Find research datasets worth reusing

opencc-by-4.0Feb 2023View details →

Cell shape analysis

Cell shape analysis --------------------------------------------- This package contains custom-written code associated with the paper "Direct observation of a crescent-shape chromosome in Bacillus subtilis" By Ti&scaron;ma et al. 2023 See README's in subdirectories for specific content description  Notes: ---------------------------------------------- ## general * code for extended cell analysis as used in the Cees Dekker Lab, 2017-onwards ## associated publications 1. [current submission] 'Direct observation of a crescent-shape chromosome in Bacillus subtilis' Authors Milo&scaron; Ti&scaron;ma, Florian Patrick Bock, Jacob Kerssemakers, Aleksandre Japaridze, Stephan Gruber, Cees Dekker, submitted (2023). 2. MukBEF-dependent chromosomal organization in widened Escherichia coli Authors Aleksandre Japaridze, Raman van Wee, Christos Gogou, Jacob WJ Kerssemakers, Cees Dekker; (2022) 3. Direct observation of independently moving replisomes in Escherichia coli Authors Aleksandre Japaridze, Christos Gogou, Jacob WJ Kerssemakers, Huyen My Nguyen, Cees Dekker, Nat.Com (2020) 4. Direct imaging of the circular chromosome in a live bacterium Authors Fabai Wu, Aleksandre Japaridze, Xuan Zheng, Jakub Wiktor, Jacob WJ Kerssemakers, Cees Dekker, Nat.Com (2019) ## package description This package collects code to analyze multi-channel microscope images of cells. Patterns of interest may be extended, such as a fully labeled chromosome, or compact, as one or multiple labeled spots. The code is used to select and screen individual cells and to quantify locations of above labels.  ## package contents * custom-written source code [Matlab]  * test data to demonstrate the software code ## system requirements and used software * Windows 10, 64bits  * Matlab Version 2021 [custom source code] * Oufti Version ....  * Fiji(ImageJ) Version 1.52a * Dip_image analysis software Version 2.9 ## installation guide * please follow the installation instructions provided by above package distributors * installation of all platforms should take less than a day on a standard PC ## demo and instructions for use * the code is divided in three main subsections (that are to be used in this order) and one extra:     * 'crop': works on microsocopic data, outputs data organized per cells     * 'donut': in-depth analysis on chromosome patterns and other labels ('spots')     * 'repli': follow-up analysis on spots     * 'topical subjects': shorter code platforms for various analyses * see 'README.md' in each subdirectory for a detailed step-by-step descriptions, in order of appearance or importance in the analysis pipeline * see 'README_code_annotations.md' in each subdirectory for a full collection of all custom function descriptions * demo data for two cells is provided to illustrate workings and settings. Running this demo data typically only takes a few minutes * for new data, follow the setup as described via the demo data    ## contributions ----------------------------------------------  * F. Wu and X. Zheng originally wrote and assembled the original code package * J.Kerssemakers organized, re-edited and expanded the 'crop' code package * J.Kerssemakers wrote the 'donut' and 'repli' code packages * A. Japaridze, Raman van Wee, Christos Gogou and M.Tisma contributed to analysis design * J.Kerssemakers and M.Tisma contributed to 'topical subjects' ## disclaimer --------------------------------------------- This code was custom written and shared to illustrate used algorithms, analysis pathways etc. in relation to published results. The code was regularly used and tested. However, small bugs, not relevant for the data analysis as present in publications may still be around. Interested users are expected to have a sufficient knowledge of Matlab code to understand and adapt the code to their own wishes.

FIGURE 7 in Taxonomic revision of Cypridopsis silvestrii comb. nov. (Ostracoda, Crustacea) from Patagonia, Argentina with morphometric analysis of their intraspecific shape variability and sexual dimorphism

FIGURE 7. Nonmetric Multidimensional Scaling (n-MDS) plot for male and female outlines, normalized for area from ET lake. Inset shows superposition of the virtual mean shape outline of males (light blue) and females (black).

FIGURE 5. Cypridopsis silvestrii comb. nov. A in Taxonomic revision of Cypridopsis silvestrii comb. nov. (Ostracoda, Crustacea) from Patagonia, Argentina with morphometric analysis of their intraspecific shape variability and sexual dimorphism

FIGURE 5. Cypridopsis silvestrii comb. nov. A. Hemipenis (UNC-PMIC 160 male). B. T1 (UNC-PMIC 153 ES female). C. Lpp (UNC-PMIC 161 male). D. Rpp (UNC-PMIC 161 male). E Zenker organ (UNC-PMIC 160 male). F. T2 (UNC-PMIC 153 ES female). G. Genital hooks (UNC-PMIC 154 ES female). H. T3 (UNC-PMIC 153 ES female). I.UR (UNC-PMIC 154 ES female). Scale bar: 100 µm.

FIGURE 1 in Taxonomic revision of Cypridopsis silvestrii comb. nov. (Ostracoda, Crustacea) from Patagonia, Argentina with morphometric analysis of their intraspecific shape variability and sexual dimorphism

FIGURE 1. Geographic location of the surveyed lakes. The upper left map shows Argentina with the Patagonian region in dark grey. The lower left map shows the sampled region with the four surveyed areas (black boxes), which correspond to the regions A, B, C, and D. Grey polygons correspond to water bodies (surveyed in black). The legends indicate the altitude in meters above sea level (masl) based on a digital elevation model (DEM; source www.earthexplorer.usgs.gov.gov), where the upper (A and B), and lower (C and D) panels share the same scale.

FIGURE 2. Cypridopsis silvestrii comb. nov. A in Taxonomic revision of Cypridopsis silvestrii comb. nov. (Ostracoda, Crustacea) from Patagonia, Argentina with morphometric analysis of their intraspecific shape variability and sexual dimorphism

FIGURE 2. Cypridopsis silvestrii comb. nov. A ES female RV external view (UNC-PMIC 149). B ET female RV external view (UNC-PMIC 153). C ET male RV external view (UNC-PMIC 158). D–E ES female LV external view (PMIC 148). F ET male LV external view (UNC-PMIC 158), G ET female Cp dorsal view (UNC-PMIC 154). H ET male Cp dorsal view (UNC- PMIC 159). I–K ES female RV internal view (PMIC 149). L–N ES female LV internal view (UNC-PMIC 148). O ET female Cp ventral view (UNC-PMIC 155). P ETC RV external view (UNC-PMIC 162). Q ETC LV internal view (UNC-PMIC 163). R He female LV internal view (UNC-PMIC 164). Scale bar= 300 µm; E= 50 µm and I, K, L, N= 100 µm.

FIGURE 9 in Taxonomic revision of Cypridopsis silvestrii comb. nov. (Ostracoda, Crustacea) from Patagonia, Argentina with morphometric analysis of their intraspecific shape variability and sexual dimorphism

FIGURE 9. Boxplots showing carapace length (a), Height (b) and H:L (c) of ETf, ETm, ETC, and ES populations. The line within the box marks the median; the lower and upper boundaries of the box indicate the 25th and 75th percentiles, respectively. Error bars above and below the box indicate the 90th and 10th percentiles, respectively, and black points indicate outliers.

FIGURE 6 in Taxonomic revision of Cypridopsis silvestrii comb. nov. (Ostracoda, Crustacea) from Patagonia, Argentina with morphometric analysis of their intraspecific shape variability and sexual dimorphism

FIGURE 6. Nonmetric Multidimensional Scaling (n-MDS) plot showing shape variability (valve outlines were normalized for area) of extant and subfossil populations, with superimposition of reconstructed mean shape outline of each population.

opencc-by-4.0May 2024View details →

Comparative 3D Shape Analysis of the Iwo Eleru Mandible, Nigeria

<pre>The uploaded dataset contains IDs, grouping information and landmark data of fossil individuals included in the publication "Comparative 3D Shape Analysis of the Iwo Eleru Mandible, Nigeria" in PaleoAnthropology. The remaining comparative samples of South African modern humans can be obtained by emailing K. Harvati directly upon permission from the their respecitve repositories. The catalogue numbers of these comparative samples and respective repositories are listed in Supplementary Table 1 of the manuscript. </pre>

opencc-by-4.0Sep 2024View details →

statistical analysis and data: How personality shapes gaze behavior without compromising subtle emotion recognition.

<pre>CODE: script_gca_X.R: scripts used for the growth curve analysis fits script_fits_brms_no_tb.r: script containing the fits (except those related to the crowth curve analysis). inpact_script_contrasts.r: contrasts of every fits (call inpact_script_plot.r and inpact_script_save.r) inpact_script_plot.r: plots inpact_script_save: save contrasts to csv and xlsx files DATA: cluster_df.Rda: personality neutral.Rda: neutral trials sdtg.Rda: signal detection theory parameters resp_emo.Rda : raw data to emotional trials df_et4_propn.Rda: eye tracking data, first exposure phase of the emotional trials (0-1000ms) df_et4_prop.Rda: eye tracking data, second exposure phase of the emotional trials (1000-2000ms) df_n4_propn.Rda: eye tracking data, first exposure phase of the neutral trials (0-1000ms) df_n4_prop.Rda: eye tracking data, second exposure phase of the neutral trials (1000-2000ms) gca_e_contrast.Rda: contrasts of the growth curve analysis fit for the eye Area of Interest (AOI) gca_n_contrast.Rda: contrasts of the growth curve analysis fit for the nose AOI gca_m_contrast.Rda: contrasts of the growth curve analysis fit for the mouth AOI X.rds: fits STIMULI: - videos of the neutral and emotional facial expressions - backward masks</pre>

opencc-by-4.0Apr 2024View details →

Data supporting the analysis of lymphatic endothelial cell junctions and shape

Data in support of:  Dynamic cytoskeletal regulation of cell shape supports resilience of lymphatic endothelium Hans Schoofs1#, Nina Daubel1#, Sarah Schnabellehner1, Max Grönloh2, Sebastián Palacios Martínez3, Aleksi Halme4, Amanda M. Marks1, Marie Jeansson1, Sara Barcos5, Cord Brakebusch6, Rui Benedito7, Britta Engelhardt5, Dietmar Vestweber8, Konstantin Gängel1, Fabian Linsenmeier9, Sebastian Schürmann9, Pipsa Saharinen4,10, Jaap D. van Buul2,3,11, Oliver Friedrich9, Richard S. Smith12, Mateusz Majda13, and Taija Mäkinen1,4,10*   1Uppsala University, Department of Immunology, Genetics and Pathology, Dag Hammarskjölds väg 20, 751 85 Uppsala, Sweden. 2Department of Medical Biochemistry at the Amsterdam UMC, location AMC, The Netherlands. 3Department of Molecular Cytology, Leeuwenhoek Centre for Advanced Microscopy at Swammerdam Institute for Life Sciences at the University of Amsterdam, The Netherlands. 4Translational Cancer Medicine Program and Department of Biochemistry and Developmental Biology, University of Helsinki, Haartmaninkatu 8, 00014 Helsinki, Finland. 5Theodor Kocher Institute, University of Bern, Bern, Switzerland. 6Biotech Research and Innovation Center, University of Copenhagen, Ole Maaløes Vej 5, 2200 Denmark. 7Centro Nacional de Investigaciones Cardiovasculares, Melchor Fernández Almagro 3, E-28029 Madrid, Spain. 8Max Planck Institute for Molecular Biomedicine, Münster, Germany. 9Institute of Medical Biotechnology, Department of Chemical and Biological Engineering, Friedrich-Alexander-University, Erlangen-Nürnberg, Paul-Gordan-Str.3, 91052 Erlangen, Germany. 10Wihuri Research Institute, Haartmaninkatu 8, 00290 Helsinki, Finland. 11Amsterdam UMC, Sanquin Research and Landsteiner Laboratory, The Netherlands. 12John Innes Centre, Norwich Research Park, Norwich NR4 7UH, UK. 13Department of Plant Molecular Biology, University of Lausanne, CH-1015 Lausanne, Switzerland. #These authors contributed equally. *Corresponding author: Taija Mäkinen, E-mail: <a href="mailto:taija.makinen@igp.uu.se">taija.makinen@igp.uu.se</a>, <a href="mailto:taija.makinen@helsinki.fi">taija.makinen@helsinki.fi</a>   DATASET A: Annotated cell-cell junction types in lymphatic capillaries of wild type mouse ear skin at different ages  __________________________________________________________________________________________________________ Contents <ul> <li>SOURCE DATA Fig1 FINAL. xlsx</li> <li>3w <ul> <li>animal 1</li> <li>animal 2</li> <li>animal 3</li> <li>animal 4</li> <li>animal 5</li> <li>sprouts</li> </ul> </li> <li>5w <ul> <li>animal 1</li> <li>animal 2</li> <li>animal 3</li> <li>animal 4</li> <li>animal 5</li> <li>diaphragm <ul> <li>Overview of diaphragm and high mag. of different capillary ends</li> </ul> </li> <li>trachea</li> </ul> </li> <li>25w <ul> <li>animal 1</li> <li>animal 2</li> <li>animal 3</li> <li>animal 4</li> <li>animal 5</li> <li>diaphragm</li> <li>trachea</li> </ul> </li> </ul> File legends C1 images: inverted LYVE1 signal (.tif) C2 images: inverted VE-cadherin signal (.tif) MAX images: RGB merge of LYVE1 (cyan) and VE-cadherin (red) (.tif) "NAME".roi: Regions of interest (ROI) of annotated junctions can be imported in ImageJ Methods Junctional classification:  Analysis of junction morphology was done on blunt-ended initial lymphatic capillaries in the segment between the intial tip and the first valve. Junction types were quantified in Z-stack projection by numbering of individual lobes of LYVE1 and VE-cadherin-stained LECs and subsequent categorizing of lobe-associated junctions based on VE-cadherin signal. Four categories were defined: 1) Button junction – a punctate VE-cadherin+ deposit at the neck of LYVE1+ lobe/overlap, with no detectable VE-cadherin at the borders of the overlap, 2) Curvilinear junction – unsegmented(continous) or segmented (discontinuous) distribution of VE-cadherin within one border of LYVE1+ lobe/cellular overlap, 3) Double junction – unsegmented(continous) or segmented (discontinuous) distribution of VE-cadherin within both borders of LYVE1+ lobe/cellular overlap, and 4) LYVE1- curvilineair junction – unsegmented(continous) linear VE-cadherin distribution at cell-cell contacts in the absence of LYVE1. Wild-type C57BL/6J mice were used for analysis of junction types, and 4-5 blunt ended vessels per mouse from five mice per age group and condition were analysed; in total 1785 junctions were annoted Imaging: Confocal images were obtained using a Leica Stellaris 5 confocal microscope equipped with 405 nm and white light lasers, 63x/1.3 HC PL APO CORR CS2 Glycerol immersion objective, and Leica LAS X software. Images were aquired at 1.51 digital zoom using a 2048x2048 resolution Tissue processing and staining:   Tissues were fixed in 4% paraformaldehyde for 2 h at RT and permeabilized in 0.3% Triton X-100 in PBS (PBST) for 10 min. After blocking in PBST with 2% bovine serum albumin, 1% FBS for 2 h, tissues were incubated with primary antibodies in blocking buffer overnight, followed by PBST washing and incubation with fluorescent dye-conjugated secondary antibodies for 2 h. All incubation steps were carried out at RT. Prior to mounting in Mowiol, samples were repeatedly washed in PBST and water. Antibodies used: Goat anti-mouse VE-cadherin (R&D Systems, AF1002; 1:200), Rat anti-mouse LYVE1 (R&D Systems, MAB2125; 1:200)   DATASET B: Finite element method (FEM) simulations of cellular stresses _______________________________________________________________ The FEM simulations were performed with MorphoMechanX using available models adapted from Sapala et al, eLife 7, e32794 (2018). A regular cylindrical grid 45 µm wide and 200 µm long was created and outlines from the cells of a lymphatic vessel were projected onto it and smoothed. These cells were then extruded inward to make 3D volumetric cells with a depth of 2 µm and triangulated using a threshold area of 4 µm.  The template was then used as the reference configuration for triangular 3 node membrane elements which were given a thickness of 0.1um. An isotropic St. Venant material model (linear, large deformation) was used with the Young's modulus set to 100 kPa to match a 10 kPa cell level Young's modulus estimated from the literature (ignoring the cell ends, the 2 x 0.1 µm membrane thickness occupied roughly 1/10th the cross-sectional area of the cell that were 2 µm deep). A uniform internal pressure was applied normal to the inside faces of the elements, which cancels out on the shared walls between cells. For simulations with a lower pressure inside the vessel, the inside faces were assigned a higher pressure. Stresses were visualized as the trace of the stress tensor.

opencc-by-4.0Oct 2024View details →

A Cross-Continental Analysis of How Regional Cues Shape Developers' Stack Overflow Contributions – Replication Package

Stack Overflow provides a wide range of knowledge for the software development community. Despite the importance of these platforms, several studies have shown that digital information tends to cluster geographically, which limits knowledge access that is otherwise necessary for innovation. The proposed study highlights the dynamics of users from different geographical backgrounds within Stack Overflow, which entails intra-country interactions, predominant topics of discourse, as well as their communication patterns. Finally, the study highlights that regional behavioural variations stem beyond cultural factors, encompassing technological advancement, entrepreneurial ventures, and workforce composition.  This replication package is provided for those interested in further examining our research methodology.

Figure 5 in Analysis of shape variability and life history strategies of Illex argentinus in the northern extreme of species distribution as a tool to differentiate spawning groups

Figure 5. Scatterplot of the relationship of PC1 on body weight separated by group (a), on body weight separated by sex (b), OvWBW (c) and NgWBW (d) for females separated by group.

Figure 3 in Analysis of shape variability and life history strategies of Illex argentinus in the northern extreme of species distribution as a tool to differentiate spawning groups

Figure 3. Schematic representation of the series of growth increments (GINC) read over the dorsal surface of the gladius, the filtering process and the back-calculation of the gladius growth.

Figure 1 in Analysis of shape variability and life history strategies of Illex argentinus in the northern extreme of species distribution as a tool to differentiate spawning groups

Figure 1. Spatial representation of the study area. (a–c) Positions of the samples of Illex argentinus collected from trawlers south-southeast of Brazil between 22° and 33°S and 45 and 722 m depth from 2001 to 2013. (b) Samples used in geometric morphometric analysis. (c) Samples used in traditional morphometric analysis. (d) Samples collected during a research cruise during August of 2004 in the same area to identify size-selective processes. Lines in maps represent 100, 300 and 700 m depth.

Figure 4 in Analysis of shape variability and life history strategies of Illex argentinus in the northern extreme of species distribution as a tool to differentiate spawning groups

Figure 4. Length distributions of the (a) Local Group (LG) and the (b) Migratory Group (MG) captured south-southeast of Brazil between 2009 and 2013. Scatterplots of the first (PC1) and second (PC2) components of the principal component analysis using body landmarks separated by group (c) and by sex (d). Scatterplot of relationship of PC1 on centroid size separated by group (e) and by sex (f).

Figure 2 in Analysis of shape variability and life history strategies of Illex argentinus in the northern extreme of species distribution as a tool to differentiate spawning groups

Figure 2. Landmark configuration on the body of Illex argentinus. Dashed line represents the longitudinal axis of the body.

Figure 8 in Analysis of shape variability and life history strategies of Illex argentinus in the northern extreme of species distribution as a tool to differentiate spawning groups

Figure 8. Length distributions of Illex argentinus captured in south-southeastern Brazil between 22° and 33°S and 45 and 722 m depth from 2001 to 2013.

Figure 7 in Analysis of shape variability and life history strategies of Illex argentinus in the northern extreme of species distribution as a tool to differentiate spawning groups

Figure 7. (a) Mean gladius length (GL) in research cruise, and (b) mean individuals recent growth trajectories of squid captured in research cruise reconstructed from gladius.

Figure 10 in Analysis of shape variability and life history strategies of Illex argentinus in the northern extreme of species distribution as a tool to differentiate spawning groups

Figure 10. Size differentiation of squid groups during the period of growth reconstructed expressed by the variation of the coefficient of asymmetry (g1) of the length frequency distributions by growth interval (days).

Figure 9 in Analysis of shape variability and life history strategies of Illex argentinus in the northern extreme of species distribution as a tool to differentiate spawning groups

Figure 9. Gladius length frequency distributions of Illex argentinus reconstructed for the last 15 days before the capture from the measured increments on the gladius for the trawls 2–4 (T2-4) and trawls 9–14 (T9-14) of the research cruise.