dataesr/scientific-paragraphs-categorization huggingface dataset:
• Wound area assessment Epithelialization is an essential component of wound healing and is used as a defining parameter of a successful wound closure. Wound areas measurement of different groups of rats are gathered in Table  5. The healing progression was estimated over 8 days by regularly checking the dimension of the wound zone. Data herein showed that all treated groups revealed significant healing effects at the end of the experiment (8 days). Nevertheless, delayed wound healing processes were observed in group I compared to group VI, which presented a significantly quicker closure time. Closure of the wounds seemed to be totally accomplished in the MLWSP-treated group, reaching 0. 05 cm against 0. 25 cm in "Cytol Basic" group, 0. 36 cm in the glycerol group, and 0. 89 cm in the physiological serum group (Table  5 ).
FT-IR Spectrometric Analysis FT-IR spectrum of MLWSP was determined on a Nicolet FTIR spectrometer equipped with horizontal attenuated total reflection (ATR) accessory. The internal crystal reflection was made from zinc selenide and had a 45 • angle of incidence to the IR beam. The spectrum was acquired at 4 cm -1 resolution, and the measurement range was 500-4000 cm -1 (mid-IR region) at room temperature. The spectral data were analyzed by the OPUS 3. 0 data collection software program (Bruker, Ettlingen, Germany).
Statistical Analysis All experiments were done in triplicate, and data were expressed as mean value ± standard deviation. The statistical analyzes were performed using the one-way analysis of variance (ANOVA) procedure with the Statistical Package for the Social Sciences (SPSS) version 20. 0 software (IBM Corp. 2011, Armonk, NY, USA). When p < 0. 05, differences were considered as statistically significant according to Fisher's LSD test.
Ongoing and future climate change driven expansion of aeroallergen-producing plant species comprise a major human health problem across Europe and elsewhere. There is an urgent need to produce accurate, temporally dynamic maps at the continental level, especially in the context of climate uncertainty. This study aimed to restore missing daily ragweed pollen data sets for Europe, to produce phenological maps of ragweed pollen, resulting in the most complete and detailed high-resolution ragweed pollen concentration maps to date. To achieve this, we have developed two statistical procedures, a Gaussian method (GM) and deep learning (DL) for restoring missing daily ragweed pollen data sets, based on the plant's reproductive and growth (phenological, pollen production and frost-related) characteristics.
DL model performances were consistently better for estimating seasonal pollen integrals than those of the GM approach. These are the first published modelled maps using altitude correction and flowering phenology to recover missing pollen information. We created a web page (http://euragweedpollen. gmf. u-szeged. hu/), including daily ragweed pollen concentration data sets of the stations examined and their restored daily data, allowing one to upload newly measured or recovered daily data. Generation of these maps provides a means to track pollen impacts in the context of climatic shifts, identify geographical regions with high pollen exposure, determine areas of future vulnerability, apply spatially-explicit mitigation measures and prioritize management interventions. † He passed away in 2015.
Recent maps of airborne ragweed pollen concentrations for Europe presented by  Smith et al. (2013)  taken from  Skjøth et al. (2012) , were based on mean annual Ambrosia pollen levels from 368 stations using the European Aeroallergen Network (EAN) Pollen Database (https://ean. polleninfo. eu/ean). Another map of airborne ragweed pollen concentrations for Europe was published by  Prank et al. (2013) , who used data for the period 2005-2011. Liu et al. (2015 Liu et al. ( , 2016) )  used data for the period 2000-2010, simulating the production and dispersion of airborne ragweed pollen for Europe. Hamaoui-Laguel et al. (2015)  used the chemistry transport model CHIMERE and the RegCM4 regional climate model for mapping historical average pollen concentrations based on annual pollen concentration data of 51 sites across Europe.
Locations and selected years In our initial approach, we used pollen concentration data of common ragweed from 625 aerobiological stations in Europe [https://ean. polleninfo. eu/Ean/ (European Aeroallergen Network Pollen Database)]. However, many of these stations have limited or sporadic data, may be absent from key geographical regions, or have ceased operations altogether. Consequently, significant temporal and spatial gaps for determining pollen characteristics are evident. For instance, most stations have been operating since the 1980s in the western part of Europe, while all aerobiological stations in Ukraine and the south-western part of Russia came on line circa 2010. Therefore, in order to prepare a satisfactory pollen portrait for Europe trade-offs among data coverage, number of stations and length of the period are evident.
To address this issue, we decided to select a common study period covering the flowering and pollen-producing period of A. artemisiifolia (July 15 -October 15) and used only stations for which not more than 25 % of the years 1995-2010 (i. e. at most 4 years) had data coverage <40 %. This criterion is subjective, but by clarifying it, our goal was to include as many of the stations with incomplete databases as possible in the study, even if we have no restrictions regarding the temporal distribution of the data gaps per station according to the pollen season. As a result, only 67 out of the above 625 aerobiological stations were selected for a 16-year period  (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010). The year 2010 had the most complete data sets over this period, and using a similar approach for the 1995-2010 period, we selected 162 stations for this year.
Finally, we generated two data sets from the above database (Fig. 1 , 1b; Table  1 ; European Aeroallergen Network Pollen Database, https://ean. polleninfo. eu/Ean). Namely, (a)  16-year (1995-2010)  annual total ragweed pollen counts based on daily data for 67 stations and (b) annual total ragweed pollen counts for the year 2010 using daily data from 162 stations.
All pollen data from every aerobiological station were measured applying the same methodology, using the Burkard sampling equipment  (Crisp et al. , 2013) (Appendix, Sections A1 and A2). Daily weather data were downloaded from the European Climate Assessment and Dataset website (https://www. ecad. eu/dailydata/predefinedseries. php#, Blended ECA dataset,  Cornes et al. , 2018). The website provides daily meteorological data for a large number of stations over Europe.
Legends: 1: 0 < x ≤ 20; 2: 20 < x ≤ 40; 3: 40 < x ≤ 60; 4: 60 < x ≤ 80; 5: 80 < x ≤ 100. b. Geographical location of the 162 aerobiological stations used in the study comprising the total annual ragweed pollen concentrations for the year 2010. Locations of the aerobiological stations are indicated by black dots. Horizontal axis: longitude, angle in degrees; vertical axis: latitude, angle in degrees. The data density (x, %) of the stations is proportional to the size of the black dots (see Table  1)  A web page (http://euragweedpollen. gmf. u-szeged. hu/) was created, which includes the daily ragweed pollen concentration data sets, restored with the Gaussian method, for both the 67 selected stations (16-year data series, 1995-2010) and the 162 selected stations (annual data sets, 2010), with the possibility to upload newly measured or recovered daily data (Appendix, Section A5).
A1 ) is given in the appendix (Appendix, Section A6). Results are reported for eight different model configurations: (1) with and without weather information, (2) with original or normalized loss function, and (3) with a perturbation scheme working on days or windows. Performances are reported for two evaluation settings: restoring single (i. e. points) and windows of consecutive (i. e. windows) missing days. Among the generated models, two (with and without weather channel) were selected to reconstruct daily pollen concentration data for each setting, based on cross-validation results. The two resulting models were then compared on the test and test103 data sets to assess their behaviour. The test dataset was generated from the 67 stations, while the test103 dataset was generated by selecting 103 stations that belong to the 162 stations dataset but are not included in the 67 stations dataset.
For the Europe-scale maps the map corners were set to cover most of Europe since, in addition to the many stations in the western and central part of the continent, we have data from stations in Russia and Scandinavian countries, as well. For these Europe-scale maps of mean annual total (1995-2010) (Fig. 3 , 3*) and annual total (2010) (Appendix, Figs. A10, A10*) ragweed pollen concentrations a spherical trapezoid with 24 • difference of latitudes (34 • N-58 • N) and 53 • difference of longitudes (11 • W-42 • E) was selected. The maps of the phenological characteristics (Fig. 4 ), peak pollenrelated characteristics (Fig. 5 ) and frost-related characteristics (Appendix, Fig. A11 ) are based on the data from 67 stations only, located mostly in eastern and central Europe. The geographical coordinate corners of these maps were inclusive of those parts of Europe where data were available.
This allowed better visualization for pollen relevancy of the maps. Using the same projection as detailed above, a spherical trapezoid with 15 • difference in latitudes (40 • N-55 • N) and 32 • difference of longitudes (5 • W-27 • E) was selected. The Europe-scale pollen concentration maps were constructed using the interpolated pollen data from 67 and 162 stations, respectively. The maps of ragweed pollen-related characteristics are based on the data from 67 stations. Spatial predictions were made using latitude, longitude, as well as elevation information. Elevation information was available for the measuring stations. Elevation data for the interpolation sites were extracted from the ETOPO1 Global Relief Model  (Amante and Eakins, 2009). ETOPO1 is a 1 arc-minute global relief model of Earth's surface that integrates land topography and ocean bathymetry.
It was built from numerous global and regional data sets. All interpolation was executed and the maps were created using the R software (version 3. 1. 2) (R Core Team, 2014). The maps were generated in two stages. First, using the scattered measured data points a high-resolution raster map was computed using Bayesian Gaussian Process Modelling as implemented in the spTimer package  (Bakar and Sahu, 2014). The model was fitted by the spt. Gibbs function using the package default settings ("GP" model, "geodetic:km" distance method). The raster resolution was set to 15 pixels per geographical degree in both latitude and longitude dimensions, which corresponds to a spatial resolution of about 4 arcminutes. Because the predictions were made using a stochastic modelling process, the resulting pixels show the predicted trends with a stochastic pattern.
To facilitate a better interpretation of the results, the predicted values were smoothed using a 3 × 3 averaging filter. The plots were generated using the ggplot package  (Kahle and Wickham, 2013). For the pollen concentration maps, the measured data were mapped with the generalized log transform before interpolation and the interpolated results were mapped back to the original scale using the inverse log transform. This is a commonly used technique to ensure that the interpolated values fall within a sensible range (in our case only nonnegative values can be interpreted). Standard rectangular shaped raster maps were created. In addition, the region covered by our maps extended the minimal enclosing rectangle of the measuring stations to show some influential regions around the stations at extreme locations.
3. 1). Restored(DL) data sets: restoration of the missing daily data occurred by using convolutional auto-encoder models with weather data 1 (ConvAE + Weather) (see Section 2. 3. 2). Weather data 1 : daily values of maximum temperature, minimum temperature, mean temperature and precipitation. Fig. 3 *. 16-year mean annual total ragweed pollen concentrations for Europe on base-10 logarithmic colour scale, with the geographical locations of the aerobiological stations, using the (a) raw data sets, (b) restored (GM) data sets, and (c) restored(DL) data sets, July 15 -October 15, 1995-2010 (pollen grains ⋅ m -3 of air). The stations (1) with mean annual pollen concentration smaller than 100 pollen grains ⋅ m -3 of air, as well as (2) the areas beyond ±100 m from the altitude of the stations within their 150 km radius are excluded from mapping, and (3) the maximum distance is 150 km to each station.
Horizontal axis: longitude, angle in degrees; vertical axis: latitude, angle in degrees. Locations of the aerobiological stations are indicated by black dots. Restored(GM) data sets: restoration of the missing daily data occurred on the assumption that the seasonal distribution of daily pollen concentrations is normal (Gaussian Method) (see Section 2. 3. 1). Restored(DL) data sets: restoration of the missing daily data occurred by using convolutional auto-encoder models with weather data 1 (ConvAE + Weather) (see Section 2. 3. 2). Weather data 1 : daily values of maximum temperature, minimum temperature, mean temperature and precipitation.
Evaluation of the map accuracy with the original and restored datasets The final aim of the GM and DL models was to estimate a more accurate ATPC value that leads to an accuracy improvement in the generated maps. We compared the reconstruction abilities of the two models considering a set of 25 stations from the "test103" dataset. These stations had no missing data for the 2010 season, therefore their ATPC is considered correct. We removed different percentages of data (from 10 % to 80 %) from the time series of the 25 stations and asked the model to reconstruct the real ATPC. We evaluated two scenarios. First, we assessed model accuracy in reconstructing ATPC values with R 2 , RMSE and Mean Absolute Error (MAE). Second, we compared the Mean Absolute Percentage Error (MAPE) between the maps generated with real and perturbed data, and the MAPE between the maps generated with real and reconstructed data (Appendix, Section A7: A7.
Comparison of the maps prepared on the raw and restored pollen concentration data sets The original, namely raw, data sets consist of annual average pollen concentrations for the  16-year period (1995-2010) , without restoring the missing daily pollen concentration data. For every station in the restored databases, the missing data for given days were restored by entering the values generated by both the Gaussian method (GM) (see Section 2. 3. 1) and the deep learning (DL) (see Section 2. 3. 2). For the study period, eight stations (11. 9 %), showed the lowest data coverage, while 20 stations (29. 9 %) indicated the highest data density (Table  1 ). We prepared mean annual total ragweed pollen concentration maps for Europe for the 16-year period, July 15 -October 15, 1995-2010, some including all stations (Fig.
Comparison of the maps of raw and restored maximal pollen counts and their date Peak values of the mean maximum daily ragweed pollen concentrations were observed in the Pannonian Plain within the Carpathian Basin (Fig. 5a ). Areas exceeding 600 pollen grains m -3 /day cover the central and southern parts, as well as the north-eastern part of this area, for Europe, using a base-10 logarithmic colour scale, with the geographical location of the aerobiological stations, using the raw data sets, 16 years, from 1995 to 2010. The stations are: (1) with mean annual pollen concentration smaller than 100 pollen grains ⋅ m -3 of air, as well as (2) the areas beyond ±100 m from the altitude of the stations within their 150 km radius are excluded from mapping, and (3) the maximum distance is 150 km to each station.
Horizontal axis: longitude, angle in degrees; vertical axis: latitude, angle in degrees. Locations of the aerobiological stations are indicated by black dots. respectively. [The daily ragweed pollen concentration above 100 pollen grains m -3 during the main flowering period is many times higher than the minimum concentration values causing symptoms  (Déchamp et al. , 1997; Makra et al. , 2005) ]. The date of the mean maximum daily ragweed pollen counts changes within a very narrow time frame (between late August and early September), with the earliest peak values in the Pannonian Plain, while the latest ones occur in western Lombardy and France, respectively (Fig. 5b ). We found that DL model performances were consistently better than GM for estimating ATPC with 10 % to 70 % data removal considering R 2 and RMSE.
The novelty of our research can be summarized in the fact that we applied two approaches, i. e. the Gaussian method and deep learning, to restore incomplete ragweed pollen data sets to improve pollen mapping. As such, this study improves on previous mapping approaches which used interpolation-oriented methods (e. g. European Aeroallergen Network Pollen Database, https://ean. polleninfo. eu/ean;  Skjøth et al. , 2012; Smith et al. , 2013). To the best of our knowledge, this is the first attempt in the literature to restore incomplete pollen data sets. In addition, we developed maps of ragweed phenology (means of start, end and duration of the pollen season), quantity-related characteristics (means of maximum daily pollen concentrations and the day of the mean maximum daily pollen concentrations) and frost-related parameters (means of last frost day in spring, first frost day in fall and the duration of the frost-free period) for Europe that have not been generated by previous research (e.
g. the CORINE Land Cover Database, as a pan-European land cover inventory does not contain data on the following countries: Croatia, Belarus, Ukraine, and Russia), and (III) information is missing for several years (the CORINE Land Cover Database inventory is only available for the following years based on the period we are  examining: 1990, 2000, and 2006. In a previous paper,  Deák et al. (2013)  examined the potential change in land use for one of the stations included in this paper (Szeged, Hungary). They found that land use changes did not influence ragweed pollen concentration over the Szeged area in the period examined (http://www. eea. europa. eu/publications/COR0landcover)  (Deák et al. , 2013).
Suitability of the distribution maps of common ragweed and their agreement with the pollen concentration maps of ragweed for Europe Distribution databases may not accurately reflect pollen loads. For example, casual ragweed occurrences in northern Europe, where ragweed populations are small, produce a very little amount of pollen. As a result, pollen data give a better indication of variation in abundance and impacts  (Dahl et al. , 1999). In addition, distribution databases can under-record in regions like Ukraine  (Rodinkova et al. , 2018)  where monitoring is absent. At the same time, the pollen data can, in principle, help fill the gaps now that more stations in these regions are available online. However, under-recording of distributions seems to correlate with poor pollen monitoring.
, 2014) , so integrating large-scale spatial data on ragweed phenology with model predictions would be very useful for developing better models for ragweed invasion risk as a response to climate-induced changes in temperature. Distribution maps of A. artemisiifolia for Europe using the DAISIE  (Bullock et al. , 2010 ; http://www. europe-aliens. org) and the GBIF  (Bullock et al. , 2010; Cunze et al. , 2013 ; http://data. gbif. org) databases are less accurate due to lack of information (i. e. significant gaps in the data sets). Specifically, (a) the distribution of the European GBIF records for A. artemisiifolia, (b) the distribution of A. artemisiifolia in Europe either on 50 km × 50 km or 10 km × 10 km grid cells (based on the distribution maps of >40 national aerobiological services and international databases), and (c) the modelled habitat suitability of A.
Suitability of the retained stations, data restoration and interpolation maps Without appropriately accounting or compensating for missing data, data sets may be biased and, as a consequence, any projections may be imprecise. For ragweed over the majority of Europe, this is the current situation. Due to the relatively few aerobiological stations with full ragweed pollen data sets, stations with raw data sets satisfying certain conditions (see Section 2. 1) were retained and their missing data were restored through two novel procedures, i. e. the Gaussian method (GM) (see Section 2. 3. 1) and deep learning (DL) (see Section 2. 3. 2). Annual pollen concentrations of several retained stations with complete databases are very low, not even exceeding 100 pollen grains m -3. For such stations, it seems excessive to speak about a pollen season, even in some cases about a longer pollen season reaching 60 days or so.
Conclusions For the first time, we have developed detailed and up-to-date maps of the airborne pollen concentrations of ragweed across the continent of Europe. We applied two statistical approaches, i. e. the Gaussian method (GM) and deep learning (DL) model for restoring missing daily ragweed pollen data sets for Europe. Based on these techniques, we used large ragweed pollen data sets for Europe to produce maps of the mean annual ragweed pollen concentrations for the 16-year period 1995-2010 and for the year 2010. Both types of concentration maps were prepared for the raw and restored data sets, respectively. For maximum comprehensibility and dissemination, we created a web page (http://eur agweedpollen. gmf. u-szeged. hu/), including the restored daily ragweed pollen concentration data sets of both the 67 selected stations (16-year data series, 1995-2010) and the 162 selected stations (annual data sets, 2010) (Appendix, Section A5).
The ragweed pollen related maps produced and presented in this paper are unique for Europe. Maps of phenology-related characteristics (means of start, end and duration of the pollen season), quantity-related parameters (means of maximum daily pollen concentration and the day of the maximum daily pollen concentration) and frost-related characteristics (means of last frost day in spring, first frost day in fall and duration of the frost-free period) are novelties in the international literature. In addition, these are the first maps in the literature prepared using altitude correction. We found that DL model performances are consistently better than those of GM for estimating annual total pollen amounts (ATPC). Ragweed pollen concentration maps derived from the raw data sets were very similar to those based on the restored data sets produced by both the Gaussian method (GM) and deep learning (DL).
Acknowledgements The authors appreciate the advice of  Marje Prank  and  Mikhail Sofiev  ( Finnish Meteorological Institute, Helsinki, Finland ). In addition, the authors would like to thank the  European Aeroallergen Network  (EAN, https://ean. polleninfo. eu/Ean) for providing ragweed pollen data from Europe, and the  Polish Aerobiological Team  for the provision of ragweed pollen data from Poland. The study was partly implemented in the frame of the  European Cooperation   in Science and Technology (EU-COST) program , " New approaches in detection of pathogens and aeroallergens (ADOPT) ," Grant  CA18226  ( EU Framework Program Horizon   2020 ).