dataesr/scientific-paragraphs-categorization huggingface dataset:
S7 ). Figure  S8e  also shows how, when g X is large, the correlations with p 12,A reverse in sign, for extreme values of h X. Figure  S7e -f shows how the data of  Noor et al. (2001)  divide naturally into individuals with high, medium and low values of h X , and together, this explains the rationale of the test presented in Table  3. To analyze the female backcross data of  Moran et al. (2017)  for Teleogryllus, we used equation (10), which assumes that P1 is well adapted, while P2 is maladapted. We also assume that a proportion π of the paternal X chromosome is silenced. In this case, the quantities p 12 and h are calculated without considering silenced alleles, because these make no contribution to the phenotype. This rich dataset contains a wide variety of cross types, and so the full predictions for all of the relevant hybrids are listed in Table  S4.
The gray symbols for the four datasets correspond to those used in panel (A). The dotted line in panel (B) shows predictions from Fisher's model, assuming that the between-strain divergence contains limited coadaptation. The prediction uses equations (13), (14), and (11), with f P = 1. 5, and α = 3. The model predicts both the roughly linear increase in vigor with heterozygosity, and the systematic difference between BC1 and F2.
ACKNOWLEDGMENTS We are very grateful to all of the authors of the original data reanalyzed here and especially to the following, who provided clarifications or reformatting: Luisa Bresadola, Charlie Brummer, Mark A. Chapman, Camille Christe, Eric Hagg, Xionglei He, Christian Lexer, Xuehui Li, Amanda  Moehring, Bret Payseur, Michael White, and Gavin Woodruff. We are also grateful to Andrea Betancourt for advice on processing the genome annotations, and to Peter Moran, who spotted a serious mistake in an earlier draft. Stuart Baird, Jon Slate, David Waxman, and anonymous reviewers, provided helpful comments that led to substantial improvements. This work was partly funded by the Agence Nationale de la Recherche (HYSEA project, ANR-12-BSV7-0011). NB acknowledges a fellowship of the French Embassy in the United Kingdom, with Churchill College Cambridge.
Different forms of viruses that infect archaea inhabiting extreme environments continue to be discovered at a surprising rate, suggesting that the current sampling of these viruses is sparse. We describe here Sulfolobus filamentous virus 1 (SFV1), a membrane-enveloped virus infecting Sulfolobus shibatae. The virus encodes two major coat proteins which display no apparent sequence similarity with each other or with any other proteins in databases. We have used cryo-electron microscopy at 3. 7 Å resolution to show that these two proteins form a nearly symmetrical heterodimer, which wraps around A-form DNA, similar to what has been shown for SIRV2 and AFV1, two other archaeal filamentous viruses. The thin (∼ 20 Å) membrane of SFV1 is mainly archaeol, a lipid species that accounts for only 1% of the host lipids.
shibatae BEU9 was isolated from a single plaque and named Sulfolobus filamentous virus 1 (SFV1) (Fig. 1 ). In order to determine the host range of SFV1, virus replication was analyzed in collection strains of the genus Sulfolobus, including S. islandicus strains REY15A 11 , HVE10/4 11 , and LAL14/1  12  , S. solfataricus strains P1 (GenBank accession no. NZ_LT549890), P2  12  and 98/2  13  , and S. acidocaldarius strain DSM639  14. None of these strains served as a viral host. Virion morphology. From images of negatively stained samples (Fig. 1a, b ), virions of SFV1 appear as flexible filamentous particles that are 845 ± 15 nm long. Each end of the virion is tapered, measures 110 ± 12 nm in length (Supplementary Fig. 1a ), and consists of a "neck" and a mop-like structure with several thin, short fibers (Fig.
1b ). Intriguingly, in the virus preparations we frequently observed virions with significantly elongated "necks" at one or both termini, which could reach 700 nm in length (Fig. 1b  and Supplementary Fig. 1b ). Viral genome. The nucleic acid extracted from the purified SFV1 virions was insensitive to RNase A but could be digested by type II restriction endonucleases (BglII, BamHI, KpnI and XbaI), indicating that the viral genome is a dsDNA molecule. Sequencing revealed that the SFV1 genome contains 37,311 bp, including Homology searches using the BLASTP program  15  revealed that only 12 (~18%) SFV1 gene products are significantly similar (Evalue cutoff of 1e-03) to sequences in the non-redundant protein database (Supplementary Table  1 ). Eight ORFs have homologs in viruses infecting host of the order Sulfolobales.
6d ) and the pseudo-symmetrical heterodimer in AFV1 (Fig. 6e ). This is surprising since a BLAST search  15  using the VP5 sequence not only failed to find VP4 as a possible homolog, but also failed to find any other protein in the existing protein databases as having significant sequence similarity. In contrast to the compact dimers in SIRV2 and AFV1, VP5 contains a small C-terminal domain containing two short helices (Fig. 6a, b ) that projects away from the dimer. This small VP5 domain makes extensive contacts with a VP4 subunit in an adjacent helical turn (Fig. 3c ) as well as with the C-terminal domain from two other VP5 subunits in the same helical turn. It is likely that this small domain limits the axial extensibility of the virion, as AFV1, which lacks this extension, was observed to have a helical pitch that varied from ~39 to 47 Å  9.
Extraction and analysis of viral DNA. Nucleic acid was isolated from the purified virions with phenol/chloroform/isoamyl alcohol (25:24:1 vol/vol). Before extraction, the virus preparation was treated with SDS and Proteinase K with final concentrations of 0. 5% (wt/vol) and 0. 2 mg ml -1 , respectively for 1 h at 55 °C. SFV1 genome library was prepared with the NEBNext Ultra DNA library Prep Kit for Illumina (New England Biolabs), and samples were sequenced by Illumina Miseq with paried-end 150-bp read lengths (Genomics Platform, Institut Pasteur, France). The genome sequence was assembled using the CLC Genomics Workbench software package. An average coverage of 800 was obtained. ORFs were predicted by using RAST v2. 0  41. Homologs of the in silico-translated protein sequences were searched using BLASTP  15  with an upper threshold E value of 1e-3, against the non-redundant protein database at the National Center for Biotechnology Information.
Searches for distant homologs were performed by using HHpred  16  against different protein databases (PFAM (Database of Protein Families), PDB (Protein Data Bank), CDD (Conserved Domain Database), and COG (Cluster of Orthologous)). Transmembrane domains were predicted using TMHMM  42. Analysis of SFV1 structural proteins. The highly purified virions were analyzed by 4-12% gradient NuPAGE Bis-Tris precast gel (Thermo Fisher Scientific). Proteins were stained with Coomassie blue using InstantBlue (Expedeon), and the stained protein bands were excised from the gel. Proteins were in-gel tryptic digested, and the generated peptides were analyzed by nano-LC-MS/MS (Proteomics Platform, Institut Pasteur, France) using an Ultimate 3000 system (Dionex) coupled to an LTQ-Orbitrap Velos system (Thermo Fisher Scientific).
Peptide masses were searched against annotated SFV1 proteins using Andromeda  43  with MaxQuant software, version 1. 3. 0. 5  43. Glycosylation of VPs was detected by using a Pro-Q Emerald 300 glycoprotein gel stain kit (Thermo Fisher Scientific). Dissociation of SFV1 virions. The purified SFV1 virions were treated with Triton X-100 with a final concentration of 0. 5% (vol/vol) for 8 h at room temperature. After treatment, the virion fragments were separated by isopycnic gradient centrifugation in CsCl in the presence of 0. 3% (vol/vol) Triton X-100. Nine fractions were collected and analyzed by SDS-PAGE, and TEM; prior to TEM observations Triton X-100 was removed from the fractions with Bio-Beads (Bio-Rad). Light scattering bands were observed in fractions F1 and F8. In the fraction F1 were observed pleomorphic spherical particles of different sizes, most likely detached envelopes (Fig.
7a ). The nucleoprotein core of the virion was observed in fraction F8 (Fig. 7a ). Analysis of SFV1 and host cell lipids. The freeze-dried host-cell preparation and the virion preparation were directly acid hydrolyzed by refluxing with 5% HCl in methanol for 3 h to release GDGT lipids. GDGT lipids were analyzed by highperformance liquid chromatography/atmospheric pressure chemical ionizationmass spectrometry (UHPLC/APCI-MS) using an Agilent 1260 HPLC, equipped with automatic injector, coupled to a 6130 Agilent MSD and HP Chemstation software. The injection volume was 10 µl. Separation of the GDGTs was achieved in normal phase using two silica BEH HILIC columns in series (150 mm × 2. 1 mm; 1,7 µm; Waters Acquity) at a temperature of 25 °C. The mobile phases are hexane (A) and hexane:isopropanol (9:1, v-v) (B).
Each fraction, containing multiple frames, represented a dose of ~2 electrons/Å 2. All the images were first motion corrected by MotionCorr v2. 1  44  , and the first and the last fractions were removed. Then a total of 586 images (each 4k x 4k) were selected that were free from drift or astigmatism, contained visible virus filaments, and had a defocus range from 0. 5 to 3. 0 μm determined by CTFFIND3  45  , The SPIDER software package  46  was used for most subsequent steps. The CTF was corrected by multiplying each image by the theoretical CTF, both reversing phases where they need to be reversed and improving the signal-to-noise ratio. This multiplication of the images by the CTF is actually a Wiener filter in the limit of a very poor SNR. The program e2helixboxer within EMAN2  47  was used for boxing long filaments from the micrographs, and 1972 such boxes of varying length were extracted.
Acknowledgements This work was supported by  NIH   R35GM122510  (to E. H. E. ), the  European Union 's  Horizon 2020 research and innovation program  under grant agreement  685778 , project  VIRUS-X  (to D. P. ) and l' Agence Nationale de la Recherche (France)  project  ENVIRA  (to M. K. ). The cryo-EM imaging was conducted at the  Molecular Electron Microscopy Core facility  at the  University of Virginia , which is supported by the  School of Medicine  and built with  NIH  grant  G20-RR31199. The Titan Krios and Falcon II direct electron detector were obtained with  NIH   S10-RR025067  and  S10-OD018149 , respectively. Ellen Hopmans  and  Caglar Yildiz  ( NIOZ ) are thanked for analytical support.
Data availability. The sequences of the SFV1 genome and the host Sulfolobus BEU9 16S rRNA gene have been deposited in Genbank, with accession numbers MH447526 (SFV1) and MH443276 (Sulfolobus BEU9 16S). The atomic model of the nucleoprotein complex has been deposited in the Protein Data Bank (6D5F) and the reconstructed volume has been deposited in the Electron Microscopy Data Bank (EMD-7797). Other data are available from the corresponding authors upon reasonable request.
We present a draft genome assembly with a de novo prediction and automated functional annotation of coding genes, and a reference transcriptome of the Adzuki bean borer, Ostrinia scapulalis, based on RNA sequencing of various tissues and developmental stages. The genome assembly spans 419 Mb, has a GC content of 37. 4% and includes 26,120 predicted coding genes. The reference transcriptome holds 33,080 unigenes and contains a high proportion of a set of genes conserved in eukaryotes and arthropods, used as quality assessment of the reconstructed transcripts. The new genomic and transcriptomic data presented here significantly enrich the public sequence databases for the Crambidae and Lepidoptera, and represent useful resources for future researches related to the evolution and the adaptation of phytophagous moths.
All libraries were sequenced by LGC Genomics GmbH (Berlin, Germany) on an Illumina HiSeq 2000 platform using the paired-end protocol. Between 234 and 351 million DNA raw reads were generated per library (Table  1 ). Assembly and scaffolding of the cleaned reads were done with the software Allpaths-LG [14], followed by GapCloser for closing gaps in the assembled scaffolds. The resulting scaffolding was then further improved by integrating independent reconstructed transcript data after RNA sequencing (see Supplementary material for details). The OSCA v1. 2 draft genome assembly consisted of 50,738 scaffolds, representing 419 Mb with a mean read coverage of 50 reads per base (Table  2 ). N50 and N90 were 29,308 and 3051 bp, respectively. Scaffold lengths ranged between 883 bp and 619. 8 kb. The genome assembly had a GC content of 37.
Acknowledgements This work was supported by a grant from  INRA (AIP Bioressources 2011  (grant id  SCOOP, INRA )). Most of the bioinformatics analyses were performed on the CBGP HPC computational platform (Montferrier-sur-Lez, France, http://www6. montpellier. inra. fr/cbgp_eng/Platforms/Computing-platform). We are grateful to the  bioinformatics platforms ABiMS (Roscoff, France , http://abims. sb-roscoff. fr/), GenOuest (Rennes, France, https://www. genouest. org/)and  Genotoul (Toulouse, France , http:// bioinfo. genotoul. fr/)for providing access to their computational resources. We also thank  Denis Bourguet  for his comments on an earlier version of the manuscript.
To ascertain that this negative finding did not result from insufficient statistical power, we performed a bootstrap analysis using a published dataset in which vigilance changes have been investigated  18  , testing whether a group size of 14, as used in the present study, is sufficient to detect vigilance changes. Indeed, vigilance-related changes in thalamo-cortical connectivity can reliably (1-ß = 0. 8) be detected in sample sizes as the one used in this study. Circadian and homeostatic factors. The lack of an association with vigilance state rather suggests an endogenous modulation of resting state cortical activity by circadian and/or homeostatic factors. When we examined the region of the suprachiasmatic nucleus in the hypothalamus  20  , which is the central pacemaker of the circadian system in the brain, we did not observe a modulation of resting-state BOLD variance as a function of ToD (repeated measures ANOVA, all voxels F(5, 65) < 4.
ToD-effects in BOLD signal variance during visual perception. Therefore, we further analyzed fMRI data during the visual detection task. We first tested whether ToD modulates taskrelated BOLD SD in the visual cortex during visual detection in the same way as it modulates visual cortex BOLD SD during rest using a t-test (cluster-extent based threshold: k = 112, P < 0. 05, FWE corrected; cluster-defining primary threshold of t(40) > 3. 31, P < 0. 001, uncorrected; n = 9). Task-related BOLD SD in two clusters in the primary and secondary visual cortex was reduced at 8:00 and 20:00 h, as compared to midday measurements (Fig. 3a  and b ). Reductions ranged from 7. 4 to 26. 8% of BOLD SD values at 14:00 h. In comparison to resting state, BOLD SD during the visual detection task was overall decreased in both the primary and secondary visual cortex (Rest-task: t(8) = 3.
Because of non-completion of the questionnaire and missing actigraphy data (see above), sleep debt could not be assessed in three participants. Mean ± SD sleep debt of the participants was 1. 81 ± 1. 00 (range = 0. 17-3. 09). Higher values indicate higher sleep debt. Because actigraphy-based sleep estimates often under-estimate the duration of sleep, as compared to selfreports  43  , this quantification possibly overestimates the true extent of sleep pressure in the population. Before each fMRI session, body temperature was measured with an ear thermometer and the participants' sleep propensity was assessed with a self-report questionnaire based on a modified version of the Epworth Sleepiness Scale (ESS) questionnaire [44], in which participants were asked to rate their sleep propensity by projecting their actual vigilance state in the situations provided by the questionnaire to quantify diurnal sleepiness.
Data acquisition and preprocessing of resting-state fMRI data. Resting-state data were collected using a 3 Tesla MRI-scanner (Siemens MAGNETOM Allegra, syngo MR 2004A, Erlangen, Germany). A total of 12 resting-state sessions of a gradient-echo T2*-weighted transverse echo-planar imaging sequence (EPI) were acquired for each participant. A resting-state session comprised 210 EPI volumes (6 sessions on two subsequent days = 12 × 210 volumes for each participant). Each volume contained 30 axial slices acquired in a sequential manner, covering the whole brain (TR/TE/flip angle = 2000ms/30 ms/90°, FOV = 192 mm, matrix size (resolution) = 64 × 64, voxel size of 3 × 3 × 3 mm³, distance factor 25%). The first 4 volumes were discarded to avoid magnetic saturation effects. Spatial preprocessing was performed using standard algorithms implemented in SPM 12 (Statistical Parametrical Mapping 12, Version 6225; Wellcome Trust Center for Neuroimaging, London, UK; http://www.
A high-resolution T1-weighted 3D magnetization-prepared rapid acquisition gradient echo (3D MP-RAGE) sequence was also acquired for each participant (144 sagittal slices, 1 slab, TR/TE/TI/flip angle = 2250 ms/2. 6 ms/900 ms/9°, FOV = 256 mm, matrix size (resolution) = 224 × 256, voxel size 1 × 1 × 1. 10 mm³, distance factor 50%). The high-resolution anatomical T1 sequence was used to exclude any structural anomalies and to map the functional results onto an averaged brain with higher anatomical resolution (see Supplementary Fig. 1 ). Single subject analyses of resting-state fMRI data. Three variables of interest were calculated from the resting-state BOLD time series for each session (ToD) separately, using in-house scripts in MATLAB 7. 7. Whole brain, voxel-wise standard deviation (SD) of the BOLD signal: We calculated the voxel-wise SD of the BOLD time series for each ToD, resulting in an SD-map for each ToD.
We obtained voxel-wise SD with: SD ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P n i¼1 x i À x ð Þ 2 n À 1 s {x 1 , x 2 , …, x n } represents the single values of a time series of length n and x is the mean value of the whole time series. Whole brain, voxel-wise amplitude of low-frequency fluctuation (ALFF) of the BOLD signal: The ALFF of the BOLD signal represents the power spectral density within the frequency band of 0. 01-0. 1 Hz  48, 49. Since the power spectrum of a time series describes how the variance of the data is distributed over the frequency domain, BOLD ALFF is analogous to BOLD SD in the given frequency range. We first transformed the BOLD time series into the frequency domain with the fast Fourier transform algorithm and then obtained BOLD ALFF by averaging the resulting power spectrum across the frequency band of interest  50.
All statistical brain maps were corrected for multiple comparisons by using cluster-extent based thresholding at P < 0. 05, FWE. The cluster-defining threshold was set at P < 0. 001 (uncorrected). We also report the MNI coordinates of local maxima in these clusters. The denomination of local maxima was adopted from the SPM Anatomy toolbox  51. To identify the ToD-differences driving the main effect of ToD in BOLD SD, we performed additional post-hoc t-tests. First, BOLD SD values from significant clusters were averaged within each cluster and extracted for each ToD and participant separately using MarsBaR. Then, using IBM SPSS Statistics (Version 22; IBM, Armonk, NY, USA; https://www. ibm. com/ analytics/us/en/technology/spss/), we performed a repeated measures ANOVA with subsequent pairwise comparisons between all ToD (dependent, paired samples, two-tailed t-tests with Bonferroni correction, significance threshold at P < 0.
Fixation blocks were not further analyzed, because the experimental conditions were similar to resting-state. Using SPM 12 for group statistical analyses, task-related BOLD SD in the visual cortex was probed for the ToDdependent effects observed during resting-state. As with the resting-state data, task data on the two consecutive days were averaged voxel-wise at each ToD for each participant. We tested for BOLD SD reductions at 08:00 and 20:00 h compared to midday measurements (11:00, 14:00, and 17:00 h) using a one-sample t-test. The analysis was restricted to the visual cortex cluster that showed a main effect of ToD during resting-state and results were thus corrected within this mask at P < 0. 05, FWE cluster-corrected (cluster-forming threshold P < 0. 001, uncorrected). Values from significant clusters were averaged within each cluster and extracted for each ToD and participant separately using MarsBaR and used for subsequent analyses (see below).
We ascertained the consistency of the effect by using the global conjunction hypothesis  53  of the contrasts (08:00 h separately against 11:00, 14:00, and 17:00 h) and (20:00 h separately against 11:00, 14:00, and 17:00 h). Comparing resting-state and task-related BOLD SD. We tested for significant overall decreases in BOLD variance during visual detection compared to rest using dependent, two-tailed, paired samples t-tests in SPSS (significance threshold at P < 0. 05, Bonferroni corrected). The peak-voxel BOLD SD values of significant clusters from the visual detection task were extracted for each ToD and participant using MarsBar, then averaged over all ToD and compared with the ToD-averaged resting-state BOLD SD values at the same coordinates. Visual detection task performance. Omission errors (i.
05. Power analysis for thalamic resting-state functional connectivity. To ascertain that the negative finding in ToD-dependent thalamocortical connectivity as an objective marker of vigilance did not result from insufficient statistical power, we performed a power analysis using a bootstrapping method on a published dataset in which vigilance changes have been investigated  18. For this re-analysis we used exactly the same parameters that we applied in our study here and investigated whether vigilance changes (N1 sleep stage vs. wakefulness) could be detected in 14 subjects in the previous dataset. In detail, a total of 93 continuous (≈3. 5 min) epochs of wakefulness and 54 of N1 sleep were selected from Tagliazucchi and Laufs  18. Two sets of 14 epochs were randomly selected (with replacement) from the wakefulness and N1 sleep sets and the voxel-wise thalamic functional connectivity (as determined by the AAL atlas 47 ) was computed.
Diatoms constitute a diverse lineage of unicellular organisms abundant and ecologically important in aquatic ecosystems. Compared to other protists, their biology and taxonomy are well-studied, offering the opportunity to combine traditional approaches and new technologies. We examined a dataset of diatom 18S rRNA-and rDNA-(V4 region) reads from different plankton size-fractions and sediments from six European coastal marine sites, with the aim of identifying peculiarities and commonalities with respect to the whole protistan community. Almost all metabarcodes (99. 6%) were assigned to known genera (121) and species (236), the most abundant of which were those already known from classic studies and coincided with those seen in light microscopy. rDNA and rRNA showed comparable patterns for the dominant taxa, but rRNA revealed a much higher diversity particularly in the sediment communities.