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697 results for “Thresholding”
Data related to the manuscript "Functional relationships between critical erosion thresholds of fine reservoir sediments and their sedimentological characteristics"
<p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Data used in the publication "Functional relationships between critical erosion thresholds of fine reservoir sediments and their sedimentological characteristics", which is accepted by the Journal of Hydraulic Engineering and will be published in a forthcoming issue.</p> <p>Beckers, F., K. Koca, S. Haun, M. Noack, S. U. Gerbersdorf, and S. Wieprecht. Forthcoming. „Functional relationships between critical erosion thresholds of fine reservoir sediments and their sedimentological characteristics.” J. Hydraul. Eng. <a href="https://doi.org/10.1061/(ASCE)HY.1943-7900.00019864">https://doi.org/10.1061/(ASCE)HY.1943-7900.0001984</a></p> <p> </p> <p>The data consists of two files:</p> <ul> <li>GBS.txt contains the sediment data of the reservoir <em>Großer Brombachsee</em></li> <li>SBT.txt contains the sediment data of the reservoir <em>Schwarzenbachtalsperre</em></li> </ul> <p>Both files contain information on the sediment depth and erosion stability separated into τ<sub>c,0</sub> and τ<sub>c,S</sub>.</p> <p>Furthermore, both files contain a collection of physico-chemical sediment parameters, including bulk density, sediment composition (Clay, Silt, Sand), percentiles (d<sub>10</sub>, d<sub>50</sub>, d<sub>90</sub>),cation exchange capacity (CEC), and organic content (TOC).</p> <p>Additionally, the SBT data contains biological sediment parameters, including chlorophyll-a (CHL-a), and extracellular polymeric substances separated into proteins (EPS-p) and carbohydrates (EPS-c).<br> -----------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>The data was collected within the transdisciplinary research project "CHARM - Challenges of Reservoir Management".</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------</p>
Data of "H2S dosimeter with controllable percolation threshold based on semi-conducting copper oxide thin films" published in JSSS 2017
<p>Raw data to the Paper "H2S dosimeter with controllable percolation threshold<br> based on semi-conducting copper oxide thin films" published in "Journal of Sensors and Sensor Systems".</p> <p>Acknowledgement and Funding in the txt.file</p>
Effect of variable thresholds on calculating linkage disequilibrium and population structure, using Plink 1.9
<p>The figure presented here shows how the number of LD-independent SNPs and the apparent population structure can change drastically, depending on what input thresholds are used for the calculations. The population sample consists of 220 fruit-fly (Drosophila melanogaster). Most of the population structure plots indicate four subpopulations, which on further investigation using Fst indicate that this is caused by defined trans-centromeric regions, without evidence for genotyping error, and probably reflective of historic admixture. In the plots of population structure, the number in each box indicates the number of independent SNPs which were used in the IBD calculatations. Points are coloured by order in which each fly was sequenced, and some error is noticable for the beige points in the top-right plots.</p> <p>The Plink program provides a useful method for selecting single-nucleotide polymorphisms (SNPs) which are independent of linkage disequilibrium (LD), and also of visualising the genetic relatedness between individuals in a population sample, using identity-by-descent analysis (IBD). The selection of LD-independent SNPs requires three user-specfied paramaters, alongside the genotype data: i. Window-size, in kilobases (Kb) within which all pairwise comparisons between SNPs will be made, ii. Step-size, in number of SNPs, iii. r2 threshold between any two SNPs, below which they are considered to be independent (fixed here at 0.5).</p>
Dataset for Main Text and SI - Squeezing the threshold of metal-halide perovskite micro-crystal lasers grown by solution epitaxy by Shuyu Zhou et al.
<p>All data published in </p> <p><strong><span>Squeezing the threshold of metal-halide perovskite micro-crystal lasers grown by solution epitaxy</span></strong></p> <p><strong><span> by </span></strong><em><span>Shuyu Zhou, Viktor Rehm, Hany A. Afify, Yufei Han, Jędrzej Korczak, Andrzej Szczerbakow, Tomasz Story, Zijian Peng, Albert These, Anastasia Barabash, Andres Osvet, Christoph J. Brabec, Klaus Götz, Tobias Unruh, Felix Hilpert, Olaf Brummel, Jörg Libuda, Wolfgang Heiss</span></em></p> <p><em><span>are summarized in this rar file. </span></em></p>
Reference genome choice and filtering thresholds jointly influence phylogenomic analyses
<p>Molecular phylogenies are a cornerstone of modern comparative biology and are commonly employed to investigate a range of biological phenomena, such as diversification rates, patterns in trait evolution, biogeography, and community assembly. Recent work has demonstrated that significant biases may be introduced into downstream phylogenetic analyses from processing genomic data; however, it remains unclear whether there are interactions among bioinformatic parameters or biases introduced through the choice of reference genome for sequence alignment and variant-calling. We address these knowledge gaps by employing a combination of simulated and empirical data sets to investigate to what extent the choice of reference genome in upstream bioinformatic processing of genomic data influences phylogenetic inference, as well as the way that reference genome choice interacts with bioinformatic filtering choices and phylogenetic inference method. We demonstrate that more stringent minor allele filters bias inferred trees away from the true species tree topology, and that these biased trees tend to be more imbalanced and have a higher center of gravity than the true trees. We find the greatest topological accuracy when filtering sites for minor allele count > 3–4 in our 51-taxa data sets, while tree center of gravity was closest to the true value when filtering for sites with minor allele count > 1-2. In contrast, filtering for missing data increased accuracy in the inferred topologies; however, this effect was small in comparison to the effect of minor allele filters and may be undesirable due to a subsequent mutation spectrum distortion. The bias introduced by these filters differs based on the reference genome used in short read alignment, providing further support that choosing a reference genome for alignment is an important bioinformatic decision with implications for downstream analyses. These results demonstrate that attributes of the study system and dataset (and their interaction) add important nuance for how best to assemble and filter short read genomic data for phylogenetic inference.</p>
ABR raw data and results from automated hearing threshold detection
<p><strong>This repository contains: </strong></p> <ol> <li><strong>data.tar.gz</strong>: raw data from Auditory Brainstem Response (ABR) measurements performed at over 4,000 mice at the German Mouse Clinic. It also contains suitably pre-processed ABR raw data of over 8,000 mice from another public data repository (10.5061/DRYAD.CV803RV) of the Wellcome Sanger Institute. All data is intended to be used by and compatible with code published on GitHub (https://github.com/ExperimentalGenetics/ABR_thresholder). <br> </li> <li><strong>results.tar.gz</strong>: results and visualisations - comparison of two new, independent automatic hearing threshold finding methods and comparison with the manual gold standard method.</li> </ol>
Data and code for figures: Ultra-Low Threshold Titanium-Doped Sapphire Whispering-Gallery Laser
<p>This zip file contains the set of data and python notebook files used to plot the data that is presented in the Figures of the paper titled, "Ultra-Low Threshold Titanium-Doped Sapphire Whispering-Gallery Laser".</p>
Dataset for "Thresholds in road network functioning on US Atlantic and Gulf barrier islands"
<p>This dataset accompanies the paper "Thresholds in road network functioning on US Atlantic and Gulf barrier islands" (<a href="https://doi.org/10.31223/X55D1G">https://doi.org/10.31223/X55D1G</a>) and is intended to be used to reproduce the analysis. In this dataset you can find the graphml files generated for each island (103 islands with drivable roads). Each intersection of the island road network is a node, and has an associated elevation and extreme water level value. Also included are the statistics for each network, and a single table with the analysis results for each of the networks with >100 nodes.</p>
Aeolian erosion thresholds for cohesive sand
<p>These are datastet corresponding to the article entitled "Aeolian erosion threshold for cohesive sand" by J.-B Besnard, P. Dupont, A. Ould El Moctar and A. Valance.</p> <p>The data set are stored in an excel file which is made of four different sheets corresponding to the data from Figure 2a, Figure 3, Figure 4 and Figure 5a of the article.<br> </p>
Data from: defining the pyro-thermal niche: do seed traits, ecosystem type and phylogeny influence thermal thresholds in seeds with physical dormancy
<p>Seeds are a key pathway for plant population recovery following disturbance. To prevent germination during unsuitable conditions, most species produce dormant seeds. In fire-prone regions, physical dormancy (PY) enables seeds to germinate after fire. The thermal niche, incorporating seed dormancy and mortality temperature responses, has not been characterised for PY seeds from fire prone environments.</p> <p>We aimed to assess variation in thermal thresholds between species with PY seeds and if the pyro-thermal niche is aligned with seed mass, ecosystem type or phylogenetic relatedness.</p> <p>We collected post heat-shock germination data for 58 Australian species that produce PY seeds. We applied species-specific thermal performance curves to define three critical thresholds (DRT<sub>50, </sub>dormancy release temperature; T<sub>opt</sub>,<sub> </sub>optimum dormancy release temperature and LT<sub>50</sub>, lethal temperature), defining the pyro-thermal niche. Each species was assigned a mean seed weight and ecosystem type. We constructed a phylogeny to account for species relatedness and calculated phylogenetic signal (h<sup>2</sup>) for LT<sub>50,</sub> T<sub>opt</sub>, and<sub> </sub>DRT<sub>50</sub>.</p> <p>Seeds of <em>Pomaderris</em> (Rhamnaceae) had the highest T<sub>opt</sub> and LT<sub>50</sub>, and <em>Pomaderris bodalla</em> having the highest DRT<sub>50 </sub>of 101.3°C. Seeds from species within this family exhibited higher temperature thresholds than those from Fabaceae. Seed mass was only influential in explaining LT<sub>50 </sub>variation.</p>
Figure2. Generation of negative feedbacks gets tuned once a TCR completes stimulation beyond the threshold l. A TCell generates activation signal to BCell once it gets stimulation of its k-TCRs.-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>A TCR at position p is stimulated if rp (x) - rn(x) > l. Figure 1 depicts this process. When a T<br> Cell receives stimulations on more than k receptors, it generates activation signal to a B Cell, as<br> represented in Figure2.</p>
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 12 Anorexia Nervosa Patients to 12 Controls-Figure 2: All axial slices of sLORETA imaging results of Resting State EEG Supra-Threshold Voxels in both the Parahippocampal (limbic) and Fusiform (temporal) Gyri illustrating decreased neuronal activity in the Anorexia Nervosa patients.
<p>Results from the sLORETA imaging indicates decreased neuronal activation within the Left<br> Fusiform Gyrus located in the Temporal lobe and Parrahippocampal gyrus, which is located in the<br> Limbic Lobe (Table 1). This correlates with other fMRI findings where patients with early onset<br> AN have exhibited reduced unilateral blood flow in the temporal lobe. The Parahippocampal and<br> Fusiform Gyri are centers that process emotions. Previous studies in which these regions have<br> shown activation involve women that have distorted perceptions of their bodies from a cognitive<br> perspective (Santel et al., 2006). It is apparent from our findings that the Fusiform Gyrus may play<br> a vital role in the processing of visual appearance of the human body. There is also a correlation<br> with the somatosensory limbic pathway in the limbic lobe, due to the similarity of function.</p>
Figure3. APCs A1-A4 connected within range of cohesion-factor-threshold form members of one ARB-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>Figure3 depicts this process. The<br> model with the above specification then effectively detects self or non-self pathogens. In terms of<br> its application to the task of clustering, this interpretation means making the affinities high within<br> clusters and low across clusters. A pathogen corresponding to an outlier would not stimulate a TCR<br> sufficiently and may not form part of any ARB.</p>
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 12 Anorexia Nervosa Patients to 12 Controls-Figure 1: sLORETA imaging results of Resting State EEG Supra-Threshold Voxels in both the Parahippocampal (limbic) and Fusiform (temporal) Gyri illustrating decreased neuronal activity in the Anorexia Nervosa patients relative to Control participants
<p>The findings of the sLORETA analysis indicated that, the difference is statistically<br> significant (p=0.03) using a one-tailed t-test: Anorexia > Controls. The brains of the patients with<br> Anorexia Nervosa illustrated decreased neuronal activity in the Left Fusiform Gyrus and the Left<br> Parahippocampal Gyrus (p=0.03) in the resting-state brains when Anorexia Patients were sitting for<br> 3min, as compared to the Controls sitting for 3minutes.</p>
Database of physical and chemical properties and thresholds relevant to shale gas
<p>Results of the data collection effort on the chemical, physical and toxicity properties as well as permitted concentration levels in drinking water of chemicals that are frequently used in the fracking process.</p>
Supplement data for the article "The impact of OTU sequence similarity threshold on diatom-based bioassessment: A case study of the rivers of Mayotte (France, Indian Ocean)", in preparation
<p>These are supplement data for the article "The impact of OTU sequence similarity threshold on diatom-based bioassessment: A case study of the rivers of Mayotte (France, Indian Ocean)", in preparation</p> <p>The folowing files are available:</p> <ul> <li>Supplement 1. Map of Mayotte with the sampling sites and the rivers.</li> <li>Supplement 2. <em>rbcL</em> primers, reaction mixture, and conditions used for the PCR of the 312-bp <em>rbcL</em> fragment. The information provided is for a single reaction with a final volume of 25µL.</li> <li>Supplement 3. The 20 fastq files containing the demultiplexed DNA reads.</li> <li>Supplement 4. Number of sequence reads for each sample before and after the trimming procedure.</li> <li>Supplement 5. The 20 OTU lists, corresponding to the 20 SSTs, including the number of DNA reads within the 90 samples and their assigned taxonomy.</li> <li>Supplement 6. Sampling site description with sample codes, names of rivers, year, number of raw DNA reads and GPS coordinates.</li> <li>Supplement 7. Values and summary statistics for the environmental variables.</li> <li>Supplement 8. The script used in Mothur for the bioinformatic analysis from trimming to the used OTU lists.</li> </ul> <p> </p>
Data and Code for: Determination of the Signal Fluctuation Threshold of the Temperature Ion Composition Ambiguity Problem Using Monte Carlo Simulations
<p>This repository contains the datasets and scripts used to obtain the Figures of the paper "Determination of the Signal Fluctuation Threshold of the Temperature Ion Composition Ambiguity Problem Using Monte Carlo Simulations".</p> <p>The repository is organized as follows:</p> <p>- a) ISR model script</p> <p>- b) Script to visualize the two solutions of the TICA problem</p> <p>- c) Generator of uniformly selected parameters</p> <p>- d) Script to represent the space of parameters</p> <p>- Part 0) Monte Carlo simulation codes</p> <p>- Part I) Analysis of the efffect of the uncertainty on the initial guess</p> <p>- Part II) Analysis of the impact of the addition of parameters from the Plasma Line</p> <p>- Part III) Analysis of the information provided by the addition of each parameter</p> <p>- Part IV) Analysis of the effect of the uncertainty on the parameters added</p> <p>All datasets and scripts were generated and tested using Matlab 2017. Simulations have been executed in parallel on a SLURM cluster, compilation and running scripts are provided.</p>
Erosion threshold and mode of failure of biofilm surrogates
<p>This dataset provides the raw data from laboratory experiments investigating the failure mechanisms and erosion thresholds of surrogate biofilms. The experiments were conducted as part of HYDRALAB+ JRA 1 RECIPE. More than 60 erosion experiments were carried out at the FZK (Forschungszentrum Küste) using different mixtures of Xanthan Gum and sand fractions. </p>
Raw data: Supra-threshold perception and neural representation of tones presented in noise in conditions of masking release
<p>Raw data of three experiments:</p> <p>1) Exp1: Psychoacoustical masked thresholds of tone in noise masker (ASCII format)</p> <p>2) Exp2: 64ch EEG data (Biosemi data format .bdf)</p> <p>3) Exp3: Salience rating of a tone masked by various maskers and levels above masked threshold. (ASCII format)</p> <p>Preprint with details on experiments submitted to BioRxiv: https://doi.org/10.1101/575720</p>
Figure 1 in The study of fidelity measures in the context of using them as a threshold criterion in the allocation of diagnostic species
Figure 1. Detection of optimal number of clusters by the Optimclass-2 approach. From left to right: the case study of the test data set 1 and 2.
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