dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. Pedro Colombo  was supported by  UGA  though an assistantship from the  Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation , and is now supported by  CNRS  through a grant from  CMEMS. Bernard Barnier ,  Jean-Marc Molines ,  Thierry Penduff ,  Julien Le Sommer , and  Anne-Marie Treguier  are supported by  CNRS. Jérôme Chanut  is supported by  Mercator Ocean International. Research leading to these results benefited from support provided by the  LEFE/GMMC programme of INSU , the  PHC  Kolmogorov no. 38102RF , and the project  14. W03. 31. 0006  of the  Russian Ministry of Science and Higher Education  ( Bernard Barnier ,  Sergey Gulev , and  Polina Verezemskaya ). This work also benefited from many interactions with the  DRAKKAR International Research Network (IRN)  established between  CNRS ,  NOCS ,  GEOMAR , and IFRE-MER.
P. Colombo et al. :  Denmark Strait overflow in a z-coordinate eddying configuration of NEMO (v3. 6) Code and data availability. The code of the model corresponds to revision 6355 of NEMO v3. 6 STABLE (see  Madec et al. , 2016 , for more information) under the CeCILL licence. It can be downloaded from https://doi. org/10. 5281/zenodo. 3568221 (Colombo, 2019a). The namelists and the post-processing scripts can also be downloaded from the same link. The data used to initialize and perform the simulations can be downloaded from https://doi. org/10. 5281/zenodo. 3568244 (1/12 and 1/36 • horizontal resolution simulations) (Colombo, 2019b)  and https://doi. org/10. 5281/zenodo. 3568283 (1/60 • horizontal resolution simulations) (Colombo, 2019b). Model outputs and diagnostics are available upon request.
Results Endogenous and exogenous attention improve performance. Participants performed a 2-AFC orientation-discrimination task under two attentional conditions (exogenous or endogenous attention), when the cue was presented either before (pre-cue) or after (post-cue) the grating stimuli (see Methods), while their brain activity was measured with fMRI (Fig. 1 ). In each condition, we calculated performance accuracy (d′), as the main dependent variable, for each participant separately (Fig. 1B , top row). A three-way repeated measures 2 × 2 × 2 ANOVA (exogenous/endogenous × valid/invalid × pre/post-cue) revealed higher performance for valid than invalid cues (F(1,4) = 23. 6, Table  1. fMRI studies comparing endogenous and exogenous attention independently in human participants. For each study, we report the cueing manipulation for each attention condition (SOA: Stimulus Onset Asynchrony is the duration of the cue + interval before onset of the target), the task performed by the participants, the dependent variable reported in the publication, whether the analysis was based on groupaveraging statistics or ROI-based on single-participants, the cortical areas reported in the publications and showing significant activation due to cue and/or target, and whether or not eye data were monitored in the scanner or used for the subsequent fMRI analysis.
Stimuli. Stimuli were generated on a Macintosh computer using the MGL toolbox  131  in MATLAB (Math-Works). Stimuli were presented on a flat-panel display (NEC, LC-XG250 MultiSync LCD 2110; refresh rate: 60 Hz; resolution: 1024 × 768 pixels) positioned at the rear of the scanner bore and housed in a Faraday box with an electrically conductive glass front. The display, calibrated and gamma corrected using a linearized lookup table, was at a viewing distance of 172 cm from the participant, and visible through an angled mirror attached to the head coil. A central, white fixation cross (0. 3°) was presented throughout the experiment. The two stimuli were two 4-cpd gratings windowed by raised cosines (3° of diameter; 7% contrast), one in each bottom quadrant (5° horizontal eccentricity; -2. 65° altitude; 5.
To minimize participants' head movements, padding was used. For each participant, three high-resolution anatomic images were acquired in one scanning session, using a T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) sequence (FOV = 256 × 256 mm; 176 sagittal slices; 1 × 1 × 1 mm voxels), and were co-registered and averaged. Using FreeSurfer (public domain software; http://surfe r. nmr. mgh. harva rd. edu), the gray matter was segmented from these averaged anatomical volumes. All subsequent analyses were constrained only to voxels that intersected gray matter. T2*-weighted echo-planar imaging sequence (i. e. functional images; TR = 1750 ms; TE = 30 ms; flip angle = 90°) measured blood oxygen level-dependent (BOLD) changes in image intensity  139. In each volume, 28 slices covered the occipital and posterior parietal lobes and were oriented 45° to the calcarine sulcus (FOV = 192 × 192 mm; resolution = 2 × 2 × 2.
5 mm; no gap). To align functional images from different sessions to the same high-resolution anatomical volume for each participant, we acquired an additional T1-weighted anatomical images in the same slices as the functional images (spin echo; TR = 600 ms; TE = 9. 1 ms; flip angle = 90°; resolution = 1. 5 × 1. 5 × 3 mm) during each scanning session, and used them in a robust image registration algorithm. MRI data pre-processing. Imaging data were analyzed in MATLAB, using mrTools  140  and custom software. To allow longitudinal magnetization to reach steady state, the first eight volumes of each run were discarded. Spatial distortion was corrected using the B0 static magnetic field measurements performed in each session. The functional data were then motion corrected, the linear trend was removed, and a temporal high-pass filter was applied (cutoff: 0.
Acknowledgements This work was supported by:  NIH   RO1-EY019693  to MC and DJH;  NIH   RO1-EY027401  to MC; the  FYSSEN Foundation  and the  Philippe Foundation  to LD; and the  Center for Brain Imaging of New York University. We want to thank members of the  Carrasco lab , especially  Rachel Denison  and  Michael Jigo , as well as  Stephanie Badde  and  Jonathan Winawer  for constructive comments on the manuscript.
The holotype of S. longisporum is from Tunisia  (Patouillard 1894)  and the type of S. perlongisporum is from La Réunion Island. Both species have been recorded from several continents as well as from islands in the Caribbean, Indian Ocean and the Pacific. The holotype of S. cochleum is from Saint Lucia Island in the Caribbean and outside that region was only reported from Hawaii  (Martini 2020). Almost all records of these species are based on the observation of fruiting bodies and few DNA sequences are available. In this study, we use molecular data to answer (1) whether S. cochleum, S. longisporum and S. perlongisporum can be considered as distinct and monophyletic species and (2) whether the similarity in spore shape and size is reflected by the phylogenetic position. For that, we explore the variation and relationship of DNA sequences of the nuclear ribosomal ITS and 28S region from geographically scattered herbarium specimens.
Microscopy In this study, we examined 49 herbarium specimens: 37 identified as Subulicystidium perlongisporum, seven as S. cochleum and five as S. longisporum. They are preserved in the following herbaria: ARAN, FR, GB, KAS, KR, MSK, LE, LY, MG, O, PC, SP, TU and TUB (acronyms, follow http://sweetgum. nybg. org/science/ih). Detailed information on the studied collections is provided in Supplementary file 1. Sections from dried herbarium specimens were examined in 3% aqueous solution of potassium hydroxide (KOH) mixed with 1% aqueous solution of Phloxine at × 1000 magnification, using a Leica DM500 light microscope. Images were captured with a built-in ICC 50 HD Camera using Leica Application Suite EZ software versions 3. 2. 1 and 3. 4. 2 (Leica Microsystems GmbH, Wetzlar, Germany). M e a s u r e m e n t s w e r e d o n e w i t h t h e s o f t w a r e "Makroaufmaßprogramm" from Jens Rüdigs (Rüdig 2019, https://ruedig.
de/tmp/messprogramm. htm) and processed with the software "Smaff" v. 3. 2  (Wilk 2012). Where possible at least 30 basidiospores per specimen were measured for all 36 sequenced specimens (28 specimens of S. perlongisporum, 5 of S. longisporum and 3 of S. cochleum). For each specimen, an automated search for size outliers, and their exclusion was performed as described in  Ordynets et al. (2018). This resulted in 1203 measurements after excluding outliers (see Supplementary file 2). Spore sizes were divided into a 90% main range, and 5% lowest and highest values as described in  Ordynets and Denecke (2018) , by means of the package "dplyr"  (Wickham et al. 2019 ) in R v. 3. 5. 3 (R Core Team 2019). During the measurements of cystidia, the protruding bowtie crystals were included in the cystidium diameter.
In most cases, between 9 and 28 cystidia per specimen were measured (in two cases nine and in one case 38). In total 645 cystidia were measured from 36 sequenced specimens (see Supplementary file 2). These measurements were not checked for outliers and were directly summarized on specimen level with the same method as used for the spores  (Ordynets and Denecke 2018). To compare the mean size and shape of spores and cystidia between phylogenetically defined groups, the unpaired two-sample Wilcoxon test was used as implemented in the function "wilcox. test", with default settings, from the R "stats" package (R Core Team 2019).
perlongisporum, three for S. cochleum and three for S. longisporum. For the nc 28S rDNA, the numbers of sequences generated for these same species were 19, 3 and 2, respectively. The newly generated DNA sequences were submitted to GenBank  (Benson et al. 2018). Additional six ITS and four 28S sequences of S. perlongisporum and two ITS and three 28S sequences of S. longisporum available in GenBank (e. g. Volobuev 2016 ) and UNITE database  (Nilsson et al. 2018)  were used in our analyses after the corresponding herbarium specimens were examined (Table  1 ). To relate the DNA sequences of S. perlongisporum, S. cochleum and S. longisporum to the rest of the genus Subulicystidium, we combined our data for these three species with the datasets of 57 ITS and 55 28S DNA sequences of other Subulicystidium species (Table  2 ).
We used ITS and 28S DNA sequences from the holotype of S. oberwinkleri (KAS:L 1860) as an outgroup in all genus-level phylogenetic analyses. Sequences from each locus, 92 of ITS region and 86 of 28S region, were aligned in MAFFT v. 7 online  (Katoh et al. 2017) , with L-INS-i algorithm and other settings as default. The small fragments of 18S rDNA and 28S rDNA were automatically trimmed from the target ITS region with the ITSx software  (Bengtsson-Palme et al. 2013 ) implemented in the PlutoF workbench  (Abarenkov et al. 2010 ). The final ITS alignment had 659 nucleotide positions. The 28S alignment was trimmed manually to produce sequences of the same length and with fewer (if any) gaps at both ends, leaving 911 positions in the final version. ITS and 28S alignments were concatenated with the SequenceMatrix v.
BioNJ was used as a starting tree. The algorithm to explore the space of tree topologies was subtree pruning and regrafting (SPR). For branch support estimation, an approximate likelihood ratio test was requested that relies on a nonparametric, Shimodaira-Hasegawa-like procedure (SH-like aLRT,  Guindon et al. 2010). To visualize results for the genus-level concatenated ITS+ 28S dataset, the consensus tree from Bayesian analysis was plotted with both posterior probabilities from Bayesian analysis and SH-like aLRT support values from PhyML. For this, we used R script of Ordynets which is based on the R code from  Crane (2013). In the most abundant species, S. perlongisporum, we analyzed also the intraspecific genetic diversity. We produced a species-level ITS alignment with 27 sequences that after trimming with ITSx was 554 positions long.
We calculated pairwise genetic distances between these ITS sequences using "pairDistPlot" function of "adegenet" R package  (Jombart 2008)  setting the arguments as follows: model = "raw", pairwise. deletion = TRUE (i. e. with deleting the sites with missing data in a pairwise way). Phylogenetic inference for this dataset was performed in PhyML v. 3. 0 online with settings identical to those in the analysis of concatenated genuslevel dataset but with HKY85+G as the best model. The result was visualized with the R packages "ggtree"  (Yu et al. 2017) , "ggstance"  (Henry et al. 2019)  and "ggplot2"  (Wickham 2009). The multiple sequence alignments, details of phylogenetic analyses and trees were deposited in TreeBASE  (Piel et al. 2009)  under submission ID S24881. The R code for the DNA-based analyses and visualizations is available on GitHub (https://github.
Phylogenetic analyses The genus-level phylogenetic analyses based on the ITS+28S dataset showed that each of the species Subulicystidium cochleum, S. longisporum and S. perlongisporum is monophyletic (Fig. 1 ). The three species were not closely related to each other. S. cochleum (branch support PP = 1, aLRT = 0. 92) was placed as a sister species to S. acerosum on a highly supported branch (PP = 1, aLRT = 1). S. longisporum (PP = 1, aLRT = 0. 95) was nested in the clade dominated by numerous sequences of S. meridense and S. brachysporum (PP = 0. 94, aLRT = 0. 87). S. perlongisporum (PP = 1, aLRT = 0. 98) was most closely related to S. robustius and S. rarocrystallinum (occupying the next more basal node) and to S. boidinii, S. harpagum, S. parvisporum and S. tropicum and to the specimen of S.
Discussion In this study, we assessed the range of molecular (nc rDNA) and morphological variation in the long-spored species of Subulicystidium: S. cochleum, S. longisporum and S. perlongisporum. We found that each of these species was monophyletic. However, none of them was sister to any of the other. For S. cochleum and S. perlongisporum, we confirmed a transoceanic distribution pattern. We included the holotype of S. perlongisporum in our study (LY11631) and successfully obtained ITS and 28S DNA sequences from this 35-year-old specimen. We generated first DNA sequence data for the species S. cochleum. We successfully sequenced collections made in Costa Rica, Jamaica and Madagascar, but failed to obtain sequences from those made in Réunion and Zimbabwe. We provided additional morphometric and genetic data for S.
DNA sequences of Subulicystidium species besides S. perlongisporum, S. longisporum and S. used in this study with information on voucher specimens and publication source. Abbreviation "na" means sequence is not available Phylogenetic relationship of Subulicystidium based on concatenated ITS+28S nc rDNA alignment. 50% majority-rule consensus tree from Bayesian analysis is shown, with posterior probabilities above the branches and supports from approximated likelihood ratio test from the maximum likelihood estimation below the branches, both in the range from 0 to 1. NA means the absence of branch support. Tips of the tree include GenBank/UNITE accession numbers for the ITS region followed by 28S region, voucher specimen and country of collection. Tree tips are coloured according to the species name.
Acknowledgements We would like to thank the curators of herbaria and mycologists who provided loan specimens for our study:  Ibai Olariaga  (ARAN),  Markus Scholler  (KR),  Mélanie Thiébaut  (LY),  Irja Saar  (TU),  Cornelia Dilger-Endrulat  and  Franz Oberwinkler  (TUB). Ulrike Frieling  and  Sylvia Heinemann  ( University of Kassel ) and  Rasmus Puusepp  ( University of Tartu ) are deeply acknowledged for the assistance with the molecular laboratory work. We thank the reviewers and editors for their comments that helped to improve the manuscript.
Introduction The filamentous fungi belonging to Trichoderma genus have been considered with interest for their use as enzymatic producer for biocontrol strategies  [1]. The antagonistic actions exerted by Trichoderma against fungal phytopathogens involve indirect mechanisms, such as competition for nutrients or space, modification of environmental conditions, induction of plant defensive properties and antibiosis, or directly mechanisms encompassing mycoparasitism  [2, 3]. The production and secretion by Trichoderma of Cell-Wall Degrading Enzymes (CWDEs), among which chitinases  [4, 5] , glucanases  [6] [7] [8]  and proteases  [9] [10] [11]  are the most representative, playing a major and significant role in targeted host invasion  [12, 13]. Chitinases are polysaccharide hydrolases categorized into glycoside hydrolase (GH) 18, 19, 20, 23 and 48 families of the CAZy database (http://www.
Plasmids Constructions and Pichia Pastoris Transformation Plasmid constructions related to the expression of single protein (TrGL or CHIT42) and fusion proteins (TrGL-CHIT42) were carried out in pPICZαA plasmid (Invitrogen®). Three chimeric TrGL-ThCHIT42 constructions were carried out. The first one, called pPICZαA/TrGL-Thchit42, consisted of TrGL directly fused with the N terminus of ThCHIT42. The two others had a linker sequence inserted between the enzyme coding DNA sequences, (GGGGS) 1 for pPICZαA/TrGL-sp1-Thchit42 and (GGGGS) 2 for pPICZαA/TrGL-sp2-Thchit42. The genes encoding TrGL (GenBank: AB440264. 1) and ThCHIT42 (GenBank: AAA18167. 1) were, respectively, obtained by PCR amplification, using Pfu DNA polymerase (Promega TM ), from Trichoderma reseei genomic DNA and Puc57Opt-ThChit42 plasmid, containing codon optimized Chit42 gene for Pichia pastoris expression (synthesized by Proteogenix Inc.
3D Structure Simulation of Chimeric Enzyme The prediction of the 3D structures of the parental and fusion enzymes was performed by comparative modeling. The structures of parental enzymes were determined by SWISS Model (https://swissmodel. expasy. org/), the template selected for homology modeling of ThCHIT42 is Endochitinase 42 from Trichoderma harzarium ( PDB accession number 6epb. 1. A) and the one selected for the modeling of TrGL is Glucuronan Lyase A from Trichoderma reesei (PDB accession number 2zzj. 1. A). In order to merge the ThCHIT42 and TrGL models of the chimeric proteins, the MultiDomain Assembler (MDA) approach was used by UCFC Chimera software and its computational web services (http://www. rbvi. ucsf. edu/chimera/). The molecular figures were visualized by PyMol (https://pymol.
Expression of Fused TrGL-Thchit42 Constructions in Pichia Pastoris Three chimeric (TrGL-ThCHIT42, TrGL-sp1-ThCHIT42 and TrGL-sp2-ThCHIT42) and two parental (TrGL and ThCHIT42) constructions (Figure  2a ) were carried out in pPICZαA expression vector, and were expressed in P. pastoris KM71H. After 72 h methanol induction, extracellular proteins secreted in the supernatant was analyzed by SDS-PAGE and the results showed a single band both for parental and chimeric recombinants (data not shown). The quantification of extracellular protein concentration showed production yields of 96 and 136 mg/mL for, respectively, TrGL and ThCHIT42, and 60, 38 and 40 mg/mL, respectively, for TrGL-Chit42, TrGL-sp1-ThCHIT42 and TrGL-sp2-ThCHIT42. Secreted His-tagged recombinant proteins were purified using Ni-affinity chromatography (IMAC) and analyzed by SDS-PAGE.
The result showed a single protein band, slightly lower than 75 kDa for the fusion enzymes which is consistent with the expected size ranging from 72. 95 to 73. 58 kDa according to the fusion enzymes, and proteins closed to 45 and 30 kDa, respectively, for Histidine-tagged parental proteins ThCHIT42 and TrGL (Figure  2b ). Life 2020, 10, x FOR PEER REVIEW 6 of 15 proteins secreted in the supernatant was analyzed by SDS-PAGE and the results showed a single band both for parental and chimeric recombinants (data not shown). The quantification of extracellular protein concentration showed production yields of 96 and 136 mg/mL for, respectively, TrGL and ThCHIT42, and 60, 38 and 40 mg/mL, respectively, for TrGL-Chit42, TrGL-sp1-ThCHIT42 and TrGL-sp2-ThCHIT42. Secreted His-tagged recombinant proteins were purified using Ni-affinity chromatography (IMAC) and analyzed by SDS-PAGE.
The molecular mechanisms underlying caudal-to-rostral progression of Lewy body pathology in Parkinson's disease remain poorly understood. Here, we identified transcriptomic signatures across brain regions involved in Braak Lewy body stages in non-neurological adults from the Allen Human Brain Atlas. Among the genes that are indicative of regional vulnerability, we found known genetic risk factors for Parkinson's disease: SCARB2, ELOVL7, SH3GL2, SNCA, BAP1, and ZNF184. Results were confirmed in two datasets of nonneurological subjects, while in two datasets of Parkinson's disease patients we found altered expression patterns. Co-expression analysis across vulnerable regions identified a module enriched for genes associated with dopamine synthesis and microglia, and another module related to the immune system, blood-oxygen transport, and endothelial cells.
This combined gene-MRI analysis illustrates the importance of local gene expression changes on functional brain networks. More detailed knowledge about the spatial organization of transcriptomic changes in physiological and pathological conditions may aid in understanding these changes on a functional level during disease progression in PD. In the present study, we analyzed the transcriptome of brain regions involved in Braak LB stages  18  of non-neurological adult donors from the AHBA to reveal molecular factors underlying selective vulnerability to LB pathology during PD progression. We validated our findings in two independent non-neurological datasets (the Genotype-Tissue Expression project (GTEx)  19  and UK Brain Expression Consortium (UKBEC)  20  ). Further, we showed that Braak stage-related genes (BRGs) are indeed progressively disrupted in patients with incidental Lewy body disease (iLBD; assumed to represent the pre-clinical stage of PD  11, 21  ) and PD.
Results Study overview. The PD Braak staging scheme defines a temporal order of brain regions affected during the progression of the disease  18. Based on the sequence of events as postulated by Braak et al. 1  , we hypothesized that genes whose expression patterns increase or decrease across regions involved in the Braak staging scheme might contribute to higher vulnerability to LBs in PD brains (Fig. 1 ). Based on this assumption, we aimed to find (1) which genes are involved, (2) which modules of interacting genes are involved, and (3) which biological processes contribute to this vulnerability. We analyzed the regions of interest using a microarray dataset of anatomical brain regions from six individuals without any known neuropsychiatric or neurological background from the AHBA  15. Therefore, we first assigned 2334 out of 3702 brain samples to Braak stage-related regions R1-R6  18  : myelencephalon (medulla, R1), pontine tegmentum including locus coeruleus (R2), substantia nigra, basal nucleus of Meynert, CA2 of hippocampus (R3), amygdala, occipito-temporal gyrus (R4), cingulate gyrus, temporal lobe (R5), frontal lobe including the olfactory area, and parietal lobe (R6) (Supplementary Table  1 , and Supplementary Fig.
05, DAVID; Supplementary Data 3). Since the expression patterns of the 960 BRGs were observed in only six non-neurological brains from the AHBA, we used two independent datasets from non-neurological controls for validation. For each dataset we assessed whether BRGs were also differentially expressed between regions related to the most distant Braak stages, and whether the decreasing or increasing expression patterns could be replicated. First, using microarray data from 134 individuals in the UKBEC  20  , we selected brain samples corresponding to the myelencephalon (R1), substantia nigra (R3), temporal cortex (R5), and frontal cortex (R6). For the 885 BRGs present in UKBEC, 139 out of 314 (44. 3%) negatively correlated BRGs and 400 out of 571 (70. 1%) positively correlated BRGs were differentially expressed between R1 and R6 (|FC R1-R6 | > 1, BHcorrected P < 0.