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Appendix 6 in Phenotypic variability in the shield morphology of wild- vs. lab-reared eumalacostracan larvae

Appendix 6. Principal components of Stomatopoda from principal component analysis on the shield outline and percentage of total variation in the data set explained by each principal component. A: Dorsal data set. B: Lateral data set.

Appendix 5 in Phenotypic variability in the shield morphology of wild- vs. lab-reared eumalacostracan larvae

Appendix 5. Principal components of Raninidae from principal component analysis on the lateral shield outline and percentage of total variation in the data set explained by each principal component.

Appendix 4 in Phenotypic variability in the shield morphology of wild- vs. lab-reared eumalacostracan larvae

Appendix 4. Principal components of Hippoidea from principal component analysis on the shield outline and percentage of total variation in the data set explained by each principal component. A: Dorsal data set. B: Lateral data set.

Appendix 3 in Phenotypic variability in the shield morphology of wild- vs. lab-reared eumalacostracan larvae

Appendix 3. Principal components of Galatheidae from principal component analysis on the shield outline and percentage of total variation in the data set explained by each principal component. A: Dorsal data set. B: Lateral data set.

Figure 7 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)

Figure 7. Results of behavioral experiments and ERG measurements. A. Behavioral experimental equipment (DA—Dark area; SA— Standing area; LA—Light area). B. Daily activities of normal form Callosobruchus maculatus. C. Quantification of ERG voltage responses of the flight and normal form insects exposed to different light stimuli. Different letters indicate significant differences between ERG responses. D. The phototaxis responses of the flight (above) and normal (down) forms were classified into 'positive phototaxis', 'negative phototaxis', and 'no selection'. E. Comparison of the phototaxis responses of the normal form and flight form Callosobruchus maculatus in response to different colors of light. Data are presented as mean ± standard error of the mean, **p <0.01, ***p <0.001 (t tests).

Figure 4 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)

Figure 4. Three-dimensional reconstruction of the compound eye of the flight form Callosobruchus maculatus. A, D. Frontal view of the head. B, C. Lateral view of the head. E. Posterior view of the head. F. Anterior view of the head. Scale bars = 100 μm.

Figure 1 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)

Figure 1. External appearance of compound eyes of Callosobruchus maculatus obtained via SEM. A–D. Laterial view of head. E–H. Vertical view of head. A, E. Flight form female (FF). B, F. Flight form male (FM). C, G. Normal form female (NF). D, H. Normal form male (NM). Abbreviations: AS—antennal socket; CE—compound eye. Scale bars = 100 μm.

Figure 6 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)

Figure 6. Ultrastructure of compound eye of Callosobruchus maculatus. A, D. Longitudinal section of the cornea. B, E. Cross-section of the distal end of the rhabdom. C, F. Cross section of proximal end of the rhabdom. G, H. Longitudinal section of compound eye. I. Semischematic drawing of one ommatidium of Callosobruchus maculatus. A, B, C, G. Normal form male. D, E, F, H. Flight form male. Abbreviations: Co—cornea; CC—crystalline cone; PPC—primary pigment cell; SPC—secondary pigment cell; Rh—rhabdom; R1–R8—retinular cells; Rh7, Rh8—rhabdomere; PG—pigment granule; CCN—nuclei of cone cells; PCN—nuclei of primary pigment cells; RCN—nuclei of retinular cells. Scale bars: A–B, D–E = 5 μm; C, F = 0.5 μm; G–H = 10 μm.

Figure S3 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)

Figure S3. Electrophysiological waveforms of compound eyes of two types of Callosobruchus maculatus. A. White. B. Green (520–530 nm). C. Blue (460–470 nm). D. Ultraviolet (365 nm). E. Red (620–630 nm).

Figure 5 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)

Figure 5. Three-dimensional reconstruction of the compound eye of the normal form Callosobruchus maculatus. A, D. Frontal view of the head. B, C. Lateral view of the head. E. Posterior view of the head. F. Anterior view of the head. Scale bars = 100 μm.

Figure S2 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)

Figure S2. The projection of microCT of Callosobruchus maculatus. A. Flight form. B. Normal form. Abbreviations: S—baseline length of a segment; H—height.

Figure 3 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)

Figure 3. Differences in the areas and numbers of ommatidia observed in two types of Callosobruchus maculatus. A. The ommatidia areas of the flight and normal forms. B. The number of ommatidia compared between two types of Callosobruchus maculatus. *p <0.05; **p <0.01; n.s., indicates no significant difference (t tests).

Figure 2 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)

Figure 2. Partially external appearance of compound eyes of Callosobruchus maculatus obtained via SEM. A–D. Hexagonal ommatidia of compound eye (H). E–H. Pentagonal and irregular ommatidia of compound eye (P). I–L. The arrows point to the interfacetal hairs between the hexagonal ommatidia. M–P. The arrows point to the interfacetal hairs between the pentagonal and irregular ommatidia. A, E, I, M. Flight-form female (FF). B, F, J, N. Flight form male (FM). C, G, K, O. Normal form female (NF). D, H, L, P. Normal form male (NM). Scale bars = 10 μm.

opencc-by-4.0Nov 2024View details →

Phenotypic, weather, soil, and imputed genomic data for the apple REFPOP

Supporting datasets for the article "Integrative multi-environmental genomic prediction in apple" by Jung et al. (2024). Pheno_raw.xlsx – Eleven traits were assessed during up to five years from 2018 to 2022 (Year) at up to five locations* (Country). The traits evaluated were floral emergence (Flowering_begin), flowering intensity (Flowering_intensity), harvest date (Harvest_date), total fruit weight (Fruit_weight), fruit number (Fruit_number), single fruit weight (Fruit_weight_single), titratable acidity (Acidity), soluble solids content (Sugar), fruit firmness (Firmness), red over color (Color_over), and russet frequency (Russet_freq_all). Weather_raw.xlsx – Hourly measurements from 2018 to 2022 (Date) of temperature (Temperature), relative humidity (Humidity), and global radiation (Radiation) were obtained at five locations* (Location). Soil_raw.xlsx – Soil characteristics (Variable) were measured at five locations* (Group.1) and two soil depths (Group.2) in 2016. SNPs_final_2022.bed, SNPs_final_2022.bim, SNPs_final_2022.fam – imputed genomic dataset of 303,239 biallelic SNPs in the PLINK format. *The locations correspond to Belgium (BEL), Switzerland (CHE), Spain (ESP), France (FRA) and Italy (ITA).

opencc-by-4.0Oct 2024View details →

Clinical phenotypes in acute and chronic infarction explained through human ventricular electromechanical modelling and simulations

This dataset includes the meshes, model parameters, and Alya executable binary for simulating acute and chronic stage post-myocardial infarct using Alya, to replicate the results in the article <a href="https://doi.org/10.7554/eLife.93002.1">https://doi.org/10.7554/eLife.93002.1</a> For each scenario simulated, a baseline simulation folder is provide with all the required meshes, fields, and model parameters necessary to run an Alya simulation. An additional series of models with variability in ionic conductances is also included for each scenario under the folder <scenario>_pom/, under which 20 simulations are included. For each simulation, only the file describing the ionic conductance scaling factors (ventricular_cell.txt) are included, all other files required to run each particular simulation can be found in the <scenario>_baseline/ version.  The file structure is as follows: <ul> <li>Alya executable binary</li> <li>control_baseline</li> <li>control_pom</li> <li>75%_transmural_scar <ul> <li>acute <ul> <li>bz1_baseline</li> <li>bz1_pom <ul> <li>pom_id_0</li> <li>...</li> <li>pom_id_19</li> </ul> </li> <li>bz2_baseline</li> <li>bz2_pom <ul> <li>pom_id_0</li> <li>...</li> <li>pom_id_19</li> </ul> </li> <li>bz3_baseline</li> <li>bz3_pom <ul> <li>pom_id_0</li> <li>...</li> <li>pom_id_19</li> </ul> </li> </ul> </li> <li>chronic <ul> <li>rz1_baseline</li> <li>rz1_pom <ul> <li>pom_id_0</li> <li>...</li> <li>pom_id_19</li> </ul> </li> <li>rz2_baseline</li> <li>rz2_pom <ul> <li>pom_id_0</li> <li>...</li> <li>pom_id_19</li> </ul> </li> </ul> </li> <li>fast_pacing <ul> <li>alternans1</li> <li>alternans4</li> </ul> </li> </ul> </li> </ul> The simulation files in alternans4/ was use to generate results Figure 6 of the accompanying article, and alternans1/ was used to generate results Figure 7.  The Alya executable binary has been built on ARCHER2 with the following loaded modules: 1) craype-x86-rome      2) libfabric/1.12.1.2.2.0.0 3) craype-network-ofi             4) perftools-base/22.12.0              5) xpmem/2.5.2-2.4_3.30__gd0f7936.shasta   6) bolt/0.8          7) epcc-setup-env    8) load-epcc-module 9) gcc/11.2.0        10) craype/2.7.19      11) cray-dsmml/0.2.2   12) cray-mpich/8.1.23 13) cray-libsci/22.12.1.1  14) PrgEnv-gnu/8.3.3   15) tk/8.6.13            16) tcl/8.6.13     17) cray-python/3.9.13.1 18) matplotlib/3.7.2 To replicate the study, access to an installation of the code in the Nord supercomputer can be requested to <a title="mailto:mariano@elem.bio" href="mailto:mariano@elem.bio">mariano@elem.bio</a>

opencc-by-4.0Sep 2023View details →

Phenotype Driven Data Augmentation Methods for Transcriptomic Data

This repository contains the data and associated results of all experiments conducted in our work "Phenotype Driven Data Augmentation Methods for Transcriptomic Data". In this work, we introduce two classes of phenotype driven data augmentation approaches – signature-dependent and signature-independent. The signature-dependent methods assume the existence of distinct gene signatures describing some phenotype and are simple, non-parametric, and novel data augmentation methods. The signature-independent methods are a modification of the established Gamma-Poisson and Poisson sampling methods for gene expression data. We benchmark our proposed methods against random oversampling, SMOTE, unmodified versions of Gamma-Poisson and Poisson sampling, and unaugmented data.    This repository contains data used for all our experiments. This includes the original data based off which augmentation was performed, the cross validation split indices as a json file, the training and validation data augmented by the various augmentation methods mentioned in our study, a test set (containing only real samples) and an external test set standardised accordingly with respect to each augmentation method and training data per CV split.  The compressed files <code>5x5stratified_{x}percent.zip</code> contains data that were augmented on <code>x%</code> of the available real data. <code>brca_public.zip</code> contains data used for the breast cancer experiments. <code>distribution_size_effect.zip</code> contains data used for hyperparameter tuning the reference set size for the modified Poisson and Gamma-Poisson methods.  The compressed file <code>results.zip</code> contains all the results from all the experiments. This includes the parameter files used to train the various models, the metrics (balanced accuracy and auc-roc) computed including p-values, as well as the latent space of train, validation and test (for the (N)VAE) for all 25 (5x5) CV splits. PLEASE NOTE: If any part of this repository is used in any form for your work, please attribute the following, in addition to attributing the original data source  - TCGA, CPTAC, GSE20713 and METABRIC, accordingly: <pre>@article{janakarajan2025phenotype,   title={Phenotype driven data augmentation methods for transcriptomic data},   author={Janakarajan, Nikita and Graziani, Mara and Rodr{\'\i}guez Mart{\'\i}nez, Mar{\'\i}a},   journal={Bioinformatics Advances},   volume={5},   number={1},   pages={vbaf124},   year={2025},   publisher={Oxford University Press} }</pre>

opencc-by-4.0Oct 2021View details →

DeepBacs – Escherichia coli antibiotic phenotyping object detection dataset and YOLOv2 model

Training and test images of E. coli cells treated with different antibiotics for antibiotic phenotyping using YOLOv2 object detection. Additional information can be found on this <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>. Example images show predictions of drug-treated E. coli cells.   Training and test dataset Data type: Paired microscopy images (confocal fluorescence) and manual annotations Microscopy data type: Confocal fluorescence images of fixed E. coli cells stained for membrane (Nile Red) and DNA (DAPI) paired with annotations in PASCAL VOC format Microscope: Zeiss LSM710 confocal microscope with a Plan-Apo 63x oil objective (1.4 NA) Cell type: Chemically fixed E. coli NO34 cells (MreB-sfGFPsw, kindly provided by Zemer Gitai) (untreated or drug-treated); File format: .png (RGB) Image size: 400 x 400 px² (Pixel size: 84 nm)   YOLOv2 model The YOLOv2 model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 97 epochs on 153 manually annotated images (image dimensions: (400, 400, 3)) with a batch size of 16 and a custom loss function combining MSE and crossentropy losses, using the YOLOv2 ZeroCostDL4Mic notebook (v 1.12) (von Chamier & Laine et al., 2020). Key python packages used include tensorflow (v0.1.12), Keras (v 2.3.1), numpy (v 1.19.5), cuda (v 10.1.243). The training was accelerated using a Tesla P100GPU and data was augmented by a factor of 8 using rotation and flipping. The model weights can be used with the ZeroCostDL4Mic YOLOv2 notebook.   Author(s): Christoph Spahn1,2, Mike Heilemann1,3 Contact email: christoph.spahn@mpi-marburg.mpg.de   Affiliation(s):  1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany 2) ORCID: 0000-0001-9886-2263  3) ORCID: 0000-0002-9821-3578

opencc-by-4.0Nov 2021View details →

Effects of phenotypic plasticity on species coexistence

Data generated during a study on the effects of phenotypic plasticity on species coexistence. Hess_etal_Data_Competition_01.csv contains data generated during competition trials between two species (Lemna minor and Spirodela polyrhiza). Prior to the competition trials, replicate populations of these species were subject to one of two different levels of an experimental treatment - either low-frequency plasticity-induction or a high-frequency plasticity-induction. Refer to manuscript for full details. Hess_etal_Data_Traits_01.csv contains data on morphological traits of Lemna minor and Spirodela polyrhiza subject to either low- or high-frequency plasticity-induction. Refer to manuscript for full details.