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287 results for “Response to selection”

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OpenNeuro48/100

Response inhibition and selective attention in adults and children with and without ADHD

Open the record for dataset details and reuse information.

openCC0Jan 2021View details →

OpenNeuro44/100

Robust functional mapping of layer-selective responses in human lateral geniculate nucleus with high-resolution 7T fMRI

Open the record for dataset details and reuse information.

openCC0Jan 2020View details →

zenodo44/100

Highly parallel genomic selection response in replicated Drosophila melanogaster populations with reduced genetic variation

Many adaptive traits are polygenic and frequently more loci contributing to the phenotype are segregating than needed to express the phenotypic optimum. Experimental evolution with replicated populations adapting to a new controlled environment provides a powerful approach to study polygenic adaptation. Since genetic redundancy often results in non-parallel selection responses among replicates, we propose a modified Evolve and Resequence (E&R) design that maximizes the similarity among replicates. Rather than starting from many founders, we only use two inbred Drosophila melanogasterstrains and expose them to a very extreme, hot temperature environment (29°C). After 20 generations, we detect many genomic regions with a strong, highly parallel selection response in 10 evolved replicates. The X chromosome has a more pronounced selection response than the autosomes, which may be attributed to dominance effects. Furthermore, we find that the median selection coefficient for all chromosomes is higher in our two-genotype experiment than in classic E&R studies. Since two random genomes harbor sufficient variation for adaptive responses, we propose that this approach is particularly well-suited for the analysis of polygenic adaptation. See the README.txt file to get a description of the uploaded files. Scripts.zip contains annotated command lines and scripts for the project (see internal README.txt file).

opencc-by-4.0Oct 2021View details →

zenodo44/100

Functional potential and evolutionary response to long-term heat selection of bacterial associates of coral photosymbionts

Sequencing reads were assembled using the genome assembler pipeline Shovill v1.1.0. Briefly, the Shovill pipeline included read trimming using Trimmomatic v0.39, de novo assembly with SPAdes v3.15.5 and genome polishing with Pilon v1.24. After the pipeline, additional polishing was performed by mapping the reads back to the contigs with BWA v0.7.17 and sorting the resulting SAM/BAM files using SAMtools v1.15.1. Pilon v1.24 was then used to correct bases, fix mis-assemblies and fill gaps. The reformat.sh script from the Bbmap package v38.76 (-minlength=1000) was used to filter out contigs less than 1000bp. The draft genome assemblies were then annotated with Bakta v1.7.0.  Single nucleotide polymorphism (SNP) detection between WT (WTref) and SS (SSref) samples were then performed using snippy v4.6.0, where both WT and SS samples were inputted as the reference genome in turn. A subset of the snippy output files are uploaded here and contain all variants found.

opencc-by-4.0Aug 2023View details →

zenodo40/100

Data from: Stochastic phenotypic switching arises in response to directional selection in experimentally evolved multicellular yeast.

This BBC_2025__README.txt file was generated on 2025-09-24 by Beatriz Baselga Cervera GENERAL INFORMATION <ol> <li>Title of Dataset and code: Data from: Stochastic phenotypic switching arises in response to directional selection in experimentally evolved multicellular yeast.</li> </ol>   2. Author Information             Corresponding Investigator                         Name: Ph.D. Beatriz Baselga-Cervera                         Institution: University of Minnesota Twin cities, Minnesota, US.                         Email: <a href="mailto:bbaselga@umn.edu">bbaselga@umn.edu</a>; beabaselga@gmail.com             Co-investigator 1                         Name: Ph.D. Nahui Olin Medina-Chávez                         Institution: University of Minnesota Twin cities, Minnesota, US.                         Email: nmedinac@umn.edu             Co-investigator 2                         Name: Ph.D. Noah Gettle                         Institution: Wellcome Sanger Institute, Hinxton, UK.                         Email: nbgettle@gmail.com Co-investigator 3                         Name: Ph.D. Michael Travisano                         Institution: University of Minnesota Twin cities, Minnesota, US.                         Email: travisan@umn.edu     3. Data collectors: Ph.D. Beatriz Baselga-Cervera, Ph.D. Nahui Olin Medina-Chávez & Ph.D. Noah Gettle.   4. Date of data collection: 2022-2024   5. Geographic location of data collection: Saint Paul, US   6. Funding sources that supported the collection of the data: Fundación Alfonso Martín Escudero, Madrid, Spain (BBC).   7. Recommended citation for this dataset: Baselga-Cervera et al. (2024), Data from: Stochastic phenotypic switching arises in response to directional selection in experimentally evolved multicellular yeast.     DATA & FILE OVERVIEW   8. Description of dataset In this study, we address whether stochastic phenotypic switching can shape biological diversity contributing to evolutionary change across the transition from singles cells to multicellular clutters in Saccharomyces cerevisiae multicellular yeast system. Populations characterization was conducted with a Coulter Counter multisize 4, a FlowCam 3, under the optic microscope, via ACE2 gene sequencing and RNA sequencing and mathematical modeling. The populations studied were the genetically uniform diploid wild-type Saccharomyces cerevisiae Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T).   9. File list: ●      Coulter Counter size distribution data:  o   File 1 name:  File_1_Coulter_Counter_Counts_20h.csv o   File 1 description: Size distributions of Saccharomyces cerevisiae Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockout, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD at 20-hours growth. Data for: Fig. 1A, Fig. 3A and Fig. S2, Table S2 and Table S3.   o   File 2 name:  File_2_Coulter_Counter_Counts_24h.csv o   File 2 description: Size distributions of Saccharomyces cerevisiae Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockout, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD at 24-hours growth. Data for: Fig. 1A, Fig. 3A, Fig. S2, Table S2 and Table S3.   o   File 3 name:  File_3_Coulter_Counter_Counts_48h.csv o   File 3 description: Size distributions of Saccharomyces cerevisiae Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockout, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD at 48-hours growth. Data for: Fig. 1, Fig. 3A, Fig. S2, Table S2 and Table S3.   o   File name:  File_4_Coulter_Counter_Counts_Constructed_strains_diversity.csv o   File 4 description: Size distributions of the constructed ACE2 knockout and a strain containing the homozygous missense mutation (ACE2 c.1934 A>T) in YPD at 24h growth. Size distributions were obtained from populations before (initial) and five resuspended colonies obtained from small-size particles by plating the top fraction of the population after gravitational selection from three isolates per strain. Data for: Fig. 1, Fig. S2, Table S2 and Table S3.   o   File 5 name:  File_5_Coulter_Counter_Counts_Selection_Experiment.xlsx o   File 5 description: Size distributions of C1W8.1 and C1W8.2 multicellular evolved strains in YPD at 24h growth. Size distributions from the selection experiment for small-size particles by plating the top fraction of the population after gravitational selection over three cycles of selection. Data for: Fig. 2B and Fig. S6.   o   File 6 name:  File_6_Coulter_Counter_Counts_12h.xlsx o   File 6 description: Size distributions of C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockout, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD at 12-hours growth. Data for: Fig. 3A and Fig. S3.     ●      FlowCam data: o   File 7 name: File_7_Rawdata_FlowCam_all.csv o   File 7 description: FlowCam data from Saccharomyces cerevisiae Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD at 24h growth. Data for: Fig. S4.     ●      Data generated statistically: o   File 8 name: File_8_C1W8.2_overlapPairs_Selection_Experiment.csv o   File 8 description: overlapping indexes (η) of the KDE distributions were computed using the R-package ‘overlapping’ from the Coulter Counter data of the C1W8.2 derived strain over the selection experiment. Data for: Fig. S6D.   o   File 9 name: File_9_C1W8.1_overlapPairs_Selection_Experiment.csv o   File 9 description: overlapping indexes (η) of the KDE distributions were computed using the R-package ‘overlapping’ from the Coulter Counter data o   of the C1W8.1 derived strain over the selection experiment. Data for: Fig. S6C.     o   File 10 name: File_10_ overlapPairs_Constructed_strains_diversity.xlsx o   File 10 description: overlapping indexes (η) of the KDE distributions were computed using the R-package ‘overlapping’ from the Coulter Counter data of the constructed ACE2 knockout and a strain containing the homozygous missense mutation (ACE2 c.1934 A>T) in YPD at 24h growth. Size distributions were obtained from populations before (initial) and after gravitational selection of five resuspended colonies from three isolates per strain. Data for: Fig. S7.   ●      Data from ImageJ: o   File 11 name: File_11_ImageJ_analyses.xlsx o   File 11 description: ImageJ analyses of the microphotographs from Saccharomyces cerevisiae Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T). Cultures were grown in culture tubes with 10 ml of media, 50 mL Erlenmeyer flasks with 10 mL and 30 mL of media, in YPD under non-shaking and shaking at 250 rpm. YPD media was used across all conditions. Cultures were assessed after 24 hours growth at 30°C.  Microphotographs of each condition and strain were obtained with a Nikon TE2000 microscope using 10x objective.   ●      Pictures: o   File 12 name: File_12_ FlowCam_Pictures.zip o   File 12 description FlowCam IMAGES from Saccharomyces cerevisiae Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD at 24h growth. Data for: Fig. 1B.   o   File 13 name: File_13_Microphotography_controled_experimental_conditions.zip o   File 13 description: Microphotographs from Saccharomyces cerevisiae Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T). Cultures were grown in culture tubes with 10 mL of media, 50 mL Erlenmeyer flasks with 10 mL and 30 mL of media, in YPD under non-shaking and shaking at 250 rpm. YPD media was used across all conditions. Cultures were assessed after 24 hours of growth at 30°C. Pictures were obtained with a Nikon TE2000 microscope using 10x objective.     ●      Mathematical Model o   File 14 name: File_14_Mathematical_model.zip o   File 14 description: Mathematical model R code and generated values.   ●      ARN data o   File 15 name: File_15_rnaseq-final-results-Top_v_Bottom.xlsx o   File 15 description: RNA analyses final results Top vs Bottom phenotypic subdistributions. Top is used as control. o   File 16 name: File_16_Variant_Call_format_file.vcf o   File 16 description: Variant Calling analyses of the sample ARN sample Top 1. Adhesion number: SRR32105384.   ●      Time-lapse videos o   File 17 name: Supp. Video 1. C1W8.1 from 17 to 22 hours growth o   File 17 description: Supplementary Video 1. Experimentally evolved multicellular yeast video between 17 and 22 hours of growth (C1W8.1-derived strain) — time-lapse video of the formation of a single-cell propagule from a multicellular cluster. o   File 18 name: Supp. Video 2. Ace2x2KO over 26 hours growth. o   File 18 description: Supplementary Video 2. ace2Δ knockout constructed strain growth — time-lapse video of a single large multicellular cluster over 26 hours. o   File 19 name: Supp. Video 3. C1W8.1 over 6 hours growth o   File 19 description: Supplementary Video 3. Experimentally evolved multicellular yeast growth between 6 and 12 hours of growth (C1W8.1-derived strain). o   File 20 name: Supp. Video 4. C1W8.1 over 24 hours growth o   File 20 description: Supplementary Video 4. Experimentally evolved multicellular yeast growth over 24 hours (C1W8.1-derived strain) — cell division stops in small ancestral-like phenotypes. o   File 21 name: Supp. Video 5. Ace2x2KO over 24 hours growth o   File 21 description: Supplementary Video 5. ace2Δ knockout constructed strain growth — time-lapse video of multiple large multicellular clusters over 24 hours. o   File 22 name: Supp. Video 6. Ace2x2missense from 0 to 3h45m hours growth o   File 22 description: Supplementary Video 6. ace2Δ missense constructed strain growth — time-lapse video of multiple large multicellular clusters up to 3 hours 45 min.   METHODOLOGICAL INFORMATION Strains: ancestral wildtype (Y55 strains), C1W8.1 and C1W8.2 multicellular derived strains isolated after 60 days of selection in YPD media, constructed ACE2 gene knockouts, and strains containing the ACE2 missense mutation (ACE2 c.1934 A>T). Media: Growth media used in this study were Yeast Peptone Dextrose media (YPD; 1% (v/w) yeast extract, 2% (v/w) peptone, 2% (v/w) D-glucose, pH 5.8). Phenotypic characterization of the different strains was conducted in a Coulter Counter Multisizer 4 and FlowCam® 3.0 Fluid Imaging Technologies, optic microscopy and a mathematical model. Replicate populations of different individual isolates per strain were analyzed to obtain the population distributions in YPD media. RNA was extracted using an Invitrogen® PureLink RNA Mini Kit. Three out of four extracted samples per treatment with the highest RNA integrity score were submitted for TrueSeq Stranded RNA-Seq.     10. Detailed description ●      Coulter Counter size distribution data of all the populations:  o   File 1 name:  File_1_Coulter_Counter_Counts_20h.csv o   File 1 description: strains naming convention; strain_Isolate_run.pseudoreplicate. Strains: ace2x2m=strains containing the ACE2 missense mutation (ACE2 c.1934 A>T); ace2x2= ACE2 knockout; C1W8.1= C1W8.1 evolved multicellular strain; C1W8.2= C1W8.2 evolved multicellular strain; Y55= ancestral strain. §  Page 1: Column 1: Volume (um3) Column 2: Diameter (um2) Columns 3 to the last column: strains counts.   o   File 2 name:  File_2_Coulter_Counter_Counts_24h.csv o   File 2 description: strains naming convention; strain_Isolate_run.pseudoreplicate. Strains: ace2x2m=strains containing the ACE2 missense mutation (ACE2 c.1934 A>T); ace2x2= ACE2 knockout; C1W8.1= C1W8.1 evolved multicellular strain; C1W8.2= C1W8.2 evolved multicellular strain; Y55= ancestral strain. §  Page 1: Column 1: Volume (um3) Column 2: Diameter (um2) Columns 3 to the last column: strains counts.   o   File 3 name:  File_3_Coulter_Counter_Counts_48h.csv o   File 3 description: strains naming convention; strain_Isolate_run.pseudoreplicate. Strains: ace2x2m=strains containing the ACE2 missense mutation (ACE2 c.1934 A>T); ace2x2= ACE2 knockout; C1W8.1= C1W8.1 evolved multicellular strain; C1W8.2= C1W8.2 evolved multicellular strain; Y55= ancestral strain. §  Page 1: Column 1: Volume (um3) Column 2: Diameter (um2) Column 3 to the last column: strains counts.   o   File_4_Coulter_Counter_Counts_Constructed_strains_diversity.csv o   File 4 description: strains naming convention; strain_Isolate_colony_run.pseudoreplicate. Strains: ace2x2m=strains containing the ACE2 missense mutation (ACE2 c.1934 A>T); ace2x2= ACE2 knockout. §  Page 1: Column 1: Volume (um3) Column 2: Diameter (um2) Column 3 to the last column: strains counts.   o   File_5_Coulter_Counter_Counts_Selection_Experiment.xlsx o   File 5 description: strains naming convention; strain_colony.phenotype_selection.cycle_run.pseudoreplicate. Strains: C1W8.2= C1W8.2 evolved multicellular strain and C1W8.1= C1W8.1 evolved multicellular strain. §  Page 1: Column 1: Volume (um3) Column 2: Diameter (um2) Column 3 to the last column: strains counts.   o   File 6 name:  File_6_Coulter_Counter_Counts_12h.xlsx o   File 6 description: Size distributions of C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockout, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD at 12-hours growth. Data for: Fig. 3A and Fig. S3. §  Page 1: Column 1: Volume (um3) Column 2: Diameter (um2) Column 3: Time Column 4: replicate Column 5: Strain name (strain_f) Column 6: Isolate (isolate_f)         o   File 7 name: File_3_Rawdata_Flowcam_all.csv o   File 7 description: strains naming convention; ace2_isolate= ACE2 knockout; Ace2m_isolate= strain containing the ACE2 missense mutation (ACE2 c.1934 A>T); c1w82_isolate=C1W8.2 evolved multicellular strain; C1W81_isoalte C1W8.1 evolved multicellular strain; Y55_isolate=ancestral strain. §  Page 1:                                                                         Column 1: Particle ID                                                                         Column 2: Area ABD                                                                         Column 3: Aspect Ratio (Width/Length)                                                                         Column 4: Circle Fit                                                                         Column 5: Area base Diameter (ABD)                                                                         Column 6: Equivalent Spherical Diameter (ESD)                                                                         Column 7: Elongation                                                                         Column 8: Perimeter                                                                         Column 9: Roughness                         Column 10: Volume ABD-based                                                                         Column 11: Volume ESD-based                                                                         Column 12: Width                                                                         Column 13: Source. Name of the sample.   o   File 8 name: File_8_C1W8.2_overlapPairs_Selection_Experiment.csv o   File 8 description: C1W8.2 _lineage_selection.cycle= C1W8.2 evolved multicellular strain, lineage (A=ancestral, M1= lineage 1,  M2= lineage 2 , M3= lineage 3) and selection cycle  (0, 1, 2 and 3). §  Page 1: Column 1: Var1= strain 1 Column 2: Var2= strain 2 Column 3: overlap value of both strains compared.   o   File 9 name: File_9_C1W8.1_overlapPairs_Selection_Experiment.csv o   File 9 description: C1W8.1 _lineage_selection.cycle =C1W8.1 evolved multicellular strain, lineage (A=ancestral, M1= lineage 1,  M2= lineage 2 , M3= lineage 3) and selection cycle  (0, 1, 2 and 3). §  Page 1: Column 1: Var1= strain 1 Column 2: Var2= strain 2 Column 3: overlap value of both strains compared.   o   File 10 name: File_10_overlapPairs_Constructed_strains_diversity.xlsx o   File 10 description: variables naming convention; strain _isolate_colony.number. Strains; ace2x2m=strains containing the ACE2 missense mutation (ACE2 c.1934 A>T); ace2x2= ACE2 knockout. Isolate; 1,2 and 3. Colony.number; Initial=initial population and colony number (1,2,3,4 and 5). §  Page 1: Column 1: Var1= strain 1 Column 2: Var2= strain 2 Column 3: overlap value of both strains compared.   o   File 11 name: File_11_ ImageJ _analyses.xlsx o   File 11 description: ImageJ analyses of the microphotographs from Saccharomyces cerevisiae Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T). Cultures were grown in culture tubes with 10 mL of media, 50 mL Erlenmeyer flasks with 10 mL and 30 mL of media, in YPD under non-shaking and shaking at 250 rpm. YPD media was used across all conditions. Cultures were assessed after 24 hours growth at 30°C.  Microphotographs of each condition and strain were obtained with a Nikon TE2000 microscope using 10x objective. §  Page 1: Column 1: Var1= strain 1 Column 2: Var2 = strain 2 Column 3: overlap value of both strains compared.     o   File 12 name: File_12_ FlowCam_Pictures.zip o   File 12 description: FlowCam runs, images, and raw data of Saccharomyces cerevisiae Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD at 24h growth. Data for: Fig. 1B.   o   File 13 name: File_13_Microphotography_controled_experimental_conditions.zip o   File 13 description: 149 microphotographs. § Folder 1:  Images of Erlenmeyer flasks with 30ml of YPD § Folder 2:  Images of Erlenmeyer’s and tubes with 10ml of YPD   o   File 14 name: File_14_Mathematical_model.zip o   File 14 description: Mathematical model, R code, and generated values. §  Document 1:  R code of the model §  Document 2:  Resulted data from the mathematical model with different inset values of k, alpha, and beta. §  Document 2:  Resulted data from the mathematical model with different inset values of k, alpha, gamma, and beta.       o   File 15 name: File_15_rnaseq-final-results-Top_v_Bottom.xlsx o   File 15 description: §  Page 1: Column 1: number Column 2: ID Column 3: protID             Column 4: gene_symbol                     Column 5: chr             Column 6: chr_latin Column 7: location Column 8: baseMean             Column 9: log2FoldChange Column 10: lfcSE Column 11: stat             Column 12: pvalue     padj Column 13: test Column 14: log10padj             Column 15: log10baseMean Column 16: blast_pident Column 17: transcript_length             Column 18: blast_evalue Column 19: blast_bitscore Column 20: rnaID             Column 21: feature Column 22: accession Column 23: strain             Column 24: gene_accession   o   File 16 name: File_16_Variant_Call_format_file.vcf o   File 16 description: Variant Calling analyses of the  ARN sample Top 1. Adhesion number: SRR32105384.   o   File 17 name: Supp. Video 1. C1W8.1 from 17 to 22 hours growth o   File 17 description: Supplementary Video 1. Experimentally evolved multicellular yeast video between 17 and 22 hours of growth (C1W8.1-derived strain) — time-lapse video of the formation of a single-cell propagule from a multicellular cluster. The time-lapse video captures growth dynamics over this period, highlighting the formation of a single-cell propagule from a multicellular cluster on two occasions (visible in the lower left region of the frame). Images were acquired every 15 minutes using a 10x objective lens.  o   File 18 name: Supp. Video 2. Ace2x2KO over 26 hours of growth. o   File 18 description: Supplementary Video 2. ace2Δ knockout constructed strain growth — time-lapse video of a single large multicellular cluster over 26 hours. The video captures large, multicellular clusters that produce both large, multicellular and small, ancestral-like clusters. The video shows a single large multicellular cluster fragmenting into two large multicellular clusters at ~ 13 hours of growth (from 02:09 to 02:10 minutes in the time-lapse) and generating two small ancestral-like propagules at ~19 hours of growth (from 03:07 to 03:09 minutes in the time-lapse). Microphotographs were obtained at 3-minute intervals under a 10x objective over 26 hours.   o   File 19 name: Supp. Video 3. C1W8.1 over 6 hours growth o   File 19 description: Supplementary Video 3. Experimentally evolved multicellular yeast growth between 6 and 12 hours of growth (C1W8.1-derived strain). The time-lapse video captures large, multicellular clusters of the C1W8.1 strains, which produce both large, multicellular and small, ancestral-like clusters. Additionally, small ancestral-like clusters are observed undergoing cellular division —no separation is observed— during the first 2 to 3 hours, followed by a cessation of division for the remainder of the time-lapse. Images were acquired every 30 seconds using a 10x objective lens. o   File 20 name: Supp. Video 4. C1W8.1 over 24 hours growth o   File 20 description: Supplementary Video 4. Experimentally evolved multicellular yeast growth over 24 hours (C1W8.1-derived strain) — cell division stops in small ancestral-like phenotypes. The footage captures multiple large multicellular clusters undergoing fragmentation into propagules. Additionally, a small ancestral-like cluster is observed undergoing division during the first 2 to 3 hours, followed by a cessation of division for the remainder of the time-lapse (visible in the lower left region of the frame). This early division phase is evident during the first 10 seconds of the video. Images were acquired every 5 minutes using a 10x objective lens.  o   File 21 name: Supp. Video 5. Ace2x2KO over 24 hours growth o   File 21 description: Supplementary Video 5. ace2Δ knockout constructed strain growth — time-lapse video of multiple large multicellular clusters over 24 hours. The video shows multiple large multicellular clusters fragmenting into large clusters and several small ancestral-like clusters being dragged by Brownian motion and evaporation of the sample. Microphotographs were obtained at fixed intervals of 3 minutes under the 10x objective over 24 hours.  o   File 22 name: Supp. Video 6. Ace2x2missense from 0 to 3h45m hours growth o   File 22 description: Supplementary Video 6. ace2Δ missense constructed strain growth — time-lapse video of multiple large multicellular clusters up to 3 hours 45 min. The video shows multiple large multicellular clusters fragmenting into large clusters, generating two small ancestral-like propagules before being dragged by Brownian motion and evaporation of the sample. Microphotographs were obtained at 3-minute intervals under the 10x objective.

opencc-by-4.0Sep 2024View details →

dryad40/100

The interplay between prior selection, mild intermittent exposure, and acute severe exposure in phenotypic and transcriptional response to hypoxia

Hypoxia has profound and diverse effects on aerobic organisms, disrupting oxidative phosphorylation and activating several protective pathways. Predictions have been made that exposure to mild intermittent hypoxia may be protective against more severe exposure and may extend lifespan. Both effects are likely to depend on prior selection on phenotypic and transcriptional plasticity in response to hypoxia, and may therefore show signs of local adaptation. Here we report the lifespan effects of chronic, mild, intermittent hypoxia (CMIH) and short-term survival in acute severe hypoxia (ASH) in four clones of Daphnia magna originating from either permanent or intermittent habitats, the latter regularly drying up with frequent hypoxic conditions. We show that CMIH extended the lifespan in the two clones originating from intermittent habitats but had the opposite effect in the two clones from permanent habitats, which also showed lower tolerance to ASH. Exposure to CMIH did not protect against ASH; to the contrary, Daphnia from the CMIH treatment had lower ASH tolerance than normoxic controls. Few transcripts changed their abundance in response to the CMIH treatment in any of the clones. After 12 hours of ASH treatment, the transcriptional response was more pronounced, with numerous protein-coding genes with functionality in mitochondrial and respiratory metabolism, oxygen transport, and, unexpectedly, gluconeogenesis showing up-regulation. While clones from intermittent habitats showed somewhat stronger differential expression in response to ASH than those from permanent habitats, there were no significant hypoxia-by-habitat of origin or CMIH-by-ASH interactions. GO enrichment analysis revealed a possible hypoxia tolerance role by accelerating the molting cycle and regulating neuron survival through up-regulation of cuticular proteins and neurotrophins, respectively.

opencc-zeroSep 2022View details →

zenodo40/100

A new and highly robust light-responsive Azo-UiO-66 for highly selective and low energy post-combustion CO2 capture and its application in a mixed matrix membrane for CO2/N2 separation

Supporting information for publication in Journal of Materials Chemistry A, <a href="https://dx.doi.org/10.1039/C8TA03553A">https://dx.doi.org/10.1039/C8TA03553A </a>

opencc-by-4.0Mar 2018View details →

dryad40/100

Evolutionary responses of energy metabolism, development, and reproduction to artificial selection for increasing heat tolerance in Drosophila subobscura

Adaptation to warming conditions involves increased heat tolerance and metabolic changes to reduce maintenance costs and maximize biological functions close to fitness. Evidence shows that energy metabolism evolves in response to warming conditions, but we know little about how heat stress intensity determines the evolutionary responses of metabolism and life history traits. Here, we evaluated the evolutionary responses of energy metabolism and life-history traits to artificial selection for increasing heat tolerance in Drosophila subobscura, using two protocols to measure and select heat tolerance: slow and fast ramping protocols. We found that the increase in heat tolerance was associated with reduced activity of the enzymes involved in the glucose-6-phosphate branchpoint, but no changes in the metabolic rate in selected lines. We also found that the evolution of increased heat tolerance increased the early fecundity in selected lines and increased the egg-to-adult viability only in the slow-ramping selected lines. This work shows heat tolerance can evolve under different thermal scenarios but with different evolutionary outcomes on associated traits depending on the heat stress intensity. Therefore, spatial and temporal variability of thermal stress intensity should be taken into account to understand and predict the adaptive response to ongoing and future climatic conditions.

opencc-zeroDec 2021View details →

zenodo40/100

EEG Dataset for 'Decoding of selective attention to continuous speech from the human auditory brainstem response' and 'Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise'.

The repository contains the unprocessed EEG data recorded for the publications [1, 2]. For convenience, the onsets of the EEG data provided here are time-aligned with the onsets of the audio books in the 'audiobooks' folder, and the EEG data are provided in HDF5 format. Please refer to the original version of this dataset for more details. More details, as well as the original data files, are available at the original repository <a href="https://doi.org/10.5281/zenodo.7086209">here</a>. Examples of using these data (preprocessing, fitting linear models) can be found <a href="https://github.com/Mike-boop/trf-examples">here</a>. The English conditions (clean, lb, mb, hb, fM, fW) comprised a single recording session. The Dutch conditions (cleanDutch, lbDutch, mbDutch, hbDutch) comprised a separate recording session. You see which participants took part in each session in session_info.json. Please note some details about the stimulus presentation for the various listening conditions: <ul> <li>English speech-in-babble-noise (lb, mb, hb): babble noise was played by itself for one second before the audiobook track began. The babble noise was also played for one second after the audiobook track ended. Therefore, you should discard the first second and the last second from these trial during your analysis.</li> <li>Dutch speech-in-babble-noise (lbDutch, mbDutch, hbDutch): the story (narrated in Dutch) was played by itself for one second before the babble noise track began. Then, the babble noise was increased linearly in amplitude for one second. Therefore, you should discard the first two seconds from these trials during your analysis.</li> <li>Dutch in quiet, and Dutch-in-babble-noise (cleanDutch, lbDutch, mbDutch, hbDutch): some English sentences were embedded in the Dutch narratives in order to encourage attention. You should crop these from your analysis. The onsets and offsets of the English sentences (in samples, at 44100Hz) are provided in the audiobooks/*Dutch/english_onsets_info.json files.</li> <li>Competing-speakers conditions (fM, fW): sometimes the attended track is longer than the unattended track, or vice-versa. The onsets of both tracks are aligned. You should crop the trial to the length of the shortest track for your analysis.</li> </ul> If you use this data, please cite the original publications, as well as this repository [1,2,3]. [1] Etard O, Kegler M, Braiman C, Forte A E and Reichenbach T. “Decoding of selective attention to continuous speech from the human auditory brainstem response” 2019. NeuroImage 200 1–11 [2] Etard O and Reichenbach T. “Neural speech tracking in the theta and in the delta frequency band differentially encode clarity and comprehension of speech in noise” 2019. J. Neurosci. 39 5750–9 [3] Etard O and Reichenbach T. "EEG Dataset for 'Decoding of selective attention to continuous speech from the human auditory brainstem response' and 'Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise". Doi: 10.5281/zenodo.7086208

opencc-by-4.0Sep 2022View details →

dryad40/100

Adaptive immune response selects for postponed maturation and increased body size

<ol> <li>The Major Histocompatibility Complex (MHC) genes encode proteins that initiate the adaptive immune response by presenting pathogen-derived antigenic peptides to T lymphocytes. Host-pathogen coevolution drives MHC polymorphism, introducing intraspecific variation in host life expectancy. This variation interacts with optimal growth strategy, as growth increases reproductive potential. While mortality rate and body size-dependent fecundity are major factors shaping life histories, the effect of intraspecific variation in MHC-based immunity on the evolution of growth strategies and host body size remains unknown.</li> <li>Here, we model how host MHC–pathogen coevolution—and its concomitant impact on host mortality—can affect the evolution of host life-histories, as represented by age at maturation and body size. Life histories were compared in scenarios with and without adaptive immune response under equal population-level mortality rates.</li> <li>We show that host-pathogen coevolutionary dynamics select for postponed maturation and increased body size. Although MHC genes and genes that determine body size were physically unlinked, selection imposed by the Red Queen process generated linkage disequilibrium between immunocompetent MHC alleles and the maturation-postponing alleles that prolong growth phase and increase body size. Particularly large body size was attained when pathogens mutated slowly, thus allowing the advantage of resistant MHC alleles to persist over multiple generations.</li> <li>The emergence of adaptive immunity, which is pathogen-specific and enables immunological memory, is considered a major evolutionary innovation of vertebrates. Our work suggests that the adaptive immune response, mediated by polymorphic MHC genes, may drive the evolution of host body size. This form of adaptive immunity may have thus predisposed vertebrates to evolve large body sizes and exhibit the macroevolutionary patterns of increasing body size over time that have been detected in comparative studies. </li> </ol>

opencc-zeroAug 2023View details →

dryad40/100

Evolutionary responses of energy metabolism, development, and reproduction to artificial selection for increasing heat tolerance in Drosophila subobscura

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publicNov 2022View details →

dryad40/100

Data and code from: Quantitative analyses of stochastic influences on the response to phenotypic selection in a small passerine, the collared flycatcher

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publicMay 2025View details →

dryad40/100

Data and scripts for: Quantitative assessment of observed vs. predicted responses to selection

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publicJun 2021View details →

dryad40/100

The interplay between prior selection, mild intermittent exposure, and acute severe exposure in phenotypic and transcriptional response to hypoxia

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publicSep 2022View details →

dryad40/100

Adaptive immune response selects for postponed maturation and increased body size

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publicAug 2023View details →

dryad40/100

Data from: Interplay of trophic relaxation and directional selection shapes eco-evolutionary responses to selective harvest in predator-prey systems

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publicAug 2025View details →

dryad36/100

Data from: Responses of activity rhythms to temperature cues evolve in Drosophila populations selected for divergent timing of eclosion

Even though the rhythm in adult emergence and rhythm in locomotor activity are two different rhythmic phenomena that occur at distinct life-stages of the fly life cycle, previous studies have hinted at similarities in certain aspects of the organisation of the circadian clock driving these two rhythms. For instance, the period gene plays an important regulatory role in both rhythms. In an earlier study, we have shown that selection on timing of adult emergence behaviour in populations of Drosophila melanogaster leads to the co-evolution of temperature sensitivity of circadian clocks driving eclosion. In this study, we were interested in asking if temperature sensitivity of the locomotor activity rhythm has evolved in our populations with divergent timing of adult emergence rhythm, with the goal of understanding the extent of similarity (or lack of it) in circadian organisation between the two rhythms. We found that in response to simulated jetlag with temperature cycles, late chronotypes (populations selected for predominant emergence during dusk) indeed re-entrain faster than early chronotypes (populations selected for predominant emergence during dawn) to 6-h phase-delays, thereby indicating enhanced sensitivity of the activity/rest clock to temperature cues in these stocks (entrainment is the synchronisation of internal rhythms to cyclic environmental time-cues). Additionally, we found that late chronotypes show higher plasticity of phases across regimes, day-to-day stability in phases and amplitude of entrainment, all indicative of enhanced temperature sensitive activity/rest rhythms. Our results highlight remarkably similar organisation principles between emergence and activity/rest rhythms.

opencc-zeroMay 2020View details →

dryad36/100

Extending the ecology of fear: Parasite-mediated sexual selection drives host response to parasites

The 'ecology of fear' describes the negative effects natural enemies have on potential victims even when those victims are not consumed or infected. Although recent work has demonstrated parasites have non-consumptive effects (NCE) on potential hosts, how these effects vary within host populations is not well understood. We investigated how NCE vary based on host risk of infection and relative cost of infection by measuring the metabolic rate (MR) of naive Drosophila nigrospiracula exposed to an ectoparasite, Macrocheles subbadius. We tested two mutually exclusive hypotheses: 1) asymmetrical costs of infection drive adaptions for stronger responses to parasite exposure; or 2) asymmetrical risks of infection drive adaptions for stronger responses to parasite exposure. In this system, male flies have higher costs of infection relative to female flies due to parasite-mediated sexual selection; similarly, virgin females experience higher costs of infection relative to mated females. Risk of infection also varies among flies because mites preferentially infect female flies over males, and mites preferentially infect mated females over virgin females. Our results were compatible with the hypothesis that costs of infection drive the strength of response to mite risk. Female flies responded to parasite exposure with a 15.1% increase in MR, while exposed males showed a stronger response with a 31.3% increase in MR. Mated females increased their MR by 34.8% during mite exposure whereas virgin females experienced an increase of 61.2%. Our findings suggest that NCE of parasites can vary based on state-dependent costs of infection.

opencc-zeroAug 2020View details →

dryad36/100

Data from: Response to joint selection on germination and flowering phenology depends on the direction of selection

Background and Aims. Flowering and germination time are components of phenology, a complex phenotype that incorporates a number of traits. In natural populations, selection is likely to occur on multiple components of phenology at once. However, we have little knowledge of how joint selection on several phenological traits influences evolutionary response. Methods. We conducted one generation of artificial selection for all combinations of early and late germination and flowering on replicated lines within two independent base populations in the herb Campanula americana. We then measured response to selection and realized heritability for each trait. Results. Response to selection and heritability were greater for flowering time than germination time, indicating greater evolutionary potential of this trait. Selection for earlier phenology, both flowering and germination, did not depend on the direction of selection on the other trait. Whereas response to selection to delay germination and flowering was greater when selection on the other trait was in the opposite direction (e.g. early germination, late flowering), indicating a negative genetic correlation between the traits. Conclusions. The extent to which correlations shaped response to selection depended on the direction of selection. Therefore the genetic correlation between timing of germination and flowering varies across the trait distributions. The negative correlation between germination and flowering time found when selecting for delayed phenology follows theoretical predictions of constraint for traits that jointly determine life history schedule. Whereas the lack of constraint found when selecting for an accelerated phenology suggests a reduction of the covariance due to strong selection favoring earlier flowering and a shorter life cycle. This genetic architecture, in turn, will facilitate further evolution of the early phenology often favored in warming climates.

opencc-zeroDec 2017View details →

dryad36/100

Data from: Population genomics of rapid evolution in natural populations: polygenic selection in response to power station thermal effluents

Background: Examples of rapid evolution are common in nature but difficult to account for with the standard population genetic model of adaptation. Instead, selection from the standing genetic variation permits rapid adaptation via soft sweeps or polygenic adaptation. Empirical evidence of this process in nature is currently limited but accumulating. Results: We provide genome-wide analyses of rapid evolution in two Fundulus heteroclitus populations subjected to recently elevated temperatures due to coastal power station thermal effluents. Bayesian and multivariate analyses of population genomic structure reveal a substantial portion of genetic variation that is most parsimoniously explained by selection at the site of thermal effluents. An FST outlier approach in conjunction with additional conservative requirements identify significant allele frequency differentiation that exceeds neutral expectations among exposed and closely related reference populations. Genomic variation patterns near these candidate loci reveal that individuals living near thermal effluents have rapidly evolved from the standing genetic variation through small allele frequency changes at many loci in a pattern consistent with polygenic selection on the standing genetic variation. Conclusions: While the ultimate trajectory of selection in these populations is unknown, our findings suggest that polygenic models of adaptation may play important roles in large, natural populations experiencing recent selection due to environmental changes that cause broad physiological impacts.

opencc-zeroDec 2018View details →

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Last verified 2026-04-29Open record