High Quality Image Interpolation via Local Autoregressive and Nonlocal 3-D Sparse Regularization

In this paper, we propose a novel image interpolation algorithm, which is formulated via combining both the local autoregressive (AR) model and the nonlocal adaptive 3-D sparse model as regularized constraints under the regularization framework. Estimating the high-resolution image by the local AR regularization is different from these conventional AR models, which weighted calculates the interpolation coefficients without considering the rough structural similarity between the low-resolution (LR) and high-resolution (HR) images. Then the nonlocal adaptive 3-D sparse model is formulated to regularize the interpolated HR image, which provides a way to modify these pixels with the problem of numerical stability caused by AR model. In addition, a new Split-Bregman based iterative algorithm is developed to solve the above optimization problem iteratively. Experiment results demonstrate that the proposed algorithm achieves significant performance improvements over the traditional algorithms in terms of both objective quality and visual perceptionComment: 4 pages, 5 figures, 2 tables, to be published at IEEE Visual Communications and Image Processing (VCIP) 201

A Computational Model for Biomechanical Effects of Arterial Compliance Mismatch

Background. Compliance mismatch is a negative factor and it needs to be considered in arterial bypass grafting. Objective. A computational model was employed to investigate the effects of arterial compliance mismatch on blood flow, wall stress, and deformation. Methods. The unsteady blood flow was assumed to be laminar, Newtonian, viscous, and incompressible. The vessel wall was assumed to be linear elastic, isotropic, and incompressible. The fluid-wall interaction scheme was constructed using the finite element method. Results. The results show that there are identical wall shear stress waveforms, wall stress, and strain waveforms at different locations. The comparison of the results demonstrates that wall shear stresses and wall strains are higher while wall stresses are lower at the more compliant section. The differences promote the probability of intimal thickening at some locations. Conclusions. The model is effective and gives satisfactory results. It could be extended to all kinds of arteries with complicated geometrical and material factors

Efficient Optimization of F

F-measure is one of the most commonly used performance metrics in classification, particularly when the classes are highly imbalanced. Direct optimization of this measure is often challenging, since no closed form solution exists. Current algorithms design the classifiers by using the approximations to the F-measure. These algorithms are not efficient and do not scale well to the large datasets. To fill the gap, in this paper, we propose a novel algorithm, which can efficiently optimize F-measure with cost-sensitive SVM. First of all, we present an explicit transformation from the optimization of F-measure to cost-sensitive SVM. Then we adopt bundle method to solve the inner optimization. For the problem where the existing bundle method may have the fluctuations in the primal objective during iterations, an additional line search procedure is involved, which can alleviate the fluctuations problem and make our algorithm more efficient. Empirical studies on the large-scale datasets demonstrate that our algorithm can provide significant speedups over current state-of-the-art F-measure based learners, while obtaining better (or comparable) precise solutions

A Copper Single-Atom Cascade Bionanocatalyst for Treating Multidrug-Resistant Bacterial Diabetic Ulcer

Diabetic ulcers induced by multidrug-resistant (MDR) bacteria have severely endangered diabetic populations. These ulcers are very challenging to treat because the local high glucose concentration can both promote bacterial growth and limit the immune system's bactericidal action. Herein, a glucose oxidase-peroxidase (GOx-POD) dual-enzyme mimetic (DEM) bionanocatalyst, Au@CuBCats is synthesized to simultaneously control glucose concentration and bacteria in diabetic ulcers. Specifically, the AuNPs can serve as GOx mimics and catalyze the oxidation of glucose for the formation of H2O2; the H2O2 can then be further catalytically converted into OH via the POD-mimetic copper single atoms. Notably, the unique copper single atoms coordinated by one oxygen and two nitrogen atoms (CuN2O1) exhibit better POD catalytic performance than natural peroxidase. Further DFT calculations are conducted to study the catalytic mechanism and reveal the advantage of this CuN2O1 structure as compared to other copper single-atom sites. Both in vitro and in vivo experiments confirm the outstanding antibacterial therapeutic efficacy of the DEM bionanocatalyst. This new bionanocatalyst will provide essential insights for the next generation of antibiotic-free strategies for combating MDR bacterial diabetic ulcers, and also offer inspiration for designing bionanocatalytic cascading medicines

Newton-Cartan Gravity and Torsion

We compare the gauging of the Bargmann algebra, for the case of arbitrary torsion, with the result that one obtains from a null-reduction of General Relativity. Whereas the two procedures lead to the same result for Newton-Cartan geometry with arbitrary torsion, the null-reduction of the Einstein equations necessarily leads to Newton-Cartan gravity with zero torsion. We show, for three space-time dimensions, how Newton-Cartan gravity with arbitrary torsion can be obtained by starting from a Schroedinger field theory with dynamical exponent z=2 for a complex compensating scalar and next coupling this field theory to a z=2 Schroedinger geometry with arbitrary torsion. The latter theory can be obtained from either a gauging of the Schroedinger algebra, for arbitrary torsion, or from a null-reduction of conformal gravity.Comment: 21 page

Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation

Turbulent flows have historically presented formidable challenges to predictive computational modeling. Traditional numerical simulations often require vast computational resources, making them infeasible for numerous engineering applications. As an alternative, deep learning-based surrogate models have emerged, offering data-drive solutions. However, these are typically constructed within deterministic settings, leading to shortfall in capturing the innate chaotic and stochastic behaviors of turbulent dynamics. We introduce a novel generative framework grounded in probabilistic diffusion models for versatile generation of spatiotemporal turbulence. Our method unifies both unconditional and conditional sampling strategies within a Bayesian framework, which can accommodate diverse conditioning scenarios, including those with a direct differentiable link between specified conditions and generated unsteady flow outcomes, and scenarios lacking such explicit correlations. A notable feature of our approach is the method proposed for long-span flow sequence generation, which is based on autoregressive gradient-based conditional sampling, eliminating the need for cumbersome retraining processes. We showcase the versatile turbulence generation capability of our framework through a suite of numerical experiments, including: 1) the synthesis of LES simulated instantaneous flow sequences from URANS inputs; 2) holistic generation of inhomogeneous, anisotropic wall-bounded turbulence, whether from given initial conditions, prescribed turbulence statistics, or entirely from scratch; 3) super-resolved generation of high-speed turbulent boundary layer flows from low-resolution data across a range of input resolutions. Collectively, our numerical experiments highlight the merit and transformative potential of the proposed methods, making a significant advance in the field of turbulence generation.Comment: 37 pages, 31 figure

Clinical practice guidelines and real-life practice in hepatocellular carcinoma: A Chinese perspective

Liver cancer is the fourth most prevalent and the second most lethal cancer in China. Hepatitis B virus (HBV) infection represents a major risk factor for hepatocellular carcinoma (HCC). Liver ultrasonography plus alpha-fetoprotein every 6 months continues to be the predominant surveillance modality. The age-Male-ALBI-Platelets score was recommended in the recent 2022 Chinese guidelines to predict HCC occurrence. The Chinese liver cancer (CNLC) staging system proposed in the 2017 guidelines continues to be the standard model for staging with modifications in the treatment allocations. Considering the aggressive nature of HBV-associated HCC, multimodal and high-intensity strategies like the addition of immunotherapy-based systemic treatment to local therapies, including resection, ablation, and intra-arterial therapies, have been adopted in real-life practices in China. The latest Chinese guidelines recommend atezolizumab plus bevacizumab, suntilimab plus a bevacizumab analog, lenvatinib, sorafenib, donafenib, and FOLFOX (folinic acid, fluorouracil, and oxaliplatin) chemotherapy as first-line treatment without priority. Regorafenib, apatinib, camrelizumab, and tislelizumab have been added as second-line systemic therapies for patients who progressed on sorafenib. Systemic therapies adopted in real-life practice are sophisticated with various combination modalities and different sequences

Two variants on T2DM susceptible gene HHEX are associated with CRC risk in a Chinese population

Increasing amounts of evidence has demonstrated that T2DM (Type 2 Diabetes Mellitus) patients have increased susceptibility to CRC (colorectal cancer). As HHEX is a recognized susceptibility gene in T2DM, this work was focused on two SNPs in HHEX, rs1111875 and rs7923837, to study their association with CRC. T2DM patients without CRC (T2DM-only, n=300), T2DM with CRC (T2DM/CRC, n=135), cancer-free controls (Control, n=570), and CRC without T2DM (CRC-only, n=642) cases were enrolled. DNA samples were extracted from the peripheral blood leukocytes of the patients and sequenced by direct sequencing. The χ(2) test was used to compare categorical data. We found that in T2DM patients, rs1111875 but not the rs7923837 in HHEX gene was associated with the occurrence of CRC (p= 0.006). for rs1111875, TC/CC patients had an increased risk of CRC (p=0.019, OR=1.592, 95%CI=1.046-2.423). Moreover, our results also indicated that the two variants of HEEX gene could be risk factors for CRC in general population, independent on T2DM (p< 0.001 for rs1111875, p=0.001 for rs7923837). For rs1111875, increased risk of CRC was observed in TC or TC/CC than CC individuals (p<0.001, OR= 1.780, 95%CI= 1.385-2.287; p<0.001, OR= 1.695, 95%CI= 1.335-2.152). For rs7923837, increased CRC risk was observed in AG, GG, and AG/GG than AA individuals (p< 0.001, OR= 1.520, 95%CI= 1.200-1.924; p=0.036, OR= 1.739, 95%CI= 0.989-3.058; p< 0.001, OR= 1.540, 95%CI= 1.225-1.936). This finding highlights the potentially functional alteration with HHEX rs1111875 and rs7923837 polymorphisms may increase CRC susceptibility. Risk effects and the functional impact of these polymorphisms need further validation

Continuous Size-Dependent Sorting of Ferromagnetic Nanoparticles in Laser-Ablated Microchannel

This paper reports a low-cost method of continuous size-dependent sorting of magnetic nanoparticles in polymer-based microfluidic devices by magnetic force. A neodymium permanent magnet was used to generate a magnetic field perpendicular to the fluid flow direction. Firstly, FeNi3 magnetic nanoparticles were chemically synthesized with diameter ranges from 80 nm to 200 nm; then, the solution of magnetic nanoparticles and a buffer were passed through the microchannel in laminar flow; the magnetic nanoparticles were deflected from the flow direction under the applied magnetic field. Nanoparticles in the microchannel will move towards the direction of high-gradient magnetic fields, and the degree of deflection depends on their sizes; therefore, magnetic nanoparticles of different sizes can be separated and finally collected from different output ports. The proposed method offers a rapid and continuous approach of preparing magnetic nanoparticles with a narrow size distribution from an arbitrary particle size distribution. The proposed new method has many potential applications in bioanalysis field since magnetic nanoparticles are commonly used as solid support for biological entities such as DNA, RNA, virus, and protein. Other than the size sorting application of magnetic nanoparticles, this approach could also be used for the size sorting and separation of naturally magnetic cells, including blood cells and magnetotactic bacteria