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29 results for “detection probability”
Occupancy and detection probability of American Three-toed Woodpecker in a heavily-managed boreal forest of eastern Canada
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Calculations for: Detector dog work assessing probability of detection for Yellow crazy ant
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Data from: Using areas of known occupancy to identify sources of variation in detection probability of raptors: taking time lowers replication effort for surveys
Species occurring at low density can be difficult to detect and if not properly accounted for, imperfect detection will lead to inaccurate estimates of occupancy. Understanding sources of variation in detection probability and how they can be managed is a key part of monitoring. We used sightings data of a low-density and elusive raptor (white-headed vulture Trigonoceps occipitalis) in areas of known occupancy (breeding territories) in a likelihood-based modelling approach to calculate detection probability and the factors affecting it. Because occupancy was known a priori to be 100%, we fixed the model occupancy parameter to 1.0 and focused on identifying sources of variation in detection probability. Using detection histories from 359 territory visits, we assessed nine covariates in 29 candidate models. The model with the highest support indicated that observer speed during a survey, combined with temporal covariates such as time of year and length of time within a territory, had the highest influence on the detection probability. Averaged detection probability was 0.207 (s.e. 0.033) and based on this the mean number of visits required to determine within 95% confidence that white-headed vultures are absent from a breeding area is 13 (95% CI: 9–20). Topographical and habitat covariates contributed little to the best models and had little effect on detection probability. We highlight that low detection probabilities of some species means that emphasizing habitat covariates could lead to spurious results in occupancy models that do not also incorporate temporal components. While variation in detection probability is complex and influenced by effects at both temporal and spatial scales, temporal covariates can and should be controlled as part of robust survey methods. Our results emphasize the importance of accounting for detection probability in occupancy studies, particularly during presence/absence studies for species such as raptors that are widespread and occur at low densities.
Data from: Using areas of known occupancy to identify sources of variation in detection probability of raptors: taking time lowers replication effort for surveys
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Data from: Simultaneous estimation of occupancy and detection probabilities an illustration using Cincinnatian brachiopods
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Transmicron: Accurate prediction of insertion probabilities improves detection of cancer driver genes from transposon mutagenesis screens
GEO Series GSE214379. Mus musculus. 64 samples. Type: Other.
Single-cell absolute contact probability detection reveals chromosomes are organized by multiple low-frequency yet specific interactions
GEO Series GSE104961. Drosophila melanogaster. 3 samples. Type: Other.
PROBABILITY CALIBRATION BY THE MINIMUM AND MAXIMUM PROBABILITY SCORES IN ONE-CLASS BAYES LEARNING FOR ANOMALY DETECTION
PROBABILITY CALIBRATION BY THE MINIMUM AND MAXIMUM PROBABILITY SCORES IN ONE-CLASS BAYES LEARNING FOR ANOMALY DETECTION GUICHONG LI, NATHALIE JAPKOWICZ, IAN HOFFMAN, R. KURT UNGAR ABSTRACT. One-class Bayes learning such as one-class Naïve Bayes and one-class Bayesian Network employs Bayes learning to build a classifier on the positive class only for discriminating the positive class and the negative class. It has been applied to anomaly detection for identifying abnormal behaviors that deviate from normal behaviors. Because one-class Bayes classifiers can produce probability score, which can be used for defining anomaly score for anomaly detection, they are preferable in many practical applications as compared with other one-class learning techniques. However, previously proposed one-class Bayes classifiers might suffer from poor probability estimation when the negative training examples are unavailable. In this paper, we propose a new method to improve the probability estimation. The improved one-class Bayes classifiers can exhibits high performance as compared with previously proposed one-class Bayes classifiers according to our empirical results.
ECG-based score estimates the probability to detect Fabry Disease cardiac involvement
<p>Figliozzi S, Camporeale A, Boveri S, Pieruzzi F, Pieroni M, Lusardi P, Spada M, Mignani R, Burlina A, Graziani F, Pica S, Tondi L, Bernardini A, Chow K, Namdar M, Lombardi M. ECG-based score estimates the probability to detect Fabry Disease cardiac involvement. Int J Cardiol. 2021 Sep 15;339:110-117. doi: 10.1016/j.ijcard.2021.07.022. Epub 2021 Jul 16. PMID: 34274410.</p> <p>Abstract</p> <p><strong>Objectives: </strong>To elaborate an ECG-based nomogram estimating the probability to detect cardiac involvement by cardiac magnetic resonance (CMR) in Fabry Disease (FD).</p> <p><strong>Methods: </strong>119 FD patients and 26 healthy controls underwent ECG and CMR. Test (n = 88, 60%) and validation cohorts (n = 57, 40%) were randomly derived. Cardiac involvement was defined as the presence of low myocardial T1 value, a CMR-surrogate of myocardial glycosphingolipid storage. ECG changes associated with low T1 value were identified in the test cohort, included in the nomogram and then tested in the validation cohort.</p> <p><strong>Results: </strong>Sokolow-Lyon index (AUC = 0.769), ratio between P-wave and PR-segment durations (Pwave/PRsegment) (AUC = 0.778), QRS duration (AUC = 0.703), QT (AUC = 0.769) duration were independently associated with the presence of low T1 on CMR at multivariate analysis. An ECG-based nomogram including these four parameters was accurate in identifying patients with CMR evidence of glycosphingolipid storage (c-index of the derived-nomogram = 0.90 in the test group; 0.81 in the validation group).</p> <p><strong>Conclusion: </strong>We propose a practical ECG-based nomogram accurately estimating the probability to detect low T1 values by CMR in FD patients. The application of this tool in clinical practice could improve early detection of FD cardiac involvement.</p>
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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.