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Phenotype, genotype and fitness data related to genetic analysis of praziquantel response in schistosome parasites.
<p>These data are related to the study of the Genetic analysis of praziquantel response in schistosome parasites implicates a Transient Receptor Potential channel.</p> <p>Mass treatment with praziquantel (PZQ) monotherapy is the mainstay for schistosomiasis treatment. This drug shows imperfect cure rates in the field and parasites showing reduced PZQ response can be selected in the laboratory, but the extent of resistance in <em>Schistosoma mansoni</em> populations is unknown. We examined the genetic basis of variation in PZQ response in a <em>S. mansoni</em> population (SmLE-PZQ-R) selected with PZQ in the laboratory: 35% of these worms survive high dose (73 µg/mL) PZQ treatment. We used genome wide association to map loci underlying PZQ response. The major chr. 3 peak contains a transient receptor potential (Sm.TRPM_PZQ) channel (Smp_246790), activated by nanomolar concentrations of PZQ. PZQ response shows recessive inheritance and marker-assisted selection of parasites at a single Sm.TRPM_PZQ SNP enriched populations of PZQ-resistant (PZQ-ER) and sensitive (PZQ-ES) parasites showing >377 fold difference in PZQ response. The PZQ-ER parasites survived treatment in rodents better than PZQ-ES. Resistant parasites show 2.25-fold lower expression of Sm.TRPM_PZQ than sensitive parasites. Specific chemical blockers of Sm.TRPM_PZQ enhanced PZQ resistance, while Sm.TRPM_PZQ activators increased sensitivity. A single SNP in Sm.TRPM_PZQ differentiated PZQ-ER and PZQ-ES lines, but mutagenesis showed this was not involved in PZQ response, suggesting linked regulatory changes. We surveyed Sm.TRPM_PZQ sequence variation in 259 parasites from the New and Old World revealing one nonsense mutation that results in a truncated protein with no PZQ-binding site. Our results demonstrate that Sm.TRPM_PZQ underlies variation in PZQ response in <em>S. mansoni</em> and provides an approach for monitoring emerging PZQ-resistance alleles in schistosome elimination programs.</p> <p>This dataset is divided in 3 folders. Each folder has a readme detailing its content.</p> <p><strong>1-Phenotyping_data</strong></p> <p>This folder includes the data tables related to the phenotyping of the worms performed during this study. The phenotype measured was the viability of worms following PZQ treatment (i.e., PZQ response). This viability was assessed microscopically or using worm lactate production released in culture media.</p> <p>The data correspond to the following experiments:</p> <ul> <li>PZQ response of single adult male worms from SmLE and SmLE-PZQ-R populations to different doses of PZQ. This data was used to determine the PZQ IC50 of each population.</li> <li>Lactate production from single SmLE-PZQ-R adult male worms and correlation with visual observation. This was a proof-of-principle that lactate production can be used to efficiently and unbiasedly phenotype schistosome adult male worms in response to PZQ drug.</li> <li>PZQ response of single SmLE-PZQ-R adult male worms. These worms were then divided in low and high producer in response to PZQ and used to perform a genome-wide association study.</li> <li>PZQ response of single adult male worms from SmLE-PZQ-ER and SmLE-PZQ-ES populations to different doses of PZQ. This data was used to determine the PZQ IC50 of each population.</li> <li>PZQ response of single adult male worms from SmLE-PZQ-ER and SmLE-PZQ-ES populations in presence of Sm.TRPM_PZQ blocker (MB2) and activator (MV1) with and without PZQ drug.</li> <li>In vivo PZQ response of schistosome worms from SmLE-PZQ-ER and SmLE-PZQ-ES populations.</li> </ul> <p><strong>2-Genotyping_data</strong></p> <p>This folder includes the data tables related to the genotyping of the worms performed during this study. Worms were genotyping using PCR-RFLP (genotyping of single nucleotide polymorphisms (SNPs) on chr2 and chr3 QTLs) or using qPCR (genotyping of a copy number variation (CNV) on chr3 QTL).</p> <p>The data correspond to the following experiment:</p> <ul> <li>Association between PZQ response of single adult male worms from SmLE-PZQ-R population and their respective genotype on chromosome 2 (SNP) and chromosome 3 (SNP and CNV) loci.</li> </ul> <p><strong>3-Fitness_data</strong></p> <p>This folder includes the data tables related to the fitness of the parasite populations. We collected data regarding:</p> <ul> <li>The number of surviving and infected snails after exposure to SmLE-PZQ-ER or SmLE-PZQ-ES miracidia.</li> <li>The number of adult worms recovered from golden Syrian female hamsters exposed to SmLE-PZQ-ER or SmLE-PZQ-ES cercariae.</li> </ul> <p>All the data were collected during 12 generations of parasites and are used to evaluate a potential impact of PZQ resistance on the parasite fitness.</p>
Building behaviour does not drive rates of phenotypic evolution in spiders
<p>This data set contains raw data tables, scripts and supplemental figures supporting the article Wolff et al. (2021, PNAS 118: e2102693118). In our study we assembled morphometric and ecological trait data of spiders from literature and de novo measurements and observations and used this data to infer the rates of morphological change over deep time in relation to web building behaviour.</p>
Data files for manuscript "Prenatal phenotype of PNKP-related primary microcephaly associated with variants in the FHA and Phosphatase domain"
<p>#2021-08-26<br> #Summary<br> This ZIP-file contains the Excel files used for the clinical and variant analyses of the PNKP protein/gene for the manuscript "Prenatal phenotype of PNKP-related primary microcephaly associated with variants in the FHA and Phosphatase domain".</p> <p>#Folder structure<br> ./ (parent directory containing this README file and all subfolders)<br> ./Clinical/ (contains an Excel sheet with complete clinical data of 4 affected individuals)<br> ./Variants/ (contains an Excel sheet with all variant annotation and domain information used in 6 sheets)</p> <p>#Files and checksums<br> 4B5BE914E77EB6511DF3DB94C6918E45 ./Clinical/FileS2_PNKP_Clinical.xlsx<br> 0228C6EF543D597C1C800B8D78E3097D ./Variants/FileS3_PNKP_Variants.xlsx</p>
Figure 6 in High molecular and phenotypic diversity in the Merodon avidus complex (Diptera, Syrphidae): cryptic speciation in a diverse insect taxon
Figure 6. Scatterplot of individual scores from the canonical variate analysis (CVA) of female specimens of: A, sympatric populations of Merodon avidus A and M. avidus B from Dubašnica Mt (ADUB, BDUB) and Greece (AGRE, BGRE) (Wilks' L = 0.003; F(48,30) = 3.64; P <0.001); B, allopatric populations of M. avidus A from FYR MACEDONIA (AMKD), Morinj (AMOR), and the Pannonian region (APAN) (Wilks' L = 0.09; F(32,48) = 3.59; P <0.0001); C, allopatric populations of M. avidus B from Durmitor Mt (BDUR), Stara Mt (BSPL), Kopaonik Mt (BKOP), and FYR MACEDONIA (BMKD) (Wilks' L = 0.36; F(32,32) = 0.66; P <0.879); D, allopatric populations of M. avidus A and M. avidus B (Wilks' L = 0.09; F(80,245) = 1.94; P <0.0001). The amount of variation explained by each canonical axis is in parentheses.
Figure 5 in High molecular and phenotypic diversity in the Merodon avidus complex (Diptera, Syrphidae): cryptic speciation in a diverse insect taxon
Figure 5. Scatterplot of individual scores from the canonical variate analysis (CVA) of all specimens (both sexes) of metapopulations: A, Merodon avidus A from Dubašnica Mt (ADUB) (Wilks' L = 0.04; F(48,72) = 3.06; P <0.000); B, M. avidus B from Dubašnica Mt (BDUB) (Wilks' L = 0.10; F(64,174) = 2.18; P <0.000); C, M. avidus B from Durmitor Mt (BDUR) (Wilks' L = 0.10; F(64,174) = 2.21; P <0.000); D, M. avidus A from FYR MACEDONIA (AMKD) (Wilks' L = 0.002; F(112,171) = 2.40; P <0.000); E, M. avidus A from Greece (AGRE) (Wilks' L = 0.009; F(64,53) = 1.93; P <0.007). The amount of variation explained by each canonical axis is in parentheses. The number of misclassified specimens/total number of analysed
Figure 1 in High molecular and phenotypic diversity in the Merodon avidus complex (Diptera, Syrphidae): cryptic speciation in a diverse insect taxon
Figure 1. Origin of the analysed populations using wing geometric morphometrics from the Balkan Peninsula: 1, Pannonian Plain (PAN, Serbia); 2, Dubašnica Mt, E 21°59′, N 44°01′ (DUB, Serbia); 3, Stara Mt, E 22°41′, N 43°20′ (SPL, Serbia); 4, Kopaonik Mt, E 20°40′, N 43°15′ (KOP, Serbia); 5, Durmitor Mt, E 19°00′, N 43°11′ (DUR, Montenegro); 6, Morinj, E 18°40′, N 43°29′30″ (MOR, Montenegro); 7, Former Yugoslav Republic of Macedonia (MKD); 8, Greece (GRE). (Numbers within region are noted sampling sites of populations).
Improving wheat yield prediction using secondary traits and high-density phenotyping under heat stressed environments
<p>A primary selection target for wheat (Triticum aestivum) improvement is grain yield. However, the selection for yield is limited by the extent of field trials, fluctuating environments, and the time needed to obtain multiyear assessments. Secondary traits such as spectral reflectance and canopy temperature (CT), which can be rapidly measured many times throughout the growing season, are frequently correlated with grain yield and could be used for indirect selection in large populations particularly in earlier generations in the breeding cycle prior to replicated yield testing. While proximal sensing data collection is increasingly implemented with high-throughput platforms that provide powerful and affordable information, efficient and effective use of these data is challenging. The objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh. Over five growing seasons, we analyzed normalized difference vegetation index (NDVI) and CT measurements collected in elite breeding lines from the International Maize and Wheat Improvement Center at the Regional Agricultural Research Station, Jamalpur, Bangladesh. We explored several variable reduction and regularization techniques followed by using the combined secondary traits to predict grain yield. Across years, grain yield heritability ranged from 0.30 to 0.72, with variable secondary trait heritability (0.0–0.6), while the correlation between grain yield and secondary traits ranged from−0.5 to 0.5. The prediction accuracy was calculated by a cross-fold validation approach as the correlation between observed and predicted grain yield using univariate and multivariate models. We found that the multivariate models resulted in higher prediction accuracies for grain yield than the univariate models. Stepwise regression performed equal to, or better than, other models in predicting grain yield. When incorporating all secondary traits into the models, we obtained high prediction accuracies (0.58–0.68) across the five growing seasons. Our results show that the optimized phenotypic prediction models can leverage secondary traits to deliver accurate predictions of wheat grain yield, allowing breeding programs to make more robust and rapid selections.</p>
Data from: Floral scents of a deceptive plant are hyperdiverse and under population-specific phenotypic selection
<p>Floral scent is a key mediator in plant–pollinator interactions; however, little is known to what extent intraspecific scent variation is shaped by phenotypic selection, with no information yet in deceptive plants. We recorded 289 scent compounds in deceptive moth fly-pollinated <i>Arum maculatum </i>from various populations north vs. south of the Alps, the highest number so far reported in a single plant species. Scent and fruit set differed between regions, and some, but not all differences in scent could be explained by differential phenotypic selection in northern vs. southern populations. Our study is the first to provide evidence that phenotypic selection is involved in shaping geographic patterns of floral scent in deceptive plants. The hyperdiverse scent of <i>A. maculatum</i> might result from the plant's imitation of various brood substrates of its pollinators.</p>
Imaging Mass Cytometry Images (APP1) from: A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution.
<p>Four µm-thick sections were cut from the APP1 FFPE block with a microtome and used for staining with a panel of 26 antibodies targeting the main immune, stromal and epithelial cell populations of the gastrointestinal tract (Supplementary Table 2). The optimal dilution of each antibody in the panel was identified by staining and ablating FFPE appendix sections. The resulting images were reviewed by a mucosal immunologist (J.S.) and the dilution giving the best signal to background ratio was selected for each antibody (Supplementary Table 2). To perform the staining for IMC, slides were dewaxed after a one-hour incubation at 60°C, rehydrated and heat-induced antigen retrieval was performed with a pressure cooker in Antigen Retrieval Reagent-Basic (R&D Systems). Slides were incubated in a 10% BSA (Sigma), 0.1% Tween (Sigma), and 2% Kiovig (Shire Pharmaceuticals) Superblock Blocking Buffer (Thermo Fisher) blocking solution at room temperature for two hours. Each antibody was added to a primary antibody mix at the selected concentration in blocking solution and incubated overnight at 4°C. After two washes in PBS and PBS-0.1% Tween, the slides were treated with the DNA intercalator Cell-ID™ Intercalator-Ir (Fluidigm) (containing the two iridium isotopes 191Ir and 193Ir) 1.25 mM in a PBS solution. After a 30-minute incubation, the slides were washed once in PBS and once in MilliQ water and air-dried. The stained slides were then loaded in the Hyperion Imaging System (Fluidigm) imaging module to obtain light-contrast high resolution images of approximately four mm<sup>2</sup>. These images were used to select the ROI in each slide. For APP1, a one mm<sup>2</sup> ROI containing a lymphoid follicle in its whole depth alongside a portion of lamina propria and of epithelium was selected. ROIs were ablated at a o µm/pixel resolution and 200 Hz frequency.</p>
Measuring hidden phenotype: quantifying the shape of barley seeds using the Euler characteristic transform
<p>Shape plays a fundamental role in biology. Traditional phenotypic analysis methods measure some features but fail to measure the information embedded in shape comprehensively. To extract, compare and analyse this information embedded in a robust and concise way, we turn to topological data analysis (TDA), specifically the Euler characteristic transform. TDA measures shape comprehensively using mathematical representations based on algebraic topology features. To study its use, we compute both traditional and topological shape descriptors to quantify the morphology of 3121 barley seeds scanned with X-ray computed tomography (CT) technology at 127 μm resolution. The Euler characteristic transform measures shape by analysing topological features of an object at thresholds across a number of directional axes. A Kruskal–Wallis analysis of the information encoded by the topological signature reveals that the Euler characteristic transform picks up successfully the shape of the crease and bottom of the seeds. Moreover, while traditional shape descriptors can cluster the seeds based on their accession, topological shape descriptors can cluster them further based on their panicle. We then successfully train a support vector machine to classify 28 different accessions of barley based exclusively on the shape of their grains. We observe that combining both traditional and topological descriptors classifies barley seeds better than using just traditional descriptors alone. This improvement suggests that TDA is thus a powerful complement to traditional morphometrics to comprehensively describe a multitude of 'hidden' shape nuances which are otherwise not detected.</p>
Agronomic, rheological and nutritional phenotypic data of 50 spelt varieties grown at 3 locations in Switzerland during 2 growing seasons (2021-2022)
<p>This dataset contains agronomic, rheological, and nutritional parameters of 50 winter spelt varieties tested during 2 growing seasons (2021-2022) at 3 locations in Switzerland. The dataset has been used to investigate the links between genotype and phenotype of spelt varieties, published in https://doi.org/10.1007/s10681-024-03400-8.</p> <p>The field trials were performed under the Swiss Extenso (low input) conditions, conducted by Agroscope and DSP, and under organic conditions, performed by GZPK. </p> <h3>Methods </h3> <p><em>Field trials </em></p> <div>Field trials were set up over the course of two growing seasons – 2020/2021, 2021/2022 – in three sites across the Swiss Central Plateau. The experimental sites were located in Changins (46°19′ N 6°14′ E, 455m a.s.l), Delley (46°55′ N 6°58′ E, 494m a.s.l) and Feldbach (47°14'24.00" N, 8°47'9.60" E, 410m a.s.l.).</div> <div>Each variety was grown in a plot of 7.1 m<sup>2</sup> (1.5 m*4.7 m) in Changins and Delley, and 4.5 m<sup>2</sup> (1.5 m*3 m) in Feldbach. We replicated the experiment three times per location. At each site, we used a complete randomized block design, with plots being randomized within each block. Density of sowing was 180 spikelets/m<sup>2</sup>. Plots were sowed mechanically each autumn. In Changins and Delley, the plots were mechanically fertilized with 100 kg N/ha (ammonium nitrate), applied in two splits (60 at heading stage—40 at flowering stage). In Feldbach, the fields were treated organically, and therefore no synthetic fertilizer was applied.</div> <div> </div> <div> </div> <div><em>Agronomic and morphological characteristics </em></div> <div> <p>For each plot, we recorded the heading date as the day of the year, in which 50% of the ears of the plot had fully emerged from the flag leaf. Once the plants and ears were fully developed, plant height was measured in each plot, by taking the average height in centimeters from the ground to the top of five random ears, excluding awns.</p> <p>At maturity, we harvested each plot with a combine harvester (Zürn 150, Schontal-Westernhausen, Switzerland). The harvested grains were weighed first, dehusked, sorted and cleaned with a sieve cleaner, and then weighted again. We measured specific weight and water content using a Dickey–John machine (GAC 2100). Grain yield was subsequently standardized to 15% of humidity. Protein content (%) was measured at the plot level with a near-infrared instrument (ProxiMate™, Büchi instruments). Thousand kernel weight (TKW, g), as well as kernel length and width (mm), were measured at the plot level with a Marvin seed analyzer (GTA Sensorik, Neubrandenburg, Germany).</p> <p>Additional measurements in Changins: we computed harvest index for each plot by cutting 30 individual culms just before harvest. Plants were cut just above the ground, oven-dried for 3 days at 80 °C and then weighed. We then threshed, dehusked, sieved and weighed the obtained grains. The harvest index was computed by taking the ratio of grain mass over total mass.</p> <p> </p> <p><em>Rheological characteristics </em></p> </div> <div> <p>At all sites, Zeleny sedimentation value (mL) was assessed based on the International Association for Cereal Science and Technology standard method 116/1.The analyses were performed by the analytical laboratory of DSP, Delley, at the variety level for each site—i.e., grains from the three replicates per site were pooled together and subsequently milled.</p> <p>Additional measurements in Changins were done for each variety, based on a pooled sample of the three replicates. Extensograph properties of the obtained dough were assessed according to ICC standard method 114/1; area under curve (energy, cm2), resistance to extension at 5 cm extension (EE), and extensibility of the dough (mm) were measured. The analyses were performed by the accredited laboratory “Versuchsanstalt für Getreideverarbeitung” based in Austria (<a href="https://www.vfg.or.at/">https://www.vfg.or.at/</a>).</p> <p> </p> <p><em>Nutritional characteristics </em></p> </div> <div> </div> <div>We assessed the structure of starch (amylose content) and the fatty acid composition for each variety in Changins. These analyses were done by pooling grains from the three replicates in Changins and milling them. The amylose and amylopectin contents of starch were determined enzymatically via an assay based on the precipitation of amylopectin complexes with the lectin concanavalin A, according to K-Amy 06/18. The fatty acid composition was analyzed by GC-FAME, via in situ transesterification, according to the method of Ampuero Kragten et al. (<a title="Kragten SA, Collomb M, Dubois S, Stoll P (2014) Determination of fatty acid composition in feed: analytical methods. Agrarforschung Schweiz 5(9):330&ndash;337" href="https://link.springer.com/article/10.1007/s10681-024-03400-8#ref-CR36">2014</a>). These analyses were performed at the accredited analytical laboratory of Agroscope, Posieux.</div> <div> </div> <div>Kragten SA, Collomb M, Dubois S, Stoll P (2014) Determination of fatty acid composition in feed: analytical methods. Agrarforschung Schweiz 5(9):330–337</div> <div> </div> <div> </div> <div><em>DNA extraction & Genotyping </em></div> <div> </div> <div>DNA was extracted from all cultivars, and sent to TraitGenetics (SGS institute Frenius, Gatersleben DE) for SNP genotyping on the 25 K XT Infinium array for wheat.</div> <div> </div> <div> </div>
Social network differences and phenotypic divergence between stickleback ecotypes
<p><span></span></p> <p>Elucidating the mechanisms underlying differentiation between populations is essential to our understanding of ecological and evolutionary processes. While social network analysis has yielded numerous insights in behavioral ecology in recent years, it has rarely been applied to questions about population differentiation. Here, we use social network analysis to assess the potential role of social behavior in the recent divergence between two three-spined stickleback ecotypes, "whites" and "commons". These ecotypes differ significantly in their social behavior and mating systems as adults, but it is unknown when or how differences in social behavior develop. We found that as juveniles, the white ecotype was bolder and more active than the common ecotype. Furthermore, while there was no evidence for assortative shoaling preferences, the two ecotypes differed in social network structure. Specifically, groups of the white ecotype had a lower clustering coefficient than groups of the common ecotype, suggesting that groups of the white ecotype were characterized by the formation of smaller subgroups, or 'cliques'. Interestingly, ecotypic differences in clustering coefficient were not apparent in mixed groups composed of whites and commons. The formation of cliques could contribute to population divergence by restricting the social environment that individuals experience, potentially influencing future mating opportunities and preferences. These findings highlight the insights that social network analysis can offer into our understanding of population divergence and reproductive isolation.</p>
Multiplexed and scalable cellular phenotyping toward the standardized three-dimensional human neuropathology
<p>The dataset for the publication. Cite the publication if you use the datasets.</p>
Proteomics and metabolomics data associated with the end-of-life phenotype Smurf
<p>Data obtained from whole bodies of mated females of genotype Drs-GFP at 20 and 40 days for proteomics and 30 days for metabolomics.</p> <p>The data are cited in the following preprint :</p> <p>Smurfness-based two-phase model of ageing helps deconvolve the ageing transcriptional signature</p> <p>Flaminia Zane, Hayet Bouzid, Sofia Sosa Marmol, <a href="http://orcid.org/0000-0003-2448-4022"> View ORCID Profile</a>Savandara Besse, Julia Lisa Molina, <a href="http://orcid.org/0000-0002-9579-5250"> View ORCID Profile</a>Céline Cansell, Fanny Aprahamian, <a href="http://orcid.org/0000-0001-6356-1006"> View ORCID Profile</a>Sylvère Durand, Jessica Ayache, <a href="http://orcid.org/0000-0001-7709-2116"> View ORCID Profile</a>Christophe Antoniewski, <a href="http://orcid.org/0000-0002-6574-6511"> View ORCID Profile</a>Michael Rera</p> <p>doi: https://doi.org/10.1101/2022.11.22.517330</p>
Contrasting effects of two phenotypes of an alpine cushion plant on understory species drive community assembly
<p><span>In alpine systems, cushion plants act as foundation species by ameliorating local environmental conditions. Empirical studies indicate that contrasting phenotypes of alpine cushion species have different effects on understory plant species, either facilitative or competitive. Furthermore, dependent species within each community type might also exhibit different responses to each cushion phenotype, which can be clustered into several "response groups". Additionally, these species-groups specific responses to alpine cushion species phenotypes could alter community assembly. However, very few studies have assessed responses of dependent communities at species-group levels, in particular for both above- and below-ground communities. Here, we selected a loose and a tight phenotype of the alpine cushion species <em>Thylacospermum</em> <em>caespitosum</em> in two sites in northwest China, and use the relative intensity of interactions index to quantify cushion plant effects on subordinate communities of plants and soil fungi and bacteria. We assessed variations in responses of both above- and below-ground organisms to cushion plant effects at species-group level. Species-group level analyses showed that the effects of the phenotype varied among groups of each of the three community types, and different species-groups were composed of unique taxa. Additionally, we found that loose cushions enhanced stochastic processes in community assembly, for plants and soil fungi but not for soil bacteria. These variations of phenotypic effects on different species-group induced contrasting taxonomic composition between groups and altered community assembly thereby. Our study highlights the occurrence of contrasting effects of two phenotypes of a foundation cushion plant on understory plants, soil fungi and bacteria community composition, but not necessarily on their richness. We also showed that assessing responses of understory species at the species-group level allows a more realistic and mechanistic understanding of biotic interactions both for above- and below-ground communities.</span></p>
Data for: From individual behaviors to collective outcomes: fruiting body formation in Dictyostelium as a group-level phenotype
<p>Collective phenotypes, which arise from the interactions among individuals, can be important for the evolution of higher levels of biological organization. However, how a group's composition determines its collective phenotype remains poorly understood. When starved, cells of the social amoeba <em>Dictyostelium discoideum</em> cooperate to build a multicellular fruiting body, and the morphology of the fruiting body is likely advantageous to the surviving spores. We assessed how the number of strains, as well as their genetic and geographic relationships to one another, impact the group's morphology and productivity. We find that some strains consistently enhance or detract from the productivity of their groups, regardless of the identity of the other group members. We also detect extensive pairwise and higher-order genotype interactions, which collectively have a large influence on the group phenotype. Whereas previous work in <em>Dictyostelium</em> has focused almost exclusively on whether spore production is equitable when strains cooperate to form multicellular fruiting bodies, our results suggest a previously unrecognized impact of chimeric co-development on the group phenotype. Our results demonstrate how interactions among members of a group influence collective phenotypes and how group phenotypes might in turn impact selection on the individual.</p>
Genome wide scan of Orthomam for associations to several phenotypes
<p>Scan results of the Orthomam database using Pelican, including predictions at the level of sites and their aggregation at the level of genes.</p> <p>Annotation of the Orthomam phylogeny is provided for each phenotype under consideration :</p> <ul> <li>echolocation</li> <li>diurnality/nocturnality</li> <li>aquatic environment</li> <li>marine environment</li> <li>subterranean environment</li> <li>vocal learning</li> <li>domestication</li> <li>diet (herbivore/carnivore/omnivore)</li> </ul>
Phenotypic divergence of traits that mediate antagonistic and mutualistic interactions between island and continental populations of the tropical plant, Tribulus cistoides (Zygophyllaceae)
<p><span><strong>Premise</strong>:</span><span> Island systems have long served as a model for evolutionary processes due to their unique species interactions. Many studies of the evolution of species interactions on islands have focused on endemic taxa. Fewer studies have focused on how antagonistic and mutualistic interactions shape the phenotypic divergence of widespread non-endemic species living on island populations. </span></p> <p><span><strong>Methods</strong>:</span><span> We used the widespread plant <em>Tribulus</em> <em>cistoides</em> (Zygophyllaceae) to test phenotypic divergence in traits that mediate antagonistic interactions with vertebrate granivores (birds) and mutualistic interactions with pollinators and how this is explained by bioclimatic variables. We used both herbarium specimens and field-collected samples to compare phenotypic divergence between continental and island populations. </span></p> <p><span><strong>Results</strong>:</span><span> Fruits from island populations were larger than on continents, but the presence of lower spines on mericarps was lower on islands. The presence of spines was largely explained by environmental variation among islands. Petal length was on average 9% smaller on island than continental populations, an effect that was especially accentuated on the Galápagos Islands. </span></p> <p><span><strong>Conclusions</strong>:</span><span> <em>Tribulus</em> <em>cistoides</em> exhibits phenotypic divergence between island and continental habitats for antagonistic traits (seed defence) and mutualistic traits (floral traits). Further, the evolution of phenotypic traits that mediate antagonistic and mutualistic interactions depended on the abiotic characteristics of specific islands. This study shows the potential of using a combination of herbarium and field samples for comparative studies on a globally distributed species to test phenotypic divergence on island habitats.</span></p>
Shifting environmental predictors of phenotypes under climate change: A case study of growth in high latitude seabirds
<p>Climate change is altering species' traits across the globe. To predict future trait changes and understand the consequences of those changes, we need to know the environmental drivers of phenotypic change. In the present study, we use multi-decadal long datasets to determine periods of within-year environmental variation that predict growth of three seabird species. We evaluate whether these periods changed over time and use them to predict future growth under climate change. We find that predictions of trait change could be improved by considering that 1) the timing of environmental factors used to predict traits (predictive-environmental features) can change over time, and 2) the type of predictive-environmental features can change over time. We find evidence of changes in the timing of environmental predictors in all populations studied and evidence for a change in the type of predictor in the studied Arctic murre population. Environmental models of growth predict that warming conditions will decrease growth rates and bird body sizes in two species (black-legged kittiwakem <em>Rissa</em> <em>tridactyla</em>, and glaucous-winged gullm <em>Larus</em> <em>glaucescens</em>), but not the third (thick-billed murrem <em>Uria</em> <em>lomvia</em>). Consequently, climate change is likely to decrease fledging rates in the gulls and kittiwakes. Further, we find that ice-cover historically predicted murre chick growth well, but no longer does – instead air temperature is now a better predictor of murre growth. Our study highlights a need to investigate whether environmental determinants of trait variation commonly shift in a changing climate and whether such changes have implications for adaptation to novel environments.</p>
A phenotyping weeds image dataset for open scientific research
<p>This in-house-built image dataset consists of 10810 weed images captured through a dedicated phenotyping activity in quasi-field conditions. The targets are seven of the most widespread and hard-to-control weeds in wheat (but also in other winter cereals) in the Mediterranean environment.</p> <p>In the framework of open scientific research, our aim is to share low-cost and high-resolution images representing challenging agricultural environments where weather, lighting and other factors can change by the hour and affect the quality of images. This way the dataset could be used to train Artificial Intelligence architectures designed for weed recognition, allowing the implementation of tools directly available in the field for farmers and technicians for effective and timely weed management.</p> <p>The dataset encompasses weed images ranging from the post-emergence phase (i.e. the complete cotyledons unfolding) until the pre-flowering stage. The weed selection was made by considering (i) bottom-up information and specific requests by farmers and technicians, (ii) weed susceptibility to commercial formulations for chemical control <50%, reported at least twice by field technicians, (iii) the difficulty of control considering any methods, and (iv) the type of growing season (overlapping or not with wheat). The final weeds selection encompassed both monocots (<em>Avena sterilis</em> and <em>Lolium multiflorum</em>) and dicots (<em>Convolvulus arvensis</em>, <em>Fumaria officinalis</em>, <em>Papaver rhoeas</em>, <em>Veronica persica</em> and <em>Vicia sativa</em>).</p> <p>Image acquisition was facilitated by using a white panel as a background; this helped to (i) spread the light and thereby make the plants well-illuminated, while still avoiding strong shadows when using the flash and (ii) simplify image processing. The images were acquired with a Canon EOS 700D hand-held camera set in the macro mode with aperture, shutter speed, ISO and flash in auto mode. Photo capture timing, target distances and light conditions did not have a fixed pattern but were deliberately programmed to vary in such a way as to mimic field conditions. For image shooting at various times of the day, the only precaution was to frame the subject with homogeneous light conditions (full sunlight/full shade). The varied outdoor conditions (light, distance, timing) and camera type (RGB) with auto mode were essential features to make the images photos look similar to those that a user can take in a field, for example with a smartphone camera.</p> <p>After selection and categorization, images were cropped to select the region of interest following the 1:1 ratio but maintaining a minimum size of 512 x 512 pixels.</p> <p> </p> <p>More details on the dataset and its use for weed recognition tasks will be soon available in the proceedings of the forthcoming ECPA conference (2-6 July 2023, Bologna, Italy).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.