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590 results for “encounter”
Dyadic encounter unfamiliar female pairs over 2 days – 16p11.2 Del mouse strain – WT-WT - 5195_5167
<p>We evaluated the social interactions and communication between unfamiliar individuals in pairs. For these recordings of social behaviour and ultrasonic communication, we focused on pairs of individuals since we currently cannot identify the emitter of USVs when animals were interacting closely. Therefore, we recorded undisturbed dyadic interactions between two unfamiliar individuals (from two different housing cages) of the same age (14-20 weeks of age) and genotype for 47h (two days and nights). For that purpose, we coupled the Live Mouse Tracker system (plugin 931) with one CM16/CMPA microphone (Avisoft Bioacoustics, Glienecke, Germany) connected to the Avisoft Ultrasound Gate 416 (300 kHz sampling rate, 16-bit format; trigger: level of this channel; pre-trigger: 1 s; hold time: 1 s; duration > 0.005 s; trigger event: 2 % energy in 25-125 kHz with entropy < 50%; Avisoft Bioacoustics, Glienecke, Germany). LMT and Avisoft systems were synchronised based on the protocol described in (de Chaumont et al., 2021). Each upload includes the sqlite database from LMT (processed, with USV synchronised) and a zip file with all USVs sequences automatically recorded by Avisoft.</p>
Dyadic encounter unfamiliar female pairs over 2 days – 16p11.2 Del mouse strain – Del/+-Del/+ - 5257_5180
<p>We evaluated the social interactions and communication between unfamiliar individuals in pairs. For these recordings of social behaviour and ultrasonic communication, we focused on pairs of individuals since we currently cannot identify the emitter of USVs when animals were interacting closely. Therefore, we recorded undisturbed dyadic interactions between two unfamiliar individuals (from two different housing cages) of the same age (14-20 weeks of age) and genotype for 47h (two days and nights). For that purpose, we coupled the Live Mouse Tracker system (plugin 931) with one CM16/CMPA microphone (Avisoft Bioacoustics, Glienecke, Germany) connected to the Avisoft Ultrasound Gate 416 (300 kHz sampling rate, 16-bit format; trigger: level of this channel; pre-trigger: 1 s; hold time: 1 s; duration > 0.005 s; trigger event: 2 % energy in 25-125 kHz with entropy < 50%; Avisoft Bioacoustics, Glienecke, Germany). LMT and Avisoft systems were synchronised based on the protocol described in (de Chaumont et al., 2021). Each upload includes the sqlite database from LMT (processed, with USV synchronised) and a zip file with all USVs sequences automatically recorded by Avisoft.</p>
Dataset of "Early triadic interactions in the first year of life: A systematic review on object-mediated shared encounters"
<pre>Two files are available as a result of data extraction from the 51 studies included in the systematic review entitled: "Early triadic interactions in the first year of life: A systematic review on object-mediated shared encounters": (1) DataExtraction.csv -> Dataset resulting from data extraction. (2) DictionaryVariables.csv -> dictionary of the variables included in the dataset: Authors, doi, Year of publication, Study type, Design type, Data analysis strategy, Type of task, Objects, Infants' age (months), Context of interaction, and Country. </pre>
data for "A disordered encounter complex is central to the yeast Abp1p SH3 domain binding pathway"
<p>Protein-protein interactions are involved in a wide range of cellular processes. These interactions often involve intrinsically disordered proteins (IDPs) and protein binding domains. However, the details of IDP binding pathways are hard to characterize using experimental approaches, which can rarely capture intermediate states present at low populations. SH3 domains are common protein interaction domains that typically bind proline-rich disordered segments and are involved in cell signaling, regulation, and assembly. We hypothesized, given the flexibility of SH3 binding peptides, that their binding pathways include multiple steps important for function. Molecular dynamics simulations were used to characterize the steps of binding between the yeast Abp1p SH3 domain (AbpSH3) and a proline-rich IDP, ArkA. Before binding, the N-terminal segment 1 of ArkA is pre-structured and adopts a polyproline II helix, while segment 2 of ArkA (C-terminal) adopts a 310 helix, but is far less structured than segment 1. As segment 2 interacts with AbpSH3, it becomes more structured, but retains flexibility even in the fully engaged state. Binding simulations reveal that ArkA enters a flexible encounter complex before forming the fully engaged bound complex. In the encounter complex, transient nonspecific hydrophobic and long- range electrostatic contacts form between ArkA and the binding surface of SH3. The encounter complex ensemble includes conformations with segment 1 in both the forward and reverse orientation, suggesting that segment 2 may play a role in stabilizing the correct binding orientation. While the encounter complex forms quickly, the slow step of binding is the transition from the disordered encounter ensemble to the fully engaged state. In this transition, ArkA makes specific contacts with AbpSH3 and buries more hydrophobic surface. Simulating the binding between ApbSH3 and ArkA provides insight into the role of encounter complex intermediates and nonnative hydrophobic interactions for other SH3 domains and IDPs in general.</p>
Data from: How do seabirds modify their search behaviour when encountering fishing boats?
Seabirds are well known to be attracted by fishing boats to forage on offal and baits. We used recently developed loggers that record accurate GPS position and detect the presence of boats through their radar emissions to examine how albatrosses use Area Restricted Search (ARS) and if so, have specific ARS behaviours, when attending boats. As much as 78.5% of locations with a radar detection (contact with boat) during a trip occurred within ARS: 36.8% of all large-scale ARS (n=212) and 14.7% of all small-scale ARS (n=1476) were associated with the presence of a boat. During small-scale ARS, birds spent more time and had greater sinuosity during boat-associated ARS compared with other ARS that we considered natural. For, small-scale ARS associated with boats, those performed over shelves were longer in duration, had greater sinuosity, and birds spent more time sitting on water compared with oceanic ARS associated with boats. We also found that the proportion of small-scale ARS tend to be more frequently nested in larger-scale ARS was higher for birds associated with boats and that ARS behaviour differed between oceanic (tuna fisheries) and shelf-edge (mainly Patagonian toothfish fisheries) habitats. We suggest that, in seabird species attracted by boats, a significant amount of ARS behaviours are associated with boats, and that it is important to be able to separate ARS behaviours associated to boats from natural searching behaviours. Our study suggest that studying ARS characteristics should help attribute specific behaviours associated to the presence of boats and understand associated risks between fisheries.
Supplementary material for the paper "Geologically old but freshly exposed rock fragments encountered by Yutu-2 rover"
<p>The derived data presented in figures 6 and 8, and the image IDs for producing figures 1, 2, 3/4, 5, and 7 in the paper "Geologically old but freshly exposed rock fragments encountered by Yutu-2 rover" are available in this data repository.</p>
Encounters in the Republic of Heaven [attached sound examples]
<p>Sound examples attached to the article by Trevor Wishart <em>Encounters in the Republic of Heaven </em></p>
Рис. 1. Sicista betulina: 1 — Λесная мышовка, пойманная в окрестностях сеΛа КойÀа; 2 — карта распространения виÀа: зеΛеная заΛивка — ареаΛ виÀа по Burgin et al. 2020, синие звезÀочки — точки нахоÀок в национаΛьном парке «Онежское Поморье» и на СоΛовецком архипеΛаге (Черенкова 2014), красный круг — новая нахоÀка в районе Àеревни КойÀа; 3–4 — биотопы, в которых быΛ встречен виÀ в окрестностях сеΛа КойÀа Fig. 1. Sicista betulina: 1 — the northern birch mouse caught in the vicinity of Koida village; 2 — the species distribution map: the green fill — species range according to Burgin et al. 2020; the blue stars — points where the species was found in the Onezhskoye Pomorye National Park and on the Solovetsky Archipelago (Cherenkova 2014); the red circle — a new find in the area of Koida village; 3–4 — biotopes in which the species was encountered in the vicinity of Koida village in A new record of the northern birch mouse Sicista betulina (Pallas, 1779) in the north of the Arkhangelsk Region (Rodentia: Sminthidae)
Рис. 1. Sicista betulina: 1 — Λесная мышовка, пойманная в окрестностях сеΛа КойÀа; 2 — карта распространения виÀа: зеΛеная заΛивка — ареаΛ виÀа по Burgin et al. 2020, синие звезÀочки — точки нахоÀок в национаΛьном парке «Онежское Поморье» и на СоΛовецком архипеΛаге (Черенкова 2014), красный круг — новая нахоÀка в районе Àеревни КойÀа; 3–4 — биотопы, в которых быΛ встречен виÀ в окрестностях сеΛа КойÀа Fig. 1. Sicista betulina: 1 — the northern birch mouse caught in the vicinity of Koida village; 2 — the species distribution map: the green fill — species range according to Burgin et al. 2020; the blue stars — points where the species was found in the Onezhskoye Pomorye National Park and on the Solovetsky Archipelago (Cherenkova 2014); the red circle — a new find in the area of Koida village; 3–4 — biotopes in which the species was encountered in the vicinity of Koida village
Dataset (encounter histories of female bats) used in the analysis for the paper Culina et al.: Live fast, don't die young: survival reproduction trade-offs in long-lived income breeders
<p>Two data files (Md_capture_H, Mn_capture_H) represent the capture histories of females of two species (Myotis daubentonii = Md, and M. nattereri=Mn). These capture histories were used to run multi-event-capture-mark-recapture model, as described in the paper, and in the readme document in this data package.<br> </p>
FIGURE 1 in Challenges encountered during acid resin transfer preparation of fossil fish from Monte Bolca, Italy
FIGURE 1. Fourier-Transform Infrared spectrograph of the three existing polymers. Wavenumber refers to the wavelength of light and % Transmission refers to the amount of each wavelength that is transmitted through the sample. These are the parameters measured by the spectrometer to achieve an infra-red spectrum. Each peak is indicative of a different chemical bond and different substances will produce distinct patterns.
FIGURE 3. 1 in Challenges encountered during acid resin transfer preparation of fossil fish from Monte Bolca, Italy
FIGURE 3. 1, Yellowed resin shows dimpled (etched) surface. A glass plate has been added to enhance clarity. 2, Yellowed resin displaying severe cracking and near opacity.
FIGURE 2 in Challenges encountered during acid resin transfer preparation of fossil fish from Monte Bolca, Italy
FIGURE 2. Specimen NHMUK PV P1990 during preparation. 1, the block split into halves, showing mortar. 2, The underside of the block set in resin, the majority of the matrix has been removed mechanically. 3, The top of the block, with the exposed fossil, embedded in resin.
Linked collectors and determiners for: A taxonomic revision of Neodietrichia (Araneae: Linyphiidae), a rarely encountered but widespread spider taxon.
Natural history specimen data linked to collectors and determiners held within, "A taxonomic revision of Neodietrichia (Araneae: Linyphiidae), a rarely encountered but widespread spider taxon". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/ffeccc89-779c-46c9-a4f7-842299585660">https://bionomia.net/dataset/ffeccc89-779c-46c9-a4f7-842299585660</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/ffeccc89-779c-46c9-a4f7-842299585660">https://gbif.org/dataset/ffeccc89-779c-46c9-a4f7-842299585660</a>. Formatted as a Frictionless Data package.
Figure 3 in New adult host records for three Buprestidae (Coleoptera) rarely encountered in the United States and significant extension of the known geographic range of Agrilus pilosicollis Fisher
Figure 3. Dicerca mutica LeConte, ♂. a) Dorsal view. b) Ventral view. c) Lateral view. d) 5th abdomi-
Figure 2 in New adult host records for three Buprestidae (Coleoptera) rarely encountered in the United States and significant extension of the known geographic range of Agrilus pilosicollis Fisher
Figure 2. Agrilus pilosicollis Fisher ♀. a) Dorsal view. b) Ventral view. c) Lateral view. d) Lateral view of pronotum. e) Proclaw.
Sampled Encounters of Low Altitude Manned Aircraft
<p><strong>Summary</strong></p> <p>Paired encounters between two manned aircraft based on samples from Bayesian dynamic models of aircraft behavior. The encounters are uncorrelated in the sense that it is assumed that air traffic control services are not being provided during the encounter.</p> <p><strong>Description</strong></p> <p>For many aviation safety studies, aircraft behavior is represented using encounter models, which are statistical models of how aircraft behave during close encounters. They are used to provide a realistic representation of the range of encounter flight dynamics where an aircraft collision avoidance system would be likely to alert. These models represent aircraft behavior (rates) during the course of the encounter with other aircraft. Encounter models have been developed for many different manned operational contexts. For more details on encounter models, please refer to the overview on the Airspace Encounter Models GitHub organization: <a href="https://github.com/Airspace-Encounter-Models/em-overview">https://github.com/Airspace-Encounter-Models/em-overview</a>.</p> <p>The Bayesian models were sourced from <a href="https://github.com/Airspace-Encounter-Models/em-model-manned-bayes/tree/v1.3">v1.3 of the em-model-manned-bayes </a>repository while<a href="https://github.com/Airspace-Encounter-Models/em-pairing-uncor-importancesampling/tree/v1.2"> v1.2 of em-pairing-uncor-importancesampling</a> contained the software to pair samples from the Bayesian models to create encounters. This dataset contains 1.6 million encounters across 18 different encounter sets, each consisting of 100,000 unique encounters. The encounters were generated in June 2021. Please refer to the software for details on the format and variables for each encounter.</p> <p>The dataset is provided as a single .zip archive with multiple directories. Simply download the .zip file and extract. An excel file with two sheets are document the configuration parameters. The first sheet reports the tradespace and configurations for each encounter set. The model files correspond to Bayesian models from em-model-manned-bayes, while <em>tCPA</em> and <em>sample_time</em> are encounter parameters from em-pairing-uncor-importancesampling. The <em>tCPA </em>variable the desired time at which the closest point of approach occurs and <em>sample_time</em> variable is the desired total duration of the encounter in seconds. The <em>sample_time</em> value must be greater than <em>tCPA</em>. The other sheet, <em>fixed_paramters</em>, documents the fixed encounter parameters. The minimum initial horizontal and vertical separation requirements were defined by <em>R_min</em> and <em>H_min</em>. The bin edges and desired proportions defined the importance sampling criteria for the horizontal miss distance (HMD) and vertical miss distance (VMD) at the closest point of approach. The <em>encIds</em> denote that 100,000 encounters were generated for each set and that the random seed (<em>randSeed</em>) was set to one.</p> <p><strong>Distribution Statement</strong></p> <p>DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.</p> <p>© 2021 Massachusetts Institute of Technology.</p> <p>Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.</p> <p>This material is based upon work supported by the Federal Aviation Administration under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Federal Aviation Administration.</p> <p>This document is derived from work done for the FAA (and possibly others); it is not the direct product of work done for the FAA. The information provided herein may include content supplied by third parties. Although the data and information contained herein has been produced or processed from sources believed to be reliable, the Federal Aviation Administration makes no warranty, expressed or implied, regarding the accuracy, adequacy, completeness, legality, reliability or usefulness of any information, conclusions or recommendations provided herein. Distribution of the information contained herein does not constitute an endorsement or warranty of the data or information provided herein by the Federal Aviation Administration or the U.S. Department of Transportation. Neither the Federal Aviation Administration nor the U.S. Department of Transportation shall be held liable for any improper or incorrect use of the information contained herein and assumes no responsibility for anyone’s use of the information. The Federal Aviation Administration and U.S. Department of Transportation shall not be liable for any claim for any loss, harm, or other damages arising from access to or use of data or information, including without limitation any direct, indirect, incidental, exemplary, special or consequential damages, even if advised of the possibility of such damages. The Federal Aviation Administration shall not be liable to anyone for any decision made or action taken, or not taken, in reliance on the information contained herein.</p>
Text-fig. 52. The distribution of aperture and tectum types among the 34 different kinds of angiosperm pollen encountered in the Catefica mesofossil flora. in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms
Text-fig. 52. The distribution of aperture and tectum types among the 34 different kinds of angiosperm pollen encountered in the Catefica mesofossil flora.
Data from: How do seabirds modify their search behaviour when encountering fishing boats?
Open the record for dataset details and reuse information.
Data from: Behavioural mimicry among poison frogs diverges during close-range encounters with predators
Open the record for dataset details and reuse information.
Data presented in: Walking Drosophila navigate complex plumes using stochastic decisions biased by the timing of odor encounters
<p>How insects navigate complex odor plumes, where the location and timing of odor packets are uncertain, remains unclear. Here, we imaged complex odor plumes simultaneously with freely-walking flies, quantifying how behavior is shaped by encounters with individual odor packets. We found that navigation was stochastic, and did not rely on the continuous modulation of speed or orientation. Instead, flies turned stochastically with stereotyped saccades, whose direction was biased upwind by the timing of prior odor encounters, while the magnitude and rate of saccades remained constant. Further, flies used the timing of odor encounters to modulate the transition rates between walks and stops. In more regular environments, flies continuously modulate speed and orientation, even though encounters can still occur randomly due to animal motion. We find that in less predictable environments, where encounters are random in both space and time, walking flies instead navigate with random walks biased by encounter timing.</p>
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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.