Consolidation effect of visual function training on children with different degrees of amblyopia

AIM: To observe the consolidation effect of visual function training in amblyopic therapy on children with amblyopia of different degrees(mild, moderate and severe)which had been normalized. <p>METHODS: Totally 78 amblyopic children were divided into two groups: visual function training group(<i>n</i>=36, 70 eyes)and control group(<i>n</i>=42, 67 eyes). The rollback situation of the two groups in 6, 12, 24 and 36 months were observed after visual acuity reached 0.9 during treatment.<p>RESULTS: The rollback rates were both 0 in visual function training group and control group with different degrees of amblyopia after 6 months. There was no significant difference in rollback rate between the 2 groups with different degrees of amblyopia after 6 and 12 months. There was no significant difference in rollback rate between the two groups with mild amblyopia after 24 and 36 months(<i>P</i>=0.269, 0.269). However, the rollback rate in training group with moderate amblyopia was significantly different from in control group after 24 and 36 months(<i>P</i>=0.004, 0.002). There was no significant difference in rollback rate between the two groups with severe amblyopia after 24 and 36 months. <p>CONCLUSION: Visual function training can reduce the rollback rate and consolidate the effect of amblyopic treatment effectively for children with moderate amblyopia. However, the effect is not as good for children with mild and severe amblyopia

Fabrication and magnetic properties of granular Co/porous InP nanocomposite materials

A novel Co/InP magnetic semiconductor nanocomposite was fabricated by electrodeposition magnetic Co nanoparticles into n-type porous InP templates in ethanol solution of cobalt chloride. The content or particle size of Co particles embedded in porous InP increased with increasing deposition time. Co particles had uniform distribution over pore sidewall surface of InP template, which was different from that of ceramic template and may open up new branch of fabrication of nanocomposites. The magnetism of such Co/InP nanocomposites can be gradually tuned from diamagnetism to ferromagnetism by increasing the deposition time of Co. Magnetic anisotropy of this Co/InP nanocomposite with magnetization easy axis along the axis of InP square channel was well realized by the competition between shape anisotropy and magnetocrystalline anisotropy. Such Co/InP nanocomposites with adjustable magnetism may have potential applications in future in the field of spin electronics

Crystal structure of human muscle creatine kinase

This is the publisher's version, also available electronically from "http://scripts.iucr.org".The crystal structure of human muscle creatine kinase has been determined by the molecular-replacement method and refined at 3.5 Å resolution. The structures of both the monomer and the dimer closely resemble those of the other known structures in the creatine kinase family. Two types of dimers, one with a non-crystallographic twofold symmetry axis and the other with a crystallographic twofold symmetry axis, were found to occur simultaneously in the crystal. These dimers form an infinite `double-helix'-like structure along an unusual long crystallographic 31 axis

DeepSketchHair: Deep Sketch-based 3D Hair Modeling

We present sketchhair, a deep learning based tool for interactive modeling of 3D hair from 2D sketches. Given a 3D bust model as reference, our sketching system takes as input a user-drawn sketch (consisting of hair contour and a few strokes indicating the hair growing direction within a hair region), and automatically generates a 3D hair model, which matches the input sketch both globally and locally. The key enablers of our system are two carefully designed neural networks, namely, S2ONet, which converts an input sketch to a dense 2D hair orientation field; and O2VNet, which maps the 2D orientation field to a 3D vector field. Our system also supports hair editing with additional sketches in new views. This is enabled by another deep neural network, V2VNet, which updates the 3D vector field with respect to the new sketches. All the three networks are trained with synthetic data generated from a 3D hairstyle database. We demonstrate the effectiveness and expressiveness of our tool using a variety of hairstyles and also compare our method with prior art

MLF-DET: Multi-Level Fusion for Cross-Modal 3D Object Detection

In this paper, we propose a novel and effective Multi-Level Fusion network, named as MLF-DET, for high-performance cross-modal 3D object DETection, which integrates both the feature-level fusion and decision-level fusion to fully utilize the information in the image. For the feature-level fusion, we present the Multi-scale Voxel Image fusion (MVI) module, which densely aligns multi-scale voxel features with image features. For the decision-level fusion, we propose the lightweight Feature-cued Confidence Rectification (FCR) module which further exploits image semantics to rectify the confidence of detection candidates. Besides, we design an effective data augmentation strategy termed Occlusion-aware GT Sampling (OGS) to reserve more sampled objects in the training scenes, so as to reduce overfitting. Extensive experiments on the KITTI dataset demonstrate the effectiveness of our method. Notably, on the extremely competitive KITTI car 3D object detection benchmark, our method reaches 82.89% moderate AP and achieves state-of-the-art performance without bells and whistles

A refined equilibrium generative adversarial network for retinal vessel segmentation

OBJECTIVE: Retinal vessel morphological parameters are vital indicator for early diagnosis of ophthalmological diseases and cardiovascular events. However, segmentation performance is highly influenced by elusive vessels, especially in low-contrast background and lesion regions. In this work, we present an end-to-end synthetic neural network to strengthen elusive vessels segmentation capability, containing a symmetric equilibrium generative adversarial network (SEGAN), multi-scale features refine blocks (MSFRB), and attention mechanism (AM). METHOD: The proposed network is superior in detail information extraction by maximizing multi-scale features representation. First, SEGAN constructs a symmetric adversarial architecture in which generator is forced to produce more realistic images with local details. Second, MSFRB are devised to optimize the feature merging process, thereby maximally maintaining high resolution information. Finally, the AM is employed to encourage the network to concentrate on discriminative features. RESULTS: On public dataset DRIVE, STARE, CHASEDB1, and HRF, we evaluate our network quantitatively and compare it with state-of-the-art works. The ablation experiment shows that SEGAN, MSFRB, and AM both contribute to the desirable performance. Conclusion: The proposed network outperforms the existing methods and effectively functions in elusive vessels segmentation, achieving highest scores in Sensitivity, G-Mean, Precision, and F1-Score while maintaining the top level in other metrics. Significance: The satisfactory performance and computational efficiency offer great potential in clinical retinal vessel segmentation application. Meanwhile, the network could be utilized to extract detail information in other biomedical image computing

Comparison of therapeutic effects between drainage blood reinfusion and temporary clamping drainage after total knee arthroplasty in patients with rheumatoid arthritis

OBJECTIVE: To compare the therapeutic effects between drainage blood reinfusion and temporary clamping drainage after total knee arthroplasty in patients with rheumatoid arthritis to provide a basis for clinical practice. METHODS: Data from 83 patients with rheumatoid arthritis undergoing total knee arthroplasty were retrospectively analyzed. The 83 patients were divided into a drainage blood reinfusion group (DR group, n = 45) and a temporary clamping drainage group (CD group, n = 38). In the DR group, postoperative drainage blood was used for autotransfusion. In the CD group, closed drainage was adopted, and the drainage tube was clamped for 2 h postoperatively followed by patency. The postoperative drainage amount, hemoglobin level, rate and average volume of allogeneic blood transfusion, swelling and ecchymosis of the affected knee joint, time to straight-leg raising and range of active knee flexion were compared between the two groups. RESULTS: The total drainage volume was higher in the DR group than in the CD group (P = 0.000). The average volume of postoperative allogeneic blood transfusion (P = 0.000) and the decrease in the hemoglobin level 24 h after total knee arthroplasty (P = 0.012) were lower in the DR group than in the CD group. Swelling and ecchymosis of the affected knee joint, time to straight-leg raising and the range of active knee flexion were improved in the DR group compared with the CD group (all

The Blessing of Randomness: SDE Beats ODE in General Diffusion-based Image Editing

We present a unified probabilistic formulation for diffusion-based image editing, where a latent variable is edited in a task-specific manner and generally deviates from the corresponding marginal distribution induced by the original stochastic or ordinary differential equation (SDE or ODE). Instead, it defines a corresponding SDE or ODE for editing. In the formulation, we prove that the Kullback-Leibler divergence between the marginal distributions of the two SDEs gradually decreases while that for the ODEs remains as the time approaches zero, which shows the promise of SDE in image editing. Inspired by it, we provide the SDE counterparts for widely used ODE baselines in various tasks including inpainting and image-to-image translation, where SDE shows a consistent and substantial improvement. Moreover, we propose SDE-Drag -- a simple yet effective method built upon the SDE formulation for point-based content dragging. We build a challenging benchmark (termed DragBench) with open-set natural, art, and AI-generated images for evaluation. A user study on DragBench indicates that SDE-Drag significantly outperforms our ODE baseline, existing diffusion-based methods, and the renowned DragGAN. Our results demonstrate the superiority and versatility of SDE in image editing and push the boundary of diffusion-based editing methods