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9,302 results for “DETECT”
ADS-B anomaly detection in the surveillance of low-altitude aircrafts
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Data supporting: Multiple input tangential interpolation-driven damage detection of a jet trainer aircraft
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MuGNN: API Misuse Detection using Graph Neural Networks and Clustering
<div> <div>This artifact presents `MuGNN`, a novel framework for efficiently detecting API misuse in Java code. The approach leverages a `Graph Neural Network (GNN)` model to generate embeddings of Java API usage code using a custom `API Flow Graph (AFG)` representation. This representation captures execution sequences, data flow, and control flow, enabling better understanding of API usage patterns. MuGNN employs self-supervised pre-training and clustering to analyze API usage and identify potential misuse.</div> </div>
Figure 6 from: Edgecombe G, Lopez Gutierrez B, MacLeod N (2011) Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda). ZooKeys 156: 49-66. https://doi.org/10.3897/zookeys.156.1997
Figure 6 - Strobe models of five positions along the canonical variates indicated in Fig. 5. CV-1, CV-2, and CV-3 axes account for 79.5% of the observed between-species shape variation. Landmarks and semi-landmarks are superimposed in the figure to the right of each sequence to express the magnitudes and directions (arrows) of shape trends. In all models, the mesial margin of the coxa is depicted to the left, the lateral margin to the right.
Figure 5 from: Edgecombe G, Lopez Gutierrez B, MacLeod N (2011) Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda). ZooKeys 156: 49-66. https://doi.org/10.3897/zookeys.156.1997
Figure 5 - Results of the CVA of coxal shape data for all eight species, showing the subspaces formed by the first three discriminant axes, which together account for more than 79% of observed between-group shape variation. Within each subspace plot the black circles represent the coordinate locations for each of the five along-axis shape models depicted in Fig. 6.
Figure 4 from: Edgecombe G, Lopez Gutierrez B, MacLeod N (2011) Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda). ZooKeys 156: 49-66. https://doi.org/10.3897/zookeys.156.1997
Figure 4 - Scatterplots of Procrustes PCA scores for coxal shape data. The first two shape variation axes (top) together account for 62.63% of the observed shape variation; PC-2 and PC-3 axes (bottom) together account for 27.58% of the observed shape variation.
Figure 3 from: Edgecombe G, Lopez Gutierrez B, MacLeod N (2011) Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda). ZooKeys 156: 49-66. https://doi.org/10.3897/zookeys.156.1997
Figure 3 - Thereuopoda longicornis scatterplot of coxal shape data along the discriminant subspace formed by the first two CV axes, which together account for 74.17% of observed between-group shape variation.
Figure 2 from: Edgecombe G, Lopez Gutierrez B, MacLeod N (2011) Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda). ZooKeys 156: 49-66. https://doi.org/10.3897/zookeys.156.1997
Figure 2 - Landmarks (L1-L10) used in morphometric analysis. Diagonal line to L1 is the longest line from anterolateral to posteromedial corners of the coxa. Spine-bristles numbered 1-4 (blue) from interior to exterior. Throughout text, left and right coxae refer to dorsal orientation (inverted 180° relative to this ventral view).
Figure 1 from: Edgecombe G, Lopez Gutierrez B, MacLeod N (2011) Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda). ZooKeys 156: 49-66. https://doi.org/10.3897/zookeys.156.1997
Figure 1 - Ventral view of head and forcipules of Thereuopoda longicornis placed in a standard horizontal position. BM 1952.9.8.574-575, Kuching, Sarawak, Malaysia.
Figure 1 from: Rutledge C, Fierke M, Careless P, Worthley T (2013) First detection of Agrilus planipennis in Connecticut made by monitoring Cerceris fumipennis (Crabronidae) colonies. Journal of Hymenoptera Research 32: 75-81. https://doi.org/10.3897/jhr.32.4865
Figure 1 - Abandoned emerald ash borer next to the nest entrance of Cerceris fumipennis at a colony in Prospect CT.
Plot folder contents for DETECT model v1.0
<p>These files contain the Matlab script files for plotting time-series of soil CO2 at different depths and soil respiration from the DETECT model. The model is described in the paper:</p> <p>Ryan, E. M., K. Ogle, H. Kropp, Y. Carrillo, K. E. Samuels-Crow, E. Pendall (in review) Modelling soil CO2 production and transport with dynamic source and diffusion terms: Testing the steady-state assumption using DETECT v1.0<em>. </em>In Geoscientific Model Development.</p>
Figure 2 from: Degawa Y, Hosoya T, Hosaka K, Hirayama Y, Saito Y, Zhao Y-J (2015) Rediscovery of Roesleria subterranea from Japan with a discussion of its infraspecific relationships detected using molecular analysis. MycoKeys 9: 1-9. https://doi.org/10.3897/mycokeys.9.6564
Figure 2 - A single most parsimonious tree of Roesleria subterranea (ITS-5.8S sequences). Tree length = 31, CI = 0.8065, HI = 0.1935, RI = 0.8723, RC = 0.7035. The tree was constructed from a dataset of all available ITS-5.8S sequences of Roesleria subterranea from various hosts and localities. The numbers on the branches indicate the bootstrap values (BP) of 1000 replications in maximum parsimony analysis when the BP exceeds 50%. The scale bar indicates the number of substitutions. Abbreviations indicate the localities of the isolates: AU, Austria; GE, German; IT, Italy; NE, Netherlands; US, USA; JP, Japan.
Figure 1 from: Degawa Y, Hosoya T, Hosaka K, Hirayama Y, Saito Y, Zhao Y-J (2015) Rediscovery of Roesleria subterranea from Japan with a discussion of its infraspecific relationships detected using molecular analysis. MycoKeys 9: 1-9. https://doi.org/10.3897/mycokeys.9.6564
Figure 1 - Roesleria subterranea (TNS-F-38701). 1 Growing habits of two mature mazaedia on the root tip of an unidentified tree 2 Hymenium of mazaedium, showing protruding paraphyses 3 Enlarged hymenium showing developmental stages of asci and paraphyses 4 Mature ascus containing eight ascospores 5 Paraphysis 6 Discharged matured ascospores; on the right, a transversely one-septate spore is shown in side view 7 Germination of ascospores on MEYE, showing that germination tubes from one of the two-celled spores 2–6 Mounted in Meltzer's solution 7 Mounted in lacto-aniline blue. Bars: 1 = 2 mm; 2 = 50 ?m; 3–7 = 10 ?m.
Figure 1 from: Hartgerink C, Wicherts J, van Assen M (2016) The value of statistical tools to detect data fabrication. Research Ideas and Outcomes 2: e8860. https://doi.org/10.3897/rio.2.e8860
Figure 1 - The applied statistical methods to test for data fabrication in Project 1, depicting those that are combined into an overall test for data fabrication with the Fisher method. Benford's law is excluded from the overall tests because of an expected lack of utility.
Figure 2 from: Hartgerink C, Wicherts J, van Assen M (2016) The value of statistical tools to detect data fabrication. Research Ideas and Outcomes 2: e8860. https://doi.org/10.3897/rio.2.e8860
Figure 2 - Scatterplot reporting the accompanying correlation value. The raw data for variables X and Y is available in the individual points and can be extracted. Statistical methods such as terminal digit analysis can be applied to these raw data to detect data anomalies.
Figure 1 from: Rosenblad MA, Martín MP, Tedersoo L, Ryberg M, Larsson E, Wurzbacher C, Abarenkov K, Nilsson RH (2016) Detection of signal recognition particle (SRP) RNAs in the nuclear ribosomal internal transcribed spacer 1 (ITS1) of three lineages of ectomycorrhizal fungi (Agaricomycetes, Basidiomycota). MycoKeys 13: 21-33. https://doi.org/10.3897/mycokeys.13.8579
Figure 1 - Schematic illustration of the fungal ITS region and neighboring rDNA genes. The subregions ITS1, 5.8S, and ITS2 of the ITS region are indicated along with the SRP RNA in the first part of the ITS1. The absolute positions of the subregions and the SRP RNA are provided in Suppl. material 2.
Figure 3 from: Bidondo LF, Colombo RP, Recchi M, Silvani VA, Pérgola M, Martínez A, Godeas AM (2018) Detection of arbuscular mycorrhizal fungi associated with pecan (Carya illinoinensis) trees by molecular and morphological approaches. MycoKeys 42: 73-88. https://doi.org/10.3897/mycokeys.42.26118
Figure 3 Arbuscular mycorrhizal intraradical colonisation in pecan roots (a–b). A: arbuscules, AP: appressoria, ILH: intraradical longitudinal hyphae. Spore of Claroideoglomuslamellosum (c), Entrophosporainfrequens (d), Cetrasporapellucida (e), Rhizoglomusmicroaggregatum (f) inside another, dead AMF spore, resembling E.infrequens.
Figure 2 from: Bidondo LF, Colombo RP, Recchi M, Silvani VA, Pérgola M, Martínez A, Godeas AM (2018) Detection of arbuscular mycorrhizal fungi associated with pecan (Carya illinoinensis) trees by molecular and morphological approaches. MycoKeys 42: 73-88. https://doi.org/10.3897/mycokeys.42.26118
Figure 2 Rank-abundance diagrams of morphological AM fungal species (a–b) and rank-abundance diagrams of AM MOTUs (c–d) detected on C.illinoinensis rhizosphere in T0 (field) and T1 (containers) samples. RA: Relative abundance. Cl: Claroideoglomuslamellosum, Rm: Rhizoglomusmicroaggregatum, Fc: Funneliformiscoronatum, Ei: Entrophosporainfrequens, Fm: Funneliformismosseae, Gi Gigasporamargarita, Ce: Claroideoglomusetunicatum, Cp: Cetrasporapellucida, De: Diversisporaeburnea, Fr: Fuscutatarubra, Sc: Septoglomusconstrictum, Re: Rhizoglomusirregulare, Ri: Rhizoglomusintraradices, Di: Dominikiairanica, Da: Dominikiaindica, Gsp: Glomus sp.
Figure 1 from: Bidondo LF, Colombo RP, Recchi M, Silvani VA, Pérgola M, Martínez A, Godeas AM (2018) Detection of arbuscular mycorrhizal fungi associated with pecan (Carya illinoinensis) trees by molecular and morphological approaches. MycoKeys 42: 73-88. https://doi.org/10.3897/mycokeys.42.26118
Figure 1 Morphological AM fungal species accumulation curves (a) and rarefaction curves (± errors, ten replicates for each subset) of observed AM MOTUs (b) detected on C.illinoinensis rhizosphere in T0 (field) and T1 (containers) samples.
Detecting and removing sample contamination in phylogenomic data: An example and its implications for Cicadidae phylogeny (Insecta: Hemiptera)
<p class="MsoNormal">Contamination of a genetic sample with DNA from one or more non-target species is a continuing concern of molecular phylogenetic studies, both Sanger sequencing studies and Next-Generation Sequencing (NGS) studies. We developed an automated pipeline for identifying and excluding likely cross-contaminated loci based on detection of bimodal distributions of patristic distances across gene trees. When the contamination occurs between samples within a dataset, comparisons between a contaminated sample and its contaminant taxon will yield bimodal distributions with one peak close to zero patristic distance. Here we present an automated pipeline for identifying and excluding likely cross-contaminated loci based on detection of these bimodal distributions of patristic distances between taxa across gene trees. This new method does not rely on <em>a priori</em> knowledge of taxon relatedness nor does it determine the process(es) that caused the contamination. Exclusion of putatively contaminated loci from a dataset generated for the insect family Cicadidae showed that these sequences were affecting some topological patterns and branch supports, although the effects were sometimes subtle, with some contamination-influenced relationships exhibiting strong bootstrap support. Long tip branches and outlier values for one anchored phylogenomic pipeline statistic (AvgNHomologs) were correlated with the presence of contamination. While the AHE markers used here, which target hemipteroid taxa, proved effective in resolving deep and shallow level Cicadidae relationships in aggregate, individual markers contained inadequate phylogenetic signal, in part probably due to short length. The cleaned dataset, consisting of 90 genera representing 44 of 56 current Cicadidae tribes, and 429 loci, supported three of the four sampled Cicadidae subfamilies in concatenated-matrix (IQ-TREE ML) and multispecies coalescent-based (ASTRAL-III) species tree analyses, with the fourth subfamily weakly supported in the ML trees. No well-supported patterns from previous family-level Sanger sequencing studies of Cicadidae phylogeny were contradicted. One taxon (<em>Aragualna plenalinea</em>) did not fall with its current subfamily in the genetic tree, and this genus and its tribe Aragualnini is reclassified to Tibicininae following morphological re-examination. Only subtle differences were observed in trees after removal of loci for which divergent base frequencies were detected. Greater success may be achieved by increased taxon sampling and developing a probe set targeting a more recent common ancestor and longer loci. Searches for contamination are an essential step in phylogenomic analyses of all kinds and our pipeline is an effective solution.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.