Stage-specific histone modification profiles reveal global transitions in the Xenopus embryonic epigenome.

Vertebrate embryos are derived from a transitory pool of pluripotent cells. By the process of embryonic induction, these precursor cells are assigned to specific fates and differentiation programs. Histone post-translational modifications are thought to play a key role in the establishment and maintenance of stable gene expression patterns underlying these processes. While on gene level histone modifications are known to change during differentiation, very little is known about the quantitative fluctuations in bulk histone modifications during development. To investigate this issue we analysed histones isolated from four different developmental stages of Xenopus laevis by mass spectrometry. In toto, we quantified 59 modification states on core histones H3 and H4 from blastula to tadpole stages. During this developmental period, we observed in general an increase in the unmodified states, and a shift from histone modifications associated with transcriptional activity to transcriptionally repressive histone marks. We also compared these naturally occurring patterns with the histone modifications of murine ES cells, detecting large differences in the methylation patterns of histone H3 lysines 27 and 36 between pluripotent ES cells and pluripotent cells from Xenopus blastulae. By combining all detected modification transitions we could cluster their patterns according to their embryonic origin, defining specific histone modification profiles (HMPs) for each developmental stage. To our knowledge, this data set represents the first compendium of covalent histone modifications and their quantitative flux during normogenesis in a vertebrate model organism. The HMPs indicate a stepwise maturation of the embryonic epigenome, which may be causal to the progressing restriction of cellular potency during development

PMP and Climate Variability and Change: A Review

[EN] A state-of-the-art review on the probable maximum precipitation (PMP) as it relates to climate variability and change is presented. The review consists of an examination of the current practice and the various developments published in the literature. The focus is on relevant research where the effect of climate dynamics on the PMP are discussed, as well as statistical methods developed for estimating very large extreme precipitation including the PMP. The review includes interpretation of extreme events arising from the climate system, their physical mechanisms, and statistical properties, together with the effect of the uncertainty of several factors determining them, such as atmospheric moisture, its transport into storms and wind, and their future changes. These issues are examined as well as the underlying historical and proxy data. In addition, the procedures and guidelines established by some countries, states, and organizations for estimating the PMP are summarized. In doing so, attention was paid to whether the current guidelines and research published literature take into consideration the effects of the variability and change of climatic processes and the underlying uncertainties.The authors would like to acknowledge the support of the Global Water Futures Program and the Natural Sciences and Engineering Research Council of Canada (NSERC Discovery Grant RGPIN-2019-06894). The fourth author acknowledges the support of the Spanish Ministry of Science and Innovation, Project TETISCHANGE (RTI2018-093717-B-100). The first author appreciates the continuous support from the Scott College of Engineering of Colorado State University.Salas, JD.; Anderson, ML.; Papalexiou, SM.; Francés, F. (2020). PMP and Climate Variability and Change: A Review. Journal of Hydrologic Engineering. 25(12):1-16. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002003S1162512Abbs, D. J. (1999). 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Floods and droughts.” In Proc. 2nd Int. Symp. in Hydrology 27–33. Fort Collins CO: Water Resources Publications.Botero, B. A., & Francés, F. (2010). Estimation of high return period flood quantiles using additional non-systematic information with upper bounded statistical models. Hydrology and Earth System Sciences, 14(12), 2617-2628. doi:10.5194/hess-14-2617-2010Canadian Dam Association. 2013. “Dam safety guidelines.” Accessed August 24 2020. https://www.cda.ca/EN/Publications_Pages/Dam_Safety_Publications.aspx.Casas, M. C., Rodríguez, R., Nieto, R., & Redaño, A. (2008). The Estimation of Probable Maximum Precipitation. Annals of the New York Academy of Sciences, 1146(1), 291-302. doi:10.1196/annals.1446.003Casas, M. C., Rodríguez, R., Prohom, M., Gázquez, A., & Redaño, A. (2010). Estimation of the probable maximum precipitation in Barcelona (Spain). International Journal of Climatology, 31(9), 1322-1327. doi:10.1002/joc.2149Casas-Castillo, M. C., Rodríguez-Solà, R., Navarro, X., Russo, B., Lastra, A., González, P., & Redaño, A. (2016). On the consideration of scaling properties of extreme rainfall in Madrid (Spain) for developing a generalized intensity-duration-frequency equation and assessing probable maximum precipitation estimates. Theoretical and Applied Climatology, 131(1-2), 573-580. doi:10.1007/s00704-016-1998-0Castellarin, A., Merz, R., & Blöschl, G. (2009). Probabilistic envelope curves for extreme rainfall events. Journal of Hydrology, 378(3-4), 263-271. doi:10.1016/j.jhydrol.2009.09.030Chavan, S. R., & Srinivas, V. V. (2016). Regionalization based envelope curves for PMP estimation by Hershfield method. International Journal of Climatology, 37(10), 3767-3779. doi:10.1002/joc.4951Chen, X., & Hossain, F. (2018). Understanding Model-Based Probable Maximum Precipitation Estimation as a Function of Location and Season from Atmospheric Reanalysis. Journal of Hydrometeorology, 19(2), 459-475. doi:10.1175/jhm-d-17-0170.1Chen, X., & Hossain, F. (2019). Understanding Future Safety of Dams in a Changing Climate. Bulletin of the American Meteorological Society, 100(8), 1395-1404. doi:10.1175/bams-d-17-0150.1Chow, V. T. (1951). A general formula for hydrologic frequency analysis. Transactions, American Geophysical Union, 32(2), 231. doi:10.1029/tr032i002p00231Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer Series in Statistics. doi:10.1007/978-1-4471-3675-0COOKE, P. (1979). Statistical inference for bounds of random variables. Biometrika, 66(2), 367-374. doi:10.1093/biomet/66.2.367Cooley, D. (2009). Extreme value analysis and the study of climate change. Climatic Change, 97(1-2), 77-83. doi:10.1007/s10584-009-9627-xDawdy, D. R., & Lettenmaier, D. P. (1987). Initiative for Risk‐Based Flood Design. Journal of Hydraulic Engineering, 113(8), 1041-1051. doi:10.1061/(asce)0733-9429(1987)113:8(1041)Desa M., M. N., & Rakhecha, P. R. (2007). Probable maximum precipitation for 24-h duration over an equatorial region: Part 2-Johor, Malaysia. Atmospheric Research, 84(1), 84-90. doi:10.1016/j.atmosres.2006.06.005Dettinger, M. (2011). Climate Change, Atmospheric Rivers, and Floods in California - A Multimodel Analysis of Storm Frequency and Magnitude Changes1. JAWRA Journal of the American Water Resources Association, 47(3), 514-523. doi:10.1111/j.1752-1688.2011.00546.xDiaz A. J. K. Ishida M. L. Kavvas and M. L. Anderson. 2017. “Maximum precipitation estimation for five watersheds in the southern Sierra Nevada.” In Proc. 2017 World Environmental and Water Resources Congress 331–339. Reston VA: ASCE. https://doi.org/10.1061/9780784480618.032.Douglas, E. M., & Barros, A. P. (2003). Probable Maximum Precipitation Estimation Using Multifractals: Application in the Eastern United States. 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Climate Vulnerability, 25-55. doi:10.1016/b978-0-12-384703-4.00501-3Gumbel, E. J. (1958). Statistics of Extremes. doi:10.7312/gumb92958Hershfield, D. M. (1965). Method for Estimating Probable Maximum Rainfall. Journal - American Water Works Association, 57(8), 965-972. doi:10.1002/j.1551-8833.1965.tb01486.xHershfield D. M. 1977. “Some tools for hydrometeorologists.” In Proc. 2nd Conf. on Hydrometeorology. Boston: American Meteorological Society.Hosking, J. R. M., & Wallis, J. R. (1997). Regional Frequency Analysis. doi:10.1017/cbo9780511529443Hosking, J. R. M., Wallis, J. R., & Wood, E. F. (1985). Estimation of the Generalized Extreme-Value Distribution by the Method of Probability-Weighted Moments. Technometrics, 27(3), 251-261. doi:10.1080/00401706.1985.10488049Houghton, J. C. (1978). Birth of a parent: The Wakeby Distribution for modeling flood flows. Water Resources Research, 14(6), 1105-1109. doi:10.1029/wr014i006p01105Hubert, P., Tessier, Y., Lovejoy, S., Schertzer, D., Schmitt, F., Ladoy, P., … Desurosne, I. (1993). Multifractals and extreme rainfall events. Geophysical Research Letters, 20(10), 931-934. doi:10.1029/93gl01245Hundecha, Y., St-Hilaire, A., Ouarda, T. B. M. J., El Adlouni, S., & Gachon, P. (2008). A Nonstationary Extreme Value Analysis for the Assessment of Changes in Extreme Annual Wind Speed over the Gulf of St. Lawrence, Canada. Journal of Applied Meteorology and Climatology, 47(11), 2745-2759. doi:10.1175/2008jamc1665.1Ishida, K., Kavvas, M. L., Jang, S., Chen, Z. Q., Ohara, N., & Anderson, M. L. (2015). Physically Based Estimation of Maximum Precipitation over Three Watersheds in Northern California: Atmospheric Boundary Condition Shifting. Journal of Hydrologic Engineering, 20(4), 04014052. doi:10.1061/(asce)he.1943-5584.0001026Ishida, K., Kavvas, M. L., Jang, S., Chen, Z. Q., Ohara, N., & Anderson, M. L. (2015). Physically Based Estimation of Maximum Precipitation over Three Watersheds in Northern California: Relative Humidity Maximization Method. Journal of Hydrologic Engineering, 20(10), 04015014. doi:10.1061/(asce)he.1943-5584.0001175Ishida, K., Ohara, N., Kavvas, M. L., Chen, Z. Q., & Anderson, M. L. (2018). Impact of air temperature on physically-based maximum precipitation estimation through change in moisture holding capacity of air. Journal of Hydrology, 556, 1050-1063. doi:10.1016/j.jhydrol.2016.10.008Jakob D. R. Smalley J. Meighen B. Taylor and K. Xuereb. 2008. “Climate change and probable maximum precipitation.” In Proc. Water Down Under 109–120. Melbourne Australia: Engineers Australia Causal Productions.Katz, R. W. (2012). Statistical Methods for Nonstationary Extremes. Water Science and Technology Library, 15-37. doi:10.1007/978-94-007-4479-0_2Katz, R. W., Parlange, M. B., & Naveau, P. (2002). Statistics of extremes in hydrology. Advances in Water Resources, 25(8-12), 1287-1304. doi:10.1016/s0309-1708(02)00056-8Kharin, V. V., Zwiers, F. W., Zhang, X., & Hegerl, G. C. (2007). Changes in Temperature and Precipitation Extremes in the IPCC Ensemble of Global Coupled Model Simulations. Journal of Climate, 20(8), 1419-1444. doi:10.1175/jcli4066.1Kijko, A. (2004). Estimation of the Maximum Earthquake Magnitude, m max. Pure and Applied Geophysics, 161(8), 1655-1681. doi:10.1007/s00024-004-2531-4Kim, I.-W., Oh, J., Woo, S., & Kripalani, R. H. (2018). Evaluation of precipitation extremes over the Asian domain: observation and modelling studies. Climate Dynamics, 52(3-4), 1317-1342. doi:10.1007/s00382-018-4193-4Koutsoyiannis, D. (1999). A probabilistic view of hershfield’s method for estimating probable maximum precipitation. Water Resources Research, 35(4), 1313-1322. doi:10.1029/1999wr900002Kundzewicz, Z. W., & Stakhiv, E. Z. (2010). Are climate models «ready for prime time» in water resources management applications, or is more research needed? Hydrological Sciences Journal, 55(7), 1085-1089. doi:10.1080/02626667.2010.513211Kunkel, K. E., Karl, T. R., Easterling, D. R., Redmond, K., Young, J., Yin, X., & Hennon, P. (2013). Probable maximum precipitation and climate change. Geophysical Research Letters, 40(7), 1402-1408. doi:10.1002/grl.50334Lan, P., Lin, B., Zhang, Y., & Chen, H. (2017). Probable Maximum Precipitation Estimation Using the Revised Km-Value Method in Hong Kong. Journal of Hydrologic Engineering, 22(8), 05017008. doi:10.1061/(asce)he.1943-5584.0001517Langousis, A., Veneziano, D., Furcolo, P., & Lepore, C. (2009). Multifractal rainfall extremes: Theoretical analysis and practical estimation. Chaos, Solitons & Fractals, 39(3), 1182-1194. doi:10.1016/j.chaos.2007.06.004Leclerc, M., & Ouarda, T. B. M. J. (2007). Non-stationary regional flood frequency analysis at ungauged sites. Journal of Hydrology, 343(3-4), 254-265. doi:10.1016/j.jhydrol.2007.06.021Lee, J., Choi, J., Lee, O., Yoon, J., & Kim, S. (2017). Estimation of Probable Maximum Precipitation in Korea using a Regional Climate Model. Water, 9(4), 240. doi:10.3390/w9040240Lenderink, G., & Attema, J. (2015). A simple scaling approach to produce climate scenarios of local precipitation extremes for the Netherlands. Environmental Research Letters, 10(8), 085001. doi:10.1088/1748-9326/10/8/085001Lepore, C., Veneziano, D., & Molini, A. (2015). Temperature and CAPE dependence of rainfall extremes in the eastern United States. Geophysical Research Letters, 42(1), 74-83. doi:10.1002/2014gl062247López, J., & Francés, F. (2013). Non-stationary flood frequency analysis in continental Spanish rivers, using climate and reservoir indices as external covariates. Hydrology and Earth System Sciences, 17(8), 3189-3203. doi:10.5194/hess-17-3189-2013Loriaux, J. M., Lenderink, G., & Siebesma, A. P. (2016). Peak precipitation intensity in relation to atmospheric conditions and large-scale forcing at midlatitudes. Journal of Geophysical Research: Atmospheres, 121(10), 5471-5487. doi:10.1002/2015jd024274Machado, M. J., Botero, B. A., López, J., Francés, F., Díez-Herrero, A., & Benito, G. (2015). Flood frequency analysis of historical flood data under stationary and non-stationary modelling. Hydrology and Earth System Sciences, 19(6), 2561-2576. doi:10.5194/hess-19-2561-2015Markonis, Y., Papalexiou, S. M., Martinkova, M., & Hanel, M. (2019). Assessment of Water Cycle Intensification Over Land using a Multisource Global Gridded Precipitation DataSet. Journal of Geophysical Research: Atmospheres, 124(21), 11175-11187. doi:10.1029/2019jd030855Martins, E. S., & Stedinger, J. R. (2000). Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data. Water Resources Research, 36(3), 737-744. doi:10.1029/1999wr900330Mejia G. and F. Villegas. 1979. “Maximum precipitation deviations in Colombia.” In Proc. 3rd Conf. on Hydrometeorology 74–76. Boston: America Meteorological Society.Merz, R., & Blöschl, G. (2008). Flood frequency hydrology: 1. Temporal, spatial, and causal expansion of information. Water Resources Research, 44(8). doi:10.1029/2007wr006744Micovic, Z., Schaefer, M. G., & Taylor, G. H. (2015). Uncertainty analysis for Probable Maximum Precipitation estimates. Journal of Hydrology, 521, 360-373. doi:10.1016/j.jhydrol.2014.12.033Mínguez, R., Méndez, F. J., Izaguirre, C., Menéndez, M., & Losada, I. J. (2010). Pseudo-optimal parameter selection of non-stationary generalized extreme value models for environmental variables. Environmental Modelling & Software, 25(12), 1592-1607. doi:10.1016/j.envsoft.2010.05.008Nathan, R., Jordan, P., Scorah, M., Lang, S., Kuczera, G., Schaefer, M., & Weinmann, E. (2016). Estimating the exceedance probability of extreme rainfalls up to the probable maximum precipitation. 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(2018). Precise Temporal Disaggregation Preserving Marginals and Correlations (DiPMaC) for Stationary and Nonstationary Processes. Water Resources Research, 54(10), 7435-7458. doi:10.1029/2018wr022726III, J. P. (1975). Statistical Inference Using Extreme Order Statistics. The Annals of Statistics, 3(1). doi:10.1214/aos/1176343003Rakhecha, P. R., Deshpande, N. R., & Soman, M. K. (1992). Probable maximum precipitation for a 2-day duration over the Indian Peninsula. Theoretical and Applied Climatology, 45(4), 277-283. doi:10.1007/bf00865518Rakhecha, P. R., & Soman, M. K. (1994). Estimation of probable maximum precipitation for a 2-day duration: Part 2 ? north Indian region. Theoretical and Applied Climatology, 49(2), 77-84. doi:10.1007/bf00868192Rastogi, D., Kao, S., Ashfaq, M., Mei, R., Kabela, E. D., Gangrade, S., … Anantharaj, V. G. (2017). Effects of climate change on probable maximum precipitation: A sensitivity study over the Alabama‐Coosa‐Tallapoosa River Basin. 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Livestock valuation: an assessment model based on sow age

[EN] Food supply in Europe is based on the consumption of meat ¿ of which pork is the most consumed. The livestock sector represents some 40% of total agricultural production. Livestock farms need tools for business management and valuation in order to make business productivity estimates and determine compensation, as well as calculate average and marginal costs. Pig farmers need to determine the optimal time for culling a sow: meaning that for livestock depreciation it is necessary to determine the value of sows depending on their age. In this study, a model is shown for valuing a sow according to its productive life and net present value generated. In the same way as any asset in a production process, the economic value of a sow should be estimated by its contribution to the process of generating future profits. The distribution of costs depends on the size of the farm, and so three sizes of farms are considered: fewer than 250 hybrid sows; 251 to 500 sows; and more than 500 sows. The economic values of the sows were obtained according to their age and number of farrowing. The models show variations between differently sized farms.Guaita-Pradas, I.; Pérez-Salas Sagreras, JL.; Fenollosa Ribera, ML. (2017). Livestock valuation: an assessment model based on sow age. Ciência e Técnica Vitivinícola. 32(8):299-323. http://hdl.handle.net/10251/109828S29932332

Investigation of objective functions and operation rules for storage reservoirs

Submitted to Office of Water Research and Technology.September 1981.Bibliography: pages 31-32.OWRT project no. B-195-COLO

MicroRNAs and Drinking: Association between the Pre-miR-27a rs895819 Polymorphism and Alcohol Consumption in a Mediterranean Population

Recently, microRNAs (miRNA) have been proposed as regulators in the different processes involved in alcohol intake, and differences have been found in the miRNA expression profile in alcoholics. However, no study has focused on analyzing polymorphisms in genes encoding miRNAs and daily alcohol consumption at the population level. Our aim was to investigate the association between a functional polymorphism in the pre-miR-27a (rs895819 A>G) gene and alcohol consumption in an elderly population. We undertook a cross-sectional study of PREvencion con DIeta MEDiterranea (PREDIMED)-Valencia participants (n = 1007, including men and women aged 67 +/- 7 years) and measured their alcohol consumption (total and alcoholic beverages) through a validated questionnaire. We found a strong association between the pre-miR-27a polymorphism and total alcohol intake, this being higher in GG subjects (5.2 +/- 0.4 in AA, 5.9 +/- 0.5 in AG and 9.1 +/- 1.8 g/day in GG; p(adjusted) = 0.019). We also found a statistically-significant association of the pre-miR-27a polymorphism with the risk of having a high alcohol intake (> 2 drinks/day in men and > 1 in women): 5.9\% in AA versus 17.5\% in GG; p(adjusted) < 0.001. In the sensitivity analysis, this association was homogeneous for sex, obesity and Mediterranean diet adherence. In conclusion, we report for the first time a significant association between a miRNA polymorphism (rs895819) and daily alcohol consumption.This study was funded, by the Spanish Ministry of Health (Instituto de Salud Carlos III) and the Ministerio de Economia y Competitividad-Fondo Europeo de Desarrollo Regional (Projects CNIC-06/2007, RTIC G03/140, CIBER 06/03, PI06-1326, PI07-0954, PI11/02505, SAF2009-12304, AGL2010-22319-C03-03 and PRX14/00527), by the lUniversity Jaume I (Project P1-1B2013-54), by Contracts 53-K06-5-10 and 58-1950-9-001 from the U.S. Department of Agriculture Research Service, USA, by the Generalitat Valenciana (ACOMP2010-181, AP111/10, AP-042/11, ACOM2011/145, ACOMP/2012/190, ACOMP/2013/159 and ACOMP/213/165), and with the collaboration of the Real Colegio Complutense at Harvard University, Cambridge. MA, USA. Rocio Barragon's contract is funded by the Ayudas para la contratacion de personal investigador en formacion de caracter predoctoral, Programa ``VALencia Investigacion mas Desarrollo´´ (VALi+d). Conselleria d'Educacio, Investigacio, Cultura i Esport. Generalitat Valenciana, Spain (ACIF/2013/168).S

Einstein-Planck Formula, Equivalence Principle, and Black Hole Radiance

The presence of gravity implies corrections to the Einstein-Planck formula E = hν. This gives hope that the divergent blueshift in frequency, associated to the presence of a black hole horizon, could be smoothed out for the energy. Using simple arguments based on Einstein's equivalence principle, we show that this is only possible if a black hole emits, in a first approximation, not just a single particle, but thermal radiation

Electric-magnetic duality and renormalization in curved spacetimes

We point out that the duality symmetry of free electromagnetism does not hold in the quantum theory if an arbitrary classical gravitational background is present. The symmetry breaks in the process of renormalization, as also happens with conformal invariance. We show that a similar duality anomaly appears for a massless scalar field in 1 + 1 dimensions

Renormalized stress-energy tensor for spin-1/2 fields in expanding universes

We provide an explicit expression for the renormalized expectation value of the stress-energy tensor of a spin-1/2 field in a spatially flat Friedmann-Lemaitre-Robertson-Walker universe. Its computation is based on the extension of the adiabatic regularization method to fermion fields introduced recently in the literature. The tensor is given in terms of UV-finite integrals in momentum space, which involve the mode functions that define the quantum state. As illustrative examples of the method efficiency, we see how to compute the renormalized energy density and pressure in two interesting cosmological scenarios: a de Sitter spacetime and a radiation-dominated universe. In the second case, we explicitly show that the late-time renormalized stress-energy tensor behaves as that of classical cold matter. We also check that, if we obtain the adiabatic expansion of the scalar field mode functions with a similar procedure to the one used for fermions, we recover the well-known WKB-type expansion