cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.4579348.v3
title: Supplementary table 3: Pairwise Fst values (below diagonal) and corresponding p-values (above diagonal) calculated using13 microsatellites genotyped in 45 individuals from RAD sequencing resolves fine-scale population structure in a benthic invertebrate: implications for understanding phenotypic plasticity

description: The field of molecular ecology is transitioning from the use of small panels of classical genetic markers such as microsatellites to much larger panels of single nucleotide polymorphisms (SNPs) generated by approaches like RAD sequencing. However, few empirical studies have directly compared the ability of these methods to resolve population structure. This could have implications for understanding phenotypic plasticity, as many previous studies of natural populations may have lacked the power to detect genetic differences, especially over micro-geographic scales. We therefore compared the ability of microsatellites and RAD sequencing to resolve fine-scale population structure in a commercially important benthic invertebrate by genotyping great scallops (<i>Pecten maximus</i>) from nine populations around Northern Ireland at 13 microsatellites and 10 539 SNPs. The shells were then subjected to morphometric and colour analysis in order to compare patterns of phenotypic and genetic variation. We found that RAD sequencing was superior at resolving population structure, yielding higher <i>F</i><sub>st</sub> values and support for two distinct genetic clusters, whereas only one cluster could be detected in a Bayesian analysis of the microsatellite dataset. Furthermore, appreciable phenotypic variation was observed in size-independent shell shape and coloration, including among localities that could not be distinguished from one another genetically, providing support for the notion that these traits are phenotypically plastic. Taken together, our results suggest that RAD sequencing is a powerful approach for studying both population structure and phenotypic plasticity in natural populations.
DOI: 10.6084/m9.figshare.4579327
title: Data from: The global fire-productivity relationship

description: Global fire activity map, in shape (GIS) and in png (image) formats from:<br>Pausas J.G. & Ribeiro E. 2013. The global fire-productivity relationship. <i>Global Ecology and Biogeography.</i> 22: 728-736.<br>
DOI: 10.6084/m9.figshare.4579348.v2
title: Supplementary table 3 from ddRAD sequencing resolves fine-scale population structure in a benthic invertebrate with implications for understanding phenotypic plasticity

description: Pairwise Fst values (below diagonal) and corresponding p-values (above diagonal) calculated using13 microsatellites genotyped in 45 individuals.
DOI: 10.6084/m9.figshare.4579345.v3
title: Supplementary table 2: Pairwise Fst values (below diagonal) and corresponding p-values (above diagonal) calculated using (a) 13 microsatellites genotyped in 180 individuals and (b) 10,539 SNPs genotyped in 45 individuals from RAD sequencing resolves fine-scale population structure in a benthic invertebrate: implications for understanding phenotypic plasticity

description: The field of molecular ecology is transitioning from the use of small panels of classical genetic markers such as microsatellites to much larger panels of single nucleotide polymorphisms (SNPs) generated by approaches like RAD sequencing. However, few empirical studies have directly compared the ability of these methods to resolve population structure. This could have implications for understanding phenotypic plasticity, as many previous studies of natural populations may have lacked the power to detect genetic differences, especially over micro-geographic scales. We therefore compared the ability of microsatellites and RAD sequencing to resolve fine-scale population structure in a commercially important benthic invertebrate by genotyping great scallops (<i>Pecten maximus</i>) from nine populations around Northern Ireland at 13 microsatellites and 10 539 SNPs. The shells were then subjected to morphometric and colour analysis in order to compare patterns of phenotypic and genetic variation. We found that RAD sequencing was superior at resolving population structure, yielding higher <i>F</i><sub>st</sub> values and support for two distinct genetic clusters, whereas only one cluster could be detected in a Bayesian analysis of the microsatellite dataset. Furthermore, appreciable phenotypic variation was observed in size-independent shell shape and coloration, including among localities that could not be distinguished from one another genetically, providing support for the notion that these traits are phenotypically plastic. Taken together, our results suggest that RAD sequencing is a powerful approach for studying both population structure and phenotypic plasticity in natural populations.
DOI: 10.6084/m9.figshare.4579324
title: Trajectory of disability and quality-of-life in non-geriatric and geriatric survivors after severe traumatic brain injury

description: <i>Objective</i>: The objective was to investigate disability and health-related quality-of-life (HRQoL) 3, 6 and 12 months after traumatic brain injury (TBI) in non-geriatric (≤ 65 years) and geriatric patients (&gt; 65 years). <i>Methods</i>: Patients ≥ 16 years who sustained a severe TBI (Abbreviated Injury Scale of the head region &gt; 3) were included in this prospective, multi-centre study. Outcome measures were Glasgow Outcome Scale Extended (GOSE; disability), SF-12 (HRQoL). Mixed linear model analyses were performed. <i>Results</i>: Three hundred and fifty-one patients (median age = 50 years; interquartile range (IQR) = 27–67) were included; 73.2% were male and 27.6% were geriatric patients. Median GOSE at 3, 6 and 12 months was 5 (IQR = 3–7), 6 (IQR = 4–8) and 7 (IQR = 5–8); this increase (slope<sub>time</sub> = 0.22, <i>p</i> &lt; 0.0001) was age dependent (slope<sub>age*time</sub> = –0.06, <i>p</i> = 0.003). Median SF-12 physical component scale score at 3, 6 and 12 months was 42.1 (IQR = 33.6–50.7), 46.6 (IQR = 37.4–53.9) and 50.4 (IQR = 39.2–55.1); this increase (slope<sub>time</sub> = 1.52, <i>p &lt;</i> 0.0001) was not age dependent (slope<sub>age*time</sub> = –0.30, <i>p</i> = 0.083). SF-12 mental component scale scores were unchanged. <i>Conclusions</i>: Disability decreased and HRQoL improved after TBI between 3–12 months. In geriatric patients this improvement was relevant for HRQoL only.
DOI: 10.6084/m9.figshare.4579327.v2
title: Data from: The global fire-productivity relationship

description: Global fire activity map, in shape (GIS) and in png (image) formats from:<br>Pausas J.G. & Ribeiro E. 2013. The global fire-productivity relationship. <i>Global Ecology and Biogeography.</i> 22: 728-736.<br>
DOI: 10.6084/m9.figshare.4579345.v2
title: Supplementary table 2 from ddRAD sequencing resolves fine-scale population structure in a benthic invertebrate with implications for understanding phenotypic plasticity

description: Pairwise Fst values (below diagonal) and corresponding p-values (above diagonal) calculated using (a) 13 microsatellites genotyped in 180 individuals and (b) 10,539 SNPs genotyped in 45 individuals.
DOI: 10.6084/m9.figshare.4579324.v1
title: Trajectory of disability and quality-of-life in non-geriatric and geriatric survivors after severe traumatic brain injury

description: <i>Objective</i>: The objective was to investigate disability and health-related quality-of-life (HRQoL) 3, 6 and 12 months after traumatic brain injury (TBI) in non-geriatric (≤ 65 years) and geriatric patients (&gt; 65 years). <i>Methods</i>: Patients ≥ 16 years who sustained a severe TBI (Abbreviated Injury Scale of the head region &gt; 3) were included in this prospective, multi-centre study. Outcome measures were Glasgow Outcome Scale Extended (GOSE; disability), SF-12 (HRQoL). Mixed linear model analyses were performed. <i>Results</i>: Three hundred and fifty-one patients (median age = 50 years; interquartile range (IQR) = 27–67) were included; 73.2% were male and 27.6% were geriatric patients. Median GOSE at 3, 6 and 12 months was 5 (IQR = 3–7), 6 (IQR = 4–8) and 7 (IQR = 5–8); this increase (slope<sub>time</sub> = 0.22, <i>p</i> &lt; 0.0001) was age dependent (slope<sub>age*time</sub> = –0.06, <i>p</i> = 0.003). Median SF-12 physical component scale score at 3, 6 and 12 months was 42.1 (IQR = 33.6–50.7), 46.6 (IQR = 37.4–53.9) and 50.4 (IQR = 39.2–55.1); this increase (slope<sub>time</sub> = 1.52, <i>p &lt;</i> 0.0001) was not age dependent (slope<sub>age*time</sub> = –0.30, <i>p</i> = 0.083). SF-12 mental component scale scores were unchanged. <i>Conclusions</i>: Disability decreased and HRQoL improved after TBI between 3–12 months. In geriatric patients this improvement was relevant for HRQoL only.
DOI: 10.6084/m9.figshare.4579315
title: Figure plots + raw data from "Probing cytoskeletal modulation of passive and active intracellular dynamics using nanobody-functionalized quantum dots"

description: Figure plots + raw data in GraphPad Prism data format, including analysis, fitting and calculations from the paper:<br><br><i>Probing cytoskeletal modulation of passive and active intracellular dynamics using nanobody-functionalized quantum dots</i><br>Eugene A Katrukha, Marina Mikhaylova, Hugo X van Brakel, Paul M van Bergen en Henegouwen, Anna Akhmanova, Casper C Hoogenraad, Lukas C Kapitein<br>Nat. Commun. 8, 14772 doi: 10.1038/ncomms14772 (2017)<br>
DOI: 10.6084/m9.figshare.4579315.v1
title: Figure plots + raw data from "Probing cytoskeletal modulation of passive and active intracellular dynamics using nanobody-functionalized quantum dots"

description: Figure plots + raw data in GraphPad Prism data format, including analysis, fitting and calculations from the paper:<br><br><i>Probing cytoskeletal modulation of passive and active intracellular dynamics using nanobody-functionalized quantum dots</i><br>Eugene A Katrukha, Marina Mikhaylova, Hugo X van Brakel, Paul M van Bergen en Henegouwen, Anna Akhmanova, Casper C Hoogenraad, Lukas C Kapitein<br>Nat. Commun. 8, 14772 doi: 10.1038/ncomms14772 (2017)<br>
DOI: 10.6084/m9.figshare.4579309
title: Trajectories analysis code for the paper "Probing cytoskeletal modulation of passive and active intracellular dynamics using nanobody-functionalized quantum dots"

description: Matlab analysis source code from the paper:<br><br><i>Probing cytoskeletal modulation of passive and active intracellular dynamics using nanobody-functionalized quantum dots</i><br>Eugene A Katrukha, Marina Mikhaylova, Hugo X van Brakel, Paul M van Bergen en Henegouwen, Anna Akhmanova, Casper C Hoogenraad, Lukas C Kapitein<br>Nat. Commun. 8, 14772 doi: 10.1038/ncomms14772 (2017)<br>
DOI: 10.6084/m9.figshare.4579288
title: Trajectories of electroporated quantum dots in COS7 cells: non-functionalized and attached to kinesins of different families

description: Two-dimensional trajectories of quantum dots (QDs) with a measured Stokes radius of 14.4 nm inside cells under different conditions. <br><br>See further details in the paper:<br><i>Probing cytoskeletal modulation of passive and active intracellular dynamics using nanobody-functionalized quantum dots</i><br>Eugene A Katrukha, Marina Mikhaylova, Hugo X van Brakel, Paul M van Bergen en Henegouwen, Anna Akhmanova, Casper C Hoogenraad, Lukas C Kapitein<br><br>Nat. Commun. 8, 14772 doi: 10.1038/ncomms14772 (2017)<br>File unfiltered_tracks_mdf_format.zip<br>There are 6 groups of trajectories (in 6 folders): 1) QD_control (control group): <br> 2) QD_wLatrunculinA (inside cells with actin depolymerized using LatrunculinA) 3) Kinesin1(KIF5B)_QD <br> 4) Kinesin2(KIF17)_QD<br> 5) Kinesin3(KIF1A)_QD 6) Kinesin4(KIF21B)_QD<br>Files are in MDF (MTrackJ format), see supporting code for example of reading.<br><br>1)-2) correspond to Figure 1 d-f<br>3-6) correspond to Figure 2d-k, 3bd,<br><br>File filtered_tracks_txt_format.zip contains tracks in txt format that are already filtered by duration as described in the paper, for theoretical analysis purposes. See http://inadilic.fr/data/ for details.<br>
DOI: 10.6084/m9.figshare.4579309.v1
title: Trajectories analysis code for the paper "Probing cytoskeletal modulation of passive and active intracellular dynamics using nanobody-functionalized quantum dots"

description: Matlab analysis source code from the paper:<br><br><i>Probing cytoskeletal modulation of passive and active intracellular dynamics using nanobody-functionalized quantum dots</i><br>Eugene A Katrukha, Marina Mikhaylova, Hugo X van Brakel, Paul M van Bergen en Henegouwen, Anna Akhmanova, Casper C Hoogenraad, Lukas C Kapitein<br>Nat. Commun. 8, 14772 doi: 10.1038/ncomms14772 (2017)<br>
DOI: 10.6084/m9.figshare.4579288.v1
title: Trajectories of electroporated quantum dots in COS7 cells: non-functionalized and attached to kinesins of different families

description: Two-dimensional trajectories of quantum dots (QDs) with a measured Stokes radius of 14.4 nm inside cells under different conditions. <br><br>See further details in the paper:<br><i>Probing cytoskeletal modulation of passive and active intracellular dynamics using nanobody-functionalized quantum dots</i><br>Eugene A Katrukha, Marina Mikhaylova, Hugo X van Brakel, Paul M van Bergen en Henegouwen, Anna Akhmanova, Casper C Hoogenraad, Lukas C Kapitein<br><br>Nat. Commun. 8, 14772 doi: 10.1038/ncomms14772 (2017)<br>File unfiltered_tracks_mdf_format.zip<br>There are 6 groups of trajectories (in 6 folders): 1) QD_control (control group): <br> 2) QD_wLatrunculinA (inside cells with actin depolymerized using LatrunculinA) 3) Kinesin1(KIF5B)_QD <br> 4) Kinesin2(KIF17)_QD<br> 5) Kinesin3(KIF1A)_QD 6) Kinesin4(KIF21B)_QD<br>Files are in MDF (MTrackJ format), see supporting code for example of reading.<br><br>1)-2) correspond to Figure 1 d-f<br>3-6) correspond to Figure 2d-k, 3bd,<br><br>File filtered_tracks_txt_format.zip contains tracks in txt format that are already filtered by duration as described in the paper, for theoretical analysis purposes. See http://inadilic.fr/data/ for details.<br>
DOI: 10.6084/m9.figshare.4579249
title: Dataset_S1_matrix_1.txt from Computational biomechanics changes our view on insect head evolution

description: Despite large-scale molecular attempts, the relationships of the basal winged insect lineages dragonflies, mayflies and neopterans, are still unresolved. Other data sources, such as morphology, suffer from unclear functional dependencies of the structures considered, which might mislead phylogenetic inference. Here, we assess this problem by combining for the first time biomechanics with phylogenetics using two advanced engineering techniques, multibody dynamics analysis and finite-element analysis, to <i>objectively</i> identify functional linkages in insect head structures which have been used traditionally to argue basal winged insect relationships. With a biomechanical model of unprecedented detail, we are able to investigate the mechanics of morphological characters under biologically realistic load, i.e. biting. We show that a range of head characters, mainly ridges, endoskeletal elements and joints, are indeed mechanically linked to each other. An analysis of character state correlation in a morphological data matrix focused on head characters shows a highly significant correlation of these mechanically linked structures. Phylogenetic tree reconstruction under different data exclusion schemes based on the correlation analysis unambiguously supports a sistergroup relationship of dragonflies and mayflies. The combination of biomechanics and phylogenetics as it is proposed here could be a promising approach to assess functional dependencies in many organisms to increase our understanding of phenotypic evolution.
DOI: 10.6084/m9.figshare.4579249.v1
title: Dataset_S1_matrix_1.txt from Computational biomechanics changes our view on insect head evolution

description: Despite large-scale molecular attempts, the relationships of the basal winged insect lineages dragonflies, mayflies and neopterans, are still unresolved. Other data sources, such as morphology, suffer from unclear functional dependencies of the structures considered, which might mislead phylogenetic inference. Here, we assess this problem by combining for the first time biomechanics with phylogenetics using two advanced engineering techniques, multibody dynamics analysis and finite-element analysis, to <i>objectively</i> identify functional linkages in insect head structures which have been used traditionally to argue basal winged insect relationships. With a biomechanical model of unprecedented detail, we are able to investigate the mechanics of morphological characters under biologically realistic load, i.e. biting. We show that a range of head characters, mainly ridges, endoskeletal elements and joints, are indeed mechanically linked to each other. An analysis of character state correlation in a morphological data matrix focused on head characters shows a highly significant correlation of these mechanically linked structures. Phylogenetic tree reconstruction under different data exclusion schemes based on the correlation analysis unambiguously supports a sistergroup relationship of dragonflies and mayflies. The combination of biomechanics and phylogenetics as it is proposed here could be a promising approach to assess functional dependencies in many organisms to increase our understanding of phenotypic evolution.
DOI: 10.6084/m9.figshare.4579240
title: Table_S1_full_matrix.xls from Computational biomechanics changes our view on insect head evolution

description: Despite large-scale molecular attempts, the relationships of the basal winged insect lineages dragonflies, mayflies and neopterans, are still unresolved. Other data sources, such as morphology, suffer from unclear functional dependencies of the structures considered, which might mislead phylogenetic inference. Here, we assess this problem by combining for the first time biomechanics with phylogenetics using two advanced engineering techniques, multibody dynamics analysis and finite-element analysis, to <i>objectively</i> identify functional linkages in insect head structures which have been used traditionally to argue basal winged insect relationships. With a biomechanical model of unprecedented detail, we are able to investigate the mechanics of morphological characters under biologically realistic load, i.e. biting. We show that a range of head characters, mainly ridges, endoskeletal elements and joints, are indeed mechanically linked to each other. An analysis of character state correlation in a morphological data matrix focused on head characters shows a highly significant correlation of these mechanically linked structures. Phylogenetic tree reconstruction under different data exclusion schemes based on the correlation analysis unambiguously supports a sistergroup relationship of dragonflies and mayflies. The combination of biomechanics and phylogenetics as it is proposed here could be a promising approach to assess functional dependencies in many organisms to increase our understanding of phenotypic evolution.
DOI: 10.6084/m9.figshare.4579234
title: Dataset_S5_matrix_FULL_reduction.txt from Computational biomechanics changes our view on insect head evolution

description: Despite large-scale molecular attempts, the relationships of the basal winged insect lineages dragonflies, mayflies and neopterans, are still unresolved. Other data sources, such as morphology, suffer from unclear functional dependencies of the structures considered, which might mislead phylogenetic inference. Here, we assess this problem by combining for the first time biomechanics with phylogenetics using two advanced engineering techniques, multibody dynamics analysis and finite-element analysis, to <i>objectively</i> identify functional linkages in insect head structures which have been used traditionally to argue basal winged insect relationships. With a biomechanical model of unprecedented detail, we are able to investigate the mechanics of morphological characters under biologically realistic load, i.e. biting. We show that a range of head characters, mainly ridges, endoskeletal elements and joints, are indeed mechanically linked to each other. An analysis of character state correlation in a morphological data matrix focused on head characters shows a highly significant correlation of these mechanically linked structures. Phylogenetic tree reconstruction under different data exclusion schemes based on the correlation analysis unambiguously supports a sistergroup relationship of dragonflies and mayflies. The combination of biomechanics and phylogenetics as it is proposed here could be a promising approach to assess functional dependencies in many organisms to increase our understanding of phenotypic evolution.
DOI: 10.6084/m9.figshare.4579225
title: Table_S3_p_values_pairwise_correlation_all_chars.txt from Computational biomechanics changes our view on insect head evolution

description: Despite large-scale molecular attempts, the relationships of the basal winged insect lineages dragonflies, mayflies and neopterans, are still unresolved. Other data sources, such as morphology, suffer from unclear functional dependencies of the structures considered, which might mislead phylogenetic inference. Here, we assess this problem by combining for the first time biomechanics with phylogenetics using two advanced engineering techniques, multibody dynamics analysis and finite-element analysis, to <i>objectively</i> identify functional linkages in insect head structures which have been used traditionally to argue basal winged insect relationships. With a biomechanical model of unprecedented detail, we are able to investigate the mechanics of morphological characters under biologically realistic load, i.e. biting. We show that a range of head characters, mainly ridges, endoskeletal elements and joints, are indeed mechanically linked to each other. An analysis of character state correlation in a morphological data matrix focused on head characters shows a highly significant correlation of these mechanically linked structures. Phylogenetic tree reconstruction under different data exclusion schemes based on the correlation analysis unambiguously supports a sistergroup relationship of dragonflies and mayflies. The combination of biomechanics and phylogenetics as it is proposed here could be a promising approach to assess functional dependencies in many organisms to increase our understanding of phenotypic evolution.
DOI: 10.6084/m9.figshare.4579225.v1
title: Table_S3_p_values_pairwise_correlation_all_chars.txt from Computational biomechanics changes our view on insect head evolution

description: Despite large-scale molecular attempts, the relationships of the basal winged insect lineages dragonflies, mayflies and neopterans, are still unresolved. Other data sources, such as morphology, suffer from unclear functional dependencies of the structures considered, which might mislead phylogenetic inference. Here, we assess this problem by combining for the first time biomechanics with phylogenetics using two advanced engineering techniques, multibody dynamics analysis and finite-element analysis, to <i>objectively</i> identify functional linkages in insect head structures which have been used traditionally to argue basal winged insect relationships. With a biomechanical model of unprecedented detail, we are able to investigate the mechanics of morphological characters under biologically realistic load, i.e. biting. We show that a range of head characters, mainly ridges, endoskeletal elements and joints, are indeed mechanically linked to each other. An analysis of character state correlation in a morphological data matrix focused on head characters shows a highly significant correlation of these mechanically linked structures. Phylogenetic tree reconstruction under different data exclusion schemes based on the correlation analysis unambiguously supports a sistergroup relationship of dragonflies and mayflies. The combination of biomechanics and phylogenetics as it is proposed here could be a promising approach to assess functional dependencies in many organisms to increase our understanding of phenotypic evolution.