First record of the Sclerogibbidae (Hymenoptera) from the Galapagos Islands, Ecuador.

Las Islas Galápagos son de gran importancia debido a su papel en los albores y la consolidación del modelo de evolución darwiniano (Sulloway 1982); su origen geológicamente reciente y su ubicación oceánica los convierten en un laboratorio natural para el estudio de muchos procesos evolutivos y ecológicos (Schluter 1986, Grant & Grant 2009). Como consecuencia, varios grupos de organismos, como los vertebrados, han sido ampliamente estudiados (De Roy 2009, Steadman 2009); sin embargo, preguntas básicas como la riqueza de las islas requieren más estudio, y aparecen sorpresas continuamente, incluso de los grupos más obvios, como los vertebrados mismos (Gentile & Snell 2009). Los artrópodos son un grupo para el cual el inventario de las islas se ha desarrollado de manera desigual y aún requiere un gran esfuerzo a pesar de las múltiples expediciones realizadas. Según Linsley y Usinger (1966), en ese momento el mejor compendio de estudios entomológicos, desde la visita pionera de Charles Darwin en 1835 hasta 1966, se produjeron alrededor de ocho expediciones individuales y 21 grupales, lo que dio como resultado una lista de 618 especies: 192 coleópteros, 97 lepidópteros y 31 himenópteros. Curiosamente, grandes grupos de himenópteros como Braconidae, Pteromalidae y Encyrtidae no figuran en la lista o se mencionan en publicaciones con menos de cinco especies (Heraty y Herrera 2017). Roque-Álbelo y Landry (2016) enumeraron 311 especies de lepidópteros, y Heraty y Herrera (2017) compilaron un total de 71 himenópteros. Curiosamente, grandes grupos de himenópteros como Braconidae, Pteromalidae y Encyrtidae no figuran en la lista o se mencionan en publicaciones con menos de cinco especies (Heraty y Herrera 2017). Roque-Álbelo y Landry (2016) enumeraron 311 especies de lepidópteros, y Heraty y Herrera (2017) compilaron un total de 71 himenópteros. Curiosamente, grandes grupos de himenópteros como Braconidae, Pteromalidae y Encyrtidae no figuran en la lista o se mencionan en publicaciones con menos de cinco especies (Heraty y Herrera 2017).The Galapagos Islands are of great importance due to their role in the dawn and consolidation of the Darwinian model of evolution (Sulloway 1982); their recent geological origin and oceanic location are recent in a natural laboratory for the study of many evolutionary and ecological processes (Schluter 1986, Grant & Grant 2009). As a consequence, several groups of organisms, such as vertebrates, have been specifically studied (De Roy 2009, Steadman 2009); however, basic questions such as the wealth of the affected islands are more studied, and surprises continually appear, even from the most obvious groups, such as the vertebrates themselves (Gentile & Snell 2009). Arthropods are a group for which the inventory of the islands has been unevenly developed and still require a great effort despite the multiple expeditions carried out. According to Linsley and Usinger (1966), at that time the best compendium of entomological studies, from Charles Darwin's pioneering visit in 1835 to 1966, produced around eight individual and 21 group expeditions, resulting in a list of 618 species: 192 beetles, 97 lepidoptera and 31 hymenoptera. Interestingly, large groups of hymenoptera such as Braconidae, Pteromalidae, and Encyrtidae are not listed or mentioned in publications with fewer than five species (Heraty and Herrera 2017). Roque-Álbelo and Landry (2016) listed 311 species of lepidoptera, and Heraty and Herrera (2017) compiled a total of 71 hymenoptera. Interestingly, large groups of hymenoptera such as Braconidae, Pteromalidae, and Encyrtidae are not listed or mentioned in publications with fewer than five species (Heraty and Herrera 2017). Roque-Álbelo and Landry (2016) listed 311 species of lepidoptera, and Heraty and Herrera (2017) compiled a total of 71 hymenoptera. Interestingly, large groups of hymenoptera such as Braconidae, Pteromalidae, and Encyrtidae are not listed or mentioned in publications with fewer than five species (Heraty and Herrera 2017)

First record of the Sclerogibbidae (Hymenoptera) from the Galapagos Islands, Ecuador.

Las Islas Galápagos son de gran importancia debido a su papel en los albores y la consolidación del modelo de evolución darwiniano (Sulloway 1982); su origen geológicamente reciente y su ubicación oceánica los convierten en un laboratorio natural para el estudio de muchos procesos evolutivos y ecológicos (Schluter 1986, Grant & Grant 2009). Como consecuencia, varios grupos de organismos, como los vertebrados, han sido ampliamente estudiados (De Roy 2009, Steadman 2009); sin embargo, preguntas básicas como la riqueza de las islas requieren más estudio, y aparecen sorpresas continuamente, incluso de los grupos más obvios, como los vertebrados mismos (Gentile & Snell 2009). Los artrópodos son un grupo para el cual el inventario de las islas se ha desarrollado de manera desigual y aún requiere un gran esfuerzo a pesar de las múltiples expediciones realizadas. Según Linsley y Usinger (1966), en ese momento el mejor compendio de estudios entomológicos, desde la visita pionera de Charles Darwin en 1835 hasta 1966, se produjeron alrededor de ocho expediciones individuales y 21 grupales, lo que dio como resultado una lista de 618 especies: 192 coleópteros, 97 lepidópteros y 31 himenópteros. Curiosamente, grandes grupos de himenópteros como Braconidae, Pteromalidae y Encyrtidae no figuran en la lista o se mencionan en publicaciones con menos de cinco especies (Heraty y Herrera 2017). Roque-Álbelo y Landry (2016) enumeraron 311 especies de lepidópteros, y Heraty y Herrera (2017) compilaron un total de 71 himenópteros. Curiosamente, grandes grupos de himenópteros como Braconidae, Pteromalidae y Encyrtidae no figuran en la lista o se mencionan en publicaciones con menos de cinco especies (Heraty y Herrera 2017). Roque-Álbelo y Landry (2016) enumeraron 311 especies de lepidópteros, y Heraty y Herrera (2017) compilaron un total de 71 himenópteros. Curiosamente, grandes grupos de himenópteros como Braconidae, Pteromalidae y Encyrtidae no figuran en la lista o se mencionan en publicaciones con menos de cinco especies (Heraty y Herrera 2017).The Galapagos Islands are of great importance due to their role in the dawn and consolidation of the Darwinian model of evolution (Sulloway 1982); their recent geological origin and oceanic location are recent in a natural laboratory for the study of many evolutionary and ecological processes (Schluter 1986, Grant & Grant 2009). As a consequence, several groups of organisms, such as vertebrates, have been specifically studied (De Roy 2009, Steadman 2009); however, basic questions such as the wealth of the affected islands are more studied, and surprises continually appear, even from the most obvious groups, such as the vertebrates themselves (Gentile & Snell 2009). Arthropods are a group for which the inventory of the islands has been unevenly developed and still require a great effort despite the multiple expeditions carried out. According to Linsley and Usinger (1966), at that time the best compendium of entomological studies, from Charles Darwin's pioneering visit in 1835 to 1966, produced around eight individual and 21 group expeditions, resulting in a list of 618 species: 192 beetles, 97 lepidoptera and 31 hymenoptera. Interestingly, large groups of hymenoptera such as Braconidae, Pteromalidae, and Encyrtidae are not listed or mentioned in publications with fewer than five species (Heraty and Herrera 2017). Roque-Álbelo and Landry (2016) listed 311 species of lepidoptera, and Heraty and Herrera (2017) compiled a total of 71 hymenoptera. Interestingly, large groups of hymenoptera such as Braconidae, Pteromalidae, and Encyrtidae are not listed or mentioned in publications with fewer than five species (Heraty and Herrera 2017). Roque-Álbelo and Landry (2016) listed 311 species of lepidoptera, and Heraty and Herrera (2017) compiled a total of 71 hymenoptera. Interestingly, large groups of hymenoptera such as Braconidae, Pteromalidae, and Encyrtidae are not listed or mentioned in publications with fewer than five species (Heraty and Herrera 2017)

Structure of the medium formed in heavy ion collisions

We investigate the structure of the medium formed in heavy ion collisions using three different models: the Color String Percolation Model (CSPM), the Core-Shell-Color String Percolation Model (CSCSPM), and the Color Glass Condensate (CGC) framework. We analyze the radial distribution function of the transverse representation of color flux tubes in each model to determine the medium's structure. Our results indicate that the CSPM behaves as an ideal gas, while the CSCSPM exhibits a structural phase transition from a gas-like to a liquid-like structure. Additionally, our analysis of the CGC framework suggests that it produces systems that behave like interacting gases for AuAu central collisions at RHIC energies and liquid-like structures for PbPb central collisions at LHC energies.Comment: 15 pages, 8 figure

Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework

[EN] Epidemiology-based models have shown to have successful adaptations to deal with challenges coming from various areas of Engineering, such as those related to energy use or asset management. This paper deals with urban water demand, and data analysis is based on an Epidemiology tool-set herein developed. This combination represents a novel framework in urban hydraulics. Specifically, various reduction tools for time series analyses based on a symbolic approximate (SAX) coding technique able to deal with simple versions of data sets are presented. Then, a neural-network-based model that uses SAX-based knowledge-generation from various time series is shown to improve forecasting abilities. This knowledge is produced by identifying water distribution district metered areas of high similarity to a given target area and sharing demand patterns with the latter. The proposal has been tested with databases from a Brazilian water utility, providing key knowledge for improving water management and hydraulic operation of the distribution system. This novel analysis framework shows several benefits in terms of accuracy and performance of neural network models for water demand.Navarrete-López, CF.; Herrera Fernández, AM.; Brentan, BM.; Luvizotto Jr., E.; Izquierdo Sebastián, J. (2019). Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework. Water. 11(246):1-17. https://doi.org/10.3390/w11020246S11711246Fecarotta, O., Carravetta, A., Morani, M., & Padulano, R. (2018). Optimal Pump Scheduling for Urban Drainage under Variable Flow Conditions. Resources, 7(4), 73. doi:10.3390/resources7040073Creaco, E., & Pezzinga, G. (2018). Comparison of Algorithms for the Optimal Location of Control Valves for Leakage Reduction in WDNs. Water, 10(4), 466. doi:10.3390/w10040466Nguyen, K. A., Stewart, R. A., Zhang, H., Sahin, O., & Siriwardene, N. (2018). Re-engineering traditional urban water management practices with smart metering and informatics. Environmental Modelling & Software, 101, 256-267. doi:10.1016/j.envsoft.2017.12.015Adamowski, J., & Karapataki, C. (2010). Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms. Journal of Hydrologic Engineering, 15(10), 729-743. doi:10.1061/(asce)he.1943-5584.0000245Caiado, J. (2010). Performance of Combined Double Seasonal Univariate Time Series Models for Forecasting Water Demand. Journal of Hydrologic Engineering, 15(3), 215-222. doi:10.1061/(asce)he.1943-5584.0000182Herrera, M., Torgo, L., Izquierdo, J., & Pérez-García, R. (2010). Predictive models for forecasting hourly urban water demand. Journal of Hydrology, 387(1-2), 141-150. doi:10.1016/j.jhydrol.2010.04.005Msiza, I. S., Nelwamondo, F. V., & Marwala, T. (2008). Water Demand Prediction using Artificial Neural Networks and Support Vector Regression. Journal of Computers, 3(11). doi:10.4304/jcp.3.11.1-8Tiwari, M., Adamowski, J., & Adamowski, K. (2016). Water demand forecasting using extreme learning machines. Journal of Water and Land Development, 28(1), 37-52. doi:10.1515/jwld-2016-0004Vijayalaksmi, D. P., & Babu, K. S. J. (2015). Water Supply System Demand Forecasting Using Adaptive Neuro-fuzzy Inference System. Aquatic Procedia, 4, 950-956. doi:10.1016/j.aqpro.2015.02.119Zhou, L., Xia, J., Yu, L., Wang, Y., Shi, Y., Cai, S., & Nie, S. (2016). Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans. International Journal of Environmental Research and Public Health, 13(4), 355. doi:10.3390/ijerph13040355Cadenas, E., Rivera, W., Campos-Amezcua, R., & Heard, C. (2016). Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model. Energies, 9(2), 109. doi:10.3390/en9020109Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175. doi:10.1016/s0925-2312(01)00702-0Herrera, M., García-Díaz, J. C., Izquierdo, J., & Pérez-García, R. (2011). Municipal Water Demand Forecasting: Tools for Intervention Time Series. Stochastic Analysis and Applications, 29(6), 998-1007. doi:10.1080/07362994.2011.610161Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing, 11(2), 2664-2675. doi:10.1016/j.asoc.2010.10.015Campisi-Pinto, S., Adamowski, J., & Oron, G. (2012). Forecasting Urban Water Demand Via Wavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy. Water Resources Management, 26(12), 3539-3558. doi:10.1007/s11269-012-0089-yBrentan, B. M., Luvizotto Jr., E., Herrera, M., Izquierdo, J., & Pérez-García, R. (2017). Hybrid regression model for near real-time urban water demand forecasting. Journal of Computational and Applied Mathematics, 309, 532-541. doi:10.1016/j.cam.2016.02.009Di Nardo, A., Di Natale, M., Musmarra, D., Santonastaso, G. F., Tzatchkov, V., & Alcocer-Yamanaka, V. H. (2014). Dual-use value of network partitioning for water system management and protection from malicious contamination. Journal of Hydroinformatics, 17(3), 361-376. doi:10.2166/hydro.2014.014Scarpa, F., Lobba, A., & Becciu, G. (2016). Elementary DMA Design of Looped Water Distribution Networks with Multiple Sources. Journal of Water Resources Planning and Management, 142(6), 04016011. doi:10.1061/(asce)wr.1943-5452.0000639Panagopoulos, G. P., Bathrellos, G. D., Skilodimou, H. D., & Martsouka, F. A. (2012). Mapping Urban Water Demands Using Multi-Criteria Analysis and GIS. Water Resources Management, 26(5), 1347-1363. doi:10.1007/s11269-011-9962-3Buchberger, S. G., & Nadimpalli, G. (2004). Leak Estimation in Water Distribution Systems by Statistical Analysis of Flow Readings. Journal of Water Resources Planning and Management, 130(4), 321-329. doi:10.1061/(asce)0733-9496(2004)130:4(321)Candelieri, A. (2017). Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection. Water, 9(3), 224. doi:10.3390/w9030224Padulano, R., & Del Giudice, G. (2018). Pattern Detection and Scaling Laws of Daily Water Demand by SOM: an Application to the WDN of Naples, Italy. Water Resources Management, 33(2), 739-755. doi:10.1007/s11269-018-2140-0Bloetscher, F. (2012). Protecting People, Infrastructure, Economies, and Ecosystem Assets: Water Management in the Face of Climate Change. Water, 4(2), 367-388. doi:10.3390/w4020367Bach, P. M., Rauch, W., Mikkelsen, P. S., McCarthy, D. T., & Deletic, A. (2014). A critical review of integrated urban water modelling – Urban drainage and beyond. Environmental Modelling & Software, 54, 88-107. doi:10.1016/j.envsoft.2013.12.018Goltsev, A. V., Dorogovtsev, S. N., Oliveira, J. G., & Mendes, J. F. F. (2012). Localization and Spreading of Diseases in Complex Networks. Physical Review Letters, 109(12). doi:10.1103/physrevlett.109.128702Danila, B., Yu, Y., Marsh, J. A., & Bassler, K. E. (2006). Optimal transport on complex networks. Physical Review E, 74(4). doi:10.1103/physreve.74.046106Herrera, M., Izquierdo, J., Pérez-García, R., & Montalvo, I. (2012). Multi-agent adaptive boosting on semi-supervised water supply clusters. Advances in Engineering Software, 50, 131-136. doi:10.1016/j.advengsoft.2012.02.005Maslov, S., Sneppen, K., & Zaliznyak, A. (2004). Detection of topological patterns in complex networks: correlation profile of the internet. Physica A: Statistical Mechanics and its Applications, 333, 529-540. doi:10.1016/j.physa.2003.06.002Lloyd, A. L., & Valeika, S. (2007). Network models in epidemiology: an overview. World Scientific Lecture Notes in Complex Systems, 189-214. doi:10.1142/9789812771582_0008Hamilton, I., Summerfield, A., Oreszczyn, T., & Ruyssevelt, P. (2017). Using epidemiological methods in energy and buildings research to achieve carbon emission targets. Energy and Buildings, 154, 188-197. doi:10.1016/j.enbuild.2017.08.079Bardet, J.-P., & Little, R. (2014). Epidemiology of urban water distribution systems. Water Resources Research, 50(8), 6447-6465. doi:10.1002/2013wr015017De Domenico, M., Granell, C., Porter, M. A., & Arenas, A. (2016). The physics of spreading processes in multilayer networks. Nature Physics, 12(10), 901-906. doi:10.1038/nphys3865Hamilton, I. G., Summerfield, A. J., Lowe, R., Ruyssevelt, P., Elwell, C. A., & Oreszczyn, T. (2013). Energy epidemiology: a new approach to end-use energy demand research. Building Research & Information, 41(4), 482-497. doi:10.1080/09613218.2013.798142Herrera, M., Ferreira, A. A., Coley, D. A., & de Aquino, R. R. B. (2016). SAX-quantile based multiresolution approach for finding heatwave events in summer temperature time series. AI Communications, 29(6), 725-732. doi:10.3233/aic-160716Padulano, R., & Del Giudice, G. (2018). A Mixed Strategy Based on Self-Organizing Map for Water Demand Pattern Profiling of Large-Size Smart Water Grid Data. Water Resources Management, 32(11), 3671-3685. doi:10.1007/s11269-018-2012-7Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007). Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2), 107-144. doi:10.1007/s10618-007-0064-zAghabozorgi, S., & Wah, T. Y. (2014). Clustering of large time series datasets. Intelligent Data Analysis, 18(5), 793-817. doi:10.3233/ida-140669Yuan, J., Wang, Z., Han, M., & Sun, Y. (2015). A lazy associative classifier for time series. Intelligent Data Analysis, 19(5), 983-1002. doi:10.3233/ida-150754Rasheed, F., Alshalalfa, M., & Alhajj, R. (2011). Efficient Periodicity Mining in Time Series Databases Using Suffix Trees. IEEE Transactions on Knowledge and Data Engineering, 23(1), 79-94. doi:10.1109/tkde.2010.76Schmieder, R., & Edwards, R. (2011). Fast Identification and Removal of Sequence Contamination from Genomic and Metagenomic Datasets. PLoS ONE, 6(3), e17288. doi:10.1371/journal.pone.0017288Valimaki, N., Gerlach, W., Dixit, K., & Makinen, V. (2007). Compressed suffix tree a basis for genome-scale sequence analysis. Bioinformatics, 23(5), 629-630. doi:10.1093/bioinformatics/btl681Ezkurdia, I., Juan, D., Rodriguez, J. M., Frankish, A., Diekhans, M., Harrow, J., … Tress, M. L. (2014). Multiple evidence strands suggest that there may be as few as 19 000 human protein-coding genes. Human Molecular Genetics, 23(22), 5866-5878. doi:10.1093/hmg/ddu309Bermudez-Santana, C. I. (2016). APLICACIONES DE LA BIOINFORMÁTICA EN LA MEDICINA: EL GENOMA HUMANO. ¿CÓMO PODEMOS VER TANTO DETALLE? Acta Biológica Colombiana, 21(1Supl), 249-258. doi:10.15446/abc.v21n1supl.51233Cai, L., Li, X., Ghosh, M., & Guo, B. (2009). Stability analysis of an HIV/AIDS epidemic model with treatment. Journal of Computational and Applied Mathematics, 229(1), 313-323. doi:10.1016/j.cam.2008.10.067Jackson, M., & Chen-Charpentier, B. M. (2017). Modeling plant virus propagation with delays. Journal of Computational and Applied Mathematics, 309, 611-621. doi:10.1016/j.cam.2016.04.024Brentan, B. M., Meirelles, G., Herrera, M., Luvizotto, E., & Izquierdo, J. (2017). Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models. Mathematical Problems in Engineering, 2017, 1-10. doi:10.1155/2017/6343625Bhaskaran, K., Gasparrini, A., Hajat, S., Smeeth, L., & Armstrong, B. (2013). Time series regression studies in environmental epidemiology. International Journal of Epidemiology, 42(4), 1187-1195. doi:10.1093/ije/dyt092HELFENSTEIN, U. (1991). The Use of Transfer Function Models, Intervention Analysis and Related Time Series Methods in Epidemiology. 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Proyecto Piloto de Mediación Familiar en el ámbito del Trabajo Social Sanitario Hospitalario

Trabajo académico centrado en la explicación, desarrollo y evaluación de un proyecto de mediación familiar en el Hospital San Juan de Dios de Zaragoza como una experiencia piloto. Para comenzar se profundiza en los conceptos de "conflicto" y "mediación", con una breve reseña de las características principales a tener en cuenta de cada uno de ellos, para posteriormente presentar el desarrollo de dicho proyecto, análisis de los resultados obtenidos, conclusiones y recomendaciones a tener en cuenta para mejorar la calidad del servicio. Se adjuntan seis anexos que cumplimentan la información desarrollada en el trabajo

FACTORES DE RIESGOS QUIMICOS EN EL PERSONAL DE ENFERMERÍA.

In the present study the chemical risks to the ones were identified that the personnel is exposed of nursing. They were included in the study to 600 professionals of nursing, excluding al personal of conduction (Supervisors and Chief) and the ones that were found with sick leave. A risks evaluation headquarters was utilized where the chemical risks were registered. Al to carry out the sociodemographic analysis was found greater quantity of personnel of the female sex and without university training. It was determined associated pathologies to the chemical risks, relating to the exposition and with the control measures lack. It was found significance statistical that would be able us to indicate that the pathologies that declare the polled would be able to be related to the exposition.En el presente estudio se identificaron los riesgos químicos a los que se expone el personal de enfermería. Fueron incluidos en el estudio 600 profesionales de enfermería, excluyendo al personal de conducción (Supervisores y Jefes) y los que se encontraban con licencia por enfermedad. Se utilizó una matriz de evaluación de riesgos donde se registraron los riesgos químicos. Al realizar el análisis sociodemográfico se encontró mayor cantidad de personal del sexo femenino y sin capacitación universitaria. Se determinaron patologías asociadas a los riesgos químicos, relacionándolas con la exposición y con la falta de medidas de control. El análisis estadístico de los resultados indicaría que las patologías que manifiestan los encuestados podrían estar relacionadas con la exposición a agentes químicos

Effectiveness of mobile telemonitoring applications in heart failure patients: systematic review of literature and meta-analysis

Q2Q1Pacientes con Insuficiencia cardiacaClose and frequent follow-up of heart failure (HF) patients improves clinical outcomes. Mobile telemonitoring applications are advantageous alternatives due to their wide availability, portability, low cost, computing power, and interconnectivity. This study aims to evaluate the impact of telemonitoring apps on mortality, hospitalization, and quality of life (QoL) in HF patients. We conducted a registered (PROSPERO CRD42022299516) systematic review of randomized clinical trials (RCTs) evaluating mobile-based telemonitoring strategies in patients with HF, published between January 2000 and December 2021 in 4 databases (PubMed, EMBASE, BVSalud/LILACS, Cochrane Reviews). We assessed the risk of bias using the RoB2 tool. The outcome of interest was the effect on mortality, hospitalization risk, and/or QoL. We performed meta-analysis when appropriate; heterogeneity and risk of publication bias were evaluated. Otherwise, descriptive analyses are offered. We screened 900 references and 19 RCTs were included for review. The risk of bias for mortality and hospitalization was mostly low, whereas for QoL was high. We observed a reduced risk of hospitalization due to HF with the use of mobile-based telemonitoring strategies (RR 0.77 [0.67; 0.89]; I2 7%). Non-statistically significant reduction in mortality risk was observed. The impact on QoL was variable between studies, with different scores and reporting measures used, thus limiting data pooling. The use of mobile-based telemonitoring strategies in patients with HF reduces risk of hospitalization due to HF. As smartphones and wirelessly connected devices are increasingly available, further research on this topic is warranted, particularly in the foundational therapy.https://orcid.org/0000-0002-4189-4317https://orcid.org/0000-0002-8244-2958https://orcid.org/0000-0001-5401-0018https://orcid.org/0000-0003-1490-1822https://orcid.org/0000-0002-3606-2102Revista Internacional - IndexadaA1N

PATÓGENOS Y SÍNTOMAS ASOCIADOS A LA MARCHITEZ DEL TOMATE (Solanum lycopersicum L.) EN TEXCOCO MÉXICO

Se identificó a Phytophthora capsici, Rhizoctonia solani y Fusarium oxysporum como agentes causales de la marchitez del tomate en Texcoco, Edo. de México y, se evaluó la sintomatología e incidencia de la marchitez inducida por estos hongos con diferentes métodos de inoculación. Cultivos de cada hongo se inocularon en plantas de tomate con 4-5 hojas verdaderas. La inoculación de P. capsici por inmersión de raíces en solución de zoosporas fue más eficiente (96,7 % de incidencia) que la inoculación al cuello, a los 6 días después de la inoculación (ddi). Este hongo indujo marchitez, pudrición de raíz y cuello, y muerte de las plantas a los 4 ddi. R. solani, al inocularse por inmersión en solución de propágalos y a través de granos de trigo infectados con el hongo, no ocasionó la muerte de las plantas, sin embargo, la inoculación con granos de trigo provocó una incidencia de 100 %, que se manifestó en reducción de crecimiento (50 %) y en amarillamiento generalizado. F. oxysporum presentó una incidencia de 100 % a los 15 y 30 ddi, para la variedad Río Grande e híbrido Yaqui, respectivamente. Las plantas manifestaron clorosis, marchitez generalizada, necrosis de tejido vascular y finalmente la muerte

Estudio de riesgos ergonómicos y satisfacción laboral en el personal de enfermería

En el presente estudio se planteó identificar los riesgos ergonómicos a los que se expone el personal de enfermería, y determinar la contribución de estos factores ergonómicos y de los índices de satisfacción laboral como factores de riesgo laboral. Se incluyeron en el estudio a los 150 profesionales de enfermería que realizan atención directa al paciente internado. Se utilizó una matriz de evaluación de riesgos para registrar los factores ergonómicos, y un cuestionario de preguntas para medir el grado de satisfacción laboral. El análisis sociodemográfico reflejó un predominio de sexo femenino sin capacitación universitaria. En el estudio sobre los riesgos ergonómicos a los que este personal está expuesto, se observó una alta prevalencia de los mismos, acompañada de valores de riesgo relativo que permitirían explicar el alto índice de patologías encontradas. En cuanto a los niveles de satisfacción laboral, el análisis muestra conformidad en el tipo de trabajo que se realiza y la relación con los compañeros. El salario y posibilidades de ascenso son las que produjeron mayor insatisfacción. Se concluye la necesidad de mejorar las condiciones laborales y disminuir el alto índice de patologías de origen profesional incorporando medidas preventivas mediante procedimientos de control, promoción de programas de entrenamiento y capacitación