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21 results for “Model discrimination”
Dataset: Modelling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks
<p><strong>Associated publication</strong></p> <p>[1] Samuel Ponting*, <strong>Takuma Morimoto</strong>*, Hannah E. Smithson, “Modelling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks”, *equal contribution, bioRxiv, <a href="https://www.google.com/url?q=https%3A%2F%2Fdoi.org%2F10.1101%2F2022.11.02.514864&sa=D&sntz=1&usg=AOvVaw3KwSo7KmqPzBR1UMc1MHmk">https://doi.org/10.1101/2022.11.02.514864</a></p> <p>[2] Takuma Morimoto, and Hannah E. Smithson, “Discrimination of spectral reflectance under complex environmental illumination,” Journal of the Optical Society of America A, 35, 4, B244-B255 (2018) https://doi.org/10.1364/JOSAA.35.00B244</p> <p> </p> <p>Datasets contain 2 folders and 1 mat file.</p> <p> </p> <p><strong>(Folder 1) Stimuli</strong></p> <p><strong>(Folder 2) Psychophysics_data</strong></p> <p><strong>(Mat file) stimulusMagnitudeToMacLeodBoynton.mat</strong></p> <p> </p> <p>Details are described below.</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(Folder 1) Stimuli</strong></p> <p> </p> <p><strong>Overview of datasets</strong></p> <p>This Image dataset includes 57,600 images (2 gloss levels * 3 environments * 100 stimulus magnitudes * 8 hue directions * 12 camera angles from 0 to 330 degree in 30 degree step) in .mat format.</p> <p> </p> <p>The half of images were used in psychophysical experiment (camera angles: 0, 60, 120, 180, 240, 300 degrees).</p> <p>Other half images were used for testing chromatic statistics models and CNN-based models [1] (camera angles: 30, 90, 150, 210, 270, 330 degrees).</p> <p> </p> <p><strong>Each image file</strong></p> <p>Filename denotes a condition name and the camera angle as formatted in a following way.</p> <p> </p> <p>stim_”environment” _”glossiness”_”hueAngle”_”magnitude”_”cameraAngle”.mat</p> <p>e.g. “stim_en1_glossy_hue45_n45_cameraAngle90.mat”</p> <p> </p> <p>Stimulus magnitude 100 is a maximum saturation, and 1 corresponds to equal energy white (which was used as a distractor object).</p> <p> </p> <p>Each image file contains two valuables : MacLeodBoynton, XYZ</p> <p> </p> <p>Each variable contains an image of 128*128*3 pixels (height*width*channel).</p> <p> </p> <p>MacLeod-Boynton: MacLeod-Boynton chromaticity image (1st channel: L/(L+M), 2nd channel: S/(L+M), and 3rd channel L+M)</p> <p>XYZ: XYZ coordinates calculated based on 2-degree CIE 1931 xyz color matching function (1st channel: X, 2nd channel: Y, and 3rd channel Z)</p> <p> </p> <p>Luminance and L+M are both relative (normalised by the maximum luminance across all 57,600 images).</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(Folder 2) Psychophysics_data</strong></p> <p>Filename denotes the condition and observers formatted in a following way.</p> <p> </p> <p>data_”environment” _”specularities”_”sessionNumber”_”obsever”.mat</p> <p>e.g. data_en2_matte_session4_JH.mat or .csv</p> <p> </p> <p>Each file includes following variables:</p> <p> </p> <p>(Variable 1) threshold</p> <p>Thresholds are stored in MacLeod-Boynton (MB) chromaticity coordinates for all 8 hue directions (from 0 to 315 degree in 45 degree step).</p> <p> </p> <p>MacLeod-Boynton chromaticity coordinates were calculated in a following way. </p> <p>These scalings are in accordance with description in CVRL main site (Chromaticity coordinates tab ).</p> <p> </p> <p>First of all, L, M, and S cone signals were calculated based on Stockman & Sharpe cone fundamentals (energy in linear scale available at at http://www.cvrl.org).</p> <p>Each sensitivity curve was normalised to have 1.0 at the peak.</p> <p> </p> <p>Then, MB coordinates were calculated using equation (1-3).</p> <p> </p> <p>L/(L+M) = Lw*L/(Lw*L+Mw*M) - (1)</p> <p>S/(L+M) = Sw*S/(Lw*L+Mw*M) - (2)</p> <p>L+M = Lw*L+Mw*M - (3)</p> <p> </p> <p>where Lw = 0.689903; Mw = 0.348322;Sw = 1.93540.</p> <p> </p> <p>L, M and S denote L-cone, M-cone, S-cone excitations, respectively.</p> <p> </p> <p>Under this calculation, equal energy white becomes L/(L+M) = 0.7078 and S/(L+M) = 1.</p> <p> </p> <p>(Variable 2) staircase</p> <p> </p> <p>Since we ran 8 interleaved staircase (for 8 hue angles), information about 8 staircases are stored in this single variable.</p> <p>(staircase(1) corresponds to 0 degree, and staircase(8) corresponds to 315 degree)</p> <p> </p> <p>There are 5 fields:</p> <p>(i) groundtruth, (ii) response, (iii) correct, (iv) magnitude, (v) cameraAngle</p> <p> </p> <p>For each trial, the location of objects was defined in a following way.</p> <p>| 1 3 |</p> <p>| 2 4 |</p> <p> </p> <p>And each field stores following information for all trials in the staircase.</p> <p> </p> <p>(i) groundtruth</p> <p>Location of the target object</p> <p> </p> <p>(ii) response</p> <p>Location that the participant chose</p> <p> </p> <p>(iii) correct</p> <p>If the response was correct (1) or incorrect (0)</p> <p> </p> <p>(iv) magnitude</p> <p>Stimulus magnitude of target object in each trial from 1 to 100 (1 for equal energy white and 100 for maximum saturation).</p> <p> </p> <p>(v) Camera angle</p> <p>Camera angles assigned for four objects in each trial.</p> <p>This data and (i) groundtruth allow reconstruct of the exact image for each trial.</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(Mat file) stimulusMagnitudeToMacLeodBoynton.mat</strong></p> <p>This file stores a variable ‘stimulusMagnitudeToMacLeodBoynton’ (8*100*2) which describes correspondence map between stimulus magnitude and MacLeod-Boynton chromaticity.</p> <p> </p> <p>1st channel: hue direction from 0 degree to 315 degree, 45 degree step</p> <p>2nd channel: magnitude from 1 to 100</p> <p>3rd channel: MacLeod-Boynton coordinate, 1 being L/(L+M) and 2 being S/(L+M)</p> <p> </p>
Raman spectra from "Discrimination of immune cell activation using Raman micro-spectroscopy in an in-vitro & ex-vivo model"
<p>The uploaded files are data from Chaudhary et al, 2021 (Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Discrimination of immune cell activation using Raman micro-spectroscopy in an in-vitro & ex-vivo model, https://doi.org/10.1016/j.saa.2020.119118).</p> <p>There are two files in .mat format. In one (Preprocessed.mat) the data has been completely pre-processed according to the methods described in the paper.</p> <p>In the second (Unpreprocessed.mat) the data has been calibrated using the methods described in the paper, but has not received further pre-processing.</p> <p>Within both files there are datasets for the spectral measurement from each cell (‘spectra’), together with the treatment which was applied to each sample (‘treatment’) and the wavenumber at which the spectral measurements were made (‘wavenumber’).</p>
Dataset: Testing for effects of growth rate on isotope trophic discrimination factors and evaluating the performance of Bayesian stable isotope mixing models experimentally: a moment of truth?
<p><span>Discerning assimilated diets of wild animals using stable isotopes is well established where potential dietary items in food webs are isotopically distinct. With the advent of mixing models, and Bayesian extensions of such models (Bayesian Stable Isotope Mixing Models, BSIMMs), statistical techniques available for these efforts have been rapidly increasing. The accuracy with which BSIMMs quantify diet, however, depends on several factors including uncertainty in tissue discrimination factors (TDFs; <em>Δ</em>) and identification of appropriate error structures. Whereas performance of BSIMMs has mostly been evaluated with simulations, here we test the efficacy of BSIMMs by raising domestic broiler chicks (<em>Gallus gallus domesticus</em>) on four isotopically distinct diets under controlled environmental conditions, ideal for evaluating factors that affect TDFs and testing how BSIMMs allocate individual birds to diets that vary in isotopic similarity. For both liver and feather tissues,<em> δ</em><sup>13</sup>C and <em>δ </em><sup>15</sup>N values differed among dietary groups. <em>Δ</em><sup>13</sup>C of liver, but not feather, was negatively related to the rate at which individuals gained body mass. For <em>Δ</em><sup>15</sup>N, we identified effects of dietary group, sex, and tissue type, as well as an interaction between sex and tissue type</span><span><span>, </span></span><span><span>with f</span></span><span>emales having higher liver <em>Δ</em><sup>15</sup>N relative to males. For both tissues, BSIMMs allocated most chicks to correct dietary groups, especially for models using combined TDFs rather than diet specific TDFs, and those applying a multiplicative error structure. These findings provide new information on how biological processes affect TDFs and confirm that adequately accounting for variability in consumer isotopes is necessary to optimize performance of BSIMMs. Moreover, they demonstrate experimentally that these types of models reliably characterize consumed diets when appropriately parameterized.<span> </span></span></p>
Noise data for the study of consonant-in-noise discrimination using an auditory model with different speech-based decision devices
<p>The data stored in this repository correspond to two sets of 5000 speech-shaped noises (SSN) that were used in the conference paper titled "Consonant-in-noise discrimination using an auditory model with different speech-based decision devices" by the same authors, presented in the DAGA conference in Vienna, Austria, on 17/08/2021. </p> <p>The two zip files (<strong>osses2021c_S01</strong> and <strong>osses2021c_S02</strong> for participants S01 and S02, respectively) have following structure:</p> <ul> <li><strong>NoiseStim-SSN</strong>: Folder containing the 5000 noises</li> <li><strong>Results</strong>: Results of the listening experiment collected using the fastACI toolbox (https://github.com/aosses-tue/fastACI).</li> </ul> <p>To obtain similar results for other participants the same experiment has to be run using the fastACI toolbox. For instance, to collect new data for participant 'S03', you have to input the following command in MATLAB:</p> <pre><code class="language-bash">fastACI_experiment('speechACI_varnet2013','S03','SSN');</code></pre>
Visitation sequence data from: Alternative flowers affect model and mimic flower discrimination performance of bumble bees
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Data from: Evaluating presence-only species distribution models with discrimination accuracy is uninformative for many applications
Aim: Species distribution models are used across evolution, ecology, conservation, and epidemiology to make critical decisions and study biological phenomena, often in cases where experimental approaches are intractable. Choices regarding optimal models, methods, and data are typically made based on discrimination accuracy: a model's ability to predict subsets of species occurrence data that were withheld during model construction. However, empirical applications of these models often involve making biological inferences based on continuous estimates of relative habitat suitability as a function of environmental predictor variables. We term the reliability of these biological inferences "functional accuracy." We explore the link between discrimination accuracy and functional accuracy. Methods: Using a simulation approach we investigate whether models that make good predictions of species distributions correctly infer the underlying relationship between environmental predictors and the suitability of habitat. Results: We demonstrate that discrimination accuracy is only informative when models are simple and similar in structure to the true niche, or when data partitioning is geographically structured. However, the utility of discrimination accuracy for selecting models with high functional accuracy was low in all cases. Main conclusions: These results suggest that many empirical studies and decisions are based on criteria that are unrelated to models' usefulness for their intended purpose. We argue that empirical modeling studies need to place significantly more emphasis on biological insight into the plausibility of models, and that the current approach of maximizing discrimination accuracy at the expense of other considerations is detrimental to both the empirical and methodological literature in this active field. Finally, we argue that future development of the field must include an increased emphasis on simulation; methodological studies based on ability to predict withheld occurrence data may be largely uninformative about best practices for applications where interpretation of models relies on estimating ecological processes, and will unduly penalize more biologically informative modeling approaches.
Genetic data improves niche model discrimination and alters the direction and magnitude of climate change forecasts
<p>Ecological niche models (ENMs) have classically operated under the simplifying assumptions that there are no barriers to gene flow, species are genetically homogeneous (i.e., no population-specific local adaptation), and all individuals share the same niche. Yet, these assumptions are violated for most broadly distributed species. Here we incorporate genetic data from the widespread riparian tree species narrowleaf cottonwood (<i>Populus angustifolia</i>) to examine whether including intraspecific genetic variation can alter model performance and predictions of climate change impacts. We found that (1) <i>P. angustifolia</i> is differentiated into six genetic groups across its range from México to Canada, and (2) different populations occupy distinct climate niches representing unique ecotypes. Comparing model discriminatory power, (3) all genetically-informed ecological niche models (gENMs) outperformed the standard species-level ENM (3-14% increase in AUC; 1-23% increase in pROC). Furthermore, (4) gENMs predicted large differences among ecotypes in both the direction and magnitude of responses to climate change, and (5) revealed evidence of niche divergence, particularly for the Eastern Rocky Mountain ecotype. (6) Models also predicted progressively increasing fragmentation and decreasing overlap between ecotypes. Contact zones are often hotspots of diversity that are critical for supporting species' capacity to respond to present and future climate change, thus predicted reductions in connectivity among ecotypes is of conservation concern. We further examined the generality of our findings by comparing our model developed for a higher elevation Rocky Mountain species with a related desert riparian cottonwood, <i>P. fremontii</i>. Together our results suggest that incorporating intraspecific genetic information can improve model performance by addressing this important source of variance. gENMs bring an evolutionary perspective to niche modeling and provide a truly "adaptive management" approach to support conservation genetic management of species facing global change.</p>
Evaluating Bayesian stable isotope mixing models of wild animal diet and the effects of trophic discrimination factors and informative priors
<blockquote> <p>1. Ecologists quantify animal diets using direct and indirect methods, including analysis of faeces, pellets, prey items and gut contents. For stable isotope analyses of diet, Bayesian stable isotope mixing models (BSIMMs) are increasingly used to infer the relative importance of food sources to consumers. Although a powerful approach, it has been hard to test BSIMM performance for wild animals because precise, direct dietary data are difficult to collect.<br> 2. We evaluated the performance of BSIMMs in quantifying animal diets when using δ13C and δ15N stable isotope ratios from the feathers and red blood cells of common buzzard Buteo buteo chicks. We analysed mixing model outcomes with various trophic discrimination factors (TDFs), with and without informative priors, and compared these to direct observations of prey provisioned to chicks by adults at nests, using remote cameras. <br> 3. Although BSIMMs with different TDFs varied markedly in their performance, the statistical package SIDER generated TDFs for both feathers and blood that resulted in model outputs that accorded well with direct observations of prey provisioning. Using feather TDFs derived from captive peregrines Falco peregrinus resulted in estimates of diet composition that were also similar to provisioned prey, though blood TDFs from the same study performed poorly. The inclusion of informative priors, based on conventional analysis of pellet and prey remains, markedly reduced model performance.<br> 4. BSIMMs can provide accurate assessments of diet in wild animals. TDF estimates from the SIDER package performed well. The inclusion of informative priors from conventional methods in Bayesian mixing models can transfer biases into model outcomes, leading to erroneous results.</p> </blockquote>
Data from: Central place foragers and moving stimuli: a hidden-state model to discriminate the processes affecting movement
1. Human activities can influence the movement of organisms, either repelling or attracting individuals depending on whether they interfere with natural behavioural patterns or enhance access to food. To discern the processes affecting such interactions, an appropriate analytical approach must reflect the motivations driving behavioural decisions at multiple scales. 2. In this study, we developed a modelling framework for the analysis of foraging trips by central place foragers. By recognising the distinction between movement phases at a larger scale and movement steps at a finer scale, our model can identify periods when animals are actively following moving attractors in their landscape. 3. We applied the framework to GPS tracking data of northern fulmars Fulmarus glacialis, paired with contemporaneous fishing boat locations, to quantify the putative scavenging activity of these seabirds on discarded fish and offal. We estimated the rate and scale of interaction between individual birds and fishing boats and the interplay with other aspects of a foraging trip. 4. The model classified periods when birds were heading out to sea, returning towards the colony or following the closest boat. The probability of switching towards a boat declined with distance and varied depending on the phase of the trip. The maximum distance at which a bird switched towards the closest boat was estimated around 35 km, suggesting the use of olfactory information to locate food. Individuals spent a quarter of a foraging trip, on average, following fishing boats, with marked heterogeneity among trips and individuals. 5. Our approach can be used to characterise interactions between central place foragers and different anthropogenic or natural stimuli. The model identifies the processes influencing central place foraging at multiple scales, which can improve our understanding of the mechanisms underlying movement behaviour and characterise individual variation in interactions with a range of human activities that may attract or repel these species. Therefore, it can be adapted to explore the movement of other species that are subject to multiple dynamic drivers.
Prospective Validation of the ADNEX Model for Discrimination Between Benign and Malignant Adnexal Masses in Pregnancy: International Ovarian Tumour Analysis in Pregnancy Study (p-IOTA)
ClinicalTrials.gov study NCT05974618. IPD Sharing: NO. Countries: 1. Publications: 47.
Data from: Central place foragers and moving stimuli: a hidden-state model to discriminate the processes affecting movement
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Genetic data improves niche model discrimination and alters the direction and magnitude of climate change forecasts
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Data from: Evaluating presence-only species distribution models with discrimination accuracy is uninformative for many applications
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Data from: Evaluating Bayesian stable isotope mixing models of wild animal diet and the effects of trophic discrimination factors and informative priors
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Data from: Residue proximity information and protein model discrimination using saturation-suppressor mutagenesis
Identification of residue-residue contacts from primary sequence can be used to guide protein structure prediction. Using Escherichia coli CcdB as the test case, we describe an experimental method termed saturation-suppressor mutagenesis to acquire residue contact information. In this methodology, for each of five inactive CcdB mutants, exhaustive screens for suppressors were performed. Proximal suppressors were accurately discriminated from distal suppressors based on their phenotypes when present as single mutants. Experimentally identified putative proximal pairs formed spatial constraints to recover >98% of native-like models of CcdB from a decoy dataset. Suppressor methodology was also applied to the integral membrane protein, diacylglycerol kinase A where the structures determined by X-ray crystallography and NMR were significantly different. Suppressor as well as sequence co-variation data clearly point to the X-ray structure being the functional one adopted in vivo. The methodology is applicable to any macromolecular system for which a convenient phenotypic assay exists.
Data from: Residue proximity information and protein model discrimination using saturation-suppressor mutagenesis
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Data from: Machine learning biogeographic processes from biotic patterns: a new trait-dependent dispersal and diversification model with model choice by simulation-trained discriminant analysis
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Analysis of the Capnography Curve Can Allow the Discrimination of Obstructive Patients - Modeling the Capnography Curve
ClinicalTrials.gov study NCT04064580. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Developing a Discrimination Model to Diagnose ALS
ClinicalTrials.gov study NCT01995903. IPD Sharing: NO. Countries: 1. Publications: 0.
Using Dry Eye As a Disease Model, Investigators Demonstrated the Optimal Selection of Individualized Clinical Interventions and the Superiority of Dynamic Treatment Discrimination in Chinese Medicine.
ClinicalTrials.gov study NCT06605495. IPD Sharing: NO. Countries: 0. Publications: 0.
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