dataesr/scientific-paragraphs-categorization huggingface dataset:
33. We compared the mean R 2 of the SNPs associated with flowering with the mean R 2 of all the SNPs using a Wilcoxon rank test. The R 2 values of each SNP were obtained from the GF model built with observed current climate data (EWEMBI dataset). Analysis of genomic vulnerability and yield. As genomic vulnerability measures the mismatch between current and predicted future genomic variation  20  , the biggest mismatches, i. e. , vulnerabilities, should result in maladaptation and hence in less fit varieties. A negative relationship between yield (as a proxy of fitness) and genomic vulnerability could therefore be expected. We experimentally assessed the relationship between yield and genomic vulnerability using the spatial heterogeneity of climate experienced by pearl millet varieties in West Africa.
Acknowledgements The research leading to these results received funding from the  UK's National Environment Research Council (NERC) / Department for International Development (DFID) Future Climate For Africa program , under the  AMMA-2050  project (grant numbers  NE/ M020002/1  and  NE/M019934/1 ) and from  Agropolis Fondation  under  AdaptInCrops  project ( ID 1403-057 ). Y. V. is also funded by the  CGIAR Research Program on Grain Legumes and Dryland Cereals (GLDC). We acknowledge the  IRD itrop HPC (South Green Platform) at IRD Montpellier  for providing HPC resources that have contributed to the research results reported within this paper, URL: https://bioinfo. ird. fr/-http://www. southgreen. fr. We thank  Ndjibo Moussa  and  Tidjani Moussa  for field trial analyses.
g. D. acutangula, D. populnea, R. cordata). In order to fulfill these objectives, we aimed: (1) To assess whether the seeds of D. acutangula, D. populnea, R. cordata and T. populneoides are dormant or not; (2) If dormant, to determine the dormancy class of these seeds; (3) To determine light requirements for germination of these species; (4) To identify treatments to improve germination of these species.
Imbibition To determine whether the seed coat is water-permeable or water-impermeable, 5 replicates of 20 seeds from three samples (one lot without scarification (control) and two lots with two different scarification methods: mechanical scarification by sand paper for D. acutangula, D. populnea and R. cordata and by a razor blade for T. populneoides; chemical scarification by sulfuric acid (H 2 SO 4 ) during 10 min for D. acutangula, D. populnea and R. cordata and during 60 min for T. populneoides) were weighed before imbibition, using the weighing scale (with a 0. 001 g precision) of the moisture content analyzer (Denver Moisture Analyzer e Model IR120, Denver Instrument). Each seed sample was placed on moist filter paper (Whatman no. 1) in a Petri dish at ambient room temperature for 7 days.
Making seeds permeable Various treatments were assessed to make physically dormant seeds permeable. First, mechanical scarification using sand paper was performed according to P erez-García  (2009). Seeds were blotted between two sand paper sheets and scarified until stripes were visible to the observer. Chemical scarification was conducted using highly concentrated sulfuric acid (H 2 SO 4 )  (Oldham and Ransom, 2009). For this treatment, seeds were soaked in a beaker filled with concentrated sulfuric acid (98%, v/ v) during 1, 5 and 10 min for small seeds of D. acutangula, D. populnea and R. cordata and 15, 30 and 60 min for bigger seeds of T. populneoides. As another treatment, immersion in hot water was carried out according to  Baskin and Baskin (2014). Thus, seeds were soaked in heated water (80 C) in a beaker until cooling down, and let in the same beaker for 16 h.
Statistical analyses One-way analysis of variance (ANOVA) was performed to analyze seed mass increase data over treatments. Multiple pairwise means comparisons were then conducted using Tukey's range test in order to separate means. Germination percentages were analyzed by fitting generalized linear models (GLMs) with a logit link function. Binomial distribution is appropriate for proportion-based data such as germination data  (Hay et al. , 2014). In order to overcome residuals over-dispersion a quasi-binomial error distribution was thus used to analyze germination percentages. Germination percentages were compared among treatments for seeds placed in daily light. Germination percentages were then compared among light conditions for the second run. For the analyses, only germination data for treatments that exhibited germination percentages different from 0% were tested.
In order to separate means in all experiments, multiple comparisons were conducted using parametric models multiple comparisons procedures, with the "glht" function of the "multcomp" package in R. In order to compare germination speed (T 50 ) among treatments for seeds placed in daily light for the four species, GLMs were performed using Gamma error distribution with a log link function  (Chamorro et al. , 2013). Means were then separated using the same procedures as for GPs analyses. All the statistical analyses and figures presented in the result section were performed using R version 3. 3. 1 (R Core Team, 2016) with ggplot2  (Wickham, 2009) , sciplot  (Morales, 2012) , multcomp  (Hothorn et al. , 2008)  and multcompView  (Piepho, 2004).
Acknowledgements This research was co-funded by the  European Union : European regional development fund (ERDF), by the  Conseil R egional de La R eunion  and by the  Centre de Coop eration Internationale en Recherche Agronomique pour le D eveloppement (CIRAD). The first author appreciates the financial support from the  ANRT  ( Association Nationale de la Recherche et de la Technologie), Minist ere de l'Enseignement Sup erieur, de la Recherche et de l'Innovation, France. The authors greatly acknowledge the  Plant Protection Platform  ( 3P , IBISA). We thank  Antoine FRANCK  ( CIRAD, La R eunion ) for providing us with the seed photographs. We also thank the personnel of P epini ere du Th eâtre for assisting us in harvesting seeds used in this study.
Since this usually cannot be done experimentally, it is typically included in the calculation. This requires the additional cascade calculation, as presented in Section 2, which includes the atomic structure calculation of the necessary RBRs, as well as the collision dynamics calculations of the initial production cross sections. Next, we present our RBR results based on the COWAN code calculations, as well as the initial production cross sections based on our 3eAOCC approach. Reported atomic structure calculations on Li-like ion states including carbon have been quite prevalent. In particular, the energies of the 1s2lnl states for n = 2, 3 and their E1 and Auger transition rates to 1s 2 nl and 1s 2 , respectively have been reported by  Cheng [50]  and Chen  [51]  using the multi-configuration Dirac-Fock method (MCDF).
[60]. In all these publications, radiative transitions to the singly excited states 1s 2 nl are only reported. However, some selective results on carbon, for radiative transitions between doubly excited states are given in Blanke et al. [61, 62]  (including lifetimes of 1s2pnl 4 L states for n = 2 -3),  [62]  (wavelengths of 1s2s3d 4 D → 1s2s2p 4 P transitions), Laughlin  [63]  (transition rates and radiative lifetimes for 1s2snl 4 L and 1s2pnl 4 L levels with L ≤ 4). These results do not cover all the radiative and Auger transitions needed for our cascade calculations. We therefore performed our own calculations using the COWAN code  [39] , to obtain the radiative and Auger transition rates and RBRs for the C 3+ [(1s2s 3 S)n 2,4 L] states decaying to the 1s2sn l 2,4 L states. These results were used in our present calculations of the cascade repopulation of the 1s2s2p 2,4 P states during the capture process.
Our calculations were carried out using the 2018 version of the COWAN  [39]  code, which is free of certain bugs reported earlier  [64]. Our results on transition rates were compared with data from Ref. [51]  for n = 2 and n = 3 and found in good agreement. The RCE subroutine of the code that performs the least-squares fitting of the energy levels was not used due to the lack of an adequate number of experimentally determined energy levels for the C 3+ [(1s2s 3 S)n 2,4 L] states. For the autoionizing decays of the C 3+ (1s2snl) configurations with n ≤ 6, the only allowed final state is the ground state 1s 2 1 S of C 4+  [41]. Thus, the Auger decay channels reported here correspond to transitions 1s2snl (2S+1) L J → 1s 2 1 S 0 + e -(l). Moreover, our results include Auger decay rates for transitions that violate spin conservation (i.
g. , σ c 3 ( 4 P) = σ c 3 ( 4 P 1/2 ) + σ c 3 ( 4 P 3/2 ) + σ c 3 ( 4 P 5/2 ), i. e. , the c 3 cascade contributions to levels i = 1 -3 according to Table  1. R was computed up to and including third order cascades, c 3 , according to the formulation of Equation  (34). Results are shown in Figure  5 , along with the ZAPS measurements reported in  [30]. Here, we increased the number of electron configurations from 20 to 29 in the COWAN code 5 obtaining slightly smaller values for R compared to the ones previously reported  [30] , which resulted in improved agreement with experiment as seen in Figure  5. These and our previous results  [30] , essentially invalidate the frozen core approximation commonly used in the past (e. g. , Röhrbein et al. [35] , found much larger values of the ratio R with or without cascades) when considering electron capture in multi-electron, multi-open-shell quantum systems  [30].
Funding: We acknowledge support by the project "Cluster of Accelerator Laboratories for Ion-Beam Research and Applications-CALIBRA" (MIS 5002799) which is implemented under the Action "Reinforcement of the Research and Innovation Infrastructure," funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund). T. J. M. Z. and A. D. also acknowledge support from the LABEX PLAS@PAR under Grant No. ANR-11-IDEX-0004-02.
Complex interactions between the environment, genetic background and serotype To further understand the interactions between the serotype, genetic background and environment, we used the original dataset of 19 strains, including at least three from each serotype. We analyzed different capsule-related variables in all five growth media. In addition to the amount of capsule production (Figure  S2 ), the natural emergence of non-capsulated mutants (Figure  1 ), maximum growth rate, the maximum yield and biofilm formation, we also measured the hypermucoviscosity of strains, a trait linked to hypervirulence and driven, at least partly by the amount of capsule (Lin et al 2012) (Figures  S10-S12 ). Firstly, we used Principal Components Analysis (PCA) to analyze how the different factors (media, serotype or strain) explain the observed variance in the data.
Capsulated and noncapsulated colonies are clearly differentiated visually and counted separately. The competitive index of capsulated strains was calculated by dividing the ratio of CFU at T 24 over T 0. Growth curves. Overnight cultures were diluted at 1:100 in the aforementioned environments. 200 μl of each subculture was transferred in a 96-well microplate. Cell densities of cultures were estimated with a TECAN Genios™ plate reader. Absorbance (OD 600 ). Light absorbance values of each well reflect the average of 6 reads per well. Absorbance values from within-block technical replicates were averaged and these averages were used as statistically independent data points. (i) Growth rate. Minimum generation times were estimated across replicates for the 1 hour interval (ΔT) spanning the fastest growth during exponential growth phase.
Strains were then left either at 37° for one, two or four days or at room temperature for two, four, seven and 14 days. PBS was added to resuspend the cultures and diluted serially from CFU counts. Quantification of dead cells. Cells were allowed to grow as described above with a minor modification; over day cultures were used to inoculate the 96-well microtiter plates. After 16 hours of growth in M02, dead cells were quantified using the LIVE/DEAD™ BacLight™ Bacterial Viability Kit from Invitrogen, following provider instructions. Images were acquired on a Nikon Eclipse E400 microscope using the Digital Net DN100 camera control unit and a 100X objective. Cells were counted using ImageJ software (https://imagej. net/). On average, 6 pictures were taken per sample (min = 3, max = 10) and ca.
ACKNOWLEDGEMENTS We thank  Matthieu Haudiquet ,  Jean-Marc Ghigo  and  Nienke Buddelmeijer  for critical reading of the manuscript. We thank  Sylvain Brisse  for providing us with the necessary Klebsiella strains, and  Christiane Forestier  and  Damien Balestrino  for providing the pKNG101 plasmid used to generate the mutants. We would also like to thank  Jean-Marc Ghigo  and  Christophe Beloin  for the gift of E. coli S17 MFD λ-pir strain and  E. coli DH5αpir.
FUNDING This work was supported by an  ANR JCJC (Agence national de recherche)  grant [ ANR 18 CE12 0001 01  ENCAPSULATION] awarded to O. R. The laboratory is funded by a  Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases' grant  [ ANR-10-LABX-62-IBEID] , the  INCEPTION programme  [ PIA/ANR-16-CONV-0005 ], and the  FRM  [ EQU201903007835 ]. The funders had no role in the study design, data collection and interpretation, or the decision to submit the work for publication.
S4 ) housed in the Staatliches Museum für Naturkunde, Stuttgart (acronym: SMNS). The new database of egg size in modern polychelidan lobsters was generated for comparison from the MNHN collections. 10 egg diameters were measured per specimen (photograph) using ImageJ. Statistical difference between each extant specimen and MNHN. F. A58254 were tested using a Wilcoxon-Mann-Whitney test.
Microtomography. For microtomography, we used the same method as described in Jauvion et al. 2. The unbroken nodule was imaged with a v│tome│× 240 L tomograph (GE Sensing & Inspection Technologies Phoenix ×│ray) equipped with a microfocus 240 kV/320 W tube delivering a current/voltage of 220 mA/120 kV. Microtomography was performed at the AST-RX technical platform of the MNHN, Paris. Data were processed to obtain a series of virtual slices with a voxel size (cubic voxel) of 31. 4 mm. Virtual slices were saved as a series of image files in 16 bits greyscale indicating differences in absorption of X-ray within the nodule (darker for low absorption, brighter for high absorption). 1900 virtual slices with a resolution of 1340 × 1198 pixels were thus obtained. Outlines of the fossilized structure were segmented using Mimics 20.
Acknowledgements The authors would like to acknowledge  Maïté Adam  and  Patricia Wils  ( AST-RX, plateau d' Accès Scientifique à la Tomographie à Rayons X du MNHN, UMS 2700 Outils et méthodes de la systématique intégrative CNRS-MNHN, Paris ) for X-Ray acquisition and pre-treatment and Florent Goussard (MNHN,  UMR 7207 CR2P ) for technical assistance with data reconstruction. We thank  Günter Schweigert  ( SMNS ) for access to specimens. Lilian Cazes (MNHN) and  Peter Massicard  (projet e-recolnat, MNHN, Investissements d'avenir,  ANR-11-INBS-0004-RECOLNAT ) provided photographs of specimens. DA research was supported by the  China Postdoctoral Science Foundation ,  Yunnan Province Postdoctoral Science Foundation ,  NSFC  grant  41861134032 , and Yunnan Provincial Research Grants  2018FA025  and  2018IA073.
Fluorescence Binding Studies The rFel d 1 binds the fluorescent probe 1-NPN, producing a blueshift in the emission spectrum. Similarly to odorant-binding proteins (OBPs) and chemosensory proteins (CSPs)  [42] , the emission maximum occurs at 407 nm and is accompanied by a strong increase in fluorescence intensity. Figure  1  reports the actual emission spectra obtained with a rFel d 1 concentration of 1 µM and the relative binding curve obtained after processing the data with the GraphPad Software, Inc. , giving a dissociation constant of 5. 8 µM. Scatchard analysis confirmed the presence of a single binding site on the protein without any cooperativity effect and yielded a dissociation constant K 1-NPN value of 4. 8 µM. We also tested other fluorescent probes (2-NPN, 1-AMA (1-aminoanthracene), 1,8-ANS (8-anilinonaphtalene sulfonic acid), but none proved to perform better than 1-NPN (data not shown).
Fluorescence Binding Studies The rFel d 1 binds the fluorescent probe 1-NPN, producing a blueshift in the emission spectrum. Similarly to odorant-binding proteins (OBPs) and chemosensory proteins (CSPs)  [42] , the emission maximum occurs at 407 nm and is accompanied by a strong increase in fluorescence intensity. Figure  1  reports the actual emission spectra obtained with a rFel d 1 concentration of 1 µ M and the relative binding curve obtained after processing the data with the GraphPad Software, Inc. , giving a dissociation constant of 5. 8 µ M. Scatchard analysis confirmed the presence of a single binding site on the protein without any cooperativity effect and yielded a dissociation constant K1-NPN value of 4. 8 µ M. We also tested other fluorescent probes (2-NPN, 1-AMA (1-aminoanthracene), 1,8-ANS (8anilinonaphtalene sulfonic acid), but none proved to perform better than 1-NPN (data not shown).
Among the 28 putative ligands, 5 fatty acids and 9 steroids were predicted to possibly interact with rFel d 1 based on the initial 1-NPN displacement screening (Table  1 ). These compounds were therefore tested in competitive binding experiments with 1-NPN and their displacement curves are reported in Figure  2. Table  2  lists the IC50 values for the best ligands, together with their dissociation constants. These were calculated using the value for 1-NPN (K D 5. 8 µM; SD 0. 62), obtained with GraphPad software, more reliable than that evaluated from the Scatchard plot. Among the fatty acids, lauric acid exhibited the best affinity to rFel d 1 (Kd = 2. 6 µM), while oleic, linoleic, and myristic acids displayed only moderate to low affinities, and palmitic acid proved to be the weakest ligand.
Among the steroids, the strongest ligand was androstenone (Kd = 2. 4 µM), followed by progesterone and pregnenolone. These results are in agreement with the in silico docking predictions. a linear behavior, apparently indicating the presence of a single binding site without cooperativity effects. Among the 28 putative ligands, 5 fatty acids and 9 steroids were predicted to possibly interact with rFel d 1 based on the initial 1-NPN displacement screening (Table  1 ). These compounds were therefore tested in competitive binding experiments with 1-NPN and their displacement curves are reported in Figure  2. Table  2  lists the IC50 values for the best ligands, together with their dissociation constants. These were calculated using the value for 1-NPN (KD 5. 8 µ M; SD 0. 62), obtained with GraphPad software, more reliable than that evaluated from the Scatchard plot.
System Configuration All the computational analyses were carried out in a high-performance GPU workstation with Cent OS V. 7. 6 Linux and the Windows OS. The hardware specifications of the workstation (Model: LVX-1 × RTX-2080Ti) include a powerful Intel Core i9-9920X processor with 1GPU Nvidia RTX-2080Ti, 32GB RAM, running with a superfast boot-home 1 × M2-1TB NVME SSD and 2 × 8TB independent hard disks. The workstation has passed all the validation tests by the Linuxvixion GPU certified system.
Collection and Structure Conversion of Ligands Molecular structures of the 28 putative ligands (15 fatty acids and their derivatives (FA) and 13 steroids) and N-phenyl-1-naphthylamine (1-NPN) (fluorescent probe) were collected from PubChem (https://pubchem. ncbi. nlm. nih. gov/). All the 2D structures of the ligands were converted into the corresponding three-dimensional (3D) coordinates (sdf to mol2 format) using OpenBabelGUI tools V. 2. 3. 1 (http://openbabel. org). The selected compounds were used to obtain a drug-likeness score from the Lipinski rule of five (RO5) webserver (http://www. scfbio-iitd. res. in/software/drugdesign/lipinski. jsp)  [72].
Protein Grid Parameters The 3D crystal structure of rFel d 1 (PDB ID: 2EJN) was retrieved from the Protein Data Bank (PDB) (https://www. rcsb. org/). The protein dimer and ligand dataset were uploaded to the DockingServer (https://www. dockingserver. com; Virtua Drug, Hungary), a web-based interface module consisting of Gasteiger and PM6 semiempirical quantum-mechanical partial charge calculations to enhance the accuracy of docking output utilizing the AutoDock 4 method  [73]. The essential hydrogen atoms, Kollman united atom-type charges, and solvation parameters were added to the 3D structure of rFel d 1. The Gasteiger charge calculation method was selected for the protein clean step. The 3D dimensional grid box was constructed for permitting ligands to interact in the binding sites of Fel d 1.
The affinity grid parameters (nx = 23; ny = 23; nz = 23 and cx = -0. 48; cy = 0. 81; cz = 0. 22) and 0. 375 Å spacing were generated using the Autogrid program  [74]. The total Gasteiger charge of rFel d 1 was -6. 959 kcal/mol. After completion of this step, the rFel d 1 structure was prepared for the docking simulation analysis.
Concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) bridge brain connectivity across timescales. During concurrent EEG-fMRI resting-state recordings, whole-brain functional connectivity (FC) strength is spatially correlated across modalities. However, cross-modal investigations have commonly remained correlational, and joint analysis of EEG-fMRI connectivity is largely unexplored. Here we investigated if there exist (spatially) independent FC networks linked between modalities. We applied the recently proposed hybrid connectivity independent component analysis (connICA) framework to two concurrent EEG-fMRI resting-state datasets (total 40 subjects). Two robust components were found across both datasets. The first component has a uniformly distributed EEG frequency fingerprint linked mainly to intrinsic connectivity networks (ICNs) in both modalities.
METHODS We use two independent datasets acquired from two different sites. The main dataset consisted of 26 healthy subjects with 3 runs of 10 minutes of concurrent EEG-fMRI during task-free resting state  (Sadaghiani et al. , 2010). The generalization dataset consisted of 14 subjects, 20 minutes resting-state runs of concurrent EEG-fMRI  (Wirsich et al. , 2017). The data was used to extract the main joint independent components co-occurring during EEG-fMRI resting state.
Subjects and data of main dataset. Magnetic resonance (MR) was acquired in 26 healthy subjects (8 females, mean age 24. 39, age range 18-31) with no history of neurological or psychiatric illness. Ethical approval has been obtained from the local Research Ethics Committee (Comité de Protection des Personnes (CPP) Ile de France III), and informed consent has been obtained from all subjects. Three runs of 10-min eyes-closed resting state were acquired in one concurrent EEG-fMRI session (Tim-Trio 3T, Siemens, 40 slices, TR = 2. 0 s, 3. 0 × 3. 0 × 3. 0 mm, TE = 50 ms, field of view 192, FA = 78 • ). EEG was simultaneously recorded using an MR-compatible amplifier (BrainAmp MR, sampling rate 5 kHz), 62 electrodes (Easycap), referenced to FCz, 1 ECG electrode, and 1 EOG electrode, while the scanner clock was time locked with the amplifier clock  (Mandelkow et al.