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9,302 results for “DETECT”
Data from: Detecting hidden diversification shifts in models of trait-dependent speciation and extinction
The distribution of diversity can vary considerably from clade to clade. Attempts to understand these patterns often employ state-dependent speciation and extinction models to determine whether the evolution of a particular novel trait has increased speciation rates and/or decreased extinction rates. It is still unclear, however, whether these models are uncovering important drivers of diversification, or whether they are simply pointing to more complex patterns involving many unmeasured and co-distributed factors. Here we describe an extension to the popular state-dependent speciation and extinction models that specifically accounts for the presence of unmeasured factors that could impact diversification rates estimated for the states of any observed trait, addressing at least one major criticism of BiSSE (Binary State Speciation and Extinction) methods. Specifically, our model, which we refer to as HiSSE (Hidden State Speciation and Extinction), assumes that related to each observed state in the model are "hidden" states that exhibit potentially distinct diversification dynamics and transition rates than the observed states in isolation. We also demonstrate how our model can be used as character-independent diversification models that allow for a complex diversification process that is independent of the evolution of a character. Under rigorous simulation tests and when applied to empirical data, we find that HiSSE performs reasonably well, and can at least detect net diversification rate differences between observed and hidden states and detect when diversification rate differences do not correlate with the observed states. We discuss the remaining issues with state-dependent speciation and extinction models in general, and the important ways in which HiSSE provides a more nuanced understanding of trait-dependent diversification.
Data from: Automated single particle detection and tracking for large microscopy datasets
Recent advances in optical microscopy have enabled the acquisition of very large datasets from living cells with unprecedented spatial and temporal resolutions. Our ability to process these datasets now plays an essential role in order to understand many biological processes. In this paper, we present an automated particle detection algorithm capable of operating in low signal-to-noise fluorescence microscopy environments and handling large datasets. When combined with our particle linking framework, it can provide hitherto intractable quantitative measurements describing the dynamics of large cohorts of cellular components from organelles to single molecules. We begin with validating the performance of our method on synthetic image data, and then extend the validation to include experiment images with ground truth. Finally, we apply the algorithm to two single-particle-tracking photo-activated localization microscopy biological datasets, acquired from living primary cells with very high temporal rates. Our analysis of the dynamics of very large cohorts of 10 000 s of membrane-associated protein molecules show that they behave as if caged in nanodomains. We show that the robustness and efficiency of our method provides a tool for the examination of single-molecule behaviour with unprecedented spatial detail and high acquisition rates.
Data from: Efficient detection of novel nuclear markers for Brassicaceae by transcriptome sequencing
The lack of DNA sequence information for most non-model organisms impairs the design of primers that are universally applicable for the study of molecular polymorphisms in nuclear markers. Next-generation sequencing (NGS) techniques nowadays provide a powerful approach to overcome this limitation. We present a flexible and inexpensive method to identify large numbers of nuclear primer pairs that amplify in most Brassicaceae species. We first obtained and mapped NGS transcriptome sequencing reads from two of the distantly related Brassicaceae species, Cardamine hirsuta and Arabis alpina, onto the Arabidopsis thaliana reference genome, and then identified short conserved sequence motifs among the three species bioinformatically. From these, primer pairs to amplify coding regions (nuclear protein coding loci, NPCL) and exon-primed intron-crossing sequences (EPIC) were developed. We identified 2,334 universally applicable primer pairs, targeting 1,164 genes, which provide a large pool of markers as readily usable genomic resource that will help addressing novel questions in the Brassicaceae family. Testing a subset of the newly designed nuclear primer pairs revealed that a great majority yielded a single amplicon in all of the 30 investigated Brassicaceae taxa. Sequence analysis and phylogenetic reconstruction with a subset of these markers on different levels of phylogenetic divergence in the mustard family were compared with previous studies. The results corroborate the usefulness of the newly developed primer pairs, e.g., for phylogenetic analyses or population genetic studies. Thus, our method provides a cost-effective approach for designing nuclear loci across a broad range of taxa and is compatible with current NGS technologies.
Data from: Background selection and FST: consequences for detecting local adaptation
Background selection is a process whereby recurrent deleterious mutations cause a decrease in the effective population size and genetic diversity at linked loci. Several authors have suggested that variation in the intensity of background selection could cause variation in FST across the genome, which could confound signals of local adaptation in genome scans. We performed realistic simulations of DNA sequences, using recombination maps from humans and sticklebacks, to investigate how variation in the intensity of background selection affects FST and other statistics of population differentiation in sexual, outcrossing species. We show that, in populations connected by gene flow, Weir & Cockerham's (1984) estimator of FST is largely insensitive to locus-to-locus variation in the intensity of background selection. Unlike FST, however, dXY is negatively correlated with background selection. Moreover, background selection does not greatly affect the false positive rate in FST outlier studies in populations connected by gene flow. Overall, our study indicates that background selection will not greatly interfere with finding the variants responsible for local adaptation.
Data from: Detecting recent selective sweeps while controlling for mutation rate and background selection
A composite likelihood ratio test implemented in the program SweepFinder is a commonly used method for scanning a genome for recent selective sweeps. SweepFinder uses information on the spatial pattern (along the chromosome) of the site frequency spectrum (SFS) around the selected locus. To avoid confounding effects of background selection and variation in the mutation process along the genome, the method is typically applied only to sites that are variable within species. However, the power to detect and localize selective sweeps can be greatly improved if invariable sites are also included in the analysis. In the spirit of a Hudson-Kreitman-Aguadé test, we suggest adding fixed differences relative to an outgroup to account for variation in mutation rate, thereby facilitating more robust and powerful analyses. We also develop a method for including background selection, modeled as a local reduction in the effective population size. Using simulations we show that these advances lead to a gain in power while maintaining robustness to mutation rate variation. Furthermore, the new method also provides more precise localization of the causative mutation than methods using the spatial pattern of segregating sites alone.
Data from: Efficient detection of repeating sites to accelerate phylogenetic likelihood calculations
The phylogenetic likelihood function is the major computational bottleneck in several applications of evolutionary biology such as phylogenetic inference, species delimitation, model selection and divergence times estimation. Given the alignment, a tree and the evolutionary model parameters, the likelihood function computes the conditional likelihood vectors for every node of the tree. Vector entries for which all input data are identical result in redundant likelihood operations which, in turn, yield identical conditional values. Such operations can be omitted for improving run-time and, using appropriate data structures, reducing memory usage. We present a fast, novel method for identifying and omitting such redundant operations in phylogenetic likelihood calculations, and assess the performance improvement and memory savings attained by our method. Using empirical and simulated data sets, we show that a prototype implementation of our method yields up to 12-fold speedups and uses up to 78% less memory than one of the fastest and most highly tuned implementations of the phylogenetic likelihood function currently available. Our method is generic and can seamlessly be integrated into any phylogenetic likelihood implementation.
Data from: Accuracy of background matching and prey detection: predation by blue tits (Cyanistes caeruleus) indicates intense selection for highly matching prey colour pattern
1. Although background matching decreases prey detectability, resemblance between camouflaged prey and their visual background is seldom perfect. This could be because even a moderate resemblance might provide sufficient protection, and additional adjustment of colour pattern might give little benefit. Alternatively, close resemblance to background may not be attained due to trade-offs or constraints. To understand selection on colour patterns of camouflaged prey and the existence of inaccurate background matching, it is necessary to investigate how detectability of a colour pattern varies with its resemblance to the background. 2. We trained wild-caught blue tits (Cyanistes caeruleus) to search for artificial prey. We manipulated the resemblance of the artificial prey items to the visual backgrounds. 3. For the first half of the twelve repeated prey presentations we found a non-linear relationship between resemblance and detectability, such that for prey that had high background matching, a change in resemblance resulted in a larger change in detectability than an equal change in resemblance did for prey with lower background matching. However, for the second half of the presentations this relationship was linear. Moreover, in a two-patch-type habitat a prey pattern that was a compromise between the two different backgrounds did after few initial presentations equally well as the prey pattern that matched highly one of the backgrounds. 4. Our results indicate an intense selection for close matching in a single background. Yet, in the heterogeneous environment that consisted of two backgrounds the compromise, which only loosely resembled either background, provided good protection. Therefore, we conclude that cryptic colour patterns that bear only a loose resemblance to a given background, and thus represent inaccurate background matching, may be adaptive outcomes.
Data from: Genomic region detection via Spatial Convex Clustering
Several modern genomic technologies, such as DNA-Methylation arrays, measure spatially registered probes that number in the hundreds of thousands across multiple chromosomes. The measured probes are by themselves less interesting scientifically; instead scientists seek to discover biologically interpretable genomic regions comprised of contiguous groups of probes which may act as biomarkers of disease or serve as a dimension-reducing pre-processing step for downstream analyses. In this paper, we introduce an unsupervised feature learning technique which maps technological units (probes) to biological units (genomic regions) that are common across all subjects. We use ideas from fusion penalties and convex clustering to introduce a method for Spatial Convex Clustering, or SpaCC. Our method is specifically tailored to detecting multi-subject regions of methylation, but we also test our approach on the well-studied problem of detecting segments of copy number variation. We formulate our method as a convex optimization problem, develop a massively parallelizable algorithm to find its solution, and introduce automated approaches for handling missing values and determining tuning parameters. Through simulation studies based on real methylation and copy number variation data, we show that SpaCC exhibits significant performance gains relative to existing methods. Finally, we illustrate SpaCC's advantages as a pre-processing technique that reduces large-scale genomics data into a smaller number of genomic regions through several cancer epigenetics case studies on subtype discovery, network estimation, and epigenetic-wide association.
Data from: Exploring the power of Bayesian birth-death skyline models to detect mass extinction events from phylogenies with only extant taxa
Mass extinction events (MEEs), defined as significant losses of species diversity in significantly short time periods, have attracted the attention of biologists because of their link to major environmental change. MEEs have traditionally been studied through the fossil record, but the development of birth-death models has made it possible to detect their signature based on extant-taxa phylogenies. Most birth-death models consider MEEs as instantaneous events where a high proportion of species are simultaneously removed from the tree ("single pulse" approach), in contrast to the paleontological record, where MEEs have a time-duration. Here, we explore the power of a Bayesian Birth-Death Skyline (BDSKY) model to detect the signature of MEEs through changes in extinction rates under a "time-slice" approach. In this approach, MEEs are time intervals where the extinction rate is greater than the speciation rate. Results showed BDSKY can detect and locate MEEs but that precision and accuracy depend on phylogenies size and MEE intensity. Comparisons of BDSKY with the single-pulse Bayesian model, CoMET, showed a similar frequency of Type II error and neither model exhibited Type I error. However, while CoMET performed better in detecting and locating MEEs for smaller phylogenies, BDSKY showed higher accuracy in estimating extinction and speciation rates.
Detecting Code Smells in React-based Web Systems
<p>Dataset - Detecting Code Smells in React-based Web Systems</p>
Detecting Code Smells in React-based Web Systems
<p>Detecting Code Smells in React-based Web Systems</p>
GPS tracks and behaviour detection of chick-rearing streaked shearwaters at Funakoshi Oshima Island, Japan, 2018 & 2019
<p>The study of seabird behaviour has largely relied on animal-borne tags to gather information, requiring interpretation to estimate at-sea behaviours. Details of shallow-diving birds' foraging are less known than deep-diving species due to difficulty in identifying shallow dives from biologging devices. Development of smaller video loggers allow a direct view of these birds' behaviours, at the cost of short battery life. However, recordings from video loggers combined with relatively low power usage accelerometers give a means to develop a reliable foraging detection method. Combined video and acceleration loggers were attached to streaked shearwaters in Funakoshi-Ohshima Island (39'N,141'59''E) during the breeding season in 2018. Video recordings were classified into behaviours and a detection method was generated from the acceleration signals. Two foraging behaviours, surface seizing and foraging dives, are reported with video recordings. Surface seizing was comprised of successive take-offs and landings (mean duration 0.6 and 1.5s, respectively), while foraging dives were shallow subsurface dives (1.9s mean duration) from the air and water surface. Birds were observed foraging close to marine predators, including dolphins and large fish. Foraging detections were validated against video recordings (surface seizing true positive 78%, false positive 5%, foraging dive true positive 66%, false positive <1%). The detection method was implemented to data from longer duration acceleration and GPS datasets collected during the 2018 and 2019 breeding seasons. Foraging trips lasted between 1-8 days, with birds performing on average 16 surface seizing events and 43 foraging dives per day, comprising <1% of daily activity, while transit and rest took up 55% and 39%, respectively. This foraging detection method can address the difficulties of recording shallow-diving foraging behaviour and provides a means to measure activity budgets across shallow diving seabird species.</p>
Data from: Quantitative detection of rare interphase chromosome breaks and translocations by high-throughput imaging
We report a method for the sensitive detection of rare chromosome breaks and translocations in interphase cells. HiBA-FISH (High-throughput break-apart FISH) combines high-throughput imaging with the measurement of the spatial separation of FISH probes flanking target genome regions of interest. As proof-of-principle, we apply hiBA-FISH to detect with high sensitivity and specificity rare chromosome breaks and translocations in the anaplastic large cell lymphoma breakpoint regions of NPM1 and ALK. This method complements existing approaches to detect translocations by overcoming the need for precise knowledge of translocation breakpoints and it extends traditional FISH by its quantitative nature.
Data from: Detecting adaptive convergent amino acid evolution
In evolutionary genomics, researchers have taken an interest in identifying substitutions that subtend convergent phenotypic adaptations. This is a difficult question that requires distinguishing foreground convergent substitutions that are involved in the convergent phenotype from background convergent substitutions. Those may be linked to other adaptations, may be neutral or may be the consequence of mutational biases. Furthermore, there is no generally accepted definition of convergent substitutions. Various methods that use different definitions have been proposed in the literature, resulting in different sets of candidate foreground convergent substitutions. In this article, we first describe the processes that can generate foreground convergent substitutions in coding sequences, separating adaptive from non-adaptive processes. Second, we review methods that have been proposed to detect foreground convergent substitutions in coding sequences and expose the assumptions that underlie them. Finally, we examine their power on simulations of convergent changes—including in the presence of a change in the efficacy of selection—and on empirical alignments.
Detection histories for Lycalopex griseus, Lycalopex culpaeus, Leopardus guigna, and Canis familiaris and habitat destruction covariate data for Los Lagos Chile (2019)
<p>In an increasingly anthropogenic world, species face multiple interacting threats. Habitat fragmentation and domestic dogs are two perturbations threatening terrestrial mammals globally. Our aim was to determine if (1) the spatial use of domestic dogs increases with habitat destruction and (2) whether domestic dogs and habitat destruction drive the spatial use of native carnivores in a heavily degraded agricultural landscape in the central valley/Andean foothills transition of Los Lagos, Chile.</p> <p>We implemented a camera trap survey in a fragmented landscape comprised of native forest patches amidst a matrix of pastureland. We used single-species occupancy models to assess the impact of domestic dogs and habitat destruction on three mesocarnivores – the foxes, culpeo (<i>Lycalopex culpaeus</i>) and chilla (<i>Lycalopex griseus</i>) and the wild cat güiña (<i>Leopardus guigna</i>). Additionally, we compared temporal overlap of all study species (including domestic dogs).</p> <p>Detection rates for both the foxes increased with domestic dog occupancy, while factors driving occupancy differed for each of the native species. We found that a 12% projected increase in domestic dog occupancy negatively impacted the spatial use of the culpeo. Habitat loss and fragmentation were positive drivers for chilla and domestic dog occupancy. The güiña did not respond to fragmentation and other habitat covariates or domestic dog occupancy. All native carnivore species were primarily nocturnal, while the domestic dog was almost entirely diurnal.</p>
Supplementary material 3 from: Macher T-H, Schütz R, Arle J, Beermann AJ, Koschorreck J, Leese F (2021) Beyond fish eDNA metabarcoding: Field replicates disproportionately improve the detection of stream associated vertebrate species. Metabarcoding and Metagenomics 5: e66557. https://doi.org/10.3897/mbmg.5.66557
Table S3. Filtered taXon table
Supplementary material 2 from: Macher T-H, Schütz R, Arle J, Beermann AJ, Koschorreck J, Leese F (2021) Beyond fish eDNA metabarcoding: Field replicates disproportionately improve the detection of stream associated vertebrate species. Metabarcoding and Metagenomics 5: e66557. https://doi.org/10.3897/mbmg.5.66557
Table S2. Raw taXon table as created with TaxonTableTools
Supplementary material 1 from: Macher T-H, Schütz R, Arle J, Beermann AJ, Koschorreck J, Leese F (2021) Beyond fish eDNA metabarcoding: Field replicates disproportionately improve the detection of stream associated vertebrate species. Metabarcoding and Metagenomics 5: e66557. https://doi.org/10.3897/mbmg.5.66557
Table S1. BLAST taxonomy table
Supplementary material 5 from: Macher T-H, Schütz R, Arle J, Beermann AJ, Koschorreck J, Leese F (2021) Beyond fish eDNA metabarcoding: Field replicates disproportionately improve the detection of stream associated vertebrate species. Metabarcoding and Metagenomics 5: e66557. https://doi.org/10.3897/mbmg.5.66557
Figure S2
Supplementary material 4 from: Macher T-H, Schütz R, Arle J, Beermann AJ, Koschorreck J, Leese F (2021) Beyond fish eDNA metabarcoding: Field replicates disproportionately improve the detection of stream associated vertebrate species. Metabarcoding and Metagenomics 5: e66557. https://doi.org/10.3897/mbmg.5.66557
Figure S1
ScienceDex guides
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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.