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Limma-voom differential expression results for GTEx CVD and MD analyses

Supplementary File 5 for the paper entitled "Exploring the Impact of Cerebrovascular Disease and Major Depression on Non-diseased Human Tissue Transcriptomes" (doi: 10.3389/fgene.2021.696836).

Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for various single- and multi-capillary occlusion scenarios.

DOCUMENTATION - Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for various single- and multi-capillary occlusion scenarios. Correspondence: fschmid@ethz.ch (Franca Schmid, ORCID: <a href="https://orcid.org/0000-0002-0689-9366">0000-0002-0689-9366</a>) 1. Related references: The data set is published in context with the manuscript:  [1] The severity of microstrokes depends on local vascular topology and baseline perfusion.  F Schmid, G Conti, P Jenny and B Weber. eLife. 2021. Doi: 10.7554/eLife.60208 The bi-phasic blood flow simulations have been performed in realistic microvascular networks (MVNs) from the mouse somatosensory cortex first published in: [2] The cortical angiome: an interconnected vascular network with noncolumnar patterns of blood flow. P Blinder, PS Tsai, JP Kaufhold, PM Knutsen, H Suhl and D Kleinfeld. Nature Neuroscience. 2013. Doi: 10.1038/nn.3426 The bi-phasic blood flow model for realistic MVNs has first been published in: [3] Depth-dependent flow and pressure characteristics in cortical microvascular networks. F Schmid, PS Tsai, D Kleinfeld, P Jenny and B Weber. PLoS Computational Biology. 2017. Doi: 10.1371/journal.pcbi.1005392 For further information on how to perform bi-phasic blood flow simulation, please contact the corresponding authors of [1] or [3]. 2. Requirements (software): All simulations and analyses have been performed in Python 2.7. To execute the analysis script the following python libraries need to be installed: cPickle, python-igraph, pandas, seaborn, scipy. The individual analyses script can then be executed by in Python (e.g. “python plot_Figure3.py”). 3. Content: All folders are stored as compressed archives (*.tar.bz2). On unix-based system the folders can be unpacked by: "tar –jxf  ARCHIVE_NAME" 3a. Time-averaged simulation results (python dictionaries stored as python 2.7 pickle files): SimulationResults_Baseline.tar.bz2: Folders: MVN1, MVN2 Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch.pkl (generated from prepare_Figure4.py), data_spatial_distribution_AVfactor.pkl (generated from plot_Figure4.py) SimulationResults_SingleCapillaryOcclusions.tar.bz2: Folders: 1-in-1-out, 1-in-2-out, 2-in-1-out, 2-in-2-out, 2-in-2-out_high, 2-in-2-out_AL1, 2-in-2-out_AL2, 2-in-2-out_AL3, 2-in-2-out_AL4, 2-in-2-out_AL5, 2-in-2-out_closeToDA, 2-in-2-out_farFromDA Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl (only folders: 1-in-1-out, 1-in-2-out, 2-in-1-out, 2-in-2-out) SimulationResults_MultiCapillaryOcclusions.tar.bz2: Folders: vesselsOccluded_1, vesselsOccluded_3, vesselsOccluded_5, vesselsOccluded_7, vesselsOccluded_9 Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl 3b. Analysis scripts (python 2.7 scripts in folder Analyses_Scripts): For details on the figure content see [1]. The verticesDict* and the edgesDict* are converted into graph structure (python-igraph) for all analyses. The functionality of python-igraph is used heavily throughout the various analyses. helperFunctions.py: various functions used by the other analysis scripts plot_Figure1_and_Figure1-supplement_1_a-d.py: Input: SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/verticesDict.pkl Output: Figures/Figure_1/*, Supplementary_Figures/Figure_1-supplement_1_a-d/* plot_Figure2_and_Figure2_supplement_1_a-d.py: Input: SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/verticesDict.pkl Output: Figures/Figure_2/*, Supplementary_Figures/Figure_2-supplement_1_a-d/* plot_Figure3.py: Input: SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_MultiCapillaryOcclusion/*/edgesDict.pkl, SimulationResults_MultiCapillaryOcclusion/*/verticesDict.pkl  Output: Figures/Figure_3/* prepare_Figure4.py (long execution time!): Input: SimulationResults_Baseline/MVN*/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN*/verticesDict_baseline.pkl, Output: SimulationResults_Baseline/MVN*/pathsDict_allPaths_from_DA_to_AV_mainBranch.pkl plot_Figure4.py (long execution time!): Input: SimulationResults_Baseline/MVN*/* Output: SimulationResults_Baseline/MVN*/edgesDict_baseline.pkl (attribute Lfactor_median added), SimulationResults_Baseline/MVN*/data_spatial_distribution_AVfactor.pkl, Figures/Figure_4/* plot_Figure5.py: Input: SimulationResults_Baseline/MVN*/*  Output: Figures/Figure_5/* plot_Figure6.py: Input: SimulationResults_Baseline/MVN1/*, SimulationResults_SingleCapillaryOcclusion/2-in-2- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl Output: Figures/Figure_6/* 4. Attributes stored in python dictionaries: 4a. Baseline: verticesDict: contains all relevant information and data stored at vertices. <ul> <li>index: index of vertex</li> <li>pressure: time averaged pressure at vertex [mmHg]</li> <li>inflowE: list of edges delivering blood to the vertex (inflows of the vertex)</li> <li>outflowE: list of edges removing blood from the vertex (outflows of the vertex)</li> <li>coords: coordinates of the vertex [µm]</li> <li>pBC: pressure boundary conditions [mmHg], None for internal vertices</li> <li>corticalDepth: depth from cortical surface [µm]</li> <li>nkind: identifier for the vessel type. 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending vein, 4: capillary</li> </ul> edgesDict: contains all relevant information and data stored at edges. <ul> <li>diameter: effective vessel diameter [µm]. See [3] for details.</li> <li>htd: time averaged discharge hematocrit [-]. </li> <li>connectivity: tuple of vertex indices which are connected by the edge.</li> <li>mainAV: identifier for ascending venule (AV) main brain. 1: is AV main brain, 0: no AV main branch</li> <li>mainDA: identifier for descending arteriole (DA) main brain. 1: is DA main brain, 0: no DA main branch</li> <li>flow: time averaged flow rate [µm3 ms-1]</li> <li>length: tortuous vessel length [µm] See [1] and [3] for details.</li> <li>tissueVolume: topological tissue volume supplied by vessel [µm3]. See [1] for details.</li> <li>nkind: identifier for the vessel type. 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending vein, 4: capillary</li> <li>edgesFulfillingSelection: identifier if vessels fulfils selection criteria to qualify for analysis. 1: vessel included for analysis, 0: vessel not included for analysis. Details on the selection criteria are provided in [1].</li> <li>htt: time averaged tube hematocrit [-]</li> <li>RBCflux: time averaged RBC flux [RBC/s] computed from the discharge hematocrit and the flow rate.</li> <li>sign: sign describing the flow direction in the vessel. +: flow direction from source (vertex with lower index) to target (vertex with higher index), -: flow direction from target to source vertex. Based on time averaged pressure values.</li> <li>points: list of tortuous vessel coordinates of the edge [µm]. Starting at the source vertex. Ending at the target vertex.</li> <li>Lfactor_median: AV-factor of the vessel. None if no AV-factor can be assigned. See [1] for details. Attribute added by plot_Figure4.py</li> </ul> pathsDict_allPaths_from_DA_to_AV_mainBranch: contains all flow path from DA main brain to AV main branch. For details see [1]. <ul> <li>startPoint: list of vertex indices of the end point of the DA</li> <li>endPoint: list of vertex indices of the end point of the AV</li> <li>allPaths: list of lists of vertex indices describing all paths between a the associated startPoint and endPoint.</li> </ul> data_spatial_distribution_AVfactor: contains information on the spatial distribution of venule-sided capillaries (AV-factor  > 0.5). For details see [1]. <ul> <li>edges_L_mean_50um: list of all edges for which the average AV-factor in an analysis sphere of 50 µm has been computed.</li> <li>resulting_L_mean_50um: average AV-factor for an analysis sphere for 50 µm (see Figure4/AV_factor_delta_analysisSphere50_MVN*.pkl)</li> <li>shortest_distance_to_closest_vessel: list of shortest distances to any vessel for all discretization points along all venule sided capillaries.</li> <li>shortest_distance_to_Lfactor_lt_05: list of shortest distances to an arteriole-sided capillary (AV-factor < 0.5) for all discretization points along all venule sided capillaries.</li> </ul> 4b. Occlusion scenarios (both single- and multi-capillary occlusions): verticesDict: <ul> <li>index: index of vertex</li> <li>coords: coordinates of the vertex [µm]</li> <li>pressure_strokeIndex_n: time averaged pressure at vertex [mmHg] for the simulation where edge n has been occluded. For details see [1].</li> </ul> edgesDict: <ul> <li>htd_strokeIndex_n: time averaged discharge hematocrit [-] for the simulation where edge n has been occluded. For details see [1]. </li> <li>flow: time averaged flow rate [µm3 ms-1] for the simulation where edge n has been occluded. For details see [1].</li> <li>RBCflux: time averaged RBC flux [RBC/s] computed from the discharge hematocrit and the flow rate for the simulation where edge n has been occluded. For details see [1].</li> <li>htt: time averaged tube hematocrit [-] for the simulation where edge n has been occluded. For details see [1].</li> <li>connectivity: tuple of vertex indices which are connected by the edge.</li> <li>diameter_strokeIndex_n: effective vessel diameter [µm] for the simulation where edge n has been occluded (only given for multi-capillary occlusions).</li> </ul> pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased: contains all flow path from DA main brain to AV main branch (unique vertex sequences). For details see helperFunctions.py --> function convert_pathsDict_to_unique_vertexSequence. <ul> <li>startPoint_strokeIndex_n: list of vertex indices of the end point of the DA for the simulation where edge n has been occluded.</li> <li>endpoint_strokeIndex_n: list of vertex indices of the end point of the AV for the simulation where edge n has been occluded.</li> <li>allPaths_strokeIndex_n: list of lists of vertex indices describing all paths between a the associated startPoint and endpoint for the simulation where edge n has been occluded.</li> </ul>

Cryptic Diversity in Cladosporium cladosporioides Resulting from Species Delimitation Analysis

Concatenated loci alignment (.fasta), Maximum-Likelihood, Maximum-Parsimony and Bayesian reconstruction of Cladosporium cladosporioides phylogeny.  Files represent dataset of the study: "Cryptic Diversity in Cladosporium cladosporioides Resulting from Species Delimitation Analysis"   ABSTRACT Establishing a stable taxonomy is particularly important for understanding fungal biodiversity, as well as the evolution of particular traits related to symbiotic interactions. Cladosporium cladosporioides is an extremely widespread fungus involved in associations ranging from mutualistic to pathogenic, and is the most represented Cladosporium species in genetic sequence databases, such as Genbank. Although the taxonomy of Cladosporium species is subjected to frequent revisions, which nowadays mostly rely on molecular data, few studies explored cryptic diversity of this genus using the recently developed species delimitation methods. Considering a previous study which reported several hypothetical species within C. cladosporioides, here we try to fill the gap of knowledge about phylogenetic relationships for this species and tested four different methods of species delimitation using the combined DNA barcodes ITS, translation elongation factor 1 and actin. The analyses involved 105 isolates revealing that currently available sequences of C. cladosporioides in GenBank actually represent more than one species. Moreover, we reported the erroneous taxonomical assignment of several isolates that should be ascribed to C. anthropophilum. Our results revealed a certain degree of discordance among species delimitation methods, which can be efficiently treated using conservative approaches, in order to minimize the risk of considering false positives.

opencc-by-nc-nd-3.0Aug 2021View details →

SAAG Workflow Evaluation Results for Anirudh Prabhu's PhD Dissertation

Part of Anirudh Prabhu's PhD Dissertation. Contains Interpretability, Coverage and Normalized Coverage scores for 65 stories used in the SAAG workflow. More details about the evaluation can be found in Chapter 6 and Appendix F of the dissertation document.

opencc-by-nc-nd-3.0Aug 2021View details →

Text to Rule Conversion Full Results for Anirudh Prabhu's PhD Dissertation

Part of Anirudh Prabhu PhD Dissertation. Contains the results of running the text to rule conversion workflow described in chapter 5 of the dissertation. The dataset preview and its description can be found in Appendix E of the dissertation document.

opencc-by-4.0Aug 2021View details →

Data Sets and Results for "Improved data sets and evaluation methods for the automatic prediction of DNA-binding proteins"

Data sets and results for "Improved data sets and evaluation methods for the automatic prediction of DNA-binding proteins" The file "dna_binding_protein_sequences.zip" has the training and testing sets from the paper: RLL - "random_<train/test>_full_1000.csv" RSL - "random_<train/test>_40.csv" RS&LL - "random_<train/test>_40_1000.csv" RLL where included positive examples have verified DNA binding activity - "random_<train/test>_hq_1000.csv" The 10 RS&LL data sets - "random_<train/test>_40_1000.csv" + "random_<train/test>_40_1000_cv_<0-8>.csv" The results files are named similarly. See "see_results.ipynb" in the <a href="https://doi.org/10.5281/zenodo.5153683">codebase</a> that supplement these data sets The species data sets are derived from "uniprot_data_bac.tab" and "uniprot_data_not_bac.tab."  See <a href="https://doi.org/10.5281/zenodo.5153683">code</a>.  The ESM embeddings used by the XGBoost model are in "dna_binding_protein_esm.zip"

opencc-by-4.0Apr 2021View details →

Testing nauyaca: mock catalog and results

Dataset associated with the paper "Nauyaca: a new tool to determine planetary masses and orbital elements through transit timing analysis". It includes the mock catalog of planetary systems and the results of the optimization and MCMC routines.

Evaluation of the shucking of certain species of scallops contaminated with domoic acid with a view to the production of edible parts meeting the safety requirements foreseen in the Union legislation - Summary statistics on occurrence and consumption data and exposure assessment results

DomoicAcid_Raw_Occurrence_Data.CSV contains the raw occurrence dataset on Domoic Acid contaminant in scallops as extracted from EFSA DWH on the 9 June 2020, 16,369 samples presented in the opinion as described in its section 1.3.2. Occurrence data submitted to EFSA. The data is provided in .csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: outcome) to address the issue (e.g. delete record or update values in specific fields). The link to the catalogues of controlled terminologies can be found under "Related identifiers”. Annex_ DomoicAcid Table of contents Table A1 Description of FoodEx2 codes used to describe scallop species and their anatomical parts Table A2 Data cleaning steps applied to occurrence data on domoic acid in scallops Table A3 Percentage of Left-Censored data and descriptive statistics for Limits of detection (LODs) and Limits of quantification (LOQs) for domoic acid in scallops (mg/kg) Table A4 Descriptive statistics for domoic acid in scallops (mg/kg) as reported in the cleaned database (statistics weighted by number of units per sample) Table A5 Descriptive statistics  of body tissue weights (g) of scallops as submitted by data providers

Fig. 3 in Life Time Egg Production in Females of the Cricket Teleogryllus commodus WALKER 1869 (Insecta: Orthoptera): Experimental Results and Theoretical Predictions

Fig. 3: Total fecundity of female Teleogryllus commodus WALKER for different environmental temperatures (20 °C, 23 °C, 25 °C, and 30 °C). Asterisks indicate significant differences between neighbouring points (p <0.01).

Fig. 5 in Life Time Egg Production in Females of the Cricket Teleogryllus commodus WALKER 1869 (Insecta: Orthoptera): Experimental Results and Theoretical Predictions

Fig. 5: Preliminary results obtained from the computer program CRICKTHERM. Dependence of fecundity on (A) temperature, (B) photoperiod, (C) protein content in food, (D) population density (individuals per square metre).

Fig. 4 in Life Time Egg Production in Females of the Cricket Teleogryllus commodus WALKER 1869 (Insecta: Orthoptera): Experimental Results and Theoretical Predictions

Fig. 4: Results concerning the investigation of ovary weights and their dependence on environmental temperature. A) Scheme showing the preparation procedure necessary for the dissection of the ovaries. B) Graph illustrating the dependence of ovary weight in grams on environmental temperature. Ovary weights from 5-day old females (pre-oviposition period) and 10-day old females (oviposition period) have been compared. Points and error bars denote mean values and standard deviations (asterisks mark significant differences between neighbouring points, p <0.01).

Fig. 2 in Life Time Egg Production in Females of the Cricket Teleogryllus commodus WALKER 1869 (Insecta: Orthoptera): Experimental Results and Theoretical Predictions

Fig. 2: Dependence of daily cricket fecundity on environmental temperature. (A) 20 °C, (B) 23 °C, (C) 25 °C, (D) 30 °C. Black lines represent mean values, whereas dashed lines mark the standard deviations. The thick grey lines show the respective out put produced by CRICKTHERM and should give an impression about the validity of the used model.

Fig. 1 in Life Time Egg Production in Females of the Cricket Teleogryllus commodus WALKER 1869 (Insecta: Orthoptera): Experimental Results and Theoretical Predictions

Fig. 1: Window design and function of the computer program CRICKTHERM calculating the fecundity of female crickets as a function of various environmental parameters. (A) Entrance window, (B) Input window including animal information and input parameters, (C) Output window presenting the fecundity-time-relationship as a line plot as well as specific output parameter, (D) Alternative output window presenting fecundity data as mean values and standard deviations.

opencc-by-4.0Dec 2017View details →

Figure 3. Geometric morphometrics results. A in The Woodcock's head: Resolving a morphological oddity using geometric morphometrics

Figure 3. Geometric morphometrics results. A. Procrustes residuals (shape data) of the 160 superimposed configurations. The configuration of the woodcock is plotted inside to show how its skull shape maps compared to the rest of birds. B. Isolated average configuration (grey, left) and woodcock (black, right). C. Average and woodcock superimposed to show that the largest difference is in the position of the facial skeleton compared to the neurocranium. D. Same as C yet using the Thin Plate Spline deformation to further stress the craniofacial differences between the woodcock and the average. Notice how craniofacial differences are due to the "rotation" of the face and the neurocranium. E. Thin plate spline deformation of the woodcock compared to the rest of the sample within morphospace (summary of shape variance of Fig. 3A using Relative Warps Analysis, i.e., PCA). Black/thick arrow points to woodcock, Thin arrow points to Snipe, whose skull shape is definitely similar to that of the woodcock.

opencc-by-4.0Sep 2012View details →

FIG. 8. A, The tree resulting from a in Archboldomys (Muridae: Murinae) Reconsidered: A New Genus and Three New Species of Shrew Mice from Luzon Island, Philippines

FIG. 8. A, The tree resulting from a maximum-likelihood analysis of the IRBP data set under the best-fitting model (HKY + Γ4). Numbers at nodes indicate maximum-likelihood (above the line) or parsimony (below the line) bootstrap support. B, The tree resulting from a mixed-model maximum-likelihood analysis of the combined IRBP + cytochrome b data set. Numbers at nodes indicate maximum-likelihood bootstrap support (above the line) or posterior probabilities resulting from a mixed-model Bayesian analysis (below the line). Trees are rooted with Phloeomys cumingi and Batomys granti as outgroups (not shown).

opencc-by-4.0Nov 2020View details →

Text-fig. 1. Result of cumulative random counting of MN 5 localities in central Europe and the Iberian Peninsula. Ten simulations were run for each area. a. Results of the count including the average in bold, showing the clearly lower diversity in IB. b. The average lines standardized, showing similar patterns in the two areas. Note that in the simulation around thirty localities were needed to capture 80 % of the regional diversity. in Generically Speaking, A Survey On Neogene Rodent Diversity At The Genus Level In The Now Database

Text-fig. 1. Result of cumulative random counting of MN 5 localities in central Europe and the Iberian Peninsula. Ten simulations were run for each area. a. Results of the count including the average in bold, showing the clearly lower diversity in IB. b. The average lines standardized, showing similar patterns in the two areas. Note that in the simulation around thirty localities were needed to capture 80 % of the regional diversity.

opencc-zeroDec 2007View details →

FIG. 7. — Dendrograms resulting from Q in Micropaleontological parameters as proxies of late Miocene surface water properties and paleoclimate in Gavdos Island, eastern Mediterranean

FIG. 7. — Dendrograms resulting from Q-mode cluster analysis and the assemblages identified in each section.

opencc-zeroDec 2002View details →

FIG. 9 in Stomatopoda (Crustacea) from the Marquesas Islands: results of MUSORSTOM 9

FIG. 9. — Oratosquilla fabricii (Holthuis, 1941); A-D, postlarva tl 29 mm (stn DW1241); A, anterior cephalon; B, TS5-8, right dorsal; C, raptorial claw carpus, right lateral; D, uropodal protopod, right ventral; E-H, juvenile tl 30 mm (stn DW1214); E, anterior cephalon; F, TS5-8, right dorsal; G, raptorial claw carpus, right lateral; H, uropodal protopod, right ventral; I-L, tl 63 mm (stn CP1187); I, anterior cephalon; J, TS5-8, right dorsal; K, raptorial claw, right lateral; L, uropodal protopod, right ventral. Scale bar: A-H, 1.5 mm; I-L, 3 mm.

opencc-zeroDec 2002View details →

FIG. 8 in Stomatopoda (Crustacea) from the Marquesas Islands: results of MUSORSTOM 9

FIG. 8. — Neoanchisquilla tuberculata Ahyong, 1998; A, B, late pelagic larva tl 32 mm (stn CP1195); A, dorsal; B, carapace, right lateral; C-F, postlarva tl 31 mm (between Society Islands and Marquesas); C, anterior cephalon, dorsal; D, TS5-8, right dorsal; E, AS5-6, telson and uropod, dorsal; F, submedian telson teeth; G-I, juvenile tl 35 mm (stn DW1210); G, anterior cephalon, dorsal; H, TS5-8, right dorsal; I, AS5-6, telson and uropod, dorsal. Scale bar: A-E, 4 mm; F-I, 2 mm.