dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The Ptychodera flava genome project was supported by  MEXT  and  OIST, Japan. This research was supported by  USPHS  grant  HD42724  and  NASA  grant  FDNAG2-1605  to J. G. ;  USPHS  grant  HD37277  to M. W. K. ;  NASA -NNX13AI68G  to C. L. F. M. was funded by  FP7/ERC  grant [ 268513 ]. O. S. and D. S. R, and T. K. and N. S. were supported by the  Molecular Genetics Unit and Marine Genomics Unit of the Okinawa Institute of Science and Technology Graduate University , respectively. Y. -H. S. and J. -K. Y. are supported by  Academia Sinica and Ministry of Science and Technology, Taiwan. L. P. was supported by  NIH  grant  R01HD073104. The Saccoglossus kowalevskii genome project was supported by a grant from the  National Human Genome Research Institute, National Institutes of Health  ( U54 HG003273 ) to R.
New results from 20 years of monitoring using satellites over the Syrdarya basin are presented. The accuracy of satellite data is 0. 6 km 3 using a combination of MODIS data and satellite altimetry, and only 0. 2 km 3 with Landsat images representing 2-4% of average annual reservoir volume variations in the reservoirs in the Syrdarya basin. With future missions such as Sentinel-3A (S3A), Sentinel-3B (S3B) and surface water and ocean topography (SWOT), significant improvement is expected. The SWOT mission's main payload (a radar interferometer in Ka band) will furthermore provide 2D maps of water height, reservoirs, lakes, rivers and floodplains, with a temporal resolution of 21 days. At the global scale, the SWOT mission will cover reservoirs with areal extents greater than 250 × 250 m with 20 cm accuracy.
In  Gao et al (2012)  for example, radar altimetry combined with MODIS imagery have been used to compute storage variations in 34 large reservoirs globally. Data on water levels of 63 large reservoirs globally (and volume variations of 15 of them) are also available in the Hydroweb database: http://www. legos. obs-mip. fr/en/soa/hydrologie/ hydroweb/ (Cretaux et al 2011a). From the beginning of the satellite radar altimetry era (i. e. 1978, with the launch of Seasat) until now, several missions have been used to calculate water level variations over lakes and rivers: Geosat, T/P, JASON-1, JASON-2, GFO, ENVISAT, ERS-1, ERS-2, HY-2A, and SARAL/AltiKa. From now to the launch of the surface water and ocean topography (SWOT) mission in 2020, time series obtained from historical data will be extended as a result of new missions (table 1): S3A (2015) and S3B (2017),  JASON-3 (2015) , and  JASON-CS (2017).
Then, the wide swath interferometer onboard the SWOT satellite will allow coverage of the entire Earth with a hectometer resolution. The objective of this paper is to illustrate the use of satellite remote sensing data for surveying reservoirs at the regional scale using results along the transboundary Syrdarya river basin as a case study. We will show that when in situ data are lacking, then satellites can be a useful tool for water management purposes. This paper is organized as follows: section 2 provides a description of remote sensing techniques generally used to survey lakes and reservoirs for water height and volume variations. This is the methodology used for the Hydroweb database. In section 3 we present and discuss the main results obtained from satellite data in the case study of the reservoirs along the Syrdarya river basin in Central Asia.
The method has been used and validated in several studies in arid zones (Abarca-Del-Rio et al 2012, Pedinotti et al 2012, Aires et al 2013) and will be illustrated in section 3 using the Syrdarya basin case study. More information about the Hydroweb MODIS data processing can be found in Cretaux et al (2011a) and (2011b). We also recommend processing guidelines proposed by Ji et al 2009. Figure  1  provides an example based on the classification done over the Lake Powell with a Landsat-5 image collected on 9June, 2004, based on the NDWI test.
The areal extent time series in the Hydroweb database may vary slightly from that of others studies. These differences are perfectly understandable. As there is no clear definition on where a lake or reservoir starts or ends, each author performs its own delineation. Discrepancies among results may be greater due to low-resolution bias which is the inaccuracy introduced by the differences in spatial resolution between high and low-resolution data  (Boschetti et al 2004).
Reservoirs along the Syrdarya river This section presents an application case of satellite data to derive potentially useful information for water management purposes, in the context of a transboundary river basin where reservoirs play a crucial role. An internet database (named Cawater: www. cawater-info. net) of in situ quantitative information on reservoirs including water volume, inflow and outflow, and reports with metadata and maps was freely accessible during the years 2010-2011. It is now only accessible to authorized users, and we could not download in situ data for the past few years. We have used this database in order to: • compare the water volume with products inferred from remote sensing data • combine in situ and satellite data to lengthen time series of water storage variations of reservoirs • show that in cases where no in situ data are available, then the satellite data can be used although the accuracy may be lower.
cawater-info. net). These data showed that in 1994, more than 9 km 3 was diverted in this way, and again 5 km 3 in 2003. We have used data on Chardarya releases to Arnasay in combination with water level and surface measurements of the Aydarkul lake from in situ information (www. cawater-info. net) and satellite altimetry and imagery (section 3. 2) to calculate this additional contribution as follows: = - C R V, ( 4 ) where C is the sum of the additional contribution (E -P-drainage into the Arnasay floodplain), R the release of the Chardarya into the Arnasay floodplain, and ΔV, the water volume variation given by equation (  1 ). Currently, Lake Aydarkul covers between 3000 and 4000 km 2 (figure  5 ), which makes it the second most extensive water body in Central Asia, after the Aral Sea. It is worth noting that opposite priorities of each of all Syrdarya riparian countries led them to negotiate new agreements (in 1998) for more efficient transboundary water operation.
It is clear that different and constant negotiations among the four countries have not smoothed the disagreement about the priorities concerning the use of the water resources of the Syrdarya. One source of dispute comes from the reticence about sharing accurate information on the exact operational status of the reservoirs on the one hand, and needs and use for irrigation on the other hand. In the following section we will show how independent datasets based on satellite remote sensing data could be used as a tool for building trust among countries and ensuring mutual compliance of the commitment signed by all countries.
What can we learn from satellite remote sensing about the Syrdarya water management? We have used the data from the Cawater database to calculate the hypsometry relationship between water height and water volume for each reservoir. Cawater provides only water volume while water heights were obtained from satellite altimetry. The Cawater database has also allowed us to perform water budget for some reservoirs and to compare water surface variations observed by MODIS over the Arnasay floodplains with in situ data provided in Cawater database. A second objective was to use the hypsometry to reconstruct time series of parameters that are not available anymore using satellite data. These comparisons allow evaluation of the order of magnitude of accuracy of the satellite products. Figure  6  shows for example that over the Aydarkul Lake (Cawater also provided the height for this lake) accuracy of altimetry is 12 cm.
In addition to the Aydarkul Lake, there are altimetry data (sometimes on a multi-mission context) on four among the five main reservoirs along the NSC: Charvak (not presented here), Karakul, Toktogul and Chardarya. For example, the Chardarya water level is calculated over more than 20 years from a combination of T/P, ENVISAT, JASON-2 and now SARAL. In situ data of storage changes over this reservoir from 1993 to 2000 allow calculation of the hypsometry V(L), therefore storage changes can be computed up to 2014 (figure  8 ). We have also calculated water storage variations of the Chardarya and the Toktogul reservoirs using only satellite data: altimetry and Modis and Landsat images. Using water heights and water areal extents we calculated hypsometry A(L) and then using equation (1) we calculated the volume variations.
It has been possible to reconstruct more than 20 years of water storage variations on the main reservoirs along the SNC from multisatellite datasets although in situ data are currently not available. We have shown that satellite data can be used to complement in situ data, but, if necessary to fully replace them. We have indeed calculated that the accuracy of water storage variations from satellites data is low compared to in situ data in the case of Syrdarya basin.
Acknowledgments First of all the authors acknowledge the  Centre National d'Etudes Spatiales (CNES)  for financing our project through the  TOSCA  project. The authors are grateful to the  Centre for Topographic Studies of the Oceans and Hydrosphere  (CTOH, www. legos. obs-mip. fr/observations/ctoh) at  LEGOS (Toulouse, France)  for providing altimetry data in a standard and useful form. We also acknowledge the project Cawater for having provided in situ data on Aral Sea Basin hydrological data. The MODIS data are downloaded from the WIST data centre: Earth Observing System Data and Information System (EOSDIS). 2009. Earth Observing  System Clearing HOuse (ECHO)/Warehouse Inventory Search Tool (WIST) Version 10. X (online application). Greenbelt, MD: EOSDIS,  Goddard Space Flight Center (GSFC) National Aeronautics and Space Administration (NASA).
Extra males from the age-synchronized plates were supplied as needed to replace dead or injured males to maintain constant male density, and plates were inspected daily for female mortality. For all of the crosses described above, female fecundity was calculated by reserving all plates containing eggs and counting the offspring under a dissecting microscope. To facilitate this and diminish counting errors, these plates were incubated at 20 °C until the offspring were at least L4 larvae in size. Because of this delay in counting, any inviable eggs or juvenile mortality were discounted in estimates of female fecundity. Mating effects were estimated using JMP 10 (SAS Institute) via a 3-way factorial ANOVA with female strain, male strain, and length of male access as fixed effects. Posthoc tests were conducted using Tukey's Honest Significant Difference (HSD) procedure.
For the C. elegans experimental evolution lines, ten spermatids were measured from each of 20 males (i. e. , 200 spermatids total) at each of three different times points (generations 0, 30, and 60). Differences in sperm size were analyzed via an analysis of variance (ANOVA) using JMP 10 (SAS Institute). For C. remanei, strain was the sole main effect. For C. elegans, a nested ANOVA model including competition treatment, replicate line (nested within competition), and generation was fit, with competition and generation as fixed effects, and replicate line as a random effect. Specific evolutionary hypotheses were tested using contrast coefficients as a subset of the overall model.
84, indices 6754-6807, of the C. remanei Pcap Asembly v2 (Genome Sequencing Center, Washington University School of Medicine, St. Louis, Missouri, USA) using Tandem Repeats Finder v 3. 21  [52]. Primers flanking this repeat, GGAACAGAT GAGGTGATGACG and CATCTCCGCTCTCCAATGA, were designed in Primer Designer v 2. 0 (Scientific and Educational Software, Cary, NC). EM464 and SB146 are fixed for different alleles at microsatellite locus cr52. 84, approximately 210 bp and 180 bp respectively, allowing paternity assignment of F1 progeny resulting from crosses between these strains. Sperm competitive ability was measured both as the ability of a focal male to displace the sperm of the first male when introduced as the second male ("offense" or P2;  [53] ) and to avoid displacement of sperm from the second male when introduced as the first male ("defense" or P1).
Acknowledgments Support for this research was provided by  National Science Foundation  grants  DEB-0641066  and  DEB-1120417  to PCP. This project was also supported in part by grants from the  National Center for Research Resources  ( 5P20RR016463-12 ) and the  National Institute of General Medical Sciences  ( 8 P20 GM103423-12 ) from the  National Institutes of Health , the  HHMI Undergraduate Science Program , and  Bowdoin College.
Dermal absorption is a key parameter in non-dietary human safety assessments for agrochemicals. Conservative default values and other criteria in the EFSA guidance have substantially increased generation of product-specific in vitro data and in some cases, in vivo data. Therefore, data from 190 GLP-and OECD guideline-compliant human in vitro dermal absorption studies were published, suggesting EFSA defaults and criteria should be revised  (Aggarwal et al. , 2014). This follow-up article presents data from an additional 171 studies and also the combined dataset. Collectively, the data provide consistent and compelling evidence for revision of EFSA's guidance. This assessment covers 152 agrochemicals, 19 formulation types and representative ranges of spray concentrations. The analysis used EFSA's worst-case dermal absorption definition (i.
e. , an entire skin residue, except for surface layers of stratum corneum, is absorbed). It confirmed previously proposed default values of 6% for liquid and 2% for solid concentrates, irrespective of active substance loading, and 30% for all spray dilutions, irrespective of formulation type. For concentrates, absorption from solvent-based formulations provided reliable read-across for other formulation types, as did water-based products for solid concentrates. The combined dataset confirmed that absorption does not increase linearly beyond a 5-fold increase in dilution. Finally, despite using EFSA's worst-case definition for absorption, a rationale for routinely excluding the entire stratum corneum residue, and ideally the entire epidermal residue in in vitro studies, is presented.
In a typical OECD test guideline-compliant dermal absorption study  (OECD 428)  at least 4 skin replicates (in the case of current datasets, from at least two donors) are exposed to the undiluted formulation (concentrate) and one or more spray dilutions for 6-8 h to match a representative working day. Dermal absorption is measured for 24 h to match with reference dose and estimated exposure units. Dermal absorption studies preferentially use human skin, in vitro; rat skin can also be used, notably to allow bridging to in vivo studies in rats. Rat skin is anatomically significantly different (thinner stratum corneum, more hair follicles, etc. ) to human skin and typically much more permeable to chemicals, so it represents a worst-case model for human skin  (Bronaugh et al. , 1982; Chan et al.
The EFSA GD triggers the requirement for dermal absorption studies for almost all formulations (including virtually identical formulations of the same active substance) even when expert judgement indicates that reliable surrogate data are already available. To investigate the reliability of EFSA guidance, the authors initiated a project to collect, collate and systematically analyse all available dermal absorption data derived from in vitro studies using human skin, which is the preferred method in EU data requirements  (EC 1107 (EC /2009;; EC 284/2013EC). Dermal absorption values in this analysis were calculated using EFSA's worst-case assumption that the whole skin residue (except tape strips 1 and 2) is absorbed. Previously, a dataset of 190 OECD GLP and test guideline-compliant in vitro human skin studies were published  (Aggarwal et al.
Selection of studies All available GLP and OECD 428-compliant in vitro human dermal absorption studies, mostly performed between 2012 and 2014, were collected  (OECD, 1997; OECD 428, 2004). This comprised 171 studies and provided 182 dermal absorption values for concentrates and 277 for spray dilutions. The resulting dataset was then combined with the published dataset  (Aggarwal et al. , 2014). To ensure homogeneity of the dataset, the following type of studies were excluded from the new dataset, and also from the combined dataset: studies with exposure duration exceeding 10 h studies with an experimental duration exceeding 24 h studies where values for tape strips 1 and 2 were not available studies with added surfactants or adjuvants (i. e. , that were not components of the agrochemical formulation).
Since these studies are OECD test guideline and GLP compliant, adjustments for recovery (where <95%) or additions of standard deviations (where >25% of mean) required by the EFSA GD were not made as they are considered to represent unacceptable regulatory conservatism as opposed to scientifically substantiated and defined criteria. Further, upon assessing the previous dataset two errors were identified: (1) one dermal absorption estimate was mistakenly excluded from the evaluation because of a presumably negative dermal absorption value; this value was re-calculated and incorporated into the combined assessment, and (2) four values derived using an added external surfactant were included in the previous evaluation; these values were excluded from the combined assessment. However, these changes do not affect the overall conclusions previous published by  Aggarwal et al.
Data analysis EFSA Guidance (2012) provides decision logic for assessors to determine the extent to which pesticide residue in the stratum corneum (i. e. , tape strips) should be considered as absorbed. While EFSA guidance occasionally allows exclusion of all tape-strip residues from the calculation of absorbed dose, this current analysis is based on EFSA's worst-case assumption that all tape strips (except 1 and 2) are absorbed: Dermal absorption = [Radioactivity present in receptor fluid + receptor chamber wash + whole skin sample] À [radioactivity in the first two tape strips]. From the individual studies, group mean values for concentrates and spray dilutions were calculated.
Data evaluation The data were evaluated to determine default values and to assess a potential relationship between dermal absorption and physical properties of the formulation groups (liquids versus solids), formulation type, concentration, dilution factor, MW or log P ow of the active substance. Dermal absorption values are plotted individually and/or as boxplots for both concentrates and dilutions. Individual values are presented as 'dot-plots' and to avoid excessive overlapping of individual values (dots) they are presented with an extended lateral perspective (Fig. 1A  and C ). Boxplots show the minimum value, 25th percentile (lower end of the box), median (horizontal line within the box), 75th percentile (upper end of the box) and maximum value. Additionally, the 95th-percentile of a distribution is superimposed as a hollow square symbol.
Data values exceeding 1. 5-times the interquartile range are presented as dots. In all figures, 'open' and 'solid' boxplots represent the analysis of new and combined datasets, respectively. Dermal absorption values were also grouped by formulation type and dilution to assess the impact of these parameters. To investigate how initial physical form influenced dermal absorption, the data were further grouped for solid and liquid concentrates. Spray dilutions from solid and liquid concentrates are denoted as 'solid' and 'liquid' dilutions, respectively, even though the true physical form of all dilutions is of course liquid. To investigate how the formulation matrix (i. e. , formulation type) influenced dermal absorption of a concentrate, values were grouped into primarily organic-solvent base (''EC + EW + SE''), primarily waterbased/dispersed (''SL + SC + OD + FS''), or solid (''WP + WG + SG'') formulations.
Statistical analysis and software Data were extracted from study reports and summarised in stacked spreadsheet format (Microsoft Excel 2010, Redmond, WA, USA). The absorption values were calculated as described in Section 2. 2. The spreadsheet was saved as a comma-separated values-file and read into the statistical programming language R (version 3. 0. 0 (2013-04-03),  R Core Team, 2014). Graphs were created with ggplot2 (version 0. 9. 3. 1, Wickham, 2009). Medians and percentiles (25th, 75th and 95th) were calculated with the R Stats Package (version 3. 0. 0). For null hypothesis statistical significance testing, alpha was set at a = 0. 01 and statistical significance was assumed when p was less than a. The data did not appear to be normally distributed, as investigated by Q-Q (quantile-quantile) plots.
Strength of dataset and exclusion of outliers Unlike the EFSA dataset, the datasets used in this evaluation are homogeneous, being derived from a single study type (i. e. , in vitro human) which is also the preferred test method in the EU regulation for plant protection products  (EC 1107 (EC /2009;; EC 284/ 2013EC). A single worst-case EFSA definition of dermal absorption was used in all endpoint evaluations. The combined dataset is robust and representative of most if not all plant protection products (PPP) as it includes 295 studies for 152 active substances from 19 formulation types, and a wide range of chemistry (Log P ow , MW, water solubility, etc. ) and concentrations (Section 2. 1). As presented in Fig. 1A  and C  (as inverted triangles), a tiny minority (5, or 1. 1%, Table  2 ) of the dermal absorption values were considered to be outliers and were excluded from further analysis.
For these reasons, these five values (4 concentrate and 1 dilution) were excluded from further analysis. There were other dermal absorption values that appeared unrepresentative of the whole dataset but a robust justification for exclusion could not be established and so they were included in endpoint evaluations; these are discussed below for information. In one study, spray dilutions had dermal absorption values of 59 and 62% (Fig. 1C ). However, values for the same active substance tested in four other studies in the same laboratory, with the same experimental protocol at comparable dilutions were all approximately 20%. In all of the studies, the skin residue was <1% and the concentrates had similar dermal absorption values below 3%. In a similar case, dermal absorption of an active substance from 50-and 200-fold dilutions of an EC formulation was 34% and 44%, respectively (Fig.
New dataset Median, 75th and 95th percentile dermal absorption values were 0. 7, 1. 3 and 4. 9% for liquid concentrates and 0. 3, 0. 7, and 1. 5% for solid concentrates (Fig. 1B , Table  2 ). Dermal absorption from liquid concentrates was higher compared to solid concentrates, especially at the 95th percentile, therefore, different defaults for liquids and solids are proposed. Median, 75th and 95th percentile values were 7. 1, 16. 2 and 31. 7%, and 4. 1, 6. 9 and 25. 0% from spray dilutions of liquid and solid formulations, respectively (Fig. 1D , Table  2 ). Since the difference in dermal absorption from both types of spray dilutions was small, a single default was proposed and median and percentile values for all spray dilutions combined were calculated (group ''All'' in Fig. 1D ); values were 6.