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824 results for “Discrimination”
Figure 5 from: Linh NN, Hang PLB, Hue HTT, Ha NH, Hanh HH, Ton ND, Hien LTT (2022) Species discrimination of novel chloroplast DNA barcodes and their application for identification of Panax (Aralioideae, Araliaceae). PhytoKeys 188: 1-18. https://doi.org/10.3897/phytokeys.188.75937
Figure 5 Percentage of variable sites, mean pairwise distances, and correct classification percentages of all markers and combinations
Figure 4 from: Linh NN, Hang PLB, Hue HTT, Ha NH, Hanh HH, Ton ND, Hien LTT (2022) Species discrimination of novel chloroplast DNA barcodes and their application for identification of Panax (Aralioideae, Araliaceae). PhytoKeys 188: 1-18. https://doi.org/10.3897/phytokeys.188.75937
Figure 4 Results of mPTP species delimitation analysis for several markers based on ML trees A Species delimitation for marker trnQ-rps16B Species delimitation for the combination of markers 1+3+4. Bootstrap values are displayed on the branches. The red branches represent supported species delimitations. Sequences highlighted in orange originate from this study.
Figure 1 from: Linh NN, Hang PLB, Hue HTT, Ha NH, Hanh HH, Ton ND, Hien LTT (2022) Species discrimination of novel chloroplast DNA barcodes and their application for identification of Panax (Aralioideae, Araliaceae). PhytoKeys 188: 1-18. https://doi.org/10.3897/phytokeys.188.75937
Figure 1 Distribution of Panax in Vietnam and sample locations. P. vietnamensis (green) collected in Quang Nam and Kon Tum Provinces. P. vietnamensis var. fuscidiscus (brown) collected in Lai Chau Province. Panax sp. Puxailaileng (pink) collected in Nghe An Province. P. bipinnatifidus (blue) and P. stipuleanatus (yellow) collected in Lao Cai Province. The natural distribution of P. vietnamensis, P. vietnamensis var. fuscidiscus, and Panax sp. are marked as green, brown, and pink, respectively. The wild habitat for P. bipinnatifidus and P. stipuleanatus is shown in yellow, and the purple area represents the distribution region of P. vietnamensis var. langbiangensis (not included in this study).
FIGURE 1 in Comparing genetic markers' efficiencies for discrimination between two commercially important holothuroids in the Mediterranean Sea, Holothuria polii and Holothuria sanctori
FIGURE 1. Whole mount, fresh samples of Holothuria sanctori (1,2) and Holothuria polii (3,4).
Willow Flycatcher subspecies song discrimination experiment data
<p class="MsoNormal">Animals use acoustic signals to repel competitors and attract mates, and signal divergence among populations can promote reproductive isolation. <em>Empidonax</em> flycatchers are insectivorous passerine birds distributed across North and Central America that are conservative in plumage but often exhibit differences in song both between and within species. Four subspecies have been recognized within the Willow Flycatcher (<em>Empidonax traillii</em>) and previous analyses have revealed differences in song structure among a subset of these. Using reciprocal playback experiments in the field, we tested for subspecific song discrimination among these four putative Willow Flycatcher subspecies. We found that two of the western subspecies (<em>E. t. adastus </em>and<em> E. t. brewsteri</em>) showed comparable aggressive responses to playbacks of their own song and those of each other and the eastern subspecies (<em>E. t. traillii</em>) while exhibiting significantly lower responses to songs of the southwestern subspecies (<em>E. t. extimus</em>)<em>. </em>The eastern subspecies (<em>E. t. traillii</em>)<em> </em>likewise<em> </em>exhibited aggressive responses to its own song significantly greater than that to the southwestern subspecies (<em>E. t. extimus</em>) but variable responses to <em>E. t. adastus</em> and <em>E. t. brewsteri</em>. In contrast, the southwestern subspecies (<em>E. t. extimus</em>) responded more aggressively to its own song relative to all other subspecies. Our results indicate that behavioral responses reflect differences in song structure among subspecies; subspecies with similar song structure responded more strongly to subspecies with that structure and less strongly to songs most different in structure, while the subspecies with the most distinctive song responded less to songs of the other three. If male response to song reflects relative reproductive compatibility within and among subspecies, song may contribute to reproductive isolation between Willow Flycatcher subspecies.</p>
Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods
<p>The physiology of living organisms, such as living plants, is complex and particularly difficult to understand on a macroscopic, organism-holistic level. Among the many options for studying plant physiology, electrical potential and tissue impedance are arguably simple measurement techniques that can be used to gather plant-level information. Despite the many possible uses, our research is exclusively driven by the idea of phytosensing, that is, interpreting living plants’ signals to gather information about surrounding environmental conditions. As ready-to-use plant-level physiological models are not available, we consider the plant as a blackbox and apply statistics and machine learning to automatically interpret measured signals. In simple plant experiments, we expose <em>Zamioculcas zamiifolia</em> and <em>Solanum lycopersicum</em> (tomato) to four different stimuli: wind, heat, red light and blue light. We measure electrical potential and tissue impedance signals. Given these signals, we evaluate a large variety of methods from statistical discriminant analysis and from deep learning, for the classification problem of determining the stimulus to which the plant was exposed. We identify a set of methods that successfully classify stimuli with good accuracy, without a clear winner. The statistical approach is competitive, partially depending on data availability for the machine learning approach. Our extensive results show the feasibility of the blackbox approach and can be used in future research to select appropriate classifier techniques for a given use case. In our own future research, we will exploit these methods to derive a phytosensing approach to monitoring air pollution in urban areas.</p> <p>Data repository for our paper " <em>Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods</em> ", submitted to the journal Bioinspiration & Biomimetics . Please refer to the paper for more information.</p> <p> </p> <p><strong>Contents of this repository</strong></p> <ul> <li><em>mu_interface:</em> Code for our data collection plant experiments, based on Raspberry Pis and the <a href="http://cybertronica.co/?q=products/phytosensor">Cybertronica phytosensing and phytoactuating system</a>.</li> <li><em>SupplementaryCode</em>: Includes the discriminant analysis classifier, raw datasets, calculated features, test-train split and the corresponding code.</li> <li><em>dl-4-tsc:</em> Deep learning framework developed by <a href="https://doi.org/10.1007/s10618-019-00619-1">Fawaz et. al (Deep learning for time series classification: a review)</a> and adapted to our use case. </li> <li><em>DeepClassifier: </em>Trained deep learning time series classifier.</li> <li><em>classification_results.xlsx: </em>Overview of the results from the deep learning framework (accuracy, precision, recall, training time, confusion matrix) and the achieved accuracies using discriminant analysis with sequential forward section (further evaluation metrics of the discriminant analysis can be found in SupplementaryCode.</li> </ul>
PREVENTION OF VIOLENCE, EXPLOITATION, CRUELTY AND DISCRIMINATION AGAINST CHILDREN
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Coworker discrimination: An experimental study
<p>Experimental data on coworker discrimination.</p>
FIGURE 9 in The RNA-seq approach to discriminate gene expression profiles in response to Beauveria bassiana and Micrococcus luteus microbial pathogens on Actias selene (Lepidoptera: Saturniidae)
FIGURE 9. Larva and adult of Actias selene.
Figure 3A in Comparison of two morphometric methods for discriminating honey bee (Apis mellifera L.) populations in Turkey
Figure 3A. Scatter plot of principle component analysis of honey bee populations from different geographic regions based on TM (Thrace = ✳; Aegean = ×; Central Anatolia/Mediterranean = ◆; Southeastern Anatolia = △; Northeastern Anatolia = □).
Text-fig. 8. Sex estimation of Moča skull (Komárno district, southern Slovakia), linear discriminant analysis using Henke's Late Upper Palaeolithic and Mesolithic database (n = 129, f = 46, m = 83) with variables M1 (GOL) and M45 (ZYB). As the closest to the Moča skull, the following fossil samples from Mesolithic could be identified: Vlasac, Arruda 2, Moita 16, Moita 17, Téviec 2, Téviec 3. in A Late Upper Palaeolithic Skull From Moča (The Slovak Republic) In The Context Of Central Europe
Text-fig. 8. Sex estimation of Moča skull (Komárno district, southern Slovakia), linear discriminant analysis using Henke's Late Upper Palaeolithic and Mesolithic database (n = 129, f = 46, m = 83) with variables M1 (GOL) and M45 (ZYB). As the closest to the Moča skull, the following fossil samples from Mesolithic could be identified: Vlasac, Arruda 2, Moita 16, Moita 17, Téviec 2, Téviec 3.
Figure 3 in Discriminant-based study of the shell morphometric relationships of Patella caerulea (Gastropoda: Prosobranchia) of the western Mediterranean Sea
Figure 3. Multivariate analysis (A: principal component analysis [PCA]; B: discriminant function analysis [DFA]) established on all sampled individuals of P. caerulea using the mean values of the parameters ([L, W and H] for the PCA and [L, W, H, L/W, L/H and W/H] for the DFA). • Individuals of group G1. • Individuals of group G2 (see Table 1 for the abbreviations). • Centroids of both groups (G1 and G2). • Projection of the parameters (L, W, and H) on the 2 most informative axes (F1 and F2) of the PCA.
Figure 1 in Discriminant-based study of the shell morphometric relationships of Patella caerulea (Gastropoda: Prosobranchia) of the western Mediterranean Sea
Figure 1. Study area showing the sampling stations (black dots). See Table 1 for abbreviations. A: Details of the French Mediterranean coast.
Figure 7 in Discrimination of the sister hedgehog species Erinaceus concolor and E. roumanicus (Erinaceomorpha: Mammalia): a geometric morphometric approach
Figure 7. Multivariate regression analysis of shape variables vs centroid size of the mandible (closed circle: E. concolor, n = 54; open circle: E. roumanicus, n = 14).
Figure 3 in Discrimination of the sister hedgehog species Erinaceus concolor and E. roumanicus (Erinaceomorpha: Mammalia): a geometric morphometric approach
Figure 3. Box and whisker plot graphics showing variation of centroid size for dorsal cranium (a) and mandible (b) between the two species.
Figure 1 in Discrimination of the sister hedgehog species Erinaceus concolor and E. roumanicus (Erinaceomorpha: Mammalia): a geometric morphometric approach
Figure 1. Collection localities for all hedgehog specimens used in this study from Turkey. Closed circle: E. concolor and open circle: E. roumanicus. Numbers in parenthesis refer to the sample sizes for the dorsal cranium and mandible, respectively.
Figure S1 in Discrimination of the sister hedgehog species Erinaceus concolor and E. roumanicus (Erinaceomorpha: Mammalia): a geometric morphometric approach
Figure S1. Ba–c) Landmarks recorded on the dorsal surface of cranium in E. concolor and E. roumanicus, respectively. b–d) Landmarks recorded on the mandible in E. concolor and E. roumanicus, respectively.
Investigating eating symptoms in the LGBTQIA+ population: Do discrimination and adverse conditions matter?
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No effect of Italian blues on color discrimination
<p>This is the material used in Stratmann & Lohmann (2023). It contains the visual stimuli used in the experiments as well as the experiment files and the data that entered the final analysis. The two datasets were then further analyzed in comparison to the data from Winawer et al. (2007), which are not published here.</p>
Data from: Contributions of subsurface cortical modulations to discrimination of executed and imagined grasp forces through stereoelectroencephalography
Stereoelectroencephalographic (SEEG) depth electrodes have the potential to record neural activity from deep brain structures not easily reached with other intracranial recording technologies. SEEG electrodes were placed through deep cortical structures including central sulcus and insular cortex. In order to observe changes in frequency band modulation, participants performed force matching trials at three distinct force levels using two different grasp configurations: a power grasp and a lateral pinch. Signals from these deeper structures were found to contain information useful for distinguishing force from rest trials as well as different force levels in some participants. High frequency components along with alpha and beta bands recorded from electrodes located near the primary motor cortex wall of central sulcus and electrodes passing through sensory cortex were found to be the most useful for classification of force versus rest although one participant did have significant modulation in the insular cortex. This study electrophysiologically corroborates with previous imaging studies that show force-related modulation occurs inside of central sulcus and insular cortex. The results of this work suggest that depth electrodes could be useful tools for investigating the functions of deeper brain structures as well as showing that central sulcus and insular cortex may contain neural signals that could be used for control of a grasp force BMI.
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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)
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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.