1,052 research outputs found
The Sweet Smile of Mother
https://digitalcommons.library.umaine.edu/mmb-vp/3385/thumbnail.jp
Automatic alignment of surgical videos using kinematic data
Over the past one hundred years, the classic teaching methodology of "see
one, do one, teach one" has governed the surgical education systems worldwide.
With the advent of Operation Room 2.0, recording video, kinematic and many
other types of data during the surgery became an easy task, thus allowing
artificial intelligence systems to be deployed and used in surgical and medical
practice. Recently, surgical videos has been shown to provide a structure for
peer coaching enabling novice trainees to learn from experienced surgeons by
replaying those videos. However, the high inter-operator variability in
surgical gesture duration and execution renders learning from comparing novice
to expert surgical videos a very difficult task. In this paper, we propose a
novel technique to align multiple videos based on the alignment of their
corresponding kinematic multivariate time series data. By leveraging the
Dynamic Time Warping measure, our algorithm synchronizes a set of videos in
order to show the same gesture being performed at different speed. We believe
that the proposed approach is a valuable addition to the existing learning
tools for surgery.Comment: Accepted at AIME 201
Hydration of Na+, Ni2+, and Sm3+ in the Interlayer of Hectorite: A Quasielastic Neutron Scattering Study
International audienceQuasielastic neutron scattering experiments were performed with Na−hectorite, Ni−hectorite, and Sm−hectorite samples in order to find out whether Sm3+ is present in the clay interlayer as a fully hydrated cation (outer-sphere complex), or, as it follows from neutron diffraction data analysis, it is dehydrated and bound to the clay surface (inner-sphere complex). The results obtained for the Sm−hectorite were compared with other interlayer cations: strongly hydrated Ni2+ and relatively weakly hydrated Na+. It was found that water mobility in the Sm−hectorite sample is very close to the water mobility in Ni−hectorite. This is only possible if the Sm3+ ion is fully hydrated. It was shown that water molecules hydrating Ni2+ and Sm3+ exhibit diffusion mobility measurable with backscattering spectrometers. The diffusion coefficients of the exchangeable cations were found using the slow exchange approximations DNi = (0.05 − 0.14) × 10−9 m2/s and DSm = (0.04 − 0.18) × 10−9 m2/s
Long-term results of NOPHO ALL-92 and ALL-2000 studies of childhood acute lymphoblastic leukemia
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Laser-driven acceleration of quasi-monoenergetic, near-collimated titanium ions via a transparency-enhanced acceleration scheme
Laser-driven ion acceleration has been an active research area in the past two decades with the prospects of designing novel and compact ion accelerators. Many potential applications in science and industry require high-quality, energetic ion beams with low divergence and narrow energy spread. Intense laser ion acceleration research strives to meet these challenges and may provide high charge state beams, with some successes for carbon and lighter ions. Here we demonstrate the generation of well collimated, quasi-monoenergetic titanium ions with energies ∼145 and 180 MeV in experiments using the high-contrast(<10-9) and high-intensity (6× 1020 W cm-2) Trident laser and ultra-Thin (∼100 nm) titanium foil targets. Numerical simulations show that the foils become transparent to the laser pulses, undergoing relativistically induced transparency (RIT), resulting in a two-stage acceleration process which lasts until ∼2 ps after the onset of RIT. Such long acceleration time in the self-generated electric fields in the expanding plasma enables the formation of the quasi-monoenergetic peaks. This work contributes to the better understanding of the acceleration of heavier ions in the RIT regime, towards the development of next generation laser-based ion accelerators for various applications
Crowdsourcing of Histological Image Labeling and Object Delineation by Medical Students
Crowdsourcing in pathology has been performed on tasks that are assumed to be manageable by nonexperts. Demand remains high for annotations of more complex elements in digital microscopic images, such as anatomical structures. Therefore, this work investigates conditions to enable crowdsourced annotations of high-level image objects, a complex task considered to require expert knowledge. 76 medical students without specific domain knowledge who voluntarily participated in three experiments solved two relevant annotation tasks on histopathological images: (1) Labeling of images showing tissue regions, and (2) delineation of morphologically defined image objects. We focus on methods to ensure sufficient annotation quality including several tests on the required number of participants and on the correlation of participants' performance between tasks. In a set up simulating annotation of images with limited ground truth, we validated the feasibility of a confidence score using full ground truth. For this, we computed a majority vote using weighting factors based on individual assessment of contributors against scattered gold standard annotated by pathologists. In conclusion, we provide guidance for task design and quality control to enable a crowdsourced approach to obtain accurate annotations required in the era of digital pathology
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