Outcome at two years of age in a Swiss national cohort of extremely preterm infants born between 2000 and 2008

Background While survival rates of extremely preterm infants have improved over the last decades, the incidence of neurodevelopmental disability (ND) in survivors remains high. Representative current data on the severity of disability and of risk factors associated with poor outcome in this growing population are necessary for clinical guidance and parent counselling. Methods Prospective longitudinal multicentre cohort study of preterm infants born in Switzerland between 240/7 and 276/7 weeks gestational age during 2000–2008. Mortality, adverse outcome (death or severe ND) at two years, and predictors for poor outcome were analysed using multilevel multivariate logistic regression. Neurodevelopment was assessed using Bayley Scales of Infant Development II. Cerebral palsy was graded after the Gross Motor Function Classification System. Results Of 1266 live born infants, 422 (33%) died. Follow-up information was available for 684 (81%) survivors: 440 (64%) showed favourable outcome, 166 (24%) moderate ND, and 78 (11%) severe ND. At birth, lower gestational age, intrauterine growth restriction and absence of antenatal corticosteroids were associated with mortality and adverse outcome (p < 0.001). At 360/7 weeks postmenstrual age, bronchopulmonary dysplasia, major brain injury and retinopathy of prematurity were the main predictors for adverse outcome (p < 0.05). Survival without moderate or severe ND increased from 27% to 39% during the observation period (p = 0.02). Conclusions In this recent Swiss national cohort study of extremely preterm infants, neonatal mortality was determined by gestational age, birth weight, and antenatal corticosteroids while neurodevelopmental outcome was determined by the major neonatal morbidities. We observed an increase of survival without moderate or severe disability.

Highly efficient ketone body treatment in multiple acyl-CoA dehydrogenase deficiency-related leukodystrophy.

BACKGROUND Multiple acyl-CoA dehydrogenase deficiency- (MADD-), also called glutaric aciduria type 2, associated leukodystrophy may be severe and progressive despite conventional treatment with protein- and fat-restricted diet, carnitine, riboflavin, and coenzyme Q10. Administration of ketone bodies was described as a promising adjunct, but has only been documented once. METHODS We describe a Portuguese boy of consanguineous parents who developed progressive muscle weakness at 2.5 y of age, followed by severe metabolic decompensation with hypoglycaemia and coma triggered by a viral infection. Magnetic resonance (MR) imaging showed diffuse leukodystrophy. MADD was diagnosed by biochemical and molecular analyses. Clinical deterioration continued despite conventional treatment. Enteral sodium D,L-3-hydroxybutyrate (NaHB) was progressively introduced and maintained at 600 mg/kg BW/d (≈3% caloric need). Follow up was 3 y and included regular clinical examinations, biochemical studies, and imaging. RESULTS During follow up, the initial GMFC-MLD (motor function classification system, 0 = normal, 6 = maximum impairment) level of 5-6 gradually improved to 1 after 5 mo. Social functioning and quality of life recovered remarkably. We found considerable improvement of MR imaging and spectroscopy during follow up, with a certain lag behind clinical recovery. There was some persistent residual developmental delay. CONCLUSION NaHB is a highly effective and safe treatment that needs further controlled studies.

Uncertainty and Fuzzy Sets: Classifying the Situation

The so called “Plural Uncertainty Model” is considered, in which statistical, maxmin, interval and Fuzzy model of uncertainty are embedded. For the last case external and internal contradictions of the theory are investigated and the modified definition of the Fuzzy Sets is proposed to overcome the troubles of the classical variant of Fuzzy Subsets by L. Zadeh. The general variants of logit- and probit- regression are the model of the modified Fuzzy Sets. It is possible to say about observations within the modification of the theory. The conception of the “situation” is proposed within modified Fuzzy Theory and the classifying problem is considered. The algorithm of the classification for the situation is proposed being the analogue of the statistical MLM(maximum likelihood method). The example related possible observing the distribution from the collection of distribution is considered.

Validación de un instrumento para evaluar la carga del cuidador en parálisis cerebral

Esta investigación pretende desarrollar y validar una escala para evaluar la carga del cuidador del niño con parálisis cerebral (PC) de niveles funcionales GMFCS (Gross Motor Function Classification System) IV y V.

Two new atom centered fragment descriptors and scoring function enhance classification of antibacterial activity

Classification of pharmacologic activity of a chemical compound is an essential step in any drug discovery process. We develop two new atom-centered fragment descriptors (vertex indices) - one based solely on topological considerations without discriminating atomor bond types, and another based on topological and electronic features. We also assess their usefulness by devising a method to rank and classify molecules with regard to their antibacterial activity. Classification performances of our method are found to be superior compared to two previous studies on large heterogeneous data sets for hit finding and hit-to-lead studies even though we use much fewer parameters. It is found that for hit finding studies topological features (simple graph) alone provide significant discriminating power, and for hit-to-lead process small but consistent improvement can be made by additionally including electronic features (colored graph). Our approach is simple, interpretable, and suitable for design of molecules as we do not use any physicochemical properties. The singular use of vertex index as descriptor, novel range based feature extraction, and rigorous statistical validation are the key elements of this study.

Cancer classification based on direction-basis-function neural networks

Automatic molecular classification of cancer based on DNA microarray has many advantages over conventional classification based on morphological appearance of the tumor. Using artificial neural networks is a general approach for automatic classification. In this paper, Direction-Basis-Function neuron and Priority-Ordered algorithm are applied to neural networks. And the leukemia gene expression dataset is used as an example to testify the classifier. The result of our method is compared to that of SVM. It shows that our method makes a better performance than SVM.

Classification of incomplete feature vectors by radial basis function networks

The paper describes the use of radial basis function neural networks with Gaussian basis functions to classify incomplete feature vectors. The method uses the fact that any marginal distribution of a Gaussian distribution can be determined from the mean vector and covariance matrix of the joint distribution.

Fully complex-valued radial basis function networks: orthogonal least squares regression and classification

We consider a fully complex-valued radial basis function (RBF) network for regression and classification applications. For regression problems, the locally regularised orthogonal least squares (LROLS) algorithm aided with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF models, is extended to the fully complex-valued RBF (CVRBF) network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully CVRBF network. The proposed fully CVRBF network is also applied to four-class classification problems that are typically encountered in communication systems. A complex-valued orthogonal forward selection algorithm based on the multi-class Fisher ratio of class separability measure is derived for constructing sparse CVRBF classifiers that generalise well. The effectiveness of the proposed algorithm is demonstrated using the example of nonlinear beamforming for multiple-antenna aided communication systems that employ complex-valued quadrature phase shift keying modulation scheme. (C) 2007 Elsevier B.V. All rights reserved.

Need for a Government-wide budget classification structure for Federal research and development information : report to the Congress /

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Unicellular pheromone glands of the pentatomid bug Nezara viridula (Heteroptera : Insecta): Ultrastructure, classification, and proposed function

Male Nezara viridula produce sex pheromones from many independent single cells, each with a duct that opens onto the ventral abdominal surface. Despite the presence of along duct and an associated end complex (in the form of a cupule and microvillus saccule), the structural organization of the cells that comprise the gland conform to Class 1 epidermal gland cell classification : a single cell surrounds the entire secretory complex. Each cuticular cupule contains a central bed of filaments and opens into a narrow tubular ductule that leads from the base of the cupule through the epidermis to the cuticle to open externally as a pore. The cuticle of the cupule is continuous with that of the ductule and has the appearance of three layers, although the inner (middle) layer may be a gap formed during construction of the complex. In young adult males, just molted, the ultrastructure of the cells and their inclusions indicate that they are not active. The region of the cell that is distal to the abdominal cuticle is reduced and the proximal region, surrounding the duct, is enlarged when compared with sexually mature (3-4 weeks old) adult males. At maturity the pheromone cells are enlarged distally around the cupule, but are reduced to a narrow sleeve proximally, around the ductule. Two characteristic cell profiles are evident, based on the shape of the cupule and the organelle content. Type A shows a broad opening to the cupule, an abundance of mitochondria, and few vesicular bodies. Type B has an elongated, narrow, vase-like opening to the cupule, few mitochondria, and numerous vesicular bodies. Type B cells are smaller and more abundant than Type A. Distribution within the epidermal layer also differs. It is likely that the different types represent cells producing different secretion profiles. However, the secretions retained by the standard fixation protocol within mature cells of both types look similar and appear to collect as crystalline bodies within the lumen. This may represent a common storage mechanism.

GPCRTree:online hierarchical classification of GPCR function

G protein-coupled receptors (GPCRs) play important physiological roles transducing extracellular signals into intracellular responses. Approximately 50% of all marketed drugs target a GPCR. There remains considerable interest in effectively predicting the function of a GPCR from its primary sequence.

Labeling large scale social media data using budget-driven One-class SVM classification

The social media classification problems draw more and more attention in the past few years. With the rapid development of Internet and the popularity of computers, there is astronomical amount of information in the social network (social media platforms). The datasets are generally large scale and are often corrupted by noise. The presence of noise in training set has strong impact on the performance of supervised learning (classification) techniques. A budget-driven One-class SVM approach is presented in this thesis that is suitable for large scale social media data classification. Our approach is based on an existing online One-class SVM learning algorithm, referred as STOCS (Self-Tuning One-Class SVM) algorithm. To justify our choice, we first analyze the noise-resilient ability of STOCS using synthetic data. The experiments suggest that STOCS is more robust against label noise than several other existing approaches. Next, to handle big data classification problem for social media data, we introduce several budget driven features, which allow the algorithm to be trained within limited time and under limited memory requirement. Besides, the resulting algorithm can be easily adapted to changes in dynamic data with minimal computational cost. Compared with two state-of-the-art approaches, Lib-Linear and kNN, our approach is shown to be competitive with lower requirements of memory and time.