a stack of species distribution maps that is then subjected to classification, ordination or aggregation; and (iii) 'assemble and predict together', in which all species are modelled simultaneously, within a single integrated modelling process. These strategies each have particular strengths and weaknesses, depending on the intended purpose of modelling and the type, quality and quantity of data involved. 4. Synthesis and applications. The potential benefits of modelling large multispecies data sets using community-level, as opposed to species-level, approaches include faster processing, increased power to detect shared patterns of environmental response across rarely recorded species, and enhanced capacity to synthesize complex data into a form more readily interpretable by scientists and decision-makers. Community-level modelling therefore deserves to be considered more often, and more widely, as a potential alternative or supplement to modelling individual species.

Official Central Bank Interventions in the Foreign Exchange Markets: A DCC Approach with Exogenous Variables

This paper assesses the impact of official central bank interventions (CBIs) on exchange rate returns, their volatility and bilateral correlations. By exploiting the recent publication of intervention data by the Bank of England, this study is able to investigate fficial interventions by a total number of four central banks, while the previous studies have been limited to three (the Federal Reserve, Bundesbank and Bank of Japan). The results of the existing literature are reappraised and refined. In particular, unilateral CBI is found to be more successful than coordinated CBI. The likely implications of these findings are then discussed.

Modelling Breaks and Clusters in the Steady States of Macroeconomic Variables

Macroeconomists working with multivariate models typically face uncertainty over which (if any) of their variables have long run steady states which are subject to breaks. Furthermore, the nature of the break process is often unknown. In this paper, we draw on methods from the Bayesian clustering literature to develop an econometric methodology which: i) finds groups of variables which have the same number of breaks; and ii) determines the nature of the break process within each group. We present an application involving a five-variate steady-state VAR.

Bayesian forecasting with highly correlated predictors

This paper considers Bayesian variable selection in regressions with a large number of possibly highly correlated macroeconomic predictors. I show that by acknowledging the correlation structure in the predictors can improve forecasts over existing popular Bayesian variable selection algorithms.

Using VARs and TVP-VARs with Many Macroeconomic Variables

This paper discusses the challenges faced by the empirical macroeconomist and methods for surmounting them. These challenges arise due to the fact that macroeconometric models potentially include a large number of variables and allow for time variation in parameters. These considerations lead to models which have a large number of parameters to estimate relative to the number of observations. A wide range of approaches are surveyed which aim to overcome the resulting problems. We stress the related themes of prior shrinkage, model averaging and model selection. Subsequently, we consider a particular modelling approach in detail. This involves the use of dynamic model selection methods with large TVP-VARs. A forecasting exercise involving a large US macroeconomic data set illustrates the practicality and empirical success of our approach.

Partner Selection into Policy Relevant Field Experiments

This study investigates the issue of self-selection of stakeholders into participation and collaboration in policy-relevant experiments. We document and test the implications of self-selection in the context of randomised policy experiment we conducted in primary schools in the UK. The main questions we ask are (1) is there evidence of selection on key observable characteristics likely to matter for the outcome of interest and (2) does selection matter for the estimates of treatment eff ects. The experimental work consists in testing the e ffects of an intervention aimed at encouraging children to make more healthy choices at lunch. We recruited schools through local authorities and randomised schools across two incentive treatments and a control group. We document the selection taking place both at the level of local authorities and at the school level. Overall we nd mild evidence of selection on key observables such as obesity levels and socio-economic characteristics. We find evidence of selection along indicators of involvement in healthy lifestyle programmes at the school level, but the magnitude is small. Moreover, We do not find signifi cant di erences in the treatment e ffects of the experiment between variables which, albeit to a mild degree, are correlated with selection into the experiment. To our knowledge, this is the rst study providing direct evidence on the magnitude of self-selection in fi eld experiments.

Estimating the Price of ROCs

The UK government introduced the Renewable Obligation (RO), a system of tradable quotas, to encourage the installation of renewable electricity capacity. Each unit of generation from renewables created a renewable obligation certificate (ROC). Electricity generators must either; earn ROCs through their own production, purchase ROCs in the market or pay the buy-out price to comply with the quota set by the RO. A unique aspect of this regulation is that all entities holding ROCs receive a share of the buy-out fund (the sum of all compliance purchases using the buy-out price). This set-up ensures that the difference between the market price for ROCs and the buy-out price should equal the expected share of the buy-out fund, as regulated entities arbitrage these two compliance options. The expected share of the buy-out fund depends on whether enough renewable generation is available to meet the quota. This analysis tests whether variables associated with renewable generation or electricity demand are correlated with, and thus can help predict, the price of ROCs.

HSU-Robbins and Spitzer's theorems for the variations of fractional Brownian motion

Using recent results on the behavior of multiple Wiener-Itô integrals based on Stein's method, we prove Hsu-Robbins and Spitzer's theorems for sequences of correlated random variables related to the increments of the fractional Brownian motion.

Comparing population distributions from bin-aggregated sample data: an application to historical height data from France

This paper develops a methodology to estimate the entire population distributions from bin-aggregated sample data. We do this through the estimation of the parameters of mixtures of distributions that allow for maximal parametric flexibility. The statistical approach we develop enables comparisons of the full distributions of height data from potential army conscripts across France's 88 departments for most of the nineteenth century. These comparisons are made by testing for differences-of-means stochastic dominance. Corrections for possible measurement errors are also devised by taking advantage of the richness of the data sets. Our methodology is of interest to researchers working on historical as well as contemporary bin-aggregated or histogram-type data, something that is still widely done since much of the information that is publicly available is in that form, often due to restrictions due to political sensitivity and/or confidentiality concerns.

On the existence of hysteresis in the Kuramoto model with bimodal frequency distributions

We investigate the transition to synchronization in the Kuramoto model with bimodal distributions of the natural frequencies. Previous studies have concluded that the model exhibits a hysteretic phase transition if the bimodal distribution is close to a unimodal one, due to the shallowness the central dip. Here we show that proximity to the unimodal-bimodal border does not necessarily imply hysteresis when the width, but not the depth, of the central dip tends to zero. We draw this conclusion from a detailed study of the Kuramoto model with a suitable family of bimodal distributions.

Mass effects mediate coexistence in competing shrews.

Recent developments in metacommunity theory have raised awareness that processes occurring at regional scales might interfere with local dynamics and affect conditions for the local coexistence of competing species. Four main paradigms are recognized in this context (namely, neutral, patch-dynamics, species-sorting, and mass-effect), which differ according to the role assigned to ecological or life-history differences among competing species, as well as to the relative time scale of regional vs. local dynamics. We investigated the patterns of regional and local coexistence of two species of shrews (Crocidura russula and Sorex coronatus) sharing a similar diet (generalist insectivores) over four generations, in a spatially structured habitat at the altitudinal limit of their distributions. Local populations were small, and regional dynamics were strong, with high rates of extinction and recolonization. Niche analysis revealed significant habitat differentiation on a few important variables, including temperature and availability of winter resting sites. In sites suitable for both species, we found instances of local coexistence with no evidence of competitive exclusion. Patterns of temporal succession did not differ from random, with no suggestion of a colonization-competition trade-off. Altogether, our data provide support for the mass-effect paradigm, where regional coexistence is mediated by specialization on different habitat types, and local coexistence by rescue effects from source sites. The strong regional dynamics and demographic stochasticity, together with high dispersal rates, presumably contributed to mass effects by overriding local differences in specific competitive abilities.

The application of chemometrics on Infrared and Raman spectra as a tool for the forensic analysis of paints

The aim of this work is to evaluate the capabilities and limitations of chemometric methods and other mathematical treatments applied on spectroscopic data and more specifically on paint samples. The uniqueness of the spectroscopic data comes from the fact that they are multivariate - a few thousands variables - and highly correlated. Statistical methods are used to study and discriminate samples. A collection of 34 red paint samples was measured by Infrared and Raman spectroscopy. Data pretreatment and variable selection demonstrated that the use of Standard Normal Variate (SNV), together with removal of the noisy variables by a selection of the wavelengths from 650 to 1830 cm−1 and 2730-3600 cm−1, provided the optimal results for infrared analysis. Principal component analysis (PCA) and hierarchical clusters analysis (HCA) were then used as exploratory techniques to provide evidence of structure in the data, cluster, or detect outliers. With the FTIR spectra, the Principal Components (PCs) correspond to binder types and the presence/absence of calcium carbonate. 83% of the total variance is explained by the four first PCs. As for the Raman spectra, we observe six different clusters corresponding to the different pigment compositions when plotting the first two PCs, which account for 37% and 20% respectively of the total variance. In conclusion, the use of chemometrics for the forensic analysis of paints provides a valuable tool for objective decision-making, a reduction of the possible classification errors, and a better efficiency, having robust results with time saving data treatments.

IL28B polymorphism is significantly correlated with ifn anti-viral effectiveness already on first day of pegylated interferon-alpha and ribavirin therapy of chronic HCV infection

Background and Aims: Recently, single nucleotide polymorphisms (SNPs) in IL28B were shown to correlate with response to pegylated interferon-a (IFN) and ribavirin therapy of chronic HCV infection. However, the cause for the SNPs effect on therapy response and its application for direct anti-viral (DAV) treatment are not clear. Here, we analyze early HCV kinetics as function of IL28B SNPs to determine its specific effect on viral dynamics. Methods: IL28B SNPs rs8099917, rs12979860 and rs12980275 were genotyped in 252 chronically HCV infected Caucasian naïve patients (67% HCV genotype 1, 28% genotype 2-3) receiving peginterferonalfa- 2a (180 mg/qw) plus ribavirin (1000-1200 mg/qd) in the DITTO study. HCV-RNA was measured (LD = 50 IU/ml) frequently during first 28 days. Results: RVR was achieved in 33% of genotype 1 patients with genotype CC at rs12979860 versus 12-16% for genotypes TT and CT (P < 0.03). Significant (P < 0.001) difference in viral decline was observed already at day 1 (see Figure). First phase decline was significantly (P < 0.001) larger in patients with genotype CC (2.0 log) than for TT and CT genotypes (0.6 and 0.8), indicating IFN anti-viral effectiveness in blocking virion production of 99% versus 75-84%. There was no significant association between second phase slope and rs12979860 genotype in patients with a first phase decline larger than 1 log. HCV kinetics as function of IL28b SNP. The same trend (not shown) was observed for HCV genotype 2-3 patients with different SNP genotype distribution that may indicate differential selection pressure as function of HCV genotype. Similar results were observed for SNPs rs8099917 and rs12980275, with a strong linkage disequilibrium among the 3 loci allowing to define the composite haplotype best associated with IFN effectiveness. Conclusions: IFN effectiveness in blocking virion production/ release is strongly affected by IL28B SNPs, but not other viral dynamic properties such as infected cell loss rate. Thus, IFN based therapy, as standard-of-care or in combination with DAV, should consider IL28B SNPs for prediction and personalized treatment, while response to pure DAV treatment may be less affected by IL28B SNPs. Additional analyses are undergoing to pinpoint the SNP effect on IFN anti-viral effectiveness.

An Introduction to parametric and non-parametric models for bivariate positive insurance claim severity distributions

We present a real data set of claims amounts where costs related to damage are recorded separately from those related to medical expenses. Only claims with positive costs are considered here. Two approaches to density estimation are presented: a classical parametric and a semi-parametric method, based on transformation kernel density estimation. We explore the data set with standard univariate methods. We also propose ways to select the bandwidth and transformation parameters in the univariate case based on Bayesian methods. We indicate how to compare the results of alternative methods both looking at the shape of the overall density domain and exploring the density estimates in the right tail.