dataesr/scientific-paragraphs-categorization huggingface dataset:
3] [4]  Mild vision impairment was defined as presenting visual acuity in the better eye of worse than 6/12 to 6/18 inclusive. This review included investigations which were published between 1980 and July 2014. Additionally, we assessed unpublished data which were identified by members of the Vision Loss Expert Group of the GBD Study. 8 In total, nine new studies were added to the Global Vision Database for the high-income countries, Eastern and Central Europe super region, giving a total of 46 studies contributing data from 22 countries. For the statistical analysis, we fitted two hierarchical Bayesian logistic regressions to estimate vision impairment prevalence over time-by age group, gender and country-one model for the prevalence of blindness and one model for the prevalence of MSVI.
9  We modelled hierarchical linear trends over time, allowing for region-specific trends in prevalence of vision impairment in the seven world regions. Prevalence estimates were reported as posterior means along with 80% posterior uncertainty intervals (UIs). 5 We calculated trends, with UIs, of age-standardised vision impairment by calculating the difference between the 1990 and 2015 age-standardised prevalence. In order to estimate the prevalence of presenting near vision impairment due to uncorrected presbyopia (functional presbyopia), we included studies where presbyopia was defined as presenting near vision worse than N6 or N8 at 40 cm regardless of distance refractive status. For broad estimates of vision impairment including both distance and near presenting impairment, we only included data from those people whose best-corrected visual acuity was 6/12 (20/40) or better, so as to avoid double counting those with both distance and near vision impairment associated with non-refractive causes.
Global issues Division's forecasts to derive the anticipated crude numbers and age-standardised prevalence estimates. 14 [5]  resulTs The super region of interest of our meta-analysis was the focus of 46 studies (table  1 ). All of the included studies were cross-sectional population-based investigations, and the visual acuity data were ascertained through clinical examination. In the study super region in 2015, the crude prevalence of blindness for all ages was 0. 32% (80% UI 0. 13-0. 55), with a prevalence of 2. 42% (80% UI 1. 08%-4. 08%) for MSVI and 2. 33% (80% UI 0. 77-4. 38) for mild vision impairment. The crude prevalence of presbyopia was 18. 94% (5. 59-35. 22). These and age-standardised rates are given in table  2. Women were more likely to be blind or MSVI than men (high-income countries relative OR for blindness: 1.
With these surveys, only data relating to cataract and uncorrected refractive error were included in the model as causes for vision impairment. Third, the proportions of best-corrected vision attributable to causes other than cataract, AMD, glaucoma or trachoma (ie, diabetic retinopathy and corneal diseases) were calculated with data from surveys that had assessed at least cataract and AMD. Fourth, despite extensive data seeking, data were not available for many countries and years, were reported using sometimes incomparable definitions of vision impairment or were representative of a subnational or community area only. Fifth, a major part of the causes of blindness and MSVI has remained uncovered and has been included as 'other causes' in the analysis. In this updated meta-analysis, we report that 'other causes' accounted for about 23% worldwide of the causes of blindness, whereas our previously published 2010 estimate was 28%.
Several sectors need global horizontal irradiance (GHI) data for various purposes. However, the availability of a long-term time series of high quality in situ GHI measurements is limited. Therefore, several studies have tried to estimate GHI by re-analysing climate data or satellite images. Validation is essential for the later use of GHI data in the regions with a scarcity of ground-recorded data. This study contributes to previous studies that have been carried out in the past to validate HelioClim-3 version 5 (HC3v5) and the Copernicus Atmosphere Monitoring Service, using radiation service version 3 (CRSv3) data of hourly GHI from satellite-derived datasets (SDD) with nine ground stations in northeast Iraq, which have not been used previously. The validation is carried out with station data at the pixel locations and two other data points in the vicinity of each station, which is something that is rarely seen in the literature.
The H2 has been developed by changing some inputs to the model in several studies  [12] [13] [14]. Other studies also used satellite imagery with different techniques for GHI estimations  [15] [16] [17]. There are several satellite-derived datasets (SDDs) for establishing, measuring, modelling and estimating GHI, which can be found in  [18, 19]. An SDD from MSG has been used to create a solar map  [20]. Studies which merged ground data with the SDD for the same above purpose reveal that the merging technique for producing a solar map is better than interpolating ground data  [21, 22]. SDDs have also been combined with meteorological data to calibrate a GHI model  [23]. The same data combinations have also been analysed to create GHI datasets for crop modelling over Europe  [24]. SDDs have been utilised to assess long-term trends of a GHI time series  [25].
SDDs are quite useful because of the limitations of ground data for GHI applications. SDDs are necessary for many fields because they provide GHI for many areas and countries. Therefore, the validation of SDDs is crucial to investigate their reliability by using various methods in different geographical and climate areas, and several prior studies achieved that. For instance, the Satellite Application Facility on Climate Monitoring (CM SAF) dataset based on predicting GHI from MFG and MSG has been validated with the ground data at several stations in Europe in the period 1983-1985  [26]. The data from the same dataset with the H2 method for converting satellite images to GHI and others (SolarGis and Solemi) have also been validated in 22 cities in Europe  [27]. Similarly, the CM SAF dataset of GHI has been compared against ground data at 20 stations in Sweden and Norway, and the result reveals good agreement with an accuracy of 15 W/m 2 , corresponding to an error of roughly 8%  [28].
In addition, GHI retrievals from different CM SAF products have been validated against ground measurements at eight sites in Europe, under various sky conditions. [29]. More broadly, Zhang et al. [19]  have evaluated the result of the six re-analysed datasets for obtaining GHI with the ground data from measurement networks such as the Baseline Surface Radiation Network (BSRN) and others for 674 cities around the globe, with an overall bias found to be from 11-50 W/m 2. Hence, they used a large volume of data in various climate regions and countries; however, the results are shown according to the datasets and measurement networks, rather than for each station. This is useful for comparison between the SDDs and measurement networks, but it does not reflect a real situation for the individual stations.
It is necessary for studies to validate more than one SDD for comparison between them, and then select the most accurate one. Recently, GHI data from the MFG and MSG have been evaluated over India, which show an overestimation bias of 10-20% of daily mean  [33]. Some other studies have evaluated SDDs over the United States in different climate regions against several ground-based measurements  [34] [35] [36] [37]. The most popular SDD is that arranged by the Solar Radiation Data (SoDa) portal  [38] , which contains several projects; one of them is HelioClim-3 version 4 and version 5 (HC3v4-5), which are based on the H2 method for converting satellite images of MSG to GHI. Another is the Copernicus Atmosphere Monitoring Service (CAMS) Radiation Service (CRS), which is based on Heliosat-4 (H4) for the same purpose.
Those SDDs have been validated by several studies in various areas. For example, Thomas et al. [39]  have validated the hourly GHI from SDDs such as HC3v4-5 and CRS for 42 stations in Brazil. The result reveals a high correlation (an average of 96%) between HC3v4-5 and ground measurements, whereas that with CRS is lower by 2%. Similarly, r values above 0. 92 for 15 min and 0. 98 for daily GHI with a bias of roughly 5% were found when comparing HC3v4-5 and CRS to ground data at 14 stations over the world  [40]. Hourly GHI and DNI from HC3v4-5 for all-sky conditions, and using the McClear dataset for clear-sky conditions, have been validated with ground data in seven stations over Egypt, with RMSE ranges from 6-22%  [41]. Marchand et al. [42]  have validated hourly GHI from HC3v4-5 and CRS with ground data at five stations in United Arab Emirates and Oman.
The overall validation result is nearly 15% of the RMSE on average. This study aims to validate the hourly GHI from HC3v5 and CRSv3 against ground measurements at nine stations in the northeast of Iraq, being the first study validating those SDDs in that region. One objective of this study is to evaluate the spatial-temporal performance of those datasets in all-sky, clear-sky, and cloudy-sky conditions and with the clearness index. Another objective is to use a new approach for validation, which is limited in the literature, comparing the GHI from ground measurements at a station against the GHI from SDDs at each station location and the two points around it, at a spatial resolution of 5 km (corresponding to that of MSG imagery), and with each point collected from a different pixel. The study is organised as follows.
Study Site and Ground Data The study area is located in the northeast of Iraq (latitudes [34 • 08 20"-37 • 22 36"], and longitudes [42 • 32 00"-46 • 14 29"]). It has a complex topography (mountains, hills and plain areas). According to the Koppen classification  [43] , two climate regions are seen in the region, which are the Mediterranean Sea and semi-arid climates (Figure  1 ). The hourly GHI data with some other climate parameters were collected from two station types. First, the data are from tower stations. The pyranometer used for recording data in these stations is the Kipp and Zonen CMP6 Pyranometer. The data were collected for the period 2011-2014 from five stations, which lacked some years, from the Ministry of Electricity-Kurdistan Regional Government (KRG) (Table  1 ). Others are automatic stations equipped with an Vaisala QMS101 Pyranometer.
Quality Controal of GHI Measurments Data normalising and cleaning were done by setting the solar elevation angle above 15 •. Missing values were found and set as not applicable (NA). The two datasets were harmonised for true local solar time. All of the GHI ground data were tested with the BSRN tests  [45]  and other quality control tests  [46, 47]. Full information about the quality control of the ground data in this study can be found in  [46]. Systematic errors were removed from the data, and some questionable values of data according to various tests were not used in the validation process. Note: The periods in the table are available for the ground measurements; the SDDs for the same periods have been collected for each station location and points around the stations, which were used for validation.
Satellite-Derived Datasets The SoDa portal  [38]  is owned by MINES ParisTech and Transvalor. It provides a dataset of solar radiation components, which are based on converting satellite images of MSG in the field view of the SEVIRI instrument covering Europe, Africa, the Middle East and part of South America (Figure  2 ) by the HC3 and CRSv3 datasets. The hourly GHI data from HC3v5 and CRSv3 for each station location and for points around each station have been collected from the SoDa website, based on the available period of ground data. Note: The periods in the table are available for the ground measurements; the SDDs for the same periods have been collected for each station location and points around the stations, which were used for validation.
Quality Controal of GHI Measurments Data normalising and cleaning were done by setting the solar elevation angle above 15°. Missing values were found and set as not applicable (NA). The two datasets were harmonised for true local solar time. All of the GHI ground data were tested with the BSRN tests  [45]  and other quality control tests  [46, 47]. Full information about the quality control of the ground data in this study can be found in  [46]. Systematic errors were removed from the data, and some questionable values of data according to various tests were not used in the validation process.
Satellite-Derived Datasets The SoDa portal  [38]  is owned by MINES ParisTech and Transvalor. It provides a dataset of solar radiation components, which are based on converting satellite images of MSG in the field view of the SEVIRI instrument covering Europe, Africa, the Middle East and part of South America (Figure  2 ) by the HC3 and CRSv3 datasets. The hourly GHI data from HC3v5 and CRSv3 for each station location and for points around each station have been collected from the SoDa website, based on the available period of ground data.
HelioClim-3 (HC3) The HC3 dataset has been created by converting MSG images to estimate the GHI for every 15 min since February 2001 using the original H2 method. The principle of H2 is to calculate solar radiation statistically by the cloud cover index, which is created by the reflectance in the visible image of MSG and ground albedo  [30]. The method has been modified several times by various inputs. It initially refers to Cano et al. [10] , and a new method was published in  [11]. The MSG image processing in this model gives GHI. Then, a DNI and diffuse horizontal irradiance are estimated  [41]. The most common version of HC3 is v4 and v5. V4 inputs are the clear-sky model of the European Solar Radiation Atlas (ESRA) and the Linke turbidity factor. One limitation of this release is that it is not detecting a local effect on the Linke turbidity factor  [48].
The clear-sky model gives solar radiation globally every three hours as in the free cloudy-sky  [49]. HC3v5 works largely on the same principle as HC3v4, but is different because it uses the McClear model  [42]. McClear is also a model for providing solar radiation under clear-sky conditions. It counts the optical depth of atmosphere as a column, which contains aerosol, water vapour and ozone. It is provided by the Copernicus atmosphere monitoring service  [50]. The data within those datasets are Available online  [38]  for MSG coverage for free from 2004 to 2006, and with payment from 2007 upwards.
Copernicus Atmosphere Monitoring Service (CAMS), Radiation Service (CRS) CRS is a dataset of solar radiation components, which provides Heliosat-4 data using the satellite images of MSG. H4 is a modified method of their previous version. Ground albedo from MODIS and the McClear model are used in this method  [39, 48, 49]. The data are available for free from 2004 until two days before for the areas covered by MSG images. The third version of CRS is available after bias correction  [38]. This study has used CRS version 3 (CRSv3). Further information about the HC3v4-5 and CRSv3 projects can be found at SoDa  [38]  and  [39] [40] [41] [42] 48, 49, 51].
Overall, the RMSE for HC3v5 was less than that for CRSv3 (Figure  8 ). The smooth scatter density plot illustrates the residual and correlation between ground data and SDDs in some cases. For example, Figure  9  for Batufa station shows that the density of observations were mostly under the 1:1 line, which indicated a recorded negative bias, and the RMSE was acceptable for HC3v5 and high for CRSv3, while some of the other values above the 1:1 line are under 200 W/m 2. However, Figure  10  (Kalar station) shows that the majority of observations are above the 1:1 line and some points are far from the line. This corresponds to positive bias and high RMSE in the station. Low rates of bias and RMSE were recorded at Maydan station, which is shown in Figure  11  for HC3v5 and CRSv3 respectively. The best-fit line in red is nearly the same as the 1:1 line, more so for HC3v5 than CRSv3 (at the same station).
Discussion The validation results demonstrate good agreement between the ground data and SDDs in all-sky and clear-sky conditions (average r = 0. 95, bias under 6% and RMSE under 21%), unlike the results for the cloudy-sky conditions (average r = 0. 61, bias above 16% and RMSE above 61%). The results from the two neighbouring points at each station are close to the results at the station location with an average difference of 2%. Overall the performance of SDDs are in agreement with those from similar studies in other areas  [39, 41, 42, 48] , also showing a better performance for HC3v5 over CRSv3 (Figures  7  and 8 ). This is mainly related to the inputs into the models for creating each dataset, whether it is H2 or H4 (see Section 2. 3).
The low variabilities between the ground data and both SDDs are seen in Figures  5  and 6. This might be related to the geographical location and climatic condition, and another reason is that the data were aggregated, concealing some random errors between the two datasets. Hence, some error rates in winter months are related to the difficulty of the Heliosat methods to estimate GHI in cloudy conditions  [31, 51]. The performance of the clearness index (Table  4 ) is nearly the same for GHI in at all-sky conditions, which is related to the above-mentioned reasons. The interesting side of this study is that the results of the validation for both SDDs with the two neighbouring points at each station separately are slightly closer to those at the station location. The differences range between 0-2% for bias and RMSE for each point at most of the locations (Table  3 , Figures  7  and 8 ).
Conclusions The study has validated hourly GHI from two SDDs, which are HC3v5 and CRSv3, with ground data from nine stations in northeast Iraq for all-sky, clear-sky and cloudy-sky conditions in the station pixels and with two other pixels around the station in all-sky conditions. The temporal changes of ground data GHI were well represented by both SDDs; r was above 0. 94 for the all-sky and clear-sky conditions, and above 0. 82 for the clearness index in most cases, while for cloudy-skies it was between 0. 51-0. 72. The bias was negative (underestimation) for most of the cases except for two HC3v5 and three CRSv3 cases, in which it was positive (overestimation); all of the bias ranges were smaller than 8% (W/m 2 ) of the mean GHI in all-sky and clear-sky conditions, whereas for cloudy-sky conditions, it was positive and varied from one station to another, by 17-85% (W/m 2 ) of the mean GHI.
The same applies to RMSE. It ranged between 8-20% (W/m 2 ) in all of the stations for all-sky and clear-sky conditions. In contrast, the range was much higher in cloudy-sky conditions: above 56%. The differences between neighbouring pixels and at-station pixels in the SDDs compared to the ground data of GHI for each site are very small, varying by 2% in most cases. The overall performance of HC3v5 is better than that of CRSv3. Despite the ratio of errors at some stations, the SDDs are closely related to the ground data at most of the stations. However, the resolution of MSG images is 5 km in the case study. The SDDs represent hourly GHI well, and this can be used to map solar resources and possibly for modelling GHI with ground data in areas with a low number of stations. Further studies about the bias corrections in the SDDs are needed, especially for cloudy-sky conditions.
Acknowledgments: The  Higher Committee for Education Development in Iraq (HCED)  funded this study as a scholarship, the  Center for Landscape and Climate Research (CLCR)  and the  National Centre for Earth Observation  ( NERC ) supported it. The authors are extremely grateful for the assistance of the  Directorate of Meteorology-Sulaymaniyah and KRG Ministry of electricity  for providing meteorological data. The authors are grateful to  Soda Service  for allowing access and free use of GHI SDD of CRSv3 data and for a subscription to use the HC3v5 data. A special thanks to  Prof. Lucien Wald ,  MINES ParisTech-France  for his oral advice towards the study.
Ligands for Nuclear Receptors -A Potential for Adversities There are a huge number of chemicals identified as potential ligands for PXR. Shukla et al. [62]  profiled 2816 clinically used pharmaceuticals for their ability to activate human PXR and induce human CYP3A4 at the cellular level and bind human PXR at the protein level. From 7 to 11% of drugs were identified as active across the three assays, which included assay-specific and pan-active compounds. A recent computational study  [63]  developed QSAR models based on in vitro PXR binding and activation, and CYP3A4 induction of pharmaceutical database and used these QSAR models to predict 'PXR binders' among the REACH database of >70 000 molecules. Approximately 5-15% of the chemicals were predicted to be 'PXR binders'. Naturally, only a small proportion of these compounds are high-affinity ligands  [62]  and the potential risk is dependent on real exposures.
The dipping time was fixed at 15 min for polyelectrolyte solutions and 30 min for MF. After each dipping step, the samples were rinsed in three different baths (2 min each) of the buffer solution used for LbL assembly. 4. 3. DLS. Dynamic light scattering (DLS) measurements were performed using a commercial instrument (Malvern Zetasizer Nano ZS, Malvern Instruments Ltd, U. K. ) equipped with a He-Ne laser at 633 nm. Experiments were performed in the static mode on MF dilution (20 nM, PBS) at 22 °C in a polystyrene cuvette. In this mode, the device monitors the fluctuation of the intensity of scattered light from the particles at 90°angle. The raw data were processed using Zetasizer software that gives access to a size distribution histogram and average hydrodynamic diameter (d H ). The value of d H was obtained from the Stokes-Einstein equation πη = d k T D 3 H B (2) where k B is Boltzmann's constant, T is the absolute temperature (K), η is the solution viscosity (mPa s), and D is the diffusion coefficient of the objects (m 2 /s).
Introduction Quantifying air-sea fluxes is critical in understanding the weather-ocean-climate system. Numerical models need forcing by in situ measurements at an increasingly higher spatial and temporal resolution  (Zhang et al. 2006). Sea Denotes content that is immediately available upon publication as open access. Supplemental information related to this paper is available at the Journals Online website: https://doi. org/10. 1175/JTECH-D-17-0209. s1. surface wind speed is a key parameter in forcing numerical models, as well as in quantifying turbulent air-sea fluxes and gas exchanges  (Wanninkhof 2014). Observations made from satellites can provide wind speed data with near-global coverage over the ice-free oceans with a spatial and temporal resolution of about 0. 258 and 24 h (e. g. , QuikScat, ASCAT), respectively.
We deployed gliders equipped with PAM sensors in the Mediterranean Sea during winter 2012/13 in the framework of the Deep Water formation Experiment (DEWEX) experiment  (Testor et al. 2017). The gliders recorded 4 months of acoustic data, with recurring opportunities to compare our wind speed estimates with Météo-France meteorological buoys in the area. Focusing on the sound pressure level in the 3-kHz third octave band, which shows the most dynamic response to wind speed, we estimate the surface wind speed around the glider's position (0. 1-10 km 2 ) throughout the 2-21. 5 m s 21 wind speed range. Section 2 describes the experiment and the associated datasets. Section 3 presents the data acquisition and processing methods, and the results. Section 4 presents the wind speed derivation model and its performance.