Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI

[EN] Ecohydrological modeling studies in developing countries, such as sub-Saharan Africa, often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to fill this gap, but require novel methodologies to exploit their spatiotemporal information that could potentially be incorporated into model calibration and validation frameworks. The present study tackles this problem by suggesting an automatic calibration procedure, based on the empirical orthogonal function, for distributed ecohydrological daily models. The procedure is tested with the support of remote sensing data in a data-scarce environment-the upper Ewaso Ngiro river basin in Kenya. In the present application, the TETIS-VEG model is calibrated using only NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The results demonstrate that (1) satellite data of vegetation dynamics can be used to calibrate and validate ecohydrological models in water-controlled and datascarce regions, (2) the model calibrated using only satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet and (3) the proposed automatic calibration methodology works satisfactorily and it allows for a straightforward incorporation of spatio-temporal data into the calibration and validation framework of a model.The research leading to these results has received funding from the Spanish Ministry of Economy and Competitiveness and FEDER funds, through the research projects ECOTETIS (CGL2011-28776-C02-014) and TETISMED (CGL2014-58127-C3-3-R). The collaboration between Universitat Politecnica de Valencia, Universita degli studi della Basilicata and Princeton University was funded by the Spanish Ministry of Economy and Competitiveness through the EEBB-I-15-10262 fellowship.Ruiz Perez, G.; Koch, J.; Manfreda, S.; Caylor, KK.; Francés, F. (2017). Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI. HYDROLOGY AND EARTH SYSTEM SCIENCES. 21(12):6235-6251. https://doi.org/10.5194/hess-21-6235-2017S623562512112Allen, R. G., Pruitt, W. O., Wright, J. L., Howell, T. A., Ventura, F., Snyder, R., Itenfisu, D., Steduto, P., Berengena, J., Yrisarry, J. B., Smith, M., Pereira, L. S., Raes, D., Perrier, A., Alves, I., Walter, I., Elliott, R.: A recommendation on standardized surface resistance for hourly calculation of reference ET0 by the FAO56 Penman-Monteith method, Agr. Water Manage., 81, 1–22, https://doi.org/10.1016/j.agwat.2005.03.007, 2006.Andersen, F. H.: Hydrological modeling in a semi-arid area using remote sensing data, Doctoral Thesis, Department of Geography and Geology, University of Copenhagen, Denmark, 2008.Bjornsson, H. and Venegas, S. A.: A manual for EOF and SVD analyses of climate data, CCGCR Rep. 97-1, McGill University, Montréal, Canada, 52 pp., 1997.Bonaccorso, B., Bordi, I., Cancelliere, A., Rossi, G., and Sutera, A.: Spatial variability of drought: an analysis of the SPI in Sicily, Water Resour. Manage., 17, 273–296, 2003.Bond, B. J., Jones, J. A., Moore, G., Phillips, N., Post, D., and McDonnell, J. J.: The zone of vegetation influence on baseflow revealed by diel patterns of streamflow and vegetation water use in a headwater basin, Hydrol. Process., 16, 1671–1677, 2002.Brown, B. G., Gilleland, E., and Ebert, E. E.: Forecasts of spatial fields, in: Forecast Verification, John Wiley, 95–117, 2011.Caylor, K. K., D'Odorico P., and Rodriguez-Iturbe I.: On the ecohydrological organization of spatially heterogeneous semi-arid landscapes, Water Resour. Res., 42, W07424, https://doi.org/10.1029/2005WR004683, 2006.Ceballos, Y. and Ruiz de la Torre, J.: Árboles y arbustos de la España peninsular, ETSI Montes Publications, Madrid, 1979.Chen, J. M. and Cihlar, J.: Retrieving leaf area index of boreal conifer forests using Landsat TM images, Remote Sens. Environ., 55, 153–162, 1996.Cheng, L., Yaeger, M., Viglione, A., Coopersmith, E., Ye, S., and Sivapalan, M.: Exploring the physical controls of regional patterns of flow duration curves – Part 1: Insights from statistical analyses, Hydrol. Earth Syst. Sci., 16, 4435–4446, https://doi.org/10.5194/hess-16-4435-2012, 2012.Cohen, W. B., Maiersperger, T. K., Gower, S. T., and Turner, D. P.: An improved strategy for regression of biophysical variables and Landsat ETM+data, Remote Sens. Environ., 84, 561–571, 2003.Cohen, J.: Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit, Psychol. Bull., 70, 213–220, https://doi.org/10.1037/h0026256, 1968.Conradt, T., Wechsung, F., and Bronstert, A.: Three perceptions of the evapotranspiration landscape: comparing spatial patterns from a distributed hydrological model, remotely sensed surface temperatures, and sub-basin water balances, Hydrol. Earth Syst. Sci., 17, 2947–2966, https://doi.org/10.5194/hess-17-2947-2013, 2013.Coopersmith, E., Yaeger, M. A., Ye, S., Cheng, L., and Sivapalan, M.: Exploring the physical controls of regional patterns of flow duration curves – Part 3: A catchment classification system based on regime curve indicators, Hydrol. Earth Syst. Sci., 16, 4467–4482, https://doi.org/10.5194/hess-16-4467-2012, 2012.Drewry, D. T. and Albertson, J. D.: Diagnosing model error in canopy-atmosphere exchange using empirical orthogonal function analysis, Water Resour. Res., 42, W06421, https://doi.org/10.1029/2005WR004496, 2006.Fang, Z., Bogena, H., Kollet, S., Koch, J., and Vereecken, H.: Spatio-temporal validation of long-term 3D hydrological simulations of a forested catchment using empirical orthogonal functions and wavelet coherence analysis, J. Hydrol., 529, 1754–1767, 2015.Feyen, L., Kalas, M., and Vrugt, J. A.: Semi-distributed parameter optimization and uncertainty assessment for large-scale stream-flow simulation using global optimization, Hydrolog. Sci. J., 53, 293–308, https://doi.org/10.1623/hysj.53.2.293, 2008.Francés, F. and Benito, J.: La modelación distribuida con pocos parámetros de las crecidas, Ingeniería del Agua, 2, 7–24, 1995.Francés, F., Vélez, J. I., and Vélez, J. J.: Split-parameter structure for the automatic calibration of distributed hydrological models, J. Hydrol., 332, 226–240, 2008.Franssen, H. J. H, Brunner, P., Makobo, P., and Kinzelbach, W.: Equally likely inverse solutions to a groundwater flow problem including pattern information from remote sensing images, Water Resour. Res., 44, W01419, https://doi.org/10.1029/2007WR006097, 2008.Franz, T. E.: Ecohydrology of the upper Ewaso Ngiro river basin, Kenia, Doctoral Thesis, Princeton University, Princeton, NJ, USA, 2007.Franz, T. E., Caylor, K. K., Nordbotten, J. M., Rodríguez-Iturbe, I., and Celia, M. A.: An ecohydrological approach to predicting regional woody species distribution patterns in dryland ecosystems, Adv. Water Resour., 33, 215–230, 2010.Frassnacht, K. S., Gower, S. T., MacKenzie, M. D., Nordheim, E. V., and Lillesand, T. M.: Estimating the leaf area index of north central Wisconsin forests using the Landsat Thematic Mapper, Remote Sens. Environ., 61, 229–245, 1997.Friedl, M. A., Michaelsen, J., Davis, F. W., Walker, H., and Schimel, D. S.: Estimating grassland biomass and leaf area index using ground and satellite data, Int. J. Remote Sens., 15, 1401–1420, 1994.Gamon, J. A., Serrano, L., and Surfus, J. S.: The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels, Oecologia, 112, 492–501, 1997.García-Arias, A. and Francés, F.: The RVDM: modelling impacts, evolution and competition processes to determine riparian vegetation dynamics, Ecohydrology, 9, 438–459, https://doi.org/10.1002/eco.1648, 2016.Gigante, V., Iacobellis, V., Manfreda, S., Milella, P., and Portoghese, I.: Influences of Leaf Area Index estimations on water balance modeling in a Mediterranean semi-arid basin, Nat. Hazards Earth Syst. Sci., 9, 979–991, https://doi.org/10.5194/nhess-9-979-2009, 2009.GIMHA team (Vélez, I., Vélez, J., Puricelli, M., Montoya, J. J., Camilo, J. C., Bussi, G., Medici, C., Orozco, I., Ruiz-Pérez, G.): Description of the distributed conceptual hydrological model TETIS v.8, Universitat Politècnica de València, 2014.Gilleland, E., Ahijevych, D. A., Brown, B. G., and Ebert, E. E.: Verifying forecasts spatially, B. Am. Meteorol. Soc., 91, 1365–1373, 2010.Graf, A., Bogena, H. R., Drüe, C., Hardelauf, H., Pütz, T., Heinemann, G., and Vereecken, H.: Spatiotemporal relations between water budget components and soil moisture in a forested tributary catchment, Water Resour. Res., 50, 4837–4857, https://doi.org/10.1002/2013WR014516, 2014.Gribovszki, Z., Kalicz, P., Szilágyi, J., and Kucsara, M.: Riparian zone evapotranspiration estimation from diurnal groundwater level fluctuations, J. Hydrol., 349, 6–17, https://doi.org/10.1016/j.jhydrol.2007.10.049, 2008.Gutmann, E. D. and Small, E. E.: A method for the determination of the hydraulic properties of soil from MODIS surface temperature for use in land-surface models, Water Resour. Res., 46, W06520, https://doi.org/10.1029/2009WR008203, 2010.Immerzel, W. and Droogers, P.: Calibration of a distributed hydrological model based on satellite evapotranspiration, J. Hydrol., 349, 411–424, 2008.Jasechko, S., Sharp, Z. D., Gibson, J. J., Birks, S. J., Yi, Y., and Fawcett, P. J.: Terrestrial water fluxes dominated by transpiration, Nature, 496, 347–350, https://doi.org/10.1038/nature11983, 2013.Kim, G. and Barros, A. P.: Space-time characterization of soil moisture from passive microwave remotely sensed imagery and ancillary data, Remote Sens.Environ., 81, 393–403, 2002.Koch, J., Jensen, K. H., and Stisen, S.: Toward a true spatial model evaluation in distributed hydrological modeling: Kappa statistics, Fuzzy theroy, and EOF-analysis benchmarked by the human perception and evaluated against a modeling case study, Water Resour. Res., 51, 1225–1246, https://doi.org/10.1002/2014WR016607, 2015.Koch, J., Cornelissen, T., Fang, Z., Bogena, H., Diekkrüger, B., Kollet, S., and Stisen, S.: Inter-comparison of three distributed hydrological models with respect to seasonal variability of soil moisture patterns at a small forested catchment, J. Hydrol., 533, 234–249, 2016a.Koch, J., Siemann, A., Stisen, S., and Sheffield, J.: Spatial validation of large-scale land surface models against monthly land surface temperature patterns using innovative performance metrics, J. Geophys. Res.-Atmos., 121, 5430–5452, https://doi.org/10.1002/2015JD024482, 2016b.Kunnath-Poovakka, A., Ryu, D., Renzullo, L. J., and George, B.: The efficacy of calibrating hydrologic model using remotely sensed evapotranspiration and soil moisture for streamflow prediction, J. Hydrol., 535, 509–524, 2016.Landsberg, J. J. and Waring, R. H.: A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partiotioning, Forest Ecol. Manage., 95, 209–228, 1997.Law, B. E. and Waring, R. H.: Remote sensing of leaf area index and radiation intercepted by understory vegetation, Ecol. Appl., 4, 272–279, 1994.Le Roux, X., Bariac, T., and Mariotti, A.: Spatial partitioning of the soil water resource between grass and shrub components in a West African humid savanna, Oecologia, 104, 147–155, 1995.Liu, Y.: Spatial patterns of soil moisture connected to monthly-seasonal precipitation variability in a monsoon region, J. Geophys. Res.-Atmos., 108, 8856, https://doi.org/10.1029/2002JD003124, 2003.Lo, M. H., Famiglietti, J. S., Yeh, P. J., and Syed, T. H.: Improving parameter estimation and water table depth simulation in a land surface model using GRACE water storage and estimated base flow data, Water Resour. Res., 46, W05517, https://doi.org/10.1029/2009WR007855, 2010.López-Serrano, F. R., Landete-Castillejos, T., Martínez-Millán, J., and Cerro-Barja, A.: LAI estimation of natural pine forest using a non-standard sampling technique, Agr. Forest Meteorol., 101, 95–111, https://doi.org/10.1016/S0168-1923(99)00171-9, 2000.Manfreda, S. and Caylor, K. K.: On The Vulnerability of Water Limited Ecosystems to Climate Change, Water, 5, 819–833, 2013.Manfreda, S., Pizzolla, T., and Caylor, K. K.: Modelling vegetation patterns in semiarid environments, Procedia Environ. Sci., 19, 168–177, 2013.Manfreda, S., Fiorentino, M., and Iacobellis, V.: DREAM: a distributed model for runoff, evapotranspiration, and antecedent soil moisture simulation, Adv. Geosci., 2, 31–39, https://doi.org/10.5194/adgeo-2-31-2005, 2005.McCabe, M. F., Wood, E. F., Wojcik, R., Pan, M., Sheffield, J., Gao, H., and Su, H.: Hydrological consistency using multi-sensor remote sensing data for water and energy cycle studies, Remote Sens. Environ., 112, 430–444, 2008.Medlyn, B. E.: Physiological basis of the light use efficiency model, Tree Physiol., 18, 167–176, 1998.Merz, R., Parajka, J., and Bloschl, G.: Scale effects in conceptual hydrological modeling, Water Resour. Res., 45, W09405, https://doi.org/10.1029/2009WR007872, 2009.Michaud, J. and Sorooshian, S.: Comparison of simple versus complex distributed runoff models on a midsized semiarid watershed, Water Resour. Res., 30, 593–605, 1994.Montaldo, N., Rondena, R., Albertson, J. D., and Mancini, M.: Parsimonious modeling of vegetation dynamics for ecohydrologic studies of water limited ecosystems, Water Resour. Res., 41, W10416, https://doi.org/10.1029/2005WR004094, 2005.Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, 1970.Nyholm, T., Rasmussen, K. R., and Christensen, S.: Estimation of stream flow depletion and uncertainty from discharge measurements in a small alluvial stream, J. Hydrol., 274, 129–144, 2003.Ollinger, S. V., Richardson, A. D., Martin, M. E., Hollinger, D. Y., Frolking, S., Reich, P. B., Plourde, L. C., Katul, G., Munger, J. W., Oren, R., Smith, M. L., Paw, U., Bolstad, K. T., Cook, P. V., Day, B., Martin, M. C., Monson, T. A., and Schmidt, R. K. H. P.: Canopynitrogen, carbon assimilation, and albedo in temperate and boreal forests: functional relations and potential climate feedbacks, P. Natl. Acad. Sci. USA, 105, 19335–19340, 2008.Pasquato, M., Medici, M., Friend, A. D., and Francés, F.: Comparing two approaches for parsimonious vegetation modelling in semiarid regions using satellite data, Ecohydrology, 8, 1024–1036, https://doi.org/10.1002/eco.1559, 2015.Porporato, A., Laio, F., Ridolfi, L., and Rodriguez-Iturbe, I.: Plants in water-controlled ecosystems: active role in hydrologic processes and response to water stress: III. Vegetation water stress, Adv. Water Resour., 24, 725–744, 2001.Pumo, D., Noto, L. V., and Viola, F.: Ecohydrological modelling of flow duration curve in Mediterranean river basins, Adv. Water Resour., 52, 314–327, 2013.Preisendorfer, R. W. and Mobbley, C. D.: Principal component analysis in meteorology and oceanography, Vol. 425, Amsterdam, Elsevier, 1988.Quevedo, D. I. and Francés, F.: A conceptual dynamic vegetation-soil model for arid and semiarid zones, Hydrol. Earth Syst. Sci., 12, 1175–1187, https://doi.org/10.5194/hess-12-1175-2008, 2008.Rientjes, T. H. M., Muthuwatta, L. P., Bos, M. G., Booij, M. J., and Bhatti, H. A.: Multi-variable calibration of a semi-distributed hydrological model using streamflow data and satellite-based evapotranspiration, J. Hydrol., 505, 276–290, 2013.Reed, S., Koren, V., Smith, M., Zhang, Z., Moreda, F., Seo, D.-J., and DMIP participants: Overall distributed model inter-comparison project results, J. Hydrol., 298, 27–60, 2004.Rodriguez-Iturbe, I., Porporato, A., Laio, F., and Ridolfi, L.: Plants in water-controlled ecosystems: active role in hydrologic processes and response to water stress, I. Scope and general outline, Adv. Water Resour., 24, 695–705, 2001.Ruiz-Pérez, G., González-Sanchis, M., Del Campo, A. D., and Francés, F.: Can a parsimonious model implemented with satellite data be used for modelling the vegetation dynamics and water cycle in water-controlled environments?, Ecol. Model., 324, 45–53, 2016.Samaniego, L., Kumar, R., and Jackisch, C.: Predictions in a data-sparse region using a regionalized grid-based hydrologic model driven by remotely sensed data, Hydrol. Res., 42, 338–355, https://doi.org/10.2166/nh.2011.156, 2011.Sims, D. A., Luo, H., Hastings, S., Oechel, W. C., Rahman, A. F., and Gamon, J. A.: Parallel adjustments in vegetation greenness and ecosystem CO2 exchange in response to drought in a Southern California chaparral ecosystem, Remote Sens. Environ., 103, 289–303, 2006.Smith, M. B., Koren, V., Reed, S., Zhang, Z., Zhang, Y., Moreda, F., Cui, Z., Mizukami, N., Anderson, E. A., and Cosgrove, B. A.: The distributed model intercomparison project – Phase 2: Motivation and design of the Oklahoma experiments, J. Hydrol., 418–419, 3–16, https://doi.org/10.1016/j.jhydrol.2011.08.055, 2012.Smith, M. B., Koren, V., Zhang, Z., et al.: The Distributed Model Intercomparison project – Phase 2: Experiment design and summary results of the western basin experiments, J. Hydrol., 207, 300–329, https://doi.org/10.1016/j.jhydrol.2013.08.040, 2013.Stisen, S., McCabe, M. F., Refsgaard, J. C., Lerer, S., and Butts, M. B.: Model parameter analysis using remotely sensed pattern information in a multi-constraint framework, J. Hydrol., 409, 337–349, 2011.Tsang, Y. P., Hornberger, G., Kaplan, L. A., Newbold, J. D., and Aufdenkampe, A. K.: A variable source area for groundwater evapotranspiration: Impacts on modeling stream flow, Hydrol. Proc., 28, 2439–2450, 2014.Turner, D. P., Cohen, W. B., Kennedy, R. E., Fassnacht, K. S., and Briggs, J. M.: Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites, Remote Sens. Environ., 70, 52–68, 1999.van Dijk, A. I. J. M. and Renzullo, L. J.: Water resource monitoring systems and the role of satellite observations, Hydrol. Earth Syst. Sci., 15, 39–55, https://doi.org/10.5194/hess-15-39-2011, 2011.Velpuri, N. M., Senay, G. B., and Asante, K. O.: A multi-source satellite data approach for modelling Lake Turkana water level: calibration and validation using satellite altimetry data, Hydrol. Earth Syst. Sci., 16, 1–18, https://doi.org/10.5194/hess-16-1-2012, 2012.Wagener, T., Blöschl, G., Goodrich, D. C., Gupta, H., Sivapalan, M., Tachikawa, Y., and Weiler, M.: A synthesis framework for runoff prediction in ungauged basins, chap., 2, 11–28, 2013.Wi, S., Yang, Y. C. E., Steinschneider, S., Khalil, A., and Brown, C. M.: Calibration approaches for distributed hydrologic models in poorly gaged basins: implication for streamflow projections under climate change, Hydrol. Earth Syst. Sci., 19, 857–876, https://doi.org/10.5194/hess-19-857-2015, 2015.Winsemius, H. C., Savenije, H. H. G., and Bastiaanssen, W. G. M.: Constraining model parameters on remotely sensed evaporation: justification for distribution in ungauged basins?, Hydrol. Earth Syst. Sci., 12, 1403–1413, https://doi.org/10.5194/hess-12-1403-2008, 2008.Xiao, X., Zhang, Q., Braswell, B., Urbanski, S., Boles, S., Wofsy, S., Moore, B., and Ojima, D.: Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data, Remote Sens. Environ., 91, 256–270, 2004.Yang, Y., Shang, S., and Jiang, L.: Remote sensing temporal and spatial patterns of evapotranspiration and the responses to water management in a large irrigation district of North China, Agr. Forest Meteorol., 164, 112–122, 2012.Yuan, W., Liu, S., Zhou, G., Zhou, G., Tieszen, L. L., Baldocchi, D., Bernhofer, C., Gholz, H., Goldstein, A. H., Goulden, M. L., Hollinger, D. Y., Hu, Y., Law, B. E., Stoy, P. C., Vesala, T., and Wofsy, S. C.: Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes, Agr. Forest Meteorol., 143, 189–207, https://doi.org/10.1016/j.agrformet.2006.12.001, 2007.Yuan, W., Liu, S., Yu, G., Bonnefond, J.-M., Chen, J., Davis, K., Desai, A. R., Goldstein, A. H., Gianelle, D., Rossi, F., Suyker, A. E., and Verma, S. B.: Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data, Remote Sens. Environ., 114, 1416–1431, 2010.Zhang, Y. Q., Francis, H. S., Chiew, H. S., Zhang, L., and Li, H.: Use of remotely sensed actual evapotranspiration to improve rainfall-runoff modeling in southeast Australia, Am. Meteorol. Soc., 10, 969–980, 2009.Zhang, Y. Q., Viney, N. R., Chiew, F. H. S., van Dijk, A. I. J. M., and Liu Y. Y.: Improving hydrological and vegetation modelling using regional model calibration schemes together with remote sensing data, 19th International Congress on Modelling and Simulation, Perth, Australia, 12–16 December, 2011.Zhang, Y., Peña-Arancibia, J. L., McVicar, T. R., Chiew, F. H., Vaze, J., Liu, C., Lu, X., Zheng H., Wang, Y., Liu, Y., and Miralles, D. G.: Multi-decadal trends in global terrestrial evapotranspiration and its components, Sci. Rep.-UK, 6, 19124, https://doi.org/10.1038/srep19124, 2016

Provision of foot health services for people with rheumatoid arthritis in New South Wales: a web-based survey of local podiatrists

Background: It is unclear if podiatric foot care for people with rheumatoid arthritis (RA) in New South Wales (NSW) meets current clinical recommendations. The objective of this study was to survey podiatrists' perceptions of the nature of podiatric foot care provision for people who have RA in NSW.Methods: An anonymous, cross-sectional survey with a web-based questionnaire was conducted. The survey questionnaire was developed according to clinical experience and current foot care recommendations. State registered podiatrists practising in the state of NSW were invited to participate. The survey link was distributed initially via email to members of the Australian Podiatry Association (NSW), and distributed further through snowballing techniques using professional networks. Data was analysed to assess significant associations between adherence to clinical practice guidelines, and private/public podiatry practices.Results: 86 podiatrists participated in the survey (78% from private practice, 22% from public practice). Respondents largely did not adhere to formal guidelines to manage their patients (88%). Only one respondent offered a dedicated service for patients with RA. Respondents indicated that the primary mode of accessing podiatry was by self-referral (68%). Significant variation was observed regarding access to disease and foot specific assessments and treatment strategies. Assessment methods such as administration of patient reported outcome measures, vascular and neurological assessments were not conducted by all respondents. Similarly, routine foot care strategies such as prescription of foot orthoses, foot health advice and footwear were not employed by all respondents.Conclusions: The results identified issues in foot care provision which should be explored through further research. Foot care provision in NSW does not appear to meet the current recommended standards for the management of foot problems in people who have RA. Improvements to foot care could be undertaken in terms of providing better access to examination techniques and treatment strategies that are recommended by evidence based treatment paradigms. © 2013 Hendry et al.; licensee BioMed Central Ltd

Search for extended gamma-ray emission from the Virgo galaxy cluster with Fermi-LAT

Galaxy clusters are one of the prime sites to search for dark matter (DM) annihilation signals. Depending on the substructure of the DM halo of a galaxy cluster and the cross sections for DM annihilation channels, these signals might be detectable by the latest generation of $\gamma$-ray telescopes. Here we use three years of Fermi Large Area Telescope (LAT) data, which are the most suitable for searching for very extended emission in the vicinity of nearby Virgo galaxy cluster. Our analysis reveals statistically significant extended emission which can be well characterized by a uniformly emitting disk profile with a radius of 3\deg that moreover is offset from the cluster center. We demonstrate that the significance of this extended emission strongly depends on the adopted interstellar emission model (IEM) and is most likely an artifact of our incomplete description of the IEM in this region. We also search for and find new point source candidates in the region. We then derive conservative upper limits on the velocity-averaged DM pair annihilation cross section from Virgo. We take into account the potential $\gamma$-ray flux enhancement due to DM sub-halos and its complex morphology as a merging cluster. For DM annihilating into $b\overline{b}$, assuming a conservative sub-halo model setup, we find limits that are between 1 and 1.5 orders of magnitude above the expectation from the thermal cross section for $m_{\mathrm{DM}}\lesssim100\,\mathrm{GeV}$. In a more optimistic scenario, we exclude $\langle \sigma v \rangle\sim3\times10^{-26}\,\mathrm{cm^{3}\,s^{-1}}$ for $m_{\mathrm{DM}}\lesssim40\,\mathrm{GeV}$ for the same channel. Finally, we derive upper limits on the $\gamma$-ray-flux produced by hadronic cosmic-ray interactions in the inter cluster medium. We find that the volume-averaged cosmic-ray-to-thermal pressure ratio is less than $\sim6\%$.Comment: 15 pages, 11 figures, 4 tables, accepted for publication in ApJ; corresponding authors: T. Jogler, S. Zimmer & A. Pinzk

Multiwavelength Evidence for Quasi-periodic Modulation in the Gamma-ray Blazar PG 1553+113

We report for the first time a gamma-ray and multi-wavelength nearly-periodic oscillation in an active galactic nucleus. Using the Fermi Large Area Telescope (LAT) we have discovered an apparent quasi-periodicity in the gamma-ray flux (E >100 MeV) from the GeV/TeV BL Lac object PG 1553+113. The marginal significance of the 2.18 +/-0.08 year-period gamma-ray cycle is strengthened by correlated oscillations observed in radio and optical fluxes, through data collected in the OVRO, Tuorla, KAIT, and CSS monitoring programs and Swift UVOT. The optical cycle appearing in ~10 years of data has a similar period, while the 15 GHz oscillation is less regular than seen in the other bands. Further long-term multi-wavelength monitoring of this blazar may discriminate among the possible explanations for this quasi-periodicity.Comment: 8 pages, 5 figures. Accepted to The Astrophysical Journal Letters. Corresponding authors: S. Ciprini (ASDC/INFN), S. Cutini (ASDC/INFN), S. Larsson (Stockholm Univ/KTH), A. Stamerra (INAF/SNS), D. J. Thompson (NASA GSFC

Nanotechnology researchers' collaboration relationships: A gender analysis of access to scientific information

Women are underrepresented in science, technology, engineering, and mathematics fields, particularly at higher levels of organizations. This article investigates the impact of this underrepresentation on the processes of interpersonal collaboration in nanotechnology. Analyses are conducted to assess: (1) the comparative tie strength of women's and men's collaborations, (2) whether women and men gain equal access to scientific information through collaborators, (3) which tie characteristics are associated with access to information for women and men, and (4) whether women and men acquire equivalent amounts of information by strengthening ties. Our results show that the overall tie strength is less for women's collaborations and that women acquire less strategic information through collaborators. Women and men rely on different tie characteristics in accessing information, but are equally effective in acquiring additional information resources by strengthening ties. This article demonstrates that the underrepresentation of women in science, technology, engineering, and mathematics has an impact on the interpersonal processes of scientific collaboration, to the disadvantage of women scientists.Villanueva-Felez, Á.; Woolley, RD.; Cañibano Sánchez, C. (2015). Nanotechnology researchers' collaboration relationships: A gender analysis of access to scientific information. Social Studies of Science. 45(1):100-129. doi:10.1177/0306312714552347S100129451ACKER, J. (1990). HIERARCHIES, JOBS, BODIES: Gender & Society, 4(2), 139-158. doi:10.1177/089124390004002002Aitken, C., Power, R., & Dwyer, R. (2008). A very low response rate in an on-line survey of medical practitioners. Australian and New Zealand Journal of Public Health, 32(3), 288-289. doi:10.1111/j.1753-6405.2008.00232.xAngrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics. doi:10.1515/9781400829828Baruch, Y., & Holtom, B. C. (2008). Survey response rate levels and trends in organizational research. Human Relations, 61(8), 1139-1160. doi:10.1177/0018726708094863Beaver, D. D. (2001). Scientometrics, 52(3), 365-377. doi:10.1023/a:1014254214337Boardman, P. C., & Corley, E. A. (2008). University research centers and the composition of research collaborations. Research Policy, 37(5), 900-913. doi:10.1016/j.respol.2008.01.012Bourdieu, P. (1975). The specificity of the scientific field and the social conditions of the progress of reason. Social Science Information, 14(6), 19-47. doi:10.1177/053901847501400602Bourdieu, P. (1977). Outline of a Theory of Practice. doi:10.1017/cbo9780511812507Bourdieu, P. (1989). Social Space and Symbolic Power. Sociological Theory, 7(1), 14. doi:10.2307/202060Bouty, I. (2000). INTERPERSONAL AND INTERACTION INFLUENCES ON INFORMAL RESOURCE EXCHANGES BETWEEN R&D RESEARCHERS ACROSS ORGANIZATIONAL BOUNDARIES. Academy of Management Journal, 43(1), 50-65. doi:10.2307/1556385Bozeman, B., & Corley, E. (2004). Scientists’ collaboration strategies: implications for scientific and technical human capital. Research Policy, 33(4), 599-616. doi:10.1016/j.respol.2004.01.008Bozeman, B., & Gaughan, M. (2011). How do men and women differ in research collaborations? An analysis of the collaborative motives and strategies of academic researchers. Research Policy, 40(10), 1393-1402. doi:10.1016/j.respol.2011.07.002Bozeman, B., & Rogers, J. D. (2002). A churn model of scientific knowledge value: Internet researchers as a knowledge value collective. Research Policy, 31(5), 769-794. doi:10.1016/s0048-7333(01)00146-9Bozeman, B., Dietz, J. S., & Gaughan, M. (2001). Scientific and technical human capital: an alternative model for research evaluation. International Journal of Technology Management, 22(7/8), 716. doi:10.1504/ijtm.2001.002988Brass, D. J. (1985). MEN’S AND WOMEN’S NETWORKS: A STUDY OF INTERACTION PATTERNS AND INFLUENCE IN AN ORGANIZATION. Academy of Management Journal, 28(2), 327-343. doi:10.2307/256204Chompalov, I., Genuth, J., & Shrum, W. (2002). The organization of scientific collaborations. Research Policy, 31(5), 749-767. doi:10.1016/s0048-7333(01)00145-7Cook, C., Heath, F., & Thompson, R. L. (2000). A Meta-Analysis of Response Rates in Web- or Internet-Based Surveys. Educational and Psychological Measurement, 60(6), 821-836. doi:10.1177/00131640021970934Durbin, S. (2010). Creating Knowledge through Networks: a Gender Perspective. Gender, Work & Organization, 18(1), 90-112. doi:10.1111/j.1468-0432.2010.00536.xEcklund, E. H., Lincoln, A. E., & Tansey, C. (2012). Gender Segregation in Elite Academic Science. Gender & Society, 26(5), 693-717. doi:10.1177/0891243212451904Ensign, P. C. (2009). Knowledge Sharing among Scientists. doi:10.1057/9780230617131Etzkowitz, H., Kemelgor, C., & Uzzi, B. (2000). Athena Unbound. doi:10.1017/cbo9780511541414Flyvbjerg, B. (2001). Making Social Science Matter. doi:10.1017/cbo9780511810503FOX, M. F. (2001). WOMEN, SCIENCE, AND ACADEMIA. Gender & Society, 15(5), 654-666. doi:10.1177/089124301015005002Fox, M. F. (2010). Women and Men Faculty in Academic Science and Engineering: Social-Organizational Indicators and Implications. American Behavioral Scientist, 53(7), 997-1012. doi:10.1177/0002764209356234Fox, M. F., & Stephan, P. E. (2001). Careers of Young Scientists: Social Studies of Science, 31(1), 109-122. doi:10.1177/030631201031001006Fox, M. F., Sonnert, G., & Nikiforova, I. (2009). Successful Programs for Undergraduate Women in Science and Engineering: Adapting versus Adopting the Institutional Environment. Research in Higher Education, 50(4), 333-353. doi:10.1007/s11162-009-9120-4Friedkin, N. (1980). A test of structural features of granovetter’s strength of weak ties theory. Social Networks, 2(4), 411-422. doi:10.1016/0378-8733(80)90006-4Gaughan, M. (2005). Introduction to the Symposium: Women in Science. The Journal of Technology Transfer, 30(4), 339-342. doi:10.1007/s10961-005-2579-zGaughan, M., & Corley, E. A. (2010). Science faculty at US research universities: The impacts of university research center-affiliation and gender on industrial activities. Technovation, 30(3), 215-222. doi:10.1016/j.technovation.2009.12.001Granovetter, M. S. (1973). The Strength of Weak Ties. American Journal of Sociology, 78(6), 1360-1380. doi:10.1086/225469Hansen, M. T. (1999). The Search-Transfer Problem: The Role of Weak Ties in Sharing Knowledge across Organization Subunits. Administrative Science Quarterly, 44(1), 82. doi:10.2307/2667032Ibarra, H. (1992). Homophily and Differential Returns: Sex Differences in Network Structure and Access in an Advertising Firm. Administrative Science Quarterly, 37(3), 422. doi:10.2307/2393451Islam, N., & Miyazaki, K. (2009). Nanotechnology innovation system: Understanding hidden dynamics of nanoscience fusion trajectories. Technological Forecasting and Social Change, 76(1), 128-140. doi:10.1016/j.techfore.2008.03.021Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1-18. doi:10.1016/s0048-7333(96)00917-1Koch, N. S., & Emrey, J. A. . (2001). The Internet and Opinion Measurement: Surveying Marginalized Populations. Social Science Quarterly, 82(1), 131-138. doi:10.1111/0038-4941.00012Kyvik, S., & Teigen, M. (1996). Child Care, Research Collaboration, and Gender Differences in Scientific Productivity. Science, Technology, & Human Values, 21(1), 54-71. doi:10.1177/016224399602100103Lee, S., & Bozeman, B. (2005). The Impact of Research Collaboration on Scientific Productivity. Social Studies of Science, 35(5), 673-702. doi:10.1177/0306312705052359Levin, D. Z., & Cross, R. (2004). The Strength of Weak Ties You Can Trust: The Mediating Role of Trust in Effective Knowledge Transfer. Management Science, 50(11), 1477-1490. doi:10.1287/mnsc.1030.0136Lin, N. (2001). Social Capital. doi:10.1017/cbo9780511815447McFadyen, M. A., & Cannella, A. A. (2004). SOCIAL CAPITAL AND KNOWLEDGE CREATION: DIMINISHING RETURNS OF THE NUMBER AND STRENGTH OF EXCHANGE RELATIONSHIPS. Academy of Management Journal, 47(5), 735-746. doi:10.2307/20159615McFadyen, M. A., Semadeni, M., & Cannella, A. A. (2009). Value of Strong Ties to Disconnected Others: Examining Knowledge Creation in Biomedicine. Organization Science, 20(3), 552-564. doi:10.1287/orsc.1080.0388Manfreda, K. L., Bosnjak, M., Berzelak, J., Haas, I., & Vehovar, V. (2008). Web Surveys versus other Survey Modes: A Meta-Analysis Comparing Response Rates. International Journal of Market Research, 50(1), 79-104. doi:10.1177/147078530805000107Marsden, P. V., & Campbell, K. E. (1984). Measuring Tie Strength. Social Forces, 63(2), 482. doi:10.2307/2579058Mason, M. A., & Ekman, E. M. (2007). Mothers on the Fast TrackHow a New Generation Can Balance Family and Careers. doi:10.1093/acprof:oso/9780195182675.001.0001Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An Integrative Model Of Organizational Trust. Academy of Management Review, 20(3), 709-734. doi:10.5465/amr.1995.9508080335Nahapiet, J., & Ghoshal, S. (1998). Social Capital, Intellectual Capital, and the Organizational Advantage. Academy of Management Review, 23(2), 242-266. doi:10.5465/amr.1998.533225Oliver, A. L., & Liebeskind, J. P. (1997). Three Levels of Networking for Sourcing Intellectual Capital in Biotechnology. International Studies of Management & Organization, 27(4), 76-103. doi:10.1080/00208825.1997.11656719Podolny, J. M., & Baron, J. N. (1997). Resources and Relationships: Social Networks and Mobility in the Workplace. American Sociological Review, 62(5), 673. doi:10.2307/2657354Rhoton, L. A. (2011). Distancing as a Gendered Barrier. Gender & Society, 25(6), 696-716. doi:10.1177/0891243211422717Rothstein, M. G., & Davey, L. M. (1995). Gender differences in network relationships in academia. Women in Management Review, 10(6), 20-25. doi:10.1108/09649429510095999Rowley, T., Behrens, D., & Krackhardt, D. (2000). Redundant governance structures: an analysis of structural and relational embeddedness in the steel and semiconductor industries. Strategic Management Journal, 21(3), 369-386. doi:10.1002/(sici)1097-0266(200003)21:33.0.co;2-mSCOTT, D. B. (1996). SHATTERING THE INSTRUMENTAL-EXPRESSIVE MYTH. Gender & Society, 10(3), 232-247. doi:10.1177/089124396010003003Shapin, S. (1994). A Social History of Truth. doi:10.7208/chicago/9780226148847.001.0001Shrum, W., Chompalov, I., & Genuth, J. (2001). Trust, Conflict and Performance in Scientific Collaborations. Social Studies of Science, 31(5), 681-730. doi:10.1177/030631201031005002Smith-Doerr, L. (2004). Flexibility and Fairness: Effects of the Network Form of Organization on Gender Equity in Life Science Careers. Sociological Perspectives, 47(1), 25-54. doi:10.1525/sop.2004.47.1.25Stix, G. (2001). Little Big Science. Scientific American, 285(3), 32-37. doi:10.1038/scientificamerican0901-32Uzzi, B. (1996). The Sources and Consequences of Embeddedness for the Economic Performance of Organizations: The Network Effect. American Sociological Review, 61(4), 674. doi:10.2307/2096399Uzzi, B. (1997). Social Structure and Competition in Interfirm Networks: The Paradox of Embeddedness. Administrative Science Quarterly, 42(1), 35. doi:10.2307/2393808Vinck, D. (2010). The Sociology of Scientific Work. doi:10.4337/9781849807197Fan, W., & Yan, Z. (2010). Factors affecting response rates of the web survey: A systematic review. Computers in Human Behavior, 26(2), 132-139. doi:10.1016/j.chb.2009.10.015Ziman, J. M. (Ed.). (1994). Prometheus Bound. doi:10.1017/cbo978051158506

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Spatial analysis of air pollution and childhood asthma in Hamilton, Canada: comparing exposure methods in sensitive subgroups

<p>Abstract</p> <p>Background</p> <p>Variations in air pollution exposure within a community may be associated with asthma prevalence. However, studies conducted to date have produced inconsistent results, possibly due to errors in measurement of the exposures.</p> <p>Methods</p> <p>A standardized asthma survey was administered to children in grades one and eight in Hamilton, Canada, in 1994–95 (N ~1467). Exposure to air pollution was estimated in four ways: (1) distance from roadways; (2) interpolated surfaces for ozone, sulfur dioxide, particulate matter and nitrous oxides from seven to nine governmental monitoring stations; (3) a kriged nitrogen dioxide (NO₂) surface based on a network of 100 passive NO₂monitors; and (4) a land use regression (LUR) model derived from the same monitoring network. Logistic regressions were used to test associations between asthma and air pollution, controlling for variables including neighbourhood income, dwelling value, state of housing, a deprivation index and smoking.</p> <p>Results</p> <p>There were no significant associations between any of the exposure estimates and asthma in the whole population, but large effects were detected the subgroup of children without hayfever (predominately in girls). The most robust effects were observed for the association of asthma without hayfever and NO₂LUR OR = 1.86 (95%CI, 1.59–2.16) in all girls and OR = 2.98 (95%CI, 0.98–9.06) for older girls, over an interquartile range increase and controlling for confounders.</p> <p>Conclusion</p> <p>Our findings indicate that traffic-related pollutants, such as NO₂, are associated with asthma without overt evidence of other atopic disorders among female children living in a medium-sized Canadian city. The effects were sensitive to the method of exposure estimation. More refined exposure models produced the most robust associations.</p

Polarization Properties of the Weakly Magnetized Neutron Star X-Ray Binary GS 1826-238 in the High Soft State

The launch of the Imaging X-ray Polarimetry Explorer (IXPE) on 2021 December 9 has opened a new window in X-ray astronomy. We report here the results of the first IXPE observation of a weakly magnetized neutron star, GS 1826−238, performed on 2022 March 29-31 when the source was in a high soft state. An upper limit (99.73% confidence level) of 1.3% for the linear polarization degree is obtained over the IXPE 2-8 keV energy range. Coordinated INTEGRAL and NICER observations were carried out simultaneously with IXPE. The spectral parameters obtained from the fits to the broadband spectrum were used as inputs for Monte Carlo simulations considering different possible geometries of the X-ray emitting region. Comparing the IXPE upper limit with these simulations, we can put constraints on the geometry and inclination angle of GS 1826-238

Accretion geometry of the neutron star low mass X-ray binary Cyg X-2 from X-ray polarization measurements

We report spectro-polarimetric results of an observational campaign of the bright neutron star low-mass X-ray binary Cyg X-2 simultaneously observed by IXPE, NICER and INTEGRAL. Consistently with previous results, the broad-band spectrum is characterized by a lower-energy component, attributed to the accretion disc with $kT_{\rm in} \approx$ 1 keV, plus unsaturated Comptonization in thermal plasma with temperature $kT_{\rm e} = 3$ keV and optical depth $\tau \approx 4$, assuming a slab geometry. We measure the polarization degree in the 2-8 keV band $P=1.8 \pm 0.3$ per cent and polarization angle $\phi = 140^{\circ} \pm 4^{\circ}$, consistent with the previous X-ray polarimetric measurements by OSO-8 as well as with the direction of the radio jet which was earlier observed from the source. While polarization of the disc spectral component is poorly constrained with the IXPE data, the Comptonized emission has a polarization degree $P =4.0 \pm 0.7$ per cent and a polarization angle aligned with the radio jet. Our results strongly favour a spreading layer at the neutron star surface as the main source of the polarization signal. However, we cannot exclude a significant contribution from reflection off the accretion disc, as indicated by the presence of the iron fluorescence line.Comment: 10 pages, 7 figures, accepted for publication in MNRA