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15,022 results for “differentiation”
Differential Gene Expression Datasets for "Identification of candidate repurposable drugs to combat COVID‑19 using a signature‑based approach"
<p>This dataset has the unfiltered transcriptome differential expression results used in the paper "Identification of candidate repurposable drugs to combat COVID‑19 using a signature‑based approach". </p>
Figure 3 in Ecological niche differentiation among Aztec fruit-eating bat subspecies (Chiroptera: Phyllostomidae) in Mesoamerica
Figure 3. Niche overlap values for Schoener's D and Hellinger's I compared to a null distribution: (a) Artibeus a. aztecus (yellow) vs. A. a. minor (blue), (b) A. a. aztecus vs.A. A. major (red), (c) A. a. minor vs. A. a. major.
Figure 2 in Ecological niche differentiation among Aztec fruit-eating bat subspecies (Chiroptera: Phyllostomidae) in Mesoamerica
Figure 2. Maxent predicted potential distribution for (a) Artibeus a. aztecus, (b) A. a. minor, and (c) A. a. major.
Raw differential gene expression data, data S1, from: Molecular cascades and cell type-specific signatures in ASD revealed by single cell genomics
<p>Genomic profiling in post-mortem brain from autistic individuals has consistently revealed convergent molecular changes. What drives these changes and how they relate to genetic susceptibility in this complex condition is not understood. We performed deep single nuclear RNA sequencing (snRNAseq) to examine cell composition and transcriptomics, identifying dysregulation of cell type-specific gene regulatory networks (GRNs) in autism, which we corroborated using snATAC-seq and spatial transcriptomics. Transcriptomic changes were primarily cell type-specific, involving multiple cell types, most prominently interhemispheric and callosal-projecting neurons, interneurons within superficial laminae, and distinct glial reactive states involving oligodendrocytes, microglia, and astrocytes. Autism-associated GRN drivers and their targets were enriched in rare and common genetic risk variants, connecting autism genetic susceptibility and cellular and circuit alterations in the human brain. This data is the raw differential gene expression comparing ASD versus CTL subjects for each cell cluster. </p>
Pronounced differentiation on the Z chromosome and parts of the autosomes in crowned sparrows contrasts with mitochondrial paraphyly: implications for speciation
<p>When a single species evolves into multiple descendent species, some parts of the genome can play a key role in the evolution of reproductive isolation while other parts flow between the evolving species via interbreeding. Genomic evolution during the speciation process is particularly interesting when major components of the genome—for instance, sex chromosomes vs. autosomes vs. mitochondrial DNA—show widely differing patterns of relationships between three diverging populations. The golden-crowned sparrow (<em>Zonotrichia atricapilla</em>) and the white-crowned sparrow (<em>Zonotrichia leucophrys</em>) are phenotypically differentiated sister species that are largely reproductively isolated despite possessing similar mitochondrial genomes, likely due to recent introgression. We assessed variation in more than 45,000 single nucleotide polymorphisms (SNPs) to determine the structure of nuclear genomic differentiation between these species and between two hybridizing subspecies of <em>Z. leucophrys</em>. The two <em>Z. leucophrys</em> subspecies showed moderate levels of relative differentiation and patterns consistent with a history of recurrent selection in both ancestral and daughter populations, with much of the sex chromosome Z and a large region on the autosome 1A showing increased differentiation compared to the rest of the genome. The two species <em>Z. leucophrys</em> and <em>Z. atricapilla</em> show high relative differentiation and strong heterogeneity in the level of differentiation among various chromosomal regions, with a large portion of the sex chromosome (Z) showing highly divergent haplotypes between these species. Studies of speciation often emphasize mitochondrial DNA differentiation, but speciation between <em>Z. atricapilla</em> and <em>Z. leucophrys</em> appears primarily associated with Z chromosome divergence and more moderately associated with autosomal differentiation, whereas mitochondria appear highly similar due apparently to recent introgression. These results add to the growing body of evidence for highly heterogeneous patterns of genomic differentiation during speciation, with some genomic regions showing lack of gene flow between populations many hundreds of thousands of years before other genomic regions.</p>
Dataset for: "Parameter identifiability and model selection for partial differential equation models of cell invasion"
<p>This is the dataset accompanying the paper "Parameter identifiability and model selection for partial differential equation models of cell invasion" (https://arxiv.org/abs/2309.01476). It consists of a series of images taken of a barrier assay experiment to study tissue expansion of MDCK cells, along with cell density data in MATLAB format.</p> <p>File structure: the contents of the four zip files should be combined (they were split into four files for practical reasons regarding file size). The data corresponds to eight experiments, four with circular initial conditions, and four with triangular initial conditions, the associated data are located in 04-05-22 exp1/Circle and 04-05-22 exp1/Triangle respectively, each labeled "xy<n>", where <n> from 1 to 8 is an identifier for the experiment. The images under the xy<n>_Phase folders are the raw images taken of the experiment, those under the xy<n>_mask folder are processed images indicating the extend of the spread of the cell population. The DensityCellcyleFraction folder contain data files in MATLAB format. The most relevant is the "density" variable, which is a rank-3 tensor of size 150x150x77 such that density(i,j,k) corresponds to the cell density at location (x_i,y_j) and time t_k. The process for calculating the cell density is described in the paper.</p> <p>Alternatively, the density data is also provided in csv format. In the csv_data folder, xy<n>/t<k>.csv encodes a matrix representing cell density for experiment <n> at time t_k.</p> <p>The code for processing and analysing these data are provided in the "code" folder. It is also available at https://github.com/liuyue002/woundhealing .</p> <p>Abstract of the paper:</p> <p>When employing a mechanistic model to study biological systems, practical parameter identifiability is important for making predictions in a wide range of scenarios, as well as for understanding the mechanisms driving the system behaviour. We argue that parameter identifiability should be considered alongside goodness-of-fit and model complexity as criteria for model selection. To demonstrate, we use a profile likelihood approach to investigate parameter identifiability for four extensions of the Fisher--KPP model, given experimental data from a cell invasion assay. We show that more complicated models tend to be less identifiable, with parameter estimates being more sensitive to subtle differences in experimental procedures, and require more data to be practically identifiable. The results from identifiability analysis can inform model selection, as well as data collection and experimental design.</p>
Fig. 2 in Karyotype differentiation and cytotaxonomic considerations in species of Serrasalmidae (Characiformes) from the Amazon basin
Fig. 2. Partial karyotypes of Serrasalmidae species showing Ag-NORs (left side) and 18S rDNA sites (right side): a-b) Serrasalmus elongatus; c-d) Serrasalmus maculatus; e-f) Serrasalmus cf. rhombeus; g-h) Serrasalmus rhombeus; i-j) Pygocentrus nattereri; k-l) Colossoma macropomum. Numbers indicate the corresponding chromosome pairs in the karyotypes of the species.
Environmental DNA metabarcoding differentiates between micro-habitats within the rocky intertidal
<p>While the utility of environmental DNA (eDNA) metabarcoding surveys for biodiversity monitoring continues to be demonstrated, the spatial and temporal variability of eDNA, and thus the limits of the differentiability of an eDNA signal, remains under-characterized. In this study, we collected eDNA samples from distinct micro-habitats (~40 m apart) in a rocky intertidal ecosystem over their exposure period in a tidal cycle. During this period, the micro-habitats transitioned from being interconnected, to physically isolated, to interconnected again. Using a well-established eukaryotic (cytochrome oxidase subunit I) metabarcoding assay, we detected 415 species across 28 phyla. Across a variety of univariate and multivariate analyses, using exclusively taxonomically assigned data as well as all detected amplicon sequence variants (ASVs), we identified unique eDNA signals from the different micro-habitats sampled. This difference paralleled expected ecological gradients and increased as the sites became more physically disconnected. Our results demonstrate that eDNA biomonitoring can differentiate micro-habitats in the rocky intertidal only 40 m apart, that these differences reflect known ecology in the area, and that physical connectivity informs the degree of differentiation possible. These findings showcase the potential power of eDNA biomonitoring to increase the spatial and temporal resolution of marine biodiversity data, aiding research, conservation, and management efforts.</p>
Fig. 4 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 4. Distribution of pairwise values of genetic distances amongst species with allopatric areas: 1 — for the Western Palearctic genus Sylvaemus; 2 — for the Eastern Palearctic genera Apodemus and Alsomys; 3 — for the Palearctic Muridae as a whole, including species of genera Micromys and Mus.
Fig. 3 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 3. Distribution of pairwise intraspecies genetic distances within: 1 — the Western Palearctic genus Sylvaemus; 2 — the Eastern Palearctic genera Apodemus and Alsomys; 3 — in general for the Palearctic Muridae, including Micromys and Mus.
Fig. 2 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 2. Phenogram of genetic distances (Tamura, Nei, 1993) calculated from cytb sequences amongst representatives of the genera/subgenera Alsomys, Apodemus and genera Micromys, Mus, Rattus, constructed using the UPGMA algorithm. Representatives of the Arvicolidae and Cricetidae as well as S. s. dichrurus, S. flavicollis, S. (K.) mystacinus and S. (K.) epimelas were taken as outgroups.
Fig. 5 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 5. Distribution of pairwise genetic distances amongst taxa: 1 — Western Palearctic genus Sylvaemus, 2 — Eastern Palearctic genera Apodemus, Alsomys, 3 — Western Palearctic genus Sylvaemus and contrarily Eastern Palearctic genera Apodemus, Alsomys.
Fig. 1 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 1. Phenogram of genetic distances calculated from cytb sequences amongst representatives of the genera Sylvaemus, Rattus, constructed using the UPGMA algorithm, as mentioned above. Microtus arvalis (Arvicolidae) and Cricetus cricetus (Cricetidae) are used as outgroups.
Fig. 2. A in Genetic differentiation in populations of Aedes aegypti (Diptera, Culicidae) dengue vector from the Brazilian state of Maranhão
Fig. 2. A priori estimate of the probable groups of populations produced by the BAPS (Bayesian Analysis of Population Structure v 6.0) program, indicating a total of two groups.
FIGURE 5 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,
FIGURE 5 Mean (+SD) relative expression of gnrh2, fshb, lhb, fshr and lhr messenger (m)RNA in (a) the breeding season () 2n, and () 4n and (b) the non-breeding season in Carassius auratus red var. () 2n, and () 4n. (RCC,) and autotetraploid C. auratus red var. ♀ × Megalobrama amblycephala ♂ (4nRR,). T, gene detected in the testis; O, gene detected in the ovary. *, significant difference between RCC and 4nRR (P <0.05)
FIGURE 4 Deduced amino-acid sequences for the Gnrh2 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,
FIGURE 4 Deduced amino-acid sequences for the Gnrh2 () and Lhr () genes in Carassius auratus red var. (RCC) and autotetraploid C. auratus red var. ♀ × Megalobrama amblycephala ♂ (4nRR)
FIGURE 2 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,
FIGURE 2 (a) The mature eggs (scale bar = 100 μm) and (b) mature sperm (scale bar = 10 μm) of autotetraploid Carrasius auratus red var. ♀ x Megalobrama amblycephala ♂ (4nRR)
FIGURE 3 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,
FIGURE 3 Reverse-transcription (RT)-PCR analysis of the expression of (a) gnrh2, (b) fshb, (c) lhb, (d) fshr and (e) lhr messenger (m)RNA in various tissues of autotetraploid Carrasius auratus red var. ♀ x Megalobrama amblycephala ♂ (4nRR). The upper strip of each panel (a)–(e) shows the positive control of actin gene while the lower strip of each panel shows the RT-PCR amplification of the target gene
FIGURE 1 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,
FIGURE 1 The gonadal structure of Carassius auratus red var. [RCC; (a)–(c)] and autotetraploids C. auratus red var. ♀ × Megalobrama amblycephala ♂ [4nRR; (d)–(f)]: (a) ovary of 7 month-old RCC containing many phase II and a few phase III oocytes; (b) ovary of 12 month-old RCC showing many mature phase IV ova; (c) testis of 12 month-old RCC with numerous mature sperms () and a small amount of spermatocytes () in the lobules of testes; (d) ovary of 7 month-old 4nRR containing phase II and a few phase III oocytes; (e) ovary of 12 month-old 4nRR with numerous mature phase IV ova; (f) testis of 12 month-old 4nRR with numerous mature sperms () and a small amount of spermatocytes () in the lobules of testes, the scale bars: (a), (b), (d), and (e) = 100 μm; (c) and (f) = 10 μm
Figure 8 in Distribution and molecular differentiation of Culex pipiens complex species in the Middle and Eastern Black Sea Regions of Turkey
Figure 8. Unrooted haplotype network for CQ11. Each circle represents a haplotype, and lines above each link indicate mutations.
ScienceDex guides
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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.