Find research datasets worth reusing

opencc-by-4.0May 2019View details →

Proteomic data sets after selecting mitochondrial proteins from the scaffold software for Ingenuity Pathway analysis (IPA Qiagen)

List of fold change proteomic data sets of dFCM- 39 vs. 12Day  and105 vs. 12Day, cFCM- 40 vs. 12Day  and115 vs. 12Day , mouse heart 90 vs. 1 day after selecting mitochondrial proteins from the scaffold software for Ingenuity Pathway Analysis (IPA Qiagen)

opencc-by-4.0Nov 2017View details →

Source Location Privacy Aware Routing Protocols Selection Results

This is the dataset used to generate the results for the journal paper " A Decision Theoretic Framework for Selecting Source Location Privacy Aware Routing Protocols in Wireless Sensor Networks"  at Future Generation Computer Systems (FGCS) 2018.

opencc-by-4.0Nov 2017View details →

Selected Resources for a Literature Survey on Software Smells

It contains the bibliography information about the selected articles in each phase of a literature survey on software smells.

opencc-by-4.0Dec 2017View details →

Raw diffraction images of human orexin 2 receptor bound to the subtype-selective antagonist EMPA

Human orexin 2 receptor (OX2R) is a member of G protein-coupled receptors (GPCR). OX2R plays important roles in modulating feeding behavior and regulating the sleep-wake cycle. 805 small-wedge (1-6°/crystal) datasets collected from loop-harvested microcrystals using MX225HS CCD detector at a wavelength of 1 Å on BL32XU, SPring-8. The crystals belonged to space group C2 with unit cell parameters a=94.1, b=75.5, c=95.9 Å, β=111.4°. 631 datasets were merged at 1.96 Å resolution in the published result (Suno et al. Structure 2017; PDB code: 5WQC) using KAMO; see https://github.com/keitaroyam/yamtbx/wiki/Processing-OX2R-data-(5WQC) Note <ul> <li>Most frames have lipid rings.</li> <li>One dataset (not included in the published result) was lost for some reason (so 804 datasets are available).</li> </ul>

Figure 2. Example AEP-Selecting Operations for Assembler Encoding

Figure 2. Example AEP and its NDM (AEP presented on the right includes four operations and four memory cells. Operation 0 updates a single entry in NDM. To this end, it uses three consecutive memory cells. The first two cells store an address to the element in NDM being updated. To determine the final address of the element mentioned, values of registers are also used. The third memory cell used by Operation 0 stores a new value for the element. The value is scaled before NDM is updated. A pointer to the memory part of AEP where three cells used by Operation 0 are located is included in Operation itself.)

Figure 1. Diagram of AE-Selecting Operations for Assembler Encoding

Using evolutionary techniques to generate Artificial Neural Networks (ANNs) is usually connected with encoding the latter. There are many ANN encoding methods, e.g. connectivity matrix proposed by Miller, Todd and Hedge [9], Kitano’s matrix rewriting encoding scheme [7], Gruau’s cellular encoding [5], edge encoding proposed by Luke and Spector [8], Symbiotic Adaptive NeuroEvolution devised by Moriarty and Miikkulainen [10], the schemes proposed by Nolfi and Parisi [11] and Cangelosi, Parisi and Nolfi [2] or Cooperative Synapse Neuroevolution proposed by Gomez et al. [4]. Assembler Encoding (AE) [15-18] is one of such methods. It originates from the cellular and edge encoding but it also has features common with Linear Genetic Programming presented, among other things, in [12]. AE represents ANN in the form of Assembler Encoding Program (AEP) whose structure is similar to the structure of a simple assembler program. The task of AEP is to create Network Definition Matrix (NDM) containing all the information necessary to produce ANN. The process of ANN construction consists of three stages. First, Genetic Algorithm (GA) is used to produce AEPs. Next, each AEP creates and fills up NDM. Finally, NDM is transformed into ANN (Fig. 1).

Figure 7. Using ADDN and DELN by AEP-Selecting Operations for Assembler Encoding

In addition to the operations whose task is to modify a content of NDM, AE also uses a jump operation denoted as JMP. The jump makes it possible to repeatedly use the same code of AEP in different places of NDM. It is possible thanks to changing values of the registers once the jump is run. An example use of the jump is demonstrated in Fig 6. The program presented in the figure proceeds as follows. First, both registers are initiated to 0. Then, the first two operations are carried out, the result of which is visible in the top left corner of NDM. In the next step, the jump, denoted in the figure as JMP(0,2,0,*), is run. It first updates values of the registers and then control goes back to the first operation of AEP. R1 is set to 0 (Memory cell 0) whereas R2 to 2 (Memory cell 1). At this point, the two operations preceding the jump are carried out once again. This time, however, both operations update a different fragment of NDM. Since the jump is run twice, each time with different values of the registers, the first two operations of AEP are executed in three different areas of NDM.

Figure 10 Optimal matrices for functions f1 (a), f2 (b), and f3 (c)-SELECTING OPERATIONS FOR ASSEMBLER ENCODING

In the experiments, the following sets of operations were tested (a description of all the operations specified below is presented in Appendix 1): • Set 1 (all four-parameter operations used during the research reported in [15,17]): CHG, CHGC0, CHGC1, CHGC2, CHGC3, CHGC4, CHGR0, CHGR1, CHGR2, CHGR3, CHGR4, CHGM0, CHGM1, CHGM2, JMP; • Set 2 (the most effective four-parameter operations used in the previous research): CHGC0, CHGC3, CHGR0, CHGR3, CHGM0, CHGM2, JMP; • Set 3: simpler variants of operations included in Set 1, the simpler operations, unlike their more complex counterparts, always changed either the whole column or the whole row or the whole matrix, operations from this set had maximally three parameters; • Set 4 (the three-parameter operations and jumps): CHG_VALUE, CHG_MEMORY, JMP.

Figure 9. Encoding CHG_MEMORY-Selecting Operations for Assembler Encoding

The three-parameter operations are represented in a somewhat different way. Their encoded form resembles classifiers from Learning Classifier Systems [1,3,6]. Similarity between classifiers and the three-parameter operations results from the use of the so-called don’t care symbol “#” in both cases. Each encoded operation consists of four blocks of genes. The first single-bit block determines one of two possible variants of the operation (see Appendix 1). The second and third block indicate location of changes performed by the operation (don’t care symbol is used for this purpose). The last block specifies the value of an integer parameter of the operation. The example use of don’t care symbol to locate changes in NDM is illustrated in Fig 9.

Figure 8. AEP encoding scheme-Selecting Operations for Assembler Encoding

Each population delegates exactly one representative to each AEP. In the beginning, AEPs have only one operation and a sequence of data. Both the operation and data come from two different populations. Further populations including operations are successively added if AEPs cannot accomplish progress in performance over the assumed number of co-evolutionary cycles (we use term “co-evolutionary cycle” to differ it from the evolutionary generation that takes place inside a single population with operations and data).

opencc-by-4.0Jan 2018View details →

Selectivity profiling of multi-kinase inhibitors across the Human Kinome from ChEMBL

Reported is the list of 596 protein kinase pairs, consisting of 141 kinases and selectivity profiles of 10,060 multi-kinase inhibitors found in ChEMBL23 high-confidence data. For each of the reported protein kinase pairs, UniProt IDs defining the kinase forming a pair is provided, as well as the shared inhibitors and their selectivity profiles. For each target within the pair, potency value for each compound is reported as pIC50 value, as well as the absolute potency difference used to assess the selectivity profiles.

opencc-zeroApr 2024View details →

Data from: Human avoidance, selection for darkness and prey activity explain wolf diel activity in a highly cultivated landscape

Wildlife that share habitats with humans with limited options for spatial avoidance must either tolerate frequent human encounters or concentrate their activity on those periods with the least risk of encountering people. Based on 5,259 camera trap images of adult wolves from eight territories, we analyzed the extent to which diel activity patterns in a highly cultivated landscape with extensive public access (Denmark) could be explained by diel variation in darkness, human activity, and prey (deer) activity. A resource selection function that contrasted every camera observation (use) with 24 alternative hourly observations from the same day (availability), revealed that diel activity correlated with all three factors simultaneously with human activity having the strongest effect (negative), followed by darkness (positive) and deer activity (positive). A model incorporating these three effects had lower parsimony and classified use and availability observations just as well as a 'circadian' model that smoothed the use-availability ratio as a function of time of the day. Most of the selection for darkness was explained by variation in human activity, supporting the notion that nocturnality (proportion of observations registered at night vs. day at the equinox) is a proxy for temporal human avoidance. Contrary to our expectations, wolves were no more nocturnal in territories with unrestricted public access than in territories where public access was restricted to roads, possibly because wolves in all territories had few possibilities to walk more than a few hundred meters without crossing roads. Overall, Danish wolf packs were 6.5 (95% CI: 4.6-9.6) times more active at night than at daylight, which makes them amongst the most nocturnally active wolves reported so far. These results confirm the prediction that wolves in habitats with limited options for spatial human avoidance, invest more in temporal avoidance.

opencc-by-4.0Jan 2020View details →

→ Fig. 2. Representative skeletal elements of ornithosuchid archosaur Dynamosuchus collisensis gen. et sp. nov. (CAPPA/UFSM 0248) from Janner outcrop, Carnian, Late Triassic. A. Selected skull bones in left lateral view. B. Reconstruction of the skull. C. Skull in ventral view. D. Left quadrate and quadratojugal in posterodorsal view. E. Parabasisphenoid in left lateral view. F. Neural arch of an anterior cervical vertebra in anterior view. G. Centrum of a cervical vertebra in left lateral view. H. Right osteoderm in dorsal view. I. Neural arch of an anterior dorsal vertebra in left lateral view. J. Left ilium in lateral view. L. Right humerus in anterior view. M. Right forearm in medial view. N. Left manus in dorsal view. O. Right (reversed) pubis in lateral view. P. Left femur in anterior view. Q. Left fibula in lateral view. Some unpreserved portions are modified from Baczko et al. in press, for the reconstruction of the skeleton of CAPPA/UFSM 0248 (preserved elements indicated in orange) (K). Scale bars 20 mm. in The first ornithosuchid from Brazil and its macroevolutionary and phylogenetic implications for Late Triassic faunas in Gondwana

→ Fig. 2. Representative skeletal elements of ornithosuchid archosaur Dynamosuchus collisensis gen. et sp. nov. (CAPPA/UFSM 0248) from Janner outcrop, Carnian, Late Triassic. A. Selected skull bones in left lateral view. B. Reconstruction of the skull. C. Skull in ventral view. D. Left quadrate and quadratojugal in posterodorsal view. E. Parabasisphenoid in left lateral view. F. Neural arch of an anterior cervical vertebra in anterior view. G. Centrum of a cervical vertebra in left lateral view. H. Right osteoderm in dorsal view. I. Neural arch of an anterior dorsal vertebra in left lateral view. J. Left ilium in lateral view. L. Right humerus in anterior view. M. Right forearm in medial view. N. Left manus in dorsal view. O. Right (reversed) pubis in lateral view. P. Left femur in anterior view. Q. Left fibula in lateral view. Some unpreserved portions are modified from Baczko et al. in press, for the reconstruction of the skeleton of CAPPA/UFSM 0248 (preserved elements indicated in orange) (K). Scale bars 20 mm.

Engineering dynamic gates in binding pocket of penicillin G acylase to selectively degrade bacterial signaling molecules

(01-mutants_design.tar.gz) Mutants design: <ol> <li>Input structures of ecPGA from the PDB database (PDB IDs: 1GK9, 1GM7, and 1GM9), processed to resemble wild-type state, repaired by RepairPDB module of FoldX 4</li> <li>Double-point mutants preparation, analysis and filtering: <ol> <li>text files including configuration for FoldX 4</li> <li>inputs and outputs of CAVER 3.02 calculations on FoldX 4 PDB files of ecPGA double-point mutants</li> <li>input configuration file and TransportTools library 0.9.4 calculations outputs generated based on the inputs produced in step 01 and 02 above</li> <li>CSV files containing complete information about FoldX 4 stability prediction and geometrical properties from CAVER 3.02 and TransportTools library version 0.9.4 for ecPGA double-point mutants</li> </ol> </li> <li>Triple-point mutant preparation, analysis and filtering: <ol> <li>text files including configuration for FoldX 4</li> <li>inputs and outputs of CAVER 3.02 calculations on FoldX 4 PDB files of ecPGA triple-point mutants</li> <li>input configuration file and TransportTools library 0.9.4 calculations outputs generated based on the inputs produced in step 01 and 02 above</li> <li>CSV files containing complete information about FoldX 4 stability prediction and geometrical properties from CAVER 3.02 and TransportTools library version 0.9.4 for ecPGA triple-point mutants</li> </ol> </li> </ol> (02-docking.tar.gz) Preparation of protein-ligand complexes using molecular docking for wild-type ecPGA and 6 best designed triple-point mutants with 6 various bacterial signaling molecules: <ol> <li>PDB files of ligand, PDBQT files of the receptor and PDB files of the complexes selected from docking experiment:</li> </ol> Full names of presented protein variants: ecPGA_wt, wild-type Escherichia coli penicillin G acylase LAF, Phe138αLeu & Met142αAla & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_22 LSF, Phe138αLeu & Met142αSer & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_98 MAF, Phe138αMet & Met142αAla & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_23 MSF, Phe138αMet & Met142αSer & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_99 VAF, Phe138αVal & Met142αAla & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_30 YAF, Phe138αTyr & Met142αAla & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_33 Full names of presented AHLs: C06, N-hexanoyl-L-homoserine lactone; C06-3O, N-3-oxo-hexanoyl-L-homoserine lactone; C08, N-octanoyl-L-homoserine lactone; C08-3O, N-3-oxo-octanoyl-L-homoserine lactone; C10, N-decanoyl-L-homoserine lactone; C12-3O, N-3-oxo-dodecanoyl-L-homoserine lactone (03-protein_ligand_MDs.tar.gz) Ligand-enzyme complexes molecular dynamics for wild-type ecPGA and 6 best designed triple-point mutants with 6 various bacterial signaling molecules: <ol> <li>Force field parameters in Amber format</li> <li>Input coordinates *.inpcrd, parameters *.parm7 and *.pdb files for each complex ready for simulation in Amber</li> <li>Amber input files *.in for minimization, equilibration and production runs</li> <li>Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format</li> <li>Simulation output files for each stage of the minimization, equilibration and production runs in Amber *.mdout format</li> <li>Output files generated during post-processing of production runs trajectories in a form of text files generated by cpptraj</li> </ol> (04-free_enzymes_MDs.tar.gz) Free enzymes molecular dynamics of 3 best triple-point ecPGA (VAF, YAF and MSF) mutants prioritized based on protein-ligand molecular dynamics simulations and experimental assays: <ol> <li>Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and *.pdb files for each complex ready for simulation in Amber format</li> <li>Amber input files *.in for minimization, equilibration and production runs</li> <li>Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format</li> <li>Simulation output files for each stage of the minimization, equilibration and production runs in Amber *.mdout format</li> <li>Post-processing analysis of generated trajectories: <ol> <li>Text files with distances, CSV files containing result of PCA and clustering, PNG files with clustered PCA results</li> <li>Inputs and outputs of MDpocket analysis and visualization of the pocket frequency grid as an isomesh</li> <li>CAVER input configuration files in text format, CAVER output data including parsed CSV and text files for visualization of entrance opening time evolution and cavity profiles inspection</li> <li>cpptraj generated text files including RMSD, distances and chi1 angles measurements</li> </ol> </li> </ol> All plots were generated using matplotlib or seaborn Python libraries. Figures containing structural representations were generated using PyMOL 2.0.1. (05-ecPGA_VAF_YAF_MSF_penG_MDs.tar.gz) PenG-enzyme complexes molecular dynamics for wild-type ecPGA and 3 best designed triple-point mutants (VAF, YAF, MSF): <ol> <li>PenG force field parameters in Amber (GAFF) format</li> <li>Input coordinates *.inpcrd, parameters *.parm7 and *.pdb files for each complex ready for simulation in Amber</li> <li>Amber input files *.in for minimization, equilibration and production runs</li> <li>Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format</li> <li>Simulation output files for each stage of the minimization, equilibration and production runs in Amber *.mdout format and analysis output files generated during post-processing of production runs trajectories in a form of text files</li> <li>Reactive Stabilization Score [RSS] statistics summarized in CSV files</li> </ol>

Selection drives divergence of eye morphology in sympatric Heliconius butterflies

When populations experience different sensory conditions, natural selection may favor sensory system divergence, affecting peripheral structures and/or downstream neural pathways. We characterized the outer eye morphology of sympatric Heliconius species from different forest types and their first-generation reciprocal hybrids to test for adaptive visual system divergence and hybrid disruption. In Panama, Heliconius cydno occurs in closed forests, whereas Heliconius melpomene resides at the forest edge. Among wild individuals, H. cydno has larger eyes than H. melpomene, and there are heritable, habitat-associated differences in the visual brain structures that exceed neutral divergence expectations. Notably, hybrids have intermediate neural phenotypes, suggesting disruption. To test for similar effects in the visual periphery, we reared both species and their hybrids in common garden conditions. We confirm that H. cydno has larger eyes and provide new evidence that this is driven by selection. Hybrid eye morphology is more H. melpomene-like despite body size being intermediate, contrasting with neural trait intermediacy. Overall, our results suggest that eye morphology differences between H. cydno and H. melpomene are adaptive, and that hybrids may suffer fitness costs due to a mismatch between the peripheral visual structures and previously described neural traits that could affect visual performance.

Data from: Stochastic character mapping, Bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria

Cyanobacteria are the only prokaryotes to have evolved oxygenic photosynthesis paving the way for complex life. Studying the evolution and ecological niche of cyanobacteria and their ancestors is crucial for understanding the intricate dynamics of biosphere evolution. These organisms frequently deal with environmental stressors such as salinity and drought, and they employ compatible solutes as a mechanism to cope with these challenges. Compatible solutes are small molecules that help maintain cellular osmotic balance in high-salinity environments, such as marine waters. Their production plays a crucial role in salt tolerance, which, in turn, influences habitat preference. Among the five known compatible solutes produced by cyanobacteria (sucrose, trehalose, glucosylglycerol, glucosylglycerate, and glycine betaine), their synthesis varies between individual strains. In this study, we work in a Bayesian stochastic mapping framework, integrating multiple sources of information about compatible solute biosynthesis in order to predict the ancestral habitat preference of Cyanobacteria. Through extensive model selection analyses and statistical tests for correlation, we identify glucosylglycerol and glucosylglycerate as the most significantly correlated with habitat preference, while trehalose exhibits the weakest correlation. Additionally, glucosylglycerol, glucosylglycerate, and glycine betaine show high loss/gain rate ratios, indicating their potential role in adaptability, while sucrose and trehalose are less likely to be lost due to their additional cellular functions. Contrary to previous findings, our analyses predict that the last common ancestor of Cyanobacteria (living at around 3180 Ma) had a 97% probability of a high salinity habitat preference and was likely able to synthesize glucosylglycerol and glucosylglycerate. Nevertheless, cyanobacteria likely colonized low-salinity environments shortly after their origin, with an 89% probability of the first cyanobacterium with low-salinity habitat preference arising prior to the Great Oxygenation Event (2460 Ma). Stochastic mapping analyses provide evidence of cyanobacteria inhabiting early marine habitats, aiding in the interpretation of the geological record. Our age estimate of ~2590 Ma for the divergence of two major cyanobacterial clades (Macro- and Microcyanobacteria) suggests that these were likely significant contributors to primary productivity in marine habitats in the lead-up to the Great Oxygenation Event, and thus played a pivotal role in triggering the sudden increase in atmospheric oxygen.

opencc-by-4.0May 2024View details →

Charge-Selective Photocatalytic Degradation of Organic Dyes using Halloysite Nanotubes

This study explores the use of Halloysite NanoTubes (HNTs) as photocatalysts capable of decomposing organic dyes under exposure to visible or ultraviolet light. We observe that the extent of RhB photocatalytic degradation in 100 min in the presence of the HNTs is ~4 times higher compared to that of bare RhB. Moreover, under optimized conditions, the as-extracted photodegradation rate of RhB (~0.0022 /min) is comparable to that of the previously reported work on the photodegradation of RhB in the presence of tubular nanostructures. A parallel effect is observed for anionic Coumarin photodegradation, albeit less efficiently. By leveraging the unique properties of HNTs, a family of naturally occurring nanotube structures, this research offers valuable insights for optimizing photocatalytic systems in the pursuit of effective and eco-friendly solutions for environmental remediation.

opencc-by-4.0Mar 2024View details →

GEO gene expression dataset recompute for selected tumor samples

<div> We aligned and quantified RNA-Seq data present in GEO with a standardized pipeline to homogenize data preprocessing for downstream applications. All uploaded files are UTF-8, <code>.csv</code>-formatted matrices. The <code>*_expected_count.csv.gz</code> files are unlogged, raw expression counts as reported by <code>rsem-quantify-expression</code> (see details below). The associated <code>*_metadata.csv.gz</code> files contain metadata pertinent to each column of the corresponding expression matrix. Some metadata files may have more rows than the associated number of columns. This is for series that were only partially RNA-Seq based (e.g. combinated RNA-Seq plus miRNA-Seq samples in the same GEO accession ID). Metadata columns are derived from GEO series files, and follow their definitions. See each GEO entry directly to determine metadata meaning. Each recompute has at least the <code>gene_id</code> column holding Ensembl Gene IDs. The remaining columns are ENA run accession IDs of the specific recomputed samples. Each associated metadata has at least the following columns: <ul> <li><code>geo_accession</code>: The GEO sample ID of the sample.</li> <li><code>ena_sample</code>: The ENA sample ID of the sample.</li> <li><code>ena_run</code>: The ENA run accession ID of the sample, to be cross-referenced with the expression matrices.</li> </ul> The remaining columns are derived from GEO metadata files and other ENA-provided data. Please refer to the <code>x.FASTQ</code> package for more information. <h3>Pipeline Details</h3> The alignment and quantification was made with the <code>x.FASTQ</code> tool available <a href="https://github.com/TCP-Lab/x.FASTQ">on Github</a> installed locally on an Arch Linux machine on commit <code>3a93dd77a70df59c74f7b15216c26f12cd918e81</code> running the Linux <code>6.7.8-zen1-1-zen</code> kernel with a <code>11th Gen Intel i7-1185G7 (8)</code> CPU and a <code>Intel TigerLake-LP GT2 [Iris Xe Graphics]</code> GPU. Please note that no sample filtering or omissions were done based on sample quality or sequencing depth. However, sensible trimming (e.g. low-quality bases and common adapters) was performed on all the samples. Reference genome was downloaded from Ensembl, version <code>hg38</code>. STAR was used to create the index genome with overhang set to <code>149</code>. </div>

Genetic diversity and efficacy of natural selection in spiders with pre-copulatory sexual cannibalism

Factors that increase reproductive variance among individuals act to reduce effective population size (Ne), which accelerates loss of genetic diversity and decreases efficacy of purifying selection. These factors include sexual cannibalism, offspring investment, and mating system. Pre-copulatory sexual cannibalism where the female consumes the male prior to mating exacerbates this effect. We performed comparative transcriptomics in two spider species, the cannibalistic Trechaleoides biocellata and the non-cannibalistic T. keyserlingi, to generate genomic evidence to support these predictions. First, we estimated heterozygosity and found that genetic diversity is relatively lower in the cannibalistic species. Second, we calculated dN/dS ratios as a measure of purifying selection, higher dN/dS ratio indicated relaxed purifying selection in the cannibalistic species. These results are consistent with the hypothesis that sexual cannibalism impacts operational sex ratio and demographic processes, which interact with evolutionary forces to shape the genetic structure of populations. However, other factors such as the mating system and life-history traits contribute to shape Ne. Comparative analyses across multiple contrasting species-pairs would be required to disentangle these effects. Our study highlights that extreme behaviours such as pre-copulatory cannibalism may have profound eco-evolutionary effects.

Data for: Selection on the joint action of pairs leads to divergent adaptation and coadaptation of care-giving parents during pre-hatching care

The joint actions of animals in partnerships or social groups evolve under both natural selection, from the wider environment, and social selection, imposed by other members of the pair or group. We used experimental evolution to investigate how jointly expressed actions evolve upon exposure to a new environmental challenge. Our work focused on the evolution of carrion nest preparation by pairs of burying beetles Nicrophorus vespilloides, a joint activity undertaken by the pair but typically led by the male. In previous work, we found that carrion nest preparation evolved to be faster in experimental populations without post-hatching care (No Care lines) than with post-hatching care (Full Care lines). Here we investigate how this joint activity evolved. After 15 generations of experimental evolution, we created heterotypic pairs (No Care females with Full Care males, and No Care males with Full Care females) and compared their carrion nest making with homotypic No Care and Full Care pairs. We found that pairs with No Care males prepared the nest more rapidly than pairs with Full Care males, regardless of the female's line of origin. We discuss how social coadaptations within pairs or groups could act as a post-mating barrier to gene flow.