The Impact of a 1-Year COVID-19 Extension on Undergraduate Dentistry in Dundee:Final Year Students’ Perspectives of Their Training in Oral Surgery

Background: The detrimental impact of the COVID-19 pandemic on dental education prompted the Scottish Government to fund an additional year to the dental course to ensure that the students had the necessary clinical experience. The aim of the study was to better understand the final year student perceptions of this extension on their oral surgery experience at the University of Dundee. Methods: This mixed methods study consisted of an anonymous online questionnaire and a focus group. Results: Forty-one students (69.3%) completed the questionnaire and ten students participated in the focus group. Thirty-six (88.8%) students agreed that the oral surgery teaching provided sufficient knowledge to undertake independent practice. All of the students felt confident to carry out an extraction, and the majority of them (n = 40, 95%) felt confident to remove a retained root, however, their confidence with surgery was lower. Conclusion: The extension gave the students sufficient experience in oral surgery to gain confidence in clinical skills and an appropriate level of knowledge in preparation for the next phase of their career. Most of the students agreed that the extension was necessary and beneficial. This cohort graduated with more oral surgery experience than any of the students did in the previous 4 years from Dundee and with experience that was comparable with the students at other schools in the pre-COVID-19 era

Bariatric surgery and its impact on cardiovascular disease and mortality : A systematic review and meta-analysis

Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.Peer reviewedPostprin

A High-Resolution Spectroscopic Search for the Remaining Donor for Tycho's Supernova

In this paper, we report on our analysis using Hubble Space Telescope astrometry and Keck-I HIRES spectroscopy of the central six stars of Tycho's supernova remnant (SN 1572). With these data, we measured the proper motions, radial velocities, rotational velocities, and chemical abundances of these objects. Regarding the chemical abundances, we do not confirm the unusu- ally high [Ni/Fe] ratio previously reported for Tycho-G. Rather, we find that for all metrics in all stars, none exhibit the characteristics expected from traditional SN Ia single-degenerate-scenario calculations. The only possible exception is Tycho-B, a rare, metal-poor A-type star; however, we are unable to find a suitable scenario for it. Thus, we suggest that SN 1572 cannot be explained by the standard single-degenerate model.Comment: 34 pages, 11 Figures, revised and resubmitted to Ap

Glial cells are functionally impaired in juvenile neuronal ceroid lipofuscinosis and detrimental to neurons.

The neuronal ceroid lipofuscinoses (NCLs or Batten disease) are a group of inherited, fatal neurodegenerative disorders of childhood. In these disorders, glial (microglial and astrocyte) activation typically occurs early in disease progression and predicts where neuron loss subsequently occurs. We have found that in the most common juvenile form of NCL (CLN3 disease or JNCL) this glial response is less pronounced in both mouse models and human autopsy material, with the morphological transformation of both astrocytes and microglia severely attenuated or delayed. To investigate their properties, we isolated glia and neurons from Cln3-deficient mice and studied their basic biology in culture. Upon stimulation, both Cln3-deficient astrocytes and microglia also showed an attenuated ability to transform morphologically, and an altered protein secretion profile. These defects were more pronounced in astrocytes, including the reduced secretion of a range of neuroprotective factors, mitogens, chemokines and cytokines, in addition to impaired calcium signalling and glutamate clearance. Cln3-deficient neurons also displayed an abnormal organization of their neurites. Most importantly, using a co-culture system, Cln3-deficient astrocytes and microglia had a negative impact on the survival and morphology of both Cln3-deficient and wildtype neurons, but these effects were largely reversed by growing mutant neurons with healthy glia. These data provide evidence that CLN3 disease astrocytes are functionally compromised. Together with microglia, they may play an active role in neuron loss in this disorder and can be considered as potential targets for therapeutic interventions

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). A numerical modeling study to investigate the assumptions used in the calculation of probable maximum precipitation. Water Resources Research, 35(3), 785-796. doi:10.1029/1998wr900013Alexander, G. N. (1963). Using the probability of storm transposition for estimating the frequency of rare floods. Journal of Hydrology, 1(1), 46-57. doi:10.1016/0022-1694(63)90032-5Balkema, A. A., & de Haan, L. (1974). Residual Life Time at Great Age. The Annals of Probability, 2(5). doi:10.1214/aop/1176996548Beauchamp J. 2010. “Estimation d’une PMP et d’une CMP d’un bassin versant septentional en contexte de changements climatiques.” Ph.D. dissertation École de Technologie Supérieure Université du Québec.Ben Alaya, M. A., Zwiers, F., & Zhang, X. (2018). Probable Maximum Precipitation: Its Estimation and Uncertainty Quantification Using Bivariate Extreme Value Analysis. Journal of Hydrometeorology, 19(4), 679-694. doi:10.1175/jhm-d-17-0110.1Benson M. A. 1973. “Thoughts on the design of design floods. 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. Journal of Hydrometeorology, 4(6), 1012-1024. doi:10.1175/1525-7541(2003)0042.0.co;2El Adlouni, S., Ouarda, T. B. M. J., Zhang, X., Roy, R., & Bobée, B. (2007). Generalized maximum likelihood estimators for the nonstationary generalized extreme value model. Water Resources Research, 43(3). doi:10.1029/2005wr004545Elíasson, J. (1994). Statistical Estimates of PMP Values. Hydrology Research, 25(4), 301-312. doi:10.2166/nh.1994.0010Elíasson, J. (1997). A statistical model for extreme precipitation. Water Resources Research, 33(3), 449-455. doi:10.1029/96wr03531England, J. F., Jarrett, R. D., & Salas, J. D. (2003). Data-based comparisons of moments estimators using historical and paleoflood data. Journal of Hydrology, 278(1-4), 172-196. doi:10.1016/s0022-1694(03)00141-0Fernandes, W., Naghettini, M., & Loschi, R. (2010). A Bayesian approach for estimating extreme flood probabilities with upper-bounded distribution functions. Stochastic Environmental Research and Risk Assessment, 24(8), 1127-1143. doi:10.1007/s00477-010-0365-4Fisher, R. A., & Tippett, L. H. C. (1928). Limiting forms of the frequency distribution of the largest or smallest member of a sample. Mathematical Proceedings of the Cambridge Philosophical Society, 24(2), 180-190. doi:10.1017/s0305004100015681Fontaine, T. A., & Potter, K. W. (1989). Estimating Probabilities of Extreme Rainfalls. Journal of Hydraulic Engineering, 115(11), 1562-1575. doi:10.1061/(asce)0733-9429(1989)115:11(1562)Foufoula-Georgiou, E. (1989). A probabilistic storm transposition approach for estimating exceedance probabilities of extreme precipitation depths. Water Resources Research, 25(5), 799-815. doi:10.1029/wr025i005p00799Francés, F. (1998). Using the TCEV distribution function with systematic and non-systematic data in a regional flood frequency analysis. Stochastic Hydrology and Hydraulics, 12(4), 267-283. doi:10.1007/s004770050021Frances, F., Salas, J. D., & Boes, D. C. (1994). Flood frequency analysis with systematic and historical or paleoflood data based on the two-parameter general extreme value models. Water Resources Research, 30(6), 1653-1664. doi:10.1029/94wr00154Gao, M., Mo, D., & Wu, X. (2016). Nonstationary modeling of extreme precipitation in China. Atmospheric Research, 182, 1-9. doi:10.1016/j.atmosres.2016.07.014García-Marín, A. P., Morbidelli, R., Saltalippi, C., Cifrodelli, M., Estévez, J., & Flammini, A. (2019). On the choice of the optimal frequency analysis of annual extreme rainfall by multifractal approach. Journal of Hydrology, 575, 1267-1279. doi:10.1016/j.jhydrol.2019.06.013Gilroy, K. L., & McCuen, R. H. (2012). A nonstationary flood frequency analysis method to adjust for future climate change and urbanization. Journal of Hydrology, 414-415, 40-48. doi:10.1016/j.jhydrol.2011.10.009Groisman, P. Y., Knight, R. W., & Zolina, O. G. (2013). Recent Trends in Regional and Global Intense Precipitation Patterns. 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. Journal of Hydrology, 543, 706-720. doi:10.1016/j.jhydrol.2016.10.044Nathan R. J. and S. K. Merz. 2001. “Estimation of extreme hydrologic events in Australia: Current practice and research needs.” Paper 13 in Proc. Hydrologic research needs for dam safety 69–77. Washington DC: FEMA.Nobilis, F., Haiden, T., & Kerschbaum, M. (1991). Statistical considerations concerning Probable Maximum Precipitation (PMP) in the Alpine Country of Austria. Theoretical and Applied Climatology, 44(2), 89-94. doi:10.1007/bf00867996NWS (National Weather Service). 2015. “Regions covered by different NWS PMP documents (as of 2015) (map).” Accessed May 1 2020. https://www.nws.noaa.gov/oh/hdsc/studies/pmp.html.Ohara, N., Kavvas, M. L., Anderson, M. L., Chen, Z. Q., & Ishida, K. (2017). Characterization of Extreme Storm Events Using a Numerical Model–Based Precipitation Maximization Procedure in the Feather, Yuba, and American River Watersheds in California. Journal of Hydrometeorology, 18(5), 1413-1423. doi:10.1175/jhm-d-15-0232.1Ohara, N., Kavvas, M. L., Kure, S., Chen, Z. Q., Jang, S., & Tan, E. (2011). Physically Based Estimation of Maximum Precipitation over American River Watershed, California. Journal of Hydrologic Engineering, 16(4), 351-361. doi:10.1061/(asce)he.1943-5584.0000324Papalexiou, S. M. (2018). Unified theory for stochastic modelling of hydroclimatic processes: Preserving marginal distributions, correlation structures, and intermittency. Advances in Water Resources, 115, 234-252. doi:10.1016/j.advwatres.2018.02.013Papalexiou, S. M., AghaKouchak, A., & Foufoula‐Georgiou, E. (2018). A Diagnostic Framework for Understanding Climatology of Tails of Hourly Precipitation Extremes in the United States. Water Resources Research, 54(9), 6725-6738. doi:10.1029/2018wr022732Papalexiou, S. M., & Koutsoyiannis, D. (2006). A probabilistic approach to the concept of Probable Maximum Precipitation. Advances in Geosciences, 7, 51-54. doi:10.5194/adgeo-7-51-2006Papalexiou, S. M., & Koutsoyiannis, D. (2012). Entropy based derivation of probability distributions: A case study to daily rainfall. Advances in Water Resources, 45, 51-57. doi:10.1016/j.advwatres.2011.11.007Papalexiou, S. M., & Koutsoyiannis, D. (2013). Battle of extreme value distributions: A global survey on extreme daily rainfall. Water Resources Research, 49(1), 187-201. doi:10.1029/2012wr012557Papalexiou, S. M., & Koutsoyiannis, D. (2016). A global survey on the seasonal variation of the marginal distribution of daily precipitation. Advances in Water Resources, 94, 131-145. doi:10.1016/j.advwatres.2016.05.005Papalexiou, S. M., Koutsoyiannis, D., & Makropoulos, C. (2013). How extreme is extreme? An assessment of daily rainfall distribution tails. Hydrology and Earth System Sciences, 17(2), 851-862. doi:10.5194/hess-17-851-2013Papalexiou, S. M., Markonis, Y., Lombardo, F., AghaKouchak, A., & Foufoula‐Georgiou, E. (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. Journal of Geophysical Research: Atmospheres, 122(9), 4808-4828. doi:10.1002/2016jd026001Reed D. W. and E. J. Stewart. 1989. “Focus on rainfall growth estimation.” In Proc. 2nd National Hydrology Symp. Sheffield UK: British Hydrological Society.Rezacova, D., Pesice, P., & Sokol, Z. (2005). An estimation of the probable maximum precipitation for river basins in the Czech Republic. Atmospheric Research, 77(1-4), 407-421. doi:10.1016/j.atmosres.2004.10.011Rouhani, H., & Leconte, R. (2016). A novel method to estimate the maximization ratio of the Probable Maximum Precipitation (PMP) using regional climate model output. Water Resources Research, 52(9), 7347-7365. doi:10.1002/2016wr018603Rouha

Lattice Thermal Conductivity of Quartz at High Pressure and Temperature from the Boltzmann Transport Equation

The thermal conductivities along the basal and hexagonal directions of α-quartz silica, the low-temperature form of crystalline SiO2, are predicted from the solution of the Boltzmann transport equation combined with the van Beest, Kramer, and van Santen potential for the temperature up to 900 K and the pressure as high as 4 GPa. The thermal conductivities at atmospheric pressure, which show a negative and nonlinear dependence on temperature, are in reasonable agreement with the experimental data. The influence of pressure on thermal conductivity is positive and linear. The pressure (P) and temperature (T) dependences of the thermal conductivity (λ) in basal and hexagonal directions are fitted to a function of the form λ = (b + cP) Ta. The thermal conductivity, influenced by temperature and pressure, is analyzed based on phonon properties, including spectral thermal conductivity, dispersion relation, phonon density of states, phonon lifetime, and phonon probability density distribution function

Herbert Simon's decision-making approach: Investigation of cognitive processes in experts

This is a post print version of the article. The official published can be obtained from the links below - PsycINFO Database Record (c) 2010 APA, all rights reserved.Herbert Simon's research endeavor aimed to understand the processes that participate in human decision making. However, despite his effort to investigate this question, his work did not have the impact in the “decision making” community that it had in other fields. His rejection of the assumption of perfect rationality, made in mainstream economics, led him to develop the concept of bounded rationality. Simon's approach also emphasized the limitations of the cognitive system, the change of processes due to expertise, and the direct empirical study of cognitive processes involved in decision making. In this article, we argue that his subsequent research program in problem solving and expertise offered critical tools for studying decision-making processes that took into account his original notion of bounded rationality. Unfortunately, these tools were ignored by the main research paradigms in decision making, such as Tversky and Kahneman's biased rationality approach (also known as the heuristics and biases approach) and the ecological approach advanced by Gigerenzer and others. We make a proposal of how to integrate Simon's approach with the main current approaches to decision making. We argue that this would lead to better models of decision making that are more generalizable, have higher ecological validity, include specification of cognitive processes, and provide a better understanding of the interaction between the characteristics of the cognitive system and the contingencies of the environment

The silicon supplement 'Monomethylsilanetriol' is safe and increases the body pool of silicon in healthy Pre-menopausal women.

BACKGROUND: Monomethylsilanetriol (MMST) has been used for decades as an oral silicon supplement for bone and connective tissue health, although there are no formal data on its in vivo utilisation or safety following sustained dosing. METHODS: To investigate whether MMST contributes to the body pool of silicon and, secondly, to establish its safety following 4 weeks' supplementation in humans, twenty-two healthy pre-menopausal women (22-38 years) were recruited and supplemented with MMST at the maximum daily recommended dose (10.5 mg Si/day) for 4 weeks in a double-blind, randomised, placebo-controlled, cross-over design (i.e. 8 weeks in total). Fasting serum and urine samples were collected at baseline and at the end of the 4-week supplementation/placebo periods for analysis of total silicon by inductively coupled plasma optical emission spectrometry, MMST by proton nuclear magnetic resonance spectroscopy and full serum biochemistry. Participants also reported on, by questionnaire, their health, well-being and quality of life at 0, 4 and 8 weeks. RESULTS: Overall, 4-weeks supplementation with MMST significantly increased total fasting Si concentrations in serum and urine (P ≤ 0.003; paired t-test). MMST was semi-quantifiable in serum and quantifiable in urine, but only accounted for ca. 50% and 10%, respectively, of the increased total-Si concentration. There were no reported adverse effects (i.e. changes to health and well-being) or serum biochemical changes with MMST versus placebo. CONCLUSIONS: Our data indicate that oral MMST is safe, is absorbed and undergoes sufficient metabolism in vivo to raise fasting serum silicon levels, consistent with other well absorbed forms of dietary silicon (e.g. orthosilicic acid). It thus appears to be a suitable silicon supplement

Resolution of the Distance Ambiguity for Galactic HII Regions

We resolve the kinematic distance ambiguity for 266 inner Galaxy HII regions out of a sample of 291 using existing HI and 13CO sky surveys. Our sample contains all HII regions with measured radio recombination line (RRL) emission over the extent of the 13CO Boston University-Five College Radio Astronomy Observatory Galactic Ring Survey (18 deg, < l < 55 deg. and |b| < 1) and contains ultra compact, compact, and diffuse HII regions. We use two methods for resolving the distance ambiguity for each HII region: HI emission/absorption (HIEA) and HI self-absorption (HISA). We find that the HIEA and HISA methods can resolve the distance ambiguity for 72% and 87% of our sample, respectively. When projected onto the Galactic plane, this large sample appears to reveal aspects of Galactic structure, with spiral arm-like features at Galactocentric radii of 4.5 and 6 kpc, and a lack of HII regions within 3.5 kpc of the Galactic center. Our HII regions are approximately in the ratio of 2 to 1 for far verses near distances. The ratio of far to near distances for ultra-compact HII regions is 2.2 to 1. Compact HII regions are preferentially at the near distance; their ratio of far to near distances is 1.6 to 1. Diffuse HII regions are preferentially at the far distance; their ratio of far to near distances is 3.8 to 1. This implies that the distinction between ultra compact and compact HII regions is due largely to distance, and that the large angular size of diffuse HII regions is not due solely to proximity to the Sun.Comment: Accepted to Ap