dataesr/scientific-paragraphs-categorization huggingface dataset:
5% FDR. (C) Evolutionary conservation (PhastCons) scores were calculated for each nucleotide across the genome, subdivided by genome annotation and plotted by bins of increasing FST. The PhastCons score for each genome category is significantly higher for increasing bins of FST (Wilcoxon rank sum p value < 2e-16). A total of 4,484 genes lie within 25 kb of either an alternately fixed variant or a highly divergent 10kb window (2. 5%FDR). Pathway enrichment analysis  (Ben-Ari Fuchs, Lieder et al. 2016)   2 ). We observed enrichment for the mouse phenotypes 'abnormal cognition' (p < 3. 3e-15), 'abnormal learning and memory' (p < 2. 6e-15), 'abnormal craniofacial morphology' (p < 4. 9e-11) and 'abnormal social/conspecific interaction' (p < 7. 1e-14). We used the list of differentiated genes to query an Allen Brain Atlas dataset that reports gene expression in hundreds of brain regions  (Hawrylycz, Lein et al.
2012). Rock-sanddivergent genes were enriched for the basomedial nucleus of the amygdala, a sub-region of the telencephalon (padj = 0. 001) that regulates fear, anxiety, and physiological responses to territorial intruders in rodents  (Adhikari, Lerner et al. 2015 , Mesquita, Abreu et al. 2016) , and has been linked to Social Anxiety Disorder in humans  (Carvalho, Nóbrega et al. 2020). Finally, we queried databases of genes involved in human disease. Genes near divergent variants are significantly enriched for factors implicated in neurological disease like Autism Spectrum Disorder (SFARI  (Abrahams, Arking et al. 2013 ), Fisher's exact test p value < 2e-16) and disorders related to the neural crest  (Piñero, Bravo et al. 2017) , (Fisher's exact test p value < 2e-16). Given the prevalence of evolutionarily conserved, non-coding, divergent rock-sand variants and genome-wide enrichment for craniofacial and neural crest biology, we examined overlap with published datasets of mammalian neural crest and craniofacial enhancers  (Rada-Iglesias, Bajpai et al.
2012 , Attanasio, Nord et al. 2013). These data allow us to identify craniofacial and cranial neural crest cell (CNCC) enhancers conserved between mammals and cichlids and fixed variants between rock and sand species within these conserved regulatory elements. A total of 275 craniofacial enhancer elements and 234 human CNCC enhancers are evolutionarily conserved between mammals and cichlids. We found divergent rock-sand mutations within the enhancer elements of key genes integral to CNCC specification and migration (SI Table  2 ). Notably, from both datasets, fixed rock-sand variants were found within the enhancer region of the gene nr2f2, a nuclear receptor and master neural crest regulator (Simoes-Costa and Bronner 2015). Rock-sand divergent variants were similarly located within craniofacial enhancers of three genes (yap1, fat4, rere) that function in the Hippo signaling pathway, as well as within enhancers of irx3 and axin2.
9e-07), 'abnormal telencephalon morphology' (p < 3. 95e-07), 'abnormal spatial learning' (p < 9. 9e-07) and pathways 'axon guidance' (p < 3. 3e-05), 'oxytocin signaling' (p < 5. 2e-05) and 'estrogen signaling' (p < 1. 8e-04) (SI Table  4 ). Matches against the Allen Brain Atlas database of gene expression yielded enrichment for exclusively sub-regions of the telencephalon: CA3, hippocampus (padj = 4. 6e-6), CA2, hippocampus (padj = 7. 3e-5), CA4, hippocampus (padj = 0. 001), claustrum (padj = 0. 001), subiculum (padj = 0. 003), dentate gyrus (padj = 0. 03) and the basomedial nucleus of the amygdala (padj = 0. 03). The hippocampus encodes episodic memory and spatial representations of the environment  (Olton, Becker et al. 1979) , and more recently its subregions have been shown to play critical roles in anxiety, social interaction, and social memory formation  (Hitti and Siegelbaum 2014 , Zou, Chen et al.
Genetically Divergent Regions Vcftools  (Danecek, Auton et al. 2011 ) was used to calculate FST (--weir-fst-pop) between the 8 rock and 14 sand species. Variants with FST = 1 were noted to be alternately fixed between rock and sand lineages in our dataset. FST was also measured across 10kb windows (--fst-window-size). Significance thresholds were marked using the fdrtool package in R. All variants were annotated using Snpeff 4. 3i  (Cingolani, Platts et al. 2012). We tested the genes within 25 kb of significantly differentiated variants for enrichment of functional categories. The cichlid gene names were converted to human analogs using Treefam based mapping  (Ramakrishnan Varadarajan, Mopuri et al. 2018 ) and functional enrichment was determined using the TOPPFUN web-browser interface  (Chen, Bardes et al.
PhastCons analysis Pairwise alignments were generated using lastz v1. 02  (Harris 2007) , with the following parameters: "B=2 C=0 E=150 H=0 K=4500 L=3000 M=254 O=600 Q=human_chimp. v2. q T=2 Y=15000". This was followed by using USCS genome utilities (https://genome. ucsc. edu/util. html, https://hgdownload. soe. ucsc. edu/admin/exe/linux. x86_64/FOOTER) axtChain tool with -minScore=5000. Additional tools with default parameters were then used following the UCSC whole-genome alignment paradigm (http://genomewiki. ucsc. edu/index. php/Whole_genome_alignment_howto) in order to obtain a contiguous pairwise alignment. Multiple alignments were generated from pairwise alignments with the multiz v11. 2  (Blanchette, Kent et al. 2004 ) program, using default parameters and the following pre-determined phylogenetic tree: ((((M.
zebra, P. nyererei), A. burtoni), N. brichardi), O. niloticus) in agreement with Brawand et al. (Brawand, Wagner et al. 2014). Sequence conservation scores were then obtained using PhastCons  (Siepel, Bejerano et al. 2005 ) with a phylogenetic model estimated by the phyloFit  (Siepel and Haussler 2004)  program, both from the PHAST software package (v. 1. 3). The model fitting was done using default parameters. PhastCons was run in two iterations, first to obtain the free parameters of the model (--estimate-trees and --no-postprobs) and then using the output from this we ran PhastCons again to attain the conservation scores with --target-coverage 0. 3 --expected-length 100.
After in situ hybridization, embryos were immunostained for pSMAD 1,5,8 protein, using published protocols  (Tucker, Mintzer et al. 2008). Embryos were then bathed in Vectashield (Vector Labs) containing DAPI and placed in a specially built mold  (White, Sylvester et al. 2015 ) that accommodates the large yolk and holds the embryo upright. Embryos were then scanned using a Zeiss LSM 700-405 confocal microscope and processed using LSM 700 software and Image J.
Quantitative F2 Analysis We quantified irx1b in F2 at stage 9 and separated by genotypic class. The 74 heterozygous rock X sand F2 embryos were dissected to remove most of the yolk and the total RNA was extracted from each individual using an RNA Extraction Kit (Qiagen). The amount of mRNA specific to each allele of irx1b was quantified by using the RNA-to-Ct kit (Invitrogen) and the custom probes. The delta Ct for each heterozygote was generated with the equation, 2^(allele from damallele from sire). We tested the data with an ANOVA, followed by a Tukey's multiple comparison test to determine significance between genotype classes.
RNA input was normalized to 1µg and libraries were prepared using the TruSeq Stranded mRNA Sample Prep Kit (Illumina-Kit A). Libraries were again quantified, quality assessed, and normalized for sequencing on the HiSeq 2500 Illumina Sequencing System (Georgia Tech Genomics Core, standard practices). Experimental design and raw files can be accessed on the NCBI Gene Expression Omnibus database under the accession number GSE122500.
Differential Gene Expression Analysis Raw sequence reads from whole brain transcriptomes were quality controlled using the NGS QC Toolkit  (Patel and Jain 2012). Raw reads with an average PHRED quality score below 20 were filtered out. Filtered reads were also trimmed of low-quality bases at the 3' end. High quality sequence reads were aligned to the M. zebra reference genome MZ_UMD2a  (Conte, Joshi et al. 2019 ) using TopHat v2. 0. 9  (Trapnell, Pachter et al. 2009 ). On average, across all samples, over 95% of reads mapped to the reference genome. The resulting TopHat2 output bam files were sorted and converted to sam files using samtools v0. 19  (Li, Handsaker et al. 2009). Sorted sam files were used as input for the HTSeq-count v0. 6. 1 program to obtain fragment counts for each locus  (Anders, Pyl et al.
2015). Fragment counts were scale-normalized across all samples using the calcNormFactors function in the edgeR package v3. 6. 8  (Robinson, McCarthy et al. 2010). Relative consistency among replicates and samples was determined via the Multidimensional scaling (MDS) feature within the edgeR package in R. The native R function hclust(dist) used to cluster samples. Scale-normalized fragment counts were converted into log2 counts per million reads mapped (cpm) with precision weights using voom and fit to a linear model using limma v3. 20. 9  (Ritchie, Phipson et al. 2015). Pairwise contrasts were constructed between socially rock and socially sand samples. After correcting for multiple comparisons using the Benjamini-Hochberg method (Hochberg and Benjamini 1990), genes were considered differentially expressed between socially rock and socially sand samples if they exhibited both a fold change ≥ 2 and Padj < 0.
Acknowledgements: This work was supported by grants from the  NIH  ( R01GM101095 ,  2R01DE019637-10  to JTS and  F32GM128346-01A1  to ZVJ) and the  Human Frontiers Science Program  ( RGP0052/2019  to JTS). We would like to thank  Shweta Biliya  and the  Genomics Core at Georgia Tech  for invaluable assistance with the NGS sequencing. We also thank the members of the  Streelman lab  for comments on this manuscript.
Strain isolation and phenotypic tests Strain Marseille-Q2069 was initially isolated by direct seeding of seeding of 50μL of sample on a homemade R2A (Reasoner's 2A agar) incubated in aerobiosis at 31°C, MALDI-TOF mass spectrometry (MS) protein analysis was carried out using a Microflex spectrometer (Bruker Daltonics, Bremen, Germany)  [8]. Spectra from strain Marseille-Q2069 were imported into the MALDI BioTyper software (version 3. 0, Bruker) and analyzed by standard pattern matching (with default parameter settings). The study was validated by the ethics committee ID-RCB: 2019-A01508-49. Different growth temperatures  (21, 28, 30, 37, 45 and 56°C) , atmosphere conditions, anaerobic, aerobic and microaerophilic (CampyGEN, Oxoid, USA), pH  (5,5; 6. 5; 7. 5; 8. 5)  and NaCl concentration (5, 10, 15 g/L) were tested.
Genome annotation and genome comparison Genome annotation was obtain through the NCBI Prokaryotic Genome Annotation Pipeline  [12]. The genome sequence data were uploaded to the Type (Strain) Genome Server (TYGS), a free bioinformatics platform available under https://tygs. dsmz. de, for a whole genome-based taxonomic analysis  [13]. Determination of closest type strain genomes was done in two complementary ways: first, all user genomes were compared against all type strain genomes available in the TYGS database via the MASH algorithm, a fast approximation of intergenomic relatedness  [14] , and, the ten type strains with the smallest MASH distances chosen per user genome. Second, an additional set of ten closely related type strains was determined via the 16S rDNA gene sequences. These were extracted from the user genomes using RNAmmer  [15]  and each sequence was subsequently BLASTed  [16]  against the 16S rDNA gene sequence of each of the currently 12983 type strains available in the TYGS database.
This was used as a proxy to find the best 50 matching type strains (according to the bitscore) for each user genome and to subsequently calculate precise distances using the Genome BLAST Distance Phylogeny approach (GBDP) under the algorithm 'coverage' and distance formula d5  [17]. These distances were finally used to determine the 10 closest type strain genomes for each of the user genomes. All pairwise comparisons among the set of genomes were conducted using GBDP and accurate intergenomic distances inferred under the algorithm 'trimming' and distance formula d5. 100 distance replicates were calculated each. Digital DDH values and confidence intervals were calculated using the recommended settings of the GGDC2. Complementarily, the degree of genomic similarity of strain Marseille-Q2069 with closely related species was estimated using the OrthoANI software with default parameters  [18].
Strain identification and classification Strain Marseille-Q2069 was isolated from the hand skin swab of a 25-year-old healthy. Strain Marseille-Q2069 failed to be identified by our systematic MALDI-TOF MS screening, suggesting that the corresponding species was not in the databasehttps://www. mediterranee-infection. com/acces-ressources/base-de-donnees/urms-data-base/. Strain Marseille-Q2069 reference spectra was generated (.
and analyzed within all the IHU databases available Chryseobacterium sp spectra and does not belong to any known cluster. (Figure. 2) Moreover, strain Marseille-Q2069 T exhibited a 99. 02% 16S rRNA sequence similarity Chryseobacterium haifense strain H38 type strain (GenBank accession no. NR_044167. 1), the phylogenetically closest bacterium standing in nomenclature. (Figure. 3. A) Furthermore, a digital DNA-DNA hybridization analysis between these two organisms revealed a maximum identity similarity of only 65. 6% (Figure. 3. B and Table. 1). and an OrthoANI parameter provided a value of 95. 73%. (Figure. 4) Taken altogether these results confirm the status of this strain as a new member of the Chryseobacterium genus for which the name Chryseobacterium manosquense strain Marseille-Q2069 T is proposed.
Funding sources MB is PhD granted by the collaboration between M&L Laboratories and Aix Marseille University referenced PVM :2018-200. This study was supported by the French State managed by the National Research Agency under the "Investissements d'avenir (Investments for the Future)" program under the reference ANR-10-IAHU-03 (Méditerranée Infection) and by the Région Provence-Alpes-Côte-d'Azur and the European funding FEDER PRIMI.
Figure. 1 ) 1 and analyzed within all the IHU databases available Chryseobacterium sp spectra and does not belong to any known cluster. (Figure. 2) Moreover, strain Marseille-Q2069 T exhibited a 99. 02% 16S rRNA sequence similarity Chryseobacterium haifense strain H38 type strain (GenBank accession no. NR_044167. 1), the phylogenetically closest bacterium standing in nomenclature. (Figure. 3. A) Furthermore, a digital DNA-DNA hybridization analysis between these two organisms revealed a maximum identity similarity of only 65. 6% (Figure. 3. B and Table. 1). and an OrthoANI parameter provided a value of 95. 73%. (Figure. 4) Taken altogether these results confirm the status of this strain as a new member of the Chryseobacterium genus for which the name Chryseobacterium manosquense strain Marseille-Q2069 T is proposed.
, 2010)  (http://www. consort-statement. org/checklists). When submitting neurofeedback results for publication, we encourage researchers to include the checklist (Fig. 2 ), ideally using the application available at www. rtfin. org/CREDnf. Alternatively, the checklist can be downloaded from the Supplementary material and the final column can be filled with the relevant text from your manuscript, or the page number identifying where in the manuscript each item is addressed. This checklist does not aim to inhibit the exploration of novel directions in neurofeedback research. On the contrary, it advocates robust designs and clear reporting to promote informed research decisions that can effectively build upon previous work. These guidelines are a first iteration. As neurofeedback research progresses, we invite the community to provide comments for improving this checklist (see rtfin.
Pre-experiment Item 1a. Preregister experimental protocol and planned analyses This item is essential for clinical and replication studies, and is encouraged for others. Preregister, for example, on a platform such as www. o sf. io, as a randomized controlled trial (RCT) on ClinicalTrials. gov or the European Union Clinical Trials Register (EUCTR), or by submitting a registered report (see www. cos. io/rr for information concerning registered reports). Clearly label primary and secondary outcome variables. Indicate the number, frequency, and duration of neurofeedback sessions. In the publication, report which analyses were preregistered, which were exploratory, and disclose any potential deviations from the preregistered protocol. Examples: (i) See The.
Item 3d. Report methods used for online data processing and artefact correction This item is essential. For example, detection and rejection/correction of ocular and muscular artefacts (EEG, MEG), and of cardio-respiratory and movement artefacts (functional MRI). Examples: (i) 'Before the start-baseline measurement, an EOG calibration method (3 min) was implemented that calculates the subjectspecific, artifact-associated frequency band. This was used for all following measurements for eye blink detection and rejection during further measurements (for details see  Huster et al. , 2014). Thus, the subject-specific artifact-associated frequency band that was calculated in the EOG calibration measure was monitored. Whenever the mean amplitudes of a 2 s segment was higher than the subject-specific artifactassociated frequency band (minus one standard deviation), the segment was rejected and not used for feedback.
Outcome measures (behaviour) Item 6a. Include measures of clinical or behavioural significance, defined a priori, and describe whether they were reached This item is essential. For example, by using MCIDs to establish the magnitude of an effect to interpret as clinically meaningful (see  Engel et al. , 2018; Lakens et al. , 2018  for an overview on establishing MCID values and smallest effect sizes of interest). Many of these values remain open to discussionexplain the reasoning behind the value used. Moreover, collect data on acceptability, safety, and adverse effects. In this paper, we are using the term 'behaviour' in the broad sense to encompass all non-physiological measures, including self-reports. Examples: (i) 'Minimal clinically important differences (MCIDs) were defined as "the smallest differences in scores in the domain of interest, which patients perceive as beneficial, and which would mandate, in the absence of troublesome side effects and excessive costs, a change in the patient's management".
Image analysis Image acquisition and analysis are summarised in Movie 1. For each field of view, we have a bright field video of the cilia motility (BF) and a video of the FL. Each video is analysed with custom MATLAB scripts to obtain the following information. (1) Cell position: using the BF video, cell locations were first roughly identified by the pixels for which the standard deviation of the intensity over time was larger than a threshold value (found with Otsu's method) (Fig. 1C ). For each of these validated pixels, CBF was then calculated as the frequency with the highest peak in the Fourier Transform signal of pixel intensities over time  (Sisson et al. , 2003; Dimova et al. , 2005; Ryser et al. , 2007). Single multiciliated cells were identified as the group of geometrically connected pixels where the frequency standard deviations did not vary more than a threshold s max f ¼ 1 Hz.
(2) Ciliary beating direction: we assumed, as recently validated in  Faubel et al. (2016)  that the cilia-driven flow corresponds to the beating orientation of underlying cilia. The fluid flow generated by the ciliated cells was measured from the FL video with particle image velocimetry (PIV) method PIVLab, MATLAB  (Thielicke and Stamhuis, 2014). Using a PIV algorithm, we extract a displacement vector field between all consecutive frames of the movie. Then, the displacement vector fields were averaged over time to obtain a smooth result of the flow field. For the PIV measurements, we used a grid with a spacing of 32 pixels (7. 6 µm). In this way we can think of each field of view as divided in boxes of area A box =7. 6×7. 6 µm 2 , where for each box we have the corresponding displacement vector.
The range of shear stress was chosen in agreement with the shear stress shown to be generated by the cilia-driven flow in the brain  (Faubel et al. , 2016). After the 3 days of constant fluid perfusion, the Transwell chips were disassembled from the flow-setup and cells imaged with and without tracer particles, following protocols described in the Materials and Methods. For numerous cells in the centre of the channels, we gathered data of their position, ciliary beating frequency (CBF), magnitude and direction of the cilia driven flow v i ! , where i is the index corresponding to the box containing the cell. These measurements were performed in the absence of external flows. We measured the global cilia alignment using an alignment parameter defined as: F ¼ 1 N X N i¼1 b v i Á ê; ð1Þ where b v i ¼ v i ! =jv i j is the unit vector pointing in the direction of the cilia-driven flow, ê is the unit vector pointing in the direction of the previously applied external flow, and N is the total number of boxes where ciliated cells are detected.
However, if the body under consideration is slender, then the boundary element method requiring surface integrations reduces to a contour integral called slender body theory  (Johnson, 1980; Das and Lauga, 2018, 2019). Here, we employ slender body theory to study the hydrodynamics of multiple filaments exposed to shear flow. A detailed description of the simulation methods can be found at https://doi. org/10. 5281/zenodo. 4305524. The beating pattern of mammalian cilia is generally more extended in the direction of the power stroke  (Hamilton et al. , 2020). For this reason, in the presence of a constant external flow, a beating cilium feels a net average torque during its beating cycle  (Guirao and Joanny, 2007). To quantify this effect, we first analyse the beating pattern (waveform on the cilium, over time) of brain cilia.
4D ). It is worth noting that in the simulations cilia spanned areas never overlap, whereas these do overlap in the same multiciliated cells. Then an exact comparison between the two would require knowledge of the cilia spanned area overlap. We then changed the ciliated fraction area in the simulations by changing the distance between cilia in the direction perpendicular to the beating (d ⊥ =0. 08, 0. 16, 0. 24, 0. 32, 0. 40). The shear flow acts as a forcing for the system that is solved numerically. The calculation method of the net force and torque on each filament is provided at https://doi. org/10. 5281/zenodo. 4110844. Since the cilia are very close to each other in the beating direction, the strongest hydrodynamic interactions of a cilium is with its neighbour in the perpendicular direction.
The results presented here are for the smallest spacing in beating direction, d k ¼ 1:866. On increasing the spacing d k , we did not find any discernible changes in the hydrodynamic screening effect. Lastly, the effect of a rigid surface is modelled using appropriate images that satisfy the no-slip hydrodynamic boundary condition by construction. The surface is placed at a small distance of 0. 05 below the cilium's immobile end, to avoid numerical singularities in the slender body theory method, as described at https://doi. org/10. 5281/zenodo. 4305524. We impose a shear flow, u 1 ¼ _ gzê y , along the y-axis, where _ g ¼ 1 is the dimensionless shear rate. The angle between the cilia beating direction, bk , and the shear flow is denoted as θ (Fig. 4C ). The no-slip velocity boundary condition applies on the substrate.