Pituitary blastoma: a pathognomonic feature of germ-line DICER1 mutations.

Individuals harboring germ-line DICER1 mutations are predisposed to a rare cancer syndrome, the DICER1 Syndrome or pleuropulmonary blastoma-familial tumor and dysplasia syndrome [online Mendelian inheritance in man (OMIM) #601200]. In addition, specific somatic mutations in the DICER1 RNase III catalytic domain have been identified in several DICER1-associated tumor types. Pituitary blastoma (PitB) was identified as a distinct entity in 2008, and is a very rare, potentially lethal early childhood tumor of the pituitary gland. Since the discovery by our team of an inherited mutation in DICER1 in a child with PitB in 2011, we have identified 12 additional PitB cases. We aimed to determine the contribution of germ-line and somatic DICER1 mutations to PitB. We hypothesized that PitB is a pathognomonic feature of a germ-line DICER1 mutation and that each PitB will harbor a second somatic mutation in DICER1. Lymphocyte or saliva DNA samples ascertained from ten infants with PitB were screened and nine were found to harbor a heterozygous germ-line DICER1 mutation. We identified additional DICER1 mutations in nine of ten tested PitB tumor samples, eight of which were confirmed to be somatic in origin. Seven of these mutations occurred within the RNase IIIb catalytic domain, a domain essential to the generation of 5p miRNAs from the 5' arm of miRNA-precursors. Germ-line DICER1 mutations are a major contributor to PitB. Second somatic DICER1 "hits" occurring within the RNase IIIb domain also appear to be critical in PitB pathogenesis.

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.

How do the different components of episodic memory develop? Role of executive functions and short-term feature-binding abilities.

This study investigated the development of all 3 components of episodic memory (EM), as defined by Tulving, namely, core factual content, spatial context, and temporal context. To this end, a novel, ecologically valid test was administered to 109 participants aged 4-16 years. Results showed that each EM component develops at a different rate. Ability to memorize factual content emerges early, whereas context retrieval abilities continue to improve until adolescence, due to persistent encoding difficulties (isolated by comparing results on free recall and recognition tasks). Exploration of links with other cognitive functions revealed that short-term feature-binding abilities contribute to all EM components, and executive functions to temporal and spatial context, although ability to memorize temporal context is predicted mainly by age.

Ex-PRESS R-50 miniature glaucoma implant insertion under the conjunctiva combined with cataract extraction

Rapport de synthèse : Le glaucome à angle ouvert est une neuropathie optique chronique progressive pour laquelle de nombreux traitements tant médicaux que chirurgicaux ont été proposés. La prise en charge chirurgicale s'articule principalement autour de deux chirurgies filtrantes, la trabéculectomie et la sclérectomie profonde avec implant de collagène. Cependant, les complications postopératoires de ces deux interventions étant relativement fréquentes, la recherche s'est orientée vers des traitements alternatifs dont la mise en place de micro-drains. Ces implants de drainage diminuent la pression intraoculaire en créant un court-circuit du flux d'humeur aqueuse de la chambre antérieure vers l'espace sous-conjonctival avec formation d'une bulle de filtration. L'implant Ex-PRESS R-50 est un implant miniature (2.5 mm de long pour 400 µm de diamètre) en acier inoxydable et biocompatible. La présente étude s'est proposée d'étudier l'efficacité et la sécurité de l'implant miniature Ex-Press R-50 lors d'une opération combinée cataracte-glaucome. Trente-cinq yeux de 35 patients (âge moyen: 75 ans) ont été inclus dans l'étude. Tous les patients ont bénéficié d'une opération de la cataracte par phacoemulsification et mise en place d'un implant de chambre postérieure suivie de l'implantation du micro-drain. Les pressions intraoculaires préopératoires et postopératoires, la meilleure acuité visuelle corrigée, le nombre de médicaments anti-glaucomateux ainsi que le type et le nombre de complications ont été évalués mensuellement puis tous les 6 mois pendant 4 ans. Le succès total a été défini par une pression postopératoire finale inférieure à 18mmHg sans traitement médical associé, le succès partiel par une pression postopératoire finale inférieure à 18mmHg avec ou sans traitement médical associé.. Le suivi moyen a été de 36.9 mois avec une baisse de la pression intraoculaire significative d'environ 25%. Une augmentation de l'acuité visuelle a été observée après l'opération de la cataracte et le nombre de médicaments anti-glaucomateux a été réduit de 57%. Dix patients ont bénéficié d'un traitement supplémentaire de la bulle de filtration par injection d'anti-métabolite (mitomycine C). Nous avons observé 8 complications majeures (4 érosions conjonctivales et 4 obstructions de l'orifice interne du micro-drain), toutes suivies de l'ablation de l'implant et de la réalisation d'une chirurgie classique du glaucome. En se basant sur les courbes de Kaplan-Meier à 48 mois, le taux de succès total était de 32.7% et le succès partiel de 53.7%. Nous pouvons conclure suite à ce travail que l'implant miniature Ex-PRESS R-50 est associé à un nombre trop élevé de complications, même si les cas non compliqués ont bénéficié d'une baisse significative de la pression intraoculaire. La modification de l'architecture du micro-drain ainsi que de la technique chirurgicale devrait augmenter le taux de succès.

Recent advances in remote sensing image processing

Remote sensing image processing is nowadays a mature research area. The techniques developed in the field allow many real-life applications with great societal value. For instance, urban monitoring, fire detection or flood prediction can have a great impact on economical and environmental issues. To attain such objectives, the remote sensing community has turned into a multidisciplinary field of science that embraces physics, signal theory, computer science, electronics, and communications. From a machine learning and signal/image processing point of view, all the applications are tackled under specific formalisms, such as classification and clustering, regression and function approximation, image coding, restoration and enhancement, source unmixing, data fusion or feature selection and extraction. This paper serves as a survey of methods and applications, and reviews the last methodological advances in remote sensing image processing.

Determination of the Enantiomers of Mianserin and its Metabolites in Plasma by Capillary Electrophoresis After Liquid-Liquid Extraction and On-Column Sample Preconcentration

Capillary electrophoresis has drawn considerable attention in the past few years, particularly in the field of chiral separations because of its high separation efficiency. However, its routine use in therapeutic drug monitoring is hampered by its low sensitivity due to a short optical path. We have developed a capillary zone electrophoresis (CZE) method using 2mM of hydroxypropyl-β-cyclodextrin as a chiral selector, which allows base-to-base separation of the enantiomers of mianserin (MIA), desmethylmianserin (DMIA), and 8-hydroxymianserin (OHMIA). Through the use of an on-column sample concentration step after liquid-liquid extraction from plasma and through the presence of an internal standard, the quantitation limits were found to be 5 ng/mL for each enantiomer of MIA and DMIA and 15 ng/mL for each enantiomer of OHMIA. To our knowledge, this is the first published CE method that allows its use for therapeutic monitoring of antidepressants due to its sensitivity down to the low nanogram range. The variability of the assays, as assessed by the coefficients of variation (CV) measured at two concentrations for each substance, ranged from 2 to 14% for the intraday (eight replicates) and from 5 to 14% for the interday (eight replicates) experiments. The deviations from the theoretical concentrations, which represent the accuracy of the method, were all within 12.5%. A linear response was obtained for all compounds within the range of concentrations used for the calibration curves (10-150 ng/mL for each enantiomer of MIA and DMIA and 20-300 ng/mL for each enantiomer of OHMIA). Good correlations were calculated between [(R) + (S)]-MIA and DMIA concentrations measured in plasma samples of 20 patients by a nonchiral gas chromatography method and CZE, and between the (R)- and (S)-concentrations of MIA and DMIA measured in plasma samples of 37 patients by a previously described chiral high-performance liquid chromatography method and CZE. Finally, no interference was noted from more than 20 other psychotropic drugs. Thus, this method, which is both sensitive and selective, can be routinely used for therapeutic monitoring of the enantiomers of MIA and its metabolites. It could be very useful due to the demonstrated interindividual variability of the stereoselective metabolism of MIA.

Polyethylene glycol and polyvinylpirrolidone effect on bacterial rRNA extraction and hybridization from cells exposed to tannins

In order to detect fluctuations in ruminal microbial populations due to forage tannins using 16S ribosomal RNA (rRNA) probes, recovery of intact rRNA is required. The objective of this work was to evaluate the effect of polyethylene glycol (PEG) and polyvinylpirrolidone (PVP) on extraction of bacterial rRNA, in the presence of tannins from tropical legume forages and other sources, that hybridize with oligonucleotide probes. Ruminococcus albus 8 cells were exposed to 8 g/L tannic acid or 1 g/L condensed tannins extracted from Acacia angustissima, banana (Musa sp.) skin, Desmodium ovalifolium, red grape (Vitis vinifera) skin and Inga edulis, or no tannins. Cells were rinsed with Tris buffer pH 7 containing either 8% PEG or 6% PVP prior to cell lysis. Total RNA samples rinsed with either PEG or PVP migrated through denaturing agarose gels. The 16S rRNA bands successfully hybridized with a R. albus species-specific oligonucleotide probe, regardless of tannin source. The effect of rinsing buffers on the density of 16S rRNA bands, as well as on the hybridization signals was compared. There were significant effects (P<0.01) when the controls were compared to either buffer treatments due to tannin type, buffer used and the interaction of tannin type and buffer. The significant interaction indicates the influence of tannin type on the parameters evaluated.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

Bump time-frequency toolbox: a toolbox for time-frequency oscillatory bursts extraction in electrophysiological signals

Background: oscillatory activity, which can be separated in background and oscillatory burst pattern activities, is supposed to be representative of local synchronies of neural assemblies. Oscillatory burst events should consequently play a specific functional role, distinct from background EEG activity – especially for cognitive tasks (e.g. working memory tasks), binding mechanisms and perceptual dynamics (e.g. visual binding), or in clinical contexts (e.g. effects of brain disorders). However extracting oscillatory events in single trials, with a reliable and consistent method, is not a simple task. Results: in this work we propose a user-friendly stand-alone toolbox, which models in a reasonable time a bump time-frequency model from the wavelet representations of a set of signals. The software is provided with a Matlab toolbox which can compute wavelet representations before calling automatically the stand-alone application. Conclusion: The tool is publicly available as a freeware at the address: http:// www.bsp.brain.riken.jp/bumptoolbox/toolbox_home.html

Fast Approximation of Nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems

This paper proposes a very fast method for blindly approximating a nonlinear mapping which transforms a sum of random variables. The estimation is surprisingly good even when the basic assumption is not satisfied.We use the method for providing a good initialization for inverting post-nonlinear mixtures and Wiener systems. Experiments show that the algorithm speed is strongly improved and the asymptotic performance is preserved with a very low extra computational cost.

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

A Robust Multiple Feature Approach To Endpoint Detection In Car Environment Based On Advanced Classifiers

In this paper we propose an endpoint detection system based on the use of several features extracted from each speech frame, followed by a robust classifier (i.e Adaboost and Bagging of decision trees, and a multilayer perceptron) and a finite state automata (FSA). We present results for four different classifiers. The FSA module consisted of a 4-state decision logic that filtered false alarms and false positives. We compare the use of four different classifiers in this task. The look ahead of the method that we propose was of 7 frames, which are the number of frames that maximized the accuracy of the system. The system was tested with real signals recorded inside a car, with signal to noise ratio that ranged from 6 dB to 30dB. Finally we present experimental results demonstrating that the system yields robust endpoint detection.

Coherency and sharpness measures by using ICA algorithms. An investigation for Alzheimer’s disease discrimination

In this paper, we present a comprehensive study of different Independent Component Analysis (ICA) algorithms for the calculation of coherency and sharpness of electroencephalogram (EEG) signals, in order to investigate the possibility of early detection of Alzheimer’s disease (AD). We found that ICA algorithms can help in the artifact rejection and noise reduction, improving the discriminative property of features in high frequency bands (specially in high alpha and beta ranges). In addition to different ICA algorithms, the optimum number of selected components is investigated, in order to help decision processes for future works.