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FIG. 6 in Horse size and domestication: Early equid bones from the Czech Republic in the European context
FIG. 6. — Size comparison between horses from the Czech Republic (dark boxes and dots) and adjacent regions (light boxes) within: A, 15000-5600 BC; B, 5600- 4200 BC. X-axis, locations (in brackets: culture and/or country). Statistics for Hadí cave and Těšetice-Kyjovice based on the same data as the Magdalenien and early Lengyel, respectively (in Fig. 3; Table 3). Statistics for Mirnoe, Kniegrotte, Bärenkeller, LBK (Germany), Sakarovka and Dereivka taken from Benecke & Driesch (2003), for Lausnitz based on Teichert (1963), for Szabadszállás-Tözegtelep on Vörös (1981). Others as in Fig. 3. Abbreviations: CR, Czech Republic; LBK, Linear Pottery c.
FIG. 4 in Horse size and domestication: Early equid bones from the Czech Republic in the European context
FIG. 4. — LSI distributions of Equus postcranial breadths/depths based on Czech datasets from selected periods and E. przewalskii presented by histograms. Based on the same data as in Fig. 3 (outliers incl.). Period codes correspond to other Tables and Figures. A, period 1 – Late Paleolithic (Magdalenien); B, pe- riod 3 – Early Lengyel (Těšetice-Kyjovice site); C, period 6 – Early Eneolithic (TRB); D, period 8 – Middle Eneolithic (Řivnáč culture); E, period 10 – Early Bronze (Únětice culture); F, period 11 – Late Bronze (KnovÍz culture); G, Equus przewalskii; X axes, LSI scale; Y axes, frequency; Red curve, Kernel density; Dotted curve, fit (estimated) normal distribution. Analysed by Past 3.02 statistical software.
FIG. 10 in The oldest erymnochelyine turtle skull, Ragechelus sahelica n. gen., n. sp., from the Iullemmeden basin, Upper Cretaceous of Africa, and the associated fauna in its geographical and geological context
FIG. 10. — Podocnemididae from Ibeceten, south-western Niger, Senonian, Gularo-Intergular pattern, MNHN.F.IBC coll. A-F, Erymnochelyine Erymnochelys group, variability in shape of plates and scutes: alternative epiplastral and entoplastral combinations: A, epiplastron IBC560 and entoplatron IBC1898; B, epiplastron IBC560 and entoplastron IBC1903; C, entoplastron IBCx1; D, IBCx2, fragmentary epiplastron; E, epiplastron IBC1893 and entoplastron IBC1898; F, epiplastron IBC1893 and entoplastton IBC542. Podocnemididae indet., primitive intergular pattern; G, IBC1899, entoplastron. Ventral views. Scale bar: 2 cm.
FIG. 9. — Ragechelus sahelica n. gen., n in The oldest erymnochelyine turtle skull, Ragechelus sahelica n. gen., n. sp., from the Iullemmeden basin, Upper Cretaceous of Africa, and the associated fauna in its geographical and geological context
FIG. 9. — Ragechelus sahelica n. gen., n. sp., Indamane, southwestern Niger, late Maastrichtian; detail of the skull, cavum tympani area, holotype MNHN- RA-2018.0031. Abbreviations: ant, antrum squamosum; cq, commissura quadrati; ica+Et, incisura columellae auris with Eustachian tube. Left lateral view. Scale bar: 2 cm.
FIG. 8. — Ragechelus sahelica n. gen., n in The oldest erymnochelyine turtle skull, Ragechelus sahelica n. gen., n. sp., from the Iullemmeden basin, Upper Cretaceous of Africa, and the associated fauna in its geographical and geological context
FIG. 8. — Ragechelus sahelica n. gen., n. sp., Indamane, southwestern Niger, late Maastrichtian; detail of the skull, holotype MNHN-RA-2018.0031, showing the rounded carotid foramen for entrance in te besicranium, at the back of the deep cavum pterygoideum, below the (broken here) podocnemidid pterygoid wing; Abbreviations:boc, basioccipital; bsph, basisphenoid; car can, enlarged carotid foramen; cav pter, cavum pterygoideum; pw, break of the pterygoid wing at its posterior base; q, quadrate; q art, area articularis quadrati. Ventral view. Scale bar: 2 cm.
FIG. 7. — Ragechelus sahelica n. gen., n in The oldest erymnochelyine turtle skull, Ragechelus sahelica n. gen., n. sp., from the Iullemmeden basin, Upper Cretaceous of Africa, and the associated fauna in its geographical and geological context
FIG. 7. — Ragechelus sahelica n. gen., n. sp., Indamane, southwestern Niger, late Maastrichtian; interpretative drawing of the skull, holotype MNHN-RA-2018.0031. Abbreviations: aaq, area articularis quadrati; boc, basioccipital; bsph, basiphenoid; car c, carotid canal; cav pter, cavum pterygoideum; co, condylus occipitalis; col-Et, columella auris with the Eustachian tube passage; cq, commissura quadrati; fpp, foramen palatinum posterius; fp, fenestra postotica; imc, intermediate maxillo-palatine crest; ju, jugal; mc, medial maxillo-palatine crest; ms, muscle insertion zone; mx, maxilla; pal, palatine; pmx, premaxilla; po, postorbital; ppo, processus paroccipitalis opisthotici; pter w, pterygoid wing; ptp, processus trochlearis pterygoideus; q, quadrate. Ventral view. Scale bar: 4 cm.
FIG. 6. — Ragechelus sahelica n. gen., n in The oldest erymnochelyine turtle skull, Ragechelus sahelica n. gen., n. sp., from the Iullemmeden basin, Upper Cretaceous of Africa, and the associated fauna in its geographical and geological context
FIG. 6. — Ragechelus sahelica n. gen., n. sp., Indamane, southwestern Niger, late Maastrichtian. Lateral view of the skull, holotype MNHN-RA-2018.0031. Abbreviations: an sq, antrum squamosum; co, condylus occipitalis; com q, commissura quadrati; fpp; foramen palatinum posterius; fr, frontal; ica+Et, incisura columellae auris with the Eustachian tube; ju, jugal; l pfr, left prefrontal; mq, meatus quadrati; mx, maxilla; na, external nare; pal, palatine; pfr, prefrontal; pmx, premaxilla; paq, processus articularis quadrati; pmx, premaxilla; po, postorbital; ppo, processus paroccipitalis opisthotici; pro, prootic; pter, pterygoid; ptp, processus trochlearis pterygoideus; q, quadrate; r paq, right processus articularis quadrati; r pter, right pterygoid; soc, supraoccipital; sq, squamosal; V, foramen trigemini; black arrow, position of the foramen stapediotemporale; blue and green dotted lines, hypothetic positions for the skull lateral notch border; red line, border of the palatal medial crest. Scale bar: 4 cm.
FIG. 5. — Ragechelus sahelica n. gen., n in The oldest erymnochelyine turtle skull, Ragechelus sahelica n. gen., n. sp., from the Iullemmeden basin, Upper Cretaceous of Africa, and the associated fauna in its geographical and geological context
FIG. 5. — Ragechelus sahelica n. gen., n. sp., Indamane, southwestern Niger, late Maastrichtian; photographs of the skull, holotype MNHN-RA-2018.0031: A-F, dorsal, ventral, left lateral, anterior, right lateral and posterior views. Scale bar: 4 cm.
FIG. 3 in The oldest erymnochelyine turtle skull, Ragechelus sahelica n. gen., n. sp., from the Iullemmeden basin, Upper Cretaceous of Africa, and the associated fauna in its geographical and geological context
FIG. 3. — Geological map of Iullemmeden basin. Extract from Greigert (1961), focused on the northeastern basin part, area of Kao to Ibeceten with Mont Indamane (Mt Igdaman). Legend, from Greigert (1961): Cr 9-8, including (from top to bottom, [Mt Indamane Maastrichtian outcropping]: 1, Upper sandstones; 2, Mosasaurus shales; 3, Lower sandstones. Cr7, lower and middle Senonian, with gypsum [including Ibéceten outcropping]; Cr6, Turonian; Cr6b, Turonian (white limestones); Cr6a, lower Turonian (Nigericeras zone); CR6a-b, lower Turonian and Upper Cenomanian (Neolobites vibrayeani zone, Tegama group sandstones); e III-VI, lower Eocene; ct, terminal continental (simplified); qa2, filled fossil valleys; qd1, fixed oriented recent dunes (barchans); F, fossils at Mont Indamane and Ilatarda.
FIG. 2 in The oldest erymnochelyine turtle skull, Ragechelus sahelica n. gen., n. sp., from the Iullemmeden basin, Upper Cretaceous of Africa, and the associated fauna in its geographical and geological context
FIG. 2. — Geographical location, northern to southern, of: Mont In Tahout area (Nigeremys locality), Indamane (Ragechelus saherica n. gen., n. sp. locality), Ibeceten (Erymnochelyine locality) and Ilatarda, fossil localities with turtles (stars), in southwestern Niger, Tahoua district between Niamey and Agades, Iullemeden basin, Upper Cretaceous. Purple line, raised edge of the Upper Cretaceous outcropping (symbols: "10" in Fig. 1, "Cr 9-8" in Fig. 3), overhanging the reg with dunes including the Ibeceten Senonian outcropping (Cr7 in Fig. 3).
FIG. 4 in The oldest erymnochelyine turtle skull, Ragechelus sahelica n. gen., n. sp., from the Iullemmeden basin, Upper Cretaceous of Africa, and the associated fauna in its geographical and geological context
FIG. 4. — Log, simplified stratigraphic section, from Greigert (1966: pl. 37)'s Mont Indamane, presenting 16 banks, from bottom to top: alternately, 1, 3, 5, gypsiferous sandy marls and 2, 4, fine and silty sandstones; at top of 5, large dinosaur site (of Greigert et al. [1954]); 6, grey and black gypsiferous marls; 7, gypsiferous marls; 8, phosphatic breccia: fish, crocodile, batoids, sawfish (bone bed of the new turtle skull); 9, white sandstones: turtles, selachians; 10, black marls, salt; 11, 13, 15, yellow marls [with Libycoceras and Laffiteines]; 12, lumachella, with Rs (Veniella [Roudaireia] ouressensis); 14, lumachella; 16, ferrugineous sandstones (overlying crust). C LS, bank C in Lingham-Soliar (1991 [after David Ward]); D et al., Dikouma et al. (1993, 1994); F, Formation; G, Greigert (1966); MS, banks 8-10, 11/14, 19 and 25 in Moody & Sutcliffe (1991). Not to scale.
FIG. 1 in The oldest erymnochelyine turtle skull, Ragechelus sahelica n. gen., n. sp., from the Iullemmeden basin, Upper Cretaceous of Africa, and the associated fauna in its geographical and geological context
FIG. 1. — Geological map of the Iullemeden basin, extract from the Geological map of Africa, 1:10 millionth (Thiéblemont & Chêne 2016). Numbers: 1, Quaternary, 2.6-0 Ma, sedimentary; 5, Paleogene to Pleistocene, 66-0.012 Ma, sedimentary; 7, Tertiary, 66-2.6 Ma, sedimentary; 10, Upper Cretaceous, 100.5-66 Ma. Sedimentary; 12, Lower Cretaceous, 145-100.5 Ma, sedimentary; 39, Paleozoic; 45, 46, Proterozoic; 70, 86, 87, Archean. Blue square, area represented Fig. 2 (geographical map). Orange square, area represented in Fig. 3 (Greigert's geological map).
Data from: The acacia ants revisited: convergent evolution and biogeographic context in an iconic ant/plant mutualism
Phylogenetic and biogeographic analyses can enhance our understanding of multispecies interactions by placing the origin and evolution of such interactions in a temporal and geographical context. We use a phylogenomic approach—ultraconserved element sequence capture—to investigate the evolutionary history of an iconic multispecies mutualism: Neotropical acacia ants (Pseudomyrmex ferrugineus group) and their associated Vachellia hostplants. In this system, the ants receive shelter and food from the host plant, and they aggressively defend the plant against herbivores and competing plants. We confirm the existence of two separate lineages of obligate acacia ants that convergently occupied Vachellia and evolved plant-protecting behaviour, from timid ancestors inhabiting dead twigs in rainforest. The more diverse of the two clades is inferred to have arisen in the Late Miocene in northern Mesoamerica, and subsequently expanded its range throughout much of Central America. The other lineage is estimated to have originated in southern Mesoamerica about 3 Myr later, apparently piggy-backing on the pre-existing mutualism. Initiation of the Pseudomyrmex/Vachellia interaction involved a shift in the ants from closed to open habitats, into an environment with more intense plant herbivory. Comparative studies of the two lineages of mutualists should provide insight into the essential features binding this mutualism.
Example data map compressed with ISO29500-2 with 3 entry point for ArcGIS, MiraMon and OWS context file.
<p>A simple map consisting of a 1:1 000 000 country boundaries vector file, produced by the FAO (United Nations – FAOStat; geodata.grid.unep.ch/options.php?selectedID=2135) on top of a 5’ digital elevation model raster file (produced by the NOAA and NGDC; geodata.grid.unep.ch/options.php?selectedID=1414). Data has been obtained from the UNEP EDE Data Portal (UNEP 2013). Vector file is a Shapefile (a de facto standard) (ESRI 1998), while the raster file consists of either a raw signed 16-bit data or a long known TIFF file (Adobe 1992, Perkins 1995). Metadata and symbolization files are included. OPC specifies how to explicitly relate different parts using .rels files. These files are XML files with the same name as that of its respective source part, adding “.rels” and placed in a “rels” folder. Each of these files lists the target parts related to its source and the semantics of this relation.</p> <p>OPC can define entry points to the data by listing them in a “.rels” part in the root “rels” folder. In this file, three map files: for the ESRI software a world.mxd map, for the MiraMon software a world.mmm map, and a world.xml map in the form of an atom file following the new Web Service common standard (OGC OWS) context document. A geospatial application reading the package will determine which entry part it better supports to start recovering the data.</p>
Listening preferences for the different reproduction systems Stereo, Surround, and Wave Field Synthesis in the context of popular music
<p>We did a paired comparison preference test where listeners rated their listening preference for four different pop musical pieces presented by WFS, stereo or surround. The musical pieces were all mixed by the same person in order to try to minimize the influence of the mix on the ratings, but still trying to get the best out of every system, see [1] for details. The mixes are available at https://doi.org/10.14279/depositonce-5173.</p> <p>Here, we provide the results of the 22 listeners that participated in the experiment together with an analysis which calculates a Bradley-Terry-Luce model after Wickelmayer et al. [2].</p> <p>[1] Hold, C., Wierstorf, H., Raake, A. (2016), “The Difference Between Stereophony and Wave Field Synthesis in the Context of Popular Music,” 140th AES Convention, Paper 9533</p> <p>[2] https://cran.r-project.org/web/packages/eba/index.html</p>
Context modulation of learned attention deployment.
<p>Eye tracking data and statistical analysis of:</p> <p>Uengoer, M., Pearce, J. M., Lachnit, H., & Koenig, S. (2017). Context modulation of learned attention deploymentReward draws the eye, uncertainty holds the eye: Associative learning modulates distractor interference in visual search. Learning & Behavior. doi:10.3758/s13420-017-0277-y</p> <p>Abstract: In three experiments, we investigated the contextual control of attention in human discrimination learning. In each experiment, participants initially received discrimination training in which the cues from Dimension Awere relevant in Context 1 but irrelevant in Context 2, whereas the cues from Dimension B were irrelevant in Context 1 but relevant in Context 2. In Experiment 1, the same cues from each dimension were used in Contexts 1 and 2, whereas in Experiments 2 and 3, the cues from each dimension were changed across contexts. In each experiment, participants were subsequently shifted to a transfer discrimination involving novel cues from either dimension, to assess the contextual control of attention. In Experiment 1, measures of eye gaze during the transfer discrimination revealed that Dimension A received more attention than Dimension B in Context 1, whereas the reverse occurred in Context 2. Corresponding results indicating the contextual control of attention were found in Experiments 2 and 3, in which we used the speed of learning (associability) as an indirect marker of learned attentional changes. Implications of our results for current theories of learning and attention are discussed.</p> <p><br> Please see related identifier 10.5281/zenodo.583223 for analysis of:<br> Koenig, S., Uengoer, M., & Lachnit, H. (2017). Attentional bias for uncertain cues of shock in human fear conditioning: Evidence for attentional learning theory. Frontiers in Human Neuroscience. doi: 10.3389/fnhum.2017.00266.</p> <p>Please see related identifier 10.5281/zenodo.583233 for analysis of:<br> Koenig, S., Kadel, H., Uengoer, M., Schubö, A., & Lachnit, H. (2017). Reward Draws the Eye, Uncertainty Holds the Eye: Associative Learning Modulates Distractor Interference in Visual Search. Frontiers in Behavioral Neuroscience,11, 128. doi: 10.3389/fnbeh.2017.00128.</p> <p> </p> <p> </p>
Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation
<p>Data accompanying the manuscript "Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation".</p> <p>Note: For ATAC fragment files, e,caQTL full cis scan summary files, clustering objects, please see the CMDGA portal (https://cmdga.org/search/?searchTerm=stephen-parker%3AVarshney2024)<br>For raw data including fastq files, please see dbGaP repo phs001048.v3.p1</p> <p>Data in this repository includes:</p> <p>Filename: Description</p> <p>1. list of 8,666 genes for which exon-only counts were considered. See methods section "Adjusting RNA counts for overlapping gene annotations" in the manuscript.</p> <p>2. nucleus_sample_cluster_map.tsv: nucleus-sample-cluster map with other QC info. <br># index: nucleus identified syntax <modality>.<batch>.NM.<10X channel>.<barcode> <br># UMAP_1, UMAP_2: UMAP coordinates for visualization<br># modality: rna or atac<br># batch: processing batch identifier<br># hqaa_umi: high quality autosomal alignments (HQAA) for atac nuclei, unique molecular identifier (UMI) for tna <br># fraction_mitochondrial: fraction of reads mapping to the mitochondrial genome<br># cohort: sample cohort<br># tss_enrichment: TSS enrichment for atac nuclei<br># coarse_cluster_name: cluster name</p> <p>3. peaks.tar.gz: snATAC peak features including:<br># consensus-summits.bed: consensus summits along with the cell type that the summits was highest in.<br># narrow peaks in clusters<br># consensus summit feature (summit +- 150bp) identified in each cluster - these were used in GWAS enrichments.</p> <p>4. snrna-cell-type-specific-genes.tsv: Normalized expression scores for genes in each cell-type cluster</p> <p>5. eqtl_permute.tar.gz: Permutation scan eQTL in each cell-type cluster. Columns: <br># variant: syntax <chrom>:<hg38 pos>:<ref>:<alt><br># effect_allele: effect allele (was the alt allele)<br># other_allele: non-effect allele<br># feature: gene name<br># featureCoordinates_tss: gene TSS<br># p-value: nominal p value<br># beta: slope/beta of the linear regression. Keyed on the alt allele<br># se: standard error of the slope<br># snp: SNP ID<br># strand: gene strand<br># n_variants_tested: number of variants tested for the gene<br># distance_var_pheno: distance of the variant with the gene TSS<br># n_effective_tests: number of effective tests<br># p_beta: beta distribution adjusted p value<br># qvalue: qvalue (Storey)</p> <p>6. caqtl_permute.tar.gz: # Permutation scan caQTL in each cell-type cluster. Columns: <br># variant: syntax <chrom>:<hg38 pos>:<ref>:<alt><br># effect_allele: effect allele (was the alt allele)<br># other_allele: non-effect allele<br># feature: peak feature coordinates<br># p-value: nominal p value<br># beta: slope/beta of the linear regression. Keyed on the alt allele<br># se: standard error of the slope<br># snp: SNP ID<br># n_variants_tested: number of variants tested for the gene<br># distance_var_pheno: distance of the variant with the gene TSS<br># n_effective_tests: number of effective tests<br># p_beta: beta distribution adjusted p value<br># qvalue: qvalue (Storey)</p> <p>7. eqtl_credible_sets.tar.gz: # eQTL credible set. The file name denotes the egene and the signal hit id. Bed file columns: <br># 1: snp chromosome<br># 2: snp start<br># 3: snp end<br># 4: snp chrom_pos_ref_alt<br># 5: Bayes Factor <br># 6: PIP<br># 7: SNP rsid</p> <p>8. caqtl_credible_sets.tar.gz: # caqtl credible set. The file name denotes the capeak and the signal hit id. Bed file columns: <br># 1: snp chromosome<br># 2: snp start<br># 3: snp end<br># 4: snp chrom_pos_ref_alt<br># 5: Bayes Factor <br># 6: PIP<br># 7: SNP rsid</p> <p>9. cicero_all.tar.gz # Cicero coaccessibility results. Columns<br># Peak 1: Macs2 narrowpeak coordinate for peak 1<br># Peak 2: Macs2 narrowpeak coordinate for peak 2<br># coaccess: Cicero coaccessibility score</p> <p>10. cicero_gene_tss.tar.gz: Cicero coaccessibility results between peak and genes. Macs2 narrow peaks in the TSS+1kb upstream region are assigned that gene name. Columns<br># Cicero coaccessibility results between peak and genes. Macs2 narrow peaks in the TSS+1kb upstream region are assigned that gene name.Columns<br># Peak 1: Macs2 narrowpeak coordinate for peak 1<br># gene_name: Assigned gene<br># Peak 2: Macs2 narrowpeak coordinate for peak 2<br># coaccess: Cicero coaccessibility score<br>## Peak1 is the narrowpeak in the TSS region, peak2 is the distal peak</p> <p>11. mash.tar.gz Mashr results for e/caQTL - lfsr, posterior means and posterior SD for each tested eSNP-eGene, caSNP-caPeak pair. </p> <p>12. cellregmap.tar.gz: Cellregmap results for endothelial nucleus-level eQTL scans.<br>## Persistent genetic effect beta_g was calculated in a simple association model. <br>## An interaction model was fit to test for GxC effect. columns:<br># rho1, g2, e1, and eps2 are variance component measures outputs from CellRegMap corresponding to interaction, genetic, environment and residual variance components. <br># p_nominal: nominal p from cellRegMap<br># kind: model kind in CellRegMap - simple association or interaction<br># beta_g: Persistent genetic effect<br># gene_name: gene name for eQTL or peak feature name for caQTL<br># context: context used either factors (continuous) or subclusters (discrete)<br># snp: index snp for which model is fit. This is the most significant identified snp from our standard e,caQTL scans. chrom-hg38pos-rsid</p> <p><br>13. coloc-eqtl-caqtl.tsv: # Summary of eQTL-caQTL coloc in each cluster. Columns:<br># nsnps: Number of SNPs in the region<br># eqtl_hit: SNP with the highest Bayes factor in the SuSiE eQTL credible set<br># caqtl_hit: SNP with the highest Bayes factor in the SuSiE caQTL credible set<br># PP.H0.abf: Coloc posterior probability for no signal<br># PP.H1.abf: Coloc posterior probability for signal in dataset 1<br># PP.H2.abf: Coloc posterior probability for signal in dataset 2<br># PP.H3.abf: Coloc posterior probability for different signals in datasets 1 and 2<br># PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2<br># idx1: Index of the SuSiE credible set for dataset 1<br># idx2: Index of the SuSiE credible set for dataset 2<br># cluster: cluster name<br># egene: eGene name<br># capeak: caPeak coordinates</p> <p>14. cit-mrs-summary.tsv: Summary from CIT and MR Steiger directionality tests. Columns:<br># cluster: cluster name<br># egene: eGene name<br># capeak: caPeak coordinates<br># eqhit: SNP with the highest Bayes factor in the SuSiE eQTL credible set<br># cahit: SNP with the highest Bayes factor in the SuSiE caQTL credible set<br># p.cit_c_c-e: P value for CIT causal cahit-ca-to-e model<br># q.cit_c_c-e: q value for CIT causal cahit-ca-to-e model<br># p.cit_rc_c-e: P value for CIT reverse-causal eqhit-ca-to-e model <br># q.cit_rc_c-e: value for CIT reverse-causal eqhit-ca-to-e model <br># p.cit_c_e-c: P value for CIT causal eqhit-e-to-ca model<br># q.cit_c_e-c: q value for CIT causal eqhit-e-to-ca model<br># p.cit_rc_e-c: P value for CIT reverse-causal cahit-e-to-ca model <br># q.cit_rc_e-c: q value for CIT reverse-causal cahit-e-to-ca model <br># cit_direction: Direction inferred from CIT <br># correct_causal_direction--ca-to-e: MR Steiger directionality test - is ca-to-e direction correct?<br># correct_causal_direction--e-to-ca: MR Steiger directionality test - is e-to-ca direction correct?<br># sensitivity_ratio--ca-to-e: MR Steiger Sensitivity ratio for ca-to-e model <br># sensitivity_ratio--e-to-ca: MR Steiger Sensitivity ratio for e-to-ca model<br># steiger_test--ca-to-e: MR Steiger directionality test P value for ca-to-e model<br># steiger_test--e-to-ca: MR Steiger directionality test P value for e-to-ca model<br># steiger_q--ca-to-e: MR Steiger directionality test q value for ca-to-e model<br># steiger_q--e-to-ca: MR Steiger directionality test q value for e-to-ca model<br># mrs_direction: Direction inferred from MR Steiger<br># direction: Direction inferred requiring consistent results between CIT and MR Steiger directionality test</p> <p>15. coloc-gwas-eqtl.tsv and<br>16. coloc-gwas-caqtl.tsv # Summary of e/caQTL coloc with GWAS in each cluster. Columns:<br># nsnps: Number of SNPs in the region<br># gwas_hit: SNP with the highest bayes factor in the SuSiE GWAS credible set<br># eqtl_hit: SNP with the highest bayes factor in the SuSiE eQTL credible set<br># caqtl_hit: SNP with the highest bayes factor in the SuSiE caQTL credible set<br># PP.H0.abf: Coloc posterior probability for no signal<br># PP.H1.abf: Coloc posterior probability for signal in dataset 1<br># PP.H2.abf: Coloc posterior probability for signal in dataset 2<br># PP.H3.abf: Coloc posterior probability for different signal in datasets 1 and 2<br># PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2<br># idx1: Index of the SuSiE credible set for dataset 1<br># idx2: Index of the SuSiE credible set for dataset 2<br># cluster: cluster name<br># egene: eGene name<br># capeak: caPeak coordinates<br># p12min: Min prior p12 where the PP H4 > 0.5. Lower this value, more robust is the colocalization<br># trait: GWAS trait name<br># gwas_locus: GWAS locus name for the coloc test - a 250kb left and right flanking genomic window on this SNP was considered for testing coloc between all pairs of GWAS/QTL signals identified in this region <br># traitname: Expanded GWAS trait name<br># variable_type: GWAS type <br># source: Source of GWAS - either UKBB or other study</p> <p>17. supplementary_tables.xlsx: Supplementary tables from the manuscript.<br>Information included in sheets:<br>1. "marker_genes": Marker genes known from literature used to annotate clusters<br>2. "n_nuclei": n pass-QC nuclei per modality-sample-cluster</p> <p>2. "snrna_GO_enrichment": GO term enrichment: matrix of cluster vs top 2 GO terms</p> <p>3. "qtl_scan_info": e/caQTL scan info<br>cluster: cluster<br>ntested_eqtl: N genes tested for eQTL<br>nsig_eqtl: N significant (5% FDR) eGenes<br>n_pheno_pcs_eqtl: N phenotype PCs considered for eQTL<br>ratio_eqtl: Ratio of N eGenes/N genes tested<br>nsig_caqtl: N peaks tested for caQTL<br>ntested_caqtl: N significant (5% FDR) caPeaks<br>n_pheno_pcs_caqtl: N phenotype PCs considered for caQTL<br>ratio_caqtl: Ratio of N caPeaks/N peaks tested<br>nsamples_eqtl: N samples for eQTL<br>nsamples_caqtl: N samples for caQTL</p> <p>4. "gwas_trait_list": GWAS trait info<br>trait: GWAS trait ID<br>traitname: GWAS trait description<br>variable_type: GWAS type. case/control (cc), continuous_irnt=continuous inverse-normal transformed<br>source: GWAS source<br>doi: GWAS study DOI</p> <p>5. "traits_in_ldsc_baseline" - list of annotations included in the baseline model for LDSC</p> <p>6. "gwas_enrichment_in_peaks" GWAS enrichment in cluster peaks (S-LDSC)</p> <p>7. "gwas_enrichment_in_qtl_peaks" GWAS enrichment in QTL peaks (fGWAS) # fGWAS results comparing GWAS enrichment in type 1 annotations<br>CI_lower_ln, estimate_ln, CI_upper_ln: natural log of lower confidence interval, estimate, and upper confidence interval<br>trait: trait id<br>traitname: trait name<br>annotation: annotation<br>sig: 1 if CIs don't overlap 0, otherwise 0</p> <p>8. t2d_gwas_caqtl_coloc and<br>9. t2d_gwas_eqtl_coloc:<br>Summary of e,caQTL coloc with T2D GWAS in each cluster, along with target gene nominations. Columns:<br>nsnps: Number of SNPs in the region<br>gwas_hit: SNP with the highest bayes factor in the SuSiE GWAS credible set<br>eqtl_hit: SNP with the highest bayes factor in the SuSiE eQTL credible set<br>caqtl_hit: SNP with the highest bayes factor in the SuSiE caQTL credible set<br>PP.H0.abf: Coloc posterior probability for no signal<br>PP.H1.abf: Coloc posterior probability for signal in dataset 1<br>PP.H2.abf: Coloc posterior probability for signal in dataset 2<br>PP.H3.abf: Coloc posterior probability for different signal in datasets 1 and 2<br>PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2<br>idx1: Index of the SuSiE credible set for dataset 1<br>idx2: Index of the SuSiE credible set for dataset 2<br>cluster: cluster name<br>egene: eGene name<br>capeak: caPeak coordinates<br>p12min: Min prior p12 where the PP H4 > 0.5. Lower this value, more robust is the colocalization<br>trait: GWAS trait id<br>diamante_gwas_locus: GWAS signal from the DIAMANTE 2018 study. Some signals that our SuSiE runs identified were not present in the original study in which case this column is NA<br>traitname: Expanded GWAS trait name<br>capeak_in_tss: caPeak in TSS + 1kb upstream region of a gene<br>gene_target_standard_cicero: caPeak coaccessible with TSS peak of a gene considering nuclei from all samples for co-accessibility<br>gene_target_allelic_cicero: caPeak coaccessible with TSS peak of a gene considering nuclei from samples homozygous for the caSNP allele associated with increased accessibility<br>gwashit_nominal_egene: gwas_hit nominally associated with these genes nominated in the columns capeak_in_tss, gene_target_standard_cicero, and gene_target_allelic_cicero</p> <p>10. MPRA results for the C2CD4A locus</p>
Amyloid-motif-dependent tau self-assembly is modulated by isoform sequence context
<p><span>The microtubule-associated protein tau is implicated in neurodegenerative diseases characterized by amyloid formation. Mutations associated with frontotemporal dementia increase tau aggregation propensity and disrupt its endogenous microtubule-binding activity. However, the structural relationship between aggregation propensity and biological activity remains unclear. We employed a multi-disciplinary approach, including computational modeling, NMR, cross-linking mass spectrometry, and cell models to engineer tau sequences that modulate its structural ensemble. Our findings show that substitutions near the conserved 'PGGG' </span><span>β</span><span>-turn motif informed by tau isoform context reduce tau aggregation in vitro and cells and can even counteract aggregation from disease-associated proline-to-serine mutations. Engineered tau sequences maintain microtubule binding and explain why 3R isoforms exhibit reduced pathogenesis compared to 4R. We propose a simple mechanism to reduce the formation of pathogenic tau species while preserving biological function, thus offering insights for therapeutic strategies aimed at reducing tau protein misfolding in neurodegenerative diseases.</span></p> <p><strong>Description of Source Data and Supplementary Data</strong>: All MD, NMR (peptide and tauRD), ThT, XL-MS, MT stabilization, MT:tau modeling, and cell-based aggregation data are available in the Source_Data directory as Data S1, Data S2, Data S3, Data S4, Data S5, Data S6, Data S7, and Data S8, respectively. Supplementary Data for raw MD trajectory files, structure files for MSM modeling and validation file, and tau:MT modeling are available as "Supplementary_Data_MD_Trajectories_Structures", "Supplementary_Data_MSM_models_Structures" and "Supplementary_Data_MT-tau_complex_models", respectively."</p>
Webis-Context-SciSumm-2023
<p>The Webis-Context-SciSumm-2023 is a large scale dataset suitable for studying contextualized summarization of scientific papers. The corpus contains approximately 540K computer science papers encompassing 4.6M citation texts and relevant information for these citations from the cited papers. The subset (approximately 25K papers) provided contains abstractive summaries of the relevant content from LLaMA (V1) and Vicuna (13B) models. </p><p>The summaries for the completed dataset will be updated on completion (due to computational constraints).</p>
Semantic annotation of PLoS journal citation contexts
<p>Dataset </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.