dataesr/scientific-paragraphs-categorization huggingface dataset:
Hence, the flexibility of the classification technique in this respect is highly required. The method should be adaptive and interpretable, hence dependent on the given speech dataset but relying on a robust technique whose interpretation can be derived according to the scenario of interest (i. e. TD-SD-SV, TD-SI-SV, etc. ). Our proposed solution is achieved by building upon existing methodologies and adapting them to work with non-stationary signals more effectively. In this way, more robust features reducing sensitivity and enhancing performance in attack mitigation are achieved. Our robust method for speech synthesis spoofing attacks combines EMD and MFCCs with a multi-kernel learning SVM classifier framework. The new formulated feature libraries called EMD-MFCCs are explored and compared in various real data studies of different complexities.
This procedure is replaced with the proposed methodology through a functional characterisation of the EMD and its basis functions. Therefore, rather than learning the formants through piece-wise functions using DNN complex layer structures, we extract them through the EMD and construct a simpler classifier. We propose a sparse architecture that replaces the DNN learning the formants with an EMD basis representation requiring far fewer parameters and can be applied to small and large datasets. It is computationally very efficient and, through an MKL ensemble method, achieves high accuracy levels in performances similar to the ones often achieved by the DNN when combined. From a speech scenario perspective, text-dependent, speaker-dependent and text-independent, speakerindependent speaker verification systems have been tested.
Since nutrient remobilization processes are known to affect the contents of both fresh weight (FW) and dry weight (DW), we first analyzed these parameters in order to find a good way to normalize our data when comparing the different leaf ranks. While FW per leaf area was relatively constant between the leaf ranks (excepted for L3, which showed minor changes), DW per leaf area was significantly reduced in the oldest leaves, which resulted in a significant increase in the FW/DW ratio (Figure  1A-C ). The decrease in DW per leaf area confirmed that nutrient remobilization processes were occurring between the leaf ranks. Consequently, all measured data in the study were normalized per dry weight content. Somewhat surprisingly, chlorophyll content per dry weight was not significantly changed between the different leaf ranks, whereas this was the case by normalizing chlorophyll content per leaf area (Figure  1D ).
S-methyl-l-cysteine Metabolites 2020, 10, x 4 of 16 gradual increase in their nitrogen remobilization processes. Moreover, since the strong activation of chlorophyll degradation in old leaves is always associated with a decrease in chloroplast volume and chloroplast size  [49] , our result suggested that L3 leaves were at the onset of senescence. Next, we quantified amino acid contents in the different leaf ranks (complete dataset available in Supplementary Table  S1 ). From this dataset, we performed a principal component analysis for amino acids showing a normal distribution of their values (Figure  2A, B ). The two first major axes selected from the PCA (Dim1 and Dim2) explained up to 68% of the variations between the leaf ranks and succeeded to visually separate L15 leaves from other leaf ranks, along a "South-South-East North-North-West" axis (from Figure  2B ).
Asn, SMCSO, Pro, Gln and Thr contents remained stable between the leaf ranks L11, L7 and L3, while some changes between L11-L7 and L3 could have been expected, due to their respective leaf status (source versus senescent). Val content was significantly higher only in L7 leaves compared with other leaf ranks, thus becoming a metabolic fingerprint specific to L7 leaves. Overall, we have identified four leaf ranks with a gradual increase in their source status and a specific metabolic fingerprint associated with amino acid metabolism. The specific variation in Asn, Gln, SMCSO, Pro, Thr and Val between leaves with contrasted sink/source balances suggested that their associated nitrogen metabolic fluxes might be affected by nitrogen remobilization processes. Next, we quantified amino acid contents in the different leaf ranks (complete dataset available in Supplementary Table  S1 ).
From this dataset, we performed a principal component analysis for amino acids showing a normal distribution of their values (Figure  2A, B ). The two first major axes selected from the PCA (Dim1 and Dim2) explained up to 68% of the variations between the leaf ranks and succeeded to visually separate L15 leaves from other leaf ranks, along a "South-South-East North-North-West" axis (from Figure  2B ). The loading plot (Figure  2A ) showed the Pearson correlation coefficient for the variation in each amino acid with respect to the spatial separation of the leaf groups in the Dim1-Dim2 plane. We found that Asn, Gln, Pro and SMCSO showed strong positive Pearson correlation coefficients (0. 89, 0. 93, 0. 93 and 0. 85, respectively) along the leaf rank separation axis. Interestingly, Thr and Val showed negative Pearson correlation coefficients along a relatively similar axis (0.
Second, a strong part of the activity of the chloroplastic Gln synthetase enzyme in illuminated plant leaves is devoted to the reassimilation of the NH 4 lost by the activity of the photorespiratory enzyme Gly decarboxylase (GDC)  [50]. Therefore, Gly appeared to be a relevant 15 N-source to ensure a sufficiently high and rapid label incorporation through amino acid metabolism. Following the transfer of leaf discs to the [ 15 N] L -glycine media, the concentration and labeling dynamics of amino acids were measured at three time points (30, 60 and 120 min) with a UPLC-DAD system and a HPLC-HRMS system, respectively. The present analysis focused on amino acids found in major quantities (>1 µmoL g -1 DW in at least half of the samples). The complete dataset of all detected amino acids is available in Supplementary Material Tables  S2  and S3.
Metabolites 2020, 10, x 6 of 16 Second, a strong part of the activity of the chloroplastic Gln synthetase enzyme in illuminated plant leaves is devoted to the reassimilation of the NH4 lost by the activity of the photorespiratory enzyme Gly decarboxylase (GDC)  [50]. Therefore, Gly appeared to be a relevant 15 N-source to ensure a sufficiently high and rapid label incorporation through amino acid metabolism. Following the transfer of leaf discs to the [ 15 N]L-glycine media, the concentration and labeling dynamics of amino acids were measured at three time points (30, 60 and 120 min) with a UPLC-DAD system and a HPLC-HRMS system, respectively. The present analysis focused on amino acids found in major quantities (>1 µmoL g -1 DW in at least half of the samples). The complete dataset of all detected amino acids is available in Supplementary Material Tables  S2  and S3.
We thus applied an alternative approach, ScalaFlux  [48] , which can quantify fluxes through any metabolic subnetwork of interest by modeling label propagation directly from the metabolic precursor of this subnetwork. The flux calculations are thus purely based on information from within the subnetwork of interest, and no additional knowledge about the surrounding network (such as label propagation through upstream reactions or the labeling of the extracellular nutrient) is required. Based on the accessible isotopic data and on the Plant Metabolics Network (PMN) database (www. plantcyc. org), we identified three independent subsystems for which fluxes can be calculated: The biosynthesis of Val and Pro from Glu, and the biosynthesis of Thr from Asp. Following the ScalaFlux workflow  [48] , we constructed a flux model for each of these subsystems (Figure  5A ).
The flux calculations are thus purely based on information from within the subnetwork of interest, and no additional knowledge about the surrounding network (such as label propagation through upstream reactions or the labeling of the extracellular nutrient) is required. Based on the accessible isotopic data and on the Plant Metabolics Network (PMN) database (www. plantcyc. org), we identified three independent subsystems for which fluxes can be calculated: The biosynthesis of Val and Pro from Glu, and the biosynthesis of Thr from Asp. Following the ScalaFlux workflow  [48] , we constructed a flux model for each of these subsystems (Figure  5A ). All flux models contain only two reactions: A biosynthetic reaction and a sink reaction that operate at the same rate to reflect the observed metabolic pseudo steady-state.
Local Estimation of Metabolic Fluxes Using ScalaFlux, a Scalable 15 N-INST-MFA Approach Flux calculations were carried out using the ScalaFlux approach implemented in the IsoSim v2. 0. 0 software (https://github. com/MetaSys-LISBP/IsoSim), as detailed in  [48]. First, three metabolic subsystems were defined to represent Thr, Val and Pro biosynthesis (Figure  5A ). Fluxes were then estimated using a two-steps approach by (i) defining analytical functions of the local label input and (ii) fitting experimental concentration and labeling dynamics of the metabolic intermediate, as detailed in the ScalaFlux publication. The objective function (defined as the sum of squared weighted differences between measured and simulated data) was minimized using the nlsic optimization algorithm  [63]. For each biological replicate, the goodness-of-fit was verified using a chi-square test  [48, 64] , assuming a precision on the isotopic measurements of 0.
Statistical Analysis Results are expressed as the mean ± standard deviation (SD) of n independent biological replicates (n = 3-5). The means of different leaf ranks were compared together with one-way ANOVA followed by a post-hoc Tukey's HSD test for multiple pairwise comparison, or by a Student test (p-value < 0. 05 for each test), by using R base  [65]. Principal component analysis was achieved with the R package FactoMineR  [66].
Supplementary Materials: The following are available online at http://www. mdpi. com/2218-1989/10/4/150/s1, Table  S1 : Complete dataset of free amino acid contents from the four leaf ranks having a different sink/source balance, Table  S2 : Complete dataset of fractional 15 N enrichment of major amino acids during a kinetic of labeling with [ 15 N] L -Glycine on leaf ranks having a different sink/source balance, Table  S3 : Complete dataset of content of major amino acids during a kinetic of labeling with [ 15 N] L -Glycine on leaf ranks having a different sink/source balance, Table  S4 : Complete dataset of observed and estimated fractional 15 N enrichment of Pro, Val and Thr, Table  S5 : Complete dataset of estimated metabolic fluxes, Code S1. R scripts used to calculate fluxes in each leaf and each plant from amino acid concentrations and labeling dynamics.
Acknowledgments: We are grateful to  Sophie Rolland  for performing C and N analyses. We thank  Carole Deleu  for providing the SMCSO standard. We acknowledge the  MetaToul (Metabolomics & Fluxomics Facitilies, Toulouse, France)  and its staff members for technical support and access to HRMS facilities. MetaToul is part of the national infrastructure MetaboHUB- ANR-11-INBS-0010. We thank members of the " Plateau de Profilage Métabolique et Métabolomique " ( P2M2 ) for access to the analytical systems and the "experiment group" for help with growing B. napus plants (IGEPP,  INRA Rennes ).
Sequence Data Set The groups of peptides used in this study and the corresponding data sources are given in Table  S1. Detailed information for each targeting, signaling, antimicrobial and random peptides are given in Tables  S2-S5. Antimicrobial peptides were extracted from the CAMPR3 database  [42]. We selected the families of antimicrobial peptides based on the following criterion: (i) activity experimentally validated; (ii) documented amphiphilic α-helical structure, with at least one family member with a resolved 3D structure available in the Protein Data Bank  [43] ; (iii) documented activity of bacterial membrane destabilization. For 3 families comprising peptides with very different structures, additional filtering was applied. We recovered only the bacteriocin of type IIa that are characterized by an amphiphilic helix  [44]  in BACTIBASE  [45].
We selected only those defensins with a resolved 3D structure with at least 5 consecutive residues in a α-helix and only those cathelicidins with a resolved structure or defined as an amphiphilic helical peptide in  [46]  via UNIPROT. As a negative control, we retrieved the cyclotides of globular structure. TPs with experimentally-confirmed cleavage sites were recovered from proteomic studies (see Table  S1 ). 200 eSPs were randomly extracted among 4707 confirmed eSPs from the Signal Peptide Website (http://www. signalpeptide. de). eSPs were selected so as to follow the length distributions of the peptides from HA-RAMP Class I families. A total of 200 random peptides were generated following the amino-acid frequencies observed in the Uniprot database and the length distribution of TPs. Data sets were curated to exclude sequences shorter than 12 amino acids or longer than 100 amino acids, following the length distributions of the peptides from HA-RAMP Class I families.
Peptide Description and Auto-Cross Covariance (ACC) Terms As amino acid descriptors, we used the Z-scales established by Hellberg and colleagues  [47]. ACC terms between Z-scales were computed as described previously in  [48]. ACC terms combine auto-covariance (same z-scale i = j) and cross-covariance (different z-scale i j) of neighboring amino acids over a window of 4 residues (lags (l) ranging from 1 to 4). There are thus nine nearest neighbor ACC terms for the 3 z-scales factors, yielding 36 ACC terms per peptide of length N. Each ACC term is defined for a given Z-scale couple (i, j) as follows: ACC i,j,l = N n=1 -l z i,n × z j,n+l N -l The Z-scales values were retrieved from the AAindex database (https://www. genome. jp/aaindex/) via the R package protr (v1. 5-0). ACC terms were calculated with the acc function from the same R package and pre-processed by mean-centering and scaling to unit variance.
K-Means Clustering Peptides were clustered based on the Euclidean distance defined above by k-means (scikit-learn Python package version 0. 21. 2). Centroid initialization was performed with the 'k-means++' method with the best inertia among 100 runs for k ranging from 2 to 10. The selected k value  (2)  is that leading to the best average silhouette coefficient-a measure of the clustering quality  [51] -over all peptides.
Distance Trees Euclidean distances between pairs of 36-dimensional vectors defining each peptide with their ACC terms were used to compute a distance tree between the studied HA-RAMPs, with the neighbor-joining implementation of the scikit-bio Python library, version 0. 5. 5. To evaluate the robustness of bipartitions on that NJ tree, we built 1000 trees from bootstrap ACC vectors and determined internode certainty (IC) and tree certainty (TC) measures  [52]  implemented in RaxML v8. 2. 12  [53]. Tree annotation and display was performed with iTOL v5. 5  [54].
Vizualisation of Peptide Properties The two (or three) principal components of a principal component analysis (PCA) of the peptides defined by their 36 ACC terms were used for visualization of the peptide's properties. The weights of each variable in the PCA are summarized by correlation circles in Figure  S5. Analyzes were performed with the scikit-learn Python package version 0. 21. 2. Box plots were generated with the R ggplot2 package version 3. 1. 0.
Microscopy For confocal imaging (Figure  5  and Figure  S9 ), cells were subjected to 0. 1 µM MitoTracker Red CMXRos (ThermoFisher) for 30 min in the dark and washed with TAP prior to imaging on an upright SP5 confocal microscope (Leica). Venus (excitation 514 nm/532-555 nm emission) and MitoTracker (561 nm/573-637 nm) were imaged sequentially to avoid crosstalk, each alongside chlorophyll autofluorescence (670-750 nm emission) to eliminate cells that had moved between images (chlorophyll data from 514 nm excitation shown in the figures). Epifluorescence images (Figures  S7  and S10 ) were taken on an Axio Observer. Z1 inverted microscope (Zeiss) equipped with an ORCA-flash4. 0 digital camera (Hamamatsu) and a Colibri. 2 LED system (Zeiss) for excitation at 505 nm for Venus (filter 46HE YFP shift free, 520-550 nm emission) and 470 nm for chlorophyll autofluorescence (filter set 50, 665-715 nm emission) with cells in poly-l-lysine (Sigma Aldrich) coated 8-well µ-slides (Ibidi).
Fiji was also used to measure image background intensities from outside the cell for the Venus channel, or from within the cell but outside the organelle in the case of the MitoTracker and chlorophyll channels. Backgrounds values were then subtracted in R (version 3. 6. 1) and Pearson correlation coefficients at each pixel between channels were calculated as a measure of co-localization  [59]  using the cor function from the stats package (v3. 6. 2).
Statistical Analysis Chi 2 tests were used to analyze the distribution of peptides according to their functional group among the different k-means clusters (Figure  1 ). Wilcoxon test for all paired comparison with a Holm correction were used to analyze the distributions of peptide features (Figures  2  and 3 ). One-way analysis of variance (ANOVA) and Tukey post-hoc were used to compare the Pearson correlation coefficients to analyze fluorescence intensities (Figure  S9 ). A p-value threshold of 0. 05 was used for all tests. All statistical calculations were performed with the stats package (v3. 6. 2) of the R version 3. 6. 1 and with functions from the Python scipy module (v1. 2. 3). represented (note that some peptides have no predicted amphipathic helix). Stars indicate significant differences (Wilcoxon tests, p-value < 0.
Peptide Families and Their Descriptors We performed a comparative analysis of different functional groups of peptides (Table  S1 ). We selected TPs from Chlamydomonas reinhardtii, Arabidopsis thaliana, Saccharomyces cerevisiae and Homo sapiens for which both the subcellular location of the targeted protein and the cleavage site have been experimentally determined (Table  S2 ). We retrieved from the CAMPR3 database  [42]  31 HA-RAMP families with a documented amphiphilic domain (see Material and Methods) and, as negative control, the cyclotide family of globular AMPs (Table  S4 ). We also considered a set of peptides, hereafter referred to as secretory signal peptides (SPs), which function as address tags, just as TPs do, but target a different subcellular compartment. SPs have a well-established evolutionary link and all use Sec-type translocation systems  [66] [67] [68].
Acknowledgments: We thank  Richard Kuras ,  Ulrike Endesfelder ,  Michael Schroda  and  Moritz Meyer  for fruitful discussions and advice,  Masayuki Onishi  and the  Pringle lab , for sharing their bi-cistronic expression plasmids,  Pierre Crozet  for advice on plasmid design,  Zhou Xu  and  Simon Desjardin  for help with microscopy, and  Barry Bruce  and  Prakitchai Chotewutmontri  for their help in the correct implementation of Hsp70 binding site and FGLK motif search scripts.
Funding: This research was funded by the annual funding from the  Centre National de la Recherche Scientifique and Sorbonne University  to UMR 7141, by the  ChloroMitoRAMP ANR  grant ( ANR-19-CE13-0009 ) and by  LabEx Dynamo  ( ANR-LABX-011 ). ODC was supported by The  Rothschild Foundation , the  Labex dynamo and the ChloroMitoRAMP  grant. CG was supported by the  MATHTEST ANR  grant ( ANR-18-CE13-0027 ).
Introduction The world of proteins is so diverse in their amino acid sequences, structural states and functions that in order to navigate efficiently between them we need their systematic classification and annotation. Although all known three-dimensional protein structures can be found in the Protein Data Bank (PDB)  [1] , significant efforts have been undertaken to further classify these structures. The best known protein structural classification databases, CATH  [2]  and SCOP  [3] , put the secondary structure of proteins at the forefront of their classification. This concept has led to a simple hierarchical classification of most protein structures, especially those with globular structures. Over the last two decades, a number of non-globular structures have been determined, containing tandem repeats (TRs) in their sequence and structure  [4] -  [6].
The new version of RepeatsDB also introduces a finer classification of repeat regions. Attempts to classify in detail a particular type of TR-containing proteins  [28]  revealed that RepeatsDB needs at least two additional classification levels. Similarly to the four-level classification schemas used in CATH  [2]  and SCOP  [3] , RepeatsDB 3. 0 provides "Class", "Topology", "Fold" and "Clan" levels. An additional "Family" level, not yet available, defines groups of homologous repeats within a clan and is defined in collaboration with the Pfam database  [27]. Finally, in addition to a revised classification, RepeastDB 3. 0 includes redesigned web server and interface to improve user experience and data curation. New features allow to compare the position of repeats over different protein structures, to evaluate sequence and structural similarity within a repeated region and navigate the classification.
Progress and new features Database content Since its first release, RepeatsDB aimed at the annotation of all these features in repeat proteins, either automatically or through manual curation. The RepeatsDB 3. 0 automatic annotation pipeline processes the entire Protein Data Bank with a new version of RepeatsDB-lite  [18]. The algorithm is based on the repeat unit library, and allows to predict the position of repeat units in the PDB chains, insertions within and between units, as well as the RepeatsDB classification. RepeatsDB supports the visualization of this data by showing the detected repeats in the PDB sequence and structure, allowing navigation of the TR classification and supporting complex queries. The new database version includes several strategies to support the standardisation of repeat phases.
Data generation pipeline and updates The starting point for RepeatsDB is the entire PDB  [1]. At each PDB update, repeat candidates are extracted with RepeatsDB-lite  [18]  to confirm the presence of repeat regions and provide detailed unit information. PDB chains annotated as containing a repeat region are then clustered at 100% sequence identity. The clusters that map to regions that were already annotated as repeats in previous database releases are automatically added to the database. This pipeline can be automated and will allow regular update of RepeatsDB as well as interoperability with other biological databases. Clusters mapping to new candidate repeat regions require manual inspection to confirm the presence of repeats in at least one representative group entry. Once this is confirmed by an expert evaluation, clusters are included in RepeatsDB.
If the exact position of repeat units is also revised and/or manually annotated, the entry is labeled as "reviewed". The PDB chains detected as containing repeats are then annotated with additional information retrieved from SIFTS  [30] , to map PDB chain identifiers to UniProt  [31]  and other biological databases, such as Pfam  [27]. These data support a comprehensive validation carried out by visual inspection, generating RepeatsDB reviewed entries at the level of the PDB chains or at the level of UniProt entries.