Tornadoes and waterspouts in Catalonia (1950–2009)

This paper presents a preliminary climatology of tornadoes and waterspouts in Catalonia (NE Iberian Peninsula). A database spanning 60 yr (1950–2009) has been developed on the basis of information collected from various sources such as weather reports, insurance companies, newspapers and damage surveys. This database has been subjected to a rigorous validation process, and the climatology describes its main features: timing, spatial pattern, and trends in the tornado and waterspout distribution. Results show the highest concentration of tornadoes from August to October, the highest density in the heavily populated coastal areas and a growing positive trend that is likely more closely linked to an increase in observation and perception rather than a real climatic trend

The INTERPRET Decision-Support System version 3.0 for evaluation of Magnetic Resonance Spectroscopy data from human brain tumours and other abnormal brain masses.

Background Proton Magnetic Resonance (MR) Spectroscopy (MRS) is a widely available technique for those clinical centres equipped with MR scanners. Unlike the rest of MR-based techniques, MRS yields not images but spectra of metabolites in the tissues. In pathological situations, the MRS profile changes and this has been particularly described for brain tumours. However, radiologists are frequently not familiar to the interpretation of MRS data and for this reason, the usefulness of decision-support systems (DSS) in MRS data analysis has been explored. Results This work presents the INTERPRET DSS version 3.0, analysing the improvements made from its first release in 2002. Version 3.0 is aimed to be a program that 1st, can be easily used with any new case from any MR scanner manufacturer and 2nd, improves the initial analysis capabilities of the first version. The main improvements are an embedded database, user accounts, more diagnostic discrimination capabilities and the possibility to analyse data acquired under additional data acquisition conditions. Other improvements include a customisable graphical user interface (GUI). Most diagnostic problems included have been addressed through a pattern-recognition based approach, in which classifiers based on linear discriminant analysis (LDA) were trained and tested. Conclusions The INTERPRET DSS 3.0 allows radiologists, medical physicists, biochemists or, generally speaking, any person with a minimum knowledge of what an MR spectrum is, to enter their own SV raw data, acquired at 1.5 T, and to analyse them. The system is expected to help in the categorisation of MR Spectra from abnormal brain masses

Pattern Recognition Analysis of MR Spectra

The need for multivariate analysis of magnetic resonance spectroscopy (MRS) data was recognized about 20 years ago, when it became evident that spectral patterns were characteristic of some diseases. Despite this, there is no generally accepted methodology for performing pattern recognition (PR) analysis of MRS data sets. Here, the data acquisition and processing requirements for performing successful PR as applied to human MRS studies are introduced, and the main techniques for feature selection, extraction, and classification are described. These include methods of dimensionality reduction such as principal component analysis (PCA), independent component analysis (ICA), non-negative matrix factorization (NMF), and feature selection. Supervised methods such as linear discriminant analysis (LDA), logistic regression (LogR), and nonlinear classification are discussed separately from unsupervised and semisupervised classification techniques, including k –means clustering. Methods for testing and metrics for gauging the performance of PR models (sensitivity and specificity, the ‘Confusion Matrix’, ‘k –fold cross-validation’, ‘Leave One Out’, ‘Bootstrapping’, the ‘Receiver Operating Characteristic curve’, and balanced error and accuracy rates) are briefly described. This article ends with a summary of the main lessons learned from PR applied to MRS to date

Automatic relevance source determination in human brain tumors using Bayesian NMF.

The clinical management of brain tumors is very sensitive; thus, their non-invasive characterization is often preferred. Non-negative Matrix Factorization techniques have been successfully applied in the context of neuro-oncology to extract the underlying source signals that explain different tissue tumor types, for which knowing the number of sources to calculate was always required. In the current study we estimate the number of relevant sources for a set of discrimination problems involving brain tumors and normal brain. For this, we propose to start by calculating a high number of sources using Bayesian NMF and automatically discarding the irrelevant ones during the iterative process of matrices decomposition, hence obtaining a reduced range of interpretable solutions. The real data used in this study come from a widely tested human brain tumor database. Simulated data that resembled the real data was also generated to validate the hypothesis against ground truth. The results obtained suggest that the proposed approach is able to provide a small range of meaningful solutions to the problem of source extraction in human brain tumors

Semi-supervised source extraction methodology for the nosological imaging of glioblastoma response to therapy.

Glioblastomas are one the most aggressive brain tumors. Their usual bad prognosis is due to the heterogeneity of their response to treatment and the lack of early and robust biomarkers to decide whether the tumor is responding to therapy. In this work, we propose the use of a semi-supervised methodology for source extraction to identify the sources representing tumor response to therapy, untreated/unresponsive tumor, and normal brain; and create nosological images of the response to therapy based on those sources. Fourteen mice were used to calculate the sources, and an independent test set of eight mice was used to further evaluate the proposed approach. The preliminary results obtained indicate that was possible to discriminate response and untreated/unresponsive areas of the tumor, and that the color-coded images allowed convenient tracking of response, especially throughout the course of therapy

A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases

Machine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to become mainstream in medical practice. In this brief paper, we define a machine learning-based analysis pipeline for helping in a difficult problem in the field of neuro-oncology, namely the discrimination of brain glioblastomas from single brain metastases. This pipeline involves source extraction using k-Meansinitialized Convex Non-negative Matrix Factorization and a collection of classifiers, including Logistic Regression, Linear Discriminant Analysis, AdaBoost, and Random Forests

Fine mapping of the peach pollen sterility gene (Ps/ps) and detection of markers for marker-assisted selection

In peach, pollen sterility, expressed as absence of pollen in the anthers, segregates as an undesired trait in breeding programs. Pollen fertility screening in progenies is not a common practice mainly because it does not affect fruit set since cross-pollination is frequent. It is also a time-consuming activity that coincides with the busy pollination season. Segregation for this trait could be avoided by using molecular markers to identify appropriate parents or male sterile plants for early culling in progenies expected to segregate, thus increasing breeding efficiency. In peach, pollen sterility is determined by a recessive allele in homozygosis of the major gene, Ps/ps, located on chromosome 6. In this work, using a conventional mapping approach combined with bulked segregant analysis using resequencing data, we fine mapped Ps to a region of almost 160 kb and developed molecular markers for marker-assisted breeding. These markers were validated in plant materials from three peach breeding programs, including progenies, advanced selections, and cultivars, allowing us to determine that the frequency of the ps allele is high (0.23) and also to infer the genotypes of a large collection of cultivars and advanced breeding lines.info:eu-repo/semantics/acceptedVersio

The development and characterisation of a bacterial artificial chromosome library for Fragaria vesca

<p>Abstract</p> <p>Background</p> <p>The cultivated strawberry <it>Fragaria ×ananassa </it>is one of the most economically-important soft-fruit species. Few structural genomic resources have been reported for <it>Fragaria </it>and there exists an urgent need for the development of physical mapping resources for the genus. The first stage in the development of a physical map for <it>Fragaria </it>is the construction and characterisation of a high molecular weight bacterial artificial chromosome (BAC) library.</p> <p>Methods</p> <p>A BAC library, consisting of 18,432 clones was constructed from <it>Fragaria vesca </it>f. <it>semperflorens </it>accession 'Ali Baba'. BAC DNA from individual library clones was pooled to create a PCR-based screening assay for the library, whereby individual clones could be identified with just 34 PCR reactions. These pools were used to screen the BAC library and anchor individual clones to the diploid <it>Fragaria </it>reference map (FV×FN).</p> <p>Findings</p> <p>Clones from the BAC library developed contained an average insert size of 85 kb, representing over seven genome equivalents. The pools and superpools developed were used to identify a set of BAC clones containing 70 molecular markers previously mapped to the diploid <it>Fragaria </it>FV×FN reference map. The number of positive colonies identified for each marker suggests the library represents between 4× and 10× coverage of the diploid <it>Fragaria </it>genome, which is in accordance with the estimate of library coverage based on average insert size.</p> <p>Conclusion</p> <p>This BAC library will be used for the construction of a physical map for <it>F. vesca </it>and the superpools will permit physical anchoring of molecular markers using PCR.</p

Fine mapping of the peach pollen sterility gene (Ps/ps) and detection of markers for marker-assisted selection

Altres ajuts: CERCA Programme/Generalitat de CatalunyaIn peach, pollen sterility, expressed as absence of pollen in the anthers, segregates as an undesired trait in breeding programs. Pollen fertility screening in progenies is not a common practice mainly because it does not affect fruit set since cross-pollination is frequent. It is also a time-consuming activity that coincides with the busy pollination season. Segregation for this trait could be avoided by using molecular markers to identify appropriate parents or male sterile plants for early culling in progenies expected to segregate, thus increasing breeding efficiency. In peach, pollen sterility is determined by a recessive allele in homozygosis of the major gene, Ps/ps, located on chromosome 6. In this work, using a conventional mapping approach combined with bulked segregant analysis using resequencing data, we fine mapped Ps to a region of almost 160 kb and developed molecular markers for marker-assisted breeding. These markers were validated in plant materials from three peach breeding programs, including progenies, advanced selections and cultivars, allowing us to determine that the frequency of the ps allele is high (0.23) and also to infer the genotypes of a large collection of cultivars and advanced breeding lines

Angiotensin II type 1/adenosine A2A receptor oligomers: a novel target for tardive dyskinesia

Tardive dyskinesia (TD) is a serious motor side effect that may appear after long-term treatment with neuroleptics and mostly mediated by dopamine D2 receptors (D2Rs). Striatal D2R functioning may be finely regulated by either adenosine A2A receptor (A2AR) or angiotensin receptor type 1 (AT1R) through putative receptor heteromers. Here, we examined whether A2AR and AT1R may oligomerize in the striatum to synergistically modulate dopaminergic transmission. First, by using bioluminescence resonance energy transfer, we demonstrated a physical AT1R-A2AR interaction in cultured cells. Interestingly, by protein-protein docking and molecular dynamics simulations, we described that a stable heterotetrameric interaction may exist between AT1R and A2AR bound to antagonists (i.e. losartan and istradefylline, respectively). Accordingly, we subsequently ascertained the existence of AT1R/A2AR heteromers in the striatum by proximity ligation in situ assay. Finally, we took advantage of a TD animal model, namely the reserpine-induced vacuous chewing movement (VCM), to evaluate a novel multimodal pharmacological TD treatment approach based on targeting the AT1R/A2AR complex. Thus, reserpinized mice were co-treated with sub-effective losartan and istradefylline doses, which prompted a synergistic reduction in VCM. Overall, our results demonstrated the existence of striatal AT1R/A2AR oligomers with potential usefulness for the therapeutic management of TD