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1,163 results for “demonstrators”
Laparoscopic LSCI demonstration
<p>Background: Intestinal resection causes inevitable vascular damage that cannot always be seen during an intraoperative clinical assessment of local intestinal perfusion. If left unaltered, impaired perfusion can lead to complications such anastomotic leakage (AL). Therefore, we demonstrate the use of a novel laparoscopic Laser Speckle Contrast Imaging (LSCI)-based approach to assess local intestinal perfusion during the construction of intestinal anastomoses. Methods: Three segments were isolated from the small intestine of a pig and their perfusion was compromised by coagulating 7-8 mesenteric arteries. Both clinical assessment and LSCI was used to detect the induced perfusion deficits and guide a transsection in either a well perfused, marginally perfused or poorly perfused tissue area within the segment. Bowel ends were then used to create three differently perfused anastomoses: well perfused/well perfused (anastomosis segment 1), well perfused/poorly perfused (anastomosis segment 2), and poorly perfused/poorly perfused (anastomosis segment 3). After construction of the anastomoses, a final perfusion assessment using both clinical assessment and LSCI was executed to evaluate the vascular viability of the anastomosis. Results: Laparoscopic LSCI enabled continuous assessment of local intestinal perfusion and allowed for detection of perfusion deficits in real-time. The imaging feedback precisely guided the surgical procedure and, when evaluating the final anastomotic perfusion, LSCI was able to visualize the varying degrees of perfusion whereas standard clinical assessment yielded only minor differences in visual appearance of the tissue. Conclusions: In this technical note we demonstrate a novel LSCI-based approach for intraoperative perfusion assessment. With its ability to continuously visualize perfusion in real-time, laparoscopic LSCI has significant potential to optimize anastomotic surgery in the near future.</p>
CROSSBOW TC_01.01.01 final demonstration results
<p>This data set represents final demonstration results for the Test Case 1.1 from the High Level Use case 1 of the CROSSBOW project. It contains information about values: ERIC [MW], TIC [MW], TIF [MW], Loop flow [%], Optimal adequacy transaction [MW] and Net adequacy exchange [MW] for 9 areas (AL, BA, BG, GR, ME; MK, HR, RO and RS) and 366 timestamps (of Week 9 and Week 10 of 2021). </p> <p>All information about RAA methodology and terms used in this data set could be found in document <em>D4.2 CROSSBOW Regional Operation Centre Balancing Cockpit (ROC-BC).</em></p>
CROSSBOW HLU3-UC1-TC1 & HLU3-UC3-TC1 AM Market prices in the demonstration period
<p>CROSSBOW RES-CC continuously participates in the CROSSBOW AM ID market. The dataset comprises some of the market results that are used in the demonstrations. Fields are:</p> <ul> <li>Time slot (hourly)</li> <li>BSP (data is aggregated by hour and BSP)</li> <li>Energy forecasted</li> <li>Energy price in ID market</li> </ul>
Stable Isotope Mixing Models Demonstrate the Role of an Invasive Plant in Wetland Songbirds Food Webs
<p>We used analysis of natural abundance stable isotopes of <sup>13</sup>C and <sup>15</sup>N in song sparrow blood, invertebrate food sources, <em>L. latifolium </em>seeds,<em> </em>and other marsh<em> </em>plant seeds to inform Bayesian, concentration-dependent mixing models that predicted average song sparrow diets. Data presented are the csv files and R markdown code for the isotope analysis.</p>
MorphoGraphX2: Datasets that demonstrate how to create positional information with local coordinate systems
<p>Confocal image data sets and segmented meshes from various plant organs including the Arabidopsis root, flower, gynoecium, meristem, embryo and ovule. The data sets are used to demonstrate features available in MorphoGraphX software (<a href="http://www.MorphoGraphX.org">www.MorphoGraphX.org</a>), and how to use positional information to add spatial context to quantitative cellular data. Also included are longform video tutorials, and source code for the MorphoGraphX software.</p>
UF & UAB's Phase 2 Demonstration Study: Developing a Model to Support Transportation System Decisions considering the Experiences of Drivers of all Age Groups with Autonomous Vehicle Technology (Project A3)
<p>Enclosed you will find the data collected during our STRIDE Phase II research project (A3) and a data dictionary.</p>
Fig. 17. Lycosa piochardi Simon, 1876, live females demonstrating common dorsal coloration morphs. A in Lycosa Latreille, 1804 (Araneae, Lycosidae) of Israel, with a note on Geolycosa Montgomery, 1904
Fig. 17. Lycosa piochardi Simon, 1876, live females demonstrating common dorsal coloration morphs. A. Sandy morph, Midreshet Ben-Gurion. B. Dark brown morph, Mt. Gilboa. C. Light brown with dark brown median bands, Mt. Hermon. D. Light brown morph, Modi'in. Photos by I. Armiach Steinpress.
Flight demonstration of a miniature atomic scalar magnetometer based on a microfabricated rubidium vapor cell
<p>Data acquired by the Miniature Absolute Scalar Magnetometer (MASM) on the low-flying sounding rocket of the Twin Rockets to Investigate Cusp Electrodynamics 2 (TRICE-2) mission. The data columns are as follows:</p> <p>epoch time: CDF Epoch Time</p> <p>time: year, month, day, hour, minute, second</p> <p>lfreq [Hz]: Larmor frequency in Hertz</p> <p>Bt [nT]: Measured total magnetic field in nano-Tesla</p> <p>Btm [nT]: Total magnetic field of the IGRF model in nano-Tesla</p> <p>Bxm [nT]: Magnetic field Bx-component of the IGRF model in ECEF coordinates and nano-Tesla</p> <p>Bym [nT]: Magnetic field By-component of the IGRF model in ECEF coordinates and nano-Tesla</p> <p>Bzm [nT]: Magnetic field Bz-component of the IGRF model in ECEF coordinates and nano-Tesla</p> <p>flight time: Flight time with respect to launch in seconds</p> <p>ecef x pos [m]: ECEF X coordinate in meters</p> <p>ecef y pos [m]: ECEF Y coordinate in meters</p> <p>ecef z pos [m]: ECEF Z coordinate in meters</p> <p>ecef x vel [m/s]: ECEF X velocity in meters per second</p> <p>ecef y vel [m/s]: ECEF Y velocity in meters per second</p> <p>ecef z vel [m/s]: ECEF Z velocity in meters per second</p> <p>lat [deg]: Geopgraphic latitude in degrees</p> <p>lon [deg]: Geographics longitude in degrees</p> <p>alt [km]: Altitude in kilometers</p> <p>vcsel temp: VCSEL temperature in engineering units</p> <p>gas cell temp: Rubidium vapor cell temperature in engineering units</p> <p>bx mag: Magnetic field Bx-component measured by the mission's science magnetometer in ECEF coordinates and nano-Tesla</p> <p>by mag: Magnetic field By-component measured by the mission's science magnetometer in ECEF coordinates and nano-Tesla</p> <p>bz mag: Magnetic field Bz-component measured by the mission's science magnetometer in ECEF coordinates and nano-Tesla</p> <p>bt mag: Total magnetic field measured by the mission's science magnetometer in ECEF coordinates and nano-Tesla</p> <p>btf mag: Filtered total magnetic field measured by the mission's science magnetometer in ECEF coordinates and nano-Tesla</p>
Text-fig. 2. Briveichthys chantepieorum gen. et sp. nov. a, b: whitened photograph and drawing of the skull, GMC 15, scale bars 5 mm; c: anterior part of the left and right frontal in dorsal view, a conspicuous concave anterior edge of the bones is demonstrated, GMC 18, whitened, scale bar 5 mm. Abbreviations: Clei – cleithrum, De – dentalosplenial, Dpt – dermopterotic, Dsph – dermosphenotic, Ext? – extrascapular?, Fr – frontal, Gul – lateral gular, Hy – hyomandibula, Mx – maxilla, Na – nasal, Pa – parietal, Pop – preoperculum, ppl – parietal pit lines, Pt – posttemporal, Rbr – branchiostegal rays, Scl – supracleithrum, soc – supraorbital canal, Sop – suboperculum, sr – sclerotic ring. in New Actinopterygians From The Permian Of The Brive Basin, And The Ichthyofaunas Of The French Massif Central
Text-fig. 2. Briveichthys chantepieorum gen. et sp. nov. a, b: whitened photograph and drawing of the skull, GMC 15, scale bars 5 mm; c: anterior part of the left and right frontal in dorsal view, a conspicuous concave anterior edge of the bones is demonstrated, GMC 18, whitened, scale bar 5 mm. Abbreviations: Clei – cleithrum, De – dentalosplenial, Dpt – dermopterotic, Dsph – dermosphenotic, Ext? – extrascapular?, Fr – frontal, Gul – lateral gular, Hy – hyomandibula, Mx – maxilla, Na – nasal, Pa – parietal, Pop – preoperculum, ppl – parietal pit lines, Pt – posttemporal, Rbr – branchiostegal rays, Scl – supracleithrum, soc – supraorbital canal, Sop – suboperculum, sr – sclerotic ring.
Text-fig. 7. Cornacaeae (a–j), Icacinaceae (k–o). a–e: Mastixia. USNM PAL 772364. Scale bar = 1 cm. a: Lateral view of eroded endocarp – the opposite side being missing and the endocarp broken near its mid point, reflected light, palladium coated. b–e: Micro-CT scan surface renderings. b: Rotated 90° from the view in (a). c: Rotated 90° from the view in (b). d: Rotated 90° from (c), exhibiting the damaged "back" face of the endocarp. e: Axillary view of the endocarp; the opposite end missing as apparent in (d). f–j: Cf. Nyssa. DMNH EPI.47808. Scale bar = 1 cm. Micro-CT scan surface renderings. f: Intact face of the endocarp; note ridges and "apical" point. g: Eroded (?gnawed; note horizontal grooving) opposite face of the endocarp. h: Lateral view of the endocarp, eroded/gnawed face to left. i: Apical view, eroded/gnawn portion below. j: Basal view of endocarp. k–o: Iodes DMNH-EPI.47807. Micro-CT scan surface renderings. Scale bar = 1 cm. k: Face view of endocarp. l: Opposite face of endocarp. m: Lateral view demonstrating the compressed nature of the endocarp, note thickened suture marking the probable track of the primary bundle. n: Apical view, primary bundle trace to right. o: Basal view, primary bundle trace to right. in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.
Text-fig. 7. Cornacaeae (a–j), Icacinaceae (k–o). a–e: Mastixia. USNM PAL 772364. Scale bar = 1 cm. a: Lateral view of eroded endocarp – the opposite side being missing and the endocarp broken near its mid point, reflected light, palladium coated. b–e: Micro-CT scan surface renderings. b: Rotated 90° from the view in (a). c: Rotated 90° from the view in (b). d: Rotated 90° from (c), exhibiting the damaged "back" face of the endocarp. e: Axillary view of the endocarp; the opposite end missing as apparent in (d). f–j: Cf. Nyssa. DMNH EPI.47808. Scale bar = 1 cm. Micro-CT scan surface renderings. f: Intact face of the endocarp; note ridges and "apical" point. g: Eroded (?gnawed; note horizontal grooving) opposite face of the endocarp. h: Lateral view of the endocarp, eroded/gnawed face to left. i: Apical view, eroded/gnawn portion below. j: Basal view of endocarp. k–o: Iodes DMNH-EPI.47807. Micro-CT scan surface renderings. Scale bar = 1 cm. k: Face view of endocarp. l: Opposite face of endocarp. m: Lateral view demonstrating the compressed nature of the endocarp, note thickened suture marking the probable track of the primary bundle. n: Apical view, primary bundle trace to right. o: Basal view, primary bundle trace to right.
TomoTwin Demonstration Dataset
<p>This is a demo dataset for <a href="https://www.biorxiv.org/content/10.1101/2022.06.24.497279v1">TomoTwin</a></p> <p>The ZIP file contains all necessary files for this demo.</p> <p>A readme.txt give instructions how to run it.</p> <p>More information can be found in the <a href="https://tomotwin-cryoet.readthedocs.io">official documentation of TomoTwin</a>.</p> <p>TomoTwin is open source and the code can be found on <a href="https://github.com/MPI-Dortmund/tomotwin-cryoet">GitHub</a>.</p>
INDIGO Virtual Router Demonstration
<p>The INDIGO-DataCloud project has been developing solutions to enable<br> virtualized networking in heterogeneous clouds. Two particular areas<br> were addressed. Firstly intra-site networking capability, wherein<br> development focused primarily on allowing users to procure private<br> virtual networks within separate cloud sites over a standardized<br> interface (OCCI). Secondly, research end development within INDIGO-<br> DataCloud focused on inter-site networking, i.e., interconnecting local<br> virtual networks across multiple cloud sites, effectively spanning such<br> networks over geographically distant sites. The combined solution<br> consisting of intra-site virtual network procurement and deployment of<br> virtual routers to span across such local networks – also a result of<br> development in INDIGO-DataCloud – will be demonstrated in real-world<br> heterogeneous environment.</p>
RoboJam Demonstration Video
<p>A video demonstration of RoboJam, a machine-learning system for generating musical responses in a touchscreen music system. This system uses a recurrent neural network (RNN) to generate sequences of touchscreen interactions and absolute timings. This video accompanies the paper "RoboJam: A Musical Mixture Density Network for Collaborative Touchscreen Interaction".</p>
Fig. 2 in The fossil record of camelids demonstrates a late divergence between Bactrian camel and dromedary
Fig. 2. Time-calibrated equiparsimonious trees. At each node, the probability density computed by diversification is shown (in red, all displaying a left skew). The age of each fossil record (in million years) is shown as a brown bar along each branch, which extends from the oldest to the youngest plausible age for each record. Darker shades represent overlapping possible age ranges, whereas brown dots represent very well-dated fossils. Extant taxa are in bold. A monophyletic Camelus is diagnosed by the loss of p3 and a smaller P3. The Paracamelus clade is diagnosed by a long muzzle. Camelus grattardi lacks derived characters of other representatives of the Camelus clade, the paraglenoid process, a shallower infra-orbital shelf, an oblique ascending ramus of the mandible, a thickened corpus, a broader P4 relative, and long ligament scars on the phalanges. The position of the poorly studied Camelus knoblochi relative to extant forms rests only on the morphology of the choanae.
Fig. 1 in The fossil record of camelids demonstrates a late divergence between Bactrian camel and dromedary
Fig. 1. Probability density histograms of speciation (cladogenesis), extinction and fossilization rates for the three equiparsimonious trees. All rates are in events per lineage and per million years. The height of each box of the plots is proportional to the posterior probability for the corresponding rate to be in the interval delineating its base.
Supplementary material for "Cretaceous lacewing larvae with binocular vision demonstrate the convergent evolution of sophisticated simple eyes" by Haug C. et al.
<p>Supplementary matereial including R-Code and data for elliptic Fourier analysis for the publication: "<span>Cretaceous lacewing larvae with binocular vision demonstrate the convergent evolution of sophisticated simple eyes" </span></p> <p><span>Carolin Haug, Roland R. Melzer, Florian Braig, Simon J. Linhart, Derek E. G. Briggs, Alejandro Caballero, Yanzhe Fu, Gideon T. Haug, Marie K. Hörnig, Joachim T. Haug</span></p> <p> </p> <p><span>Abstract: </span></p> <p><span>Many insects and their relatives are renowned for sophisticated compound eyes, which are also preserved in the fossil record. Yet there are also other types of eyes, notably the so-called stemmata of holometabolans, such as beetles, bees, and butterflies. Stemmata are not as effective as compound eyes, except in some predatory larvae. Here we report three lacewing larvae with large forward-directed stemmata from Cretaceous Kachin amber, Myanmar. The stemmata are large relative to those of other fossil lacewing larvae, comparable to the simple eyes of modern larvae capable of image formation. The head is very wide in one larva, representing a new type of morphology as demonstrated by a quantitative comparison of the head and stylets of over 400 fossil and extant lacewing larvae. The arrangement of the exceptionally large stemmata of the larvae reported here provides stereoscopic vision. These new specimens demonstrate the convergent evolution of highly developed simple eyes in at least two additional lineages of lacewings, showcasing the enormous diversity of lacewing larvae in the Cretaceous.</span></p>
Fig. 5 in Geometric morphometric analysis of cyclical body shape changes in color pattern variants of Cichla temensis Humboldt, 1821 (Perciformes: Cichlidae) demonstrates reproductive energy allocation
Fig. 5. Relative mean GSI vs. relative mean HSI of color pattern variants of Cichla temensis. Points for GSI represent the mean value for each CPV grade as compared to the range encountered. Points for HSI represent the mean value for each CPV grade compared to the range encountered.
Fig. 3 in Geometric morphometric analysis of cyclical body shape changes in color pattern variants of Cichla temensis Humboldt, 1821 (Perciformes: Cichlidae) demonstrates reproductive energy allocation
Fig. 3. Biplot of the uniform components in each direction (UniX and UniY) of morphometrical differences in 80 specimens of Cichla temensis in 4 color variation patterns (CPV) as measured by 9 Thin Plate Spline (TPS) distortion variables (V1-V9). Colored numbers indicate the CPV grade of individuals. The total spread of scores among individuals of each CPV are indicated by an envelope (solid line polygon) calculated as the minimum convex hull for that group. Position in the plot relative to other individuals indicates the degree of similarity in morph. Vectors point in the direction of gradient change for that TPS variable and the magnitude indicates the strength of the gradient. Angles between vectors indicate the TPS interset correlations.
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 3. Demonstrating the process of classification by applying the ANN, then the SVM
<p>Essentially, if the number of nodes in the hidden layers increases, processing time increases, and the resultant ANN will suffer from over-fitting. Conversely, too small of a number of hidden layers will cause under-fitting for the ANN. In our setting, the number of hidden layers and the number of nodes in each hidden layer were fixed at five. Concerning the learning rate and momentum settings, these impact sensitive training performances are set to optimal values obtained via a grid search technique. The number of nodes in the output layer was three because there are three different class labels (i.e., 2Dchart, bar, and pie) in our datasets. We used the ANN here because our datasets have nonlinear separation, and the ANN is also highly applicable to nonlinear modeling. Thus the ANN with multiple hidden layers was an optimal candidate; however, since the ANN is a black box learning approach, it is difficult to interpret implicit relationships between inputs and outputs.</p>
Cloudkeeper AWS Demonstration
<p>Demonstration of the Cloudkeeper backend for Amazon Web Services.</p>
ScienceDex guides
Understand access before you commit
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.