18,649 research outputs found
Towards automated visual flexible endoscope navigation
Background:\ud
The design of flexible endoscopes has not changed significantly in the past 50 years. A trend is observed towards a wider application of flexible endoscopes with an increasing role in complex intraluminal therapeutic procedures. The nonintuitive and nonergonomical steering mechanism now forms a barrier in the extension of flexible endoscope applications. Automating the navigation of endoscopes could be a solution for this problem. This paper summarizes the current state of the art in image-based navigation algorithms. The objectives are to find the most promising navigation system(s) to date and to indicate fields for further research.\ud
Methods:\ud
A systematic literature search was performed using three general search terms in two medical–technological literature databases. Papers were included according to the inclusion criteria. A total of 135 papers were analyzed. Ultimately, 26 were included.\ud
Results:\ud
Navigation often is based on visual information, which means steering the endoscope using the images that the endoscope produces. Two main techniques are described: lumen centralization and visual odometry. Although the research results are promising, no successful, commercially available automated flexible endoscopy system exists to date.\ud
Conclusions:\ud
Automated systems that employ conventional flexible endoscopes show the most promising prospects in terms of cost and applicability. To produce such a system, the research focus should lie on finding low-cost mechatronics and technologically robust steering algorithms. Additional functionality and increased efficiency can be obtained through software development. The first priority is to find real-time, robust steering algorithms. These algorithms need to handle bubbles, motion blur, and other image artifacts without disrupting the steering process
Fast and adaptive fractal tree-based path planning for programmable bevel tip steerable needles
© 2016 IEEE. Steerable needles are a promising technology for minimally invasive surgery, as they can provide access to difficult to reach locations while avoiding delicate anatomical regions. However, due to the unpredictable tissue deformation associated with needle insertion and the complexity of many surgical scenarios, a real-time path planning algorithm with high update frequency would be advantageous. Real-time path planning for nonholonomic systems is commonly used in a broad variety of fields, ranging from aerospace to submarine navigation. In this letter, we propose to take advantage of the architecture of graphics processing units (GPUs) to apply fractal theory and thus parallelize real-time path planning computation. This novel approach, termed adaptive fractal trees (AFT), allows for the creation of a database of paths covering the entire domain, which are dense, invariant, procedurally produced, adaptable in size, and present a recursive structure. The generated cache of paths can in turn be analyzed in parallel to determine the most suitable path in a fraction of a second. The ability to cope with nonholonomic constraints, as well as constraints in the space of states of any complexity or number, is intrinsic to the AFT approach, rendering it highly versatile. Three-dimensional (3-D) simulations applied to needle steering in neurosurgery show that our approach can successfully compute paths in real-time, enabling complex brain navigation
Computing Heavy Elements
Reliable calculations of the structure of heavy elements are crucial to
address fundamental science questions such as the origin of the elements in the
universe. Applications relevant for energy production, medicine, or national
security also rely on theoretical predictions of basic properties of atomic
nuclei. Heavy elements are best described within the nuclear density functional
theory (DFT) and its various extensions. While relatively mature, DFT has never
been implemented in its full power, as it relies on a very large number (~
10^9-10^12) of expensive calculations (~ day). The advent of leadership-class
computers, as well as dedicated large-scale collaborative efforts such as the
SciDAC 2 UNEDF project, have dramatically changed the field. This article gives
an overview of the various computational challenges related to the nuclear DFT,
as well as some of the recent achievements.Comment: Proceeding of the Invited Talk given at the SciDAC 2011 conference,
Jul. 10-15, 2011, Denver, C
Nuclear Theory and Science of the Facility for Rare Isotope Beams
The Facility for Rare Isotope Beams (FRIB) will be a world-leading laboratory
for the study of nuclear structure, reactions and astrophysics. Experiments
with intense beams of rare isotopes produced at FRIB will guide us toward a
comprehensive description of nuclei, elucidate the origin of the elements in
the cosmos, help provide an understanding of matter in neutron stars, and
establish the scientific foundation for innovative applications of nuclear
science to society. FRIB will be essential for gaining access to key regions of
the nuclear chart, where the measured nuclear properties will challenge
established concepts, and highlight shortcomings and needed modifications to
current theory. Conversely, nuclear theory will play a critical role in
providing the intellectual framework for the science at FRIB, and will provide
invaluable guidance to FRIB's experimental programs. This article overviews the
broad scope of the FRIB theory effort, which reaches beyond the traditional
fields of nuclear structure and reactions, and nuclear astrophysics, to explore
exciting interdisciplinary boundaries with other areas.
\keywords{Nuclear Structure and Reactions. Nuclear
Astrophysics. Fundamental Interactions. High Performance
Computing. Rare Isotopes. Radioactive Beams.Comment: 20 pages, 7 figure
Analyzing Machupo virus-receptor binding by molecular dynamics simulations
In many biological applications, we would like to be able to computationally
predict mutational effects on affinity in protein-protein interactions.
However, many commonly used methods to predict these effects perform poorly in
important test cases. In particular, the effects of multiple mutations,
non-alanine substitutions, and flexible loops are difficult to predict with
available tools and protocols. We present here an existing method applied in a
novel way to a new test case; we interrogate affinity differences resulting
from mutations in a host-virus protein-protein interface. We use steered
molecular dynamics (SMD) to computationally pull the machupo virus (MACV) spike
glycoprotein (GP1) away from the human transferrin receptor (hTfR1). We then
approximate affinity using the maximum applied force of separation and the area
under the force-versus-distance curve. We find, even without the rigor and
planning required for free energy calculations, that these quantities can
provide novel biophysical insight into the GP1/hTfR1 interaction. First, with
no prior knowledge of the system we can differentiate among wild type and
mutant complexes. Moreover, we show that this simple SMD scheme correlates well
with relative free energy differences computed via free energy perturbation.
Second, although the static co-crystal structure shows two large
hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate
that one of them may not be important for tight binding. Third, one viral site
known to be critical for infection may mark an important evolutionary
suppressor site for infection-resistant hTfR1 mutants. Finally, our approach
provides a framework to compare the effects of multiple mutations, individually
and jointly, on protein-protein interactions.Comment: 33 pages, 8 figures, 5 table
Computational Steering of Cluster Formation in Brownian Suspensions
We simulate cluster formation of model colloidal particles interacting via
DLVO (Derjaguin, Landau, Vervey, Overbeek) potentials. The interaction
potentials can be related to experimental conditions, defined by the pH-value,
the salt concentration and the volume fraction of solid particles suspended in
water. The system shows different structural properties for different
conditions, including cluster formation, a glass-like repulsive structure, or a
liquid suspension. Since many simulations are needed to explore the whole
parameter space, when investigating the properties of the suspension depending
on the experimental conditions, we have developed a steering approach to
control a running simulation and to detect interesting transitions from one
region in the configuration space to another. The advantages of the steering
approach and the restrictions of its applicability due to physical constraints
are illustrated by several example cases.Comment: 9 pages, 4 figures, submitted to Proceedings of the Fourth
International Conference on Mesoscopic Methods in Engineering and Science
(ICMMES) 2007 (Munich, Germany), revised version, 2 figures exchanged, some
parts rephrase
A LightGBM-Based EEG Analysis Method for Driver Mental States Classification
Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography-
(EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated.
However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a
challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is
based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers,
such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin
nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision
efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of
driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state
prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)
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