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105 results for “IMPACT DETECTION”
Dataset for publication: An inter-laboratory study characterizes the impact of bioinformatic approaches on genome-based cluster detection for foodborne bacterial pathogens
<p>This dataset is part of a dry-lab interlaboratory study conducted across Germany, regarding bacterial outbreak detection based on NGS data, with a focus on bioinformatic analysis of four species to identify potential variability caused by different data analysis approaches and human interpretation. Participants were asked to follow their usual in-house protocols while adhering to the general guidelines. A quality assessment (with sample exclusion) was followed by 7-gene Multilocus-Sequence Typing (MLST), core genome Multilocus Sequencing Typing (cgMLST), and SNP calling. The participants were then asked to identify clusters. The study was not intended to resemble a standard proficiency test with a passing/failing grade, but rather to investigate and quantify obvious variability in the results and, where possible, the reasons for it. For this purpose, the datasets included borderline cases in terms of quality.</p>
Data from: A tale of two studies: detection and attribution of the impacts of invasive plants in observational surveys
1.Short-term experiments cannot characterize how long-lived, invasive shrubs influence ecological properties that can be slow to change, including native diversity and soil fertility. Observational studies are thus necessary, but often suffer from methodological issues. 2.To highlight ways of improving the design and interpretation of observational studies that assess the impacts of invasive plants, we compare two studies of nutrient cycling and earthworms along two separate gradients of invasive shrub abundance. By considering the divergent sampling strategies and statistical analyses of these two studies, and interpreting their contradictory results in the context of other studies, we also aim to better describe the impacts of the focal invader, Rhamnus cathartica. 3.In a new study of a single site in Minnesota, we observed positive correlations between buckthorn abundance and soil pH, soil nutrient pools, nutrient fluxes through leaf litterfall, earthworm abundance, and root biomass. Multiple regression models showed these relationships persisted after accounting for variability in soil texture and tree species composition. For a separate, more expansive study in Illinois, other authors reported little to no correlation between buckthorn abundance and 10 soil properties, including earthworm abundance, pH, and nutrient concentrations. However, like many other studies, their regression models only assessed predictors related to invader abundance. R2 values for models of ecosystem properties ranged from 0-0.79 (adjusted-R2) for our study in Minnesota and from <0.05-0.16 (unadjusted) for the prior study in Illinois. 4.Differences in sampling error and use of predictor variables between the two studies likely explain the contrasting results. 5.Synthesis and applications. To reduce the uncertainty of conclusions from observational studies of invasive plants, future studies must ensure that heterogeneity of soils and vegetation is adequately accounted for in the sampling strategy and statistical analyses (e.g., analysis of covariance, multiple regression). Particular attention should be given to ecosystem properties with variability that likely predates the invader (e.g., geophysical features and tree community composition). In our study, effects of buckthorn on ecosystem properties were not only robust to the inclusion of potentially confounding predictors, but also consistent with expectations based on ecological stoichiometry and mass balance of element flow.
The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure
<p>Dataset and Python code complementing the publication </p> <p>Kaiser, S.; Boike, J.; Grosse, G.; Langer, M. The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure. <em>Remote Sens.</em> <strong>2022</strong>, <em>14</em>, 6107. https://doi.org/10.3390/rs14236107</p> <ul> <li><strong>AROSICS.zip</strong> contains the orthomosaic of 2018 shifted to 2019 with the AROSICS algorithm. The .txt file contains the x-/y-shift in map units [m].</li> <li><strong>CC_DistancePointClouds.zip</strong> contains the distance point clouds as calculated via Multiscale Model to Model Comparison (M3C2 after Lague et. al, 2013) at each post-processing level (I-IV) and the validation.</li> <li><strong>CC_PointCloudProcessing.zip</strong> contains the point clouds at post-processing levels II-IV.</li> <li><strong>ODM_Orthomosaics.zip</strong> contains the orthomosaics of 2018 and 2019 as processed in WebODM (based on OpenDroneMap).</li> <li><strong>ODM_PointClouds.zip</strong> contains the raw point clouds of 2018 and 2019 (post-processing level I) as processed in WebODM (based on OpenDroneMap).</li> <li><strong>PointCloudStatistics.zip</strong> contains the M3C2 distance statistics at each post-processing level (I-IV) and the validation for the whole point cloud and the two subsets.</li> <li><strong>Python_ChangeDetection.zip</strong> contains the Python (v 3.6) script for calculating the displacement vectors Dx, Dy, Dz for each distance point cloud, rasterizing the attribute "vertical displacement (Dz)" of the distance point cloud with the highest accuracy (post-processing level IV), applying a Sobel edge detection filter to highlight high image gradients and clustering the image into two categories: change (high image gradient) and no change (low image gradient). Needed data input is <strong>CC_DistancePointClouds.zip.</strong></li> <li><strong>Subsets.zip </strong>contains shapefiles of the two subsets.</li> </ul>
Evaluating Impact of NIRAF Detection for Identifying Parathyroid Glands During Parathyroidectomy
ClinicalTrials.gov study NCT04299425. IPD Sharing: YES. Countries: 1. Publications: 13.
Data from: A tale of two studies: detection and attribution of the impacts of invasive plants in observational surveys
Open the record for dataset details and reuse information.
Heterogeneity in the rate of molecular sequence evolution substantially impacts the accuracy of detecting shifts in diversification rates
<p>As species richness varies along the tree of life, there is a great interest in identifying factors that affect the rates by which lineages speciate or go extinct. To this end, theoretical biologists have developed a suit of phylogenetic comparative methods that aim to identify where shifts in diversification rates had occurred along a phylogeny and whether they are associated with some traits. Using these methods, numerous studies have predicted that speciation and extinction rates vary across the tree of life. In this study we show that asymmetric rates of sequence evolution rates lead to systematic biases in the inferred phylogeny, which in turn lead to erroneous inferences regarding lineage diversification patterns. The results demonstrate that as the asymmetry in sequence evolution rates increases, so does the tendency to select more complicated models that include the possibility of diversification rate shifts. These results thus suggest that any inference regarding shifts in diversification pattern should be treated with great caution, at least until any biases regarding the molecular substitution rate have been ruled out.</p>
Miniaturization eliminates detectable impacts of drones on bat activity
<p>A new way to survey wildlife populations may be possible with advancements in drones, or unmanned aerial vehicles (UAVs) that render aerial technology more accessible and promote surveying in inapproachable habitats. However, it remains unclear whether UAV disturbance deters animals, which would make this method inaccurate for data collection and hazardous to wildlife welfare. This study addresses the viability of UAV use for wildlife research by measuring the effects of UAV flight on acoustic bat detection and comparing bat activity in response to varying UAV models. Depending on the way UAVs effect bat detection rate, it may be possible to identify whether wildlife surveys should be done with UAVs and the drone models best suited for this purpose. The results reveal that larger and louder UAVs deterred significantly more bats, and the smallest and quietest model had no effect on bat detection. Indeed, drone noise was positively correlated with drone size, but drone size had little effect on the range of frequencies emitted. While detecting bats with small and quiet UAVs may be possible, complications still arise with acoustic detection and the species-specific effects of drone flight. The reliability of automatic identification with the acoustic detecting software is limited, as over a quarter of detections were triggered by non-bat noises yet still classified as bats (25.99%). Overall, using drones for wildlife detection should be approached with caution, as this study illustrates that some drones deter and disturb wild bats. If drones are used in wildlife habitat, consider flying smaller and quieter models, which are significantly less disturbing. Otherwise, large and loud drones will likely deter more animals and skew the results of the survey.</p>
Appendix of "Impact of Change Granularity in Refactoring Detection"
<p>This is the dataset for ICPC 2022 Impact of Change Granularity in Refactoring Detection, which contains data about coarse-grained refactorings in 19 open source repositories.</p> <p>There are 19 csv files in this dataset. Each of the csv contains 8 rows:</p> <p>1. repository: repository name <br> 2. commit(s): commit SHA-1 hash<br> 3. detected_refactoring_type: refactoring type detected in that commit<br> 4. description: description for that refactoring<br> 5. leftSideLocations: refactoring start place<br> 6. rightSideLocations: refactoring end place<br> 7. is_effective: whether this refactoring is a coarse-grained refactoring (null for refactoring whose coarse-granularity is equal to 1)<br> 8. granularity: coarse-granularity of this refactoring</p> <p>Note that refactorings detected using RefactoringMiner(2.2) with invalid locations has been excluded.</p>
Data from: Early detection of human impacts using acoustic monitoring: an example with forest elephants
<p>The impacts of human activities and climate change on animal populations often take considerable time before they are reflected in typical measures of population health such as population size, demography, and landscape use. Earlier detection of such impacts could enhance the effectiveness of conservation strategies, particularly for species with slow population growth. Passive acoustic monitoring is increasingly used to estimate occupancy and population size, but this tool can also monitor subtle shifts in behavior that might be early indicators of changing impacts. Here we use data from an acoustic grid, monitoring 1250 km<sup>2</sup> of forest in the northern Republic of Congo, to study how forest elephants (<em>Loxodonta cyclotis</em>) assess the risk of poaching across a landscape that includes a national park as well as active and inactive logging concessions. By quantifying emerging patterns of behavior at the population level, arising from individual-based decisions, we gain an understanding of how elephants perceive their landscape along an axis of human disturbance. Forest elephants in relatively undisturbed forests are active nearly equally day and night. However, they become more nocturnal when exposed to a perceived risk such as poaching. We assessed elephant perception of risk by monitoring changes in the likelihood of nocturnal activity relative to differing levels of human activity. We show that logging is perceived to be a risk on short-time and small spatial scales but with little effect on animal density. However, risk avoidance persisted in areas with relatively easy access to poachers and in more open habitats where poaching has historically been concentrated. Increased nocturnal activity is a common response in many animals to human intrusion on the landscape. Provided a species is acoustically active, passive acoustic monitoring can measure changes in human impact at the early stages of such change, informing management priorities.</p>
Dataset and code for article "Expanded detection and impact of BAP1 alterations in cancer"
<p>Dataset and code for article titled "Expanded detection and impact of <em>BAP1 </em>alterations in cancer". Includes Singularity image and Nextflow code for somatic variant calling pipeline. Also includes figure code (qmd, html, and pdf formats), analysis code (qmd), and public raw and processed data of reasonable file size. Controlled-access data and large file size data (e.g., combined dataset of pan-cancer individual RNA-seq files) must be downloaded separately from the TCGA Genomic Data Commons or PanCanAtlas Publication pages. R code written in R version 4.4.1 and provided with R environment (renv) information.</p>
Bridging the Chromosome-Centric and Biology and Disease Human Proteome Projects: Accessible and automated tools for interpreting biological and pathological impact of protein sequence variants detected via proteogenomics
<p>Bridging the Chromosome-Centric and Biology and Disease Human Proteome Projects: Accessible and automated tools for interpreting biological and pathological impact of protein sequence variants detected via proteogenomics</p>
Detectability and impact of repetitive surveys on threatened West African crocodylians: Data M1
<p>West African crocodylians are among the most threatened and least studied crocodylian species globally. Assessing population status and establishing a basis for population monitoring is the highest priority action for this region. Monitoring of crocodiles is influenced by many factors that affect detectability, including environmental variables and individual or population-level wariness. We investigated how these factors affect detectability and counts of the Critically Endangered <em>Mecistops cataphractus</em> and the newly recognized <em>Crocodylus suchus</em>. We implemented 195 repetitive surveys at 38 sites across Côte d'Ivoire between 2014 and 2019. We used an occupancy-based approach and a count-based GLMM analysis to determine the effect of environmental and anthropogenic variables on detection, and modeled crocodile wariness over repetitive surveys. Despite their rarity and level of threat, detection probability of both species was relatively high (0.75 for <em>M. cataphractus </em>and 0.81 for <em>C. suchus</em>), but a minimum of two surveys was required to infer absence of either species with 90% confidence. We found that detection of<em> M. cataphractus</em> was significantly negatively influenced by fishing net encounter rate, while high temperature for the previous 48h of the day of the survey increased <em>C. suchus</em> detection. Precipitation and aquatic vegetation had significant negative and positive influence, respectively, on <em>M. cataphractus</em> counts and showed the opposite effect for <em>C. suchus</em> counts. We also found that fishing encounter rate had a significant negative effect on <em>C. suchus</em> counts. Interestingly, survey repetition did not generally affect wariness for either species, though there was some indication that at least <em>C. suchus</em> was more wary by the fourth replicate. These results are informative for designing future survey and monitoring protocols for these threatened crocodylians in West Africa, and for other endangered crocodylians globally.</p>
Impact ionization dust detection with compact, hollow and fluffy dust analogs
<p>Datasets for the Planetary and Space Science article "Impact ionization dust detection with compact, hollow and fluffy dust analogs" (DOI: 10.1016/j.pss.2022.105536). The data contains two folders for dust impacts on the IIT and CAT targets of CDA. The folders contain separate tables with data for each run. Each table shows the registered CDA impacts for the corresponding run with impact time in UTC (determined by CDA) and all additional information about the impact that could be reconstructed from the measurements of CDA and other detectors (impact velocity, dust mass, ...). Data that could not be reconstructed are represented by NAN values in the tables.</p>
The Impact of Total Body Skin Examination on Skin Cancer Detection
ClinicalTrials.gov study NCT00765193. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Phase 4 Trial Assessing the ImPact of Residual Inflammation Detected Via Imaging TEchniques, Drug Levels and Patient Characteristics on the Outcome of Dose TaperIng of Adalimumab in Clinical Remissi
ClinicalTrials.gov study NCT02198651. IPD Sharing: YES. Countries: 14. Publications: 2.
Evaluating Impact of Near Infrared Autofluorescence (NIRAF) Detection for Identifying Parathyroid Glands During Parathyroidectomy
ClinicalTrials.gov study NCT05022641. IPD Sharing: NO. Countries: 1. Publications: 1.
Management of Device Detected AT and Impact of Device Treatment Algorithms on Atrial Fibrillation
ClinicalTrials.gov study NCT04172883. IPD Sharing: NO. Countries: 1. Publications: 4.
The Impact and Detection of Driving Impairments Associated With Acute Cannabis Smoking
ClinicalTrials.gov study NCT02849587. IPD Sharing: YES. Countries: 1. Publications: 12.
Detectability and impact of repetitive surveys on threatened West African crocodylians: Data M1
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Data from: No detectable deployment impacts of solar-powered GPS devices for long-term use on a small shorebird
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International Brain Laboratory public data
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OpenNeuro
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