Ultra-early intravenous stroke thrombolysis: do all patients benefit similarly?

BACKGROUND AND PURPOSE: We previously reported increased benefit and reduced mortality after ultra-early stroke thrombolysis in a single center. We now explored in a large multicenter cohort whether extra benefit of treatment within 90 minutes from symptom onset is uniform across predefined stroke severity subgroups, as compared with later thrombolysis. METHODS: Prospectively collected data of consecutive ischemic stroke patients who received IV thrombolysis in 10 European stroke centers were merged. Logistic regression tested association between treatment delays, as well as excellent 3-month outcome (modified Rankin scale, 0-1), and mortality. The association was tested separately in tertiles of baseline National Institutes of Health Stroke Scale. RESULTS: In the whole cohort (n=6856), shorter onset-to-treatment time as a continuous variable was significantly associated with excellent outcome (P<0.001). Every fifth patient had onset-to-treatment time≤90 minutes, and these patients had lower frequency of intracranial hemorrhage. After adjusting for age, sex, admission glucose level, and year of treatment, onset-to-treatment time≤90 minutes was associated with excellent outcome in patients with National Institutes of Health Stroke Scale 7 to 12 (odds ratio, 1.37; 95% confidence interval, 1.11-1.70; P=0.004), but not in patients with baseline National Institutes of Health Stroke Scale>12 (odds ratio, 1.00; 95% confidence interval, 0.76-1.32; P=0.99) and baseline National Institutes of Health Stroke Scale 0 to 6 (odds ratio, 1.04; 95% confidence interval, 0.78-1.39; P=0.80). In the latter, however, an independent association (odds ratio, 1.51; 95% confidence interval, 1.14-2.01; P<0.01) was found when considering modified Rankin scale 0 as outcome (to overcome the possible ceiling effect from spontaneous better prognosis of patients with mild symptoms). Ultra-early treatment was not associated with mortality. CONCLUSIONS: IV thrombolysis within 90 minutes is, compared with later thrombolysis, strongly and independently associated with excellent outcome in patients with moderate and mild stroke severity.

Lyfe-cycle effects on household expenditures: A latent-variable approach

Using data from the Spanish household budget survey, we investigate life-cycle effects on several product expenditures. A latent-variable model approach is adopted to evaluate the impact of income on expenditures, controlling for the number of members in the family. Two latent factors underlying repeated measures of monetary and non-monetary income are used as explanatory variables in the expenditure regression equations, thus avoiding possible bias associated to the measurement error in income. The proposed methodology also takes care of the case in which product expenditures exhibit a pattern of infrequent purchases. Multiple-group analysis is used to assess the variation of key parameters of the model across various household life-cycle typologies. The analysis discloses significant life-cycle effects on the mean levels of expenditures; it also detects significant life-cycle effects on the way expenditures are affected by income and family size. Asymptotic robust methods are used to account for possible non-normality of the data.

An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System

The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.

Canonical correspondence analysis in social science research

The use of simple and multiple correspondence analysis is well-established in socialscience research for understanding relationships between two or more categorical variables.By contrast, canonical correspondence analysis, which is a correspondence analysis with linearrestrictions on the solution, has become one of the most popular multivariate techniques inecological research. Multivariate ecological data typically consist of frequencies of observedspecies across a set of sampling locations, as well as a set of observed environmental variablesat the same locations. In this context the principal dimensions of the biological variables aresought in a space that is constrained to be related to the environmental variables. Thisrestricted form of correspondence analysis has many uses in social science research as well,as is demonstrated in this paper. We first illustrate the result that canonical correspondenceanalysis of an indicator matrix, restricted to be related an external categorical variable, reducesto a simple correspondence analysis of a set of concatenated (or stacked ) tables. Then weshow how canonical correspondence analysis can be used to focus on, or partial out, aparticular set of response categories in sample survey data. For example, the method can beused to partial out the influence of missing responses, which usually dominate the results of amultiple correspondence analysis.

Variable Kernel estimates: On the impossibility of tuning the parameters

For the standard kernel density estimate, it is known that one can tune the bandwidth such that the expected L1 error is within a constant factor of the optimal L1 error (obtained when one is allowed to choose the bandwidth with knowledge of the density). In this paper, we pose the same problem for variable bandwidth kernel estimates where the bandwidths are allowed to depend upon the location. We show in particular that for positive kernels on the real line, for any data-based bandwidth, there exists a densityfor which the ratio of expected L1 error over optimal L1 error tends to infinity. Thus, the problem of tuning the variable bandwidth in an optimal manner is ``too hard''. Moreover, from the class of counterexamples exhibited in the paper, it appears thatplacing conditions on the densities (monotonicity, convexity, smoothness) does not help.

PyRate: a new program to estimate speciation and extinction rates from incomplete fossil data

Despite the advancement of phylogenetic methods to estimate speciation and extinction rates, their power can be limited under variable rates, in particular for clades with high extinction rates and small number of extant species. Fossil data can provide a powerful alternative source of information to investigate diversification processes. Here, we present PyRate, a computer program to estimate speciation and extinction rates and their temporal dynamics from fossil occurrence data. The rates are inferred in a Bayesian framework and are comparable to those estimated from phylogenetic trees. We describe how PyRate can be used to explore different models of diversification. In addition to the diversification rates, it provides estimates of the parameters of the preservation process (fossilization and sampling) and the times of speciation and extinction of each species in the data set. Moreover, we develop a new birth-death model to correlate the variation of speciation/extinction rates with changes of a continuous trait. Finally, we demonstrate the use of Bayes factors for model selection and show how the posterior estimates of a PyRate analysis can be used to generate calibration densities for Bayesian molecular clock analysis. PyRate is an open-source command-line Python program available at http://sourceforge.net/projects/pyrate/.

Testing financing constraints on firm investment using variable capital

We consider a dynamic multifactor model of investment with financing imperfections,adjustment costs and fixed and variable capital. We use the model to derive a test offinancing constraints based on a reduced form variable capital equation. Simulation resultsshow that this test correctly identifies financially constrained firms even when the estimationof firms investment opportunities is very noisy. In addition, the test is well specified inthe presence of both concave and convex adjustment costs of fixed capital. We confirmempirically the validity of this test on a sample of small Italian manufacturing companies.

Biased quantitative measurement of interval ordered homothetic preferences

We represent interval ordered homothetic preferences with a quantitative homothetic utility function and a multiplicative bias. When preferences are weakly ordered (i.e. when indifference is transitive), such a bias equals 1. When indifference is intransitive, the biasing factor is a positive function smaller than 1 and measures a threshold of indifference. We show that the bias is constant if and only if preferences are semiordered, and we identify conditions ensuring a linear utility function. We illustrate our approach with indifference sets on a two dimensional commodity space.

Spread spectrum magnetic resonance imaging.

We propose a novel compressed sensing technique to accelerate the magnetic resonance imaging (MRI) acquisition process. The method, coined spread spectrum MRI or simply s(2)MRI, consists of premodulating the signal of interest by a linear chirp before random k-space under-sampling, and then reconstructing the signal with nonlinear algorithms that promote sparsity. The effectiveness of the procedure is theoretically underpinned by the optimization of the coherence between the sparsity and sensing bases. The proposed technique is thoroughly studied by means of numerical simulations, as well as phantom and in vivo experiments on a 7T scanner. Our results suggest that s(2)MRI performs better than state-of-the-art variable density k-space under-sampling approaches.

Analysis of matched matrices

We consider the joint visualization of two matrices which have common rowsand columns, for example multivariate data observed at two time pointsor split accord-ing to a dichotomous variable. Methods of interest includeprincipal components analysis for interval-scaled data, or correspondenceanalysis for frequency data or ratio-scaled variables on commensuratescales. A simple result in matrix algebra shows that by setting up thematrices in a particular block format, matrix sum and difference componentscan be visualized. The case when we have more than two matrices is alsodiscussed and the methodology is applied to data from the InternationalSocial Survey Program.

What risks do people perceive in everyday life? A perspective gained from the experience sampling method (ESM)

The experiential sampling method (ESM) was used to collect data from 74 parttimestudents who described and assessed the risks involved in their current activitieswhen interrupted at random moments by text messages. The major categories ofperceived risk were short-term in nature and involved loss of time or materials relatedto work and physical damage (e.g., from transportation). Using techniques of multilevelanalysis, we demonstrate effects of gender, emotional state, and types of risk onassessments of risk. Specifically, females do not differ from males in assessing thepotential severity of risks but they see these as more likely to occur. Also, participantsassessed risks to be lower when in more positive self-reported emotional states. Wefurther demonstrate the potential of ESM by showing that risk assessments associatedwith current actions exceed those made retrospectively. We conclude by notingadvantages and disadvantages of ESM for collecting data about risk perceptions.

Insects associated with exposed decomposing bodies in the Colombian Andean Coffee Region

In Colombia, mainly classic forensic medicine methods were used to clarify crimes until 2004. However, other disciplines, including forensic entomology, started to be considered only after the New Accusatory System introduction in Bogotá and the Coffee Region in 2005. In order to provide tools for obtaining evidentiary material elements in judicial trials, it is presented here the succession of insects throughout the decomposition process of an exposed carcass of Sus scrofa Linnaeus 1758 (Suidae) and the Occurrence Matrix of colonizing species. This process was evaluated under ambient conditions in the Andean rural area of the city of Pereira, in the Mundo Nuevo district, located in a pre-montane Wet Forest area, from October to November 2006. A sampling period of 27 days and 3198 individuals were collected. We found these colonizing species in the following stages of decomposition: Lucilia eximia (Wiedemann, 1819) fresh; Hemilucilia semidiaphana (Rondani, 1850), Oxelytrum discicolle (Brullé, 1840), and Cochliomyia macellaria (Fabricius 1775) bloated; Chrysomya albiceps (Wiedemann 1819), Compsomyiops verena (Walker, 1849), Ophyra aenescens (Wiedemann, 1830) and Musca domestica Linnaeus, 1758 active; Fannia sp. advanced and Stearibia nigriceps (Meigen, 1826) remains. This study provides support tools to define the Post Mortem Interval that may be used by experts from government institutions and laboratories officially accredited.