Lake-size dependency of wind shear and convection as controls on gas exchange

High-frequency physical observations from 40 temperate lakes were used to examine the relative contributions of wind shear (u*) and convection (w*) to turbulence in the surface mixed layer. Seasonal patterns of u* and w* were dissimilar; u* was often highest in the spring, while w * increased throughout the summer to a maximum in early fall. Convection was a larger mixed-layer turbulence source than wind shear (u */w*-1 for lakes* and w* differ in temporal pattern and magnitude across lakes, both convection and wind shear should be considered in future formulations of lake-air gas exchange, especially for small lakes. © 2012 by the American Geophysical Union.Jordan S. Read, David P. Hamilton, Ankur R. Desai, Kevin C. Rose, Sally MacIntyre, John D. Lenters, Robyn L. Smyth, Paul C. Hanson, Jonathan J. Cole, Peter A. Staehr, James A. Rusak, Donald C. Pierson, Justin D. Brookes, Alo Laas, and Chin H. W

Distinctive effects of allochthonous and autochthonous organic matter on CDOM spectra in a tropical lake

Despite the increasing understanding about differences in carbon cycling between temperate and tropical freshwater systems, our knowledge on the importance of organic matter (OM) pools on light absorption properties in tropical lakes is very scarce. We performed a factorial mesocosm experiment in a tropical lake (Minas Gerais, Brazil) to evaluate the effects of increased concentrations of al-lochthonous and autochthonous OM, and differences in light availability on the light absorption characteristics of chromophoric dissolved organic matter (CDOM). Autochthonous OM deriving from phytoplankton (similar to Chl a) was stimulated by addition of nutrients, while OM from degradation of terrestrial leaves increased allochthonous OM, and neutral shading was used to manipulate light availability. Effects of the additions and shading on DOC, Chl a, nutrients, total suspended solid concentrations (TSM) and spectral CDOM absorption were monitored every 3 days. CDOM quality was characterized by spectral indices (S250-450, S275-295, S350-450, S-R and SUVA(254)). Effects of carbon sources and shading on the spectral CDOM absorption was investigated through principal component (PCA) and redundancy (RDA) analyses. The two different OM sources affected CDOM quality very differently and shading had minor effects on OM levels, but significant effects on OM quality, especially in combination with nutrient additions. Spectral indices (S250-450 and S-R) were mostly affected by allochthonous OM addition. The PCA showed that enrichment by allochthonous carbon had a strong effect on the CDOM spectra in the range between 300 and 400 nm, while the increase in autochthonous carbon increased absorption at wavelengths below 350 nm. Our study shows that small inputs of allochthonous OM can have large effects on the spectral light absorption compared to large production of autochthonous OM, with important implications for carbon cycling in tropical lakes.Peer reviewe

Wind and trophic status explain within and among-lake variability of algal biomass

Phytoplankton biomass and production regulates key aspects of freshwater ecosystems yet its variability and subsequent predictability is poorly understood. We estimated within-lake variation in biomass using high-frequency chlorophyll fluorescence data from 18 globally distributed lakes. We tested how variation in fluorescence at monthly, daily, and hourly scales was related to high-frequency variability of wind, water temperature, and radiation within lakes as well as productivity and physical attributes among lakes. Within lakes, monthly variation dominated, but combined daily and hourly variation were equivalent to that expressed monthly. Among lakes, biomass variability increased with trophic status while, within-lake biomass variation increased with increasing variability in wind speed. Our results highlight the benefits of high-frequency chlorophyll monitoring and suggest that predicted changes associated with climate, as well as ongoing cultural eutrophication, are likely to substantially increase the temporal variability of algal biomass and thus the predictability of the services it provides.Peer reviewe

Detecting spatio-temporal mortality clusters of European countries by sex and ag

[EN] Background: Mortality decreased in European Union (EU) countries during the last century. Despite these similar trends, there are still considerable differences in the levels of mortality between Eastern and Western European countries. Sub-group analysis of mortality in Europe for different age and sex groups is common, however to our knowledge a spatio-temporal methodology as in this study has not been applied to detect significant spatial dependence and interaction with time. Thus, the objective of this paper is to quantify the dynamics of mortality in Europe and detect significant clusters of mortality between European countries, applying spatio-temporal methodology. In addition, the joint evolution between the mortality of European countries and their neighbours over time was studied. Methods: The spatio-temporal methodology used in this study takes into account two factors: time and the geographical location of countries and, consequently, the neighbourhood relationships between them. This methodology was applied to 26 European countries for the period 1990-2012. Results: Principally, for people older than 64 years two significant clusters were obtained: one of high mortality formed by Eastern European countries and the other of low mortality composed of Western countries. In contrast, for ages below or equal to 64 years only the significant cluster of high mortality formed by Eastern European countries was observed. In addition, the joint evolution between the 26 European countries and their neighbours during the period 1990-2012 was confirmed. For this reason, it can be said that mortality in EU not only depends on differences in the health systems, which are a subject to national discretion, but also on supra-national developments. Conclusions: This paper proposes statistical tools which provide a clear framework for the successful implementation of development public policies to help the UE meet the challenge of rethinking its social model (Social Security and health care) and make it sustainable in the medium term.The authors are grateful for the financial support provided by the Ministry of Economy and Competitiveness, project MTM2013-45381-P. Adina Iftimi gratefully acknowledges financial support from the MECyD (Ministerio de Educacion, Cultura y Deporte, Spain) Grant FPU12/04531. Francisco Montes is grateful for the financial support provided by the Spanish Ministry of Economy and Competitiveness, project MTM2016-78917-R. The research by Patricia Carracedo and Ana Debon has been supported by a grant from the Mapfre Foundation.Carracedo-Garnateo, P.; Debón Aucejo, AM.; Iftimi, A.; Montes-Suay, F. (2018). Detecting spatio-temporal mortality clusters of European countries by sex and ag. International Journal for Equity in Health. 17:1-19. https://doi.org/10.1186/s12939-018-0750-zS11917Anderson TW, Goodman LA. Statistical Inference about Markov Chains. Ann Math Stat. 1957; 28(1):89–110.Anselin L. Local Indicators of Spatial Association–LISA. Geographical Anal. 1995; 27(2):93–115.Bilbao-Ubillos J. Is there still such a thing as the ‘European social model’?. Int J Soc Welf. 2016; 25:110–25.Bivand R. spdep: Spatial Dependence:Weighting Schemes, Statistics and Models. 2012. R package version 0.5-53. http://CRAN.R-project.org/package=spdep .Bivand R, Hauke J, Kossowski T. Computing the Jacobian in Gaussian Spatial Autoregressive Models: An Illustrated Comparison of Available Methods. Geographical Anal. 2013; 45(2):150–79.Bivand R, Keitt T, Rowlingson B. rgdal: Bindings for the Geospatial Data Abstraction Library. 2016. R package version 1.1-10. https://CRAN.R-project.org/package=rgdal .Bivand R, Lewin-Koh N. maptools: Tools for Reading and Handling Spatial Objects. 2016. R package version 0.8-39 https://CRAN.R-project.org/package=maptools .Bonneux L, Huisman C. de Beer J. Mortality in 272 European regions, 2002-2004: an update. Eur J Epidemiol. 2010; 25(1):77–85. Reporting year: 2010.Charpentier A. Computational Actuarial Science with R. Chapman y Hall/CRC. 2014.Cliff AD, Ord JK. Spatial autocorrelation. London: Pion; 1973.Cutler D, Deaton A, Lleras-Muney A. The Determinants of Mortality. J Econ Perspect. 2006; 20(3):97–120.Debón A, Chaves L, Haberman S, Villa F. Characterization of between-group inequality of longevity in European Union countries. Insur Math Econ. 2017; 75:151–65.Fleiss J, Levin B, Paik M. Statistical Methods for Rates and Proportions: Wiley; 2013.Gordon M. Gmisc: Descriptive Statistics, Transition Plots, and More. 2016. R package version 1.3.1. https://CRAN.R-project.org/package=Gmisc .Hinde A. Demographic methods. Routledge: Routledge; 1998.Hyndman RJ, Booth H, Tickle L, Maindonald J. demography: Forecasting mortality, fertility, migration and population data. 2014. package version 1.18. https://CRAN.R-project.org/package=demography .Human Mortality Database. University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany). 2016. Available at www.mortality.org or www.humanmortality.de (data downloaded on 12th July 2016).Hatzopoulos P, Haberman S. Common mortality modeling and coherent forecasts. An empirical analysis of worldwide mortality data. Insurance Math Econ. 2013; 52(2):320–37.Iftimi A, Montes F, Santiyán AM, Martínez-Ruiz F. Space–time airborne disease mapping applied to detect specific behaviour of varicella in Valencia, Spain Spatial Spatio-Temporal Epidemiol. 2015; 14:33–44.Julious S, Nicholl J, George S. Why do we continue to use standardized mortality ratios for small area comparisons?. J Public Health. 2001; 23(1):40–6.Laurent T, Ruiz-Gazen A, Thomas-Agnan C. GeoXp: An R package for exploratory spatial data analysis. J Stat Softw. 2012; 47(2):1–23.Leon DA. Trends in European life expectancy: a salutary view. Int J Epidemiol. 2011; 40:271–7.Li H, Li L, Wu B, Xiong Y. The End of Cheap Chinese Labor. J Econ Perspect. 2013; 26(4):57–74.Mackenbach JP, Karanikolos M, McKee M. The unequal health of Europeans: successes and failures of policies. The Lancet. 2013; 381(9872):1125–34.Meslé F. Mortality in Central and Eastern Europe: Long-term trends and recent upturns. Demographic Res. 2004; 2:45–70.Meslé F, Vallin J. Mortality in Europe: The divergence between East and West. Population (English Edition). 2002; 57(1):157–97.Moran PAP. Notes on continuous stochastic phenomena. Biometrika. 1950; 37(1-2):17–23.Moran PAP. A Test for the Serial Independence of Residuals. Biometrika. 1950; 37(1/2):178–81.Neuwirth E. RColorBrewer: ColorBrewer Palettes. R package version. 2014; 1:1–2. https://CRAN.R-project.org/package=RColorBrewer .Oleckno WA. Epidemiology: concepts and methods: Waveland Press, Inc.; 2008.Quah D. Galton’s Fallacy and Tests of the Convergence Hypothesis. Scand J Econ. 1993; 95(4):427–43.R Core Team. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. 2015. https://www.R-project.org/ .Rey S. In: Fischer MM, Nijkamp P, (eds).Spatial Dynamics and Space-Time Data Analysis. Berlin, Heidelberg: Springer: Handbook of Regional Science; 2014, pp. 1365–83.Rey SJ. Spatial Empirics for Economic Growth and Convergence. Geogr Anal. 2001; 33(3):195–214.Riffe T. Reading Human Fertility Database and Human Mortality Database data into R. Technical Report TR-2015-004, MPIDR. 2015.Schofield R, Reher D, Bideau A. The Decline of Mortality in Europe. International studies in demography. Oxford: Clarendon Press; 1991.Shaw M, Orford S, Brimblecombe N, Dorling D. Widening inequality in mortality between 160 regions of 15 European countries in the early 1990s. Soc Sci Med. 2000; 50(7-8):1047–58.Spinakis A, Anastasiou G, Panousis V, Spiliopoulos K, Palaiologou S, Yfantopoulos J. Expert Review and Proposals for Measurement of Health Inequalities in the European Union. European Commission. Technical report,Luxembourg: European Commission Directorate General for Health and Consumers; 2011. http://ec.europa.eu/health/social_determinants/docs/full_quantos_en.pdf .Staehr K. Economic transition in Estonia. Background, reforms and results In: Rindzeviciute E, editor. Contemporary Change in Estonia. Baltic and East European Studies. Sodertorns hogskola: Baltic and East European Studies: 2004. p. 437–67.Trnka L, Dankova D, Zitova J, Cimprichova L, Migliori GB, Clancy L, Zellweger J. Survey of BCG vaccination policy in Europe: 1994-96. Bull World Health Organ. 1998; 76(1):85–91.United Nations Inter–agency Group for Child Mortality Estimation. Levels & Trends in Child Mortality: Report 2013. New York: Technical report, United Nations Children’s Fund; 2013. Avaliable at www.who.int/maternal_child_adolescent/documents/levels_trends_child_mortality_2013.pdf Accessed 27 Oct 2016.Vågerö D. The east–west health divide in Europe: Growing and shifting eastwards. Eur Rev. 2010; 18(01):23–34.Vaupel JW, Zhang Z, van Raalte AA, Vaupel JW, Zhang Z, van Raalte AA. Life expectancy and disparity: an international comparison of life table data. BMJ Open. 2011; 1:e000128.Wickham H, Chang W. devtools: Tools to Make Developing R Packages Easier. R package version 1.11.1. 2016. https://CRAN.R-project.org/package=devtools .Wilcox R. Introduction to robust estimation and hypothesis testing, 3rd Edition.San Diego: Academic Press; 2012

Managing changes initiated by industrial big data technologies : a technochange management model

With the adoption of Internet of Things and advanced data analytical technologies in manufacturing firms, the industrial sector has launched an evolutionary journey toward the 4th industrial revolution, or so called Industry 4.0. Industrial big data is a core component to realize the vision of Industry 4.0. However, the implementation and usage of industrial big data tools in manufacturing firms will not merely be a technical endeavor, but can also lead to a thorough management reform. By means of a comprehensive review of literature related to Industry 4.0, smart manufacturing, industrial big data, information systems (IS) and technochange management, this paper aims to analyze potential changes triggered by the application of industrial big data in manufacturing firms, from technological, individual and organizational perspectives. Furthermore, in order to drive these changes more effectively and eliminate potential resistance, a conceptual technochange management model was developed and proposed. Drawn upon theories reported in literature of IS technochange management, this model proposed four types of interventions that can be used to copy with changes initiated by industrial big data technologies, including human process intervention, techno-structural intervention, human resources management intervention and strategic intervention. This model will be of interests and value to practitioners and researchers concerned with business reforms triggered by Industry 4.0 in general and by industrial big data technologies in particular

Diel surface temperature range scales with lake size

Ecological and biogeochemical processes in lakes are strongly dependent upon water temperature. Long-term surface warming of many lakes is unequivocal, but little is known about the comparative magnitude of temperature variation at diel timescales, due to a lack of appropriately resolved data. Here we quantify the pattern and magnitude of diel temperature variability of surface waters using high-frequency data from 100 lakes. We show that the near-surface diel temperature range can be substantial in summer relative to long-term change and, for lakes smaller than 3 km2, increases sharply and predictably with decreasing lake area. Most small lakes included in this study experience average summer diel ranges in their near-surface temperatures of between 4 and 7°C. Large diel temperature fluctuations in the majority of lakes undoubtedly influence their structure, function and role in biogeochemical cycles, but the full implications remain largely unexplored

Diel surface temperature range scales with lake size

Ecological and biogeochemical processes in lakes are strongly dependent upon water temperature. Long-term surface warming of many lakes is unequivocal, but little is known about the comparative magnitude of temperature variation at Diel timescales, due to a lack of appropriately resolved data. Here we quantify the pattern and magnitude of Diel temperature variability of surface waters using high-frequency data from 100 lakes. We show that the near-surface Diel temperature range can be substantial in summer relative to long-term change and, for lakes smaller than 3 km2, increases sharply and predictably with decreasing lake area. Most small lakes included in this study experience average summer Diel ranges in their near-surface temperatures of between 4 and 7°C. Large Diel temperature fluctuations in the majority of lakes undoubtedly influence their structure, function and role in biogeochemical cycles, but the full implications remain largely unexplored

Translational pharmacology of an inhaled small molecule αvβ6 integrin inhibitor for idiopathic pulmonary fibrosis

The αvβ6 integrin plays a key role in the activation of transforming growth factor-β (TGFβ), a pro-fibrotic mediator that is pivotal to the development of idiopathic pulmonary fibrosis (IPF). We identified a selective small molecule αvβ6 RGD-mimetic, GSK3008348, and profiled it in a range of disease relevant pre-clinical systems. To understand the relationship between target engagement and inhibition of fibrosis, we measured pharmacodynamic and diseaserelated end points. Here we report, GSK3008348 binds to αvβ6 with high affinity in human IPF lung and reduces downstream pro-fibrotic TGFβ signaling to normal levels. In human lung epithelial cells, GSK3008348 induces rapid internalization and lysosomal degradation of the αvβ6 integrin. In the murine bleomycin-induced lung fibrosis model, GSK3008348 engages αvβ6, induces prolonged inhibition of TGFβ signaling and reduces lung collagen deposition and serum C3M, a marker of IPF disease progression. These studies highlight the potential of inhaled GSK3008348 as an anti-fibrotic therapy

Atmospheric stilling leads to prolonged thermal stratification in a large shallow polymictic lake

To quantify the effects of recent and potential future decreases in surface wind speeds on lake thermal stratification, we apply the one-dimensional process-based model MyLake to a large, shallow, polymictic lake, Võrtsjärv. The model is validated for a 3-year period and run separately for 28 years using long-term daily atmospheric forcing data from a nearby meteorological station. Model simulations show exceptionally good agreement with observed surface and bottom water temperatures during the 3-year period. Similarly, simulated surface water temperatures for 28 years show remarkably good agreement with long-term in situ water temperatures. Sensitivity analysis demonstrates that decreasing wind speeds has resulted in substantial changes in stratification dynamics since 1982, while increasing air temperatures during the same period had a negligible effect. Atmospheric stilling is a phenomenon observed globally, and in addition to recent increases in surface air temperature, needs to be considered when evaluating the influence of climate change on lake ecosystems