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Vocal behavior in spotted seals (Phoca largha) and implications for passive acoustic monitoring
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Brainport, Automated valet parking, parking spot detected by drone
<p><strong>Scenario description</strong>:</p> <p>The drone receives AVP command message (Message type AutoPilot.DroneAVPCommand) from PMS via the IBM IoT Platfom. The command message contains the instruction about the selected parking spots to be checked. The drone takes off and fly to the corresponding parking spots detects the occupancy (FREE or OCCUPIED) of the parking spot and publishes the message from type AutoPilot.ParkingSpotDetection to the PMS via IBM Watson IoT platform und return to the lading position and landed. During the flight the drone sends continuously the message about it current position and some status information as message from type AutoPilot.PositionEstimate to the PMS via IoT Platform.</p> <p><strong>Session description</strong>:</p> <p>Selection of one free parking spot to be check (see command message contain), the drone detects the parking spot and publish the occupancy information to the PMS for parking management purpose</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Figure 2 in Nesting biology of the Spotted Nightingale-Thrush (Catharus dryas) and comparison of life histories in the genus Catharus
Figure 2. Reproductive phenology of C. dryas. The black bars show the total number of nests encountered monthly with eggs. The white bars show the ones encountered with nestlings. The red line represents the precipitation values recorded in the Manu National Park between 2000 and 2012, obtained from http://www.worldweatheronline.com/. Note the synchrony between the start of the rains and egg laying, and the end of reproduction with the peak of rains.
Figure 1 in Nesting biology of the Spotted Nightingale-Thrush (Catharus dryas) and comparison of life histories in the genus Catharus
Figure 1. Photographic evidence of Catharus dryas nesting characteristics. (a) The two nest layers: external layer composed principally of moss, and the internal layer built with thick and dark root networks. This photo is a courtesy of Sharon Beals, from the collection of the Western Foundation of Vertebrate Zoology. (b) Greenish-blue eggs with brown speckles. (c) Evidence of the high moss density on the vegetation at the nest's locations. (d) Three-day old nestling. (e) Ten-day old nestling. (f) 13-day old nestling.
Figure 6 in Nesting biology of the Spotted Nightingale-Thrush (Catharus dryas) and comparison of life histories in the genus Catharus
Figure 6. Absolute character values for all Catharus species. Values correspond to the minimum range. White bars represent Temperate species, meanwhile grey bars represent Tropical species. C. dryas is highlighted by a dark grey colour. Asterisk (*) indicates not available information for those species.
Figure 5 in Nesting biology of the Spotted Nightingale-Thrush (Catharus dryas) and comparison of life histories in the genus Catharus
Figure 5. Nestling growth rate throughout the nestling period in C. dryas. (a) Mass, (b) wing and (c) tarsus growth. The nestling age corresponds to the days elapsed after hatching. The intervals correspond to standard deviation and the numbers in the first panel to the sample size (number of nestling) for each specific age.
Figure 3 in Nesting biology of the Spotted Nightingale-Thrush (Catharus dryas) and comparison of life histories in the genus Catharus
Figure 3. Mean nest attendance per day across the incubation period of C. dryas based on data from 12 nests. The error bars correspond to standard deviation and the numbers above the bars to the number of nests monitored. Statistical analysis indicates no-change nest attendance through the incubation period.
Figure 4 in Nesting biology of the Spotted Nightingale-Thrush (Catharus dryas) and comparison of life histories in the genus Catharus
Figure 4. Daytime incubation behaviour in C. dryas. (a) Egg (black points) and environmental (grey points) temperatures, and the variation by hour measured as standard deviation. (b) Time on the nest, measured as the number of minutes in the nest. (c) Number of off-bout trips. (d) Length of off-bout trips. These figures are based on data from seven nest monitored during 52 days. This plot shows a relative constant pattern of egg temperature, nest attendance and the length of trips across the day.
FIGURE 4 in Description of a new group of species of Edessa Fabricius, 1803 (Hemiptera: Pentatomidae: Edessinae) with translucent spot on hemelytra
FIGURE 4. Edessa brunneofasciata sp. n. A–C Male, pygophore; A—dorsal view; B—posterior view; C—ventral view. D— Female, genital plates (Scale = 1mm).
FIGURE 2 in Description of a new group of species of Edessa Fabricius, 1803 (Hemiptera: Pentatomidae: Edessinae) with translucent spot on hemelytra
FIGURE 2. Edessa translucida sp. n. A–C Male, pygophore; A—dorsal view; B—posterior view; C—ventral view. D—Female, genital plates (Scale = 1mm).
FIGURE 1. Edessa stalii. A–C in Description of a new group of species of Edessa Fabricius, 1803 (Hemiptera: Pentatomidae: Edessinae) with translucent spot on hemelytra
FIGURE 1. Edessa stalii. A–C Male, pygophore; A—dorsal view; B—posterior view; C—ventral view. D—Female, genital Plates (dr—dorsal rim; vr—ventral rim; proc—proctiger; gp—genital superior process; pa—parameres; gc8—gonocoxite 8; gc9—gonocoxite 9; la8—laterotergite 8; la9—laterotergite 9; x—abdominal segment X. Scale = 1mm).
Data from: Transcriptome sequencing reveals signatures of positive selection in the spot-tailed earless lizard
<p><span><span><span><span><span><span><span><span><span><span><span>The continual loss of threatened biodiversity is occurring at an accelerated pace. High-throughput sequencing technologies are now providing opportunities to address this issue by aiding in the generation of molecular data for many understudied species of high conservation interest. Our overall goal of this study was to begin building the genomic resources to continue investigations and conservation of the Spot-Tailed Earless lizard. Here we leverage the power of high-throughput sequencing to generate the liver transcriptome for the Northern Spot-Tailed Earless Lizard (<i>Holbrookia lacerata</i>)<i> </i>and Southern Spot-Tailed Earless Lizard (<i>Holbrookia</i> <i>subcaudalis</i>), which have declined in abundance in the past decades, and their sister species, the Common Lesser Earless Lizard (<i>Holbrookia maculata</i>). Our efforts produced high quality and robust transcriptome assemblies validated by <b>1</b>) quantifying the number of processed reads represented in the transcriptome assembly and <b>2</b>) quantifying the number of highly conserved single-copy orthologs that are present in our transcript set using the BUSCO pipeline. We found 1,361 1-to-1 orthologs among the three <i>Holbrookia </i>species, <i>Anolis carolinensis</i>, and <i>Sceloporus undulatus</i>. We carried out dN/dS selection tests using a branch-sites model and identified a dozen genes that experienced positive selection in the <i>Holbrookia</i> lineage with functions in development, immunity, and metabolism. Our single-copy orthologous sequences additionally revealed significant pairwise sequence divergence (~.73%) between the Northern <i>H. lacerata</i> and Southern <i>H.</i> <i>subcaudalis </i>that further supports the recent elevation of the Southern Spot-Tailed Earless Lizard to full species<i>.</i></span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Prevalence of afebrile malaria and development of risk-scores for gradation of villages: a study from a hot-spot in Odisha
Introduction: Malaria is a public health emergency in India and Odisha. The national malaria elimination programme aims to expedite early identification, treatment and follow-up of malaria cases in hot-spots through a robust health system, besides focusing on efficient vector control. This study, a result of mass screening conducted in a hot-spot in Odisha, aimed to assess prevalence, identify and estimate the risks and develop a management tool for malaria elimination. Methods: Through a cross-sectional study and using WHO recommended Rapid Diagnostic Test (RDT), 13221 individuals were screened. Information about age, gender, education and health practices were collected along with blood sample (5 µl) for malaria testing. Altitude, forestation, availability of a village health worker and distance from secondary health center were captured using panel technique. A multi-level poisson regression model was used to analyze association between risk factors and prevalence of malaria, and to estimate risk scores. Results: The prevalence of malaria was 5.8% and afebrile malaria accounted for 79 percent of all confirmed cases. Higher proportion of Pv infections were afebrile (81%). We found the prevalence to be 1.38 (1.1664 - 1.6457) times higher in villages where the Accredited Social Health Activist (ASHA) didn't stay; the risk increased by 1.38 (1.0428 - 1.8272) and 1.92 (1.4428 - 2.5764) times in mid- and high-altitude tertiles. With regard to forest coverage, villages falling under mid- and highest-tertiles were 2.01 times (1.6194 - 2.5129) and 2.03 times (1.5477 - 2.6809), respectively, more likely affected by malaria. Similarly, villages of mid tertile and lowest tertile of education had 1.73 times (1.3392 - 2.2586) and 2.50 times (2.009 - 3.1244) higher prevalence of malaria. Conclusion: Presence of ASHA worker in villages, altitude, forestation, and education emerged as principal predictors of malaria infection in the study area. An easy-to-use risk-scoring system for ranking villages based on these risk factors could facilitate resource prioritization for malaria elimination.
Data from: Great spotted cuckoo eggshell microstructure characteristics can make eggs stronger
Obligate avian brood parasites lay stronger eggs than their hosts or non-parasitic relatives because they are rounder and have a thicker eggshell. Additionally, some other characteristics of the brood parasitic eggshells related to their microstructure such as size and orientation of calcite crystal units could also contribute to generating even stronger shells. An eggshell microstructure formed by small randomly oriented calcite crystal units can increase the robustness of the eggshells of birds. Here, the eggshell microstructure of avian brood parasites as well as their hosts have been characterized in detail, using X-ray diffraction analyses to estimate the size and degree of orientation of calcite crystal units making the eggshell. Specifically, the brood parasitic great spotted cuckoo (Clamator glandarius) and two hosts (jackdaws, Corvus monedula and magpie, Pica pica) and one non-host species (the pigeon, Columba livia domestica) were considered. Calcite crystal of the eggshell of the brood parasitic species was smaller and more randomly oriented than those of the eggshells of non-parasitic species, which suggest that eggshell microstructure would contribute to explain why parasitic eggs are more resistant to breakage than those of their hosts.
Data from: Spotting the pests of tomorrow - Sampling designs for detection of species associations with woody plants
Aim: Early warning against potentially harmful organisms of woody plant species can be achieved by sampling sentinel plants in exporting countries. However, it is unclear where sentinel plants can best be located, and how many samples are required and when and how often sampling optimally should take place for the adequate assessment of the biodiversity associated with the target plant species. We aimed to review spatial and temporal factors affecting associate biodiversity of single woody plant species and to develop guidance for the design of global biodiversity sampling studies. Location: Worldwide. Taxon: Insects and Fungi. Methods: Literature about factors affecting the diversity of insects and fungi in association with single plant species on global, regional, local and different temporal scales was reviewed. Case studies of insect and fungal diversity, primarily collected on single plant species, and the cost of collecting and analysing samples from locations around the world were analysed. Results: The review of the literature illustrated various factors affecting diversity, and the case studies allowed quantification of the relative impact of some spatial, temporal and financial aspects on captured biodiversity and, thus, illustrate the need to consider all possible factors that may affect the result of the sampling when deciding on a sampling design. Main conclusions: Our study illustrates the factors that should be considered when deciding on the location and timing of sampling for sentinel plants, which is important because of the trade-off between the number of samples and sampling locations needed to detect many of the species which may be potential pests, and the cost of (repeated) sampling in many locations. Decisions about the sampling design must be based on the objective of the sampling, but our recommendations apply irrespective of the targeted plant species or country.
Water-borne and plasma corticosterone are not correlated in spotted salamanders
<p>Water-borne hormone measurement is a noninvasive method suitable for amphibians of all sizes that are otherwise difficult to sample. For this method, containment-water is assayed for hormones released by the animal. Originally developed in fish, the method has expanded to amphibians, but requires additional species-specific validations. We wanted to determine physiological relevance of water-borne corticosterone in spotted salamanders (<i>Ambystoma maculatum</i>) by comparing concentrations to those taken using established corticosterone sampling methods, such as plasma. Using a mixture of field and laboratory studies, we compared water-borne corticosterone levels to other traditional methods of sampling corticosterone for spotted salamander larvae, metamorphs, and adults. Despite multiple attempts, and detecting differences between age groups, we found no correlations between water-borne and plasma corticosterone levels in any age group. Water-borne sampling measures a rate of release; whereas plasma is the concentration circulating in the blood. The unique units of measurement may inherently prevent correlations between the two. These two methods may also require different interpretations of the data and the physiological meaning. We also note caveats with the method, including how to account for differences in body size and life history stages. Collectively, our results illustrate the importance of careful validation of water-borne hormone levels in each species in order to understand its physiological significance.</p>
FIGURE 7. A in A new white-spotted moray eel, Gymnothorax aurocephalus sp. nov. (Muraenidae Muraeninae) from Andaman Sea, India
FIGURE 7. A. Gymnothorax aurocephalus; B. Gymnothorax pseudotile; C. Gymnothorax punctatus; D. Gymnothorax smithi; E. Gymnothorax tile.
FIGURE 6 in A new white-spotted moray eel, Gymnothorax aurocephalus sp. nov. (Muraenidae Muraeninae) from Andaman Sea, India
FIGURE 6. Lateral view of head of holotype of Gymnothorax aurocephalus sp. nov. A. Photograph of fresh specimen showing densely packed golden spots close to rictus; B. Formalin-preserved specimen.
FIGURE 8. A in A new white-spotted moray eel, Gymnothorax aurocephalus sp. nov. (Muraenidae Muraeninae) from Andaman Sea, India
FIGURE 8. A.Gymnothorax cf. sokotrensis, undescribed (USNM 438252; 610 mm TL), Myanmar; B. Lateral view of head showing larger white blotches (photo: O. Alvheim).
FIGURE 3 in A new white-spotted moray eel, Gymnothorax aurocephalus sp. nov. (Muraenidae Muraeninae) from Andaman Sea, India
FIGURE 3. Gymnothorax aurocephalus sp. nov., holotype (EBRC/ZSI/11800; 723 mm TL). A. Photograph of fresh specimen; B. Formalin-preserved specimen.
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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.