99 research outputs found

    Algunas soluciones exactas para una ecuación de Klein Gordon

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    In solving practical problems in science and engineering arises as a direct consequence differential equations that explains the dynamics of the phenomena. Finding exact solutions to this equations provides importan information about the behavior of physical systems. The Lie symmetry method allows tofind invariant solutions under certain groups of transformations for differential equations.This method not very well known and used is of great importance in the scientific community. By this approach it was possible to find several exactinvariant solutions for the Klein Gordon Equation uxx − utt = k(u). A particularcase, The Kolmogorov equation uxx − utt = k1u + k2un was considered.These equations appear in the study of relativistic and quantum physics. The general solutions found, could be used for future explorations on the study for other specific K(u) functions.Al resolver problemas prácticos en ciencia e ingeniería surge como consecuencia directa las ecuaciones diferenciales que explican la dinámica de los fenómenos. Encontrar soluciones exactas a estas ecuaciones proporciona información importante sobre el comportamiento de los sistemas físicos. El método de simetría de Lie permite encontrar soluciones invariantes bajo ciertos grupos de transformaciones para ecuaciones diferenciales. Este método, poco conocido y utilizado, es de gran importancia en la comunidad científica. Mediante este enfoque, fue posible encontrar varias soluciones exactas invariables para la ecuación de Klein Gordon uxx - utt = k (u). Un caso particular, se consideró la ecuación de Kolmogorov uxx - utt = k1u + k2un. Estas ecuaciones aparecen en el estudio de la física relativista y cuántica. Las soluciones generales encontradas podrían utilizarse para futuras exploraciones en el estudio para otras funciones específicas de K (u)

    Propuestas para una reforma laboral democrática

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    Este trabajo, presenta trece propuestas complementarias sobre los temas centrales de la reforma laboral: la contracción individual y colectiva, con si variable de "subcontratación"; la transparencia con la proposición concreta de creación del registro nacional de contratos y sindicatos; los salarios y la jornada laboral, temas clave en el proyecto de país de la izquierda democrátic

    Evaluación actual y propuestas para el desarrollo urbano en México

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    La revista Problemas del Desarrollo recoge en su colección de ediciones especiales las disertaciones y ponencias que se presentan en los seminarios, simposia y conferencias a que convoca y realiza anualmente el Instituto de Investigaciones Económicas de la Universidad Nacional Autónoma de México. El propósito es contribuir a la mayor difusión de textos de contenido analítico, descriptivo y propositivo que se presentan en tales actos y que tienen gran valor para el mejor conocimiento y comprensión de la estructura y problemática económica y social de nuestro país. Pero también la revista quiere convertirse en foro abierto para la expresión de comentarios y ampliaciones que deseen hacer los lectores de estas ediciones especiales sobre temas particulares de los comprendidos en cada uno de sus libros. Estos aportes, por lo tanto, serán muy bien recibidos. Para ello, solo se requiere que los lectores los remitan, por escrito, al Instituto de Investigaciones Económicas, Torre II de Humanidades, Ciudad Universitaria

    Formas de Hispanidad

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    Este texto presenta estudios sobre las múltiples formas de hispanidad, desarrollados en los últimos años por destacados investigadores del mundo hispánico que, poco a poco, han estado construyendo un nuevo espacio de investigación para una creciente y activa comunidad científica. En este libro el lector encontrará estudios con enfoques desde la ciencia política, la teoría política, la historia, la filosofía, la sociología, la economía, los estudios literarios y culturales, entre otras perspectivas académicas. Los aportes de cada aproximación teórica y disciplinar están orientados al logro de una meta común: la de reconstruir y reinterpretar la tradición histórica hispánica, desmantelando prejuicios ideológicamente provocados, con el fin de comprender los fenómenos políticos que la caracterizan. Por las mismas razones este libro se sitúa en el debate sobre las formas de escritura de la historia, que no es sólo un debate de teoría de la historia sino también de filosofía de lo histórico

    Air quality and urban sustainable development: the application of machine learning tools

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    [EN] Air quality has an efect on a population¿s quality of life. As a dimension of sustainable urban development, governments have been concerned about this indicator. This is refected in the references consulted that have demonstrated progress in forecasting pollution events to issue early warnings using conventional tools which, as a result of the new era of big data, are becoming obsolete. There are a limited number of studies with applications of machine learning tools to characterize and forecast behavior of the environmental, social and economic dimensions of sustainable development as they pertain to air quality. This article presents an analysis of studies that developed machine learning models to forecast sustainable development and air quality. Additionally, this paper sets out to present research that studied the relationship between air quality and urban sustainable development to identify the reliability and possible applications in diferent urban contexts of these machine learning tools. To that end, a systematic review was carried out, revealing that machine learning tools have been primarily used for clustering and classifying variables and indicators according to the problem analyzed, while tools such as artifcial neural networks and support vector machines are the most widely used to predict diferent types of events. The nonlinear nature and synergy of the dimensions of sustainable development are of great interest for the application of machine learning tools.Molina-Gómez, NI.; Díaz-Arévalo, JL.; López Jiménez, PA. (2021). Air quality and urban sustainable development: the application of machine learning tools. International Journal of Environmental Science and Technology. 18(4):1-18. https://doi.org/10.1007/s13762-020-02896-6S118184Al-Dabbous A, Kumar P, Khan A (2017) Prediction of airborne nanoparticles at roadside location using a feed–forward artificial neural network. Atmos Pollut Res 8:446–454. https://doi.org/10.1016/j.apr.2016.11.004Antanasijević D, Pocajt V, Povrenović D, Ristić M, Perić-Grujić A (2013) PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Sci Total Environ 443:511–519. https://doi.org/10.1016/j.scitotenv.2012.10.110Brink H, Richards JW, Fetherolf M (2016) Real-world machine learning. Richards JW, Fetherolf M (eds) Manning Publications Co. Berkeley, CA. https://www.manning.com/books/real-world-machine-learning. Accessed 26 Apr 2020Cervone G, Franzese P, Ezber Y, Boybeyi Z (2008) Risk assessment of atmospheric emissions using machine learning. Nat Hazard Earth Syst 8:991–1000. https://doi.org/10.5194/nhess-8-991-2008Chen S, Kan G, Li J, Liang K, Hong Y (2018) Investigating China’s urban air quality using big data, information theory, and machine learning. Pol J Environ Stud 27:565–578. https://doi.org/10.15244/pjoes/75159Corani (2005) Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning. Ecol Model 185:513–529. https://doi.org/10.1016/j.ecolmodel.2005.01.008Cruz C, Gómez A, Ramírez L, Villalva A, Monge O, Varela J, Quiroz J, Duarte H (2017) Calidad del aire respecto de metales (Pb, Cd, Ni, Cu, Cr) y relación con salud respiratoria: caso Sonora, México. Rev Int Contam Ambient 33:23–34. https://doi.org/10.20937/RICA.2017.33.esp02.02de Hoogh K, Héritier H, Stafoggia M, Künzli N, Kloog I (2018) Modelling daily PM2.5 concentrations at high spatio-temporal resolution across Switzerland. Environ Pollut 233:1147–1154. https://doi.org/10.1016/j.envpol.2017.10.025Franceschi F, Cobo M, Figueredo M (2018) Discovering relationships and forecasting PM10 and PM2.5 concentrations in Bogotá, Colombia, using Artificial Neural Networks, Principal Component Analysis, and k-means clustering. Atmos Pollut Res 9:912–922. https://doi.org/10.1016/j.apr.2018.02.006García N, Combarro E, del Coz J, Montañes E (2013) A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): a case study. Appl Math Comput 219:8923–8937. https://doi.org/10.1016/j.amc.2013.03.018Gibert K, Sànchez-Màrre M, Sevilla B (2012) Tools for environmental data mining and intelligent decision support. In iEMSs. Leipzig, Germany. http://www.iemss.org/society/index.php/iemss-2012-proceedings. Accessed 26 Nov 2018Gibert K, Sànchez-Marrè M, Izquierdo J (2016) A survey on pre-processing techniques: relevant issues in the context of environmental data mining. Ai Commun 29:627–663. https://doi.org/10.3233/AIC-160710Gounaridis D, Chorianopoulos I, Koukoulas S (2018) Exploring prospective urban growth trends under different economic outlooks and land-use planning scenarios: the case of Athens. Appl Geogr 90:134–144. https://doi.org/10.1016/j.apgeog.2017.12.001Holloway J, Mengersen K (2018) Statistical machine learning methods and remote sensing for sustainable development goals: a review. Remote Sens 10:1–21. https://doi.org/10.3390/rs10091365Ifaei P, Karbassi A, Lee S, Yoo Ch (2017) A renewable energies-assisted sustainable development plan for Iran using techno-econo-socio-environmental multivariate analysis and big data. Energy Convers Manag 153:257–277. https://doi.org/10.1016/j.enconman.2017.10.014Kadiyala A, Kumar A (2017a) Applications of R to evaluate environmental data science problems. Environ Prog Sustain 36:1358–1364. https://doi.org/10.1002/ep.12676Kadiyala A, Kumar A (2017b) Vector time series-based radial basis function neural network modeling of air quality inside a public transportation bus using available software. Environ Prog Sustain 36:4–10. https://doi.org/10.1002/ep.12523Karimian H, Li Q, Wu Ch, Qi Y, Mo Y, Chen G, Zhang X, Sachdeva S (2019) Evaluation of different machine learning approaches to forecasting PM2.5 mass concentrations. Aerosol Air Qual Res 19:1400–1410. https://doi.org/10.4209/aaqr.2018.12.0450Krzyzanowski M, Apte J, Bonjour S, Brauer M, Cohen A, Prüss-Ustun A (2014) Air pollution in the mega-cities. Curr Environ Health Rep 1:185–191. https://doi.org/10.1007/s40572-014-0019-7Lässig K, Morik (2016) Computat sustainability. Springer, Berlin. https://doi.org/10.1007/978-3-319-31858-5Li Y, Wu Y-X, Zeng Z-X, Guo L (2006) Research on forecast model for sustainable development of economy-environment system based on PCA and SVM. In: Proceedings of the 2006 international conference on machine learning and cybernetics, vol 2006. IEEE, Dalian, China, pp 3590–3593. https://doi.org/10.1109/ICMLC.2006.258576Liu B-Ch, Binaykia A, Chang P-Ch, Tiwari M, Tsao Ch-Ch (2017) Urban air quality forecasting based on multi- dimensional collaborative support vector regression (SVR): a case study of Beijing-Tianjin-Shijiazhuang. PLoS ONE 12:1–17. https://doi.org/10.1371/journal.pone.0179763Lubell M, Feiock R, Handy S (2009) City adoption of environmentally sustainable policies in California’s Central Valley. J Am Plan Assoc 75:293–308. https://doi.org/10.1080/01944360902952295Ma D, Zhang Z (2016) Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere. J Hazard Mater 311:237–245. https://doi.org/10.1016/j.jhazmat.2016.03.022Madu C, Kuei N, Lee P (2017) Urban sustainability management: a deep learning perspective. Sustain Cities Soc 30:1–17. https://doi.org/10.1016/j.scs.2016.12.012Mellos K (1988) Theory of eco-development. In: Perspectives on ecology. Palgrave Macmillan, London. https://doi.org/10.1007/978-1-349-19598-5_4Ni XY, Huang H, Du WP (2017) Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data. Atmos Environ 150:146–161. https://doi.org/10.1016/j.atmosenv.2016.11.054Oprea M, Dragomir E, Popescu M, Mihalache S (2016) Particulate matter air pollutants forecasting using inductive learning approach. Rev Chim 67:2075–2081Paas B, Stienen J, Vorländer M, Schneider Ch (2017) Modelling of urban near-road atmospheric PM concentrations using an artificial neural network approach with acoustic data input. Environments 4:1–25. https://doi.org/10.3390/environments4020026Pandey G, Zhang B, Jian L (2013) Predicting submicron air pollution indicators: a machine learning approach. Environ Sci Proc Impacts 15:996–1005. https://doi.org/10.1039/c3em30890aPeng H, Lima A, Teakles A, Jin J, Cannon A, Hsieh W (2017) Evaluating hourly air quality forecasting in Canada with nonlinear updatable machine learning methods. Air Qual Atmos Health 10:195–211. https://doi.org/10.1007/s11869-016-0414-3Pérez-Ortíz M, de La Paz-Marín M, Gutiérrez PA, Hervás-Martínez C (2014) Classification of EU countries’ progress towards sustainable development based on ordinal regression techniques. Knowl Based Syst 66:178–189. https://doi.org/10.1016/j.knosys.2014.04.041Phillis Y, Kouikoglou V, Verdugo C (2017) Urban sustainability assessment and ranking of cities. Comput Environ Urban 64:254–265. https://doi.org/10.1016/j.compenvurbsys.2017.03.002Saeed S, Hussain L, Awan I, Idris A (2017) Comparative analysis of different statistical methods for prediction of PM2.5 and PM10 concentrations in advance for several hours. Int J Comput Sci Netw Secur 17:45–52Sayegh A, Munir S, Habeebullah T (2014) Comparing the performance of statistical models for predicting PM10 concentrations. Aerosol Air Qual Res 14:653–665. https://doi.org/10.4209/aaqr.2013.07.0259Shaban K, Kadri A, Rezk E (2016) Urban air pollution monitoring system with forecasting models. IEEE Sens J 16:2598–2606. https://doi.org/10.1109/JSEN.2016.2514378Sierra B (2006) Aprendizaje automático conceptos básicos y avanzados Aspectos prácticos utilizando el software Weka. Madrid Pearson Prentice Hall, MadridSingh K, Gupta S, Rai P (2013) Identifying pollution sources and predicting urban air quality using ensemble learning methods. Atmos Environ 80:426–437. https://doi.org/10.1016/j.atmosenv.2013.08.023Song L, Pang S, Longley I, Olivares G, Sarrafzadeh A (2014) Spatio-temporal PM2.5 prediction by spatial data aided incremental support vector regression. In: International joint conference on neural networks. IEEE, Beijing, pp 623–630. https://doi.org/10.1109/IJCNN.2014.6889521Souza R, Coelho G, da Silva A, Pozza S (2015) Using ensembles of artificial neural networks to improve PM10 forecasts. Chem Eng Trans 43:2161–2166. https://doi.org/10.3303/CET1543361Suárez A, García PJ, Riesgo P, del Coz JJ, Iglesias-Rodríguez FJ (2011) Application of an SVM-based regression model to the air quality study at local scale in the Avilés urban area (Spain). Math Comput Model 54:453–1466. https://doi.org/10.1016/j.mcm.2011.04.017Tamas W, Notton G, Paoli C, Nivet M, Voyant C (2016) Hybridization of air quality forecasting models using machine learning and clustering: an original approach to detect pollutant peaks. Aerosol Air Qual Res 16:405–416. https://doi.org/10.4209/aaqr.2015.03.0193Toumi O, Le Gallo J, Ben Rejeb J (2017) Assessment of Latin American sustainability. Renew Sustain Energy Rev 78:878–885. https://doi.org/10.1016/j.rser.2017.05.013Tzima F, Mitkas P, Voukantsis D, Karatzas K (2011) Sparse episode identification in environmental datasets: the case of air quality assessment. Expert Syst Appl 38:5019–5027. https://doi.org/10.1016/j.eswa.2010.09.148United Nations, Department of Economic and Social Affairs (2019) World urbanization prospects The 2018 Revision. New York. https://doi.org/10.18356/b9e995fe-enWang B (2019) Applying machine-learning methods based on causality analysis to determine air quality in China. Pol J Environ Stud 28:3877–3885. https://doi.org/10.15244/pjoes/99639Wang X, Xiao Z (2017) Regional eco-efficiency prediction with support vector spatial dynamic MIDAS. J Clean Prod 161:165–177. https://doi.org/10.1016/j.jclepro.2017.05.077Wang W, Men C, Lu W (2008) Online prediction model based on support vector machine. Neurocomputing 71:550–558. https://doi.org/10.1016/j.neucom.2007.07.020WCED (1987) Report of the world commission on environment and development: our common future: report of the world commission on environment and development. WCED, Oslo. https://doi.org/10.1080/07488008808408783Weizhen H, Zhengqiang L, Yuhuan Z, Hua X, Ying Z, Kaitao L, Donghui L, Peng W, Yan M (2014) Using support vector regression to predict PM10 and PM2.5. In: IOP conference series: earth and environmental science, vol 17. IOP. https://doi.org/10.1088/1755-1315/17/1/012268WHO (2016) OMS | La OMS publica estimaciones nacionales sobre la exposición a la contaminación del aire y sus repercusiones para la salud. WHO. http://www.who.int/mediacentre/news/releases/2016/air-pollution-estimates/es/. Accesed 26 Nov 2018Yeganeh N, Shafie MP, Rashidi Y, Kamalan H (2012) Prediction of CO concentrations based on a hybrid partial least square and support vector machine model. Atmos Environ 55:357–365. https://doi.org/10.1016/j.atmosenv.2012.02.092Zalakeviciute R, Bastidas M, Buenaño A, Rybarczyk Y (2020) A traffic-based method to predict and map urban air quality. Appl Sci. https://doi.org/10.3390/app10062035Zeng L, Guo J, Wang B, Lv J, Wang Q (2019) Analyzing sustainability of Chinese coal cities using a decision tree modeling approach. Resour Policy 64:101501. https://doi.org/10.1016/j.resourpol.2019.101501Zhan Y, Luo Y, Deng X, Grieneisen M, Zhang M, Di B (2018) Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment. Environ Pollut 233:464–473. https://doi.org/10.1016/j.envpol.2017.10.029Zhang Y, Huan Q (2006) Research on the evaluation of sustainable development in Cangzhou city based on neural-network-AHP. In: Proceedings of the fifth international conference on machine learning and cybernetics, vol 2006. pp 3144–3147. https://doi.org/10.1109/ICMLC.2006.258407Zhang Y, Shang W, Wu Y (2009) Research on sustainable development based on neural network. In: 2009 Chinese control and decision conference. IEEE, pp 3273–3276. https://doi.org/10.1109/CCDC.2009.5192476Zhou Y, Chang F-J, Chang L-Ch, Kao I-F, Wang YS (2019) Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. J Clean Prod 209:134–145. https://doi.org/10.1016/j.jclepro.2018.10.24

    The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

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    The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its first two years of operation (July 2014-2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey (eBOSS); the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020, and will be followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14 happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov 2017 (this is the "post-print" and "post-proofs" version; minor corrections only from v1, and most of errors found in proofs corrected

    Unexpected Role for Helicobacter pylori DNA Polymerase I As a Source of Genetic Variability

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    Helicobacter pylori, a human pathogen infecting about half of the world population, is characterised by its large intraspecies variability. Its genome plasticity has been invoked as the basis for its high adaptation capacity. Consistent with its small genome, H. pylori possesses only two bona fide DNA polymerases, Pol I and the replicative Pol III, lacking homologues of translesion synthesis DNA polymerases. Bacterial DNA polymerases I are implicated both in normal DNA replication and in DNA repair. We report that H. pylori DNA Pol I 5′- 3′ exonuclease domain is essential for viability, probably through its involvement in DNA replication. We show here that, despite the fact that it also plays crucial roles in DNA repair, Pol I contributes to genomic instability. Indeed, strains defective in the DNA polymerase activity of the protein, although sensitive to genotoxic agents, display reduced mutation frequencies. Conversely, overexpression of Pol I leads to a hypermutator phenotype. Although the purified protein displays an intrinsic fidelity during replication of undamaged DNA, it lacks a proofreading activity, allowing it to efficiently elongate mismatched primers and perform mutagenic translesion synthesis. In agreement with this finding, we show that the spontaneous mutator phenotype of a strain deficient in the removal of oxidised pyrimidines from the genome is in part dependent on the presence of an active DNA Pol I. This study provides evidence for an unexpected role of DNA polymerase I in generating genomic plasticity

    Shells and humans: molluscs and other coastal resources from the earliest human occupations at the Mesolithic shell midden of El Mazo (Asturias, Northern Spain)

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    Human populations exploited coastal areas with intensity during the Mesolithic in Atlantic Europe, resulting in the accumulation of large shell middens. Northern Spain is one of the most prolific regions, and especially the so-called Asturian area. Large accumulations of shellfish led some scholars to propose the existence of intensification in the exploitation of coastal resources in the region during the Mesolithic. In this paper, shell remains (molluscs, crustaceans and echinoderms) from stratigraphic units 114 and 115 (dated to the early Mesolithic c. 9 kys cal BP) at El Mazo cave (Asturias, northern Spain) were studied in order to establish resource exploitation patterns and environmental conditions. Species representation showed that limpets, top shells and sea urchins were preferentially exploited. One-millimetre mesh screens were crucial in establishing an accurate minimum number of individuals for sea urchins and to determine their importance in exploitation patterns. Environmental conditions deduced from shell assemblages indicated that temperate conditions prevailed at the time of the occupation and the morphology of the coastline was similar to today (rocky exposed shores). Information recovered relating to species representation, collection areas and shell biometry reflected some evidence of intensification (reduced shell size, collection in lower areas of exposed shores, no size selection in some units and species) in the exploitation of coastal resources through time. However, the results suggested the existence of changes in collection strategies and resource management, and periods of intense shell collection may have alternated with times of shell stock recovery throughout the Mesolithic.This research was performed as part of the project “The human response to the global climatic change in a littoral zone: the case of the transition to the Holocene in the Cantabrian coast (10,000–5000 cal BC) (HAR2010-22115-C02-01)” funded by the Spanish Ministry of Economy and Competitiveness. AGE was funded by the University of Cantabria through a predoctoral grant and IGZ was funded by the Spanish Ministry of Economy and Competitiveness through a Juan de la Cierva grant. We also would like to thank the University of Cantabria and the IIIPC for providing support, David Cuenca-Solana, Alejandro García Moreno and Lucia Agudo Pérez for their help. We also thank Jennifer Jones for correcting the English. Comments from two anonymous reviewers helped to improve the paper

    Peripheral T-lymphocytes express WNT7A and its restoration in leukemia-derived lymphoblasts inhibits cell proliferation

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    <p>Abstract</p> <p>Background</p> <p>WNT7a, a member of the Wnt ligand family implicated in several developmental processes, has also been reported to be dysregulated in some types of tumors; however, its function and implication in oncogenesis is poorly understood. Moreover, the expression of this gene and the role that it plays in the biology of blood cells remains unclear. In addition to determining the expression of the <it>WNT7A </it>gene in blood cells, in leukemia-derived cell lines, and in samples of patients with leukemia, the aim of this study was to seek the effect of this gene in proliferation.</p> <p>Methods</p> <p>We analyzed peripheral blood mononuclear cells, sorted CD3 and CD19 cells, four leukemia-derived cell lines, and blood samples from 14 patients with Acute lymphoblastic leukemia (ALL), and 19 clinically healthy subjects. Reverse transcription followed by quantitative Real-time Polymerase chain reaction (qRT-PCR) analysis were performed to determine relative <it>WNT7A </it>expression. Restoration of WNT7a was done employing a lentiviral system and by using a recombinant human protein. Cell proliferation was measured by addition of WST-1 to cell cultures.</p> <p>Results</p> <p>WNT7a is mainly produced by CD3 T-lymphocytes, its expression decreases upon activation, and it is severely reduced in leukemia-derived cell lines, as well as in the blood samples of patients with ALL when compared with healthy controls (<it>p </it>≤0.001). By restoring <it>WNT7A </it>expression in leukemia-derived cells, we were able to demonstrate that WNT7a inhibits cell growth. A similar effect was observed when a recombinant human WNT7a protein was used. Interestingly, restoration of <it>WNT7A </it>expression in Jurkat cells did not activate the canonical Wnt/β-catenin pathway.</p> <p>Conclusions</p> <p>To our knowledge, this is the first report evidencing quantitatively decreased <it>WNT7A </it>levels in leukemia-derived cells and that <it>WNT7A </it>restoration in T-lymphocytes inhibits cell proliferation. In addition, our results also support the possible function of <it>WNT7A </it>as a tumor suppressor gene as well as a therapeutic tool.</p

    Additional records of metazoan parasites from Caribbean marine mammals, including genetically identified anisakid nematodes

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    Studies of marine mammal parasites in the Caribbean are scarce. An assessment for marine mammal endo- and ectoparasites from Puerto Rico and the Virgin Islands, but extending to other areas of the Caribbean, was conducted between 1989 and 1994. The present study complements the latter and enhances identification of anisakid nematodes using molecular markers. Parasites were collected from 59 carcasses of stranded cetaceans and manatees from 1994 to 2006, including Globicephala macrorhynchus, Kogia breviceps, Kogia sima, Lagenodelphis hosei, Mesoplodon densirostris, Peponocephala electra, Stenella longirostris, Steno bredanensis, Trichechus manatus. Tursiops truncatus, and Ziphius cavirostris. Sixteen species of endoparasitic helminthes were morphologically identified, including two species of acanthocephalans (Bolbosoma capitatum, Bolbosoma vasculosum), nine species of nematodes (Anisakis sp., Anisakis brevispiculata, Anisakis paggiae, Anisakis simplex, Anisakis typica, Anisakis ziphidarium, Crassicauda anthonyi, Heterocheilus tunicatus, Pseudoterranova ceticola), two species of cestodes (Monorygma grimaldi, Phyllobothrium delphini), and three species of trematodes (Chiorchis groschafti, Pulmonicola cochleotrema, Monoligerum blairi). The nematodes belonging to the genus Anisakis recovered in some stranded animals were genetically identified to species level based on their sequence analysis of mitochondrial DNA (629 bp of mtDNA cox 2). A total of five new host records and six new geographic records are presented.L'articolo è disponibile sul sito dell'editore http://www.springerlink.com
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