1,245 research outputs found
Continuous Curvilinear Capsulorhexis in Cataract Surgery Using a Modified 3-Bend Cystotome
We modified a 2-bend cystotome for continuous curvilinear capsulorhexis (CCC) in manual or phacoemulsification cataract surgery to improve the safety and ease of performance. A 26G needle was converted into a cystotome with 3 bends. In this retrospective study, the performance of modified 3-bend cystotome was compared with conventional 2-bend cystotome. During cataract surgery, in the 3-bend cystotome group, mean completion time of CCC was shorter, mean times of viscoelastic agent supplement were less, and CCC success rate was higher than that in 2-bend group. Complication incidence, such as postoperative transient corneal edema and irreparable V-shaped tear, was also lower in 3-bend group. No posterior capsular rupture or no other complication was observed in either group. A polymethyl methacrylate intraocular lens or a hydrogel intraocular lens was implanted in the capsular bag in all eyes. We conclude that it is safe and efficient to accomplish a CCC using the 3-bend cystotome due to its ability to sustain the anterior chamber depth (ACD) and keep the posterior lip intact. Using the 3-bend cystotome also allowed for an adequate view into the anterior chamber from lack of wound deformation
Knowledge from Large-Scale Protein Contact Prediction Models Can Be Transferred to the Data-Scarce RNA Contact Prediction Task
RNA, whose functionality is largely determined by its structure, plays an
important role in many biological activities. The prediction of pairwise
structural proximity between each nucleotide of an RNA sequence can
characterize the structural information of the RNA. Historically, this problem
has been tackled by machine learning models using expert-engineered features
and trained on scarce labeled datasets. Here, we find that the knowledge
learned by a protein-coevolution Transformer-based deep neural network can be
transferred to the RNA contact prediction task. As protein datasets are orders
of magnitude larger than those for RNA contact prediction, our findings and the
subsequent framework greatly reduce the data scarcity bottleneck. Experiments
confirm that RNA contact prediction through transfer learning using a publicly
available protein model is greatly improved. Our findings indicate that the
learned structural patterns of proteins can be transferred to RNAs, opening up
potential new avenues for research.Comment: Minor revision. The code is available at
https://github.com/yiren-jian/CoT-RNA-Transfe
Demonstrating anyonic fractional statistics with a six-qubit quantum simulator
Anyons are exotic quasiparticles living in two dimensions that do not fit
into the usual categories of fermions and bosons, but obey a new form of
fractional statistics. Following a recent proposal [Phys. Rev. Lett. 98, 150404
(2007)], we present an experimental demonstration of the fractional statistics
of anyons in the Kitaev spin lattice model using a photonic quantum simulator.
We dynamically create the ground state and excited states (which are six-qubit
graph states) of the Kitaev model Hamiltonian, and implement the anyonic
braiding and fusion operations by single-qubit rotations. A phase shift of
related to the anyon braiding is observed, confirming the prediction of
the fractional statistics of Abelian 1/2-anyons.Comment: revised version 3, revTex, 4.3 pages, 4 figures, notes and reference
adde
Optical Transition and Momentum Transfer in Atomic Wave Packets
It is shown that the population Rabi-floppings in a lossless two-level atom,
interacting with a monochromatic electromagnetic field, in general are
convergent in time. The well-known continuous floppings take place because the
restricted choosing of initial conditions, that is when the atom initially is
chosen on ground or excited level before the interaction, simultaneously having
a definite value of momentum there. The convergence of Rabi-floppings in atomic
wave-packet-states is a direct consequence of Doppler effect on optical
transition rates (Rabi-frequencies): it gradually leads to ''irregular''
chaotic-type distributions of momentum in ground and excited energy levels,
smearing the amplitudes of Rabi-floppings. Conjointly with Rabi-floppings, the
coherent accumulation of momentum on each internal energy level monotonically
diminishes too.Comment: 6 pages, 10 Figure
CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution
Recently, deep convolution neural networks (CNNs) steered face
super-resolution methods have achieved great progress in restoring degraded
facial details by jointly training with facial priors. However, these methods
have some obvious limitations. On the one hand, multi-task joint learning
requires additional marking on the dataset, and the introduced prior network
will significantly increase the computational cost of the model. On the other
hand, the limited receptive field of CNN will reduce the fidelity and
naturalness of the reconstructed facial images, resulting in suboptimal
reconstructed images. In this work, we propose an efficient CNN-Transformer
Cooperation Network (CTCNet) for face super-resolution tasks, which uses the
multi-scale connected encoder-decoder architecture as the backbone.
Specifically, we first devise a novel Local-Global Feature Cooperation Module
(LGCM), which is composed of a Facial Structure Attention Unit (FSAU) and a
Transformer block, to promote the consistency of local facial detail and global
facial structure restoration simultaneously. Then, we design an efficient Local
Feature Refinement Module (LFRM) to enhance the local facial structure
information. Finally, to further improve the restoration of fine facial
details, we present a Multi-scale Feature Fusion Unit (MFFU) to adaptively fuse
the features from different stages in the encoder procedure. Comprehensive
evaluations on various datasets have assessed that the proposed CTCNet can
outperform other state-of-the-art methods significantly.Comment: 12 pages, 10 figures, 8 table
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