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74 results for “dynamic detection”
Raw data from: Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila, part 2
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Data from: Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila
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Raw data from: Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila, part 3
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Data from: Detecting the population dynamics of an autosomal sex-ratio distorter transgene in malaria vector mosquitoes
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Raw data from: Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila, part 1
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Data from: Slow cortical dynamics generate context processing and novelty detection
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Sea otters in a California estuary: Detecting temporal and spatial dynamics with volunteer monitoring
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Data from: Metabarcoding on planktonic larval stages: an efficient approach for detecting and investigating life cycle dynamics of benthic aliens
High-throughput sequencing (HTS) technologies offer new promise to support surveillance programs targeting marine non-indigenous species (NIS). Metabarcoding might surpass traditional monitoring methods, for example through its ability to detect rare species, a key feature in early detection of NIS. Another interest of this approach is the identification of organisms difficult to identify based on morphology only (e.g., early developmental stages), making it relevant in the context of management programs. Because many marine benthic NIS have a bi-phasic bentho-pelagic life cycle, targeting their pelagic larval stages in zooplankton may allow early detection and assessment of their establishment and potential spread. We illustrate this approach with an analysis of bulk-DNA retrieved from a time-series of zooplankton samples collected over 22 months in one bay in Brittany (France). Using HTS of amplicons obtained with two markers (COI and 18S) and a metabarcoding approach, 12 NIS were identified and their temporal larval dynamics were monitored. Importantly, we chose to focus on a closed list of species, from four metazoan classes encompassing 52 NIS reported within the study area or nearby seas, with molecular references available or obtained locally for 42 of them. The use of a custom-designed database allowed the detection of three NIS otherwise not detected when using public databases. Interestingly, NIS known to have a short-lived larval stage were detected (e.g., the bryozoan Bugula neritina or the tunicate Corella eumyota). For two molluscs Ruditapes philippinarum and Crepidula fornicata, metabarcoding results were compared to those obtained using traditional methods (i.e., barcoding of individual larvae and morphology, respectively) to show the reliability of the approach in detecting and assessing the extent of their reproductive periods. Our results also revealed that the Pacific oyster Crassostrea gigas, a notorious invasive species, failed to reproduce in the study bay, showing that metabarcoding on larval stages also provides information regarding the establishment success (or failure) of NIS. While metabarcoding has its limitations and biases, this study demonstrates its effectiveness for surveillance of targeted NIS, notably to support management strategies like the European Marine Strategy Framework Directive (MSFD).
Data from: Crop-associated virus infection in a native perennial grass: reduction in plant fitness and dynamic patterns of virus detection
To understand the eco-evolutionary significance of plant viruses in nature, we must (i) quantify the effects of infection on plant fitness and (ii) recognize that native plants are increasingly exposed to crop-associated viruses. Studies of perennials are particularly needed: most of our knowledge of plant-virus interactions is from annuals, yet long-lived species dominate landscapes. Here we used aster models for life-history analysis and longitudinal measures of plant virus status to evaluate multi-year consequences of crop virus infection in a native perennial. We used Barley yellow dwarf virus acquired from wheat to inoculate seedlings of Panicum virgatum L. (switchgrass), a North American prairie grass. We grew inoculated and mock-inoculated individuals of two ecotypes for 3 years in the field. We measured plant size, infection status and fitness components. Aster modelling provided integrated multi-year measures of fitness. Crop virus inoculation reduced multi-year native plant fitness by 30% over 2 years despite generally asymptomatic infection and evidence of resistance. This reduction was greater than predicted from individual fitness components or most size measures. Ecotypes differed in response, with the lowland ecotype experiencing higher apparent recovery from infection. Virus treatment in the upland ecotype delayed flowering phenology and reduced seed filling. Synthesis. Our use of field experimentation, surveys of plant infection status and aster modelling demonstrates a rigorous and broadly applicable approach for quantifying the effects of viruses and other microbes on multi-year plant fitness. We found that a crop virus had negative multi-year effects on native plant fitness even after infection was no longer detected. Viruses may have substantial effects on native vegetation with domestication of landscapes and agricultural expansion.
Data from: Detecting elusive aspects of wildlife ecology using drones: new insights on the mating dynamics and operational sex ratios of sea turtles
Offspring and breeding (operational) sex ratios (OSR) are a key component of demographic studies. While offspring sex ratios are often relatively easy to measure, measuring OSRs is often far more problematic. Yet highly skewed OSRs, and a lack of male-female encounters, may be an important extinction driver. Using loggerhead sea turtles (Caretta caretta) as a case study, we showed the utility of drones, i.e. unmanned aerial vehicles (UAVs) to distinguish adult males and females in a marine breeding area, using a combination of morphological characteristics (tail length) and behavioural differences (active mating, courting and searching by males versus resting by females). Through repeated surveys, we documented seasonal changes in the OSR. While the number, and ratio, of males and females on the breeding grounds changed massively, the ratio of receptive females (derived from the rate of influx of new individuals to the area) to breeding males remained close to 1:1 for much of the period before nesting commenced. Hence, we show how large imbalances in the number of adult males and females may translate into relatively balanced OSRs. Our results suggest that the departure of males from the breeding grounds is linked to a decline in female receptivity, with female sea turtles being known to store sperm to ensure high clutch fertility throughout the nesting season. In conclusion, while we detected up to three times more females than males at the breeding ground, at present, OSRs appear stable. However, because most males breed annually (versus biannually by females), there might only be ˜100 males in the adult population (i.e. adult sex ratio of 1:7.5), which might become further skewed under expected climate change scenarios; thus, we need to identify the minimum number of males required to prevent extinction. Finally, we highlight the use of UAVs for assessing the mating dynamics of other marine, terrestrial or avian species, in which adults might exhibit visually detectable differences, such as sexual dimorphism, external body characteristics or grouping tendencies.
Dynamics of island mass effect: detection input & output data
<p>"dynamic_IMEdetection.zip" contains a dynamic island mass effect detection software package.</p> <p>To run:</p> <ul> <li>Unzip "dynamic_IMEdetection.zip"</li> <li>Download input data</li> <li>change path, region to process, and options in "main_dynamicIME_detection.m" to work with the location of the unzipped "dynamic_IME_input_output_data" directory.</li> <li>Run "main_dynamicIME_detection.m"</li> </ul> <p>"dynamic_IME_input_output_data.zip" contains the input data and ouput IME and BO masks of the "dynamic_IMEdetection" software for four case studies:</p> <ul> <li>I04 = Rapa Nui</li> <li>I07 = Society Islands</li> <li>I09 = Samoa</li> <li>I18 = Fiji-Tonga</li> </ul> <p>Input data are custom multi-satellite products computed using the custom-made satellite binning software found here: <a href="../doi/10.5281/zenodo.13376824">10.5281/zenodo.13376824</a>.</p> <p>Output IME and BO masks are computed using the dynamic IME detection software (i.e. "dynamic_IMEdetection") which works with the original naming (i.e. "I04", "I07", "I09", and "I18") and subdirectory path of of the share data "dynamic_IME_input_output_data".</p> <p>Output IME/BO mask, chlorophyll concentration, and modelled surface current timeseries in *.gif format:</p> <ul> <li>RapaNui_8Dbin_20160520-0712_20161205-2230_map-time-series.gif = Rapa Nui between 2016/05/20 and 2016/12/05</li> <li>SocietyIsl_8Dbin_20160622-1012_20170209-0945_map-time-series.gif = Society Islands between 2016/06/22 and 2017/02/09</li> <li>Samoa_8Dbin_20160810-1115_20170226-1030_map-time-series.gif = Samoa between 2016/08/10 and 2017/02/26</li> <li>FijiTonga_8Dbin_20170218-1240_20170906-1205_map-time-series.gif = Fiji - Tonga between 2017/02/18 and 2017/09/06</li> </ul>
Ancient geological dynamics impact neutral biodiversity accumulation and are detectable in phylogenetic reconstructions
<p><strong>Aim</strong> Landmasses have been continuously modified by tectonic activity, the breakup and collision of landmasses is thought to have generated or suppressed ecological opportunities, altering the rates of speciation, dispersal and extinction. However, the extent to which the signatures of past geologic events are retained in modern biodiversity patterns - or obliterated by recent ecological dynamics - remains unresolved. We aim to identify the fingerprint of different scenarios of geological activity on phylogenetic trees and geographic range size distributions.<br> <strong>Location</strong> Global.<br> <strong>Time period</strong> Geological time.<br> <strong>Major taxa studied</strong> Theoretical predictions for any taxa.<br> <strong>Methods </strong>We conducted spatially explicit simulations under a neutral model of range evolution, speciation and extinction for three different geological scenarios that differed in their geological histories. We set a limit in the number of populations that locally can coexist which along with the geographic boundaries of landmasses, influences the rate of range expansion.<br> <strong>Results</strong> Our results demonstrate regions of similar size, age and ecological limits will differ in richness and macroevolutionary patterns based solely on the geological history of landmass breakup-collision even in the absence of species' ecological differences i.e., neutrality. When landmasses collide, regional richness is higher, lineages exhibit more similar rates of speciation and phylogenetic trees are more balanced than in the geologically static scenario. Stringent local limits to coexistence yield lower regional diversity but in general do not affect our ability to distinguish geological scenarios.<br> <strong>Main conclusions</strong> These findings provide an alternative explanation for existence of some hotspots of diversity in areas of high geological activity. Although a limit in the number of coexisting species largely influences regional diversity, its contribution to phylogenetic patterns is lower than variation in per-capita rates of speciation and extirpation. Importantly, these findings demonstrate the potential for inferring past geological history from distributions of phylogenies and range sizes.</p>
Change Detection in Dynamic Environments by Means of a Robotic Swarm - Digital Appendix
<p>Digital Appendix for my thesis Change Detection in Dynamic Environments by Means of a Robotic Swarm.</p> <p>In the zip-file you find a README that guides you through the structure and where to find the plots for the respective experiments.</p> <p><strong>Change Detection of Dynamic Environments by Means of a Robotic Swarm - Digital Appendix</strong></p> <p>The following folder structure holds all research data of my conducted experiments(h5-logfiles and plots). The Python-Script "show_h5.py" can be used to read out the logfile in h5-format (<em>$python3 show_h5.py expample_logfilename.h5</em>). However, this shouldn't be necessary because all plots are already generated.</p> <p>To find the results you want to see, this is a small guide through the structure:</p> <ol> <li> <p>First the trials are divided into the respective methods (BOCPD, PELT, DBB, DBBCPD). In the folders you find the experiments for the specific method.</p> </li> <li> <p>In the folders of BOCPD and PELT you find the results for the different feedback types and their combinations. The id for each feedback is noted in parentheses (e.g. XX_(id)_feedback_description). Feedback combinations have their ids added up (e.g. XX_(id1+...+idn)_feedback_description).</p> </li> <li> <p>In the folder to each feedback type the different test trials can be found. This means varying environment difficulties and parameter settings. In the name of the folders this information can be found (e.g. XX_method_environmentdifficulty_parametersetting).</p> </li> </ol> <p>All experiments follow the same procedure as long as it is stated otherwise. Each trial consists of 20 individual runs with a duration of 6000 seconds. At half time (3000 s) a change to the opposite fill ratio occurs (fill ratio of 1.0 defines a completely white and one of 0.0 a completely black environment).</p> <p><strong>Environment difficulty</strong></p> <ul> <li> <p>0901 --> easy environment, fill ratio changed from 0.9 to 0.1</p> </li> <li> <p>0703 --> easy environment, fill ratio changed from 0.7 to 0.3</p> </li> <li> <p>0604 --> easy environment, fill ratio changed from 0.6 to 0.4</p> </li> <li> <p>055045 --> easy environment, fill ratio changed from 0.55 to 0.45</p> </li> </ul> <p><strong>Parameter Setting</strong></p> <p>The setting is in the name of the folder composed of: feedbackID: intervalLength amountNeighbors</p> <ul> <li> <p>3c:50s3n --> feedback 3c with a 50s interval and 3 neighbors</p> </li> </ul> <p>In these folders all plots of the respective runs can be found showing a Boxplot of all 20 runs and for each run the swarm belief, the decision distribution and the reset histogram (before/after the change)</p>
Dynamic web page change content detection
<p>This dataset contains 4 parts. "SimilarWeb dataset with screenshots" is created by scraping web elements, their CSS, and corresponding screenshots in three different time intervals for around 100 web pages. Based on this data, the "SimilarWeb dataset with SSIM column" is created with the target column containing the structural similarity index measure (SSIM) of the captured screenshots. This part of the dataset is used to train machine learning regression models. To evaluate approach, "Accessible web pages dataset" and "General use web pages dataset" parts of the dataset are used.</p>
Plasticity in Collective Decision-Making for Robots: Creating Global Reference Frames, Detecting Dynamic Environments, and Preventing Lock-ins
<p>Swarm robots operate as autonomous agents and a swarm as a whole gets autonomous by its capability of collective decision-making.<br> Despite intensive research on models of<br> collective decision-making, the implementation in multi-robot systems is still challenging.<br> Here, we advance the state of the art by introducing more plasticity to the decision-making process and by increasing the task difficulty.<br> Most studies on large-scale multi-robot decision-making are limited to one instance of an iterated exploration-dissemination phase followed by successful and permanent convergence.<br> We investigate a dynamic environment that requires constant collective monitoring of option qualities.<br> Once a significant change in qualities is detected by the swarm, it has to collectively reconsider its previous decision accordingly.<br> This is only possible by preventing lock-ins, a global consensus state of no return.<br> In addition, we introduce a task of increased difficulty as the robots must locate themselves to assess the quality of an option.<br> Using local communication, swarm robots propagate hop-count information throughout the swarm to form a global reference frame.<br> We successfully validate our implementation in many swarm robot experiments concerning robustness to disruptions of the reference frame, scalability, and adaptivity to a dynamic environment.</p>
Expanded dynamic methylome and quantitative trait detection by long-read epigenome profiling of personal DNA
<p>Scripts and methylation frequencies for "Expanded dynamic methylome and quantitative trait detection by long-read epigenome profiling of personal DNA" manuscript.</p>
Pupillary dynamics reflect the impact of temporal expectation on detection strategy - data
<p>This repository contains data from work that investigated how humans can extract hidden temporal cues from the occurrences of probabilistic targets and utilize them to inform target detection in a complex acoustic stream, using a combination of behavioral measures and pupillometry. It accompanies the article entitled: " <strong>Pupillary dynamics reflect the impact of temporal expectation on detection strategy</strong>" from the same authors. It contains:<br> - preprocessed pupil data for all subjects<br> - similarly matched behavior data for all subjects<br> - custom code for both behavioral and pupil analysis</p>
EMBEDWATCH: Dynamic Root Cause Detection of Spatial Memory Errors in Embedded Systems
<p>Dataset of firmwares used for the experiments of the paper <em>EMBEDWATCH: Dynamic Root Cause Detection of Spatial Memory Errors in Embedded Systems</em></p>
Detecting Autologous Transfusion by Measuring Alterations in the Dynamics of Red Blood Cell Maturation and Recycling
ClinicalTrials.gov study NCT02684747. IPD Sharing: NO. Countries: 1. Publications: 10.
Diagnostic Significance of Single Center, Open and Prospective Evaluation of <Sup>18<Sup>F-FDG PET/CT Dynamic Imaging and Genomic Sequencing in Detecting Metastatic Lesions and Differentiating Multipl
ClinicalTrials.gov study NCT03679936. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
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.