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Data for: The biomechanics of tooth strength: testing the utility of simple models for predicting fracture in geometrically complex teeth
<p>Teeth must fracture foods while avoiding being fractured themselves. This study evaluated dome biomechanical models used to describe tooth strength. Finite element analysis (FEA) tested whether the predictions of the dome models applied to the complex geometry of an actual tooth. A finite element model (FEM) was built from microCT scans of a human M3. The FEA included three loading regimes simulating contact between 1) a hard object and a single cusp tip, 2) a hard object and all major cusp tips, and 3) a soft object and the entire occlusal basin. Our results corroborate the dome models with respect to the distribution and orientation of tensile stresses, but document heterogeneity of stress orientation across the lateral enamel. This implies that high stresses might not cause fractures to fully propagate between cusp tip and cervix under certain loading conditions. The crown is most at risk of failing during hard object biting on a single cusp. Geometrically simple biomechanical models are valuable tools for understanding tooth function but do not fully capture aspects of biomechanical performance in actual teeth whose complex geometries may reflect adaptations for strength.</p>
Gene structure prediction results and execution commands and options by GINGER and similar tools
<p>Gene structure prediction results, execution commands, and options by GINGER and similar tools</p> <ul> <li>genome.tar.gz ... genome sequence data used for benchmark test in the paper. </li> <li>input_of_EVM.tar.gz ... input dataset for EVM</li> <li>input_of_GINGER.tar.gz ... input dataset for GINGER</li> <li>input_of_MAKER.tar.gz ... input dataset for MAKER</li> <li>reference_annotation.tar.gz ... gff data used for benchmark test in the paper.</li> <li>result_of_EVM.tar.gz .... results and stats information of EVM (including intermediate result file of <em>C.elegans</em>, <em>D. melanogaster</em>, <em>O. sativa</em>, <em>D. rerio</em>, and <em>H. sapiens</em>)</li> <li>result_of_GINGER.tar.gz .... results and stats information of GINGER (including intermediate result files of <em>C.elegans</em>)</li> <li>result_of_MAKER.tar.gz .... results and stats information of MAKER (including intermediate result files of <em>C.elegans</em>)</li> </ul>
ExtendedData Fig. 9 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
ExtendedData Fig. 9 | Correlationbetweenseasonalityanddisturbance. At thelocallandscapelevel (a), seasonalityiscalculatedasthestandarddeviationof meanmonthlytemperaturevaluesthroughouttheyearatthelandscapecentroid (n = 31). Highdisturbancemeans 50% of thestudylandscapeareaoverlaps areasofhighnatural (forexamplestorms,glaciers,fires) orAnthropogenic (for exampleforestloss).Boxplotsshowmedian,interquartile range,andwhiskers toextremevalues (outliersaredatapoints>1.5x quartiles).Statisticsarefrom atwo-sided Wilcoxon test.Atthespecieslevel (b), communitymeanvalues (n = 31), arecalculatedusingspecies' distributionalseasonalityanddisturbance scores.Disturbanceiscalculatedastheproportionof thespeciesbreedingrange whichoverlapsareasofhighnatural (forexamplestorms,glaciers,fires) or anthropogenic (forexampleforestloss) disturbance.Seasonalityiscalculated asthestandarddeviationof meanmonthlytemperaturevaluesthroughoutthe year,averagedacrossallgridcellsinthespecies' breedingrange.Statisticsare fromalinearregressionwith Gaussianerrors;purplelineshowsmodelfit;shaded areais 95% confidenceintervals.
ExtendedData Fig. 8 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
ExtendedData Fig. 8 | Relationshipbetweendispersallimitation (nHWI) anddiet. Datashownfor (a) 276 birdspeciessampledacross 18 temperate studylandscapes,and (b) 817 birdspeciessampledacross 13 tropicalstudy landscapes.Dietaryclasseswith <5 specieswereremovedfromtheanalysis.Diet classificationsarefrom Tobiasand Pigot110. F-statisticand P-valuearecalculated withatwo-way ANOVA.Boxplotsshowmedian,interquartile range,andwhiskers toextremevalues (outliersaredatapoints>1.5x quartiles).
ExtendedData Fig. 7 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
ExtendedData Fig. 7 | Predictorsofdispersallimitationinbirds. Results shownareoutputsof phylogeneticleastsquaresmodelpredictingdispersal limitation (nHWI) acrossallbirdspeciessampled,includinglong-distance migrants (swallowimage,dark bars;n = 1034); onlyresidentspeciesandshort distance/partialmigrants (thrushimage,medium bars;n = 921); orresident speciesonly (pittaimage,palebars;n = 858). Panelspresentthreesetsofmodels withincreasingcomplexity:aunivariatemodelwithsinglepredictor (a,d), and multivariatemodelswithtwo (b,e) andthree (c,f) predictors.Eachpredictor iscalculatedatthespecieslevelbyaveragingacross landscapeswhereeach speciesispresent.Disturbance (red) iscalculatedastheproportionofspecies breedingrangewhichoverlapsareasofhigh natural (e.g. storms,glaciers,fires) oranthropogenic (e.g. forestloss) disturbance.Absolutelatitude (yellow) is calculatedasthecentroidlatitudeof thespeciesbreedingrange.Seasonality (blue) iscalculatedasthestandarddeviationof meanmonthlytemperature valuesthroughouttheyear,averagedacrossallgridcellsinthebreedingrange. a–c, EffectsiZeestimatesaregivenwith 95% confidenceintervals;anegative effectindicatesreduceddispersallimitation (thatisincreased dispersalability). R2 and AICvaluesarecalculatedforfullsamplemodelsonly.d–f, Proportion of independentvariationexplainedbyeachmodelcovariate,calculatedusing hierarchicalpartitioning.
ExtendedData Fig. 6 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
ExtendedData Fig. 6 | Driversoffragmentationsensitivitywithnatural disturbances. Resultsof Bayesianphylogeneticmixedeffectmodelspredicting fragmentationsensitivityfor 1564 birdpopulations (n = 1034 species). Populationswereclassifiedasfragmentationsensitiveiftheywereidentifiedas 'Forest-core' by BIOFRAG. Restrictedanalysisassignedfragmentationsensitivity onlyto 'Forestspecialists' (a); Expandedanalysisassignedfragmentation sensitivitytoboth ' Forestspecialist' and ' Forestassociated' species (b; see Methods).Bayesianposteriordistributionisshownabovetheline;effectsiZe estimateswithcredibleintervals (CI) belowtheline (68%: thickerrorbars; 95%: thinerrorbars).HigheffectsiZesindicateapositiveassociationwith fragmentationsensitivity;loweffectsiZesindicateanegativeassociation. Finchandhawksilhouettesindicatethatbothmodelswererunonacomplete sample. Historicaldisturbanceisabinaryvariable (1/0) calculatedusingnatural disturbance (forexamplefires,storms & glaciation) layersonly.
ExtendedData Fig. 2 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
ExtendedData Fig. 2 | Thelatitudinalgradientinaveragedispersallimitation ofbirdassemblages.Datapoints (colouredbylevelofhistoricaldisturbance) showthecommunitymeanvaluesforavianassemblagessampledat 31 study landscapesmappedin Fig.1. Theoverallgradientisnotexplainedbylandscape disturbancehistory.Absolutelatitudeisthecentroidlatitudeofallsampling pointsineachstudylandscape.Mean dispersallimitationisthenegative (thatis inverse) hand-wingindex (nHWI) averagedacrossallspeciesintheassemblage; nHWIislogarithmicallyscaled (log(1/HWI)) forvisualiZation.Statisticsarefroma linearmodelwith Gaussianerrors;purplelineshowsmodelfit (R2 = 0.44); shaded regionshowsthestandarderrorof theregressioncoefficient.
Fig. 3 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
Fig. 3 | Fragmentationsensitivityincreaseswithdispersallimitationinbird assemblages.a, Variationin fragmentationsensitivity anddispersalability plottedonaconsensusphylogenetictree.Eachbranchrepresentsagenus (n = 441), withdataattipsaveragedacrossfamilies (n = 115) forvisualiZation. Branchcoloursindicatedispersallimitation (leastdispersivespeciesin red); tipcoloursshowtheproportionoffragmentation-sensitivespeciesineach family (expandedanalysis;mostsensitivein yellow).b, Datapoints (coloured bylevelofhistoricaldisturbance) aremeansfor 31 studylandscapes.Foreach assemblage,fragmentationsensitivityisassignedtoforest-corespecies withhighforestdependency (Restrictedanalysis),andmeandispersal limitationisthenHWIaveragedacrossallspecies;nHWIislogarithmically scaled (log(1/HWI)) forvisualiZation.StatisticsarefromageneraliZedlinear modelwithquasi-binomialerrors;purplelineshowsmodelfit (R2 = 0.180); shadedregionshows 95% confidenceintervals.Boxplotsin b showthesame distributionswithmedianvalue,interquartilerangeandwhiskerstoextreme values (outliersaredatapoints>1.5× quartiles).Resultsfortheexpandedsample areshownin ExtendedData Fig. 4.
Fig. 2 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
Fig. 2 | Globalpatternsoflandscapedisturbanceanddispersallimitation. a, Thepresenceofnatural oranthropogenichistoricaldisturbancesrecorded ineachgridcell.Naturaldisturbancepressures (bright red) includemajorfires, stormsandglaciation;theseeventshavetypicallypersisted forlongerperiods oftimeandmaycausecompleteremovalofforestbiota.Anthropogenicforest loss (palered) representsmore recentdisturbancethatoftenalterscomposition oflocalassemblageswithoutcompleteeradication.b, VariationinnHWI averagedacross speciesoccurringineachgridcell,rangingfromlow (blue) to high (red) dispersallimitation.Dispersallimitationdataarecalculatedfrom measurementsof 10,562 birdspecies,logarithmicallyscaledforvisualiZation (log(1/HWI)).Yellowdotsshowstudylandscapes (21 from BIOFRAG;10 from additionalsampling).Gridcellsin a and b are 2.5 arcminutes. c,d, Hypothetical relationships:extinctionfilterspredictthatfragmentationsensitivityis negativelyassociatedwithhistoricaldisturbance (c), whiledispersal-related mechanismspredictthatfragmentationsensitivityispositivelyassociated with dispersallimitation (d).
Fig. 1 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
Fig. 1 | Hypothesespredictingthedistributionoffragmentation-sensitive species. Thetoppathway (a) illustrateshow ' extinctionfilters' linkedto historicaldisturbances (forexample, fireandanthropogenicforestloss) canbenon-random,removingspeciestraitsassociatedwithsensitivityto disturbanceandretainingmoreresilientsurvivors.Tropicalbirdcommunities thathavelargelyavoidedseverehistoricaldisturbancetheoreticallycontain morespecieswithdisturbance-sensitivetraits (suchaspoordispersaland ecologicalspecialiZation),accentuatingtheimpactsofforestfragmentation (b). Backgroundturnoverofspecies,shownin (b) butpresentinallpathways, israndomwithrespecttodisturbance-sensitivetraits.Adifferentmechanism involvestheevolutionofflightadaptationstocopewithseasonalfluctuations intemperatureandresources (including vegetation,insects,flowersand fruits).In birds, thepredominantadaptationtoseasonalityinvolvesincreased mobility (fromlocaldispersaltolong-distancemigration),sohighly seasonal communitieslackdispersal-limitedspecies,potentiallyincreasingtheir resiliencetoforestfragmentation (c) incomparisonwithclimaticallystable regions (b). Relativespeciesrichnessisshownbythenumberofbirdsilhouettes inthecommunity.
ExtendedData Fig. 5 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
ExtendedData Fig. 5 | Driversoffragmentationsensitivitywith Anthropogenicdisturbances.Resultsof Bayesianphylogeneticmixedeffect modelspredictingfragmentationsensitivityfor 1564 birdpopulations (n = 1034 species).Populations wereclassifiedasfragmentationsensitiveif they wereidentifiedas 'Forest-core' by BIOFRAG. Restrictedanalysisassigned fragmentationsensitivityonlyto 'Forestspecialists' (a); Expandedanalysis assignedfragmentationsensitivityto both 'Forestspecialist' and 'Forest associated' species (b; see Methods). Bayesianposteriordistributionisshown abovetheline;effectsiZeestimateswithcredibleintervals (CI) belowtheline (68%: thickerrorbars;95%:thinerrorbars).HigheffectsiZesindicateapositive associationwithfragmentationsensitivity;loweffect siZesindicateanegative association.Finchandhawksilhouettesindicatethatbothmodelswererunon acompletesample.Historicaldisturbanceisabinaryvariable (1/0) calculated usinganthropogenicdisturbance (forestloss) only.
ExtendedData Fig. 1 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
ExtendedData Fig. 1 | Correlationbetweendisturbanceandlatitude. Study landscapesexposedtohighlevelsofhistoricaldisturbance (n = 16 landscapes; red) tendtobefoundathigherlatitudesthanlandscapesexposedtolowerlevels ofhistoricaldisturbance (n = 15 landscapes;blue).Disturbancelevelisestimated fromglobalmapsof majorhistoricaldisturbance (forexamplefire,glaciation). Absolutelatitudeisthecentroidlatitudeof allsamplingpointsineachstudy landscape.Boxplotsshowthemedian, interquartilerangeandwhiskers extendingtoextremevalues.Statisticsshowresultsof two-sided Wilcoxon ranksum testindicatingthatdisturbanceandlatitudearecorrelated (without accountingforspatialauto-correlation).
ExtendedData Fig. 4 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
ExtendedData Fig. 4 | Correlationbetweenfragmentationsensitivityand dispersallimitationinbirds. Datapoints (colouredbylevelofhistorical disturbance) arecommunitymeanvaluesforavian assemblagesat 31 study landscapesmappedin Fig.1. Foreachassemblage,fragmentationsensitivity isassignedtospecieswith 'Forest-core' habitatpreferenceandeitherahigh or mediumforestdependency (Expandedanalysis).Meandispersallimitationis thenegative (thatisinverse) hand-wingindex (nHWI) averagedacross allspecies intheassemblage;nHWIislogarithmicallyscaled (log(1/HWI)) forvisualiZation. StatisticsarefromageneraliZedlinearmodelwithquasi-binomialerrors;purple lineshowsmodelfit (R2 = 0.270); shadedregionshows 95% confidenceintervals. Adjacentboxplotsshowthesamedistributionwithmedianvalue,interquartile range,andwhiskerstoextremevalues (outliersaredatapoints>1.5x quartiles).
ExtendedData Fig. 3 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
ExtendedData Fig. 3 | Correlationbetweenfragmentationsensitivityand latitudeinbirds. Datapoints (colouredbylevelofhistoricaldisturbance) are communitymeanvaluesforavianassemblagesat 31 studylandscapesmapped in Fig.1. Foreachassemblage,fragmentationsensitivityisassignedto (a) Forest-specialistspecieswith 'Forest-core' habitatpreference (Restrictedanalysis),and (b) Forest-associatedspecieswith ' Forest-core' habitatpreference (Expanded analysis).Absolutelatitudeistheabsolutecentroidlatitudeofallsampling pointsineachstudylandscape.StatisticsarefromgeneraliZedlinearmodels withquasi-binomialerrors;purplelineshowsmodelfit (Restrictedanalysis:R2 = 0.2559, Expandedanalysis:R2 = 0.3208); shadedregionshowsthe 95% confidence intervals.
Fig. 5 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
Fig. 5 | Predictorsofdispersallimitationinbirds. Resultsshownareoutputs ofphylogeneticgeneraliZedleast-squaresmodelspredictingdispersal limitation (nHWI) acrossallbirdspeciessampled,includinglong-distance migrants (swallowimage,dark bars;n = 1,034), onlyresidentspeciesandshort distance/partialmigrants (thrushimage,medium bars;n = 921) oronlyresident species (pittaimage, palebars;n = 858). Panelspresentthreesetsofmodels withincreasingcomplexity:aunivariatemodelwithsinglepredictor (a,d), and multivariatemodelswithtwo (b,e) andthree (c,f) predictors.Eachpredictoris calculatedatthespecieslevelbyaveragingacrosslandscapeswhere eachspecies ispresent.Disturbance (red) isthelocalbinarydisturbancescore,latitude (yellow) istheabsolutelatitudeof thelandscapecentroids andseasonality (blue) isthestandarddeviationof meanmonthlytemperaturevalues.a–c, Effect-siZeestimateswith 95% confidenceintervals;anegativeeffectindicates reduceddispersallimitation (thatis,increaseddispersalability).R2 and Akaike informationcriterion (AIC) valuesarecalculatedforfullsamplemodelsonly. d–f, Proportionof independentvariationexplainedbyeachmodelcovariate, calculatedusinghierarchicalpartitioning.
Fig. 4 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds
Fig. 4 | Dispersallimitation (nHWI) explainsvariationinfragmentation sensitivity.Resultsof Bayesianphylogeneticmixed-effect modelspredicting fragmentationsensitivityforall 1,564 birdpopulations (n = 1,034 species). Populationswereclassifiedasfragmentationsensitiveiftheywereidentifiedas 'Forest-core' by BIOFRAG. Restrictedanalysisassignedfragmentationsensitivity onlyto 'Forestspecialists' (a); Expandedanalysisassignedfragmentation sensitivitytoboth ' Forestspecialist' and ' Forestassociated' species (b; see Methods).Bayesianposteriordistributionisshownabovetheline;effect-siZe estimateswithcredibleintervals (CI) arebelowtheline (thickerrorbars,68%; thinerrorbars,95%).HigheffectsiZesindicateapositiveassociationwith fragmentationsensitivity;loweffectsiZesindicateanegativeassociation. Historicaldisturbanceisabinaryvariable (1/0) calculatedusingalldisturbance layers (forestloss,glaciation,stormsandfires).
Quantitative prediction of excited-state decay rates for radical anion photocatalysts
<p>The input and output files related to the DFT calculations carried out for the work entitled "Quantitative prediction of excited-state decay rates for radical anion photocatalysts" are loaded in this repository.</p> <p>The zip file contains the optimized geometries of the compounds involved in the job.</p> <pre> </pre>
Data_for_Rational_Prediction_of_PROTAC_Compatible_PPI
<p>Tar file containing the dataset used for the "Rational prediction of PROTAC compatible ligase-target interfaces" manuscript by Pereira et al.</p>
A novel immune-related prognostic signature based on Chemoradiotherapy sensitivity predicts long-term survival in patients with esophageal squamous cell carcinoma.
<p>Raw Data for PeerJ</p>
Novel M2-like tumor-associated macrophage-related biomarkers predict prognosis of Acute myeloid leukemia patients.
<p>The supplementary material for the Novel M2-like tumor-associated macrophage-related biomarkers predict prognosis of Acute myeloid leukemia patients.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.