Therapeutic strategies in acute myocardial infarction. Results of STIM 93 registry

A registry was set up by the national college of cardiologists practicing in general hospitals in February 1993. The data concerned mode of admission, demographic details, initial clinical and haemodynamic evaluation and hospital outcome. Special attention was given to the electrocardiographic changes before and, in patients receiving thrombolytic therapy, after treatment. An analysis of predictive factors for mortality was performed both in the group of patients "revascularised" and in the group treated conventionally. One thousand and twenty three cases from 327 centres were analysed. There were 1292 men and 531 women, with an average age of 67.9 years. The average time interval from onset of symptoms to hospital admission was 5 h 30 min, 56.8% of patients arriving within 6 hours. 36.4% of the population underwent thrombolysis or angioplasty, 75% of patients under 75 years of age admitted before the 5th hours underwent a procedure of myocardial revascularisation. The hospital mortality was 14%, 8.7% in those revascularised and 17% in patients treated conventionally. Factors predictive of mortality were age, female gender, Killip Classes III or IV, systolic blood pressure of less than 100 mmHg, heart rate of more than 100/min and contraindications of thrombolysis. The maximum ST depression, the sum of ST depression, the sum of ST elevation and depression, were also significant predictive factors of a fatal hospital outcome in the whole population group and in patients treated conventionally. In the reperfused group, only the initial sum of ST elevation and depression was predictive of mortality: 120 minutes after the beginning of thrombolysis, the sum of ST elevations and of elevations and depressions was predictive of twice the mortality when the values exceeded 0.6 mv and 1.4 mv respectively.     

Drawings of graphs on surfaces with few crossings

We give drawings of a complete graphK _n withO(n ⁴ log² g/g) many crossings on an orientable or nonorientable surface of genusg 2. We use these drawings ofK _n and give a polynomial-time algorithm for drawing any graph withn vertices andm edges withO(m ² log² g/g) many crossings on an orientable or nonorientable surface of genusg 2. Moreover, we derive lower bounds on the crossing number of any graph on a surface of genusg 0. The number of crossings in the drawings produced by our algorithm are within a multiplicative factor ofO(log² g) from the lower bound (and hence from the optimal) for any graph withm 8n andn ²/m g m/64.The research of the third and the fourth authors was partially supported by Grant No. 2/1138/94 of the Slovak Academy of Sciences and by EC Cooperative action IC1000 Algorithms for Future Technologies (Project ALTEC). A preliminary version of this paper was presented at WG93 and published in Lecture Notes in Computer Science, Vol. 790, 1993, pp. 388–396.     

Yearly stepwise increments of the growth hormone dose results in a better growth response after four years in girls with Turner syndrome. Dutch Working Group on Growth Hormone

To optimize the growth promoting effect of growth hormone (GH), 65 previously untreated girls with Turner syndrome (TS), chronological age (CA) 2-11 yr, were randomized into 3 dosage regimen groups: A, B, and C, with a daily recombinant-human GH dose during 4 study years of 4-4-4-4, 4-6-6-6, and 4-6-8-8 IU/m2 b.s. The first GH dosage increase in groups B and C resulted in a significantly higher mean height velocity (HV) compared with constant dose group A. During the third year, when the dose was raised again only in group C, mean HV was significantly higher in groups B and C than in group A, and in group C compared with group B. In year 4 only group C mean HV remained significantly higher than group A. The pattern of change in HSDSCA (Dutch-Swedish-Danish Turner references) was identical; however, in year 4 mean delta HSDSCA in group B also remained significantly higher than group A. After 4 yr GH treatment, the following was determined. 1) The mean delta HSDSCA was significantly higher for groups B and C compared with group A, but not significantly different between groups B and C. 2) Although significantly higher compared with estimated values for untreated Dutch girls with TS, bone maturation of the GH treated girls was not significantly different between groups. 3) It was positively related with the degree of bone age (BA) retardation at start of study and negatively with baseline CA. 4) Both the modified Index of Potential Height (mIPHRUS) and a recently developed Turner-specific final height (FH) prediction method (PTSRUS), based on regression coefficients for H, CA, and bone age, showed significant increases in mean FH prediction, without significant differences between groups. PTSRUS values were markedly higher than the mIPHRUS values. Dose dependency could be shown for the area under the curve (AUC) for GH, but delta HSDSCA was not linearly related with AUC. Baseline GH binding protein (BP) levels were in 84% of the cases within the normal age range; the decrease in mean levels after 6 months GH was not significant. Mean insulin-like growth factor I (IGF-I) and IGFBP-3 plasma levels increased significantly, without significant differences between groups. delta HSDSCA during GH was dependent on IGF-I plasma levels at baseline and during the study period, beta-0.002 and beta-0.0004. Thus, a stepwise GH-dosing approach reduced the "waning" effect of the growth response after 4 yr treatment without undue bone maturation. FH prediction was not significantly different between treatment groups. Irrespective of the GH dose used, initiation of GH treatment at a younger age was beneficial after 4 yr GH when expressed as actual cm gained or as gain in FH prediction, but was not statistically significant when expressed as delta HSDSCA over the study period.     

Interprocedural Array Region Analyses

Many program optimizations require exact knowledge of the sets of array elements that are referenced in or that flow between statements or procedures. Some examples are array privatization, generation of communications in distributed memory machines, or compile-time optimization of cache behavior in hierarchical memory machines. Exact array region analysis is introduced in this article. These regions exactly represent the effects of statements and procedures upon array variables. To represent the flow of these data, we also introduce two new types of array region analyses: in and out regions. The intraprocedural propagation is presented, as well as a general linear framework for interprocedural analyses, which handles array reshapes. The intra- and inter-procedural propagation of array regions is implemented in pips, the interprocedural parallelizer of fortran programs developed at École des mines de Paris.     

Motion of points and lines in the uncalibrated case

In the present paper we address the problem of computing structure and motion, given a set point and/or line correspondences, in a monocular image sequence, when the camera is not calibrated.Considering point correspondences first, we analyse how to parameterize the retinal correspondences, in function of the chosen geometry: Euclidean, affine or projective geometry. The simplest of these parameterizations is called the FQs-representation and is a composite projective representation. The main result is that considering N+1 views in such a monocular image sequence, the retinal correspondences are parameterized by 11 N–4 parameters in the general projective case. Moreover, 3 other parameters are required to work in the affine case and 5 additional parameters in the Euclidean case. These 8 parameters are calibration parameters and must be calculated considering at least 8 external informations or constraints. The method being constructive, all these representations are made explicit.Then, considering line correspondences, we show how the the same parameterizations can be used when we analyse the motion of lines, in the uncalibrated case. The case of three views is extensively studied and a geometrical interpretation is proposed, introducing the notion of trifocal geometry which generalizes the well known epipolar geometry. It is also discussed how to introduce line correspondences, in a framework based on point correspondences, using the same equations.Finally, considering the F Qs-representation, one implementation is proposed as a motion module, taking retinal correspondences as input, and providing and estimation of the 11 N–4 retinal motion parameters. As discussed in this paper, this module can also estimate the 3D depth of the points up to an affine and projective transformation, defined by the 8 parameters identified in the first section. Experimental results are provided.     

Inferring global pereeptual contours from local features

We address the problem of contour inference from partial data, as obtained from state-of-the-art edge detectors.We argue that in order to obtain more pereeptually salient contours, it is necessary to impose generic constraints such as continuity and co-curvilinearity.The implementation is in the form of a convolution with a mask which encodes both the orientation and the strength of the possible continuations. We first show how the mask, called the Extension field is derived, then how the contributions from different sites are collected to produce a saliency map.We show that the scheme can handle a variety of input data, from dot patterns to oriented edgels in a unified manner, and demonstrate results on a variety of input stimuli.We also present a similar approach to the problem of inferring contours formed by end points. In both cases, the scheme is non-linear, non iterative, and unified in the sense that all types of input tokens are handled in the same manner.This research was supported by the Advanced Research Projects Agency of the Department of Defense and was monitored by the Air Force Office of Scientific Research under Contract No. F49620-90-C-0078, and by a NSF Grant under award No. IRI-9024369. The United States Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright notation hereon.     

Surrogate modeling approximation using a mixture of experts based on EM joint estimation

An automatic method to combine several local surrogate models is presented. This method is intended to build accurate and smooth approximation of discontinuous functions that are to be used in structural optimization problems. It strongly relies on the Expectation−Maximization (EM) algorithm for Gaussian mixture models (GMM). To the end of regression, the inputs are clustered together with their output values by means of parameter estimation of the joint distribution. A local expert is then built (linear, quadratic, artificial neural network, moving least squares) on each cluster. Lastly, the local experts are combined using the Gaussian mixture model parameters found by the EM algorithm to obtain a global model. This method is tested over both mathematical test cases and an engineering optimization problem from aeronautics and is found to improve the accuracy of the approximation.     

Democratic instance selection: A linear complexity instance selection algorithm based on classifier ensemble concepts

Instance selection is becoming increasingly relevant due to the huge amount of data that is constantly being produced in many fields of research. Although current algorithms are useful for fairly large datasets, scaling problems are found when the number of instances is in the hundreds of thousands or millions. When we face huge problems, scalability becomes an issue, and most algorithms are not applicable.Thus, paradoxically, instance selection algorithms are for the most part impracticable for the same problems that would benefit most from their use. This paper presents a way of avoiding this difficulty using several rounds of instance selection on subsets of the original dataset. These rounds are combined using a voting scheme to allow good performance in terms of testing error and storage reduction, while the execution time of the process is significantly reduced. The method is particularly efficient when we use instance selection algorithms that are high in computational cost. The proposed approach shares the philosophy underlying the construction of ensembles of classifiers. In an ensemble, several weak learners are combined to form a strong classifier; in our method several weak (in the sense that they are applied to subsets of the data) instance selection algorithms are combined to produce a strong and fast instance selection method.An extensive comparison of 30 medium and large datasets from the UCI Machine Learning Repository using 3 different classifiers shows the usefulness of our method. Additionally, the method is applied to 5 huge datasets (from three hundred thousand to more than a million instances) with good results and fast execution time.     

Evaluation of artery visualizations for heart disease diagnosis

Heart disease is the number one killer in the United States, and finding indicators of the disease at an early stage is critical for treatment and prevention. In this paper we evaluate visualization techniques that enable the diagnosis of coronary artery disease. A key physical quantity of medical interest is endothelial shear stress (ESS). Low ESS has been associated with sites of lesion formation and rapid progression of disease in the coronary arteries. Having effective visualizations of a patient's ESS data is vital for the quick and thorough non-invasive evaluation by a cardiologist. We present a task taxonomy for hemodynamics based on a formative user study with domain experts. Based on the results of this study we developed HemoVis, an interactive visualization application for heart disease diagnosis that uses a novel 2D tree diagram representation of coronary artery trees. We present the results of a formal quantitative user study with domain experts that evaluates the effect of 2D versus 3D artery representations and of color maps on identifying regions of low ESS. We show statistically significant results demonstrating that our 2D visualizations are more accurate and efficient than 3D representations, and that a perceptually appropriate color map leads to fewer diagnostic mistakes than a rainbow color map.     

Weighting Efficient Accuracy and Minimum Sensitivity for Evolving Multi-Class Classifiers

Recently, a multi-objective Sensitivity–Accuracy based methodology has been proposed for building classifiers for multi-class problems. This technique is especially suitable for imbalanced and multi-class datasets. Moreover, the high computational cost of multi-objective approaches is well known so more efficient alternatives must be explored. This paper presents an efficient alternative to the Pareto based solution when considering both Minimum Sensitivity and Accuracy in multi-class classifiers. Alternatives are implemented by extending the Evolutionary Extreme Learning Machine algorithm for training artificial neural networks. Experiments were performed to select the best option after considering alternative proposals and related methods. Based on the experiments, this methodology is competitive in Accuracy, Minimum Sensitivity and efficiency.