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9,302 results for “DETECT”
FIGURE 2 in Detection of two cryptic taxa in Meristogenys amoropalamus (Amphibia, Ranidae) through nuclear and mitochondrial DNA analyses
FIGURE 2. Distributions of haplotype frequencies of POMC for three lineages of M. cf. amoropalamus. The size of each circle indicates the number of samples examined from each locality. 1, Wario; 2, Poring, 3, Mesilau; 4, Kamborangah; 5, Liwagu; 6, Bundu Tuhan; 7, Mahua. KNP and CRNP indicate Kinabalu National Park and Crocker Range National Park, respectively.
FIGURE 1 in Detection of two cryptic taxa in Meristogenys amoropalamus (Amphibia, Ranidae) through nuclear and mitochondrial DNA analyses
FIGURE 1. Neighbor-joining tree of a 963-bp sequence of mitochondrial 12S rRNA and Cytb (left) and 1313-bp sequences of nuclear POMC, Rag-1, and rhodopsin (right) for species of Meristogenys. Numbers above or below branches represent bootstrap support for the NJ/MP inference for the respective clade (both 1000 replicates). Nodes with asterisks indicate significant support (>95%) by Bayesian inference. Lineage names were designated by Shimada et al. (2007) and the present study. The haplotypes of M. cf. amoropalamus are given using the locality name; Bu: Bundu Tuhan, Ka: Kamborangah, Li: Liwagu, Ma: Mahua, Me: Mesilau, Po: Poring, Wa: Wario. The number of adult and larval specimens of each haplotype of M. cf. amoropalamus is shown in parenthesis.
Duplicate Bug Report Detection using an Attention-basedPre-trained Neural Language Model
<p>A BERT based Approach for Automatic Duplicate Bug Report Detection</p>
Increasing the sensitivity of hyperpolarized [15N2]urea detection by serial transfer of polarization to spin-coupled protons
<p>This upload contains the raw data, MATLAB scripts and Mathematica notebooks used for the publication.</p> <p>Figure 2, 3, 7 and 8 where generated with the Mathematica notebooks in the folder Mathematica_Notebooks.</p> <p>Figure 4 was generated with the raw data in NOE_data/20190821 and processed with the script NOE_data/20190821/H1to15NNOEin15N2urea.m</p> <p>Figure 5a is based on data in Polarization_Transfer_Data/spectral/timecourse/_fk_BHINDER_flipback_15Nto1H_hyp_20180609_01.fid processed with Polarization_Transfer_Data/spectral/timecourse/_fk_BHINDER_flipback_15Nto1H_hyp_20180609_01.fid/timecourse.m</p> <p>Figure 5b is based on Polarization_Transfer_Data/imaging/_fk_BHINDER_flipback_EPIP_hyp_10perc_20180531_01.img . From this raw data image2 - image60 are acquired after polarisation transfer. Image1 is a direct acquisition of the water resonance .</p> <p>The brightness and contrast of the images was adjusted and a montage of images 2 to 9 was created with (Fiji <a href="https://imagej.net/software/fiji/">https://imagej.net/software/fiji/</a>) .</p> <p>Figure 6 is based on data in Polarization_Transfer_Data/spectral/interleaved processed with Polarization_Transfer_Data/spectral/interleaved/comparision.m</p> <p>The script Pulse_Generation/createIRRUPT.m was used to generate the adiabatic pulses.</p>
Digital breast tomosynthesis and contrast-enhanced dual-energy digital mammography alone and in combination compared to 2D digital synthetized mammography and MR imaging in breast cancer detection and classification
<p>We uploaded the dataset of included patients of manuscript: Petrillo A, Fusco R, Vallone P, Filice S, Granata V, Petrosino T, Rosaria Rubulotta M, Setola SV, Mattace Raso M, Maio F, Raiano C, Siani C, Di Bonito M, Botti G. Digital breast tomosynthesis and contrast-enhanced dual-energy digital mammography alone and in combination compared to 2D digital synthetized mammography and MR imaging in breast cancer detection and classification. Breast J. 2020 May;26(5):860-872. doi: 10.1111/tbj.13739. Epub 2019 Dec 30. PMID: 31886607.</p>
Diffusion-Weighted MRI and Diffusion Kurtosis Imaging to Detect RAS Mutation in Colorectal Liver Metastasis
<p>We uploaded dataset including apparent diffusion coefficient (ADC), basal signal (S0), pseudo-diffusion coefficient (DP), perfusion fraction (FP), tissue diffusivity (DT) and DKI data (mean of diffusion coefficient (MD) and mean of diffusional Kurtosis (MK)) of 52 patients of the manuscript: Diffusion-Weighted MRI and Diffusion Kurtosis Imaging to Detect RAS Mutation in Colorectal Liver Metastasis. Granata V, Fusco R, Risi C, Ottaiano A, Avallone A, De Stefano A, Grimm R, Grassi R, Brunese L, Izzo F, Petrillo A. Diffusion-Weighted MRI and Diffusion Kurtosis Imaging to Detect RAS Mutation in Colorectal Liver Metastasis. Cancers (Basel). 2020 Aug 26;12(9):2420. doi: 10.3390/cancers12092420. PMID: 32858990; PMCID: PMC7565693.</p>
Collective detection based on visual information in animal groups
<p></p><p> We investigate key principles underlying individual, and collective, visual detection of stimuli, and how this relates to the internal structure of groups. While the individual and collective detection principles are generally applicable, we employ a model experimental system of schooling golden shiner fish ( Notemigonus crysoleucas ) to relate theory directly to empirical data, using computational reconstruction of the visual fields of all individuals. This reveals how the external visual information available to each group member depends on the number of individuals in the group, the position within the group, and the location of the external visually detectable stimulus. We find that in small groups, individuals have detection capability in nearly all directions, while in large groups, occlusion by neighbours causes detection capability to vary with position within the group. To understand the principles that drive detection in groups, we formulate a simple, and generally applicable, model that captures how visual detection properties emerge due to geometric scaling of the space occupied by the group and occlusion caused by neighbours. We employ these insights to discuss principles that extend beyond our specific system, such as how collective detection depends on individual body shape, and the size and structure of the group. </p><p></p>
Sling load - Dataset of software tests on column detection algorithm with laserscanner data
<p>This dataset contains data and scripts for plotting results of testing of the algorithm of column detection, using laserscanner data.</p>
Figure 4 in Distal leg morphology, subgenual organs and host detection in Stephanidae (Insecta, Hymenoptera)
Figure 4. Megischus spp., female. (A) Hind leg, median view. (B) Hind tibia proximally at level of subgenual organ, lateral view. (C) Hind tarsus, tarsomeres 1–3 (different species from the one illustrated in Figure 4A).
Figure 1 in Distal leg morphology, subgenual organs and host detection in Stephanidae (Insecta, Hymenoptera)
Figure 1. Schlettererius cinctipes, female, foreleg and mid-leg. (A) Fore leg, median view. (B) Basal part of fore tibia, median view, with dome concealing subgenual organ. (C) Fore tarsus, tarsomeres 2–5. (D) Mid leg, lateral view.
Figure 6 in Distal leg morphology, subgenual organs and host detection in Stephanidae (Insecta, Hymenoptera)
Figure 6. Parastephanellus sp., female ovipositing. Redrawn after Rodd (1951, figures 1 and 3). Early (top) and late (bottom) stage. X indicates the point of entry of the ovipositor into the wood, triangles the position of the subgenual organs in the different legs.
CNN for image-based sediment detection applied to a large terrestrial and airborne dataset
<p>This repository contains data sets and model used in the manuscript "CNN for image-based sediment detection applied to a large terrestrial and airborne dataset" submitted to Earth Surface Dynamics (2021) by Xingyu Chen, Marwan A. Hassan, and Xudong Fu.</p>
Fig. 1 in First Detection of the Exotic Longhorn Beetle Batocera parryi (Hope) (Coleoptera: Cerambycidae) in Europe
Fig. 1. Habitus (dorsal, lateral, and ventral views, respectively) of the female specimen of Batocera parryi captured in northern Spain during October 2014.
Fig 1. Ernobius mollis. A in Ernobius mollis (Linnaeus) (Coleoptera: Ptinidae: Ernobiinae), an Adventive Wood-Boring Beetle Detected in Western Canada
Fig 1. Ernobius mollis. A) Dorsal habitus (modified from Klimaszewski et al. 2017), B) Aedeagus, parameral and lateral view (left to right). Scale bars = 1 mm (A), 0.5 mm (B).
Data of Black-Box Side-Channel Detection and Mitigation for Internet of Things
<p>This repository contains the datasets used in the experiments of the paper "Black-Box Side-Channel Detection and Mitigation for Internet of Things" and the results of the experiments.</p>
Learning-based landmark detection in pelvis X-Rays with attention mechanism: Data from the Osteoarthritis Initiative
<p>We proposed a new landmark detection task based on the data from Osteoarthritis Initiative. We chose high-quality 544 images taken by the Swissray scanner from OAI bone image dataset. Images of the patients with hip replacement were excluded owing to the lack of anatomical structures that must be predicted, and some of the existing images that influenced the doctors labeling the landmarks were also excluded. In total, data of 524 subjects were collected to develop and validate a deep learning system to predict the landmarks in the pelvis images. According to our research problem, we invited professional doctors to label femoral head center, upper acetabulum rim, innermost point of subchondral sclerosis and teardrop in the pelvis X-Ray, as shown in the Figure 4. Total images were annotated by a radiologist with 7 years of working experience from the First Hospital of Jilin University, and the anatomical positions labeled were reviewed by orthopedic surgeon with 7 years of working experience from the Second Hospital of Jilin University.</p> <p>There are two files in this dataset. "Coordinates_information.xlsx" stored the coordinates of landmarks labeled by radiologist. "original_information.xlsx" was download from Osteoarthritis Initiative, which stored the basic information about X-rays.</p>
Data from: Using singleton densities to detect recent selection in Bos taurus
<p>Many quantitative traits are subject to polygenic selection, where several genomic regions undergo small, simultaneous changes in allele frequency that collectively alter a phenotype. The widespread availability of genome data, along with novel statistical techniques, has made it easier to detect these changes. We apply one such method, the 'Singleton Density Score', to the Holstein breed of<i> Bos taurus</i> to detect recent selection (arising up to around 740 years ago). We identify several genes as candidates for targets of recent selection, including some relating to cell regulation, catabolic processes, neural-cell adhesion and immunity. We do not find strong evidence that three traits that are important to humans – milk protein content, milk fat content, and stature – have been subject to directional selection. Simulations demonstrate that since <i>B. taurus</i> recently experienced a population bottleneck, singleton numbers are reduced limiting the power of SDS methods. These results inform on which genes underlie recent genetic change in <i>B. </i><i>taurus</i>, while providing information on how polygenic selection can be best investigated in future studies.</p>
Multiple Sclerosis lesions detection by a hybrid Watershed-Clustering algorithm
<p>Computer Aided Diagnosis (CAD) systems have been developing in the last years with the aim of helping the diagnosis and monitoring of several diseases. We present a novel CAD system based on a hybrid Watershed-Clustering algorithm for the detection of lesions in Multiple Sclerosis. Magnetic Resonance Imaging scans (FLAIR sequences without gadolinium) of 20 patients affected by Multiple Sclerosis with hyperintense lesions were studied. The CAD system consisted of the following automated processing steps: images recording, automated segmentation based on the Watershed algorithm, detection of lesions, extraction of both dynamic and morphological features, and classification of lesions by Cluster Analysis. The investigation was performed on 316 suspect regions including 255 lesion and 61 non-lesion cases. The Receiver Operating Characteristic analysis revealed a highly significant difference between lesions and non-lesions; the diagnostic accuracy was 87% (95% CI: 0.83–0.90), with an appropriate cut-off of 192.8; the sensitivity was 77% and the specificity was 87%. In conclusion, we developed a CAD system by using a modified algorithm for automated image segmentation which may discriminate MS lesions from non-lesions. The proposed method generates a detection out-put that may be support the clinical evaluation.</p>
Individual TE expression detection
<p>This data set was used to study and compare tools with respect to detecting the expression of transposable elements at the level of individual instances.</p>
Graphical integrity issues in open access publications: detection and patterns of proportional ink violations
<p>Here are all the bar charts we annotated manually and used to train the graphical integrity detector. For more detail about our method, please visit our GitHub repository. (https://github.com/sciosci/Graphical_Integrity_Issues)</p>
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.