Proceso y análisis estadístico de los servicios médicos legales realizados que brinda el Instituto de Medicina Legal y Ciencias Forenses 2011-2018

Objetivo: El presente informe explica el proceso de gestión para la obtención de los datos y análisis de la información estadística forense realizados entre los años 2011 y 2018, respecto a los servicios médicos legales que brinda el Instituto de Medicina Legal y Ciencias Forenses, reconocimiento médico legal, psicológicos, necropsias de ley, exámenes auxiliares y pericias especializadas, realizados en niños, adolescentes, jóvenes, adulto y adulto mayor. Método: La investigación es de enfoque cualitativo, tipo explicativo y diseño experimental. Resultados: La mayor cantidad de servicios médicos legales a nivel nacional son realizados por el distrito fiscal de Lima. Se logró identificar la mayor cobertura de los servicios médicos legales, realizado por distrito fiscal relacionado al sexo y grupo Etario y también se logró identificar la mayor cobertura de los exámenes auxiliares de los laboratorios forenses, realizado por distrito fiscal. Asimismo, se evidencio el incremento respecto a la cantidad de pericias realizadas por la Gerencia de Peritajes y el Equipo Forense en materia Ambiental - EFOMA; así como también la cantidad total de individuos entregados por el Equipo Forense Especializado - EFE a nivel nacional. Conclusiones: Con la aplicación del proceso de la gestión de la información estadística se realizó el sinceramiento de la información forense y se logró identificar el incremento de los diversos servicios médicos legales que brinda el Instituto de Medicina Legal y Ciencias Forenses a través de los años 2011- 2018 a nivel nacional realizado por distrito fiscal relacionado al sexo y grupo Etario

Uncertainties in estimates of the oxidative capacity of the urban atmosphere : a modeling and measurement approach

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 2007.Includes bibliographical references (p. 203-212).Mobile emissions represent a significant fraction of the total anthropogenic emissions burden in megacities and have a deleterious effect on air quality at local and regional scales. Due to the significant sources of uncertainties involved during the estimation of mobile emissions, an adequate treatment of emission uncertainties is critical during the design of air quality control strategies using AQMs. This thesis focuses on quantifying the effects of parametric uncertainties of input emission fields on model uncertainties of ozone predictions. We obtained direct measurements of mobile emission sources in the Mexico City Metropolitan Area (MCMA) using a novel measurement technique and quantified the magnitude and variability of key pollutant species. This analysis allowed a direct evaluation of the emissions inventory used in AQMs for the MCMA. Measured selected VOCs and NOy showed a strong dependence on traffic mode and indicated a larger than expected burden of emitted NOx and aldehydes. Our measurements of benzene, toluene, formaldehyde, and acetaldehyde in the MCMA indicate that the emissions of these toxic pollutants are similar or higher than for some US cities. We derived approximate historical trends of the VOC/NOx emission ratio and quantified the impact of changes of mobile emission sources on the photochemical levels using the Brute Force Method and Direct Decoupled Method sensitivity techniques with the CAMx model. The model reasonably reproduces concentrations of ozone and VOCs and accurately those of CO and NOx but over predicts OH by about 25% and severely under-predicts HO2 by a factor of 2 to 3 suggesting that the radical formation pathways in current state of the art AQMs should be revised.(cont.) The model successfully reproduces the corresponding relative changes in historical observations of ozone peak and diurnal average concentrations and suggests a current moderate VOC-sensitive regime. The analysis of the model's sensitivity coefficients to individual perturbations of VOC group species as described by the SAPRC99 chemical mechanism showed that the model is particularly sensitive to aromatics, higher alkenes, and formaldehyde emissions. We found, however, that NOx, olefins and aromatic species can potentially contribute significantly to uncertainties in ozone predictions.by Miguel Angel Zavala-Perez.Ph.D

Evaluation of WRF mesoscale simulations and particle trajectory analysis for the MILAGRO field campaign

Accurate numerical simulations of the complex wind flows in the Mexico City Metropolitan Area (MCMA) can be an invaluable tool for interpreting the MILAGRO field campaign results. This paper uses three methods to evaluate numerical simulations of basin meteorology using the MM5 and WRF models: statistical comparisons with observations, "Concentration Field Analysis" (CFA) using measured air pollutant concentrations, and comparison of flow features using cluster analysis. CFA is shown to be a better indication of simulation quality than statistical metrics, and WRF simulations are shown to be an improvement on the MM5 ones. Comparisons with clusters identifies an under-representation of the drainage flows into the basin and an over-representation of wind shear in the boundary layer. Particle trajectories simulated with WRF-FLEXPART are then used to analyse the transport of the urban plume and show rapid venting and limited recirculation during MILAGRO. Lagrangian impacts were identified at the campaign supersites, and age spectra of the pollutants evaluated at those same sites. The evaluation presented in the paper show that mesoscale meteorological simulations are of sufficient accuracy to be useful for MILAGRO data analysis.National Science Foundation (U.S.) (Award ATM-0511803)National Science Foundation (U.S.) (Award ATM-0810950)National Science Foundation (U.S.) (Award ATM-0810931)Molina Center for Energy and the Environmen

Revealing patterns of local species richness along environmental gradients with a novel network tool

How species richness relates to environmental gradients at large extents is commonly investigated aggregating local site data to coarser grains. However, such relationships often change with the grain of analysis, potentially hiding the local signal. Here we show that a novel network technique, the “method of reflections”, could unveil the relationships between species richness and climate without such drawbacks. We introduced a new index related to potential species richness, which revealed large scale patterns by including at the local community level information about species distribution throughout the dataset (i.e., the network). The method effectively removed noise, identifying how far site richness was from potential. When applying it to study woody species richness patterns in Spain, we observed that annual precipitation and mean annual temperature explained large parts of the variance of the newly defined species richness, highlighting that, at the local scale, communities in drier and warmer areas were potentially the species richest. Our method went far beyond what geographical upscaling of the data could unfold, and the insights obtained strongly suggested that it is a powerful instrument to detect key factors underlying species richness patterns, and that it could have numerous applications in ecology and other fields

Induction of auxin biosynthesis and WOX5 repression mediate changes in root development in Arabidopsis exposed to chitosan

[EN] Chitosan is a natural polymer with applications in agriculture, which causes plasma membrane permeabilisation and induction of intracellular reactive oxygen species (ROS) in plants. Chitosan has been mostly applied in the phylloplane to control plant diseases and to enhance plant defences, but has also been considered for controlling root pests. However, the effect of chitosan on roots is virtually unknown. In this work, we show that chitosan interfered with auxin homeostasis in Arabidopsis roots, promoting a 2-3 fold accumulation of indole acetic acid (IAA). We observed chitosan dose-dependent alterations of auxin synthesis, transport and signalling in Arabidopsis roots. As a consequence, high doses of chitosan reduce WOX5 expression in the root apical meristem and arrest root growth. Chitosan also propitiates accumulation of salicylic (SA) and jasmonic (JA) acids in Arabidopsis roots by induction of genes involved in their biosynthesis and signalling. In addition, high-dose chitosan irrigation of tomato and barley plants also arrests root development. Tomato root apices treated with chitosan showed isodiametric cells respect to rectangular cells in the controls. We found that chitosan causes strong alterations in root cell morphology. Our results highlight the importance of considering chitosan dose during agronomical applications to the rhizosphere.This work was supported by AGL 2015 66833-R Grant from the Spanish Ministry of Economy and Competitiveness Grant AGL 2015. We would like to thank Drs Isabel Lopez-Diaz and Esther Carrera for plant hormone quantitation (IBMCP, Valencia, Spain). Part of this work was filed for a patent (P201431399) by L. V. Lopez-Llorca, F. Lopez-Moya and N. Escudero as inventors. We would like to thank Dr Michael Kershaw (University of Exeter) for his English revision and critical comments of the manuscript. We also thank Ms Marta Suarez-Fernandez (University of Alicante) and Mr Alfonso Prieto for their technical support. All the authors reviewed and approved the manuscript.Lopez-Moya, F.; Escudero, N.; Zavala-Gonzalez, EA.; Esteve-Bruna, D.; Blazquez Rodriguez, MA.; Alabadí Diego, D.; Lopez-Llorca, LV. (2017). Induction of auxin biosynthesis and WOX5 repression mediate changes in root development in Arabidopsis exposed to chitosan. Scientific Reports. 7:1-14. https://doi.org/10.1038/s41598-017-16874-5S1147Savary, S., Ficke, A., Aubertot, J.-N. & Hollier, C. Crop losses due to diseases and their implications for global food production losses and food security. Food Sec. 4(4), 519–37 (2012).Pimentel, D., Zuniga, R. & Morrison, D. Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecol. Econ. 52(3), 273–88 (2005).El-Hadrami, A. & Adam, L. R. Hadrami El, I. & Daayf, F. Chitosan in plant protection. Mar. Drugs. 8(4), 968–987 (2010).Kumar, R. M. N. V. A review of chitin and chitosan applications. React. Funct. Poly. 46(1), 1–27 (2000).Mayakrishnan, V., Kannappan, P., Abdullah, N. & Ali, A. A. B. Cardioprotective activity of polysaccharides derived from marine algae: an overview. Trends Food Sci. Technol. 30, 98–104 (2013).Lopez-Moya, F. et al. Carbon and nitrogen limitation increase chitosan antifungal activity in Neurospora crassa and fungal human pathogens. Fun. Biol. 119(2-3), 154–69 (2015).Benhamou, N. & Theriault, G. Treatment with chitosan enhances resistance of tomato plants to the crown and root rot pathogen Fusarium oxysporum f. sp. radicis-lycopersici. Physiol Mol Plant Path. 44(1), 33–52 (1992).Ohta, K., Taniguchi, A., Konishi, N. & Hosoki, T. Chitosan treatment affects plant growth and flower quality in Eustoma grandiflorum. HortScience. 34(2), 233–234 (1999).Van, S. N., Minh, H. D. & Anh, D. N. Study on chitosan nanoparticles on biophysical characteristics and growth of Robusta coffee in green house. Biocatal Agric Biotechnol. 2(4), 289–294 (2013).Kananont, N., Pichvangkura, R., Chanprame, S., Chadchawan, S. & Limpanavech, P. Chitosan specificity for the in vitro seed germination of two Dendrobium orchids (Asparagales: Orchidaceae). Sci. Hortic. 124(2), 239–247 (2010).Malerba, M. & Cerana, R. Chitosan Effects on Plant Systems. Int. J. Mol. Sci. 17(7), pii: E996 (2016).Uthairatanakij, A., da Silva, J. A. T. & Obsuwan, K. Chitosan for improving orchid production and quality. Orchid Sci. Biotechnol. 1(1), 1–5 (2007).Limpanavech, P. et al. Chitosan effects on floral production, gene expression, and anatomical changes in the Dendrobium orchid. Sci. Hortic. 116(1), 65–72 (2008).Nge, K. L., New, N., Chandrkrachang, S. & Stevens, W. F. Chitosan as a growth stimulator in orchid tissue culture. Plant Sci. 170(6), 1185–90 (2006).Khan, T. A., Peh, K. K. & Ch’ng, H. S. Reporting degree of deacetylation values of chitosan: the influence of analytical methods. J. Pharma. Sci. 5(3), 205–12 (2002).Iriti, M. & Faoro, F. Bioactivity of grape chemicals for human health. Nat. Prod. Commun. 4(5), 611–34 (2009).Pitta-Alvarez, S. I. & Giulietti, A. M. Influence of chitosan, acetic acid and citric acid on growth and tropane alkaloid production in transformed roots of Brugmansia candida effect of medium pH and growth phase. Plant Cell Tissue Organ Cult. 59(1), 31–38 (1999).Sivanandhan, G. et al. Chitosan enhances with anolides production in adventitious root cultures of Withania somnifera (L.) Dunal. Ind. Crops Prod. 37, 124–129 (2012).Chatelain, P. G., Pintado, M. E. & Vasconcelos, M. W. Evaluation of chitooligosaccharide application on mineral accumulation and plant growth in Phaseolus vulgaris. Plant Sci. 214(15), 134–40 (2014).El-Tantawi, E. M. Behaviour of tomato plants as affected by spraying with chitosan and aminofort as natural stimulator substances under application of soil organic amendments. Pak. J. Biol. Sci. 12(17), 1164–73 (2009).Sharp, R. G. 2013. A review of the applications of chitin and its derivatives in agriculture to modify plant-microbial interactions and improve crop yields. Agronomy. 3, 757–793 (2013).Aranega-Bou, P., de la O Leyva, M., Finiti, I., García-Agustín, P. & González-Bosch, C. Priming of plant resistance by natural compounds. Hexanoic acid as a model. Front. Plant Sci. 1(5), 488 (2014).Kwak, J. M., Nguyen, V. & Schroeder, J. I. The role of reactive oxygen species in hormonal responses. Plant Physiol. 141(2), 323–9 (2006).Lopez-Moya, F. & Lopez-Llorca, L. V. Omics for investigating chitosan as an antifungal and gene modulator. J. Fungi. 2(1), 1–11 (2016).Issak, M. et al. Neither endogenous abscisic acid nor endogenous jasmonate is involved in salicylic acid-, yeast elicitor-, orchitosan-induced stomatal closure in Arabidopsis thaliana. Biosc. Biotechnol. Biochem. 77(5), 1111–3 (2013).Vidhyasekaran, P. Switching on Plant Innate Immunity Signaling Systems: Bioengineering and Molecular Manipulation Of PAMP PIMP PRR Signaling Complex. Signaling and Communication In Plants. Chapt. 3. Switching on Plant Immune Signaling Systems using Microbe-Associated Molecular Patterns/section 3.6 Manipulation of Plant Immune System using Chitosan 144–148, (Springer, 2016).Baque, Md. A., Shiragi, Md. H. K., Lee, E. J. & Paek, K.-Y. Elicitor effect of chitosan and pectin on the biosynthesis of anthraquinones, phenolics and flavonoids in adventitious root suspension cultures of Morinda citrifolia (L.). Aust. J. Crop Sci. 6(9), 1349–1355 (2012).Khalil, M. S. & Badawy, M. E. I. Nematicidal activity of a biopolymer chitosan at different molecular weights against root-knot nematode. Meloidogyne incognita. Plant Prot. Sci. 48(4), 170–178 (2012).Sarkar, A. K. et al. Conserved factors regulate signalling in Arabidopsis thaliana shoot and root stem cell organizers. Nature. 446(7137), 811–4 (2007).Ding, Z. & Friml, J. Auxin regulates distal stem cell differentiation in Arabidopsis roots. Proc. Natl. Acad. Sci. USA 107(26), 12046–12051 (2010).Tian, H. et al. WOX5-IAA17 feedback circuit-mediated cellular auxin response is crucial for the patterning of root stem cell niches in Arabidopsis. Mol. Plant. 7(2), 277–89 (2014).Tiwari, S. B., Hagen, G. & Guilfoyle, T. The roles of auxin response factor domains in auxin-responsive transcription. Plant Cell. 15(2), 533–43 (2003).Doares, S. H., Syrovets, T., Weiler, E. W. & Ryan, C. A. Oligogalacturonides and chitosan activate plant defensive genes through the octadecanoid pathway. Proc. Natl. Acad. Sci. USA 92(10), 4095–4098 (1995).Chandra, S. et al. Chitosan nanoparticles: A positive modulator of innate immune responses in plants. Sci. Rep. 5, 15195 (2015).Escudero, N. et al. Chitosan enhances parasitism of Meloidogyne javanica eggs by the nematophagous fungus Pochonia chlamydosporia. Fun. Biol. 120(4), 572–85 (2016).Escudero, N. et al. Chitosan Increases Tomato Root Colonization by Pochonia chlamydosporia and Their Combination Reduces Root-Knot Nematode Damage. Front. Plant Sci. 8, 1415 (2017).Atkinson, N. J., Lilley, C. J. & Urwin, P. E. Identification of Genes Involved in the Response of Arabidopsis to Simultaneous Biotic and Abiotic Stresses. Plant Physiol. 162(4), 2028–2041 (2013).Kazan, K. Auxin and the integration of environmental signals into plant root development. Ann Bot 112(9), 1655–1665 (2013).Overvoorde, P., Fukaki, H. & Beeckman, T. Auxin control of root development. Cold Spring Harb. Perspect. Biol. 2(6), a001537 (2010).Petricka, J. J., Winter, C. M. & Benfey, P. N. Control of Arabidopsis Root Development. Ann. Rev. Plant Biol. 63, 563–590 (2012).Iriti, M. & Faoro, F. Chitosan as a MAMP, searching for a PRR. Plant Signal Behav. 4(1), 66–68 (2009).Jones, A. M. A new look at stress: abscisic acid patterns and dynamics at high-resolution. New Phytol. 210(1), 38–44 (2015).Wasternack, C. Jasmonates: An Update on Biosynthesis, Signal Transduction and Action in Plant Stress Response, Growth and Development. Ann. Bot. 100(4), 681–697 (2007).Wasternack, C. & Hause, B. Jasmonates: biosynthesis, perception, signal transduction and action in plant stress response, growth and development. Ann. Bot. 111(6), 1021–1058 (2013).Kazan, K. Diverse roles of jasmonates and ethylene in abiotic stress tolerance. Trends Plant Sci. 20(4), 219–29 (2015).Ning, Y., Liu, W. & Wang, G. L. Balancing Immunity and Yield in Crop Plants. Trends Plant Sci., 1385(17), 30206–6 (2017). pii: S1360.Zhang, R. Q., Zhu, H. H., Zhao, H. Q. & Yao, Q. Arbuscular mycorrhizal fungal inoculation increases phenolic synthesis in clover roots via hydrogen peroxide, salicylic acid and nitric oxide signaling pathways. J. Plant Physiol. 170(1), 74–9 (2013).Dar, T. A., Uddin, M., Masroor, A. M., Hakeem, K. R. & Jaleel, H. 2015. Jasmonates counter plant stress: A Review. Environ. Exp. Bot. 115, 49–57 (2015).Kazan, K. & Lyons, R. 2016. The link between flowering time and stress tolerance. J Exp Bot. 67(1), 47–60 (2016).Jeong, S. et al. Rootin, a compound that inhibits root development through modulating PIN-mediated auxin distribution. Plant Sci. 233, 116–26 (2015).Amborabé, B. E., Bonmort, J., Fleurat-Lessard, P. & Roblin, G. Early events induced by chitosan on plant cells. J. Exp. Bot. 59(9), 2317–2324 (2008).Chen, X. Y. & Kim, J. Y. Callose synthesis in higher plants. Plant Signal Behav. 4(6), 489–492 (2009).Vasil’ev, L. A. et al. Chitosan-induced programmed cell death in plants. Biochem (Mosc). 74(9), 1035–43 (2009).Friml, J. et al. Efflux-dependent auxin gradients establish the apical-basal axis of Arabidopsis. Nature. 426(6963), 147–153 (2003).Xu, J. et al. A molecular framework for plant regeneration. Science. 311(5759), 385–8 (2006).Cheng, Y., Dai, X. & Zhao, Y. Auxin biosynthesis by the YUCCA flavin monooxygenases controls the formation of floral organs and vascular tissues in Arabidopsis. Genes Dev. 20(13), 1790–1799 (2006).Cao, H., Bowling, S. A., Gordon, A. S. & Dong, X. Characterization of an Arabidopsis mutant that is nonresponsive to inducers of Systemic Acquired Resistance. Plant Cell. 6(11), 1583–92 (1994).Dobón, A., Wulff, B. B., Canet, J. V., Fort, P. & Tornero, P. An allele of Arabidopsis COI1 with hypo- and hypermorphic phenotypes in plant growth, defence and fertility. Plos One. 8(1), e55115 (2013).Fernández-Calvo, P. et al. The Arabidopsis bHLH transcription factors MYC3 and MYC4 are targets of JAZ repressors and act additively with MYC2 in the activation of jasmonate responses. Plant Cell. 23(2), 701–15 (2011).Ripoll, J. J., Ferrándiz, C., Martínez-Laborda, A. & Vera, A. PEPPER, a novel K-homology domain gene, regulates vegetative and gynoecium development in Arabidopsis. Dev. Biol. 289(2), 346–59 (2006).Bordallo, J. J. et al. Colonization of plant roots by egg-parasitic and nematode-trapping fungi. New Phytol. 154(2), 491–499 (2002).Palma-Guerrero, J., Jansson, H. B., Salinas, J. & Lopez-Llorca, L. V. Effect of chitosan on hyphal growth and spore germination of plant pathogenic and biocontrol fungi. J. App. Microbiol. 104(2), 541–53 (2008).Seo, M., Jikumaru, Y. & Kamiya, Y. Profiling of hormones and related metabolites in seed dormancy and germination studies. Methods Mol. Biol. 773, 99–111 (2011).Sabatini, S. et al. An auxin-dependent distal organizer of pattern and polarity in the Arabidopsis root. Cell. 99, 463–472 (1999).Weigel, D. & Glazebrook, J. Arabidopsis: A Laboratory Manual. (Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press, 2002).Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods. 25, 402–408 (2001).Escudero, N. & Lopez-Llorca, L. V. Effects on plant growth and root-knot nematode infection of an endophytic GFP transformant of the nematophagous fungus Pochonia chlamydosporia. Symbiosis. 57(1), 33–42 (2012).Maciá-Vicente, J. G., Jansson, H. B., Talbot, N. J. & Lopez-Llorca, L. V. Real-time PCR quantification and live-cell imaging of endophytic colonization of barley (Hordeum ytvulgare) roots by Fusarium equiseti and Pochonia chlamydosporia. New Phytolo. 182(1), 213–28 (2009).Underwood, A. J. Experiments in ecology: their logical design and interpretation using analysis of variance. (Cambridge University Press, Cambridge, 1997).Bartlett, M. S. 1937. Properties of sufficiency and statistical tests. Proc. Royal. Soci. London Math. 160, 268–282 (1937)

Evaluation of the volatility basis-set approach for the simulation of organic aerosol formation in the Mexico City metropolitan area

New primary and secondary organic aerosol modules have been added to PMCAMx, a three dimensional chemical transport model (CTM), for use with the SAPRC99 chemistry mechanism based on recent smog chamber studies. The new modeling framework is based on the volatility basis-set approach: both primary and secondary organic components are assumed to be semivolatile and photochemically reactive and are distributed in logarithmically spaced volatility bins. This new framework with the use of the new volatility basis parameters for low-NOx [low - NO subscript x] and high-NOx [high - NO subscript x] conditions tends to predict 4–6 times higher anthropogenic SOA concentrations than those predicted with older generation of models. The resulting PMCAMx-2008 was applied in Mexico City Metropolitan Area (MCMA) for approximately a week during April of 2003. The emission inventory, which uses as starting point the MCMA 2004 official inventory, is modified and the primary organic aerosol (POA) emissions are distributed by volatility based on dilution experiments. The predicted organic aerosol (OA) concentrations peak in the center of Mexico City reaching values above 40 μg [mu g] m−3 [m superscript -3]. The model predictions are compared with Aerosol Mass Spectrometry (AMS) observations and their Positive Matrix Factorization (PMF) analysis. The model reproduces both Hydrocarbon-like Organic Aerosol (HOA) and Oxygenated Organic Aerosol (OOA) concentrations and diurnal profiles. The small OA underprediction during the rush hour periods and overprediction in the afternoon suggest potential improvements to the description of fresh primary organic emissions and the formation of the oxygenated organic aerosols respectively, although they may also be due to errors in the simulation of dispersion and vertical mixing. However, the AMS OOA data are not specific enough to prove that the model reproduces the organic aerosol observations for the right reasons. Other combinations of contributions of primary, aged primary, and secondary organic aerosol production rates may lead to similar results. The model results suggest strongly that during the simulated period transport of OA from outside the city was a significant contributor to the observed OA levels. Future simulations should use a larger domain in order to test whether the regional OA can be predicted with current SOA parameterizations. Sensitivity tests indicate that the predicted OA concentration is especially sensitive to the volatility distribution of the emissions in the lower volatility bins.Seventh Framework Programme (European Commission)European UnionMEGAPOLI (Project) (Grant agreement no. 212520)Molina Center for Energy and the EnvironmentUnited States. National Oceanic and Atmospheric Administration. Office of Global Programs (Grant NA08OAR4310565)National Science Foundation (U.S.) (Grant ATM-0528634)National Science Foundation (U.S.) (Grant ATM-0528227)United States. Dept. of Energy. Office of Biological and Environmental Research. Atmospheric Science Program (DEFG0208ER64627

Perception of bullying in sixth year students of a Lima public university school of medicine 2015

Introducción: Las conductas agresivas y discriminatorias afectan la salud física y mental incluso de quienes las cometen. Los estudiantes están sometidos a situaciones de estrés, las cuales aumentan el riesgo de desarrollar dichas conductas y a sufrir sus consecuencias. Por otro lado, la Escuela de Medicina tiene como propósito formar integralmente profesionales médicos, con capacidad de evitar situaciones de violencia y convivir pacíficamente. Es por ello el interés de describir este problema en la Escuela de Medicina de la Universidad Nacional Mayor de San Marcos, Lima, Perú. Objetivo: Abordar la situación de violencia (bullying) en estudiantes de Medicina Humana del 6° año que no habían participado en el Programa de Formación Integral en la UNMSM. Diseño: Estudio descriptivo-transversal. Lugar: Sede docente “Instituto Nacional de Salud del Niño”, Lima, Perú. Participantes: Muestra conformada por 93 (63%) estudiantes matriculados en el sexto año de la Facultad de Medicina que llevaron el curso de Pediatría durante los meses de julio a noviembre de 2015. Intervenciones: Se utilizó un instrumento desarrollado y validado por la Defensoría del Pueblo de España, modificado y corregido por Hoyos y col. Se calcularon las frecuencias y porcentajes de las respuestas. Resultados: Las conductas de violencia fueron más prevalentes entre estudiantes, entre ellas la conducta de agresión verbal más practicada fue la de expresarse de mala manera (40,8%). Conclusiones: La existencia de violencia se presenta en un alto porcentaje de la población estudiada y están implicados en ella docentes y estudiantes.Introduction. Aggressive and discriminatory behavior affects physical and mental health even on those who commit them. Students are subjected to stress, which increases the risk of those behaviors and makes them likely to suffer from its consequences. On the other hand, the Medical School aims to educate medical professionals integrally, with abilities to avoid violence and live peacefully. That is why the interest of describing this problem in Universidad Nacional Mayor de San Marcos, Lima, Peru’s Medical School. Objective. To address violence in students from the sixth year of Human Medicine who had not participated in the Integral Formation Program in UNMSM. Design: Descriptive, transversal study. Setting. "Instituto Nacional del Niño", Lima, Peru, teaching hospital. Participants. Sample consisted in 93 (63%) students enrolled in the sixth year of the Faculty of Medicine who took the course of Pediatrics during the months of July to November 2015. Interventions. An instrument developed and validated by the Spanish Office of the Ombudsman, modified and corrected by Hoyos et al was used. Frequencies and percentages of responses were calculated. Results. Violent behaviors were more prevalent among students; the most practiced behavior was to express badly (40.8%). Conclusions. The existence of violence occurs in a high percentage of the studied population; teachers and students are involved

Application of positive matrix factorization to on-road measurements for source apportionment of diesel- and gasoline-powered vehicle emissions in Mexico City

The goal of this research is to quantify diesel- and gasoline-powered motor vehicle emissions within the Mexico City Metropolitan Area (MCMA) using on-road measurements captured by a mobile laboratory combined with positive matrix factorization (PMF) receptor modeling. During the MCMA-2006 ground-based component of the MILAGRO field campaign, the Aerodyne Mobile Laboratory (AML) measured many gaseous and particulate pollutants, including carbon dioxide, carbon monoxide (CO), nitrogen oxides (NOx) [(NO subscript x)], benzene, toluene, alkylated aromatics, formaldehyde, acetaldehyde, acetone, ammonia, particle number, fine particulate mass (PM2.5) [(PM subscript 2.5)], and black carbon (BC). These serve as inputs to the receptor model, which is able to resolve three factors corresponding to gasoline engine exhaust, diesel engine exhaust, and the urban background. Using the source profiles, we calculate fuel-based emission factors for each type of exhaust. The MCMA's gasoline-powered vehicles are considerably dirtier, on average, than those in the US with respect to CO and aldehydes. Its diesel-powered vehicles have similar emission factors of NOx [NO subscript x] and higher emission factors of aldehydes, particle number, and BC. In the fleet sampled during AML driving, gasoline-powered vehicles are found to be responsible for 97% of total vehicular emissions of CO, 22% of NOx [NO subscript x], 95–97% of each aromatic species, 72–85% of each carbonyl species, 74% of ammonia, negligible amounts of particle number, 26% of PM2.5 [PM subscript 2.5], and 2% of BC; diesel-powered vehicles account for the balance. Because the mobile lab spent 17% of its time waiting at stoplights, the results may overemphasize idling conditions, possibly resulting in an underestimate of NOx [NO subscript x] and overestimate of CO emissions. On the other hand, estimates of the inventory that do not correctly account for emissions during idling are likely to produce bias in the opposite direction.The resulting fuel-based estimates of emissions are lower than in the official inventory for CO and NOx [NO subscript x] and higher for VOCs. For NOx [NO subscript x], the fuel-based estimates are lower for gasoline-powered vehicles but higher for diesel-powered ones compared to the official inventory. While conclusions regarding the inventory should be interpreted with care because of the small sample size, 3.5 h of driving, the discrepancies with the official inventory agree with those reported in other studies.National Science Foundation (U.S.) (Grant ATM-0528170)National Science Foundation (U.S.) (Grant ATM-0528227)United States. Dept. of Energy (Grant DE-FG02-05ER63982)United States. National Aeronautics and Space AdministrationMolina Center for Energy and the Environmen

Rediscovering Kemp’s Ridley Sea Turtle (<em>Lepidochelys kempii</em>): Molecular Analysis and Threats

Sea turtles are reptiles that have inhabited the earth for 100 million years. These are divided into 2 families (Cheloniidae and Dermochelyidae) and 7 species of sea turtles in the world: the leatherback turtle (Dermochelys coriacea); hawksbill turtle (Eretmochelys imbricata); Kemp’s ridley (Lepidochelys kempii); olive ridley (L. olivacea); Loggerhead turtle (Caretta caretta); flatback sea turtle (Natator depressus) and green turtle (Chelonia mydas). In particular, Kemp’s ridley is included in the red list of IUCN categorized as “critically endangered”. The most important site around the Word is in Rancho Nuevo, Tamaulipas, Mexico. Where 80–95% of the world’s nesting is concentrated. Other nesting areas are Tepeguajes and Barra del Tordo, in Tamaulipas, and with less intensity in Veracruz (Lechuguillas and El Raudal beaches) and South Padre Island, Texas, USA. They deposit an average of about 90 eggs and hatching takes 40 to 60 days. Therefore, they are vulnerable to different anthropogenic activities and sources of pollution, such as heavy metals, which can cause toxic effects that are harmful to the turtles, damage their physiology and health. To understand the real situation about health and genetic parameters it is necessary to analyze biochemical and molecular factors in this species