dataesr/scientific-paragraphs-categorization huggingface dataset:
The fMRI IC weights of the component are in line with previous findings extracting VIS-SM independent components from different fMRI datasets  (Amico et al. , 2017; Contreras et al. , 2017). On the other hand, the γ-banddriven electrophysiological FC VIS-FS component is in line with the results of  Wirsich et al. (2017)  who observed a global contribution of delta but a contribution of gamma to predicting diffusion MRI connectivity specifically in visual areas. The results reported here are consistent with this pattern, as the VIS-FS component is mainly driven by gamma oscillations, whereas delta does not contribute to this component and only contributes to the global pattern of the ICN-FG component (see Figure  4A ). The dominance of the gamma band in this component tied with an occipitally focused fMRI counterpart may be surprising in light of the well-known predominance of alpha power in occipitoparietal cortex.
, 2016). In short, a partial preference of EEG and fMRI for nonoverlapping classes of pathways could explain how a spatially divergent connectivity pattern could be tied across the two modalities in the VIS-FS component. To summarize, the observation of two robust bimodal components, one with convergent and one with divergent topography across EEG and fMRI (ICN-FG and VIS-FS, respectively), would support the view that the two data modalities capture partly overlapping and partly independent neural activity. From a systems neuroscience point of view, the concurrent convergence and divergence of multimodal connectivity patterns reflect recent findings of computational modeling of scalefree dynamics. In line with the convergent aspect, a large body of literature is showing that computational models of FC generated from structure show a common pattern across several timescales  (Deco et al.
Methodological Considerations, Limitations, and Future Directions Like in any data-driven approach (and in rsfMRI in general) preprocessing steps remain arbitrary. In particular, here the parameters of PCA-based variance reduction and number of final ICs can slightly change the results. We tried to avoid this parameter-dependence by fitting the optimal parameter to the maximum correlation between two independent datasets. Nevertheless, the analysis design still remains flexible and parameter sets most likely also depend on SNR of any given dataset (recording length, field strength of the MR scanner). Also, the selection criteria for stable ICAs defined by  (Amico et al. , 2017)  remain arbitrary, and further work should explore the parameters of the stability criteria or use other approaches to define ICA stability (e.
g. , an adequate null model). Another important parameter might be the number of subjects used in the approach. The here-used generalization dataset had only 14 subjects with a resting-state acquisition of 20 min (as compared to the 26 subjects of the main dataset with each 30-min total of resting-state acquisition). Note that this relatively small number of subjects might result in an overfitting of the ICA weights to overly general features ignoring more detailed features. As shown in Figure  3  (and Supporting Information Figures  S1  and S2 ), the ICN-FG component is detectable for more parameter configurations in the main dataset than in the generalization dataset (13 vs. 7 configurations). This suggests that it is easier to extract stable ICs when more data points/subjects are available.
Note, however, that this was not true for the VIS-FS component (we found five configurations for the main dataset, whereas the generalization dataset had nine stable configurations). It remains unclear if increasing the sample size will help to solve this variability of the ICA approach (see  Amico & Goñi, 2018a, and Supporting Information). These constraints notwithstanding, the bimodal connICA approach provides novel information complementing those obtained from other methodologies. Deligianni et al. (2014)  predicted EEG from fMRI and vice versa using canonical correlation analysis. Another approach would be to link multimodal connectomes by using partial least squares  (Mišić et al. , 2016). As compared to those correlational approaches, the choice of using ICA follows the general assumption that static connectivity holds independent mixed connectivity signals  (Amico et al.
Results: In this paper, we start by presenting rules to compute a genomic relationship matrix (GRM) for the X-chromosome (G X ) without making any assumption on dosage compensation, and based on coding of gene content with 0/1 for males and 0/1/2 for females. This coding adjusts naturally to previously derived pedigree-based relationships (S) for the X-chromosome. When needed, we propose to accommodate and estimate dosage compensation and genetic heterogeneity across sexes via multiple trait models. Using a Holstein dairy cattle dataset, including males and females, we then empirically illustrate that realized relationships (G X ) matches expectations (S). However, G X presents high deviations from S. G X has also a lower dimensionality compared to the autosomal GRM. In particular, individuals are frequently identical along the entire chromosome.
Note that if a different coding of gene content is applied, similar applications can be performed by rescaling S. Indeed, if {0,1,2} coding is chosen for females and any coding {0,k} is chosen for males to construct a particular G X , then S can be rescaled appropriately by multi- plying the (male, male) part by k 2 and the (male, female) and (female, male) parts by k. Rescaling S might be more convenient than recoding genotypes in already existing databases. H X = S 11 -S 12 S -1 22 S 21 + S 12 S -1 22 G X S -1 22 S 21 S 12 S -1 22 G X G X S -1 22 S 21 G X , H X -1 = S -1 + 0 0 0 G -1 X -S -1.
Empirical comparison of pedigree-based and genomic relationships for markers on the X-chromosome Data The dataset used in our study consisted in a sample of 6085 French Holstein individuals genotyped with the BovineSNP50 or the BovineHD genotyping arrays (Illumina, San Diego, CA). These 637 sires and 5448 dams corresponded to the French Holstein parents that have a phenotype for global recombination rate in the study by Kadri et al. [32]. For the autosomes, we conserved the 30,127 markers selected by Kadri et al. [32]  after discarding monomorphic markers or those with a low call rate (lower than 0. 95), markers that deviated from Hardy-Weinberg proportions, that had more than 10 Mendelian inconsistences or were associated with putative map errors. Similar filtering rules were applied to a set of 853 SNPs mapping to the X-chromosome in the ARS-UCD1.
Heritability of gene content Forneris et al. [19]  proposed to estimate the heritability of gene content to perform quality control of genotypes. Here, we estimated heritability of gene content for the 744 markers on the X-chromosome (X-specific part) by using either the autosomal additive relationship matrix A , or the X-chromosome relationship matrix S. In both cases, the relationship matrices were computed from pedigree data. The gene content was equal to the number of copies of the reference alleles (ranging from 0 to 2 in females and 0 to 1 in males). Variance components were estimated with the AI-REML algorithm implemented in the blupf90 package  [36]. Comparisons allowed to check which relationship matrix ( S or A ) best fits the data, and at the same time, whether covariances of gene content at a locus are correctly described by S as expected (i.
Pedigree-based and realized relationships in the French Holstein cattle data Expected (pedigree-based) and realized (marker-based) additive genetic relationships between the 6085 French Holstein individuals were estimated. The estimated correlations between polygenic effects for selected categories of individuals are in Table  2 , for the X-chromosome, for the 29 autosomes jointly, and for chromosome 2. We observe that the genomic relationships obtained with genetic markers fit the pedigree-based expectations (genomic relationships are a bit lower because observed, not base, allele frequencies were used), although with some variability. The realized genomic relationships were clearly closer to the expected relationships derived for the X-chromosome with the rules from Fernando and Grossman  [16]  than to the expectations derived for the autosomes.
Acknowledgements We thank two anonymous reviewers for their helpful comments and suggestions. The authors gratefully acknowledge  Didier Boichard  ( INRAE, France ) and  Sébastien Fritz  ( Allice, France ) for providing the material used in this study. This work was part of the  LoCO motifs research project  (Grant  T. 0080. 20 )funded by the  Fonds de la Recherche Scientifique-FNRS (F. R. S. -FNRS ). This project has received funding from the  European Unions '  Horizon 2020 Research & Innovation program  under grant agreement  N°772787  -SMARTER. It was also partly supported by the  Toulouse Midi-Pyrénées bioinformatics platform. Tom Druet is Senior Research Associate from the  F. R. S. -FNRS. We used the supercomputing facilities of the " Consortium d'Equipements en Calcul Intensif en Fédération Wallonie-Bruxelles " (CECI), funded by the  F.
Funding This work was funded by the  Fonds de la Recherche Scientifique-FNRS (F. R. S. -FNRS)  under Grant  T. 0080. 20  (" LoCO motifs" research project ), by the  European Unions '  Horizon 2020 Research & Innovation program  under grant agreement  N°772787 -SMARTER  and by the  Toulouse Midi-Pyrénées bioinformatics platform.
3c. Meanwhile, mobilities of those tested ML-FinFETs devices are extracted to be in the range of 1-6 cm 2 V À1 s À1 , as shown in the statistics in Fig. 3d. Optimizing the material growth conditions to improve the intrinsic mobility of the material is our next plan. At this stage, electrical performances such as SS and mobility of the ML-FinFET can be further improved to meet the criteria for future application. Nevertheless, modelings of a ML-FinFET with 4nm gate length and 0. 65 nm W fin were carried out to address the performance of such ideal cases, using the finite element software COMSOL Multiphysics model (Methods). As shown in Fig. 4a , the model device was built with three parts, i. e. , channel, source, and drain regions, where the gate area was separated with 2 nm dielectric layer of HfO 2.
Acknowledgements This work is supported by the  National Key R&D Program of China  ( 2019YFA0307800 ,  2017YFA0206302 , and  2016YFB0401104 ) and the  National Natural Science Foundation of China (NSFC)  with Grants  11974357 ,  U1932151 , and  51627801. D. -M. Sun  acknowledges the  Strategic Priority Research Program  of  Chinese Academy of Sciences  (Grant No. XDB30000000 ),  Key Research Program of Frontier Sciences CAS  Grant No. ZDBS-LY-JSC027 , and supports from  NSFC  with Grants  51272256 ,  61422406 , and  61574143. S. Liu  acknowledges supports from  NSFC  ( 21705036  and  21975067 ) and  Natural Science Foundation of Hunan Province, China  ( 2018JJ3035 ). S. Qiu.
FUNDING The Almeida lab has been supported by  FCT-JPCOFUND/0004/2015 ; Alzheimer's  Association Research Grant  ( AARG-19-618007 );  Maratona da Saúde ;  H2020   Spreading Excellence and Widening Participation ,  H2020-WIDESPREAD-01-2016-2017-TeamingPhase2-GA739572 ;  iNOVA4Health  ( UID/Multi/04462/2019 ), a program financially supported by  Fundação para a Ciência e Tecnologia (FCT)/Ministério da Educação e Ciência , through national funds and co-funded by  FEDER  under the  PT2020 Partnership Agreement. CG's salary is supported by  FCT-CEECIND/00410/2017. FM has been the recipient of an  FCT doctoral fellowship  ( PD/BD/128344/2017 ). CP has been the recipient of an  FCT doctoral fellowship  ( SFRH/BD/128374/2017 ).
The taxonomic status and nomenclature of these taxa is reevaluated to determine the adequate name usage through the application of the International Code of Nomenclature for algae, fungi, and plants  (Turland et al. 2018). Vegetative and floral structures of living plants were photographed with a Nikon® 7100 and Nikon® 810 camera using a AF-S VR Micro-NIKKOR 105 mm f/2. 8G IF-ED lens. Those photographs were used to prepare Lankester Composite Dissection Plates (LCDP) using Adobe Photoshop®. Plates include habit, flower, dissected perianth, column, anther cap and pollinarium, as well as other taxonomically informative characters depending on the taxa illustrated. LCDPs are prepared in such a way that they are easily comparable among the diverse taxa. All photographs are by the authors except when indicated otherwise.
We are unable at this time to be sure and have indicated that uncertainty with yellow (wild) and red (cultivated or uncertain) points in the map. Further studies incorporating distribution and frequency data, as well as ecological requirements, will be needed to assess the conservation priorities among these Vanilla species. The Global Biodiversity Information Facility (GBIF) is one of the most important sources of raw data for accessing species occurrence records which are used to assess distributions in studies that go from inferring the impact of climate change and spread of invasive species, to determining priorities for conservation, food security and human health. The database relies on the correct determination of the specimen vouchers from which it feeds. Despite their best curation efforts specimens are not always correctly identified and most users will not or cannot double check them.
Luckily digital vouchers are available in the GBIF gallery for some of the specimens in the database. A careful inspection of the vouchers for the ten species discussed in this paper reveals that all of them include incorrectly determined specimens, even when disregarding synonymies. In the case of V. hartii, V. odorata, V. phaeantha and V. pompona, the false positives represent up to 15% of the digitally available vouchers for those species. But in the case of V. inodora and V. mexicana incorrectly determined vouchers represent about 50% of all those available. What could be consequences for those studies on non-taxonomical if these misidentifications were generalized? The adequate determination of taxa used in nontaxonomic biological studies is fundamental. Correctly determined taxa set a strong foundation in order to reach the research objectives and are essential for drawing robust conclusions.
ACKnoWledGments. We thank  Rudolf Jenny  for providing historic literature and illustrations. We are especially thankful to  Ernst Vitek  (W) and  Bruno Wallnöfer  (W),  Phil Cribb  (K) and  André Schuiteman  (K),  Germán Carnevali  (CICY) and  Silvia Hernandez  (CICY),  Bruce Holst  (SEL) and  Shawn McCourt  (SEL),  Julio Betancur  (COL) and  Henry Agudelo Zamora (COL) , and  Eric Hágsater (AMO) ,  Elizabeth Santiago Ayala  (AMO) and  Rolando Jimenez  (AMO), all of who kindly provided access to and images of specimens kept in their collections. Rafaela Campostrini Forzza  and  Paula Leitman  of the Projecto Reflora kindly provided several type images of Brazilian taxa. Sebastião Neto (HB) kindly provided photos of the type of V. denticulata. Franco Pupulin (JBL) aided in the analysis of the original materials of V.
In addition, host RNA dependent editing of SARS-CoV-2 genome may have important consequences on the fate of the virus and the patient  35. We independently analyzed 5,156 SARS-CoV-2 sequences available in GenBank and 27,153 sequences available in GISAID for sequence variation in the -1 PRF region. We found that the sequence has remained largely unchanged during the pandemic, with observed changes maintaining the overall architecture of the -1 PRF signals. In the small percentage of in GenBank and GISAID entries with changes in the PRF region relative to the reference sequence, we identified a recurrent C to U change at position 13,536. This position lies in the functional center of the three-stemmed pseudoknot. Interestingly, the conversion of the G 13493 -C 13536 Watson-Crick pair into a wobble G-U pair results in a pseudoknot structure that closely resembles its counterpart in the Middle East respiratory syndrome coronavirus (MERS-CoV).
Comparative analysis of -1 PRF SARS-CoV-2 sequences obtained from GenBank and GISAID reveals a frequent change. We initially assessed the frequency of mutations in -1 frameshift signal region of SARS-CoV-2 (Figure  1 ) by analyzing sequences that were available in GenBank as of June 6, 2020. Only sequences longer than 29 kb were analyzed. Of the 5,156 sequences, 4,947 had -1 PRF signal identical to that of the Wuhan-Hu-1 reference sequence (NC_045512. 2)  7. The alignment of the sequences that did not contain an identical -1 PRF signal showed that within the pseudoknot region, position 13536 was most variable; it was changed from C in the reference strain to U in most cases (Supplementary Figure  1 ). This change was found in 12 isolates (0. 23% frequency) from various origins. We refer to the C 13536 -to-U change as a type 1 mutation.
In SARS-CoV, the hairpin is capped by a UNCG tetraloop  38, 39  whereas it is an AGNN tetraloop in SARS-CoV-2  40, 41  (Figure  1 ). In SARS-CoVs, the stem consists of 8 to 10 base pairs. In the 35-nucleotide attenuator region of the SARS-CoV-2 isolates from the GenBank dataset, only 2. 6% of the strains had attenuator hairpin sequences that differed from that of the Wuhan-Hu-1 strain (Supplementary Figure  2 ). Most of these strains had ambiguities in this region (more than 5 ambiguous in the 35-nucleotide region analyzed). Only 21 entries that had no ambiguities had mutations in the attenuator stem-loop, and none of the mutations were observed more than once. Therefore, in this hairpin, we did not identify a specific nucleotide with a strong frequency of change. A C U U G C U A U G C G A U G C A G U C C A AUCGUUUUUAAACGGGUUU G A A U G U G G C G G U C U U A C A C C G U G C G G C A U G C A G C C C C A G G A C A C U A G U G U C A U G C U G U A U C A A U C G G G UUUG 5'.
Thus, it is possible that the C 13536 to U change that we identified resulted from an RNA editing event. Bases that are different between the two sequences are highlighted in red in the MERS-CoV sequence (NC_019843, GenBank)  47. Bases that differ between SARS-CoV (NC_004718, GenBank)  48  and SARS-CoV-2 are indicated on the SARS-CoV-2 sequence with blue circles; deletions and insertions are indicated with blue triangles. Bases important for -1 PRF efficiency in SARS-CoV are marked by green boxes. The type 1 change that we have identified in SARS-CoV-2 is indicated by a red box. The type 1 mutation likely results in a non-canonical G-U pair as is observed in MERS-CoV.
DISCUSSION As in all sequenced coronaviruses, a -1 frameshift is necessary for expression of SARS-CoV-2 early proteins such as the RNA-dependent RNA polymerase. Ribosomal frameshifting is stimulated by a three-stemmed RNA pseudoknot. As the COVID-19 pandemic continues, SARS-CoV-2 will likely evolve as it adapts to its human host  34. Here we focused on the sequence variability of the -1 PRF signals by analysis of 5,156 SARS-CoV-2 sequences that were available in the GenBank as of June 6, 2020 and 27,153 entries from GISAID available as of June 7, 2020. We found that the 85-nucleotide -1 PRF region is highly conserved with 95. 7% and 97. 2% of isolates, respectively, identical in this region to the reference sequence, Wuhan-Hu-1 (NC_045512. 2). Among the isolates with differences from the reference sequence in the -1 PRF region, there is a recurrent change.
METHODS Sequence analysis. Genomic sequences of SARS-CoV-2 isolates were downloaded from the SARS-CoV-2 data hub of the NCBI Virus website  56. All the sequences longer than 29,000 bases that were released by June 6, 2020 were analyzed (5,156 sequences). Sequences that did not contain a region identical to the -1 frameshift signal of Wuhan-Hu-1 strain (5'-TTTAAACGGGTTTGCGGTGTAAGTGCAGCCCGTCTT ACACCGTGCGGCACAGGCACTAGTACTGATGTCGT ATACAGGGCTTTTG-3')  7, 33  were selected. From the selected sequences, a 2,500-base region (11,500 to 14,000 bases from the 5' end) containing the 85 nucleotides of the -1 PRF region of ORF1a/b were extracted and aligned using ClustalX software  57. For attenuator hairpin, the same analysis was performed using the 35 nucleotides including this hairpin-forming sequence: (5'-CCCATGCTTCAGTCAGCTGATGCACAATCGTTTTT-3').
For the analysis of data from GISAID, sequences available as of June 7, 2020 were downloaded from GISAID Initiative EpiCoV platform using "complete", "high coverage only", and "low coverage excluded" filtering functions. Sequences derived from animals (bat and pangolin) were excluded. A total of 27,153 sequences were analyzed as described for the GenBank sequence analysis. A full acknowledgements table of the laboratories where the clinical specimens and/or virus isolates were obtained and laboratories where data have been generated was obtained from the GISAID website and is included as Supplementary Table  1.
Statistical tests Intention-to-treat analyses were performed. Normally and non-normally distributed quantitative parameters were expressed as mean ± SD or median [interquartile range], respectively. Their comparisons relied on Student's t-test or Mann-Whitney U test, respectively. Qualitative parameters were expressed in count (%) and compared with Fisher's exact test. The Epi info™ version 7. 2. 2. 6 (CDC, Atlanta, GA, USA, 2011) and the RStudio version 1. 2. 1335 (R Foundation for Statistical Computing, Vienna, Austria) softwares were used. A p < 0. 05 was considered signi cant.
Sample and design A total of 163 Business Administration students (78 female, mean age 19. 37, SD 1. 57) participated in this experiment,  3 which lasted around 55 min. Subjects were recruited via Erasmus Research Participation System, which rewards students with course credit for participation in scientific research. The experiment used a mixed between/withinsubjects design (see Table  1 ) with two randomly assigned between-subjects conditions: individual decision making (IDM) and collective decision making (CDM). Experimental sessions were run on computers in sessions of two (CDM) or four (IDM) subjects sitting adjacently in separated cubicles. The experiment was programmed in Matlab, and instructions were provided on a separate sheet (see Online Appendix A). An instructor was present at all times to answer any questions subjects might have with regard to the procedure.
Gene Sequencing and Data Analysis of 16S rRNA The V1-V3 and the V3-V4 regions of the 16S rRNA genes were amplified by PCR. The V1-V3 bacterial universal primers were selected  [25, 26]. Primers 27F (3 -AGAGTTTGATCATGGCTCAG-5 ) and 1492R (3 -TACGGYTACCTTGTTACGACTT-5 ) were used for the V3-V4 amplification fragment. For each sample, an amplified DNA quality check was performed. The amplified DNA was then sequenced by the Eurofins Genomics platform (Ebersberg, Germany) using an Illumina ® MiSeq Technology. The FROGS (find rapidly OTUs with Galaxy solution) pipeline (version 3. 1) was used to retrieve the operational taxonomic units (OTUs) under the Galaxy environment  [27]. This pipeline includes raw FASTQ files demultiplexing and a preprocess step to filter and delete sequences with unexpected lengths, with ambiguous bases (N) and the ones that do not have primer sequences at both 3 and 5 ends.
purR Gene Sequencing and Data Analysis The purR gene was amplified by PCR with primers purR-324F (5 -YACTCCATCAAATC TTCGTAAAAT-3 ), purR-811R (5 -TGTCATTAAATATATTTCCCAATTGAACA-3 ) described by Frantzen et al. [7]. Forward (5 -TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG) and reverse (5 -GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG) Illumina ® adapter overhangs were added to the 5 ends of primers to enable Nextera XT DNA indexing of the PCR products. The libraries were sequenced using an Illumina ® MiSeq technology on the Eurofins Genomics platform (Ebersberg, Germany). The attempted amplicon size was 487 pb  [7]. The data were analyzed using the DADA2 pipeline (v. 1. 12, http://benjjneb. github. io/dada2/ tutorial. html), which retrieves biological sequences from reads by modelling the Illumina-sequencing errors  [31].