Schizophrenia and complications of pregnancy and labor: an individual patient data meta-analysis.

Several epidemiological studies have reported an association between complications of pregnancy and delivery and schizophrenia, but none have had sufficient power to examine specific complications that, individually, are of low prevalence. We, therefore, performed an individual patient meta-analysis using the raw data from case control studies that used the Lewis-Murray scale. Data were obtained from 12 studies on 700 schizophrenia subjects and 835 controls. There were significant associations between schizophrenia and premature rupture of membranes, gestational age shorter than 37 weeks, and use of resuscitation or incubator. There were associations of borderline significance between schizophrenia and birthweight lower than 2,500 g and forceps delivery. There was no significant interaction between these complications and sex. We conclude that some abnormalities of pregnancy and delivery may be associated with development of schizophrenia. The pathophysiology may involve hypoxia and so future studies should focus on the accurate measurement of this exposure.

Data-driven clustering : new methods and applications

Abstract : This work is concerned with the development and application of novel unsupervised learning methods, having in mind two target applications: the analysis of forensic case data and the classification of remote sensing images. First, a method based on a symbolic optimization of the inter-sample distance measure is proposed to improve the flexibility of spectral clustering algorithms, and applied to the problem of forensic case data. This distance is optimized using a loss function related to the preservation of neighborhood structure between the input space and the space of principal components, and solutions are found using genetic programming. Results are compared to a variety of state-of--the-art clustering algorithms. Subsequently, a new large-scale clustering method based on a joint optimization of feature extraction and classification is proposed and applied to various databases, including two hyperspectral remote sensing images. The algorithm makes uses of a functional model (e.g., a neural network) for clustering which is trained by stochastic gradient descent. Results indicate that such a technique can easily scale to huge databases, can avoid the so-called out-of-sample problem, and can compete with or even outperform existing clustering algorithms on both artificial data and real remote sensing images. This is verified on small databases as well as very large problems. Résumé : Ce travail de recherche porte sur le développement et l'application de méthodes d'apprentissage dites non supervisées. Les applications visées par ces méthodes sont l'analyse de données forensiques et la classification d'images hyperspectrales en télédétection. Dans un premier temps, une méthodologie de classification non supervisée fondée sur l'optimisation symbolique d'une mesure de distance inter-échantillons est proposée. Cette mesure est obtenue en optimisant une fonction de coût reliée à la préservation de la structure de voisinage d'un point entre l'espace des variables initiales et l'espace des composantes principales. Cette méthode est appliquée à l'analyse de données forensiques et comparée à un éventail de méthodes déjà existantes. En second lieu, une méthode fondée sur une optimisation conjointe des tâches de sélection de variables et de classification est implémentée dans un réseau de neurones et appliquée à diverses bases de données, dont deux images hyperspectrales. Le réseau de neurones est entraîné à l'aide d'un algorithme de gradient stochastique, ce qui rend cette technique applicable à des images de très haute résolution. Les résultats de l'application de cette dernière montrent que l'utilisation d'une telle technique permet de classifier de très grandes bases de données sans difficulté et donne des résultats avantageusement comparables aux méthodes existantes.

Transvaginal specimen extraction in colorectal surgery: current state of the art.

Aim  Background The expected benefit of transvaginal specimen extraction is reduced incision-related morbidity. Objectives A systematic review of transvaginal specimen extraction in colorectal surgery was carried out to assess this expectation. Method  Search strategy The following keywords, in various combinations, were searched: NOSE (natural orifices specimen extraction), colorectal, colon surgery, transvaginal, right hemicolectomy, left hemicolectomy, low anterior resection, sigmoidectomy, ileocaecal resection, proctocolectomy, colon cancer, sigmoid diverticulitis and inflammatory bowel diseases. Selection criteria Selection criteria included large bowel resection with transvaginal specimen extraction, laparoscopic approach, human studies and English language. Exclusion criteria were experimental studies and laparotomic approach or local excision. All articles published up to February 2011 were included. Results  Twenty-three articles (including a total of 130 patients) fulfilled the search criteria. The primary diagnosis was colorectal cancer in 51% (67) of patients, endometriosis in 46% (60) of patients and other conditions in the remaining patients. A concurrent gynaecological procedure was performed in 17% (22) of patients. One case of conversion to laparotomy was reported. In two patients, transvaginal extraction failed. In left- and right-sided resections, the rate of severe complications was 3.7% and 2%, respectively. Two significant complications, one of pelvic seroma and one of rectovaginal fistula, were likely to have been related to transvaginal extraction. The degree of follow up was specified in only one study. Harvested nodes and negative margins were adequate and reported in 70% of oncological cases. Conclusion  Vaginal extraction of a colorectal surgery specimen shows potential benefit, particularly when associated with a gynaecological procedure. Data from prospective randomized trials are needed to support the routine use of this technique.

Identification and visualization of multidimensional antigen-specific T-cell populations in polychromatic cytometry data.

An important aspect of immune monitoring for vaccine development, clinical trials, and research is the detection, measurement, and comparison of antigen-specific T-cells from subject samples under different conditions. Antigen-specific T-cells compose a very small fraction of total T-cells. Developments in cytometry technology over the past five years have enabled the measurement of single-cells in a multivariate and high-throughput manner. This growth in both dimensionality and quantity of data continues to pose a challenge for effective identification and visualization of rare cell subsets, such as antigen-specific T-cells. Dimension reduction and feature extraction play pivotal role in both identifying and visualizing cell populations of interest in large, multi-dimensional cytometry datasets. However, the automated identification and visualization of rare, high-dimensional cell subsets remains challenging. Here we demonstrate how a systematic and integrated approach combining targeted feature extraction with dimension reduction can be used to identify and visualize biological differences in rare, antigen-specific cell populations. By using OpenCyto to perform semi-automated gating and features extraction of flow cytometry data, followed by dimensionality reduction with t-SNE we are able to identify polyfunctional subpopulations of antigen-specific T-cells and visualize treatment-specific differences between them.