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2,774 results for “Agent”
Fig. 1 in Trichomonas stableri n. sp., an agent of trichomonosis in Pacific Coast band-tailed pigeons (Patagioenas fasciata monilis)
Fig. 1. Micrographs of T. gallinae and T. stableri trophozoites. (A) T. gallinae, illustrating the four anterior flagella, and the extension of the axostyle. Note the slender appearance of the trophozoite with a near elliptical form. (B) Slender form of T. stableri is shown with protruding axostyle and four anterior flagella. (C) Rounded form of T. stableri is shown with the four anterior flagella. All scale bars are 5 µm.
Fig. 2 in Trichomonas stableri n. sp., an agent of trichomonosis in Pacific Coast band-tailed pigeons (Patagioenas fasciata monilis)
Fig. 2. Consensus phylogenetic tree of ITS1/5.8S/ITS2 sequences (260 bp alignment) made by Bayesian inference. Legend denotes substitutions per site. All posterior probabilities are>90% unless otherwise noted. ITS region Sequence Groups refer to designations described in Gerhold et al. (2008). BTPI, band-tailed pigeon; BWHA, broadwinged hawk; COHA, Cooper's hawk; ECDO, Eurasian collared dove, GRDO, common ground dove; HOFI, house finch; MODO, mourning dove; ROPI, rock pigeon; WWDO, white-winged dove.
Fig. 3 in Trichomonas stableri n. sp., an agent of trichomonosis in Pacific Coast band-tailed pigeons (Patagioenas fasciata monilis)
Fig. 3. Consensus phylogenetic tree of partial Fe-hydrogenase gene sequences (750 bp alignment) made by Bayesian inference. Legend denotes substitutions per site. All node posterior probabilities are>90% unless otherwise noted.
Fig. 5 in Population structure of the soft tick Ornithodoros maritimus and its associated infectious agents within a colony of its seabird host Larus michahellis
Fig. 5. Spatial autocorrelation in the total tick number of counted nests, measured as Moran's I, across three distance classes: a, 1st visit; b, 2nd visit; c, 3rd visit; d, 4th visit; e, 5th visit; f, 6th visit. Circles indicate the autocorrelation coefficients. The same results were obtained with female count numbers.
Fig. 2 in Population structure of the soft tick Ornithodoros maritimus and its associated infectious agents within a colony of its seabird host Larus michahellis
Fig. 2. Histogram presenting the mean number of ticks observed in all nests over time. Bars represent mean standard errors of the total number of ticks.
Fig. 3 in Population structure of the soft tick Ornithodoros maritimus and its associated infectious agents within a colony of its seabird host Larus michahellis
Fig. 3. Boxplot representations of tick numbers in counted and collected nests over time: a, females only; b, males only; c, nymphs. The box shows the median as a line across the middle and the quartiles (25th and 75th percentiles) at either end. Extremities represent the minimal and maximal values and circles represent outliers.
Fig. 1 in Population structure of the soft tick Ornithodoros maritimus and its associated infectious agents within a colony of its seabird host Larus michahellis
Fig. 1. Map showing the position of the 30 tracked nests on Carteau Island, in the Camargue region of France (represented by the red point on the bottom right map). Orange points represent the 15 nests in which ticks were counted and released. The green points are those nests where all ticks were counted and collected. Stars within the points represent the nests in which ticks were used for the screening of infectious agents. Boxes indicate the number of ticks screened and the detected infectious agents: Ana: Anaplasma spp.; Bab: Babesia spp.; Bar: Bartonella spp.; Bor: Borrelia spp.; Cox: Coxiella-like symbiont; Fra: Francisella-like symbiont; Ri: Rickettsia helvetica; Ri-like: Rickettsia-like symbiont. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 4 in Population structure of the soft tick Ornithodoros maritimus and its associated infectious agents within a colony of its seabird host Larus michahellis
Fig. 4. Spatial autocorrelation in total tick number estimated by Moran's I (Sokal and Oden, 1978). Data are from the first visit in the colony and include nests of both treatments. Ten distance classes representing 10 m between marked nests have been defined. No index value was significantly different from zero. The same results were obtained using female count data only (results not shown).
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 19. AnyLogic Implementation of Decision-Making Architechture in an Virtual Autonomous Agent Environment
<p>Figure 19 shows a screenshot of the test implementation. The picture in the middle shows the modules and interfaces<br> of the decision-making architecture which were realized by so-called “active objects” and “ports”.<br> On the left side, the implemented modules are listed. In the right lower corner of the figure, the<br> agents and the virtual environment are displayed. The environment comprises different “objects”<br> (food sources, obstacles, predators, other agents, etc.). In order to survive, the agents have to access<br> food sources. However, the accessing of food sources bears difficulties and risks.</p>
Intention Reconsideration in Wumpus World And Intentional Inference in Adolescents-Figure 3. Screen showing the agent finding the gold.
<p>The novelty of a map, that is, an 8x8 board added to the world, is introduced in the MCWW design, as opposed to the CWW version. This map is responsible for showing on the screen the agent's knowledge base or the inferences she makes as she moves and receives the different perceptions on the board. In this way, it is easier for the experimental subjects to predict the possible movements of the agent. As an example, observe a sequence of screens of the board of our version MCWW in which it is seen that the agent kills the Wumpus (the goal of Intention 2). See the figures 1-3.</p>
Intention Reconsideration in Wumpus World And Intentional Inference in Adolescents-Figure 2. Screen showing the agent hunting the Wumpus.
<p>The novelty of a map, that is, an 8x8 board added to the world, is introduced in the MCWW design, as opposed to the CWW version. This map is responsible for showing on the screen the agent's knowledge base or the inferences she makes as she moves and receives the different perceptions on the board. In this way, it is easier for the experimental subjects to predict the possible movements of the agent. As an example, observe a sequence of screens of the board of our version MCWW in which it is seen that the agent kills the Wumpus (the goal of Intention 2). See the figures 1-3.</p>
Intention Reconsideration in Wumpus World And Intentional Inference in Adolescents-Figure 1. Screen showing the agent in the cave.
<p>The novelty of a map, that is, an 8x8 board added to the world, is introduced in the MCWW design, as opposed to the CWW version. This map is responsible for showing on the screen the agent's knowledge base or the inferences she makes as she moves and receives the different perceptions on the board. In this way, it is easier for the experimental subjects to predict the possible movements of the agent. As an example, observe a sequence of screens of the board of our version MCWW in which it is seen that the agent kills the Wumpus (the goal of Intention 2). See the figures 1-3.</p>
Partially automatically annotated corpus to predict gestural cues in Embodied Conversational Agents
<p>#Structure of the corpus</p> <p>This corpus has been built using speeches of Spanish politicians freely available <a href="http://www.congreso.es/portal/page/portal/Congreso/Congreso/Intervenciones">here</a> along with their transcriptions.</p> <p>Each transcription has been analyzed in terms of:</p> <ul> <li>Surface Syntactic Structure*</li> <li>Deep Syntactic Structure*</li> <li>Morphology (Part of Speech)*</li> <li>Communicative Structure</li> </ul> <p>Gestures (beat vs. no gesture tags) have been annotated using the videos.</p> <p>*All those features have been automatically retrieved using the parser freely available in https://github.com/TalnUPF/miis. The other features have been annotated manually.</p> <p>#Concerns about the corpus</p> <p>This corpus has been mostly annotated manually. Annotation agreement has not been computed.</p> <p>Moreover, it is small. In order to extract reliable correlations from it, it should be extended.</p>
John Baillie of Leys, political agent at Lucknow, at the Durbar of Ghāzi al-Dīn Ḥaydar Shāh
<p><a href="https://en.wikipedia.org/wiki/John_Baillie_of_Leys">John Baillie of Leys</a> (1772–1833), political agent at Lucknow, at the Durbar of Ghāzi al-Dīn Ḥaydar Shāh (<a href="https://en.wikipedia.org/wiki/Urdu">Urdu</a>:غازی الدیں حیدر شاہ) (c. 1769-19 October 1827).</p>
uGIM: week monitorization data of a microgrid with five agents (04/08/2019 - 10/08/2019)
<p>uGIM is a microgrid intelligent management software that can represent individual microgrid’s players using a multi-agent approach. This dataset has data regarding a week (from 04-08-2019 to 10-08-2019) of a microgrid with five players (all offices). All agents have consumption and generation data. One of the agents also has sensor data, such as temperature, movement and humidity.</p> <p>In uGIM, agents are deployed in the player’s facilities using single-board computers. All the data in this dataset is read and stored in five single-board computers. Each agent integrates several resources. In this microgrid deployment, all resources use TCP/IP communication. However, uGIM supports more protocols, such as Modbus/RTU, and Modbus/TCP.</p> <p>uGIM related publications:<br> - Gomes, L., Vale, Z., & Corchado, J. M. (2020). Microgrid management system based on a multi-agent approach: An office building pilot. Measurement: Journal of the International Measurement Confederation, 154. <a href="http://doi.org/10.1016/j.measurement.2019.107427">https://doi.org/10.1016/j.measurement.2019.107427</a><br> - Gomes, L., Vale, Z. A., & Corchado, J. M. (2020). Multi-Agent Microgrid Management System for Single-Board Computers: A Case Study on Peer-to-Peer Energy Trading. IEEE Access, 8, 64169–64183. <a href="http://doi.org/10.1109/ACCESS.2020.2985254">https://doi.org/10.1109/ACCESS.2020.2985254</a><br> - Gomes, L. (2020). μGIM - Microgrid intelligen management system based on a multi-agent approach and the active participation of end-users [Universidad de Salamanca]. <a href="http://doi.org/10.14201/gredos.144238">https://doi.org/10.14201/gredos.144238</a><br> - Gomes, L., Spínola, J., Vale, Z., & Corchado, J. M. (2019). Agent-based architecture for demand side management using real-time resources’ priorities and a deterministic optimization algorithm. Journal of Cleaner Production, 241, 118154. <a href="http://doi.org/10.1016/j.jclepro.2019.118154">https://doi.org/10.1016/j.jclepro.2019.118154</a></p> <p> </p> <p><em>(if you used this dataset in your publications, please send us your information so we can add your publication to the list above)</em></p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
Figura 1 in Uso de Tenebrio molitor Linnaeus 1758 (Coleoptera: Tenebrionidae) como agente biológico para la limpieza de osamentas de pequeños vertebrados
Figura 1. Larvas de Tenebrio molitor, contenedor con sustrato y ejemplares procesados después de 3 meses. Larvas (a), cubeta con la crianza de las larvas y los ejemplares sobre el sustrato (b); ejemplares luego de tres meses de limpieza: cabeza de rata (c), cabeza de gallina (d), carcasa de pollo (e) y carcasa de diamante mandarÍn (f). / Tenebrio molitor larvae, container with substrate and specimens processed after 3 months. Larvae (a), bucket with the rearing of larvae and the specimens on the substrate (b); specimens after three months of cleaning: rat head (c), hen head (d), chicken carcass (e) and diamond Mandarin carcass (f).
Figura 2 in Uso de Tenebrio molitor Linnaeus 1758 (Coleoptera: Tenebrionidae) como agente biológico para la limpieza de osamentas de pequeños vertebrados
Figura 2. Daño producido por la acción de las larvas de Tenebrio molitor. Carcasa de diamante mandarÍn con esternón dañado (a), detalle del esternón de diamante mandarÍn con daño indicado por flechas (b), ausencia del anillo esclerótico del ojo por destrucción completa en cabeza de diamante mandarÍn (c) y cabeza de pollo (d). e = esternón. / Damage caused by the action of Tenebrio molitor larvae. Diamond Mandarin carcass with damaged sternum (a), detail of sternum of diamond Mandarin with damage indicated by arrows (b), absence of sclerotic ring of the eye due to complete destruction in diamond Mandarin head (c) and chicken head (d). e = sternum.
Figura 1 in Uso de Linepithema humile Mayr, 1868 (Hymenoptera: Formicidae) como agente biológico para la limpieza de osamenta de Philodryas chamissonis (Wiegmann, 1834) (Squamata: Colubridae)
Figura 1. Proceso de limpieza de esqueleto de culebra de cola larga. a.) Ejemplar recolectado. b) Dispositivo con el ejemplar y el cebo de azúcar. c) y d) Esqueleto limpio. / Long-tailed snake skeleton cleaning process. a.) Collected specimen. b) Device with the specimen and sugar bait. c) and d) Clean skeleton.
Fig. 2 in Oviposition of Aedes aegypti Linnaeus, 1762 and Aedes albopictus Skuse, 1894 (Diptera: Culicidae) under laboratory and field conditions using ovitraps associated to different control agents, Manaus, Amazonas, Brazil
Fig. 2. Total Aedes aegypti and Aedes albopictus adults obtained through egg collection in ovitraps containing grass infusion (control) and associated with different control agents. Legend: *(gI + Bti) – grass infusion + Bacillus thuringiensis israelensis; *(gI + Ss) – grass infusion + Saccharopolyspora spinosa; *(dW + Th) – distilled water + Toxorhynchites haemorrhoidalis; *(gI + P) – grass infusion + Pyriproxyfen;* (gI) – grass infusion (control).
Fig. 1 in Oviposition of Aedes aegypti Linnaeus, 1762 and Aedes albopictus Skuse, 1894 (Diptera: Culicidae) under laboratory and field conditions using ovitraps associated to different control agents, Manaus, Amazonas, Brazil
Fig. 1. Ovitraps Positivity Index (OPI) and Egg Density Index (EDI) with association of grass infusion with different control agents using ovitraps in the period ranging from June to July 2015, Manaus – Amazonas – Brazil. Legend: *(gI + Bti) – grass infusion + Bacillus thuringiensis israelensis; * (gI + Ss) – grass infusion + Saccharopolyspora spinosa; * (dW + Th) – distilled water + Toxorhynchites haemorrhoidalis; *(gI + P) – grass infusion + Pyriproxyfen; * (gI) – grass infusion (control).
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.