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130 results for “categorization”
Categorized chlorophyll index map: Urva Reservoir in northern Brazil
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Categorized chlorophyll index map: Ilha Solteira Reservoir in southeastern Brazil.
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Categorized chlorophyll index map: Ranco Reservoir in southern Chile.
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Auditory Emotion Word Primes Influence Emotional Face Categorization in Children and Adults, but Not Vice Versa
<p>Abstract</p> <p>In order to assess how the perception of audible speech and facial expressions influence one another for the perception of emotions, and how this influence might change over the course of development, we conducted two cross-modal priming experiments with three age groups of children (6-, 9-, and 12-years old), as well as college-aged adults. In Experiment 1, 74 children and 24 adult participants were tasked with categorizing photographs of emotional faces as positive or negative as quickly as possible after being primed with emotion words presented via audio in valence-congruent and valence-incongruent trials. In Experiment 2, 67 children and 24 adult participants carried out a similar categorization task, but with faces acting as visual primes, and emotion words acting as auditory targets. The results of Experiment 1 showed that participants made more errors when categorizing positive faces primed by negative words versus positive words, and that 6-year-old children are particularly sensitive to positive word primes, giving faster correct responses regardless of target valence. Meanwhile, the results of Experiment 2 did not show any congruency effects for priming by facial expressions. Thus, audible emotion words seem to exert an influence on the emotional categorization of faces, while faces do not seem to influence the categorization of emotion words in a significant way.</p>
D2.11 Categorization of practices in the Platforms M17
<p>Deliverable 2.11 presents the updated list of practices titles and respective categories, added to the B-THENET “Practices” and “R&I” platforms. The dataset format is a table, and it is generated by a plugin. The overall process used to obtain the dataset was described in the previous Deliverable 2.10</p>
FIGURE 3. Species Richness per Ecoregion. The richness data per ecoregion were categorized into classes with equal intervals, using 14 in Revealing the Baja California Peninsula's Hidden Treasures: An Annotated checklist of the native bees (Hymenoptera: Apoidea: Anthophila)
FIGURE 3. Species Richness per Ecoregion. The richness data per ecoregion were categorized into classes with equal intervals, using 14 breaks. However, the map displays only the eight categories where ecoregional richness is concentrated. Ecoregion: Coastal Sage Matorral (CSM); Chaparral (Ch); Baja California Mountains (BCM); Succulent Coastal Matorral (SCM); Lower Colorado Desert (LCD); Central Desert (CD); Vizcaíno Desert (VD); Gulf Coast (GC); La Giganta Ranges (GR); Magdalena Plains (MP); Tropical Dry Forest (TDF); Cape Mountains (CM); Sarcocaulescent Shrubland (SS).
Clinical Categorization Algorithm (Clical) and Machine-Learning Approach (Srf-clical) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-world Evidence from Istituto Nazionale Tumori Irccs Fondazione Pascale, Napoli, Italy.
<p>Raw-data related to a manuscript submitted to "Cancers" journal - MDPI - https://www.mdpi.com/journal/cancers</p> <p><strong>Manuscript Title</strong>: Clinical Categorization Algorithm (Clical) and Machine-Learning Approach (Srf-clical) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-world Evidence from Istituto Nazionale Tumori Irccs Fondazione Pascale, Napoli, Italy.</p> <p><strong>Authors:</strong> Gabriele Madonna1,#, Giuseppe V. Masucci2,3,#, Mariaelena Capone1, Domenico Mallardo1, Antonio Maria Grimaldi1, Ester Simeone1, Vito Vanella1, Lucia Festino1, Marco Palla1, Luigi Scarpato1, Marilena Tuffanelli1, Grazia D’angelo1, Lisa Villabona2, Isabelle Krakowski2,4, Hanna Eriksson2,3, Felipe Simao5, Rolf Lewensohn2,3, Paolo Antonio Ascierto1,+</p> <p><strong>Affiliations</strong>:</p> <p>1 Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione "G. Pascale", Napoli, Italy</p> <p>2 Theme Cancer, Karolinska University Hospital, Stockholm, Sweden</p> <p>3 Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden</p> <p>4 Theme Inflammation, Karolinska University Hospital Stockholm, Sweden</p> <p>5 Genevia technologies OY, Tampere, Finland</p> <p># these authors equally contributed</p> <p>+ Corresponding author</p> <p><strong>Abstract of submitted Manuscript:</strong> The real-life application of immune checkpoint inhibitors (ICI) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICI at Istituto Nazionale Tumori IRCCS Fondazione “G. Pascale” of Napoli Italy (INT-NA). To compare patients’ clinical variables (age, Lactate Dehydrogenase (LDH), Neutrophil-Lymphocyte Ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive non-hierarchical way, a Clinical Categorization Algorithm (CLICAL) was defined and validated by the application of machine learning, Survival Random Forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms convened into predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with a 95% accuracy.</p> <p><strong>Funding: </strong>This research was funded by Italian Ministry of Health (IT-MOH) through “Ricerca Corrente”, grants number M2-2. Additional funding [N#184093) from the Stockholm Cancer Society and King Gustav V’s Jubilee foundation Stockholm.</p>
Developmental Changes in Gaze Behavior and the Effects of Auditory Emotion Word Priming in Emotional Face Categorization
<p>Data used for statistical analyses in the journal article "Developmental Changes in Gaze Behavior and the Effects of Auditory Emotion Word Priming in Emotional Face Categorization" published in the journal Multisensory Research (online publication date: 16 September 2021).</p>
Data from: Sound categorization by crocodilians
<p>Dataset, acoustic signals and all original statistical codes used in the article "Sound categorization by crocodilians".</p>
Heatmap of global collection units Any collection object in any museum can be categorized into only one of the 304 cells (19 collection types by 16 geographic regions).A "collection unit" is a single museum's holdings within a single cell. For 73 museums, there are 22,192 possible collection units. The heatmap shows the 1957 collection units with more than 10,000 objects. See supplementary materials for details and for a heatmap of the 242 collection units with more than 1 million objects. in A global approach for natural history museum collections
Heatmap of global collection units Any collection object in any museum can be categorized into only one of the 304 cells (19 collection types by 16 geographic regions).A "collection unit" is a single museum's holdings within a single cell. For 73 museums, there are 22,192 possible collection units. The heatmap shows the 1957 collection units with more than 10,000 objects. See supplementary materials for details and for a heatmap of the 242 collection units with more than 1 million objects.
Dynamics and maintenance of categorical responses in primary auditory cortex during task engagement
<p>Source data used in the paper: "Dynamics and maintenance of categorical responses in primary auditory cortex during task engagement" (BiorXiv, 2022), Chillale RK, Shamma S, Ostojic S, Boubenec Y. (doi: https://doi.org/10.1101/2022.12.19.521141)</p> <p>This data repository contains following folders: <br> - Pelardon (Ferret-P) and Timanoix(Ferret-T) as described in the figures of the paper<br> - Each folder contains sub-folders 1. Spike_soring and 2. TrialStructures and Channels_all.mat and SessionsInfo_AllSess.mat<br> - This data can used to generate figures in the paper using publicly available code (https://github.com/rupeshjnu/A1-Category) <br> - Spike_sorting folder contains recording sessions corresponding to SessionInfo_AllSess.mat file with the session number mentioned in the code<br> - Each Spike_sorting folder contains files<br> - TrialStructure folders corresponds to behavioral files corresponding to the recording sessions</p> <p><br> </p>
Corvids optimize working memory by categorizing continuous stimuli
<p>This repository contains numerical source data for graphs and charts (in a MATLAB struct) presented in our main manuscript: Corvids optimize working memory by categorizing continuous stimuli (Apostel, Panichello, Buschman, Rose).</p> <p>Contact: aylin.klarer@ruhr-uni-bochum.de, jonas.rose@ruhr-uni-bochum.de</p>
The Development of Categorization
ClinicalTrials.gov study NCT00001950. IPD Sharing: Not stated. Countries: 1. Publications: 2.
A Cardiac Registry to Evaluate and Manage the hsTnI Categorical CVD Risk in Subjects Undergoing Preventive Health Checks (PHC).
ClinicalTrials.gov study NCT04903041. IPD Sharing: NO. Countries: 1. Publications: 8.
The Role of Motion in Infants' Ability to Categorize
ClinicalTrials.gov study NCT00362076. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Sasanlimab As Maintenance Treatment Based on Clinical Response to Neoadjuvant Treatment in Molecularly Categorized Muscle Invasive Bladder Cancer Patients
ClinicalTrials.gov study NCT06623162. IPD Sharing: NO. Countries: 1. Publications: 8.
Identification and Research of Impacts of Social Categorization Process in Nurse Triage (SOCIAL SORTING)
ClinicalTrials.gov study NCT03379961. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Control of adaptive action selection by secondary motor cortex during flexible visual categorization
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Data for: Correlated evolution of categorical characters under a simple model
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Data from: Rapid categorization of natural face images in the infant right hemisphere
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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)
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.