dataesr/scientific-paragraphs-categorization huggingface dataset:
Monitoring of experiments Oryzias latipes fish were maintained in suitable and stable conditions, and no fish died during the whole experimentation (Fig. 1  and Dataset S2). During the first 28-days long exposure (d0-d28), concentrations of Microcystis aeruginosa in the three treatments (d28_1, d28_10 and d28_100) corresponded to expected levels, 1. 0 ± 0. 2, 10. 0 ± 0. 8 and 100. 2 ± 7. 8 µg. L -1 Chla, respectively. Microcystin levels in water were 0. 4 ± 0. 0 and 10. 4 ± 2. 1 µg MC-LR eq. L -1 in the d28_10 and d28_100 treatments, respectively, while microcystin was below detection level (< 0. 15 µg. L -1 ) in d28_1. During the second M. aeruginosa exposure (d33-d39), fish were exposed to 102. 8 ± 2. 9 µg. L -1 Chla and 11. 2 ± 2. 8 µg MC-LR eq. L -1. The metabolic content of Microcystis aeruginosa cultures was examined (see Supplementary Note 2 and Table  S1 ).
Diversity and composition of the gut bacterial microbiota after 28 days of exposure At d28, gut communities display between 42 and 219 ASVs with higher average bacterial richness (136 ± 56 ASVs) and evenness (0. 556) reported in fish guts exposed to d28_Z8, and lower average bacterial richness (82 ± 25 ASVs) and evenness (0. 391) in the treatment d28_0 (Dataset S4). Gut-associated species richness was highest in the d28_Z8 treatment (LMM, p < 0. 05), while no differences are observed among replicates within each treatment (LMM, p > 0. 05). The Shannon index increases slightly with Microcystis concentration (d28_1: 2. 09, d28_10: 2. 14, d28_100: 2. 28). Visual comparisons on individual plots of principal coordinates analyses (PCoA) based on the weighted or unweighted UniFrac distances suggest changes occur in terms of both abundances as well as community membership when fish are exposed to M.
Metagenome-based comparison of gut communities in d28_0 and d28_100 Metagenome datasets obtained from 5 fish guts from d28_0 and 5 others from d28_100 yielded negligible amounts of Archaea and non-fish Eukaryota sequences. Among the 32 bacterial bins obtained, 11 are found to be differentially abundant between the two treatments (p < 0. 05, Wilcoxon rank-sum test) (Fig. S2b ). Eight are more abundant in d28_100, including seven assigned to the same taxa as ASVs (Flavobacterium, Aeromonas, Gemmobacter, Rhizobiales), and vary in the same way as ASV counts (Fig. S2a, b ). Of the 3 bins found significantly more abundant in d28_0, bin24 is affiliated to the Firmicutes, with a majority of coding sequences associated to ZOR0006 (58. 06%), the genus corresponding to aforementioned ASV1662. Gene functions were investigated by analysing enzymes (KO) potentially produced by bins and their associated pathways.
S3a ). In muscles, differences occur (p < 0. 001) especially between d28_0 and both d28_Z8 and d28_1 (p = 0. 01), and between d28_100 and all other treatments (p = 0. 01) except d28_0 (p = 0. 33) (Fig. S3b ). A joint analysis of gut metabolites and bacterial communities was performed, as described by Singh and colleagues  (45). The application of this multi-omics method has required the initial development of a new protocol for the extraction of both metabolites and DNA from the single gut samples of very small organisms. The resulting combined dataset consists of two matrices of the 70 abundant ASVs, i. e. representing at least 1% of reads in at least one sample, and the 1674 metabolites, sufficiently well correlated together (76%) to explore associations between ASVs and metabolites. The relevance network illustrates correlations between the most highly associated ASVs and metabolites discriminating among the five treatments.
Four ASVs (ASV1349, ASV1564, ASV475, ASV1995) appear negatively correlated with several metabolites and are the least abundant in d28_0 (Fig. 2e  and Fig. 3b, c ). Differently, the two other ASVs, namely ASV1543 (Epulopiscium) and ASV2350 (Barnesiellaceae), are positively correlated with few metabolites and are the most abundant in d28_0 (Fig. 3b, c ). Unfortunately, only few of the metabolites presenting high correlation with abundant ASVs could be annotated, and these mainly corresponded to amino acids or small peptides (Dataset S5).
Occurrence of dominant ASVs in compartments other than gut Dominant ASVs were searched for in fish food, M. aeruginosa culture, water, biofilm, and faeces (Fig. S6 ). ASV1620 (Cetobacterium) is most abundant in guts (44. 3 ± 20. 4%) then in faeces (28. 6 ± 15. 3%), congruent with its best matches in the database which are fish gastrointestinal bacteria (zebrafish, Nile Tilapia). ASV1349 (Flavobacterium) and ASV1564 (Aeromonas) are most abundant in faeces (8. 8 ± 9. 6% and 7. 8 ± 2. 8%, respectively), then guts (2. 1 ± 4. 4% and 3. 4 ± 2. 6%, respectively) and biofilms (2. 0 ± 3. 3% and 3. 4 ± 2. 5%, respectively). This is congruent with their respective BLASTN hits with bacteria from fish intestinal tracts or aquatic environments. ASV1644 (unassigned Bacteria) is also most abundant in faeces (10.
Availability of data and materials 16S rRNA gene sequencing and shotgun raw data are deposited into the GenBank SRA database under the BioProject PRJNA746242 (samples SAMN20080275 to SAMN20080438). Files with metabolite tables from mass spectrometry analyses, and the rarefied table of ASVs will be available upon acceptance of the manuscript. The detailed protocol for the extraction of metabolites and DNA will be deposited on protocols. io upon acceptance of the manuscript. The full R code and QIIME 2 script will be available on Zenodo upon acceptance of the manuscript.
DNA isolation, sequencing and molecular analyses Total genomic DNA was extracted from ventral integument samples of approximately 5 x 5 mm by using the DNeasy Blood & Tissue Kit (Qiagen). For 8 species, regions of the nuclear 28S rRNA and mitochondrial 16S rRNA, tRNAs Ala, Leu and Ser, NADH dehydrogenase (ND1) and COI were amplified by polymerase chain reaction (PCR), with the primers and conditions described in Perez-Losada et al. [10, 11]. PCR products were purified and sequenced by the C. A. C. T. I Genomics service (University of Vigo). DNA sequences obtained in this study are available in Genbank, under accession numbers MZ153163-MZ153169, MZ156573-MZ156588 and MZ235293-MZ235314. Sequences reported by Domı ´nguez et al. [5, 12] , Perez-Losada et al. [10, 11, 13] , Paoletti et al. [14]  and de Sosa et al.
[15] , including representatives of most of the Lumbricidae genera and two members of the closest families (Hormogastridae and Criodrilidae), were downloaded from Genbank and used as a reference data set. The species included are listed in Supporting Information S1 File. Sequences were aligned with MAFFT v. 7  [16]  with default settings and concatenated with BioEdit  [17] , resulting in a matrix of 3,201 bp. The best fitting evolutionary model for each partition was selected with jModelTest v. 2. 1. 3 [18]  by applying the Akaike information criterion (AIC  [19] ), and Bayesian information criterion (BIC  [20] ). GTR + I + G was selected as the best-fitting evolutionary model for COI, 28S and ND1, HKY+I+G was selected for 16S and HKY+G was selected for tRNAs. Maximum Likelihood analysis was performed with RaxML-NG  [21]  as implemented in the CIPRES Science Gateway V.
This work was supported by a grant from the  Systematic Research Fund  and by  Xunta de Galicia. Consellerı ´a de Cultura, Educacion e Ordenacion Universitaria. Secretaria Xeral de Universidades  under grant  ED431B 2019/038. DFM was funded by a  Juan de La Cierva-Formacio  ´n grant ( FJCI-2017-32895 ) from the  Spanish Ministry of Sciences, Innovation and Universities.
Eventually, one site (AS1, close to GBA) was equipped with a four-component (short-and longwave, incoming and outgoing) radiation sensor. The location of these instruments is indicated in Figure  2. Eventually, a 2nd sodar (Remtech PA2) was installed close to the GBA tower, with the characteristics of 15 min, 25 m, 75 m, and 500 m regarding the time resolution, vertical resolution, and lowest and highest heights of observation, respectively. The KASCADE-2017 field campaign data are archived at: https://kascade. sedoo. fr, accessed on 10 August 2021. Atmosphere 2021, 12, x FOR PEER REVIEW 5 of 29 sodar (Metek PCS. 2000-24) is installed. This sodar measures the profile of the horizontal wind. The lowest height of observation is 60 m, and the highest is around 400 m, with a possible variation according to the propagation conditions of sound waves.
4. 2. Representation of the Ground We used maps with resolutions close to the resolutions of WRF and not too resourceintensive. From SRTM topographic maps  [45, 46] , we extracted a 90 m resolution map for domain D5, whereas coarser resolutions were used for the other domains, up to 5 arcmin for the 9 km domain. The representation of the land cover in the model has received particular attention, the methodology of which will be detailed in a forthcoming paper. Here, in order to go beyond the usual USGS land use dataset and its associated lookup tables, we took advantage of the Corine Land Cover (CLC) land use well suited for our European area, and we developed an aggregation method preventing misclassification when different land use categories are present in a single cell, at any given resolution.
In contrast, at the finest resolution (right plots), the CV is clearly represented, and all stations are in different cells of the model. arcminutes for the 9 km domain. The representation of the land cover in the model has received particular attention, the methodology of which will be detailed in a forthcoming paper. Here, in order to go beyond the usual USGS land use dataset and its associated lookup tables, we took advantage of the Corine Land Cover (CLC) land use well suited for our European area, and we developed an aggregation method preventing misclassification when different land use categories are present in a single cell, at any given resolution. Although we developed new lookup tables (described in a forthcoming paper) assigning physical parameters (albedo, roughness length, etc.
Output Learning Data Collection (Experiment II) For a reliable data, we collected output learning by real try-on. The concrete implementation scheme is as follows (Figure  2 ), wearers tried on all garments in the database. Finally, all the garments were divided into three categories: fit garments  1 ,  2 , … ,   , tight garments  +1 ,  +2 , … ,  + and loose garment  ++1 ,  ++2 , … ,  ++. Fit garments were labeled "1", tight garments were labeled "2" and loose garments were labeled "3". We used the data comprised of "1", "2" and "3" as output learning data to train the proposed model.
03 and the number of training iterations to 500. BP-ANNs training  [38] :Actually, BP-ANNs training process is a process for the connection weights adjustment. It mainly contains the following nine stages. 1) Initialize of the connection weights of the networks ; 2) Select a sample from the training dataset as the input of the networks ; 3) Calculate the output value of the networks ; 4) Calculate the error of the networks output related to the real value; 5) Adjust the connection weights by using the feedback from the output layer to the input layer; 6) Repeat Steps 3), 4) and 5) until the error is acceptable; 7) Select another sample of the training set data and repeat the above steps until the convergence of the algorithm. BP-ANNs prediction: After identifying the connection weights by the previous training step, the proposed model in Figure  3  can be used to predict garment fit level by inputting the measured digital clothing pressures of a new garment.
Learning Data Collection and Garment Fit Evaluation Model Training According to the method of input learning data collection (Experiment I), we used a measurement method, which were used in our previous researches  [14, 15] , to acquire digital clothing pressures. As shown in Figure  4 , firstly, measuring points F 1 , F 2 , …, F 15 and B 1 , B 2 , …, B 5 were equally mapped on front and back pieces of the pants' patterns respectively (Note: the 'parts below knee have little effect on clothing fit; therefore, we did not map measuring points on these parts); secondly, we used CLO 3D Modelist software to try the patterns on a parametric human model, whose body dimensions were equal to the real body dimensions; finally, we measured digital clothing pressures of 72 pairs of pants according to the 20 predefined points.
The collected data of digital clothing pressures was as the input learning and test data used for model construction and validation. According to the method of output learning data collection (Experiment II), 72 pairs of pants were evaluated by real try-on. We defined fit, tight and loosed as follows: If a wearer feels neither tight nor loose in each part of a garment, the garment is considered fit for the wearer; If a wearer feels tight in one place of a garment; the garment is considered tight for the wearer; If a wearer feels loose in one place of a garment, the garment is considered loose for the wearer. We labeled fit garments with "1", tight garments with "2" and loose garments with "3". The data containing "1", "2" and "3" was as the output learning and test data to construct and validate our proposed model.
Garment Fit Evaluation Model's Prediction Accuracy Calculation The validation flowchart is shown in Figure  5. The data collected in Experiment I and II were divided into training data and testing data. Firstly, we used the training data to train our proposed BP-ANNs based fit evaluation model. That is to adjust the weights of neural networks. After learning from training data, the test data is used for validating the model's prediction accuracy. In order to obtain more reasonable validation result, we use the K-fold cross-validation to test the approach by calculating the prediction accuracies of the proposed models. The principle of K-fold cross validation is introduced as follows. The data set is divided into  subsets, and the holdout method is repeated  times. Each time, one of the  subsets is used as the test set and the other -1 subsets are put together to form a training set.
Prediction methods Prediction Accuracy Fit evaluation method based on BP-ANNs 93% Fit evaluation method based on Random Forest 83% Fit evaluation method based on Bayesian Classifier 81% Fit evaluation method based on Discriminant Analysis 78% Traditional fit evaluation method by real try-on Less than 50% Note: In the training process, the number of the hidden layer nodes is 10, and the number of trees of the Random Forest is 50. Prediction Accuracy is the average of 10 K-fold cross validations (K=10). Fit evaluation is an important link in clothing product development and sales. In this section, we gave an application to evaluate garment fit using our proposed method for garment e-shopping. As shown in Figure  6 , a customer chooses a garment in a garment online shopping store; according to the customer's body dimensions, we adjust the 3D parametric mannequin; then we find the selected garment's patterns from pattern databased, and try the patterns on the adjusted 3D parametric mannequin; next, we measure digital clothing pressures and input the pressures into our proposed model; the model predicts garment fit automatically finally.
Discussion The traditional garment fit evaluation methods require customers having a real try-on; it leads to high garment manufacturing costs. Therefore, we proposed a data-learning-based method to predict garment fit. In this model, the digital clothing pressure data collected by virtual try and garment fit condition data collected by real try-on have mapping relationships. Even though some differences between virtual and real clothing pressures exist, the reliability of the proposed model is unaffected. The main contributing factor on the reliability of the proposed model is the outputting data collected by real try-on. Therefore, real try-on is very important for output data collection. As the proposed model learns from the experimental data collected by real try-on, the prediction accuracy is reliable.
For garment e-shopping, our proposed method requires customers providing their body dimensions. With the advent of 3D body scanner, body dimensions can be measured automatically and rapidly using this device. The 3D body scanner can not only measure body dimensions, but also acquire human body's point cloud data. 3D human model constructed based on the point cloud is more accurate than body dimensions. Therefore, combining 3D body scanning technology, the fit evaluation methods based on data learning technology could predict garment fit more accurately. Although our method has many advantages, it also has some shortcomings. One of the biggest limitations of our proposed method is that, for another type of garment, the proposed model should be re-trained.
To do so, we computed the ratio S i /S toluene , using data from another experiment performed with the same instrument the year before (data from  Loubet et al. , 2021) , as well as the using data from the literature  (Koss et al. 2018) , with an instrument similar to ours, though not exactly the same. This exercise (Table  B2 ) shows that for most compounds the systematic error made by assuming a single k is limited to less than 40% error, which is the range expected from theoretical estimations of k. However, for ethanol, formaldehyde, methanol and propene, the bias may be high (from ~3 to ~40), with a tendency to underestimate the actual mixing ratio. For methanol, a specific calibration was performed in this study against another PTRMS which was itself precisely calibrated. For the ethanol, formaldehyde and propene the bias may have been be large, and especially for ethanol.
BVOC tentative identification Identification was attempted with the software PTR-MS VIEWER 3 (Ionicon) coupled with the NIST database. Exact identification of all ions presented in this study was not possible. Indeed because of the principle of mass spectrometry, distinction of compounds having the same molecular weight (isobaric) or the same chemical formula (isomeric) was not possible. Thus, some compounds have been suggested for these masses based on the literature, and in comparison with GC-MS measurements in a campaign conducted in 2018 at the same farm  (Kammer et al. , 2019). The concentration of the m/z 43. 018 compound strongly correlated (0. 98 correction factor during ripening and senescence) with the m/z 61. 029 (acetic acid), which strongly suggest that the m/z 43. 018 is a fragment of acetic acid.
Comparison with eddy covariance fluxes obtained in a nearby field the year before Given the limited number of replicates of our experimental design with only two wheat plants, no statistically significant differences could be assessed to determine if there is a real effect of plant development stages on BVOC emissions. To overcome this difficulty, we used Eddy covariance (EC) fluxes measured with the same instrument at the same site one year prior to the present study. These measurements by EC were taken during the same developmental stages and on the same soil. These data were compared with the emission factors reported here and their variations with plant developmental stages. These data were reported by  Loubet et al. (2021) , and are referred to as the COV3ER-2016 dataset in the following sections.
The data were taken from the repository associated with this publication (https://doi. org/10. 15454/IRZ9XX), and are not detailed here. Similarly to what was done in this study with net plant photosynthesis (Fig. 1 ), ripening, senescence 1, and senescence 2 periods were determined based on measured net ecosystem exchange (Appendix D). For each of these periods, the standard emission factors estimated in this study were used to compute the SEF-based whole plant VOC emissions using air temperature and incoming photosynthetically active radiation as measured in COV3ER-2016 and equations (  5 )-(  7 ). These SEF-based whole plant VOC emissions were compared to hourly measured fluxes in COV3ER-2016 which were scaled to μg g -1 (DM) h -1 by multiplying the fluxes in nmol m -2 s -1 by ion mass × 3600/1000 × AGB, where AGB is the total above ground biomass that was 1224 g m -2 during that period.
for methanol and acetone, but lower SEF and higher γ age for acetaldehyde in this study compared to Guenther (Table  3 ). One the contrary, COV3ER2016 compares very well with  Bachy et al. (2020) , both in terms of SEF and γ age for methanol and acetaldehyde. For acetaldehyde this study COV3ER-2016 and Bachy et al. all agree well in terms of SEF and γ age , while acetone emissions are larger in COV3ER2016 than in the three other datasets, and γ age is lower than 1 in COV3ER-2016. This comparison suggests that methanol emissions by chamber measurements give larger emissions (by a factor of ~3-~4) compared to whole ecosystem fluxes, probably due to soil and plant recapture which would correspond to a large fraction of the emissions. Moreover, the effect of senescence on methanol emissions is negligible in chamber measurements but large and consistent at the canopy scale (factor of 3).
Acknowledgements This study was supported by the  region Île-de-France  ( DIM R2DS ,  Développement soutenable Réseau francilien de Recherche sur le Développement Soutenable , project n •  1656 ),  ADEME  ( COV3ER , project n •  1562C0032  and  RAVISA , project n •  1762C0006 ) and the  EU ICOS-RI Research Infrastructure. The  PTR-Qi-ToF-MS  is a national instrument supported by  ANAEE-FR   services  ( ANR  project n •  11-INBS-0001 ). SB acknowledges the European Union's  Horizon 2020 research and innovation program  ( Marie-Sklodowska-Curie  grant agreement n •  674911-IMPACT ). We gratefully thank  Dominique Tristant  and  Yves Python  from the  AgroParisTech  farm for giving access to the fields and sharing their information on practices, and  Pascal Duprix  for helping building the chambers.
Confocal microscopy To induce expression of either the dual-color FRT reporter or the GFPdeg system, NGM plates with nematodes were incubated for 15 min floating in a 34°C water bath. For evaluation of recombination efficiency and specificity, nematodes were left to recover at 20°C for 2-3 hours prior to imaging. For GFP degradation experiments, recovery was at 25°C for 1-24 hours as indicated for each experiment. For imaging, nematodes were anesthetized in 5 μl 10 mM levamisole HCl on 2% agarose pads and covered by a cover slip. Images were acquired with a Nikon Eclipse Ti microscope equipped with Plan Fluor 40×/1. 3 and Plan Apo VC 60×/1. 4 objectives and an A1R scanner using a pinhole of 1. 2 airy units  [20]. Cells with GFP signal as an indication of a recombination event in the dual-color reporter as well as mNG coexpression with FLP were evaluated from z-stacks acquired with the Nikon NIS software.
Lifespan experiments Worms were cultured at 16°C until L4 larval stage. In each replica, 20 L4 larvae per strain were transferred to newly seeded NGM plates and incubated for 15 min floating in a 34°C water bath to induce the GFPdeg system. They were then continuously incubated at 25°C. During the reproductive period, the live adults were transferred to new plates each day to not confuse founders with their progeny. Throughout the experiment, the number of live and dead animals were recorded. Animals were censored if they disappeared, were damaged during transfer or died due to internal hatching. Statistical analysis was done in R using the survival package (version 3. 2-7  [46] ), which calculates Chi-square statistics based on a logrank test.