dataesr/scientific-paragraphs-categorization huggingface dataset:
5. 0 50 Linux patched with PREEMPT-RT, is able to run Simulink models thanks to the RT-MaG toolbox. With its breakout board (Pinto-TH), it features 4 PWM outputs (and 2 are already translated to 5. 0V by the RCB), 1 SPI bus (with 2 Chip Select), 2 serial ports, 1 WIFI interface (54 MB/s), 8 GPIO and 6 10-bit ADC. The Overo AirSTORM can be easily programmed via the RT-MaG toolbox, which makes it easy to test and validate various control strategies. In addition, the board provides a wireless link (WIFI) making it really easy to monitor data and tune parameters in real time via a ground station. The various links between the hardware components of X4-MaG are shown in figure 2. The Gumstix and the NanoWii communicate with each other via an RS232 link at 115200 Baud. The Gumstix sends the four motor input reference signals to the Nanowii, which sends the IMU's data back 92 X4-MaG: A Low-Cost Open-Source Micro-Quadrotor and Its Linux-Based Controller International Journal of Micro Air Vehicles.
Moreover, the ease of access to sequencing facilities was expected by a large community to counterbalance the impact of the taxonomic impediment in conservation and basic biodiversity sciences  [28]. A potential solution to the gap in global information systems in taxonomy was proposed by the international Barcode of Life project (iBOL) through the creation of a database system enabling the repository of sequences but also offering a workbench for a collective assembly of the DNA barcode libraries  [29, 35]. This project led to the launch in 2005 of the Barcode of Life datasystem (www. boldsystems. org) as a solution to an interactive online database enabling collective assembly and curation of the libraries. The development of such a global system had to face several challenges: (i) identification based on molecular data should be reproducible to enable a universal use, (ii) owing to the state of the art of the world inventory of living beings, the system should ensure the storage of collateral data enabling further taxonomic studies, (iii) open access to the data should be guaranteed in agreement with the Access and Benefit Sharing (ABS) principle established by the CBD.
DNA barcoding: toward the establishment of a global information system The first step in the development of a global system of molecular identification is the constitution of the DNA barcodes reference libraries for known species based either on large scale field sampling campaigns  [18, [36] [37] [38]  or sequencing collections in natural history museums whenever possible (e. g. birds and insects  [39, 40] ). In order to ensure the reproducibility of molecular identifications based on DNA barcodes reference libraries, however, the BOLD database currently hosts specimen records for which essentially, seven data elements are listed (Fig. 1 ): 1. Species name 2. Voucher data 3. Collection record 4. Identifier of the specimen would have to face the undescribed diversity of earth biotas [e. g.
From the taxonomic impediment to DNA barcoding During the second conference of the parties of the Convention on Biological Diversity (CBD) held in Jakarta in 1995, the participant countries have explicitly formulated through the concept of taxonomic impediment the major concern raised by the worldwide community of taxonomists since the 90's about the increasing disinterest from governments and funding agencies for taxonomy. Unfortunately, several global initiatives such as the Global Taxonomic Initiative (GTI) launched in the context of the CBD early in 2002 failed to embrace a massive adhesion and failed to help reach the CBD goal to slow-down the pace of species loss by 2010. Several challenges prevented the emergence of a global project including the settlement a reserved keyword for those records in an INSDC database that meet a higher quality standard and are compliant with the following requirements: 1.
To further improve reliability and reproducibility of DNA barcoding, the Consortium for the Barcode of Life (cBOL), in cooperation with GenBank and the other members of the International Nucleotide Sequence Database Collaboration (INSDC), have created and implemented the BARCODE data standard. "BARCODE" is few discrepancies between molecules and phenotypes  [36, 39]. It proved also, however, to constitute a powerful approach when dealing with hyperdiverse tropical fauna by facilitating the delineation of new evolutionary lineages representing instances of new species sometimes at unexpected rates as recently emphasized in arthropods and crustaceans  [11] [12] [13] 20, 42, 43]  or fishes  [18, 19, 37, 44]. Worth mentioning, DNA barcoding also revealed some overlooked taxa in well-studied temperate faunas  [36, 39, 40].
In the context of an integrative taxonomy workflow, BOLD makes the most of the DNA barcode data standards by introducing an access to all primary data at any steps and promoting more sustainable practices during the production of DNA sequences for taxonomic purposes. Along the same line, several other initiatives have been launched to summarize and ease access to primary data and biological repositories for taxonomic purposes. The Global Registry of Biorepositories (GRBio), for instance, aims at offering a consolidated clearinghouse of information about biological collections and repositories worldwide in order to facilitate electronic linkages of this information (http://grbio. org). The accessibility to species description has been recently considered from a data storage perspective recently by Species-ID (http:// species-id.
Architectural measurements were performed at the elementary architectural structure (EAS) stage  (Crespel et al. , 2013)  and took place from 21 May to 8 July 2013. Plants that had reached this stage were stored for no longer than 1 week at 2 • C in a dark cold chamber in order to prevent their growth and branching  (Girault et al. , 2008; Djennane et al. , 2014)  and avoid any experimental bias. Ten plants per cultivar for each treatment were randomly chosen and defoliated. Architectural measurements were carried out using a Fastrack digitizer (Polhemus, Colchester, VT, USA) and data were recorded using PiafDigit software  (Donès et al. , 2006). This software makes it possible to both store measurements made at the two observation scales and to construct a 3D real-time representation of the plant architecture.
Architectural Data Analysis All the data were subjected to a two-way analysis of variance (ANOVA) to determine genotype and watering effects and their interaction. Multiple comparisons of means (Newman-Keuls test) were performed. Differences with p < 0. 05 were considered to be significant. Data analyses were carried out using R 3. 0. 2 software. The percentages of the differences between WR-treated plants and the control for each architectural variable were calculated and summed as absolute values. Each value was then normalized by dividing by the value corresponding to the least responsive genotype, Hw336. The intensity of the architectural response to water restriction of each genotype was then assessed based on these ratios.
5 kV (negative mode), source block and desolvation gas temperatures of 130 and 500 • C, respectively. Nitrogen was used to assist the cone and desolvation (150 and 800 L h -1 , respectively), argon was used as the collision gas at a flow rate of 0. 18 mL min -1. Samples were reconstituted in 140 µL of 50/50 acetonitrile/H 2 O (v/v) per mL of injected volume. The limit of detection (LOD) and the limit of quantification (LOQ) were extrapolated for each hormone from calibration curves and samples using the Quantify module of MassLynx software, version 4. 1.
Physiological Data Analysis Data collected were divided as follows: (A) data obtained at the end of WRP1 and WRP2 that characterize plant responses to water restriction; and (B) data obtained during WWP1 and WWP2 at least 15 days after the beginning of the re-watering, which characterize the recovery capacity of the plant. Each group of data was analyzed separately along with its corresponding control data. When using a large number of variables it is often not clear which ones are relevant and/or redundant to describe the observed variability. Therefore, data were first subjected to a principal component analysis (PCA) to select variables that (i) best explain the physiological variability and (ii) are the least correlated among themselves  (Ky et al. , 2013; Granda et al. , 2014; Takahashi et al.
, 2014). PCA is a well-established dimensionality reduction method  (Eklöv et al. , 1999; Pagola et al. , 2009; Verbanck et al. , 2013)  that makes it possible to describe a multidimensional dataset using a small number of variables while retaining as much information as possible. The reduction is achieved by transforming the original variables into a new set of independent continuous variables, referred to as the principal components (PCs). The selected variables were subjected to a Kruskal-Wallis test to evaluate the genotype and watering effects and to a Mann-Whitney-Wilcoxon test for comparisons of means. Differences with p < 0. 05 were considered to be significant. Data analyses were carried out using R 3. 0. 2 software.
Selecting the Most Relevant Water Stress-Related Variables Twelve water stress-related variables were measured at the end of each WRP (dataset A) and at least 15 days after the beginning of each WWP (dataset B) for control plants and WR-treated plants. They were then subjected to a PCA (Figure  6 ). For the analysis of dataset A, the first two PCs accounted for 32. 0 and 22. 7%, respectively, or 54. 7% of the total variability. For the analysis of dataset B, the first two PCs accounted for 30. 9 and 25. 1%, or 56. 0% of the total variability. For each of the two analyses, relevant variables were selected based on one criterion: to contribute the most to the major component formation. For the analysis of dataset A, ABA (19. 6%) and sucrose (17. 1%) were selected for PC1, and conjugated cytokinin (cCK) (21.
4%), SA (18. 3%), and intermediate cytokinin (iCK) (18. 3%) were selected for PC2 (Table  2 ). For the analysis of dataset B, ABA (20. 2%) and sucrose (22. 2%) were chosen for PC1, and proline (25. 5%), glucose (19. 2%), and fructose (18. 7%) were chosen for PC2. Since glucose and fructose were highly correlated (r = 0. 99) and thus gave redundant information, fructose was arbitrarily selected. Therefore, the most explanatory variables of the observed variability for water restriction were ABA, sucrose, cCK, SA and iCK, and the ones best explaining the observed variability for re-watering were ABA, sucrose, fructose, and proline. The Kruskal-Wallis test showed no significant difference either between the two WRP or between the two WWP for these variables. Thus, data obtained within each group were pooled for further analyses.
Image acquisition was performed with an inverted microscope and a laser scanning confocal imaging system (Leica-SP8) using a HCX PL APO CS 63 ϫ 1. 20 oil immersion objective. Activation was performed with 22 l agonists in HEPES-BSA buffer (final concentration ACh, 10 M, LPA 1 M) or with 22 l vehicle as a negative control. Images were acquired in multiposition mode (4 positions) over 15 min with zstacks. Each video was generated using FIJI software and represent all the z-stack images at a given time-frame. Figure. Preparation-Figs. of microscopy images were prepared with FigureJ  (24).
However, the whole screening procedure, the analysis of the movies and the criteria for selecting the potential hit proteins were performed in blind by using the identification number associated with each vector without the knowledge of the protein encoded. The final analysis of the proteomic data was achieved with the knowledge of the proteins encoded. The Name and Swissprot ID of each screened protein is indicated in Table  I. We screened 140 fluorescent fusion proteins that had previously been determined as localizing in the cytoplasm of resting cells  (7)  and 53 that were localized both in the cytoplasm and the nucleus. Each of these proteins was screened once with the fluorophore ECFP fused to its amino-terminal part and once with the fluorophore EYFP fused to its carboxyl-terminal part (unless otherwise stated).
Fig. 1B  indicates the repartition of the screened proteins according to the known biological processes in which they are described in the uniprot database at http://www. uniprot. org/. The screened proteins belong to 13 categories of biological processes, with the addition of two categories for proteins that are still of unknown function (22% of the screened proteins) and for clearly mislocalized proteins (4% of the screened proteins), such as integral membrane proteins and those that should be secreted. Categories representing around 10% of the screened proteins are: cell cycle, protein transport, signal transduction, and transcription regulation. Fifty-six proteins were selected as potential hits and were classified into three different classes according to their localization depending on the activated state of the cells (1, special localization before activation; 2, fast translocation upon activation (usually recorded in the video of activation); 3, slow translocation observed several minutes or hours after NKA activation).
7C  and 7  videos on the website). These assemblies of spots are probably at the level of the interaction between the plasma membrane and the surface of the glass slide. After several hours of NKA activation, the small individual spots rearranged into several short linear succession of dots, usually in lamellipodia (Fig. 7D ). Our data illustrate a very peculiar localization of PLEKHH2 and indicate that it responds to activation of a prototypical Gqcoupled receptor. SPATC1L-This speriolin-like protein is of unknown function, whereas speriolin is a spermatogenic cell-specific centrosomal protein  (50). Before activation, CFP-SPATC1L was distributed in the cytoplasm, the nucleus and in the perinuclear region. After one hour of activation, fluorescence was also detectable at the top of the cell-cell junction (see the 5 videos on the website).
Acknowledgments-This work is dedicated to the memory of  Hans WD Matthes  for all we have learned from him. We thank  Pierrick Bossert ,  CNRS , the excellent webmaster and conceptor of the GFP-cDNA Live and G-Protein Coupled Receptors website. We are grateful to all members of the imaging center at IGBMC particularly Pascal Kessler. We would like to thank members of the  Galzi/Simonin lab , particularly  Renaud Wagner  and  Brigitte Ilien  for support,  Valerie Utard  for maintenance of the cell culture facility and  Jeremy Garwood  for critical reading of the manuscript.
One run took 60 min, including the regeneration step at 100% buffer B and the equilibration step at 100% buffer A. Ionization was performed with a 1. 3-kV spray voltage applied to an uncoated capillary probe (10 m tip inner diameter; New Objective, Woburn, MA, USA). Peptide ions were analyzed using Xcalibur 2. 2 (Thermo Electron) with the following data-dependent acquisition steps: (1) MS scan (mass-to-charge ratio (m/z) 400 to 1,400, 70 000 resolution, profile mode), (2) MS/MS (17,500 resolution, collision energy ϭ 30%, profile mode). Step 2 was repeated for the eight major ions detected in step 1. Dynamic exclusion was set to 40 s. Xcalibur raw data files were transformed to mzXML open source format using msconvert software in the ProteoWizard 3. 0. 3706 package  (59). During conversion, MS and MS/MS data were centroided.
MS Data Availability-The raw MS output files were deposited online using PROTICdb database (60 -62) at the following URL: http:// moulon. inra. fr/protic/heterosyeast2. Protein Identification-Protein identification was performed using the custom database described in Blein-Nicolas et al. (58). This database, containing 10,851 entries, was constructed from the translations of all systematically named ORFs of S. cerevisiae and S. uvarum downloaded from the Saccharomyces Genome Database (SGD project, http://www. yeastgenome. org/, versions dated October 5, 2010 and  December 15, 2003, respectively). The proteins of S. cerevisiae and of S. uvarum encoded by orthologous genes were attributed unique labels. A contaminant database containing the sequences of standard contaminants and the sequences of 16 proteins of Vitis vinifera previously identified in extracts of yeast grown in grape juice was also interrogated.
The decoy database comprised the reverse protein sequences of the custom database. Database search was performed with X!Tandem (version 2011. 12. 01. 1; http://www. thegpm. org/TANDEM/) with the following settings. Enzymatic cleavage was declared as a trypsin digestion with one possible misscleavage. Carboxyamidomethylation of cysteine residues and oxidation of methionine residues were set to static and possible modifications, respectively. Precursor mass precision was set to 10 ppm. Fragment mass tolerance was 0. 02 Th. A refinement search was added with the same settings, except that protein N-ter acetylations were also searched. Only peptides with an E-value smaller than 0. 05 were reported. Identified proteins were filtered and sorted by using X!Tandem-Pipeline (version 3. 3. 0, http://pappso.
inra. fr/bioinfo/xtandempipeline/). Criteria used for protein identification were (i) at least two different peptides identified with an E-value smaller than 0. 05 and (ii) a protein E-value (product of unique peptide E-values) smaller than 10 Ϫ4. These criteria led to a false discovery rate estimated by using the decoy database of 0. 12% and 1. 15% for peptide and protein identification, respectively. Peptide Quantification and Processing Intensity Data-Peptides were quantified based on extracted ion chromatograms using Mass-ChroQ software version 1. 2. 2  (63)  with the parameters given in File S1. The detection threshold on min and max were set at 30,000 and 50,000, respectively. Due to progressive fouling of the quadrupole, sensitivity losses were observed over time, leading to a global decrease of measured intensities, particularly for hydrophobic peptides.
To avoid bias on the estimation of total protein abundances in hybrids, we removed parent-specific peptides by using peptides presenting presence/absence variation among parental strains as a proxy. However, parent-specific peptides were confounded with species-specific peptides, which represented nearly 65% of the valid peptides. To exploit as far as possible the data available for intraspecies crosses, we thus split the dataset into three subsets: one contained S. cerevisiae triplets (hybrid and its parents), another contained S. uvarum triplets, and the last one contained interspecific triplets. Parent-specific peptides were removed separately in the three subsets. To finish, in order to estimate the peptide effect properly, peptides quantified in less than four strains ϫ temperature combinations in a given subset of data were removed.
Detection of Protein Abundance Changes-Protein abundances were estimated independently in the three subsets of data by using log͑I istr ͒ ϭ kst ϩ D i ϩ B r ϩ C tr ϩ ⑀ istr where I istr is the normalized intensity value for peptide i in strain s, temperature t, and replicate r, kst is the natural logarithm of the abundance of protein k in strain s and temperature t, B r ϳ N ͑0, B 2 ͒ is an error due to the biological variation of replicate, C str ϳ N ͑0, C 2 ͒ is an error due to the technical variation of sample str, D i ϳ N ͑0, D 2 ͒ is an error due to the LC-MS response of peptide i, and ⑀ istr ϳ N ͑0, ⑀ 2 ͒ is the residual error. Estimation of the parameters of the model was performed as described in Blein-Nicolas et al. (64). Protein abundance changes were detected by multiple test procedure across four different contrasts: (i) hybrid-mean of parents, (ii) hybrid-parent 1 , (iii) hybridparent 2 , (iv) parent 1 -parent 2.
These 1,396 proteins belonged to 16 functional categories following the MIPS Functional Catalogue Database (68) (Fig. S1 , Table  S6 ). Metabolism was the most represented category, with 534 proteins (31. 1% coverage; Fig. S1 ). Representation of protein abundances as a heat map showed that the strain ϫ temperature combinations were separated in three main clusters corresponding globally to S. uvarum strains, interspecific hybrids, and S. cerevisiae strains (Fig. 1, clusters A,  B,  and C , respectively). Interspecific hybrids differed from all the other strains by a cluster of proteins that were globally more abundant than in the other strains (Fig. 1 , cluster II). S. uvarum strains and S. cerevisiae strains differed by two clusters of proteins: one containing proteins that were more abundant in S.
Heterosis for Protein Abundance Is Partly Related to the Complexity of Transcriptional Regulation-To determine the extent to which the factors controlling protein abundances could be involved in heterosis for protein abundance, we focused on the transcription factors (TFs) possibly involved in the regulation of the genes encoding the proteins quantified in our study. A total of 162 TFs with a consensus DNA-binding sequence were retrieved from the Yeastract database (www. yeastract. com; 71-74). On average, the genes encoding proteins that were heterotic in at least one hybrid ϫ temperature combination were putative targets of a higher number of TFs than the genes encoding non-heterotic proteins (27. 7 versus 21. 4; Mann-Whitney test p ϭ 1. 96 ϫ 10 Ϫ15 ; Fig. 7A ). In addition, a significant correlation was found between the number of putative TFs of a gene and the proportion of hybrids ϫ temperature combinations in which the encoded protein was heterotic (Kendall correlation test, r ϭ 0.
Vibrational measurements Specific dynamic modulus of elasticity and damping coefficient were measured by non-contact forcedreleased vibrations of free-free slender beams (e. g. Obataya et al. 2000). The employed device and testing procedure are described in detail in  Brémaud et al. 2012. Specimens, supported by thin threads located at nodal lines of the first bending mode, were set into flexural vibration using an electronic magnet facing a small piece of iron glued to one end of the specimen. The displacement at a belly of vibration was measured through a laser triangulation sensor (Figure  2 ). The emission of frequency sweeps and signal acquisition, processing and analysis were piloted through a development in Labview ® software  (Brémaud 2006 , Brémaud et al. 2012). Specific dynamic modulus of elasticity (E'/ρ, calculated from first resonance frequency and dimensions measured at each step) and damping coefficient (tanδ, calculated both from the frequency bandwidth or "quality factor" and from the logarithmic decrement of amplitude after stopping the excitation) were determined as the average of three repetitions on each specimen.
Corrective factors and statistical analyses As water treatments can change mass and specific gravity by removing extractives, this contribution of extractives was taken into account for the calculation of changes in EMC according to Hernandez 2007, and for the calculation of changes in E′/ρ according to  Brémaud et al. 2011 and Se Golpayegani et al. 2012. All data were statistically analyzed using one way ANOVA test combined with Duncan Post-hoc in SPSS ® for α= 0,05 and α=0,01.
ACKNOWLEDGMENTS Authors are grateful to  Service de Coopération et d'Action Culturelle (SCAC) of the French embassy in Tehran and Center for International Scientific Studies and Collaboration (CISSC)  for supporting this project ( Gundishapur  project, Egide nb 20714UJ). Authors are also grateful to  Samad Zare , the professional Iranian lute maker who provided the raw material and shared his knowledge and experience through this study.