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13,587 results for “specification”
Data from: Intra-specific variation in responses to habitat restoration: Could artificial reefs increase spatiotemporal segregation between migratory phenotypes of lake sturgeon?
<p>Habitat restoration is an important tool used to conserve biodiversity and restore species, but its effects are notoriously difficult to predict. Although outcomes of restoration projects are usually assessed using indices of species abundance and diversity, phenotypic differences among individuals within species are likely associated with differing responses to restored habitats. Here, we use lake sturgeon (<span>Acipenser fulvescens</span>) as a case study to illustrate how responses to habitat restoration can differ between phenotypes and potentially lead to unanticipated effects on populations. North America<span>'</span>s St. Clair River supports one of the largest remaining populations of lake sturgeon but has lost much spawning habitat due to its role as a major industrial corridor between the Laurentian Great Lakes Erie and Huron. Two artificial reefs were recently built in the lower and middle segments of the river to increase the available sturgeon spawning habitat. Interestingly, lake sturgeon in the St. Clair River express different migratory phenotypes that may be associated with different likelihoods of colonizing artificial reefs. Acoustic telemetry revealed that artificial reefs were more likely to be used by sturgeon that migrated downstream to overwinter in Lake St. Clair than those that migrated upstream to overwinter in Lake Huron. Furthermore, increasing time spent at the artificial reefs by Lake St. Clair migrants was associated with later arrival to and shorter occupancy of the river<span>'</span>s only natural spawning site, the primary location where the two phenotypes have opportunity to interbreed. Additional research is necessary to determine the ultimate impacts of the artificial reefs on lake sturgeon populations; nevertheless, our study showed phenotype-specific opportunity to colonize restored habitat and a mechanism through which this could lead to changes in gene flow. Our results illustrate the importance of considering intra-specific diversity when planning restoration projects and assessing the effects on populations.</p>
Data to run analyses present in the paper: Cell specificity of human regulatory annotations and their genetic effects on gene expression
<p>Data for the manuscript:</p> <p>Varshney A., H. VanRenterghem, P. Orchard, A. P. Boyle, M. L. Stitzel, <em>et al.</em>, 2019 Cell Specificity of Human Regulatory Annotations and Their Genetic Effects on Gene Expression. Genetics 211: 549–562. <a href="https://doi.org/10.1534/genetics.118.301525">https://doi.org/10.1534/genetics.118.301525</a></p> <p>The code to generate these data are at GitHub: https://github.com/ParkerLab/regulatoryAnnotations_comparisons</p> <p>Also see processed data available in this previous Zenodo record: https://zenodo.org/record/1413623</p>
Supplementary material 1 from: Xu M, Liu Y, Möller E, LaGreca S, Moya P, Wang X, Timdal E, de Boer H, Barreno E, Wang L, Thüs H, Andrésson Ó, Magnússon KP, Ólafsdóttir ES, Heiðmarsson S (2023) Mycobiont-specific primers facilitate the amplification of mitochondrial small subunit ribosomal DNA: a focus on the lichenized fungal genus Melanelia (Ascomycota, Parmeliaceae) in Iceland. MycoKeys 96: 57-75. https://doi.org/10.3897/mycokeys.96.100037
Multiple sequence alignment for fungal mtSSU primer design in the family Parmeliaceae (except for Usnea)
Data for: Host-specific plasmid evolution explains the variable spread of clinical antibiotic-resistance plasmids
<p><span>Antibiotic resistance encoded on plasmids is a pressing global health problem. Predicting which plasmids spread in the long term remains very challenging, even though some key parameters influencing plasmid stability have been identified, such as plasmid growth costs and horizontal transfer rates. Here, we show these parameters evolve in a strain-specific way among clinical plasmids and bacteria, and this occurs rapidly enough to alter the relative likelihoods of different bacterium-plasmid combinations spreading. We used experiments with <em>Escherichia</em> <em>coli</em> and antibiotic-resistance plasmids isolated from patients, paired with a mathematical model, to track long-term plasmid stability (beyond antibiotic exposure). Explaining variable stability across six bacterium-plasmid combinations required accounting for evolutionary changes in plasmid-stability traits, whereas initial variation of these parameters </span><span>was a relatively poor predictor of long-term outcomes</span><span>. Evolutionary trajectories were specific to particular bacterium-plasmid combinations, as evidenced by genome sequencing and genetic manipulation. This revealed epistatic (here, strain-dependent) effects of key genetic changes affecting horizontal plasmid transfer. Several genetic changes involved mobile elements and pathogenicity islands. Rapid strain-specific evolution can thus outweigh ancestral phenotypes as a predictor of plasmid stability. Accounting for strain-specific plasmid evolution in natural populations could improve our ability to anticipate and manage successful bacterium-plasmid combinations.</span></p>
Dataset associated with Inferring Cell-Type-Specific Causal Gene Regulatory Networks during Human Neurogenesis
<p>Full summary statistics for QTLs generated in study titled "Inferring Cell-Type-Specific Causal Gene Regulatory Networks during Human Neurogenesis"</p> <p>The big "data" folder includes datasets for each model under subfolders Model 1A, Model 1B and Model 2 as following tree</p> <p>data<br> │ ├───Model1A<br> │ │ ├───caQTL<br> │ │ │ ├───neuron<br> │ │ │ └───progenitor<br> │ │ └───eQTL<br> │ │ ├───neuron<br> │ │ └───progenitor<br> │ ├───Model1B<br> │ └───Model2</p> <p> </p> <p> </p>
A Model-driven and Template-based Approach for Requirements Specification : Case studies
<p>This dataset includes the requirements specified using our templates as part of our case studies :</p> <ol> <li>ARINC-653 case study : Time management requirements.</li> <li>ARINC-653 case study : Intra-partition communication requirements.</li> <li>AUTOSAR case study : Crypto service manager requirements.</li> <li>Traditional software case study : Requirements from Kaggle.</li> </ol>
Fig. 3 in Age- and sex-specific survivorship of the Southern Hemisphere long-finned pilot whale (Globicephala melas edwardii)
Fig. 3.—Age-specific mortality rates (qx) for male and female longfinned pilot whales (Globicephala melas edwardii) mass-stranded on the New Zealand coast between 2006 and 2017. Points are based on traditional life table calculations (qx) and smoothed curves were fitted using the Siler model (Siler qx). Age-class = age x to x + 1.
Fig. 1 in Age- and sex-specific survivorship of the Southern Hemisphere long-finned pilot whale (Globicephala melas edwardii)
Fig. 1.—Age distribution of female (n = 227), male (n = 154), and unknown sex (n = 3) long-finned pilot whales (Globicephala melas edwardii) mass-stranded on the New Zealand coast between 2006 and 2017.
Fig. 2 in Age- and sex-specific survivorship of the Southern Hemisphere long-finned pilot whale (Globicephala melas edwardii)
Fig. 2.—Age-specific survivorship (lx) for male and female longfinned pilot whales (Globicephala melas edwardii) mass-stranded on the New Zealand coast between 2006 and 2017. Points are based on traditional life table calculations (lx) and smoothed curves were fitted using the Siler model (Siler lx). Age-class = age x to x + 1.
FIGURE 2–5. Optical microscope view. 2 in Specificity of fleas associated with opossums in a landscape gradient in the Paranaense Rainforest Ecoregion
FIGURE 2–5. Optical microscope view. 2) Modified posterior segments of a male specimen of A. (A.) a. ronnai. 3) Detail of the metatibia of Polygenis (P.) r. roberti with seven dorsal notches, the subterminal one with three setae. 4–5) Genitalia and modified posterior segments of 4) female and 5) male specimens of Polygenis (P.) r. roberti.
Figure 4. Clade 3 in Host conservatism or host specialization? Patterns of fungal diversification are influenced by host plant specificity in Ophiognomonia (Gnomoniaceae: Diaporthales)
Figure 4. Clade 3 of the genus Ophiognomonia. Spatial evolutionary and ecological vicariance analysis (SEEVA) results. Divergence indices of four host plant variables: host plant order, family, genus, and species. Histograms of divergence indices (0–1) for each variable are mapped onto the maximum-likelihood tree from the GARLI analysis for 17 species of Ophiognomonia. After applying a Bonferroni correction (P ≤ 0.0034), the statistically significant divergence indices are indicated by asterisks at each node (*). Nodes are labelled as clade #:node #. The host plant families are represented as shaded boxes. The complete host–fungus associations are listed in Table 1. Patterns of host conservatism, specialization, or switching are indicated by bold arrows. Nodes 3:3, 3:4, 3:5, 3:9, 3:11, 3:12, 3:13, and 3:14 are not supported by maximum-likelihood bootstrap values of ≥ 70%.
Figure 2. Clade 1 in Host conservatism or host specialization? Patterns of fungal diversification are influenced by host plant specificity in Ophiognomonia (Gnomoniaceae: Diaporthales)
Figure 2. Clade 1 of the genus Ophiognomonia. Spatial evolutionary and ecological vicariance analysis (SEEVA) results. Divergence indices of four host plant variables: host plant order, family, genus, and species. Histograms representing divergence indices (0–1) for each variable are mapped onto the maximum-likelihood tree from the GARLI analysis for 16 species of Ophiognomonia. After applying a Bonferroni correction (P ≤ 0.0034), the statistically significant divergence indices are indicated by asterisks at each node (*). Nodes are labelled as clade #:node #. The host plant orders and families are represented as shaded boxes. The complete host–fungus associations are listed in Table 1. Patterns of host conservatism, specialization, or switching are indicated by bold arrows. Only node 1:15 is not supported by a maximumlikelihood bootstrap value of ≥ 70%.
Figure 1 in Host conservatism or host specialization? Patterns of fungal diversification are influenced by host plant specificity in Ophiognomonia (Gnomoniaceae: Diaporthales)
Figure 1. Morphology of the species Ophiognomonia alni-viridis. From the top, left to right: perithecia on an overwintered leaf of Alnus sinuata; single perithecium extracted from host tissue; single ascospore; and single ascus.
Figure 3. Clade 2 in Host conservatism or host specialization? Patterns of fungal diversification are influenced by host plant specificity in Ophiognomonia (Gnomoniaceae: Diaporthales)
Figure 3. Clade 2 of the genus Ophiognomonia. Spatial evolutionary and ecological vicariance analysis (SEEVA) results. Divergence indices of four host plant variables: host plant order, family, genus, and species. Histograms of divergence indices (0–1) for each variable are mapped onto the maximum-likelihood tree from the GARLI analysis for 14 species of Ophiognomonia. After applying a Bonferroni correction (P ≤ 0.0039), the statistically significant divergence indices are indicated by asterisks at each node (*). Nodes are labelled as clade #:node #. The host plant orders and families are represented as shaded boxes. The complete host–fungus associations are listed in Table 1. Patterns of host conservatism, specialization, or switching are indicated by bold arrows. All branches are supported by maximum-likelihood values ≥ 70%.
Dataset to article "Distinct traits of structural and regulatory evolutional conser-vation of human genes with specific focus on major cancer mo-lecular pathways"
<p>Abstract: Evolution of protein coding genes has both structural and regulatory components. The first can be assessed by measuring the ratio of non-synonymous to synonymous nucleotide sub-stitutions. The second component can be measured as the normalized proportion of transposable elements that used as the regulatory elements. For the first time, we characterized in parallel the regulatory and structural evolutionary profiles for 10,890 human genes and 2,972 molecular pathways. We observed ~0.1 correlation between the structural and regulatory metrics at the gene level, which appeared much higher (~0.4) at the pathway level. We deposited the data in the publicly available database RetroSpect accessible at https://retrospect.oncobox.com. We also analyzed evolutionary dynamics of six cancer pathways of two major axes: Notch/WNT/Hedgehog and AKT/mTOR/EGFR. The Hedgehog pathway had both components slower, whereas the Akt pathway had clearly accelerated structural evolution. In particular, the major hub nodes Akt and beta-catenin showed both components strongly decreased, whereas two major regulators of Akt TCL1 and CTMP had outstandingly high evolutionary rates. We al-so noticed structural conservation of serine/threonine kinases and the genes related to guanosine metabolism in cancer signaling: GPCRs, G proteins, and small regulatory GTPases (Src, Rac, Ras); however, this was compensated by the accelerated regulatory evolution.</p>
Age-specific survival of territorial and nonterritorial male chamois
<p><span>How alternative reproductive tactics (ARTs) are maintained in wildlife populations is one of the major questions in evolutionary biology. As a dominant status, territoriality is typically linked to increased mating opportunities, and one explanation for why this behaviour coexists with other tactics is that dominance implies survival costs. Such a trade-off may occur in the Northern chamois <em>Rupicapra</em> <em>rupicapra</em>, as reproductive advantages of territorial males over nonterritorial males could be counterbalanced by a reduction in survival mediated through energy expenditure, stress and parasitic infections, ultimately favouring ART coexistence. Here, we analysed age-dependent survival probabilities of territorial (n=15) and nonterritorial (n=16) adult chamois using information collected over 12 years between 2010 and 2021 in the Gran Paradiso National Park (Western Italian Alps). Survival rates were estimated with a CMR approach using Burnham's joint modelling of live encounter and dead recovery data. The model selection procedure, based on AICc value minimisation, supported a linear decrease of survival with age but did not match our predictions, as territorial chamois did not have lower survival rates than nonterritorial chamois. In contrast, territorial males appeared to enjoy reproductive success at lower survival costs. This, in turn, supports the role of other factors, such as snow-dependent environmental stochasticity, in the maintenance of ARTs in chamois populations. The limited sample size, however, calls for caution in interpretation, and </span><span>long-term studies of lifetime reproductive success and survival are necessary to clarify</span><span> the mechanisms underlying the expression and coexistence of different reproductive behaviours in this species.</span></p>
FIG. 3 in Phylogeny and biogeography of wild roses with specific attention to polyploids
FIG. 3. Network representing the relationships among copies of GAPDH obtained from Rosa species. A 'c' followed by a number indicates the number attributed to one particular clone sequenced. The groups are compared with our main clades from the chloroplast analyses. Purple is attributed to Rosa subgen. Platyrhodon, yellow to R. sect. Banksianae, bright orange to R. sect. Bracteatae, brown to R. sect. Laevigatae, light pink to Pimpinellifoliae clade, green to Cinnamomeae clade, light blue to Synstylae clade and a deeper blue to R. sect. Caninae. Two species have a particular colour, R. spinosissima is highlighted with a deeper pink colour and R. abyssinica with a light orange colour. Two types of copies, C1 and C2, are distinguished in our Cinnamomeae group. Some polyploids have several copies with different affinities. The names of known polyploids are in bold (in R. sect. Caninae all species are presumed to be polyploids even when the ploidy number is not exactly known).
FIG. 4 in Phylogeny and biogeography of wild roses with specific attention to polyploids
FIG. 4. Chronogram obtained from BEAST analyses of chloroplast regions. Branches are coloured according to the ancestral area reconstruction analyses (DEC model) including fossils information. A P designates our Pimpinellifoliae clade, a C our Cinnamomeae clade and an S our Synstylae clade. The names of known polyploids are in bold (in R. sect. Caninae all species are presumed to be polyploids even when the ploidy number is not exactly known).
FIG. 1 in Phylogeny and biogeography of wild roses with specific attention to polyploids
FIG. 1. Morphological diversity of flowers and fruits of a few representatives of Rosa. (A) Flower of Rosa hugonis. (B) Flower of R. moyesii. (C) Flower of R. multibracteata. (D) Flower of R. odorata. (E) Flower of R. praelucens. (F) Flower of R. prattii. (G) Flower of R. tsinglingensis. (H) Flower of R. roxburghii. (I) Flower of R. rubus. (J) Flower of R. roxburghii. (K) Flower of R. villosa. (L) Flower of R. primula. (M) Fruit of R. sikangensis. (N) Fruits of R. omeiensis. (O) Fruit of R. mairei. (P) Fruit of R. macrophylla. (Q) Fruits of R. sweginzowii.
FIG. 2 in Phylogeny and biogeography of wild roses with specific attention to polyploids
FIG. 2. Phylogenetic relationships among Rosa species as reconstructed by Maximum Likelihood analyses of three chloroplast regions (psbA-trnH spacer, trnL region and trnG region). Bootstrap values are placed as close as possible to the node supported. The ploidy level of each species is given after its name (see Erlanson, 1929, 1934, 1938; Roberts, 1977; Yokoya et al., 2000; Roberts et al., 2009; Jian et al., 2010). The names of known polyploids are in bold (in R. sect. Caninae all species are presumed to be polyploids even when the ploidy number is not exactly known). Wissemann's (2003) classification is compared with our clades. A P designates our Pimpinellifoliae clade, a C our Cinnamomeae clade, an S our Synstylae clade, Ca our Caninae clade and Ru our Rubiginae clade.
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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.