Overexpression pattern,function,and clinical value of proteasome 26S subunit non-ATPase 6 in hepatocellular carcinoma

BACKGROUND In recent years,many studies have shown that proteasome 26S subunit non-ATPase 6(PSMD6)plays an important role in the occurrence and development of malignant tumours.Unfortunately,there are no reports on the evaluation of the potential role of PSMD6 in hepatocellular carcinoma(HCC).AIM To comprehensively evaluate the overexpression pattern and clinical significance of PSMD6 in HCC tissues.METHODS This study integrated PSMD6 mRNA expression profiles from 4672 HCC and 3667 non-HCC tissues,along with immunohistochemical scores from 383 HCC and adjacent tissues,to assess PSMD6 overexpression in HCC.Clustered regularly interspaced short palindromic repeats knockout technology evaluated PSMD6’s essential role in HCC cell growth.Functional enrichment analysis explored the molecular mechanism of PSMD6 abnormalities in HCC.Drug sensitivity analysis and molecular docking analysed the effect of abnormal expression of PSMD6 on the drug sensitivity of HCC cells.RESULTS The results of 41 external and two internal datasets showed that PSMD6 mRNA(SMD=0.26,95%CI:0.09-0.42,P<0.05)and protein(SMD=2.85,95%CI:1.19-4.50,P<0.05)were significantly overexpressed in HCC tissues.The integrated analysis results showed that PSMD6 had a significant overexpression pattern in HCC tissues(SMD=0.40,95%CI:0.15-0.66,P<0.05).PSMD6 knockout inhibited HCC cell growth(chronos scores<-1).Functional enrichment implicated ribosome biogenesis and RNA splicing.Significant enrichment of signalling pathways such as RNA degradation,ribosomes,and chemical carcinogenesis—reactive oxygen species.Drug sensitivity analysis and a molecular docking model showed that high expression of PSMD6 was associated with the tolerance of HCC cells to drugs such as ML323,sepantronium bromide,and GDC0810.Overexpressed PSMD6 effectively distinguished HCC tissues(AUC=0.75,95%CI:0.71-0.79).CONCLUSION This study was the first to discover that PSMD6 was overexpressed in HCC tissues.PSMD6 is essential for the growth of HCC cells and may be involved in ribosome biogenesis and RNA splicing.

Distributed Optimal Formation Control for Unmanned Surface Vessels by a Regularized Game-Based Approach

Dear Editor,This letter explores optimal formation control for a network of unmanned surface vessels(USVs).By designing an individual objective function for each USV,the optimal formation problem is transformed into a noncooperative game.Under this game theoretic framework,the optimal formation is achieved by seeking the Nash equilibrium of the regularized game.A modular structure consisting of a distributed Nash equilibrium seeker and a regulator is proposed.

SHARP MORREY REGULARITY THEORY FOR A FOURTH ORDER GEOMETRICAL EQUATION

This paper is a continuation of recent work by Guo-Xiang-Zheng[10].We deduce the sharp Morrey regularity theory for weak solutions to the fourth order nonhomogeneous Lamm-Rivière equation △^{2}u=△(V▽u)+div(w▽u)+(▽ω+F)·▽u+f in B^(4),under the smallest regularity assumptions of V,ω,ω,F,where f belongs to some Morrey spaces.This work was motivated by many geometrical problems such as the flow of biharmonic mappings.Our results deepens the Lp type regularity theory of[10],and generalizes the work of Du,Kang and Wang[4]on a second order problem to our fourth order problems.

Overexpression of proteasome 26S subunit non-ATPase 6 protein and its clinicopathological significance in intrahepatic cholangiocarcinoma

BACKGROUND Currently,intrahepatic cholangiocarcinoma(ICC)poses a continuing,significant health challenge,but the relationship has yet to be established between ICC and the proteasome 26S subunit non-ATPase 6(PSMD6).AIM To investigate the protein expression and clinicopathological significance of PSMD6 in ICC.METHODS The potential impact of the PSMD6 gene on the growth of ICC cell lines was analyzed using clustered regularly interspaced short palindromic repeat knockout screening technology.Forty-two paired specimens of ICC and adjacent noncancerous tissues were collected.PSMD6 protein expression was determined by immunohistochemistry.Receiver operating characteristic curve analysis was performed to validate PSMD6 expression level,and its association with ICC patients’various clinicopathological characteristics was investigated.RESULTS The PSMD6 gene was found to be essential for the growth of ICC cell lines.PSMD6 protein was significantly overexpressed in ICC tissues(P<0.001),but showed no significant association with patient age,gender,pathological grade,or tumor-node-metastasis stage(P>0.05).CONCLUSION PSMD6 can promote the growth of ICC cells,thus playing a pro-oncogenic role.

Efficient anti-aliasing and anti-leakage Fourier transform for high-dimensional seismic data regularization using cube removal and GPU

Seismic data is commonly acquired sparsely and irregularly, which necessitates the regularization of seismic data with anti-aliasing and anti-leakage methods during seismic data processing. We propose a novel method of 4D anti-aliasing and anti-leakage Fourier transform using a cube-removal strategy to address the combination of irregular sampling and aliasing in high-dimensional seismic data. We compute a weighting function by stacking the spectrum along the radial lines, apply this function to suppress the aliasing energy, and then iteratively pick the dominant amplitude cube to construct the Fourier spectrum. The proposed method is very efficient due to a cube removal strategy for accelerating the convergence of Fourier reconstruction and a well-designed parallel architecture using CPU/GPU collaborative computing. To better fill the acquisition holes from 5D seismic data and meanwhile considering the GPU memory limitation, we developed the anti-aliasing and anti-leakage Fourier transform method in 4D with the remaining spatial dimension looped. The entire workflow is composed of three steps: data splitting, 4D regularization, and data merging. Numerical tests on both synthetic and field data examples demonstrate the high efficiency and effectiveness of our approach.

Analytical Method for the Wave Diffraction of Asymmetrically Arranged Breakwaters

The layout forms of several breakwater structures can be generalized as asymmetrical arrangements in actual engineering.However,the problem of wave diffraction around asymmetrically arranged breakwaters has not been adequately investigated.In this study,we propose an analytical method of wave diffraction for regular waves passing through asymmetrically arranged breakwaters,and we use the Nyström method to obtain the analytical solution numerically.We compared the results of this method with those of previous analytical solutions and with numerical results to demonstrate the validity of our approach.We also provided diffraction coefficient diagrams of breakwaters with different layout forms.Moreover,we described the analytical expression for the problem of diffraction through long-wave incident breakwaters and presented an analysis of the relationship between the diffraction coefficients and the widths of breakwater gates.The analytical method presented in this study contributes to the limited literature on the theory of wave diffraction through asymmetrically arranged breakwaters.

Balance Sparse Decomposition Method with Nonconvex Regularization for Gearbox Fault Diagnosis

As an important part of rotating machinery,gearboxes often fail due to their complex working conditions and harsh working environment.Therefore,it is very necessary to effectively extract the fault features of the gearboxes.Gearbox fault signals usually contain multiple characteristic components and are accompanied by strong noise interference.Traditional sparse modeling methods are based on synthesis models,and there are few studies on analysis and balance models.In this paper,a balance nonconvex regularized sparse decomposition method is proposed,which based on a balance model and an arctangent nonconvex penalty function.The sparse dictionary is constructed by using Tunable Q-Factor Wavelet Transform(TQWT)that satisfies the tight frame condition,which can achieve efficient and fast solution.It is optimized and solved by alternating direction method of multipliers(ADMM)algorithm,and the non-convex regularized sparse decomposition algorithm of synthetic and analytical models are given.Through simulation experiments,the determination methods of regularization parameters and balance parameters are given,and compared with the L1 norm regularization sparse decomposition method under the three models.Simulation analysis and engineering experimental signal analysis verify the effectiveness and superiority of the proposed method.

Insufficient Physical Activity among Students Aged 6-17 Years in China,2016-2017

Regular physical activity of sufficient duration and intensity can provide health benefits such as improved physical fitness,which promotes bone health,prevents hypertension,improves mental health,and reduces adiposity^([1]).Furthermore,the benefits of regular physical exercise during youth may persist into adulthood^([2]).

THE GLOBAL EXISTENCE AND ANALYTICITY OF A MILD SOLUTION TO THE 3D REGULARIZED MHD EQUATIONS

In this paper,we study the three-dimensional regularized MHD equations with fractional Laplacians in the dissipative and diffusive terms.We establish the global existence of mild solutions to this system with small initial data.In addition,we also obtain the Gevrey class regularity and the temporal decay rate of the solution.

THE SMOOTHING EFFECT IN SHARP GEVREY SPACE FOR THE SPATIALLY HOMOGENEOUS NON-CUTOFF BOLTZMANN EQUATIONS WITH A HARDPOTENTIAL

In this article, we study the smoothing effect of the Cauchy problem for the spatially homogeneous non-cutoff Boltzmann equation for hard potentials. It has long been suspected that the non-cutoff Boltzmann equation enjoys similar regularity properties as to whose of the fractional heat equation. We prove that any solution with mild regularity will become smooth in Gevrey class at positive time, with a sharp Gevrey index, depending on the angular singularity. Our proof relies on the elementary L^(2) weighted estimates.

ELLIPTIC EQUATIONS IN DIVERGENCE FORM WITH DISCONTINUOUS COEFFICIENTS IN DOMAINS WITH CORNERS

We study equations in divergence form with piecewise Cαcoefficients.The domains contain corners and the discontinuity surfaces are attached to the edges of the corners.We obtain piecewise C^(1,α) estimates across the discontinuity surfaces and provide an example to illustrate the issue regarding the regularity at the corners.

A STRONG POSITIVITY PROPERTY AND A RELATED INVERSE SOURCE PROBLEM FOR MULTI-TERM TIME-FRACTIONAL DIFFUSION EQUATIONS

In this article,we consider the diffusion equation with multi-term time-fractional derivatives.We first derive,by a subordination principle for the solution,that the solution is positive when the initial value is non-negative.As an application,we prove the uniqueness of solution to an inverse problem of determination of the temporally varying source term by integral type information in a subdomain.Finally,several numerical experiments are presented to show the accuracy and efficiency of the algorithm.

Regularized dynamic mode decomposition algorithm for time sequence predictions

Dynamic mode decomposition(DMD) aims at extracting intrinsic mechanisms in a time sequence via linear recurrence relation of its observables, thereby predicting later terms in the sequence. Stability is a major concern in DMD predictions. We adopt a regularized form and propose a Regularized DMD(Re DMD) algorithm to determine the regularization parameter. This leverages stability and accuracy. Numerical tests for Burgers' equation demonstrate that Re DMD effectively stabilizes the DMD prediction while maintaining accuracy. Comparisons are made with the truncated DMD algorithm.

A novel algorithm for evaluating cement azimuthal density based on perturbation theory in horizontal well

Cement density monitoring plays a vital role in evaluating the quality of cementing projects,which is of great significance to the development of oil and gas.However,the presence of inhomogeneous cement distribution and casing eccentricity in horizontal wells often complicates the accurate evaluation of cement azimuthal density.In this regard,this paper proposes an algorithm to calculate the cement azimuthal density in horizontal wells using a multi-detector gamma-ray detection system.The spatial dynamic response functions are simulated to obtain the influence of cement density on gamma-ray counts by the perturbation theory,and the contribution of cement density in six sectors to the gamma-ray recorded by different detectors is obtained by integrating the spatial dynamic response functions.Combined with the relationship between gamma-ray counts and cement density,a multi-parameter calculation equation system is established,and the regularized Newton iteration method is employed to invert casing eccentricity and cement azimuthal density.This approach ensures the stability of the inversion process while simultaneously achieving an accuracy of 0.05 g/cm^(3) for the cement azimuthal density.This accuracy level is ten times higher compared to density accuracy calculated using calibration equations.Overall,this algorithm enhances the accuracy of cement azimuthal density evaluation,provides valuable technical support for the monitoring of cement azimuthal density in the oil and gas industry.

A Recurrent Neural Network for Multimodal Anomaly Detection by Using Spatio-Temporal Audio-Visual Data

In video surveillance,anomaly detection requires training machine learning models on spatio-temporal video sequences.However,sometimes the video-only data is not sufficient to accurately detect all the abnormal activities.Therefore,we propose a novel audio-visual spatiotemporal autoencoder specifically designed to detect anomalies for video surveillance by utilizing audio data along with video data.This paper presents a competitive approach to a multi-modal recurrent neural network for anomaly detection that combines separate spatial and temporal autoencoders to leverage both spatial and temporal features in audio-visual data.The proposed model is trained to produce low reconstruction error for normal data and high error for abnormal data,effectively distinguishing between the two and assigning an anomaly score.Training is conducted on normal datasets,while testing is performed on both normal and anomalous datasets.The anomaly scores from the models are combined using a late fusion technique,and a deep dense layer model is trained to produce decisive scores indicating whether a sequence is normal or anomalous.The model’s performance is evaluated on the University of California,San Diego Pedestrian 2(UCSD PED 2),University of Minnesota(UMN),and Tampere University of Technology(TUT)Rare Sound Events datasets using six evaluation metrics.It is compared with state-of-the-art methods depicting a high Area Under Curve(AUC)and a low Equal Error Rate(EER),achieving an(AUC)of 93.1 and an(EER)of 8.1 for the(UCSD)dataset,and an(AUC)of 94.9 and an(EER)of 5.9 for the UMN dataset.The evaluations demonstrate that the joint results from the combined audio-visual model outperform those from separate models,highlighting the competitive advantage of the proposed multi-modal approach.

Two-Stage Approach for Targeted Knowledge Transfer in Self-Knowledge Distillation

Knowledge distillation(KD) enhances student network generalization by transferring dark knowledge from a complex teacher network. To optimize computational expenditure and memory utilization, self-knowledge distillation(SKD) extracts dark knowledge from the model itself rather than an external teacher network. However, previous SKD methods performed distillation indiscriminately on full datasets, overlooking the analysis of representative samples. In this work, we present a novel two-stage approach to providing targeted knowledge on specific samples, named two-stage approach self-knowledge distillation(TOAST). We first soften the hard targets using class medoids generated based on logit vectors per class. Then, we iteratively distill the under-trained data with past predictions of half the batch size. The two-stage knowledge is linearly combined, efficiently enhancing model performance. Extensive experiments conducted on five backbone architectures show our method is model-agnostic and achieves the best generalization performance.Besides, TOAST is strongly compatible with existing augmentation-based regularization methods. Our method also obtains a speedup of up to 2.95x compared with a recent state-of-the-art method.

Impact Force Localization and Reconstruction via ADMM-based Sparse Regularization Method

In practice,simultaneous impact localization and time history reconstruction can hardly be achieved,due to the illposed and under-determined problems induced by the constrained and harsh measuring conditions.Although l_(1) regularization can be used to obtain sparse solutions,it tends to underestimate solution amplitudes as a biased estimator.To address this issue,a novel impact force identification method with l_(p) regularization is proposed in this paper,using the alternating direction method of multipliers(ADMM).By decomposing the complex primal problem into sub-problems solvable in parallel via proximal operators,ADMM can address the challenge effectively.To mitigate the sensitivity to regularization parameters,an adaptive regularization parameter is derived based on the K-sparsity strategy.Then,an ADMM-based sparse regularization method is developed,which is capable of handling l_(p) regularization with arbitrary p values using adaptively-updated parameters.The effectiveness and performance of the proposed method are validated on an aircraft skin-like composite structure.Additionally,an investigation into the optimal p value for achieving high-accuracy solutions via l_(p) regularization is conducted.It turns out that l_(0.6)regularization consistently yields sparser and more accurate solutions for impact force identification compared to the classic l_(1) regularization method.The impact force identification method proposed in this paper can simultaneously reconstruct impact time history with high accuracy and accurately localize the impact using an under-determined sensor configuration.

Supported Ziegler-Natta catalysts from MgCl_(2)·nROH precursors and its catalytic behaviors for diene copolymerization

Heterogeneous TiCl4/MgCl_(2) type Ziegler-Natta(Z-N)catalysts with unique advantages like low cost,high activity,high stereoregularity and pretty particle morphology,contribute to more than 130 Mt polyolefin large-scale production.However,most researches related with heterogeneous Z-N catalysts focused onα-olefin polymerizations like ethylene,propylene,etc.

Uncertainty-Aware Physical Simulation of Neural Radiance Fields for Fluids

This paper presents a novel framework aimed at quantifying uncertainties associated with the 3D reconstruction of smoke from2Dimages.This approach reconstructs color and density fields from 2D images using Neural Radiance Field(NeRF)and improves image quality using frequency regularization.The NeRF model is obtained via joint training ofmultiple artificial neural networks,whereby the expectation and standard deviation of density fields and RGB values can be evaluated for each pixel.In addition,customized physics-informed neural network(PINN)with residual blocks and two-layer activation functions are utilized to input the density fields of the NeRF into Navier-Stokes equations and convection-diffusion equations to reconstruct the velocity field.The velocity uncertainties are also evaluated through ensemble learning.The effectiveness of the proposed algorithm is demonstrated through numerical examples.The presentmethod is an important step towards downstream tasks such as reliability analysis and robust optimization in engineering design.

LOW-REGULARITY SOLUTIONS TO FOKKER-PLANCK-TYPE SYSTEMS IN THE WHOLE SPACE

In this manuscript,we consider two kinds of the Fokker-Planck-type systems in the whole space.The first part involves proving the global existence and the algebraic time decay rates of the mild solutions to the Fokker-Planck-Boltzmann equation near Maxwellians if initial data satisfies some smallness in the function space L_(k)^(1)L_(T)^(∞)L_(v)^(2)∩L_(k)^(p)L_(T)^(∞)L_(v)^(2).The second part proves the global existence of the mild solutions to the Vlasov-Poisson-Fokker-Planck system in the function space L_(k)^(1)L_(T)^(∞)L_(v)^(2),and we also obtain the exponential time decay rates,which are different from the algebraic time decay rates of the Fokker-Planck-Boltzmann equation.Our analysis is based on Lk1LT∞Lv2function space introduced by Duan et al.(Comm Pure Appl Math,2021,74:932-1020),the L_(k)^(1)∩L_(k)^(p) approach developed by Duan et al.(SIAM J Math Anal,2024,56:762-800),and the coercivity property of the Fokker-Planck operator.However,it is worth pointing out that the L_(k)^(1)∩L_(k)^(p)approach is not required for the Vlasov-Poisson-Fokker-Planck system,due to the influence of the electric field term,which is different from the Fokker-Planck-Boltzmann equation in this paper and in the work of Duan et al.(SIAM J Math Anal,2024,56:762-800).