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824 results for “Discrimination”
Numerical discrimination of sets of elements by Archerfish
<p>Debates have arisen as to whether non-human animals actually can learn abstract non-symbolic numerousness or whether they always rely on some continuous physical aspect of the stimuli, covarying with number. Here we investigated archerfish (<em>Toxotes jaculatrix</em>) non-symbolic numerical discrimination with accurate control for co-varying continuous physical stimulus attributes. Archerfish were trained to select one of two groups of black dots (Exp. 1: 3 <em>vs. </em>6 elements; Exp. 2: 2 <em>vs.</em> 3 elements); these were controlled for several combinations of physical variables (elements' size, overall area, overall perimeter, density and sparsity), ensuring that only numerical information was available. Generalization tests with novel numerical comparisons (2 <em>vs.</em> 3, 5 <em>vs.</em> 8 and 6 <em>vs.</em> 9 in Exp. 1; 3 <em>vs.</em> 4, 3 <em>vs.</em> 6 in Exp. 2) revealed choice for the largest or smallest numerical group according to the relative number that was rewarded at training. None of the continuous physical variables, including spatial frequency, were affecting archerfish performance. Results provide evidence that archerfish spontaneously use abstract relative numerical information for both small and large numbers when only numerical cues are available.</p>
Reevaluating trophic discrimination factors (Δδ13C and Δδ15N) for diet reconstruction
<p>Stable isotope analysis is increasingly used to assess diet and trophic positions of animals. Such assessments require estimates of trophic discrimination factors (TDF — offset between the isotopic composition of diet and animal tissues), with imprecise applications of TDFs leading to biased conclusions in resource use. Because TDFs are unavailable for most species, ecologists often apply values from taxonomically similar species or use trophic step increases of about 1‰ for carbon (TDF-δ<sup>13</sup>C) and 3‰ for nitrogen (TDF-δ<sup>15</sup>N). Such practices may be inaccurate since TDFs vary greatly, even within a species. To better understand the factors that influence TDFs, we conducted a meta-analysis of TDF-δ<sup>13</sup>C and TDF-δ<sup>15</sup>N for mammals and quantified variation in relation to consumer type (herbivore, omnivore, carnivore) and diet source (C3-based, C4-based, marine-based, mixture). Additionally, to guide TDF choice, we used an isotopic dataset of small mammal tissues and diet items to assess how predicted dietary contributions vary with TDFs estimated using (1) taxonomic relatedness, (2) consumer type and diet source, or (3) values derived from wild animals eating natural diets. Our meta-analysis revealed that metabolic routing and interactions between consumer class, dietary source, and the protein versus energy content of diets best explained variation in TDF-δ<sup>13</sup>C values (-1.5 to 7.3‰), whereas consumer class best explained variation in TDF-δ<sup>15</sup>N values (-0.5 to 7.1‰). Our test of methods to estimate TDFs indicated that ecologists should avoid relying on taxonomic relatedness when selecting TDF-δ<sup>13</sup>C, as mixed-diet lab studies may cause misleading results for herbivores and omnivores. Additionally, field-derived estimates could help fill TDF gaps where diets within a consumer class are absent. Overall, we suggest that using standard TDF trophic step values should be abandoned, as feeding studies are often poor proxies for natural diets, particularly for herbivores and omnivores. Instead, we provide recommendations on how to select TDFs, along with a range of TDF-δ<sup>13</sup>C and TDF-δ<sup>15</sup>N values depending on diet source, consumer class, and tissue type. Use of these more refined recommendations and TDF values in isotopic assessments will improve estimates of diets and trophic interactions in natural systems, leading to a better understanding of ecological interactions and communities.</p>
Patch clamp dataset for: A massively parallel assay accurately discriminates between functionally normal and abnormal variants in a hotspot domain of KCNH2
<p><span>Many genes, including <em>KCNH2</em>, contain </span><span>'hotspot' domains associated with a high density of variants associated with disease. This has led to the suggestion that variant location can be used as evidence supporting classification of clinical variants. However, it is not known what proportion of all potential variants in hotspot domains cause loss of function. Here, we have used a massively parallel trafficking assay to characterize all single-nucleotide variants in exon 2 of <em>KCNH2, </em>a known hotspot for variants that cause long QT syndrome type 2 and an increased risk of sudden cardiac death<em>. </em>Forty-two percent of <em>KCNH2</em> exon 2 variants caused at least 50 % reduction in protein trafficking and 65% of these trafficking defective variants exerted a dominant-negative effect when co-expressed with a WT <em>KCNH2</em> allele as assessed using a calibrated patch clamp electrophysiology assay. The massively parallel trafficking assay was more accurate (AUC of 0.94) than bioinformatic prediction tools (REVEL and CardioBoost, AUC of 0.81) in discriminating between functionally normal and abnormal variants. Interestingly, over half of variants in exon 2 were found to be functionally normal, suggesting a nuanced interpretation of variants in this 'hotspot' domain is necessary. Our massively parallel trafficking assay can provide this information prospectively.</span></p>
Trafficking dataset for: A massively parallel assay accurately discriminates between functionally normal and abnormal variants in a hotspot domain of KCNH2
<p><span>Many genes, including <em>KCNH2</em>, contain </span><span>'hotspot' domains associated with a high density of variants associated with disease. This has led to the suggestion that variant location can be used as evidence supporting classification of clinical variants. However, it is not known what proportion of all potential variants in hotspot domains cause loss of function. Here, we have used a massively parallel trafficking assay to characterize all single-nucleotide variants in exon 2 of <em>KCNH2, </em>a known hotspot for variants that cause long QT syndrome type 2 and an increased risk of sudden cardiac death<em>. </em>Forty-two percent of <em>KCNH2</em> exon 2 variants caused at least 50 % reduction in protein trafficking and 65% of these trafficking defective variants exerted a dominant-negative effect when co-expressed with a WT <em>KCNH2</em> allele as assessed using a calibrated patch clamp electrophysiology assay. The massively parallel trafficking assay was more accurate (AUC of 0.94) than bioinformatic prediction tools (REVEL and CardioBoost, AUC of 0.81) in discriminating between functionally normal and abnormal variants. Interestingly, over half of variants in exon 2 were found to be functionally normal, suggesting a nuanced interpretation of variants in this 'hotspot' domain is necessary. Our massively parallel trafficking assay can provide this information prospectively.</span></p>
Adult-born granule cells improve stimulus encoding and discrimination in the dentate gyrus
<p>Heterogeneity plays an important role in diversifying neural responses to support brain function. Adult neurogenesis provides the dentate gyrus with a heterogeneous population of granule cells (GCs) that were born and developed their properties at different times. Immature GCs have distinct intrinsic and synaptic properties than mature GCs and are needed for correct encoding and discrimination in spatial tasks. How immature GCs enhance the encoding of information to support these functions is not well understood. Here, we record the responses to fluctuating current injections of GCs of different ages to study how they encode stimuli. Immature GCs produce unreliable responses compared to mature GCs, exhibiting imprecise spike timings across repeated stimulation. We use a statistical model to describe the stimulus-response transformation performed by GCs of different ages. We fit this model to the data and obtain parameters that capture GCs encoding properties. Parameter values from this fit reflect the maturational differences of the population and indicate that immature GCs perform a differential encoding of stimuli. To study how this age heterogeneity influences encoding by a population, we perform stimulus decoding using populations that contain GCs of different ages. We find that, despite their individual unreliability, immature GCs enhance the fidelity of the signal encoded by the population and improve the discrimination of similar time dependent stimuli. Thus, the observed heterogeneity confers the population with enhanced encoding capabilities.</p>
alfa-synuclein spectra for the paper "Linear discriminant analysis reveals hidden patterns in NMR chemical shifts of intrinsically disordered proteins"
<p>The experimental data for the paper "Linear discriminant analysis<br> reveals hidden patterns in NMR chemical shifts of intrinsically<br> disordered proteins" - four spectra of an intrinsically disordered<br> protein alfa-synuclein:</p> <p>3D HNCO</p> <p>4D HabCab(CO)NH</p> <p>4D (H)N(CA)CONH</p> <p>4D HNCACO</p> <p>All spectra were acquired using non-uniform sampling (schedules included<br> as 'schedule.txt' files in a format of 'Kozminski' method in VnmrJ). 3D<br> spectrum (HNCO_nuFT.ucsf) was processed using multidimensional Fourier transform<br> (http://nmr.cent3.uw.edu.pl/software, program 'toastd'). Additionally, we provide a spectrum cleaned of NUS artifacts (HNCO_artifacts_cleaned.ucsf) using program handy (http://nmr.cent3.uw.edu.pl/software, program 'handy').</p> <p>4D spectra were processed using sparse multidimensional Fourier transform<br> (http://nmr.cent3.uw.edu.pl/software, program 'reduced'), based on a<br> HNCO peak list ('peak.list' file). Files with processing parameters are<br> included as 'parameters.txt' files.</p> <p>The resulting spectra are included as Sparky .ucsf files. The files for<br> 4D spectra are the collections of 2D cross-sections with the<br> cross-section number corresponding to the numbering of HNCO peaks in the<br> 'peak.list' file.</p> <p>The folder 'Application2_assignment_transfer' contains Sparky 'ucsf' and 'save' files of the two-dimensional NH projection of the HNCO spectrum with the experimental (HSQC_exp.list) and BMRB (HSQC_bmrb.list) peak lists read into the spectrum. The experimental peaks are marked with arbitrary numbers and BMRB peaks - with names of the preceding aa-residues. The correct assignment is shown in the file 'assignment.txt'.</p>
Dataset for: Guidelines for standardising the application of discriminant analysis of principal components to genotype data
<p><span>Data and scripts required to replicate the analyses in Thia (2022) "<span class="fontstyle0">Guidelines for standardising the application of discriminant analysis of principal components to genotype data" in <em>Molecular Ecology</em>.</span></span></p> <p><span>This study aimed to address methodological misunderstandings and misuse of the DAPC method in population genetics. The analyses are used to illustrate that for genotype data comprising <em>k</em> effective populations, there are only <em>k</em><span>−</span>1 PC axes that describe populations structure, and that are biologically informative. These PC axes are the only suitable axes for modelling the among-population differences with a DA. Use of many more than <em>k</em><span>−1 PC axes leads to decreasing biological relevancy of the final DA solution, with implications for misinterpretations of population structure.</span></span></p>
Fig. 1 in Morphometric Key For The Discrimination Of Two Wood Mice Species, Apodemus Sylvaticus And A. Flavicollis
Fig. 1. Cranial measurements used in this study
Monitoring of neoadjuvant chemotherapy through time domain diffuse optics: Breast tissue composition changes and collagen discriminative potential
<p>This dataset serves as a comprehensive resource containing the raw data, analyses, and various tools utilized to produce the findings that are presented in our paper titled ‘<strong>Monitoring of neoadjuvant chemotherapy through time domain diffuse optics: breast tissue composition changes and collagen discriminative potential</strong>’. </p> <p>The aim of this clinical study is to test broad spectral range (635-1060 nm) time domain diffuse optical spectroscopy to monitor the response of breast cancer patients to neoadjuvant chemotherapy. Preliminary results from patients on changes in hemoglobin, water, lipids, collagen concentrations, and scattering parameters due to the therapy are presented. In these initial results, we hypothesize that collagen could be a possible biomarker to distinguish complete and partial responders.</p>
Fig. 7. Reconstructed denticles shapes using a in Geometric morphometric on a new species of Trichodinidae. A tool to discriminate trichodinid species combined with traditional morphology and molecular analysis
Fig. 7. Reconstructed denticles shapes using a range of 20 harmonic.
Fig. 6 in Geometric morphometric on a new species of Trichodinidae. A tool to discriminate trichodinid species combined with traditional morphology and molecular analysis
Fig. 6. Fourier harmonic power spectrum based on Elliptical Fourier analysis.
DATASET FOR: A multimodal spectroscopic approach combining mid-infrared and near-infrared for discriminating Gram-positive and Gram-negative bacteria
<h4>Description:</h4> <p>This dataset comprises a comprehensive set of files designed for the analysis and 2D correlation of spectral data, specifically focusing on ATR and NIR spectra. It includes MATLAB scripts and supporting functions necessary to replicate the analysis, as well as the raw datasets used in the study. Below is a detailed description of the included files:</p> <ol> <li> <p><strong>Data Analysis</strong>:</p> <ul> <li><strong>File Name</strong>: <code>Data_Analysis.mlx</code></li> <li><strong>Description</strong>: This MATLAB Live Script file contains the main script used for the classification analysis of the spectral data. It includes steps for preprocessing, analysis, and visualization of the ATR and NIR spectra.</li> </ul> </li> <li> <p><strong>2D Correlation Data Analysis</strong>:</p> <ul> <li><strong>File Name</strong>: <code>Data_Analysis_2Dcorr.mlx</code></li> <li><strong>Description</strong>: This MATLAB Live Script file is similar to the primary analysis script but is specifically tailored for performing 2D correlation analysis on the spectral data. It includes detailed steps and code for executing the 2D correlation.</li> </ul> </li> <li> <p><strong>Functions</strong>:</p> <ul> <li><strong>Folder Name</strong>: <code>Functions</code></li> <li><strong>Description</strong>: This folder contains all the necessary MATLAB function files required to replicate the analyses presented in the scripts. These functions handle various preprocessing steps, calculations, and visualizations.</li> </ul> </li> <li> <p><strong>Datasets</strong>:</p> <ul> <li><strong>File Names</strong>: <code>ATR_dataset.xlsx</code>, <code>NIR_dataset.xlsx</code>, <code>Reference_data.csv</code></li> <li><strong>Description</strong>: These Excel files contain the raw spectral data for ATR and NIR analyses, as well as reference datasets. Each file includes multiple sheets with detailed measurements and metadata.</li> </ul> </li> </ol> <h4>Usage Notes:</h4> <ul> <li><strong>Software Requirements</strong>: <ul> <li>MATLAB is required to run the .mlx files and utilize the functions.</li> <li><strong>PLS_Toolbox</strong>: Necessary for certain preprocessing and analysis steps.</li> <li><strong>MIDAS 2010</strong>: Available at <a href="https://www.mathworks.com/matlabcentral/fileexchange/32384-midas-2010" target="_new" rel="noreferrer">MIDAS 2010</a>, required for the 2D correlation analysis.</li> </ul> </li> <li><strong>Replication</strong>: Users can replicate the analyses by running the <code>Data_Analysis.mlx</code> and <code>Data_Analysis_2Dcorr.mlx</code> scripts in MATLAB, ensuring that the <code>Functions</code> folder is in the MATLAB path.</li> <li><strong>Data Handling</strong>: The datasets are provided in .xlsx format, which can be easily imported into MATLAB or other data analysis software.</li> </ul>
Luminoso Input Data for SemEval-2018 Task 10: "Capturing Discriminative Attributes"
<p>This is the data required to run Luminoso's entry to the SemEval-2018 task on Capturing Discriminative Attributes.</p> <p>This data includes:</p> <ul> <li>A recently-computed version of the <a href="https://github.com/commonsense/conceptnet-numberbatch">ConceptNet Numberbatch</a> word embeddings</li> <li>The output of an implementation of Semantic Matching Energy over ConceptNet</li> <li>A SQLite database containing the lead section of all articles on the <a href="http://en.wikipedia.org">English Wikipedia</a> on 2017-12-20</li> <li>The text file that that database is constructed from</li> <li>A SQLite database of words that co-occur in <a href="http://storage.googleapis.com/books/ngrams/books/datasetsv2.html">Google Books 2-grams</a></li> <li>The text file containing total counts of 2-grams in the Google Books data, which that database is constructed from</li> </ul> <p>For more information, see the paper "Luminoso at SemEval-2018 Task 10: Distinguishing Attributes Using Text Corpora and Relational Knowledge", by Robyn Speer and Joanna Lowry-Duda, to appear in the proceedings of the SemEval workshop at NAACL 2018.</p>
Fig. 1 in Isotopic discrimination and persistence of the C marker in adults of Anastrepha fraterculus (Diptera: Tephritidae) Brazilian-1 morphotype
Fig. 1. Schematic view of the diet switching experiments.
Fig. 1 in Osteometric analysis of the scapula and humerus of Rangifer tarandus and Cervus elaphus: A contribution to the discrimination of Late Pleistocene cervids
Fig. 1. Geographic location of Kiputz IX (Mutriku, Gipuzkoa).
Fig. 2 in Osteometric analysis of the scapula and humerus of Rangifer tarandus and Cervus elaphus: A contribution to the discrimination of Late Pleistocene cervids
Fig. 2. Stratigraphic section and radiocarbon dates for the locality of Kiputz IX.
Experiences of Discrimination are Associated with Microbiome and Transcriptome Alterations in the Gut
<p>Experiences of Discrimination are Associated with Microbiome and Transcriptome Alterations in the Gut. Human stool processed microbiome and transcriptomic data. </p>
Data set of study Examining the impact of cognitive control and discrimination on mental health outcomes in diverse Pakistani and Afghan communities
<div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <p>This dataset is part of a study examining the impact of cognitive control and discrimination on mental health outcomes among diverse Pakistani and Afghan communities. It includes demographic variables such as age, gender, and ethnicity, as well as psychological measures assessing cognitive control, discrimination experiences, and various mental health outcomes.</p> </div> </div> </div> </div> <div> <div> <div> <div> </div> <div><span>4o</span></div> <span></span></div> </div> </div> <div> </div> <div> <div> </div> </div> </div> <div> <div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> </div> <div> <div> </div> </div> <div> <div> <div> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div> <div> <div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Identification data for discrimination between honey bee (Apis mellifera) lineages
<p><span>The data in dw.xml file can be used for discriminate between 4 lineages of honey bee </span><em>Apis mellifera</em><span>: A, C, M, O. </span></p> <p>The discrimination is based on 19 landmarks of a forewing. The data were obtained from:</p> <p>Nawrocka, A., Kandemir, İ., Fuchs, S., & Tofilski, A. (2018). Computer software for identification of honey bee subspecies and evolutionary lineages. Apidologie, 49(2), 172-184. <a href="https://doi.org/10.1007/s13592-017-0538-y" rel="noopener">https://doi.org/10.1007/s13592-017-0538-y</a></p> <p>The dw.xml file can be used in <a href="http://drawwing.org/identifly" target="_blank" rel="noopener">IdentiFly</a> software or in R package <a href="https://github.com/DrawWing/IdentiFlyR" target="_blank" rel="noopener">IdentiFlyR</a> https://github.com/DrawWing/IdentiFlyR. </p> <p><span> </span></p>
Identification data for discrimination between regions in lineage C of honey bee (Apis mellifera)
<p>The data in dw.xml file can be used for discriminate between 5 regions in lineage C of honey bee <em>Apis mellifera</em>: Greece, Croatia and Slovenia, Italy, Romania and Moldova and European part of Turkey. </p> <p>The discrimination is based on 19 landmarks of a forewing. The data were obtained from:</p> <p>Oleksa, A., Căuia, E., Siceanu, A., Puškadija, Z., Kovačić, M., Pinto, M. A., Rodrigues, P. J., Hatjina, F., Charistos, L., Bouga, M., Prešern, J., Kandemir, İ., Rašić, S., Kusza, S., Tofilski, A. (2023). Honey bee (<em>Apis mellifera</em>) wing images: a tool for identification and conservation. GigaScience, 12, giad019. <a href="https://doi.org/10.1093/gigascience/giad019" target="_blank" rel="noopener">https://doi.org/10.1093/gigascience/giad019</a></p> <p>The dw.xml file can be used in <a href="http://drawwing.org/identifly" target="_blank" rel="noopener">IdentiFly</a> or in R package <a href="https://github.com/DrawWing/IdentiFlyR" target="_blank" rel="noopener">IdentiFlyR</a>. </p>
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.