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 $\pi$ 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