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667 results for “distinguishability”
Data from: Which traits do observers use to distinguish Batesian mimics from their models?
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Data from: Plumage genes and little else distinguish the genomes of hybridizing warblers
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Data from: Contrasting water, dry matter and air contents distinguish orthophylls, sclerophylls and succophylls (leaf succulents)
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Data from: Distinguishing the effects of selection from demographic history in the genetic variation of two sister passerines based on mitochondrial-nuclear comparison
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Data from: Quantifying "apparent" impact and distinguishing impact from invasiveness in multispecies plant invasions
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Gulf of Maine genotypes for 'Species identification based on a semi-diagnostic marker: evaluation of a simple conchological test for distinguishing blue mussels Mytilus edulis L. and M. trossulus Gould'
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Data from: Displayed trees do not determine distinguishability under the network multispecies coalescent
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Data from: Distinguishing migration events of different timing for wild boar in the Balkans
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Data From: Applying empirical dynamic modeling to distinguish abiotic and biotic drivers of population fluctuations in sympatric fishes
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Dataset: Predictive error processing distinguishes between relevant and irrelevant errors after visuomotor learning
<p>Supplementary Data for <em><strong>Predictive error processing distinguishes between relevant and irrelevant errors after visuomotor learning</strong></em><em><strong> </strong></em>article</p> <p>Dataset associated with the following publication:</p> <p>Maurer, L. K., Joch, M., Hegele, M., Maurer, H. & Müller, H. (2019). Predictive error processing distinguishes between relevant and irrelevant errors after visuomotor learning. <em>Journal of Vision</em>, <em>19(4):18</em>, 1-13. https://doi.org/10.1167/19.4.18</p>
First use of acoustic calls to distinguish cryptic fish species: Dascyllus aruanus complex as a case study
From a practical point of view, the determination of species in the wild is based on their phenotypes. Consequently, many species remain unknown because they are visually indistinguishable from described species. Although molecular methods and advances in bioacoustical analysis have been extensively used to uncover cryptic species, the combination of both methodologies is still rare and concerns only some terrestrial taxa such as insects, bats, frogs and birds. In this study, we aim to determine whether the sounds produced by different populations of fish can also be a tool to distinguish and identify cryptic species. The humbug damselfish complex, Dascyllus aruanus, is widely distributed across the Indo-Pacific Ocean and, since 2019, is thought to be composed of at least two species with Dascyllus aruanus in the Pacific Ocean and Dascyllus abudafur in the Indian Ocean. Recordings were made over a large geographical area with populations from Madagascar (Indian Ocean), Taiwan (Pacific Ocean) and French Polynesia (Society Islands). Two kinds of sounds were used for analysis: sounds associated with conspecific chases, and sounds produced during the "signal jump" of courtship behaviour. The sounds associated with signal jumps differ geographically. Acoustic feature differences between Taiwan and Madagascar align with the existence of genetic differences confirming specific status and supporting for the first time that sounds can help to discriminate cryptic species in Teleosts. However, differences in both acoustic features and genetic data can also be found between Taiwan and French Polynesia suggesting two clearly distinct populations. Using the same reasoning, we propose to resurrect the epithet "emamo" (Lesson 1830) for the Society Island humbug damselfish. Interestingly, sounds associated to conspecific chases are more variable than sounds related to signal jumps, suggesting that there are more constraints on sounds related to courtship since they would serve as indicators for species identity and contribute to premating isolation.
Novel immunoassay detecting p-Tau Thr217 distinguishes Alzheimer's Disease from other dementias
<p><b>Objective</b></p> <p>To investigate whether p-tau T217 assay in cerebrospinal fluid (CSF) can distinguishes Alzheimer's disease from other dementias and healthy controls.</p> <p><b>Methods</b></p> <p>We developed and validated a novel Simoa immunoassay to detect p-tau T217 in CSF. There was a total of 190 participants from three cohorts with AD (n = 77) and other neurodegenerative diseases (n = 69) as well as healthy subjects (n = 44).</p> <p><b>Results</b></p> <p>The p-tau T217 assay (cut-off 242 pg/ml) identified AD subjects with accuracy of 90%, with 78% positive predictive value (PPV), 97% negative predictive value (NPV), 93% sensitivity, 88% specificity compared favorably with p-tau T181 ELISA (52 pg/ml) showing 78% accuracy, 58% PPV, 98% NPV, 71% specificity; 97% sensitivity. The assay distinguished AD patients from age-matched healthy subjects (cut-off 163 pg/ml, sensitivity 98%, specificity 93%) similarly to p-tau T181 ELISA (cut-off 60 pg/ml, 96% sensitivity and 86% specificity). In AD patients, we found a strong correlation between p-tau T217-tau and p-tau T181, t-tau and Aβ40 but not with Aβ42.</p> <p><b>Conclusions</b></p> <p>This study demonstrates that p-tau T217 displayed better diagnostic accuracy than p-tau T181. The data suggests that new p-tau T217 assay has a potential as an AD diagnostic test in the clinical evaluation.</p> <p><b>Classification of Evidence</b>: </p> <p>This study provides Class III evidence that a CSF immunoassay for p-tau T217 distinguishes AD from other dementias and healthy controls.</p>
Data from: CLIP test: a new fast, simple and powerful method to distinguish between linked or pleiotropic quantitative trait loci in linkage disequilibria analysis
An important question arises when mapping quantitative trait loci (QTLs) for genetically correlated traits: is the correlation due to pleiotropy (a single QTL affecting more than one trait) and/or close linkage (different QTLs that are physically close to each other and influence the traits)? In this article, we propose the Close Linkage versus Pleiotropism (CLIP) test, a fast, simple and powerful method to distinguish between these two situations. The CLIP test is based on the comparison of the square of the observed correlation between a combination of apparent effects at the marker level to the minimal value it can take under the pleiotropic assumption. A simulation study was performed to estimate the power and alpha risk of the CLIP test and compare it to a test that evaluated whether the confidence intervals of the two QTLs overlapped or not (CI test). On average, the CLIP test showed a higher power (68%) to detect close-linked QTLs than the CI test (43%) and a same alpha risk (4%).
Data from: Distinguishing migration from isolation using genes with intragenic recombination: detecting introgression in the Drosophila simulans species complex
Background: Determining the presence or absence of gene flow between populations is the target of some statistical methods in population genetics. Until recently, these methods either avoided the use of recombining genes, or treated recombination as a nuisance parameter. However, genes with recombination contribute additional information for the detection of gene flow (i.e. through linkage disequilibrium). Methods: We present three summary statistics based on the spatial arrangement of fixed differences, and shared and exclusive polymorphisms that are sensitive to the presence and direction of gene flow. Power and false positive rate for tests based on these statistics are studied by simulation. Results: The application of these tests to populations from the Drosophila simulans species complex yielded results consistent with migration between D. simulans and its two endemic sister species D. mauritiana and D. sechellia, and between populations D. mauritiana on the islands of the Mauritius and Rodrigues. Conclusions: We demonstrate the sensitivity of the developed statistics to the presence and direction of gene flow, and characterize their power as a function of differentiation level and recombination rate. The properties of these statistics make them especially suitable for analyzing high-throughput sequencing data or for their integration within the approximate Bayesian computation framework.
Data from: Distinguishing between convergent evolution and violation of the molecular clock for three taxa
We give a non-technical introduction to convergence-divergence models, a new modeling approach for phylogenetic data that allows for the usual divergence of lineages after lineage-splitting but also allows for taxa to converge, i.e. become more similar over time. By examining the 3-taxon case in some detail we illustrate that phylogeneticists have been ``spoiled'' in the sense of not having to think about the structural parameters in their models by virtue of the strong assumption that evolution is tree-like. We show that there are not always good statistical reasons to prefer the usual class of tree-like models over more general convergence-divergence models. Specifically we show many 3-taxon data sets can be equally well explained by supposing violation of the molecular clock due to change in the rate of evolution along different edges, or by keeping the assumption of a constant rate of evolution but instead assuming that evolution is not a purely divergent process. Given the abundance of evidence that evolution is not strictly tree-like, our discussion is an illustration that as phylogeneticists we need to think clearly about the structural form of the models we use. For cases with four taxa we show that there will be far greater ability to distinguish models with convergence from non-clock-like tree models.
Data from: Distinguishing between reservoir exposure and human-to-human transmission for emerging pathogens using case onset data
Pathogens such as MERS-CoV, influenza A/H5N1 and influenza A/H7N9 are currently generating sporadic clusters of spillover human cases from animal reservoirs. The lack of a clear human epidemic suggests that the basic reproductive number R0 is below or very close to one for all three infections. However, robust cluster-based estimates for low R0 values are still desirable so as to help prioritise scarce resources between different emerging infections and to detect significant changes between clusters and over time. We developed an inferential transmission model capable of distinguishing the signal of human-to-human transmission from the background noise of direct spillover transmission (e.g. from markets or farms). By simulation, we showed that our approach could obtain unbiased estimates of R0, even when the temporal trend in spillover exposure was not fully known, so long as the serial interval of the infection and the timing of a sudden drop in spillover exposure were known (e.g. day of market closure). Applying our method to data from the three largest outbreaks of influenza A/H7N9 outbreak in China in 2013, we found evidence that human-to-human transmission accounted for 13% (95% credible interval 1%–32%) of cases overall. We estimated R0 for the three clusters to be: 0.19 in Shanghai (0.01-0.49), 0.29 in Jiangsu (0.03-0.73); and 0.03 in Zhejiang (0.00-0.22). If a reliable temporal trend for the spillover hazard could be estimated, for example by implementing widespread routine sampling in sentinel markets, it should be possible to estimate sub-critical values of R0 even more accurately. Should a similar strain emerge with R0>1, these methods could give a real-time indication that sustained transmission is occurring with well-characterised uncertainty.
Data from: Distinguishing social from nonsocial navigation in moving animal groups
Many animals, such as migrating shoals of fish, navigate in groups. Knowing the mechanisms involved in animal navigation is important when it comes to explaining navigation accuracy, dispersal patterns, population and evolutionary dynamics and consequently the design of conservation strategies. When navigating towards a common target, animals could interact socially by sharing available information directly or indirectly, or each individual could navigate by itself and aggregations may not disperse because all animals are moving towards the same target. Here, we present an analysis technique that uses individual movement trajectories to determine the extent to which individuals in navigating groups interact socially, given knowledge of their target. The basic idea of our approach is that the movement direction of individuals arises from a combination of responses to the environment and to other individuals. We estimate the relative importance of these responses, distinguishing between social and non-social interactions. We develop and test our method using simulated groups and demonstrate its applicability to empirical data in a case study on groups of guppies moving towards shelter in a tank. Our approach is generic and can be extended to different scenarios of animal group movement.
Data from: Can longitudinal generalized estimating equation models distinguish network influence and homophily? an agent-based modeling approach to measurement characteristics
Background: Connected individuals (or nodes) in a network are more likely to be similar than two randomly selected nodes due to homophily and/or network influence. Distinguishing between these two influences is an important goal in network analysis, and generalized estimating equation (GEE) analyses of longitudinal dyadic network data are an attractive approach. It is not known to what extent such regressions can accurately extract underlying data generating processes. Therefore our primary objective is to determine to what extent, and under what conditions, does the GEE-approach recreate the actual dynamics in an agent-based model. Methods: We generated simulated cohorts with pre-specified network characteristics and attachments in both static and dynamic networks, and we varied the presence of homophily and network influence. We then used statistical regression and examined the GEE model performance in each cohort to determine whether the model was able to detect the presence of homophily and network influence. Results: In cohorts with both static and dynamic networks, we find that the GEE models have excellent sensitivity and reasonable specificity for determining the presence or absence of network influence, but little ability to distinguish whether or not homophily is present. Conclusions: The GEE models are a valuable tool to examine for the presence of network influence in longitudinal data, but are quite limited with respect to homophily.
Data from: Distinguishing causes of virulence evolution: reply to Alizon & Michalakis
In a recent study of the symbiosis between bacteria and plasmids, the available evidence suggests that experimental evolution of plasmid virulence was primarily driven by within-host competition caused by superinfection. The data do not exclude the possibility, however, that a trade-off between virulence and infectious transmission to uninfected bacteria also played a minor role.
FIGURE 3 in Use of DNA barcoding to distinguish the malaria vector An opheles neivai in Colombia
FIGURE 3. DNA barcode threshold optimization for An. neivai.
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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.