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Fig. 3 in Spatio-temporal variation in prevalence and intensity of trematodes responsible for waterfowl die-offs in faucet snail-infested waterbodies of Minnesota, USA
Fig. 3. Average intensity of (a) Cyathocotyle bushiensis (Cb) and (b) Sphaeridiotrema spp. (Sg) metacercariae in each of the waterbodies studied in northcentral Minnesota during nine seasons in 2011‾2013 with 95% confidence intervals. Note that waterbody specific y-axis scales are used to highlight differences within a waterbody.
Fig. 2 in Spatio-temporal variation in prevalence and intensity of trematodes responsible for waterfowl die-offs in faucet snail-infested waterbodies of Minnesota, USA
Fig. 2. Average prevalence of (a) Cyathocotyle bushiensis (Cb) and (b) Sphaeridiotrema spp. (Sg) metacercariae in each of the waterbodies studied in northcentral Minnesota during nine seasons in 2011‾2013 with 95% confidence intervals.
Fig. 1 in Spatio-temporal variation in prevalence and intensity of trematodes responsible for waterfowl die-offs in faucet snail-infested waterbodies of Minnesota, USA
Fig. 1. Map of study area in northcentral Minnesota depicting the study lakes with county boundaries, within the state and USA.
Individual allotype responses to HEK-293T-based cell lines ex-pressing single MHC class I chain-related gene B alleles
<p><span>Figure S1.</span><span> Individual allotype responses between 64 sera collected from kidney transplant patients and 5 single </span><span>MICB</span><span> allele-expressing cell lines established in </span><span>HLA</span><span> class I, </span><span>MICA,</span><span> and </span><span>MICB</span><span>-null HEK-293T cells. </span><span>HLA</span><span> class I, </span><span>MICA,</span><span> and </span><span>MICB</span><span> genes were removed using CRISPR/Cas9 in previous studies [20, 21]. Some of the 64 sera showed responses to single </span><span>MICA</span><span> allele-expressing cell lines [21]. However, none of the 64 sera showed individual allotype responses in this study.</span></p>
Figure 14. The time that a in Manatee behavioral response to boats
Figure 14. The time that a manatee has to change its behavior (i.e., the time window from when a boat was first audible on the DTAG to when the boat reached its CPA to the manatee) plotted against boat speed at CPA. The criteria to be included in this analysis were: boat passed within 50 m of the manatee; boat did not change its speed class during the pass; it was either a single boat pass or the first within a series of boat passes; and personal watercraft (e.g., Jet Skis) were excluded.
Figure 4 in Manatee behavioral response to boats
Figure 4. The mean rate of change in roll (A), heading (B), depth (C), and fluking (D) for 16 tagged manatees during boat passes with a CPA <50 m, during boat passes with a CPA>50 m, and periods without boat noise. Categories with different letters are significantly different (P <0.05). Standard error of the mean (6 SEM) is represented by whiskers.
Figure 8 in Manatee behavioral response to boats
Figure 8. Model-estimated mean probability of a manatee making a change in depth (6 SEM) according to the order of the boat pass. "First" indicates that the boat pass was a single boat pass or the first boat pass in a series of overlapping boat passes. "Subsequent" indicates a boat pass after the first pass in a series of overlapping boat passes. Means sharing any common letters are not significantly different (P> 0.05). The fixed factor values used to generate these estimates are listed in Table 3.
Figure 11 in Manatee behavioral response to boats
Figure 11. Model-estimated mean time delay from start of boat pass until first change in manatee behavior (6 95% CI) in relation to the time available for change (i.e., time from start of pass to CPA). Results are broken out by fluking state at the start of the pass: high fluking state (orange) and low fluking state (purple). Intermediate fluking state was slightly lower but not significantly different from low fluking state (Fig. S4). The fixed factor values used to generate these estimates are listed in Table 3.
Figure 3 in Manatee behavioral response to boats
Figure 3. Visualization of the acoustic and sensor data recorded by a manatee-borne DTAG for a boat pass while the tagged manatee was resting at 3 m depth. The top plot (A) is a spectrogram (frequency vs. time) from the acoustic record; color denotes amplitude (dark red is loudest). The second plot (B) shows changes in pitch of the manatee's peduncle; the colored horizontal lines at the bottom of the plot depict fluking state: black indicates a period of fluking classified as low, green is intermediate, and red indicates high fluking behavior. The purple triangle indicates an increase in fluking rate. The third plot (C) shows the manatee's roll, the fourth (D) shows the manatee's heading (relative scale), and the bottom plot (E) shows the manatee's depth, all plotted against time (s) on the x-axis. The blue lines on the bottom three plots represent periods of time during which there was a substantial change in the corresponding parameter (e.g., depth). On the bottom plot (E), the first black vertical line on the left indicates the start of the boat pass (i.e., when it first becomes audible on DTAG record), the red line indicates the boat's closest point of approach (CPA), and the black line on the right indicates the end of the boat pass (i.e., no longer audible on the DTAG record). For this pass, the manatee changed its fluking behavior, roll, heading, and depth during the boat pass and the first change in behavior was an increase in fluking state at 5.5 s before CPA (arrow on plot B).
Figure 13 in Manatee behavioral response to boats
Figure 13. The time of a manatee's first change in behavior in relation to boat speed class for boat passes within 50 m of the manatee based on the raw data (n 5 64). (A) Time of behavioral change expressed relative to the start of the pass (i.e., when boat noise first became audible on the DTAG record). (B) Time of behavioral change expressed relative to the time at CPA of the boat. The CPA occurs at 0 s with negative values indicating changes in behavior that occurred before CPA and positive values indicating changes after CPA. The boxes represent the interquartile range, the horizontal line shows the median, and the whiskers indicate the 10th and 90th percentiles.
Figure 2 in Manatee behavioral response to boats
Figure 2. Manatees were captured and tagged in Lemon Bay during the first field season (2007) and in Placida Harbor and Gasparilla Sound during the second field season (2008). When the DTAGs were recording and the manatees were being followed by the observation boat, the manatees ranged from Sarasota Bay to Cayo Costa, between the Gulf of Mexico and the Peace River.
Figure 15 in Manatee behavioral response to boats
Figure 15. The amount of time a manatee has to change its behavior before the time of CPA in relation to boat speed class for boat passes within 50 m of the manatee based on the raw data (n 5 91). Boat passes are split between those that are single or the first in a string of overlapping passes (A) and those subsequent to the first pass in a string of overlapping passes (B). The boxes represent the interquartile range, the horizontal line shows the median, and the whiskers indicate the 10th and 90th percentiles.
Figure 5 in Manatee behavioral response to boats
Figure 5. Model-estimated mean probability (6 SEM) of any change in manatee behavior (roll, heading, depth, or fluking) during a boat pass in relation to manatee-boat distance at CPA. Means sharing any common letters are not significantly different (P> 0.05). The fixed factor values used to generate these estimates are listed in Table 3.
Figure 9 in Manatee behavioral response to boats
Figure 9. Model-estimated mean probability (6 SEM) of a manatee changing its heading (A) and mean number of heading changes per min (6 SEM) (C) according to presence of manatee chewing at the start of the boat pass. Model-estimated mean probability (6 SEM) of manatee making a change in depth (B) and mean number of depth changes per min (6 SEM) (D) according to seagrass presence at the manatee's location at the start of the boat pass. Means sharing any common letters are not significantly different (P> 0.05). The fixed factor values used to generate these estimates are listed in Table 3.
Figure 6 in Manatee behavioral response to boats
Figure 6. Model-estimated mean probability of a manatee making a change in roll (A), heading (B), depth (C), and fluking (D) (6 SEM) during a boat pass in relation to manatee-boat distance at CPA. Means sharing any common letters are not significantly different (P> 0.05). The fixed factor values used to generate these estimates are listed in Table 3.
Figure 1 in The effects of two essential oils on thefunctional response of Amblyseius swirskii (Acari: Phytoseiidae) fed on Frankliniella occidentalis (Thysanoptera: Thripidae)
Figure 1. The functional response curves and feeding percentages of Amblyseius swirskii on the first instar Frankliniella occidentalis after exposure to Mentha piperita (a1, b1), and Laurus nobilis (a2, b2) essential oils.
Figure 6. Noticeable subjective response to the distorted face to the right-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>Quite surprisingly, if the distortion is viewed in the normal upward position, it evokes strong<br> emotional response to the distorted face to the right in figure 6.</p>
Figure 5.(a)Linear (y=0.45x + 57.74) dose-response relationship between plasma propranolol to % β- adrenergeric blockade derived from healthy study participants and translate into patients with angina pectoris. This image has been adapted from(Pine et al., 1975).-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>Apharmacodynamic model,with parameters in the table below, may be used to visualize the<br> propranolol concentration-effect (β-blockade) relationship in patients suffering from angina pectoris.<br> These results have been adapted from the Pine et al article published in Circulation in 1975 which<br> identified a linear relationship plasma Propranolol (ng/mL) to an effect of % β-Adrenergic Blockade<br> in a single-oral dose of 40mg Propranolol in exercising individuals (Pine et al., 1975).</p>
Room Impulse Response measurements of a rectangular room
<p>This archive contains the data for a Gitlab hosted project (https://github.com/epfl-lts2/joint_estimation_of_room_geometry_and_modes). This archive allows users to extract the Room impulse responses (RIRs) measurements for a real rectangular room. A total of 132 measurements are included. Furthermore, users have the option to perform several post processing steps on the RIRs such as filtering, downsampling and truncation in time domain. Please refer to the guidelines pdf for more information.</p>
Data and scripts for Predictable Ecological Response to Rising CO2 of a Community of Marine Phytoplankton
<p>Rising atmospheric CO<sub>2</sub> and ocean acidification are fundamentally altering conditions for life of all marine organisms, including phytoplankton. Differences in CO<sub>2</sub> related physiology between major phytoplankton taxa lead to differences in their ability to take up and utilise CO<sub>2</sub>. These differences may cause predictable shifts in the composition of marine phytoplankton communities in response to rising atmospheric CO<sub>2</sub>. We report an experiment in which 7 species of marine phytoplankton, belonging to 4 major taxonomic groups (cyanobacteria, chlorophytes, diatoms and coccolithophores) were grown at both ambient (500 µatm) and future (1000 µatm) CO<sub>2</sub> levels. These phytoplankton were grown as individual species, as cultures of pairs of species and as a community assemblage of all seven species in two culture regimes (high-nitrogen batch cultures and lower-nitrogen semi-continuous cultures, though not under nitrogen limitation). All phytoplankton species tested in this study increased their growth rates under elevated CO<sub>2</sub> independent of the culture regime. We also find that, despite species-specific variation in growth response to high CO<sub>2</sub>, the identity of major taxonomic groups provides a good prediction of changes in population growth and competitive ability under high CO<sub>2</sub>. The CO<sub>2</sub>-induced growth response is a good predictor of CO<sub>2</sub>-induced changes in competition (R<sup>2</sup>>0.93) and community composition (R<sup>2</sup>>0.73). This study suggests that it may be possible to infer how marine phytoplankton communities respond to rising CO<sub>2</sub> levels from the knowledge of the physiology of major taxonomic groups, but that these predictions may require further characterisation of these traits across a diversity of growth conditions. These findings must be validated in the context of limitation by other nutrients. Also, in natural communities of phytoplankton, numerous other factors that may all respond to changes in CO2, including nitrogen fixation, grazing and variation in the limiting resource will likely complicate this prediction.</p>
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.