113 research outputs found
MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features
To distinguish the real pre-miRNAs from other hairpin sequences with similar stem-loops (pseudo pre-miRNAs), a hybrid feature which consists of local contiguous structure-sequence composition, minimum of free energy (MFE) of the secondary structure and P-value of randomization test is used. Besides, a novel machine-learning algorithm, random forest (RF), is introduced. The results suggest that our method predicts at 98.21% specificity and 95.09% sensitivity. When compared with the previous study, Triplet-SVM-classifier, our RF method was nearly 10% greater in total accuracy. Further analysis indicated that the improvement was due to both the combined features and the RF algorithm. The MiPred web server is available at http://www.bioinf.seu.edu.cn/miRNA/. Given a sequence, MiPred decides whether it is a pre-miRNA-like hairpin sequence or not. If the sequence is a pre-miRNA-like hairpin, the RF classifier will predict whether it is a real pre-miRNA or a pseudo one
Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolution
Four-dimensional magnetic resonance imaging (4D-MRI) is an emerging technique
for tumor motion management in image-guided radiation therapy (IGRT). However,
current 4D-MRI suffers from low spatial resolution and strong motion artifacts
owing to the long acquisition time and patients' respiratory variations; these
limitations, if not managed properly, can adversely affect treatment planning
and delivery in IGRT. Herein, we developed a novel deep learning framework
called the coarse-super-resolution-fine network (CoSF-Net) to achieve
simultaneous motion estimation and super-resolution in a unified model. We
designed CoSF-Net by fully excavating the inherent properties of 4D-MRI, with
consideration of limited and imperfectly matched training datasets. We
conducted extensive experiments on multiple real patient datasets to verify the
feasibility and robustness of the developed network. Compared with existing
networks and three state-of-the-art conventional algorithms, CoSF-Net not only
accurately estimated the deformable vector fields between the respiratory
phases of 4D-MRI but also simultaneously improved the spatial resolution of
4D-MRI with enhanced anatomic features, yielding 4D-MR images with high
spatiotemporal resolution
PyPose v0.6: The Imperative Programming Interface for Robotics
PyPose is an open-source library for robot learning. It combines a
learning-based approach with physics-based optimization, which enables seamless
end-to-end robot learning. It has been used in many tasks due to its
meticulously designed application programming interface (API) and efficient
implementation. From its initial launch in early 2022, PyPose has experienced
significant enhancements, incorporating a wide variety of new features into its
platform. To satisfy the growing demand for understanding and utilizing the
library and reduce the learning curve of new users, we present the fundamental
design principle of the imperative programming interface, and showcase the
flexible usage of diverse functionalities and modules using an extremely simple
Dubins car example. We also demonstrate that the PyPose can be easily used to
navigate a real quadruped robot with a few lines of code
PyPose: A Library for Robot Learning with Physics-based Optimization
Deep learning has had remarkable success in robotic perception, but its
data-centric nature suffers when it comes to generalizing to ever-changing
environments. By contrast, physics-based optimization generalizes better, but
it does not perform as well in complicated tasks due to the lack of high-level
semantic information and the reliance on manual parametric tuning. To take
advantage of these two complementary worlds, we present PyPose: a
robotics-oriented, PyTorch-based library that combines deep perceptual models
with physics-based optimization techniques. Our design goal for PyPose is to
make it user-friendly, efficient, and interpretable with a tidy and
well-organized architecture. Using an imperative style interface, it can be
easily integrated into real-world robotic applications. Besides, it supports
parallel computing of any order gradients of Lie groups and Lie algebras and
-order optimizers, such as trust region methods. Experiments
show that PyPose achieves 3-20 speedup in computation compared to
state-of-the-art libraries. To boost future research, we provide concrete
examples across several fields of robotics, including SLAM, inertial
navigation, planning, and control
Neutrino Physics with JUNO
The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe
Real-time Monitoring for the Next Core-Collapse Supernova in JUNO
Core-collapse supernova (CCSN) is one of the most energetic astrophysical
events in the Universe. The early and prompt detection of neutrinos before
(pre-SN) and during the SN burst is a unique opportunity to realize the
multi-messenger observation of the CCSN events. In this work, we describe the
monitoring concept and present the sensitivity of the system to the pre-SN and
SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is
a 20 kton liquid scintillator detector under construction in South China. The
real-time monitoring system is designed with both the prompt monitors on the
electronic board and online monitors at the data acquisition stage, in order to
ensure both the alert speed and alert coverage of progenitor stars. By assuming
a false alert rate of 1 per year, this monitoring system can be sensitive to
the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos
up to about 370 (360) kpc for a progenitor mass of 30 for the case
of normal (inverted) mass ordering. The pointing ability of the CCSN is
evaluated by using the accumulated event anisotropy of the inverse beta decay
interactions from pre-SN or SN neutrinos, which, along with the early alert,
can play important roles for the followup multi-messenger observations of the
next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure
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