dataesr/scientific-paragraphs-categorization huggingface dataset:
Nuclear staining and image analysis Fixed ovaries were embedded in paraffin and median sections (7 μm thickness) were performed with a microtome (HM355, microm). Nuclei were stained with DAPI (0,1 µg/ml) at room temperature for 15 minutes in the dark. Median sections were washed 1h in PBS at room temperature. Images of whole sections were acquired with a nanozoomer (HAMAMATSU). For quantitative image analyses, individual oocyte's area was measured using an image analysis software (Visilog 7.
Founding This work was funded by the TEFOR project (Agence National de la Recherche, ANR-II-INBS-0014, http://www. agence-nationale-recherche. fr/investissements-davenir/projets-finances/). VT received the funding. This work has also been supported by the ERA-Net COFASP (COFA) AquaCrispr project (Agence National de la Recherche, ANR-16-COFA-0004, http://www. agence-nationale-recherche. fr/suivi-bilan/editions-2013-et-anterieures/environnement-et-ressources-biologiques/era-net-cofaspcooperation-in-fisheries-aquaculture-and-seafood-processing/). JB received the funding. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Among these ones, the Dynamic Habitat Index (DHI) incorporates three components based on the temporal variation of canopy light absorbance through the year including the annual sum, the minimum and the monthly variation  [24, 25]. This indicator has been already taken up by studies on different taxonomic groups  [26] [27] [28]  and a recent study has compared DHI based on different MODIS products to explain breeding bird species richness in North America  [29]. However, until now, the added value of this integrative indicator of intra-annual variation, compared to data acquired at a single date, has never been tested. In this study, we used a large bird dataset collected at the French national scale to (i) compare single date NDVI data and DHI to explain and predict species richness patterns of different bird groups; (ii) identify the best NDVI acquisition date to explain and predict bird species richness; and (iii) test whether the best NDVI acquisition date depends on the dominant land cover in landscapes.
Bird Data Bird data were obtained from the French Breeding Bird Survey (FBBS), a standardized monitoring program in which volunteer ornithologists identify and record breeding birds year after year  [30]. In the FBBS program, each observer provides a point location, and a randomly 2 km × 2 km survey square shaped plot is selected around a radius of 10 km from this location. Each plot contain 10 point counts with 100 m radii which are separated from each other by 300 m at a minimum and scattered in the landscape in order to best represent the diversity of habitats (Figure  1 ). Each point count is performed twice a year during the breeding season (from April to mid-June) to record early singing species as well as last migrants. In each point count, heard or seen species are recorded during 5 min between 1 h and 4 h after sunrise.
Based on this map, the dominant climate region was estimated for each plot of bird data and defined as one qualitative predictor. An alternative would have been to use WorldClim2 data which are locally defined at 1 km 2 scale  [32]. However, these data are based on an interpolation using fewer weather stations than the map defined by  [31]  at French scale. This is why we retained the first source of data (i. e. , map of macro-climate regions). Normalized Difference Vegetation Index (NDVI) was used to provide surrogate of local vegetation cover and landscape heterogeneity patterns over France. The Terra-MODIS NDVI 16-day-composit product at 250-m spatial resolution (MOD13Q1) was retained. To accommodate for seasonal variation and evaluate the temporal effect of NDVI on models performance, five time periods in 2010 were selected (Figure  2 ): late winter (6 March 2010, NDVI 065 ), before leaves growing of deciduous tree; late spring (26 June 2010, NDVI 177 ), before winter crops harvesting; late summer (29 August 2010, NDVI 241 ), after winter crops harvesting before summer crops harvesting; end summer (14 September 2010, NDVI 257 ), after partial summer crops harvesting; begin autumn (30 September 2010, NDVI 273 ), after full crops harvesting.
At these time periods, landscape composition and heterogeneity are revealed differently in NDVI data. In winter, NDVI values are low in the deciduous forests and in the non-cultivated agricultural parcels because of absence of photosynthetic activity. In spring and summer, NDVI reaches its highest values, mainly in winter and summer crops respectively, but also in grasslands and forests. In autumn, after all crops harvesting, NDVI discriminates well forests and open habitats because of higher difference of NDVI values, agricultural parcels being bare (see Appendix E). The NDVI data of each time period were selected after checking the MODIS quality layers of all the 16-day time series products of 2010. Based on the average value of the MOD13Q1 pixel reliability over France (0 = good, 1 = marginal data, 2 = snow/ice, 3 = cloudy), the images showing the highest quality (i.
To examine whether seasonal variations effect of NDVI on the models performance depends on the landscape context, we used land cover/land use datasets to calculate landscape composition in each bird plot. The forest layer was obtained from a nationwide highly detailed vector spatial database (BDTopo ® , French mapping agency IGN) at 1-m resolution. For agricultural areas, the graphic parcel register (RPG) of 2010 was used. The French RPG database is produced annually since 2002 in application of the EU directives related to the Common Agricultural Policy. The land use information, based on a 28 class nomenclature (including several types of crops, as well as artificial and natural grasslands), is obtained from the farmers' declaration of their cultivated areas. The information is defined at the agricultural block level composed of one single field or several contiguous ones.
Statistical Analysis In a first time, we built models at national scale using the total filtered bird dataset (n = 759). We compared explanatory and predictive performances of NDVI data at several time periods with DHI data. For each of the four species richness metrics, we first constructed a model with only the climate variable, i. e. , the dominant climate region of the bird plot, as predictor (e. g. , SR woodland ~Climate). Then, we added into the climate model both NDVI variables (Avg and SD) of each time period in five separate models (e. g. , SR woodland ~Climate + Avg. NDVI 065 + SD. NDVI 065 ). Finally, we fitted a final model including DHI variables (6 predictors related to the Avg and SD of the three DHI components, see above) and the climate predictor. In a second time, we examined whether the best time period of NDVI depended on the plot land cover context.
This analysis was only applied to woodland and farmland species richness that were the best explained bird metrics at national scale. We partitioned bird national data into subsets, according to the dominant land cover of the bird plot squares. Landscape composition in each plot was calculated as the proportion of area occupied by four land covers: woody vegetation (including hedgerows), permanent grasslands, winter crops (mainly wheat and barley) and summer crops (mainly maize and sunflower). Because agricultural blocks of the land cover map (RPG) may cover partially the bird plot squares, only the blocks included by at least 70% in the plots were conserved for the estimation. Based on the land cover proportions, four subsets of bird plots were defined with the constraint to obtain a sample size large enough (i.
e. , with n > 100) and a high dominance of a specific land cover (Table  1 ). For each subset, we built models with climate and NDVI variables of each date as for the national scale. Relationships between bird species richness and predictors were explored using generalized additive models (GAMs) with R version 3. 1. 0 software  [35]  and the gam package  [36]. The general expression of GAM model is  [37] : g(E(Y|X 1 , X 2 ,. , X p )) = f 0 + f 1 (X 1 ) + f 2 (X 2 ) +. + f p (X p ) = f 0 + p ∑ i=1 f i (X i ) ( 1 ) where Y is the response variable, X i the predictors, f i the non-parametric smooth functions and g the link function relating to the expected value of Y. GAMs present the advantage to consider non-linear effects between response and predictor variables. Poisson distribution and a log link function were used to calibrate the models.
The most parsimonious models (i. e. , with the lowest Akaike Information Criterion values) were selected by stepwise backward and forward variable selection  [38]. In addition, the procedure was allowed to select from one to four degrees of smoothing (1 = no smoothing) for each quantitative variable in GAMs. The goodness of fit of the models (explanatory performance) was quantified by the amount of explained deviance (%D 2 ). The independent contribution of each variable to explain species richness was also estimated from a hierarchical partitioning procedure  [39]. The predictive performance was calculated by k-fold cross-validation  [40]. The initial dataset was splitted randomly in three groups of equal size (k = 3). Two groups were used to calibrate the model and the third group was used as a test set to evaluate predictions.
In landscapes dominated by grasslands, late spring (26 June) and late summer (29 August) were the most adequate periods for SR woodland. This is also the case in woody landscape to explain SR farmland. These time periods match with the peak of woodland and grassland vegetation productivity leading to better accurate estimations. Because the proportion of the dominant land use in the surveyed square was sometimes low in the analysis (although a majority proportion was ensured in bird plots), a deeper analysis should be further developed to confirm the results, using a most exhaustive dataset.
The analysis areas measured 5 µm × 5 µm or 10 µm × 10 µm. Images were obtained with 512 lines and 512 samples/line, which correspond to spatial resolutions of 10 nm and 20 nm, respectively, for the 5 µm and 10 µm wide images. The scan rate was 0. 2 Hz and the peak force set point was 250 pN. Finite-element simulations. Numerical simulations were carried out by finite-element simulation using the COMSOL software Version 5. 2a and the RF module. We simulated the propagation of a planar wave from silica to the air, either through a scratch approximated by a triangle or through cross-sections of several scratches obtained by AFM imaging. The incoming wave is normal to the exit surface and its wavelength is 351 nm. TM polarization is defined as the polarization perpendicular to the scratch direction.
All patients underwent a complete ophthalmological examination before switching including medical history and comorbidities (high blood pressure), best-corrected visual acuity (BCVA) on the ETDRS chart, intraocular pressure, slit-lamp examination, and SD-OCT. Based on these examinations, one of the investigators (SNB and AGB) decided to switch to aflibercept. Visual acuity and SD-OCT were repeated on a TAE basis after switching to aflibercept. e following data were collected: time interval between injections, number of injections, BCVA, and (CRT) assessed by SD-OCT. e primary endpoint was the interval between aflibercept injections after switching and comparing the previous interval between ranibizumab injections. Secondary endpoints were changes in BCVA and CRT after switching to aflibercept.
pollen). The latter was performed by examining in a Hundh 500 (Helmut Hund GmbH, Wetzlar, Germany) light microscope coupled to a digital camera (Motic m 1000, Motic, Hong Kong, China) and an image analysis system (Motic images plus software) for morphometry of the pollen grains. The sample was stored in a hermetically closed glass bottle at 4 • C until the VOCs isolation. Artificial honey (a honey-like sugar matrix) containing 448 g/L fructose and 350 g/L glucose was prepared according to the research from Castro-Vázquez et al. [12]  and spiked with reference standards (Table  1 ) representing different groups of compounds naturally occurring in various types of honey at two levels of concentration: 300 µg/L for α-pinene, terpinen-4-ol, α-terpineol, linalool, borneol, 600 µg/L for p-anisaldehyde, eugenol, vanillin, methyl syringate, caffeine, and 1000 µg/L for benzyl alcohol as well as at two-fold higher concentration for all of these compounds.
Introduction The importance of historical control data was discussed by a working group of the International Workshop on Genotoxicity Testing (IWGT) at a meeting in Basel in 2009. Various recommendations were made by the group for the use of historical control data  [1]. They focused mainly on historical negative control data pointing to its use in determining the acceptability of the experimental (concurrent) negative control in the test and as evidence of a laboratory's competency for conducting an assay. The recommendations made in this IWGT paper included:  (1)  the minimum sets of data needed for the creation of historical control datasets, (2) consideration of the distribution of the data rather than the simple ranges and (3) consideration of how such data can assist in the interpretation of results.
Recent revisions of the Organisation for Economic Co-operation and Development (OECD) Test Guidelines for the Testing of Chemicals now include an increased emphasis on the use of historical negative control data in the assessment of genotoxicity test results  [2]. They include recommendations on how to build an historical control database. For example, the OECD TG 487 for the in vitro Mammalian Cell Micronucleus Test  [3]  states that: "When first acquiring data for an historical negative control distribution, concurrent negative controls should be consistent with published negative control data where they exist. As more experimental data are added to the control distribution, concurrent negative controls should ideally be within the 95% control limits of that distribution. The laboratory's historical negative control database should initially be built with a minimum of 10 experiments but would preferably consist of at least 20 experiments conducted under comparable experimental conditions.
Laboratories should use quality control methods, such as control charts (e. g. C-charts or X-bar charts), to identify how variable their positive and negative control data are, and to show that the methodology is 'under control' in their laboratory. " The concurrent negative control data is used to assess whether the experiment meets acceptability criteria based on whether the negative control is considered to be acceptable for addition to the laboratory historical control database. The criteria for the evaluation and interpretation of results states that, in addition to an evaluation of the statistical significance and dose response, at least one or more data points must fall outside the distribution of the historical negative control. This later criterion provides a means to assess the biological significance of the results.
The OECD test guidelines also provide guidance on the acceptance criteria for inclusion of data into the historical negative control database. They recommend comparisons of the test results with the historical negative control data as one of the three conditions necessary for a definitive clear positive or negative result. As a part of the deliberations during the last revision of the in vitro test guidelines, the OECD collected a small amount of historical control data for a number of in vivo and in vitro genotoxicity tests  [4]. Data were collected from a number of laboratories following a call from the OECD for historical control data from different tests including the in vitro micronucleus test to help answer questions related to optimal cell numbers and sample size issues, based upon the expected statistical power and background incidence of genotoxic events in the negative controls.
During the course of the OECD revision discussions, a number of limitations of this data set were identified including that reporting of the data was non-standardized and some of the data sets were quite variable. Furthermore, the analysis of those data sets showed what appeared to be appreciable within and between laboratory variability  [4]. In view of these limitations, the HESI Genetic Toxicology Technical Committee (GTTC) Data Interpretation Workgroup started a project to collect and collate a set of data into a standardized database from wellestablished laboratories. To begin the project, the workgroup chose the in vitro micronucleus test using the human lymphoblastic (B-cell) TK6 cells as the prototype for collection of standardized control data and to develop methods to assess these data.
'Banks' of the cells have been set up at the Japanese Collection of Research Bioresources (JCRB) Cell Bank, Japan and subsequently at the European Collection of Authenticated Cell Cultures (ECACC), UK. The HESI GTTC Data Interpretation Workgroup put out calls in 2013 and 2014 for interested laboratories to submit data. A specially designed Excel spreadsheet was created for the collection of the data in a defined and uniform way. The Excel spreadsheet was successfully trialled by one of the participating laboratories before being sent out to the other participants. Participating laboratories were asked to complete the spreadsheet and provide answers to a series of specific questions developed by a Management Team on aspects of the conditions used in their studies. The goals of the project were:  (1)  to identify the range of data collected by proficient laboratories;  (2)  to see if any of the different 'factors' in the conduct of studies affect the variability; (3) to see whether the historical negative control data could provide information to make a recommendation of the appropriate number of experiments needed to build a historical negative control database; and (4) to establish acceptable ranges of negative control database.
Materials and methods Thirteen laboratories (four from the USA, three from Japan and six from Europe) participated. Four (C, D, J, M) used flow cytometry methods while nine (A, B, E, F, G, H, I, K, L) used other scoring methods including manual counting using light (4) or fluorescent microscopes (3), automated fluorescence microscopy using the Cellomics ArrayScan ® VTi HCS Reader  (1)  and what was described as microscopy using image analysis  (1). No laboratory provided data using more than one method on the same material. Data were collected from experiments carried out between March 2003 and August 2014 although the time frame was, in general, much narrower for the individual laboratory. For instance, one laboratory has a range from 2003 to 2014 while others spanned less than a year.
A small amount of data were collected from 24 h treatment in the absence of S9 followed by a recovery period longer than 24 h (-S9 longer). Two of the four laboratories (D, M) using flow cytometry methods only reported data from studies without the addition of S9. The data sets from all 13 laboratories (currently anonymized as A to M) were broken down into 55 separate combinations of the various conditions (e. g. ± S9, time, scoring methods and vehicles). Most laboratories provided information at the individual replicate culture level allowing assessment of the variability between replicate cultures within a laboratory. Other laboratories reported just one replicate per experiment. Two laboratories (F, I) pooled counts over two replicates and consequently there was no opportunity to assess variability between replicate cultures for these laboratories.
Data on 4642 replicates were provided by the 13 laboratories, mainly in the form of defined multiples of 1000 cells. Of these replicates, 1090 were from the four laboratories using flow cytometry. In some cases, there were a variable number of replicates within a single experiment. In one experiment, Laboratory A reported there were 25 replicates. Data, in general, were reported in one of the following formats: • Number of micronucleated cells for exactly 1000 (in a small number of cases 2000) cells; • Number of micronucleated cells from approximately 1000 cells; • Number of micronucleated cells from a variable number of cells > 1000 (non-flow cytometry methods); • Number of micronucleated cells from a variable number of cells > 1000 (flow cytometer method). Different solvents were used as vehicles including: water, dimethyl sulfoxide (DMSO), phosphate buffered saline (PBS), saline, 10% Donor Horse Serum -Roswell Park Memorial Institute (DHS-RPMI) and a vehicle just described as "Medium".
The number of replicates/experimental condition ranged from 2 to 795 (40-461 for the laboratories using flow cytometry). The number of cells/replicate scored by the nine laboratories using non-flow cytometry methods ranged from 1000 to > 2000 and 1238 to 21,772 for the four laboratories using flow cytometry methods. No restrictions were placed upon the amount of data that could be submitted except that laboratories were expected to have data from a minimum of 20 experiments, which is often considered the lower limit for the use of Quality Control methods  [7]. In general, this was achieved, although there were a number of cases where data were derived from a smaller number of experiments because the number of experiments carried out using particular combinations of conditions (such as using a specific vehicle) was small.
Statistical methods Data were analysed using the statistical procedures available in Minitab (Minitab 16 Statistical Software. Minitab, Inc. , State College, PA) and R  [8]. Data from replicates of exactly 1000 cells were analysed as integer counts. Data from replicates with variable numbers of cells (especially much greater than 1000) were analysed as proportions of micronucleated cells. In some analyses, proportions were multiplied by 1000 to give estimates of the number of micronucleated cells/1000 cells scored. The specific procedures were: one-way and nested analyses of variances, tests for extra-binomial variation (i. e. Goodness of fit to a Poisson distribution), correlations between -S9 short and +S9 short experiments (where applicable) and calculation of tolerance intervals. A number of QC methods were used: C-Charts for Poisson counts, I-Charts for individual replicate values of counts or proportions of micronucleated cells, p-Charts for proportions when the number of cells counted differed between replicates and X-bar Charts when there were a number of replicates per experiment.
Although the length of data collection varied over time (range six months to 10 years) these data show that QC methods have a role in improving a laboratory's performance and that a further reduction of intra-laboratory variability is feasible. It is not known whether laboratories provided data from all their experiments or whether those experiments that were considered to have failed to meet the laboratory's own acceptance criteria were excluded from the datasets. Laboratories may vary in the degree to which they 'self-edited' their datasets. If this was the case, then such a laboratory might be expected to report less variability than actually occurred. One of the complications in building up negative control databases is whether data can be combined across the different conditions to increase the number of replicates and achieve the 'rule of thumb' of 20 experiments for QC methods.