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990 results for “quantification”
Data from: Simultaneous delimitation of species and quantification of interspecific hybridization in Amazonian peacock cichlids (genus Cichla) using multi-locus data
BACKGROUND: Introgression likely plays a significant role in evolution, but understanding the extent and consequences of this process requires a clear identification of species boundaries in each focal group. The delimitation of species, however, is a contentious endeavor. This is true not only because of the inadequacy of current tools to identify species lineages, but also because of the inherent ambiguity between natural populations and species paradigms. The result has been a debate about the supremacy of various species concepts and criteria. Here, we utilized multiple separate sources of molecular data, mtDNA, nuclear sequences, and microsatellites, to delimit species under a polytypic species concept (PTSC) and estimate the frequency and genomic extent of introgression in a Neotropical genus of cichlid fishes (Cichla). We compared our inferences of species boundaries and introgression under this paradigm to those when species are identified under a diagnostic species concept (DSC). RESULTS: We find that, based on extensive molecular data and an inclusive species concept, 8 separate biological entities should be recognized rather than the 15 described species of Cichla. Under the PTSC, fewer individuals are expected to exhibit hybrid ancestry than under the DSC (~2% vs. ~12%), but more of the species exhibit introgression from at least one other species (75% vs. 60%). Under either species concept, the phylogenetic breadth of introgression in this group is notable, with both sister species and species from different major mtDNA clades exhibiting introgression. CONCLUSIONS: Introgression was observed to be a widespread phenomenon for delimited species in this group. While several instances of introgressive hybridization were observed in anthropogenically altered habitats, most were found in undisturbed natural habitats, suggesting that introgression is a natural but ephemeral part of the evolution of many tropical species. Nevertheless, even transient introgression may facilitate an increase in genetic diversity or transfer of adaptive mutations that have important consequences in the evolution of tropical biodiversity.
Data from: Quantification of the zygotic barrier between interbreeding taxa using gene flow data
Hybridization and introgression via interspecific gene flow are common processes in the plant kingdom. The effectiveness of these processes is governed by the strengths of multiple zygotic barriers. These barriers have often been quantified in artificial settings using laborious and time-consuming hand-pollination experiments, but their quantification is nonexistent at the landscape level. In this study, we utilized gene flow data within a spatially explicit simulation to assess the strengths of zygotic barriers. Our model system consisted of Populusnigra and its hybrid, P.×canadensis, which interbreed under natural conditions. The study population was located in the floodplain of the Eder River in Central Germany. Pollen-mediated introgression rates from hybrid males into the seeds of individual female trees were used as the target pattern using an inverse modeling approach. Simulations that treated pollen from both taxa equally revealed a large discrepancy between the observed and modeled rates of introgression for both taxa. The discrepancy was reduced by introducing a zygotic barrier against the pollen from the hybrid males. The best model outcome indicated comparably strong zygotic barriers acting against pollen-mediated introgressive gene flow into the two parental taxa, P.nigra and P.×canadensis. The sensitivity of our model was tested by applying different dispersal functions. Four common probability density functions were used along with a pollen dispersal function that had previously been fitted to gene flow data from the same dataset. The best barrier value was almost independent of the dispersal functions used here. Moreover, it was within the range previously determined in hand-pollination-based investigations, validating our model. These data indicate that the inverse modeling approach is a powerful method for quantifying hidden processes, and we discuss its use as a valuable tool for generating new insights into plant mating systems that are relevant to evolutionary biology and risk analysis in conservation efforts
Data from: Quantification and decomposition of environment-selection relationships
In nature, selection varies across time in most environments, but we lack an understanding of how specific ecological changes drive this variation. Ecological factors can alter phenotypic selection coefficients through changes in trait distributions or individual mean fitness, even when the trait-absolute fitness relationship remains constant. We apply and extend a regression-based approach in a population of Soay sheep (Ovis aries) and suggest metrics of environment-selection relationships that can be compared across studies. We then introduce a novel method which constructs an environmentally-structured fitness function. This allows calculation of full (as in existing approaches) and partial (acting separately through the absolute fitness function slope, mean fitness, and phenotype distribution) sensitivities of selection to an ecological variable. Both approaches show positive overall effects of density on viability selection of lamb mass. However, the second approach demonstrates that this relationship is largely driven by effects of density on mean fitness, rather than on the trait-fitness relationship slope. If such mechanisms of environmental dependence of selection are common, this could have important implications regarding the frequency of fluctuating selection, and how previous selection inferences relate to longer-term evolutionary dynamics.
Data from: Quantification of within- and between-farm dispersal of Culicoides biting midges using an immunomarking technique
Culicoides biting midges (Diptera, Ceratopogonidae) are vectors of arboviruses that cause significant economic and welfare impact. Local-scale spread of Culicoides-borne arboviruses is largely determined by the between-farm movement of infected Culicoides. Study of the dispersal behaviour of Culicoides by capture–mark–recapture (CMR) is problematic due to the likelihood of mortality and changes in behaviour upon capture caused by the small size and fragility of these insects, evidenced by low recapture rates. To counter the problem of using CMR with Culicoides, this study utilised an ovalbumin immunomarking technique to quantify the within- and between-farm dispersal of Culicoides in southern England. Both within- and between-farm dispersal of Culicoides was observed. Of the 9058 Culicoides collected over 22 nights of trapping, 600 ovalbumin-positive Culicoides, of 12 species including those implicated as arbovirus vectors, were collected with a maximum dispersal distance of 3125 m. This study provides the first species-level data on the between-farm dispersal of potential bluetongue, Schmallenberg and African horse sickness virus vectors in northern Europe. High-resolution meteorological data determined upwind and downwind flight by Culicoides had occurred. Cumulative collection and meteorological data suggest 15·6% of flights over 1 km were upwind of the treatment area and 84·4% downwind. Synthesis and applications. The use of immunomarking eliminates the potential adverse effects on survival and behaviour of insect collection prior to marking, substantially improving the resolution and accuracy of estimates of the dispersal potential of small and delicate vector species such as Culicoides. Using this technique, quantification of the range of Culicoides dispersal with regard to meteorological conditions including wind direction will enable improved, data-driven modelling of the spread of Culicoides-borne arboviruses and will inform policy response to incursions and outbreaks.
3D Cryo Soft X-ray Transmission Microscopy data of Intact Thick Cells for Membrane Segmentation and Quantification
<p>The datasets used for evaluation of the proposed method in R. Cárdenes and C. Zhang et al. "3D Membrane Segmentation and Quantification of Intact Thick Cells using Cryo Soft X-ray Transmission Microscopy: A Pilot Study", PloS One, 2017. (DOI: 10.1371/journal.pone.0174324)</p>
Dataset for "Joint zonated quantification of multiple parameters in hepatic lobules"
<p>Datasets for the publication "Joint zonated quantification of multiple parameters in hepatic lobules", available at Research Square <a href="https://doi.org/10.21203/rs.3.rs-4764718/v1">https://doi.org/10.21203/rs.3.rs-4764718/v1</a></p>
Combinational quantification of distinct neural projections from retrograde tracing
<p>This record contains the experimental cases with the following ids, used for data analysis of the paper. <br><br>SW190423-07<br>Sw190423-08<br>SW190423-09<br>SW190425-07<br>SW190425-08<br>SW190425-09<br>SW190425-10<br>SW190426-01<br>SW190426-02<br>SW190426-03<br>SW190816-01<br>SW190816-03</p>
MIHIC: A multiplex IHC histopathological image classification dataset for lung cancer immune microenvironment quantification
<p>A cohort of 47 TMA sections from 114 patients was collected from Liaoning cancer hospital \& Institute, where each TMA section has the size of 188,416$\times$110,080 pixels (i.e., 42660.87um$\times$24924.15um) at 40$\times$ magnification. TMA sections contain different number of tissue cores, ranging from 28 to 48. After excluding poor quality TMA sections with tissue folding, missing or contamination, there are totally 114 patients. Each patient has tissue cores with 12 different IHC stains, including CD3, CD20, CD34, CD38, CD68, CDK4, cyclin-D1, D2-40, FAP, Ki67, P53, and SMA. Two pathologists have manually labeled clear tissue regions (i.e., without controversy) in TMA sections based on visual examination via Qupath software, where six tissue types including Alveoli, Immune cells, Nerosis, Other, Stroma, Tumor were annotated. Besides the annotated six tissue types, we added one more Background type.</p> <p>To build histological classification models, we split 309,698 image patches in MIHIC dataset into three sets: training, validation and test. Note that image patches extracted from the same annotated tissue region are distributed into the same set, which avoids data leakage during classification model optimization. According to the number of extracted ROIs, train, val and test accounted for 64\%, 16\% and 20\%.</p> <h1>if you use this dataset, please cite:</h1> <pre>@article{wang2024mihic, title={MIHIC: a multiplex IHC histopathological image classification dataset for lung cancer immune microenvironment quantification}, author={Wang, Ranran and Qiu, Yusong and Wang, Tong and Wang, Mingkang and Jin, Shan and Cong, Fengyu and Zhang, Yong and Xu, Hongming}, journal={Frontiers in Immunology}, volume={15}, year={2024}, publisher={Frontiers Media SA} }</pre>
MME-only models trained with clean data for JAMES paper "Machine-learned uncertainty quantification is not magic"
<p>This tar file contains all 100 trained models in the MME-only ensemble from Experiment 1 (i.e., those trained with clean data, not with lightly perturbed data). To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.</p>
MME-only models trained with lightly perturbed data for JAMES paper "Machine-learned uncertainty quantification is not magic"
<p>This tar file contains all 100 trained models in the MME-only ensemble from Experiment 2 (i.e., those trained with lightly perturbed data). To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.</p>
MME/CRPS models trained with clean data for JAMES paper "Machine-learned uncertainty quantification is not magic"
<p>This tar file contains all 100 trained models in the MME/CRPS ensemble from Experiment 1 (i.e., those trained with clean data, not with lightly perturbed data). To pare the ensemble down to 50 models, we randomly select 50. To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.</p>
MME/CRPS models trained with lightly perturbed data for JAMES paper "Machine-learned uncertainty quantification is not magic"
<p>This tar file contains all 100 trained models in the MME/CRPS ensemble from Experiment 2 (i.e., those trained with lightly perturbed data). To pare the ensemble down to 50 models, we randomly select 50. To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.</p>
Input data and some models (all except multi-model ensembles) for JAMES paper "Machine-learned uncertainty quantification is not magic"
<p>The tar file contains two directories: data and models. Within "data," there are 4 subdirectories: "training" (the clean training data -- without perturbations), "training_all_perturbed_for_uq" (the lightly perturbed training data), "validation_all_perturbed_for_uq" (the moderately perturbed validation data), and "testing_all_perturbed_for_uq" (the heavily perturbed validation data). The data in these directories are unnormalized. The subdirectories "training" and "training_all_perturbed_for_uq" each contain a normalization file. These normalization files contain parameters used to normalize the data (from physical units to z-scores) for Experiment 1 and Experiment 2, respectively. To do the normalization, you can use the script normalize_examples.py in the code library (ml4rt) with the argument input_normalization_file_name set to one of these two file paths. The other arguments should be as follows:</p><p>--uniformize=1</p><p>--predictor_norm_type_string="z_score"</p><p>--vector_target_norm_type_string=""</p><p>--scalar_target_norm_type_string=""</p><p> </p><p>Within the directory "models," there are 6 subdirectories: for the BNN-only models trained with clean and lightly perturbed data, for the CRPS-only models trained with clean and lightly perturbed data, and for the BNN/CRPS models trained with clean and lightly perturbed data. To read the models into Python, you can use the method neural_net.read_model in the ml4rt library.</p>
WAM-IPE uncertainty quantification data
<p>In each file, one or several specific WAM-IPE outputs (quantities of interest, QoI) and the associated parameters used in latent space to generate synthetic drivers. The parameters in latent space and the QoIs can be used to build polynomial chaos expansion based surrogate model.</p>
NextClone and CloneDetective: An Integrated Nextflow Pipeline and R Package for Clonal Barcode Extraction and Quantification
<p>BAM file (chunk 26-50 of 50) for the scRNAseq data required to replicate the analyses presented at: https://phipsonlab.github.io/NextClone-analysis/.</p><p>Chunk 1-25 can be downloaded from https://zenodo.org/records/10129134.</p><p>The original BAM file was too big to fit in an entry. Thus it was split into 50 using picard:</p><blockquote><p>SplitSamByNumberOfReads -I possorted_genome_bam.bam -O split_sam/ -N_FILES 50 <i>--CREATE_MD5_FILE</i></p></blockquote><p>Before re-running all the analyses in https://phipsonlab.github.io/NextClone-analysis/, make sure you merge all 50 chunks first using picard (change xxx to point to the directory storing all the 50 chunks you have downloaded):</p><blockquote><p>outdir="xxx"</p><p>args=""</p><p><i># Loop through each BAM file</i></p><p>for file in ${outdir}/*.bam; do</p><p> args+="-I $file "</p><p>done</p><p># Do the actual merging</p><p>MergeSamFiles $args -O $outdir/merged_v2/merged_bam_v2.bam --USE_THREADING --CREATE_MD5_FILE</p></blockquote><p>Picard can be downloaded from: https://github.com/broadinstitute/picard</p>
NextClone and CloneDetective: An Integrated Nextflow Pipeline and R Package for Clonal Barcode Extraction and Quantification
<p>BAM file (chunk 1-25 of 50) for the scRNAseq data required to replicate the analyses presented at: https://phipsonlab.github.io/NextClone-analysis/.</p><p>Chunk 26-50 can be downloaded from https://zenodo.org/uploads/10129625</p><p>The original BAM file was too big to fit in an entry. Thus it was split into 50 using picard:</p><blockquote><p>SplitSamByNumberOfReads -I possorted_genome_bam.bam -O split_sam/ -N_FILES 50 <i>--CREATE_MD5_FILE</i></p></blockquote><p>Before re-running all the analyses in https://phipsonlab.github.io/NextClone-analysis/, make sure you merge all 50 chunks first using picard (change xxx to point to the directory storing all the 50 chunks you have downloaded):</p><blockquote><p>outdir="xxx"</p><p>args=""</p><p><i># Loop through each BAM file</i></p><p>for file in ${outdir}/*.bam; do</p><p> args+="-I $file "</p><p>done</p><p># Do the actual merging</p><p>MergeSamFiles $args -O $outdir/merged_v2/merged_bam_v2.bam --USE_THREADING --CREATE_MD5_FILE</p></blockquote><p>Picard can be downloaded from: https://github.com/broadinstitute/picard</p>
Image dataset for quantification of virus-infected cells
<p>The dataset contains microscopy images of hantavirus-infected cells stained with antibodies against the viral nucleocapsid protein. </p>
Karim et al; Fig-S7C-IF Raw Image for quantification-DQ-BSA Red
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Raw data for Hybrid micellar liquid chromatography separation of brimonidine tartrate and brinzolamide. Retention study and quantification in fixed dose ophthalmic suspensions
<p>Raw data for optimization of the chromatographic conditions and calibration curve construction</p>
Quantification of RNAseq and CUT&RUN from MeCP2 adult knockout hippocampus
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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.