Spanish unemployment: Normative versus analytical regionalisation procedures

In applied regional analysis, statistical information is usually published at different territorial levels with the aim of providing information of interest for different potential users. When using this information, there are two different choices: first, to use normative regions (towns, provinces, etc.), or, second, to design analytical regions directly related with the analysed phenomena. In this paper, provincial time series of unemployment rates in Spain are used in order to compare the results obtained by applying two analytical regionalisation models (a two stages procedure based on cluster analysis and a procedure based on mathematical programming) with the normative regions available at two different scales: NUTS II and NUTS I. The results have shown that more homogeneous regions were designed when applying both analytical regionalisation tools. Two other obtained interesting results are related with the fact that analytical regions were also more stable along time and with the effects of scale in the regionalisation process.unemployment, regionalisation, analytical region, normative region

Leptin induces cell migration and invasion in a FAK-Src- dependent manner in breast cancer cells

Breast cancer is the most common invasive neoplasia, and the second leading cause of the cancer deaths in women worldwide. Mammary tumorigenesis is severely linked to obesity, one potential connection is leptin. Leptin is a hormone secreted by adipocytes, which contributes to the progression of breast cancer. Cell migration, metalloproteases secretion, and invasion are cellular processes associated with various stages of metastasis. These processes are regulated by the kinases FAK and Src. In this study, we utilized the breast cancer cell lines MCF7 and MDA-MB-231 to determine the effect of leptin on FAK and Src kinases activation, cell migration, metalloprotease secretion, and invasion. We found that leptin activates FAK and Src, and induces the localization of FAK to the focal adhesions. Interestingly, leptin promotes the activation of FAK through a Src and STAT3-dependent canonical pathway. Specific inhibitors of FAK, Src and STAT3 showed that the effect exerted by leptin in cell migration in breast cancer cells is dependent on these proteins. Moreover, we established that leptin promotes the secretion of the extracellular matrix remodelers, MMP-2 and MMP-9 and invasion in a FAK and Src dependent manner. Our findings strongly suggest that leptin promotes the development of a more aggressive invasive phenotype in mammary cancer cells

Intercambio de fosforo entre agua y sedimentos generados en una laguna profunda de estabilizacion

Available from Centro de Informacion y Documentacion Cientifica CINDOC. Joaquin Costa, 22. 28002 Madrid. SPAIN / CINDOC - Centro de Informaciòn y Documentaciòn CientìficaSIGLEESSpai

KnowSeq R-Bioc package: The automatic smart gene expression tool for retrieving relevant biological knowledge

This work was funded by the Spanish Ministry of Sciences, Innovation and Universities under Project RTI2018-101674-B-I00 titled "Computer Architectures and Machine Learningbased solutions for complex challenges in Bioinformatics, Biotechnology and Biomedicine", in collaboration with the Government of Andalusia under Postdoctoral Grant P12TIC2082. The funders had no role in study design, datacollection and analysis, decision to publish, or preparation of this manuscript. The results published here are in whole or part based upon data generated by the TCGA Research Network: https:// www.cancer. gov/tcga.KnowSeq R/Bioc package is designed as a powerful, scalable and modular software focused on automatizing and assembling renowned bioinformatic tools with new features and functionalities. It comprises a unified environment to perform complex gene expression analyses, covering all the needed processing steps to identify a gene signature for a specific disease to gather understandable knowledge. This process may be initiated from raw files either available at well-known platforms or provided by the users themselves, and in either case coming from different information sources and different Transcriptomic technologies. The pipeline makes use of a set of advanced algorithms, including the adaptation of a novel procedure for the selection of the most representative genes in a given multiclass problem. Similarly, an intelligent system able to classify new patients, providing the user the opportunity to choose one among a number of well-known and widespread classification and feature selection methods in Bioinformatics, is embedded. Furthermore, KnowSeq is engineered to automatically develop a complete and detailed HTML report of the whole process which is also modular and scalable. Biclass breast cancer and multiclass lung cancer study cases were addressed to rigorously assess the usability and efficiency of KnowSeq. The models built by using the Differential Expressed Genes achieved from both experiments reach high classification rates. Furthermore, biological knowledge was extracted in terms of Gene Ontologies, Pathways and related diseases with the aim of helping the expert in the decision-making process. KnowSeq is available at Bioconductor (https://bioconductor.org/packages/KnowSeq), GitHub (https://github.com/CasedUgr/KnowSeq) and Docker (https://hub.docker.com/r/casedugr/knowseq).Spanish Ministry of Sciences, Innovation and Universities RTI2018-101674-B-I00Government of Andalusia P12TIC208

Geometrical and Monte Carlo projectors in 3D PET reconstruction

Purpose: In the present work, the authors compare geometrical and Monte Carlo projectors in detail. The geometrical projectors considered were the conventional geometrical Siddon ray-tracer (S-RT) and the orthogonal distance-based ray-tracer (OD-RT), based on computing the orthogonal distance from the center of image voxel to the line-of-response. A comparison of these geometrical projectors was performed using different point spread function (PSF) models. The Monte Carlo-based method under consideration involves an extensive model of the system response matrix based on Monte Carlo simulations and is computed off-line and stored on disk. Methods: Comparisons were performed using simulated and experimental data of the commercial small animal PET scanner rPET. Results: The results demonstrate that the orthogonal distance-based ray-tracer and Siddon ray-tracer using PSF image-space convolutions yield better images in terms of contrast and spatial resolution than those obtained after using the conventional method and the multiray-based S-RT. Furthermore, the Monte Carlo-based method yields slight improvements in terms of contrast and spatial resolution with respect to these geometrical projectors. Conclusions: The orthogonal distance-based ray-tracer and Siddon ray-tracer using PSF image-space convolutions represent satisfactory alternatives to factorizing the system matrix or to the conventional on-the-fly ray-tracing methods for list-mode reconstruction, where an extensive modeling based on Monte Carlo simulations is unfeasible.This work was supported in part by Fondo de Investigaciones Sanitarias del Instituto de Salud Carlos III (Project Nos. PI041017, PS09/01206, and CB06/01/1039), by the Ministerio de Ciencia e Innovacion (Grant No. TEC2007-61047), by the Generalitat Valenciana (Grant No. GV06/246), by the Generalitat de Catalunya (Grant No. SGR1049), Ministerio de Ciencia e Innovacion (Project No. SAF2009-08076), and by Ministerio de Ciencia e Innovacion (CDTI-CENIT) AMIT project and Comunidad de Madrid (project ARTEMIS P2009/DPI-1802). P. Aguiar was awarded a "Sara Borrell" fellowship by Fondo de Investigaciones Sanitarias del Instituto de Salud Carlos III.Peer reviewe

Geometrical and Monte Carlo projectors in 3D PET reconstruction

Purpose: In the present work, the authors compare geometrical and Monte Carlo projectors in detail. The geometrical projectors considered were the conventional geometrical Siddon ray-tracer (S-RT) and the orthogonal distance-based ray-tracer (OD-RT), based on computing the orthogonal distance from the center of image voxel to the line-of-response. A comparison of these geometrical projectors was performed using different point spread function (PSF) models. The Monte Carlo-based method under consideration involves an extensive model of the system response matrix based on Monte Carlo simulations and is computed off-line and stored on disk. Methods: Comparisons were performed using simulated and experimental data of the commercial small animal PET scanner rPET. Results: The results demonstrate that the orthogonal distance-based ray-tracer and Siddon ray-tracer using PSF image-space convolutions yield better images in terms of contrast and spatial resolution than those obtained after using the conventional method and the multiray-based S-RT. Furthermore, the Monte Carlo-based method yields slight improvements in terms of contrast and spatial resolution with respect to these geometrical projectors. Conclusions: The orthogonal distance-based ray-tracer and Siddon ray-tracer using PSF image-space convolutions represent satisfactory alternatives to factorizing the system matrix or to the conventional on-the-fly ray-tracing methods for list-mode reconstruction, where an extensive modeling based on Monte Carlo simulations is unfeasible.This work was supported in part by Fondo de Investigaciones Sanitarias del Instituto de Salud Carlos III (Project Nos. PI041017, PS09/01206, and CB06/01/1039), by the Ministerio de Ciencia e Innovacion (Grant No. TEC2007-61047), by the Generalitat Valenciana (Grant No. GV06/246), by the Generalitat de Catalunya (Grant No. SGR1049), Ministerio de Ciencia e Innovacion (Project No. SAF2009-08076), and by Ministerio de Ciencia e Innovacion (CDTI-CENIT) AMIT project and Comunidad de Madrid (project ARTEMIS P2009/DPI-1802). P. Aguiar was awarded a "Sara Borrell" fellowship by Fondo de Investigaciones Sanitarias del Instituto de Salud Carlos III.Peer reviewe

Towards Improving Skin Cancer Diagnosis by Integrating Microarray and RNA-Seq Datasets.

Many clinical studies have revealed the high biological similarities existing among different skin pathological states. These similarities create difficulties in the efficient diagnosis of skin cancer, and encourage to study and design new intelligent clinical decision support systems. In this sense, gene expression analysis can help find differentially expressed genes (DEGs) simultaneously discerning multiple skin pathological states in a single test. The integration of multiple heterogeneous transcriptomic datasets requires different pipeline stages to be properly designed: from suitable batch merging and efficient biomarker selection to automated classification assessment. This article presents a novel approach addressing all these technical issues, with the intention of providing new sights about skin cancer diagnosis. Although new future efforts will have to be made in the search for better biomarkers recognizing specific skin pathological states, our study found a panel of 8 highly relevant multiclass DEGs for discerning up to 10 skin pathological states: 2 healthy skin conditions a priori, 2 cataloged precancerous skin diseases and 6 cancerous skin states. Their power of diagnosis over new samples was widely tested by previously well-trained classification models. Robust performance metrics such as overall and mean multiclass F1-score outperformed recognition rates of 94% and 80%, respectively. Clinicians should give special attention to highlighted multiclass DEGs that have high gene expression changes present among them, and understand their biological relationship to different skin pathological states