dataesr/scientific-paragraphs-categorization huggingface dataset:
The blades of wind turbines placed in cold climate regions are exposed to the risk of icing phenomena which impact their lifetimes. This paper proposes a numerical model to simulate 50 mm ice thickness localized on the tip side of a horizontal wind turbine blade,and to study its mechanical behavior. The wind turbine blade wasmodeled with the finite element method (FEM)in ABAQUS software taking into account aerodynamic, centrifugal and inertial loads under the conditions of service of the blade. Numerical tests haveevaluated the behavior of different composite materials and compared with each other. Damage mode based on the Hashin criteria was defined. Carbon fibers were considered to be the most rigid material which results in thinner, stiffer and lighter blades.
Boundary Conditions Figure  2  illustrates the boundary condition assigned to the blade using "Encastre" type applied at the root  [16]. Wind turbines are subject to various loads applied to their entirestructure. They are defined as aerodynamic loads that aregenerated by a rotor blade, and operating on it are greatly variable in intensity because of the stochastic character of the wind field. Inertia and gravitational loadsare also taken into account in this study  [17]. The three loads were then simultaneously applied using the ABAQUS software on a horizontal blade Figure  3 , containing ice at the end of the structureas shown in the Figure  4. Table  3  provides some ice layer informationused in this study. In this work, rotor performance analysis of a wind turbines placed on a horizontal position subject to icing event was performed using ABAQUS software.
In general, the Pantanal climate is predominantly hot and humid, with an average annual rainfall of 1,500 mm and a maximum average temperature of 32℃ and a minimum of 20℃  (Alvares et al. , 2014). In this study, we considered the area recognized as the floodplain of the Brazilian Pantanal region (Fig. 1 ) within whose limits the respective records of the occurrence of the various bird species were plotted. It encompasses a perimeter of 150,000 km² exclusively in the Brazilian territory, 4. 6% of which is represented by protected areas including 18 private reserves, 1 national park, 3 state parks, 1 municipal park, 1 environmental protection area, and 1 ecological station  (Brazil, 2020). For the elaboration of our database, 199 locations in the states of Mato Grosso and Mato Grosso do Sul that presented one or more records of avifauna were evaluated.
Elias Coelho (ASEC), from the Federal University of Rio de Janeiro and at the Neotropical Fonoteca Jacques Vielliard (FNJV) of the Zoology Museum of the State University of Campinas. Regarding the photographic and sound records available on digital media, we evaluated those hosted until Mach 2021 in the WikiAves (WikiAves, 2021 -http://www. wikiaves. com), Xeno-canto (Xeno-Canto, 2021 -https://www. xeno-canto/databases. org), and Visual Resources of Ornithology/VIREO (VIREO, 2021 -http://vireo. ansp. org) databases. Records in municipalities that include stretches of plateau (e. g. , Corumbá, Miranda, Aquidauana, Coxim, Rio Verde de Mato Grosso, Cáceres, and Santo Antônio do Leverger) were carefully investigated by confirming their locations with the authors to ensure that only records collected from the floodplain were included in the study.
ACKNOWLEDGMENTS We thank  EMBRAPA Pantanal, WWF-Brazil , the  Pantanal Research Center-CPP ,  CNPq ,  Conservation International of Brazil, EarthWatch Institute , the  Manoel de Barros Foundation , and the  Federal University of Mato Grosso do Sul Foundation  for their financial and logistical support in the research projects and species inventories carried out by the authors in the Pantanal floodplain. To the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) - and the  Fundação de Amparo à Pesquisa do Estado de Mato Grosso (FAPEMAT)  for granting scholarships to AVBF and BDV. We also thank the countless Pantanal farm owners who allowed us to conduct studies on their properties. In particular, we thank the Vitor Piacentini and authors of photographic records available on the WikiAves digital platform for sharing their valuable information on species records.
Participants As part of a larger study  (Moseley et al. , 2021) , data from 647 participants were collected in person at one of 11 data collection sites: Durham University, University of Roehampton, King's College London, University College London, University of Cambridge (all UK), University Paul Val ery (France), University of Groningen (Netherlands), Charles University (Czech Republic), University of Bergen (Norway), University of British Columbia (Canada), and Swinburne University (Australia). Data were also collected for a subset of tasks on 866 participants online, but were not included in the present analysis, because not all tasks of interest (namely, auditory signal detection) were collected online, and the multivariate nature of the current analysis required all subjects to have all measures.
Participants first performed the non-forced task, followed by the forced ones. The order of the forced left and right was counterbalanced across participants. There were 36 trials in each condition, presented in a random order, including 6 homonym trials (with the same syllable presented to each ear). The homonym trials were excluded from data analysis and were used only as a data quality check (see below). The remaining 30 trials consisted of all possible combinations of the 6 syllables presented to each ear. The total number of selected syllable responses matching presentations to the right ear (right ear score, RES) and the left ear (left ear score, LES) were counted for all three conditions ('non-forced', 'forced right' and 'forced left'). A laterality index was calculated for each condition as follows: Laterality Index ¼ [(RES e LES)/(RES þ LES)] *100, and these were submitted to the multivariate analysis.
, 2011) , consisting of a male voice reading a text (taken from an instruction manual) in an emotionally neutral tone. After each burst of noise, participants were presented with the text "Did you hear speech?" and they responded by clicking a mouse button for Yes or No. For each trial, they were also then prompted to enter a confidence rating. Confidence ratings were not analyzed as part of this study. Signal detection measures sensitivity (d 0 ), and response bias (b) were estimated  (Stanislaw & Todorov, 1999). The hit rate, false alarm, sensitivity, and response bias were included in the analysis. Data from 11 participants (out of the 594 in-lab participants) were excluded from the auditory signal detection task data due to scoring a d 0 of 0 (indicating at or below chance performance), or a hit rate of 10%, or both.
Data availability and transparency statement All the data and code necessary to reproduce the results in the paper have been uploaded to a publicly accessible repository (https://osf. io/aeg5d/). The full dataset used in the original study can be found here (https://osf. io/eqy76/). No part of the secondary analysis reported in this paper was preregistered prior to the research being conducted. We report how we determined our sample size, all data exclusions (if any), all inclusion/exclusion criteria, whether inclusion/exclusion criteria were established prior to secondary data analysis, all manipulations, and all measures in the study (see Methods, section on Participants).
Funding This research was supported by the  Wellcome Trust  grant  WT108720  awarded to CF. SLR is supported by a  NHMRC senior research fellowship  ( GNT1154651 ), and WLT is supported by a  NHMRC New Investigator  project grant ( GNT1161609 ). AMC is supported by  Brain Canada , in partnership with  Health Canada , for the  Canadian Open Neuroscience Platform (CONP) initiative. EK and YZ are supported by the  Internal Grant Agency of the Ministry of Health of the Czech Republic  [grant number  AZV 17-32957A ] and by the project " Sustainability for the National Institute of Mental Health ", under grant number  LO1611. JB is supported by grants from  ERC   Advanced Grant  (ERCAdG # 693124 ), from the  Helse-Vest Samarbeidsorganet  (# 912045 ), and from the  Research Council of Norway  (NORMENT# 213363 ).
Gray outline indicates gene-poor chromosomes, while black outline indicates gene-rich chromosome. Statistics, Fisher's Exact Test comparing pairing initiated versus uninitiated. For all quantification, ��� p<0. 0001, �� p<0. 001, � p<0. 01. All data were collected from n = 2-3 testes squashes per chromosome. https://doi. org/10. 1371/journal. pgen. 1009700. g004.
Imaging, quantification, and data analysis Widefield images of meiotic squashes and cytocentrifuge meiotic cells were acquired on a Leica DMi6000 wide-field inverted fluorescence microscope using an HCX PL APO 63x/1. 40-0. 60 Oil objective (Leica Biosystems, Buffalo Grove, IL), Leica DFC9000 sCMOS Monochrome Camera, and LasX software. Whole mount images were acquired using a Zeiss LSM 780 point scanning confocal (Zeiss Microscope Systems, Jena, Germany) with high sensitivity 32 anode Hybrid-GaAsP detectors. BmN4 mitotic spreads were acquired on a Zeiss AxioObserver Z1 wide-field inverted fluorescence microscope with 100x/1. 4 oil Plan-APO objective, a Hamamatsu C13440 ORCA-Flash 4. 0 V3 Digital CMOS camera, and ZEN blue software. Super-resolution imaging of cytospin meiotic spindles and CENP-T immunofluorescence was performed using a Nikon CSU-W1 SoRa spinning disk confocal microscope (Nikon Instruments, Inc, Melville, NY), controlled by Nikon Elements AR software ver 5.
DAPI was excited by the 405 nm laser and emission filtered by Semrock FF02-447/60. Post-processing of the images was performed in Nikon Elements AR software and included denoising via Denoise. ai and 3D deconvolution in automatic mode. Super-resolution imaging of Oligopaints in whole mount ovaries was performed using a Zeiss LSM 880 Airyscan with 63x/1. 4 oil Plan-APO objective. Airyscan images were acquired in SR mode using the Zeiss 32 channel Airyscan detector, and images were processed under auto strength setting with Zen black software. Leica and Zeiss-acquired images were processed using Huygens deconvolution software (SVI, Hilversum, Netherlands). For all images, tiffs were created in ImageJ. All quantification was performed manually. All data are reported in S1 File.
Acknowledgments We would like to thank the members of the  Lei and Drinnenberg labs , as well as  Jamie Walters ,  Petr Nguyen ,  Martina Dalikova ,  Eric Joyce , and  Son Nguyen  for helpful discussion. We would also like to thank  Catherine McManus ,  Jennifer Luppino ,  Stacie Hughes ,  Jean-ReneHuynh , and  Anahi Molla-Herman  for critical reading of the manuscript. Finally, we would like to thank  Xufeng Wu  and  Jeff Reese  in the  NHLBI  and  NIDDK  microscopy cores, respectively, for assistance with super-resolution microscopy.
This work was funded by the  Intramural Program of the National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health (NIDDK ;  DK015602  to E. P. L. ), the  Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (NICHD ;  1K99HD104851  to L. F. R),  LabEx DEEP  grants from the  Agence National de la Recherche  ( ANR-11-LABX-0044  and  ANR-10-IDEX-0001-02  to I. A. D. ), an  ATIP-AVENIR research grant  from  CNRS/INSERM , the  European Research Council  (ERC;  CENEVO-758757  to I. A. D. ), and  Institut Curie (I. A. D). NIDDK  provides salary support for E. P. L. and L. F. R. Institute Curie  and  CNRS  provide salary support for  I. A. D. ERC  provides salary support for J. G. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Gene Set Enrichment Analysis (GSEA) The RNAseq datasets (6h and 12h exposure to H. zoospora) were compared to other C. elegans transcriptomic datasets using Gene Set Enrichment Analysis (GSEA) software v4. 0. 3  (Mootha et al. , 2003; Subramanian et al. , 2005). The genes that were significantly differentially expressed post pathogen exposure (FDR<0. 1) were ranked based on their b value in descending order. We used 65 gene sets for the analysis derived from  (Osman et al. , 2018; Reddy et al. , 2019)  and WormExp [  (Yang et al. , 2016) , which can be found in Table  S2 ]. Pre-ranked analysis with weighted enrichment statistic, 1000 permutations and a minimum of 15 genes overlap, was performed independently for 6h and 12h post-pathogen exposure. The NES-values of gene sets with FDR<0. 25 and nominal p value<0.
Out of these, 424 genes were found to be shared between the two timepoints (Figure  4A ). We reasoned that genes differentially expressed upon H. zoospora exposure may be shared with those responding to M. humicola infection, thereby representing a common oomycete response (COR). Indeed, we found 235 upregulated genes that were in common between the Haptoglossa and Myzocytiopsis infection datasets (Figure 4B and Table  S2 ). Gene ontology (GO) analysis (Angeles-Albores et al. , 2016) of differentially expressed genes showed significant enrichment for terms related to infection, such as "response to biotic stimulus", "defense response" and "immune system process" (Figures  4C  and S4 ). Other highly enriched GO terms were related to signalling upon stress and catabolic processes (Figures  4C  and S4 ).
Next, we compared the transcriptional response to H. zoospora with the response against other known pathogens of C. elegans using gene set enrichment analysis (GSEA)  (Mootha et al. , 2003; Subramanian et al. , 2005). We found that H. zoospora exposure at 6 and 12 hours showed significant intersection (nominal [NOM] p value < 0. 05 and false discovery rate [FDR] < 0. 25) with 25 and 14 out of 50 datasets analysed respectively (Table  S2 ). The most significant intersection was found with datasets derived from infection by M. humicola and N. parisii or the Orsay virus (Figure  4D , Table  S2 ). This is not very surprising in light of the recently reported overlap between the response to M. humicola detection and the induction of IPR upon exposure to microsporidia or impairment of the negative regulator PALS-22  (Bakowski et al.
, 2014; Chen et al. , 2017; Reddy et al. , 2019; Fasseas et al. , 2021). Some less significant overlap observed with other fungal and bacterial infection datasets  (Troemel et al. , 2006; Engelmann et al. , 2011; Yang et al. , 2015)  may be attributable to response to generic tissue damage caused by the growth of Haptoglossa inside the nematode (Figure  4D ). We previously reported that a hallmark of the C. elegans response to M. humicola infection is the induction of chil genes, which occurs upon detection of M. humicola without infection  (Osman et al. , 2018; Fasseas et al. , 2021). Among the identified COR genes, the chil gene family was also well represented (Figure  5A ), pointing to similarities in the host response against oomycetes that are phylogenetically distinct and exhibit distinct infection strategies.
Intrigued by this finding, we sought to determine the overlap between the response to H. zoospora infection and the oomycete recognition response (ORR) recently described upon exposure to an extract prepared from animals infected by M. humicola  (Fasseas et al. , 2021). We found that more than 50% of ORR genes overlapped with the H. zoospora infection datasets (Figure  5B ) and COR genes (Figure  5C  and Table  S2 ). Taken together, we conclude that the response to H. zoospora infection shares similarities with the response against M. humicola infection, and that a large part of this similarity reflects genes that are likely to be induced upon pathogen detection.
Electrotrophic enrichment in a microbial electrochemical system A Microbial Electrochemical System (MES) was filled with 1. 5 L of sterile mineral medium as previously described  [21]  with 0. 1 g/l of Cysteine-HCl and supplemented with 4mM Nitrate, continuously flushed with N2:CO2 (90:10, 100 ml/min) to anaerobic condition and set at 80°C and pH 6. 0 throughout platform monitoring. The working electrode (WE) composed of 20 cm² of carbon cloth was poised at -590mV vs SHE using SP-240 potentiostats and EC-Lab software (BioLogic, France). The WE served as the sole electron donor for electrotrophs development. The system was inoculated with 8 g of the crushed chimney in anaerobic conditions. Current consumption was monitored via the chronoamperometry method with current density and counter-electrode potential measurements taken every 10 s.
5 s, a mixing time (tm) of 100 ms and a t1 of 4 μs. The sequence enables an optimal suppression of the water signal that dominates the spectrum. One hundred twentyeight free induction decays (FID) of 65,536 data points were collected using a spectral width of 12 kHz and an acquisition time of 2. 72 s. For all the spectra, the FIDs were multiplied by an exponential weighting function corresponding to a line broadening of 0. 3 Hz and zerofilled before Fourier transformation. NMR spectra were manually phased using Topspin 3. 5 software (Bruker Biospin Corporation) and automatically baseline corrected and referenced to the TSP signal (δ = -0. 015 ppm) using Chenomx NMR suite (version 7. 5) software (Chenomx Inc. ). A 0. 3 Hz line-broadening apodization was applied prior to spectral analysis and 1 H-1 H TOCSY  [23]  and 1 H- 13 C HSQC  [24]  experiments were recorded on selected samples in order to identify the detected metabolites.
ml -1. Samples observation were performed on a confocal LSM780 microscope (ZEISS, Germany) equipped with an x10, EC PLAN-NEOFLUAR objective. Blue, green and red fluorescence emissions were acquired by excitation at 358, 488 and 561 nm, respectively, using three lasers. Image stacks (at 0. 5-1 µm steps) were acquired with GaAsP photomultiplier tube detectors. Epifluorescence micrographs were processed using the Zen software (Zeiss, Germany).
Quantitative PCR of phylogenetic orders Quantification of Bacteria, Archaea and specific phylogenetic orders retrieved in MiSeq analysis were carried out by qPCR method. SsoAdvanced™ Sybr Green Supermix was used on a CFX96 Real-Time System (C1000 Thermal Cycler, Bio-Rad Laboratories, CA) with the primers specified in table 1. 16S rRNA primers were specifically and manually designed for quantification of most represented orders using MEGA6 Software  [29]  and 16S rRNA reference sequences from Silva database (http://www. arb-silva. de). Newly designed primers were validated in silico on TestPrime software (https://www. arb-silva. de) and on different cultures of species belonging or not to targeted order. The PCR program was composed of a 5min initial denaturation step at 98°C followed by 50 cycles of 10s denaturation step at 98°C, a hybridization step of 20 s at temperature indicated in table 1 and a 40 s elongation step at 72°C, with melting curves generated at the end of each reaction to ensure product specificity.
Moreover, low production of alanine was detected after D11 to reach a maximum (0. 12 mM; D21) and decrease later. H2 production (Fig. 1B ) has appeared strongly related to the variation of current consumption (Fig. 1A ). This production has increased quickly in 24h up to 0. 025 ml. min-1, remained stable for five days and then slowly increased to reach a maximum of 0. 04 ml. min-1 at D19. This production decreased from D21, correlated with the depletion of the pyruvate leading to the decrease of current consumption (-220 mA/m²; D22). Others organic compounds have been produced during this experiment. Peaks of methanol and ethanol were measured, that reached a maximum of 9 mM and 0. 16 mM, respectively at D4 (data not shown). Moreover, slow production of acetamide (maximum of 69 µM at D12), benzoate like molecule (slow increase up to 0.
Acknowledgments This work received financial support from the  CNRS national interdisciplinary research program  ( PEPS-ExoMod 2016 ). The authors thank  Erwan Roussel  ( LM2E ,  IFREMER Brest ) helpful suggestions; Rommevaux and  Françoise Lesongeur  for sampling during the 2014 cruise; the  MIM platform (MIO, France)  for allowing providing access to their confocal microscopy facility; and the  GeT-PlaGe platform (GenoToul, France ) for sequencing. The project leading to this publication has received funding from  European FEDER Fund  under project  1166-39417. The authors declare no conflict of interest.
Real-Time PCR Complementary DNA synthesis was performed on 1 µg of total RNA using the ImProm-II TM reverse transcription system (Promega France, Melun-les-Charbonnières, France) according to the manufacturer's instructions. The resulting product was diluted 10 times in nuclease-free water. Primers were designed to amplify plant gene transcripts (Supplementary Table  2 ), including reference genes Bradi4g00660 (UBC18) and Bradi4g41850 (ACT3-like under accession number XM_003578821 in the nucleotide NCBI database) as previously determined by  Hong et al. (2008) ; (Supplementary Table  2 ). qPCR reactions were performed on 2 µL of the diluted cDNA product using 8 pmoles of each specific primer and 10 µL of SYBRGreen Master Mix in a final volume of 20 µL. Reactions were performed in a Light Cycler LC480 Real-time PCR system (Roche Diagnostics, Meylan, France).
Phylogenetic Analyses Amino acid sequences were recovered from The Cytochrome P450 Homepage  (Nelson, 2009)  or through reciprocal BLAST analysis for the Hordeum vulgare protein sequences. Phylogenetic analyses were inferred from a multiple sequence alignment generated by CLUSTAL W and by using the Maximum Likelihood method based on the JTT matrix-based model, conducted on MEGAX software  (Kumar et al. , 2018).
RESULTS The Bradi1g75310 Gene Belongs to an Oligogenic Family Homologous to the Arabidopsis MAX1 Gene and Is Specifically Induced Under F. graminearum Infection in a DON-Dependent Manner A previous study has identified Bradi1g75310 as a gene induced by a DON-producing F. graminearum [FgDON + , 6. 93 fold log 2 compared to the mock condition 96 hours post inoculation (hpi)] while it does not respond to a mutant strain unable to produce the mycotoxin [FgDON -, 0. 77 fold log 2 compared to the mock condition (96 hpi);  Pasquet et al. , 2014]. In order to validate these transcriptomic data, reverse transcription-quantitative PCR (RT-qPCR) was conducted on the same RNA samples as those used for microarrays. Results showed good correlation between microarrays and RT-qPCR data as the Bradi1g75310 gene was strongly induced by the FgDON + strain (15.
The nucleotide sequence is constituted of 5 exons and 4 introns and the 1,572-bp coding sequence encodes a putative 523 amino acid CYP (Supplementary Figure  1 ), which has been classified as belonging to the CYP711A subfamily and named BdCYP711A29  (Nelson, 2009 ). 2 This notation will be therefore used throughout this work to refer both to the protein (BdCYP711A29) and the gene (BdCYP711A29). According to this database, BdCYP711A29 belongs to an oligomeric subfamily comprising 4 other members: BdCYP711A5 (Bradi3g08360), BdCYP711A6 (Bradi1g37730), BdCYP711A30 (Bradi4g08970), and BdCYP711A31 (Bradi4g09040). Sequences from A. thaliana and Oryza sativa CYP711A proteins (1 and 5 members, respectively) were used together with B. distachyon CYP711A protein sequences to perform multiple protein sequence alignment of the full-length proteins and phylogenetic analysis.