dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification and annotation of mature seed proteins Protein bands were excised from the gel and protein digestion was carried out according to  Labas et al. (2015). The resultant peptides were extracted in 5% formic acid (10 min sonication, supernatant removed and saved), followed by incubation in 100% acetonitrile/1% formic acid (1:1, 10 min), and a final incubation with 100% acetonitrile (5 min, supernatant was again removed and saved). These two peptide extractions were pooled and dried using a SPD1010 speedvac system (Thermosavant, Thermofisher Scientific). The resultant peptide mixtures were analysed by nanoflow liquid chromatography-tandem mass spectrometry (nanoLC-MS/MS) for protein identification. Peptide separation and data acquisition were performed according to  Labas et al. (2015)  on a LTQ Orbitrap Velos mass spectrometer (ThermoFisher Scientific) coupled to an Ultimate® 3000 RSLC Ultra High Pressure Liquid Chromatographer (Dionex) controlled by Chromeleon Software (v6.
8 SR11; Dionex). MS/MS ion searches were performed using Mascot (v2. 3. 2, Matrix Science) via Proteome Discoverer 2. 1 software (ThermoFisher Scientific) against the pea gene atlas  (Alves-Carvalho et al. , 2015). The search parameters included trypsin as a protease with two allowed missed cleavages and carbamidomethylcysteine, methionine oxidation, and acetylation of N-term protein as variable modifications. The tolerance of the ions was set to 5 ppm for parent and 0. 8 Da for daughter-fragment ion matches. Peptides and proteins identified by Mascot were validated using Scaffold (v4. 8. 3, Proteome Software): protein identifications were accepted when they contained at least two identified peptides and when the probability was at least 95% and 99% as specified by the PeptideProphet and ProteinProphet algorithms, respectively  (Keller et al.
Eleven cycles of PCR were applied to amplify the libraries. Library quality was assessed using a Fragment Analyzer (Agilent Technologies) and libraries were quantified by qPCR using a Kapa Library Quantification Kit (Roche). RNA-seq experiments were performed on an Illumina HiSeq3000 using a paired-end read length of 2×150 pb with Illumina HiSeq3000 sequencing kits. The sequence quality of the raw data was assessed using the FastQC v0. 11. 2 software (http://www. bioinformatics. babraham. ac. uk/projects/fastqc) and raw data were corrected using the k-mer-based error correction software Rcorrector (-k 31)  (MacManes, 2015, Preprint; Song and Florea, 2015). The corrected data were trimmed for low-quality and adapter sequences using Trimmomatic v0. 32  (Bolger et al. , 2014)  with the following parameters: ILLUMINACLIP:TruSeq3PE2:2:40:15, LEADING/ TRAILING:2, SLIDINGWINDOW:4:15, and MINELN:25.
Assemblies from the different samples were then merged using the StringTie merge function. The Gffcompare v0. 9. 9e software (https://github. com/gpertea/gffcompare) was used to classify the predicted transcripts compared to the reference, and only new transcripts were kept (class code U, i. e. the predicted transcript is intergenic in comparison to known reference transcripts). To remove potential transposable elements (TEs) from our set of new assembled transcripts, transcript and TE coordinates were compared using the Bedtools intersect v2. 26. 0 software  (Quinlan and Hall, 2010)  with the option --wao. Transcripts with more than 50% coverage with a TE were removed from our data set. Long non-coding transcripts were identified using the FEElnc v0. 1. 0 software  (Wucher et al. , 2017)  with default parameters.
When a coding protein gene had at least one of its alternative transcripts identified as a long non-coding RNA (lncRNA), all alternative sequences were mapped against the protein databases of Medicago truncatula v4 (http:// www. medicagogenome. org/) and Arabidopsis thaliana TAIR10 (https:// www. arabidopsis. org/). When a match (e-value<0. 001) was obtained for at least one of the transcripts, the gene and its alternative transcripts were considered as coding RNAs. Coding transcripts were translated into proteins using the load, longestorf, and translate commands from the JCVI toolbox  (Tang et al. , 2015). The entire dataset was mapped against the Medicago truncatula proteome (v4) and the Arabidopsis thaliana proteome (TAIR10) using Blastp with an e-value of 0. 001. For each species, the best hit for both coverage and identity was selected.
The reliability of the Illumina RNA-seq data was validated for 11 genes using quantitative RT-PCR. A strong correlation between the RNA-seq and RT-qPCR data was obtained (R 2 =0. 97), thus validating our differential analysis (Supplementary Fig. S3 ). In total, 1394 and 1584 genes exhibited significantly higher and lower expression, respectively, in stress conditions compared to the control (Fig. 4A ), and about 40% of the transcriptomic changes (1199 out of 2976 DEGs) were shared between the S-and S-WS treatments. Only two genes (out of 2976) had opposite regulation in response to the different stresses: a ferritin gene (Psat7g247120) was down-regulated in response to S deficiency but upregulated in response to the combined stress, while a gene encoding an unknown protein (Psat5g068840) was up-and down-regulated in S-and S-WS conditions, respectively (Supplementary Table  S3 ).
Acknowledgments We thank the greenhouse team of  INRA Dijon  for their help,  Sylvie Girodet  ( UMR Agroécologie ) for CN measurements, the  GISMO platform  ( Université de Bourgogne Franche-Comté, Dijon, France ) for S measurements,  Judith Burstin  ( UMR Agroécologie ) for providing us with access to the pea genome sequence for mapping RNA-seq reads, and  Richard Thompson  and  Christine Le Signor  ( UMR Agroécologie ) for critical reading of the manuscript. The PhD grant of CH was funded by the  French Ministry for Higher Education and Research. The experiments were funded by the  European Union  under the  FP7 Program 'LEGATO'  (project no. 613551 ) except the RNA-seq analyses, which were founded by the  Groupe Roullier TIMAC Agro International  within the framework of the  FUI-SERAPIS  project.
The transcriptomics was performed in collaboration with the  GeT core facility  (http:// get. genotoul. fr)and was supported by the  France Génomique National  infrastructure, funded as part of the ' Investissement d'avenir' program  ( National Research Agency , contract  ANR-10-INBS-09 ). The highresolution mass spectrometer was financed (under the  SMHART  project no. 3569 ) by the  European Regional Development Fund , the  Conseil Régional du Centre ,  INRA , and the  French National Institute of Health and Medical Research (INSERM).
Data deposition The MS proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository  (Vizcaíno et al. , 2014)  with the dataset identifier PXD011029. The raw RNA-seq data have been deposited to the NCBI SRA database (http://www. ncbi. nlm. nih. gov/bioproject/) under accession number PRJNA517587.
For Qdots tracking experiments, a 532 nm laser (5 W, Verdi, Coherent) is used to excite Qdots (QD655, Thermofisher Scientific) in epi-fluorescence (∼1. 5 kW/cm², dichroic FF580-FDi01, Semrock, emission filter D655/40 m, Chroma). The SELFI module (described in more details in the results section) is used to record single fluorescent emitter images and includes a homemade diffraction grating based self-interferometer. The interference patterns are magnified 2× by an imaging relay (100 and 200 mm achromatic doublets in 4f configuration) then recorded with a camera (sCMOS, ORCA Flash4 V2+, Hamamatsu). Figure  1  shows a scheme of the setup and summarizes the different illumination possibilities. A homemade Labview program (National Instrument) is used to drive the acquisition setup, analyze the interferograms to retrieve the 3D position of each emitter, and render the reconstructed 3D superresolved images.
Here we demonstrated highly oblique illumination in uPAINT experiments, total internal reflection in PALM imaging and standard epi-illumination for deep tissue SPT. Note that this independence from illumination schemes is key for tissue imaging applications because intrinsic light scattering by the sample might disturb specific illumination configurations that are needed to obtain the 3D information with other methods. In this work, we have also chosen to image quasi-metrological living biological samples by studying axial molecular structuration within cellular adhesion sites. The nanoscale layering of proteins measured with SELFI is in agreement with the data obtained in fixed cells in the literature. Finally, we have demonstrated fast 3D SPT in thick samples. This paves the way to the unambiguous study of molecular nano-organization and dynamics in complex samples where 2D imaging-only can lead to biased results.
FUNDING This work was supported by CNRS, the Agence Nationale de la Recherche (ANR-15-CE16-0004-03, ANR-16-CE29-0011-03, ANR-16-CE92-0034-01), IdEx Bordeaux (ANR-10-IDEX-03-02), the France-BioImaging national infrastructure (ANR-10-INBS-04-01), and Conseil Regional Nouvelle-Aquitaine (2015-1R60301-00005204). Fondation pour la Recherche Médicale (FRM: DEI20151234402).
The algorithm produces time series of the altitudes and values of peaks and inflection points of the different radar observables. A literature review allows us to link the micro-physical processes at play during the melting process with the available polarimetric and Doppler signatures, e. g. , (i) regarding the altitude differences between the peaks of reflectivity, cross-polar correlation coefficient and differential reflectivity, as well as (ii) regarding the co-variation of the profiles of Doppler velocity spectra and cross-polar correlation coefficient. A statistical analysis of the ML based on 42 rain events (98 h of XPORT data) is then proposed. Among other results, this study indicates that (i) the mean value of the ML width in Grenoble is 610 m with a standard deviation of 160 m; (ii) the mean altitude difference between the horizontal reflectivity and the ρ HV peaks is 90 m and the mean altitude difference between the ρ HV and Zdr peaks is 30 m; (iii) even for the limited rainrate range in the dataset (0-8.
5 mm h -1 ), the "intensity effect" is clear on the reflectivity profile and the ML width, as well as on polarimetric variables such as ρ HV peak value and the Zdr enhancement in the upper part of the profile. On the contrary, the study of both the "density effect" and the influence of the 0 • C isotherm altitude did not yield significant results with the considered dataset; (iv) a principal component analysis on one hand shows the richness of the dataset since the first 2 PCs explain only 50% of the total variance and on the other hand the added-value of the polarimetric variables since they rank high in a ranking of the total variance explained by individual variables.
We will assume attenuation and beam broadening effects to be of limited importance for such close-range/high-elevation angle measurements in predominantly stratiform precipitation with low intensity. Note that the attenuation problem, and more precisely the relationship between the total differential phase and path-integrated attenuation in convective rainfall and in the melting layer, is addressed in another article  [8]. Hereinafter, we discuss in Section 2 the experimental setup and datasets used in this study and we illustrate the working principles of our ML detection algorithm. In Section 3, we revisit the literature on the melting of ice particles in the atmosphere and provide relevant illustrations, based on our radar observations, regarding micro-physical processes occurring within the melting layer.
Statistical Analysis of the ML Characteristics Here, we study the climatology of the melting layer based on the available dataset of 42 rain events. We will seek to understand the dependence of ML parameters on altitude of the 0 • C isotherm, intensity of rainfall at ground and density of snow. Being a standard hypothesis of 1D ML models  [16] , 1 to 1 correspondence of snowflake to raindrop is a question of interest. The added value of Doppler and polarimetric measurements with respect to reflectivity measurements for the analysis of melting layer is also a subject of interest in this study.
ML Boundaries and Vertical Organization of the ML Let us first consider the altitudinal dimension of ML characteristic points. Our experience with the ML detection algorithm indicates that ρ HV peak is the most consistently identified signature, and we expect it to be in between altitudes of Zh peak and Zdr peak. Figure  5  shows the probability distribution functions (pdf) of different distances between radar signatures, i. e. , top, peak and bottom of Zh, Zdr and ρ HV , with respect to the ρ HV peak altitude, computed over the entire dataset of 980 profiles; a number of statistics (mean, standard deviation and quantiles of these distributions) are listed in Table  3. We note that the pdfs of Zh and Zdr peak altitudes are quite narrow. The mean distance between ρ HV peak and Zdr peak is around 30 m, while the average distance between ρ HV peak and Zh peak is around 90 m.
With increase in diversity of shape, size and orientation of icy hydrometeors, we expect increase in Zdr. val. t2p within ML with increase in rainfall intensity. In Figure  9b  we can observe that the Zdr enhancement almost doubles (from 1. 1 dB to 1. 9 dB) as rainfall intensity increases from 0. 6 to 5. 2 mm h -1. Finally, increase in diversity in shape, size, orientation, fall speed of melting particles within the pulse volume results in significant decrease of ρ HV peak value as rainrate increases (Figure  9a ). At low rain intensity, ρ HV peak value is quite high around 0. 88 for rain-rate of 0. 6 mm h -1 ; it drops to 0. 78 around rainfall intensity of 4. 5 mm h -1. A larger dataset would be necessary to determine if the non-monotonic trends visible for ρ HV peak value and ML width for the highest rainrates are significant.
Hydrometeor density can be another explanatory variable associated with Wsnow and the reflectivity enhancement on top of the profile (Zh. val. t2p). In this sub-section, we limit ourselves to study the relationship of some of the ML descriptors with the rainrate and the 0 • C isotherm altitude, using R and ML top derived from the radar dataset available as proxies. Investigations of other explanatory variables could be possible and desirable, e. g. , with high-resolution NWP model outputs; this will be the subject of future research. Using partial correlation coefficients in Figure  7 , we computed the total correlation coefficients r 1. 23 (Appendix A. 2) of different ML characteristic values as explained variables (index 1) as a function of the two explanatory variables (ML top and R). The most interesting results are displayed in Table  6.
Faster falling hydrometeors lead to lower number concentration and a decrease in reflectivity. In order to check if this effect is visible in our dataset, in Figure  10  we examine the effect of snow density on BB enhancement. As most of the events under study are stratiform events with max rainfall intensity of 8. 47 mm h -1 , we do not have significant number of observations at the last interval i. e. , mean(Wsnow) > 2. 3 m s -1 as seen in Figure  10. For a small class of reflectivity values below ML, [25-27 dBZ], we observe a decrease in BB enhancement of 1 dBZ with the increase in snow velocity/density.
Information Content of the ML Dataset In order to investigate more deeply the information content of the ML dataset available, we have implemented a number of statistical techniques ranging from Principal Component Analysis (PCA) to more complex clustering techniques, e. g. , Kohonen's self-organizing maps. In this article we simply present results from the PCA technique. We remind that PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components (PCs). By ordering the principal components, it is possible to reduce the dimensionality of the dataset if first few PCs retain most of the variance present in all of the original dataset. Here, we have selected 18 variables (x-labels of Figure  11b ), assumed to be representative of the total variance present in vertical profiles of Zh, Zdr, ρ HV and W within the ML.
Another forecasting model was presented by  [5]. The study was based on statistical results and artificial neural networks to simulate the domestic water consumption for nine users. However, the developed models faced some difficulties to accurately predict the peak values. Limited research has been devoted to improve the forecasting of peak demand. [6]  found that this peak is affected by the socio-economic and behavior changes of the residential sector. This paper presents a methodology to predict water consumption at different scales using the ANN approach. Data were collected from a large scale demonstrator of the smart water system SunRise. The latter concerns the water distribution network consisting of 15 km of cast iron pipes supplying the Scientific Campus of Lille University located in the North of France which stands for a town of 25000 users.
Data collection and analysis Water consumption data were obtained at hourly time interval from AMRs in the period from the 1 st of January 2015 till the end of April 2016. This study is based on data related to (i) a DMA that comprises university faculties, administrative buildings and students' residences and (ii) a restaurant inside this DMA. Statistical analysis is conducted first in order to predict the water usage profile during the year and identify the boundaries of the study domain. Figure  1  shows the water consumption of the DMA and the restaurant at monthly time interval. It could be observed that the water consumption is related to the university calendar, with a significant drop during summer holidays. This is expected since teaching and administrative buildings are closed during this period and the resident students leave the campus for summer break.
approach A feed-forward back-propagation artificial neural network is used in the numerical modelling using a Matlab script. The time series of historical water consumptions are used in the model to forecast future demand. Two main data analyses were carried out: -The first analysis aimed at predicting daily water consumption and forecast the demand for the next day taking into account a lag of 1 day in order to study the effect of the water consumption of the day before on the actual demand. -The second analysis was conducted to predict the water consumption for the next day with a step of 1 hour using the historical values of the day before, with a lag length of 24 hours. This lag interval is chosen due to the transmission of recorded data at 24 hours' time interval. The input vector for the daily basis includes the day of the week DWi (i varies from 1 to 7, i.
The anomalies in time series are identified using Chebyshev's inequality  [9]. This theorem guarantees that in any probability distribution, the majority of the values are close to the mean value. Using this inequality, the values are filtered to eliminate the outliers that exceed the average plus three standard deviations (i. e. 89% of the values lies within two standard deviations). The input dataset is then divided into three sets: 70% for training, 15% for validation and the remaining 15% corresponds to the testing set. The input and target variables are normalized in the range of 0 and 1 according the following equation:  (1)  where X̅ is the normalized input or target variables X, Xmin and Xmax represent the minimum and the maximum of observation values respectively. The aim of normalization is to avoid any prioritization of the variables and to remove any arbitrary effect of similarity between the objects  [10].
Daily prediction analysis Collected data for the DMA and the restaurant are trained using the water consumption of the previous day (i. e. lag equal to 1 day). Table  1 , figures 2 and 3 show the quality of the ANN predictions. For the DMA, a good correlation is observed between recorded and predicted values (R 2 is equal to 0. 827 and RMSE is equivalent to 0. 110); the ANN model predicts well the water consumption, including the peak and minimum values. The ANN model performances for the restaurant yet better with a factor R 2 equal to 0. 902 and RSME equivalent to 0. 1.
The Basidiomycota constitutes a major phylum of the kingdom Fungi and is second in species numbers to the Ascomycota. The present work provides an overview of all validly published, currently used basidiomycete genera to date in a single document. An outline of all genera of Basidiomycota is provided, which includes 1928 currently used genera names, with 1263 synonyms, which are distributed in 241 families, 68 orders, 18 classes and four subphyla. We provide brief notes for each accepted genus including information on classification, number of accepted species, type species, life mode, habitat, distribution, and sequence information. Furthermore, three phylogenetic analyses with combined LSU, SSU, 5. 8s, rpb1, rpb2, and ef1 datasets for the subphyla Agaricomycotina, Pucciniomycotina and Ustilaginomycotina are conducted, respectively.
Phylogenetic analyses Sequences were downloaded from GenBank  (Benson et al. 2017). Six genes  (LSU, SSU, 5. 8s, ef1, rpb1 and rpb2)  were included in this study. Only species for which two or more gene sequences were available were included in the phylogenetic analyses. Sequence information is listed in Supplementary Table  1. Sequences were checked in BioEdit V. 7. 0. 4 first  (Hall 2007). Alignments were made by  Muscle 3. 8. 31   (Edgar 2004 ) for each region separately, then adjusted manually. In order to avoid substitutional saturation in third codon position, we used translated amino acid sequences for ef1, rpb1 and rpb2  (Matheny et al. 2007b). For each data set, we then combined with DNA from rDNA genes and amino acid sequences. Divergence times were estimated in  BEAST 1. 8. 4 (Drummond et al.
9 PP support. The phylogenetic relationships of the main clades in Boletales roughly agree with  Binder and Hibbett 2006  which gave a phylogenetic relationships among suborders based on a five-genes dataset. In Boletacae, Zangiodeae represented by Zangia and Harrya is recognized and supported statistically, which agrees with  Wu et al. (2014b). However, phylogenetic relationships of the other genera are not resolved because of low statistical support. The well-supported taxa were dated with an estimated divergence time for Agaricomycotina as 406 Mya; the classes ranged from 298-341 Mya; and orders from 108-259 Mya (Table  1 ). The analyses involving Agaricomycetes comprised 77 families and 352 genera, the Dacrymycetes cmprised three families and six genera, and the Tremellomycetes with 18 families and 54 genera (Fig.
The phylogeny and divergence time analyses of Ustilaginomycotina and Wallemiomycotina In the phylogenetic analyses of Ustilaginomycotina, a sixgene (LSU, SSU, 5. 8s, rpb1, rpb2 and ef1) dataset was used, which comprised of 74 species from Ustilaginomycotina, three species from Wallemiomycotina, and two species from Pucciniomycotina as the outgroup. Wallemiomycotina occupied the basal position with strong support with a divergence time of 430 Mya in MCC tree (Fig. 4 ). There were four classes In Ustilaginomycotina, of which Moniliellomycetes and Malasseziomycetes were each represented by a single sample. Ustilaginomycetes included four orders, 12 families and 45 genera and formed a strongly-supported monophyletic clade. Exobasidiomycetes was polyphyletic and comprised seven orders, 13 families and 27 genera.