dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , a relevant role for the homolog gene in other plant species, the co-localization with QTLs for monoterpene content (with a special attention to the linalool-specific QTLs on chromosomes 2 and 10, which were also detected in the Moscato Bianco genetic background by  Battilana et al. , 2009) , or the coexpression from public transcriptomic databases with genes involved in the metabolic pahways under study, which may indicate functional association according to the "guilt-byassociation" principle. Hereafter, we discuss the most interesting findings from the present work; obvioulsy, we can't exclude that additional genes not included in this microarray platform may participate in monoterpene biosynthesis, as well as we can't know a priori whether our findings will be reproduced in other Muscat varieties with a genetic background different from Moscato Bianco.
The symbols # and § indicate references for grapevine and other plant species, respectively. For details, including the correspondence between microarray probes and V1 gene predictions, see Supplementary Table  S9. in VTCdb database (http://vtcdb. adelaide. edu. au/Home. aspx) for CYP genes coexpressed with linalool synthases, as in  Ginglinger et al. (2013). This information was then added to our transcriptomic and metabolic integrated datasets. On this base, we propose some genes (VIT_15s0048g01490, VIT_15s0048g01590, VIT_18s0001g13790, VIT_00s0389g00030, VIT_00s0389g00040) and eventually additional ones as potential CYPs involved in linalool metabolism (Table  2  and Supplementary Table  S9 ). Most of these candidates have been never reported elsewhere, and thus deserve further attention.
Acknowledgements The authors acknowledge financial support from  Xiamen University  through " Research and Development of spray drying equipment, 863-project ,"  National Science Foundation of China  ( 2011AA100801-3 ), and sincerely thank  Prof. Heping Zhang  in  Inner Mongolia Agricultural University  for kindly providing the Lactobacillus strains, and  Dr. Mary Bret  for help with English language. The authors thank  Dr. Xuee Wu , and our colleagues  Lu Zhang ,  Qinchuan Zhao ,  Qian Gao  and  Xuejiao Yan  for their technical support and fruitful discussions.
Study protocol Following recruitment, participants completed all required paperwork and anthropomorphic measurements (height, weight) were collected. Firefighters received a core temperature pill that they ingested 6-12 h prior to data collection. Upon arrival on each day, firefighters were instrumented with skin temperature patches on the back of their neck and upper arm that they wore throughout the trial. Multiple pre-and post-firefighting cardiovascular measurements and chemical exposure samples (biological and PPE) were collected prior to the initiation of the live fire evaluation (these data will be reported elsewhere). The firefighter participants were then deployed to complete their firefighting work in a purpose-built live-fire research test structure. In order to safely and reliably conduct this study, a structure was designed and built to have all of the interior finishes and features of a single family dwelling, yet contained specialised safety systems and hardened construction techniques that ensured participants' safety as described in  Horn et al.
Confirmatory analyses were conducted on log-transformed data for the few non-normal data-sets, which in all cases resulted in the same determination of statistical significance. Each of these analyses was performed in SPSS (v. 23 IBM, Armonk, NY) with significance set at an alpha of 0. 05. Data describing the environmental conditions within the structure at 0. 9, 1. 5 and 2. 1 m above the floor are reported in various rooms of interest (Living Room, Dining Room, Hallway, Fire Bedrooms) for scenarios in which interior and transitional attack tactics were implemented. Maximum temperatures and hallway heat flux values recorded at each height and location throughout the structure are determined for the 'Interior attack' and 'Transitional attack' tactics and compared between these tactics using Student t-test to determine if conditions were similar prior to firefighter intervention.
Firefighter local temperatures For each scenario, two of the firefighters operating at the front of their crews on the inside of the structure (nozzleman on the attack line and lead firefighter on the search team) wore a portable temperature sensor and data acquisition system affixed to the front of the helmet. Type K thermocouples with a 0. 5 mm nominal diameter in conjunction with Omega Engineering UWTC wireless temperature sensors were used to monitor temperatures of firefighting crews. The wireless sensors incorporated internal cold junction compensation. Data was logged to an internal solid state memory and downloaded after each experiment. The sensors, programmed to a sampling rate of 0. 5 Hz, synced to the main data acquisition system before each experiment. The resolution of the sensor was 1 °C, with an accuracy of 0.
Assessment of firefighter core and skin temperature Skin (neck and arm) and core body temperatures were continuously measured throughout all data collection sessions  (Horn et al. 2016). A monitor (MiniMitter Vital Sense, Phillips Respironics, Bend, OR) was clipped to their belts before and after firefighting and carried in their bunker coat after donning their PPE. This unit communicated with and recorded data from the core temperature pill and local skin temperature patches. Participants swallowed a small disposable core temperature sensor capsule, which is designed to pass through the body and be eliminated in faeces within ~24 h. While the sensor was in the GI tract it transmitted temperature information to the remote recording device. If a firefighter retained a pill from a prior measurement day, the one ingested 6-12 h prior to activity was utilised for consistency.
Core temperature The participants' core temperatures were monitored throughout the study and baseline and maximal values were recorded. Mean and standard deviation of the maximum core temperatures recorded and the change in core temperature from baseline are reported in Table  6. Figure  5  shows representative core temperature data from firefighters who completed four different job assignments (Inside Search, Outside Command/Pump, Outside Vent and Overhaul/RIT) from a single Transitional attack scenario. It is not possible to indicate the exact time of firefighting activities on this Figure as with earlier plots due to the limitations in linking between the different data acquisitions systems utilised. However, for this scenario, Inside operations were conducted for a little over 10 min after dispatch, while overhaul operations were conducted for 17 min after Inside operations ended.
The crop production comprises only irrigated crops (less than 20% of the areas) during the dry season (Rabi, December to March), to which rain-fed cotton is added to reach 60% of the areas during the monsoon season (Kharif, June to November). Sentinel-1 radar backscatter provides useful observations during the cloudy monsoon season. The lowest irrigated area totals were found during Rabi 2016 and Kharif 2016, accounting for 3. 5 and 5% with moderate classification confusion. This confusion decreases with increasing areas of irrigated crops during Rabi 2017. During this season, 16% of rice and 6% of irrigated crops were detected after the exceptional rainfalls observed in September. Surface water in small surface reservoirs reached 3% of the total area, which corresponds to a high value. The use of both Sentinel datasets improves the method accuracy and strengthens our confidence in the resulting maps.
In 2016, the National Information System for Climate and Environment Studies (NICES) project released a national database of seasonal net sown areas (http://bhuvan. nrsc. gov. in/ data/download/index. php), which highlights the high spatiotemporal heterogeneity of land cover and its temporal dynamics. This dataset is published as a fraction of the crop sown at 5 km resolution by the National Remote Sensing Center (NRSC) laboratory, part of the Indian Space Research Organization (ISRO). A similar program, called Water Bodies Fraction (WBF) and based on the same data type, produces the areal fraction of surface water every 15 days since 2012. The short-term variations of sown areas show a high temporal variation over recent decades in the Hyderabad region, with 41 to 58% of sown areas in Kharif and 8 to 22% in Rabi between 2005 and 2016.
The seasonal restitution of the areas of inundated rice and other irrigated crops from multi-temporal high resolution satellite remote sensing is thus a promising method for estimating the surface water and groundwater requirements for irrigation. Machine learning approaches are a way to produce land cover maps from remote sensing time series. More specifically, supervised classification methods, which require a training dataset, are suitable for exploring these massive amounts of data  [21]. Less automated strategies based on user-defined thresholds to provide more accurate indices can also be envisaged, but they are more time-consuming. In this agro-hydrological context, a threshold algorithm used to classify inundated rice shows higher accuracy than supervised random forest classification  [22].
It explores the benefit of the synergy between Sentinel-1's radar backscatter advantages (not sensitive to the persistent cloud cover during the Kharif season, highly impacted by crop growth and surface water) and the Sentinel-2's multi-spectral detection, both at an appropriate spatial resolution (10 to 20 m). The estimates of surface water area will be compared to existing datasets from ISRO-NRSC, to discuss their interest for better understanding the contribution to aquifer recharge of the thousands of small surface reservoirs that constitute the water harvesting system. We have chosen the Random Forest algorithm (RF) provided with the Orfeo ToolBox (OTB) because it is a fast, open-source processor of high-resolution optical, multispectral and radar images at the terabyte scale. Highly appropriate for building a processing chain, it seems suitable for automatically producing agro-hydrological variables at a large scale  [23].
Sentinel-1 and 2 Datasets Sentinel-1 and 2 acquisitions over India began at the end of 2015. Three crop cover successions over three seasons were monitored using the existing images. Table  1  lists the acquisition dates of the Sentinel images used to quantify the irrigated areas for the three growing seasons. The study area is embedded in two Sentinel-2 tiles (44QKE and 44QKF) located north of Hyderabad (Figure  1 , orange).
Sentinel-1 and 2 Datasets Sentinel-1 and 2 acquisitions over India began at the end of 2015. Three crop cover successions over three seasons were monitored using the existing images. Table  1  lists the acquisition dates of the Sentinel images used to quantify the irrigated areas for the three growing seasons. The study area is embedded in two Sentinel-2 tiles (44QKE and 44QKF) located north of Hyderabad (Figure  1 , orange). The Sentinel-2 dataset downloaded in this study is the standard Level-1C product, which includes ortho-rectification and spatial registration on a global reference system with sub-pixel accuracy. The Sentinel-2 Level-1C product is composed of 110 km × 110 km tiles in the UTM/WGS84 projection and provides the Top-Of-Atmosphere (TOA) reflectance. We produced Sentinel-2 Level-2A using the Sen2Cor software (2.
3. 1 released by European Spatial Agency-ESA), which performs the atmospheric, terrain, and cirrus correction of TOA input data. In this semi-arid Indian area, the correction is important for cloud-free images as the seasonal variations of aerosol concentrations in the form of dry dust is high  [27]  and alters the reflectance. The Sentinel-1 dataset comprises Level-1 Ground Range Detected (GRD) data in Interferometric Wide swath mode (IW) consisting of focused SAR data that has been multi-looked and projected to ground range using the WGS84 Earth ellipsoid model. The resulting images in dual polarization (VH and VV) have a dimension of 270 × 270 km and a resolution of 10 m. The terrain correction was then applied to geocode the images by correcting SAR geometric distortions (foreshortening, layover and shadow) using the digital elevation model from the Shuttle Radar Topography Mission, producing ground-projected images.
Field Data from Land Cover Surveys in 2016 and 2017 Three seasonal surveys of land cover were carried out in February 2016, November 2016, and February 2017, corresponding to 428 plots of non-irrigated areas: bare ground or natural vegetation cover, rainfed and tree plantations (eucalyptus and teak), 286 irrigated areas: orchards (mainly mango trees), maize crops and vegetables (beans, lentils), and 192 flooded rice crop areas. The dataset was especially scrutinized for rice as the main water-consuming crop. A USB GPS receiver (G-STAR IV) was connected to GIS software (QGIS) on a laptop to position the operator in the landscape. The operator used the latest Sentinel-2 acquisition to delineate polygons containing both homogeneous land cover and reflectance observed in the field and on the Sentinel-2 image.
The dataset created from the field surveys was used to create three seasonal datasets: perennial land cover such as orchard, forest, and natural land use are preserved across the three datasets together with the irrigated, flooded, and rain-fed crop fields observed during one season. These reference data comprise nine land cover classes: inundated rice, irrigated vegetables, irrigated maize, orchards, forested area, bare ground and natural bushes, urban areas, surface water, and rain-fed cotton (only in Kharif). Each seasonal dataset is then split into two separate training and validation sets of polygons.
Training and Validation Dataset All the pixels located in the training and validation polygons are used to respectively train the RF learning algorithm and to assess the accuracy of the resulting land-cover maps. The unit of assessment is therefore the pixel for the validation procedure, the number varying with the land-cover classes and spatial resolution considered, from tens to thousands of pixels. Non-agricultural areas cover 60 to 70% of these rural areas and are sampled using large polygons, implying thousands of learning and validation pixels, whereas rice and irrigated areas consist of hundreds of small polygons with limited numbers of pixels in each (Table  2 ). The accuracy of the land cover map produced is evaluated with the precision, recall, and F-score of the targeted irrigated and inundated crop class from the confusion matrices.
F-score is the harmonic mean of the user's accuracies and the producer's accuracies. The best value of the F-score is 1, and the worst 0. We use the inundated and irrigated area estimates to compare the methods and seasons. The overall land-cover accuracy is given by the Kappa index for all classes. The size of each satellite time series dataset analyzed is also given, to assess both the impact of resolutions on detection accuracies and on irrigated surface area estimates.
Surface Water from Sentinel-1 Time Series The Sentinel-1 radar is highly sensitive to surface water which highly vary from a season to another (Figure  3 ) as the backscattered energy is low if acquisition angles are not close to the nadir, as no backscatters are received from smooth surface  [37]. Using a threshold method, surface water areas can be quantified for large region (see Figure  4 ). However, this method requires setting the threshold for each image and is not automatic. We have built a training dataset of permanent surface water areas observed in the 14 images such as permanent lakes (e. g. , Hussain Sagar Lake in the center of Hyderabad) to perform a RF classification for each image. An average of surface water extent is computed for the Kudaliar watershed area, and compared with external source of data from NRSC-ISRO, at 1 km resolution available on the web platform Bhuvan.
Detection of Non-Irrigated Areas The spatial and spectral resolution has an impact on F-scores for non-rice land covers and thus on the overall Kappa index using Sentinel-1 only or Sentinel-2 only, especially for Rabi 2017. The improvement of F-score at 20-m resolution is not observed with the Sentinel-1 and 2 dataset. The additional Sentinel-2 variables NDWI and SWIR, and a decrease in radar speckle, could explain these improvements between 10 and 20 m spatial resolutions.
Estimation of Irrigated Areas and the Uncertainty of Water Demand Quantification The extent of irrigated and flooded crops and the corresponding Irrigated Water Demand (IWD) for the watershed are presented in Table  4. Estimates of rice areas range from 1. 55 to 3. 52% but are more likely around 3. 42 or 3. 52% when detected by Sentinel-2 only and Sentinel-1 and 2, since with Sentinel-1 alone detection accuracy is low (F-score 0. 6). The preliminary results using thresholding and the RF algorithm were of the same magnitude (2. 04 to 3. 7 in  [22] ). Irrigated crop (maize and vegetables) areas range from 0. 4 to 2. 77% but the classification accuracy is low for the whole dataset. Figure  6  shows the annual variability of areas of seasonally sown crops, derived from the ISRO-NRSC data base and averaged for the Kudaliar catchment.
The sown fraction estimates (all irrigated crops in Rabi and irrigated crop plus cotton areas in Kharif) from Sentinel remote sensing data are similar to that produced by ISRO-NRSC for Rabi 2016 (5. 5%). It corresponds to a minimum observed in Rabi for the last 10 years. The Kharif 2016 sown fraction derived from Sentinel time series also corresponds to a minimum observed during the Kharif season over the last 10 years. In contrast, a maximum of sown fraction for the last 10 years is observed during for Rabi 2017, concomitant with the surface and groundwater replenishment. The Kharif cotton areas seem to be well detected by S1 only: their areas remain similar (around 20%) after introducing S2 images (only four cloud-free images during the Kharif). This estimate of 20% appears to be highly realistic: a similar figure was obtained in Kharif 2009 over the study site  [17, 18].
The Uncertainty of Irrigated Water Demand Quantification The classification accuracies for each land-use class discussed earlier have contrasting impacts on IWD estimates. A high level of confusion in classifications regarding small crop areas and/or low irrigation requirements affects only limited amounts of water that can be neglected when compared to much larger hydrological fluxes. For instance, the high uncertainty of irrigated maize and vegetable areas lead to IWD estimates ranging from 0. 9 to 5. 9 mm for Rabi 2016, a water depth to be related to the whole catchment area. These figures represent small amounts of water when compared to the aquifer recharge or the rice water demand for the same period: from 20. 2 to 45. 7 mm and more likely around 45 mm with the Sentinel-2 or Sentinel-1 and 2 datasets.
We have computed the average and standard deviation of IWD estimated using both S2 and Sentinel-1 and 2 datasets in the Rabi seasons and only the Sentinel-1 and 2 datasets in the Kharif season (since only four cloud-free Sentinel-2 images were available). The irrigated water demand estimates based on the Decision Support Tool Ground Water (DST-GW) model  [24]  were 49. 5 ± 0. 78 mm (1. 5% of uncertainty) in Rabi 2016, 44. 9 ± 2. 9 mm (6. 5% of uncertainty) in the Kharif season, and 226. 2 ± 5. 8 mm (2. 5% of uncertainty) in Rabi 2017.
Surface Water Area Dynamics Using the S1 Dataset The 18 Sentinel-1 images acquired between the end of June 2016 and February 2017 were used to classify surface water areas, using two classes: water and non-water. Figure  8  shows the surface water temporal dynamic detected from S1 backscatter using the RF algorithm over the Kudaliar watershed. This automatic classification strategy is preferred to the threshold method used in Figure  4  by the Google Earth Engine platform, which requires selection of an appropriate threshold for each image. Very good F-scores (>0. 98 for each image) were obtained from this classification using the permanent surface water training dataset. Both resolutions of Sentinel-1 (10 and 20 m) are considered in this work and give similar results. More than 200 small reservoirs were detected and over the 1000 km 2.
This detection shows fast dynamics of surface water areas impacted by rainfall and runoff collected within the reservoir, evaporation from surface water, soil infiltration and pumping for irrigation. The Water Body Fractions extracted from the Indian national database were compared with the surface water areas found with Sentinel-1. Each fraction is provided for 15 days from ResourceSat optical remote sensing at 56-m resolution (Figure  8 , bold black line). The lower resolution of the Indian optical satellite (56 m versus 10-20 m) underestimates the water bodies' areal extents: boundary pixels not fully covered by water are not classified as water, leading to an erosion of the detected water surface areas. related to the whole catchment area. These figures represent small amounts of water when compared to the aquifer recharge or the rice water demand for the same period: from 20.
2 to 45. 7 mm and more likely around 45 mm with the Sentinel-2 or Sentinel-1 and 2 datasets. We have computed the average and standard deviation of IWD estimated using both S2 and Sentinel-1 and 2 datasets in the Rabi seasons and only the Sentinel-1 and 2 datasets in the Kharif season (since only four cloud-free Sentinel-2 images were available). The irrigated water demand estimates based on the Decision Support Tool Ground Water (DST-GW) model  [24]  were 49. 5 ± 0. 78 mm (1. 5% of uncertainty) in Rabi 2016, 44. 9 ± 2. 9 mm (6. 5% of uncertainty) in the Kharif season, and 226. 2 ± 5. 8 mm (2. 5% of uncertainty) in Rabi 2017.
Surface Water Area Dynamics Using the S1 Dataset The 18 Sentinel-1 images acquired between the end of June 2016 and February 2017 were used to classify surface water areas, using two classes: water and non-water. Figure  8  shows the surface water temporal dynamic detected from S1 backscatter using the RF algorithm over the Kudaliar watershed. This automatic classification strategy is preferred to the threshold method used in Figure  4  by the Google Earth Engine platform, which requires selection of an appropriate threshold for each image. Very good F-scores (>0. 98 for each image) were obtained from this classification using the permanent surface water training dataset. Both resolutions of Sentinel-1 (10 and 20 m) are considered in this work and give similar results. More than 200 small reservoirs were detected and monitored over the 1000 km 2.
This detection shows fast dynamics of surface water areas impacted by rainfall and runoff collected within the reservoir, evaporation from surface water, soil infiltration and pumping for irrigation. The Water Body Fractions extracted from the Indian national database were compared with the surface water areas found with Sentinel-1. Each fraction is provided for 15 days from ResourceSat optical remote sensing at 56-m resolution (Figure  8 , bold black line). The lower resolution of the Indian optical satellite (56 m versus 10-20 m) underestimates the water bodies' areal extents: boundary pixels not fully covered by water are not classified as water, leading to an erosion of the detected water surface areas.