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Purpose: To study the MRI features (based on LI-RADS) and clinical characteristics of P53-mutated hepatocellular carcinoma (HCC) patients. Patients and Methods: This study enrolled 344 patients with histopathologically confirmed HCC (P53-mutated group [n = 196], non-P53-mutated group [n = 148]). We retrospectively evaluated the preoperative MRI features, clinical and pathologic features of the lesions and assigned each lesion according to the LI-RADS. MRI findings, clinical features, and pathologic findings were compared using the Student's t test, χ 2 test, and multivariable regression analysis. Results: Most HCC patients were categorized as LR-5. On multivariate analysis, the Edmondson–Steiner grade (odds ratio, 2.280; 95% CI: 1.268, 4.101; p = 0.006) and rim enhancement (odds ratio, 2.517; 95% CI: 1.095, 5.784; p = 0.030) were found to be independent variables associated with P53-mutated HCC. In the group of HCC lesions with the largest tumor diameter (LTD) greater than or equal to 10mm and less than or equal to 20mm, enhancing capsule was an independent predictor of P53-mutated HCC (odds ratio, 6.200; 95% CI: 1.116, 34.449; p = 0.037). Among the HCC lesions (20 mm ˂ LTD ≤ 50 mm), corona enhancement (odds ratio, 2.102; 95% CI: 1.022, 4.322; p = 0.043) and nodule-in-nodule architecture (odds ratio, 2.157; 95% CI: 1.033, 4.504; p = 0.041) were found to be independent risk factors for P53 mutation. Among the HCC lesions (50 mm ˂ LTD ≤ 100 mm), diameter (odds ratio, 1.035; 95% CI: 1.001, 1.069; p = 0.044) and AFP ≥ 400 (ng/mL) (odds ratio, 3.336; 95% CI: 1.052, 10.577; p = 0.041) were found to be independent variables associated with P53-mutated HCC. Conclusion: Poor differentiation and rim enhancement are potential predictive biomarkers for P53-mutated HCC, while HCCs of different diameters have different risk factors for predicting P53 mutations. [ABSTRACT FROM AUTHOR]

We present an interface-type memristor utilizing native silicon oxide nanostructures achieved through controlled plasma oxidation engineering at room temperature. This process effectively reduces the concentration of oxygen vacancies on the surface of the native silicon oxide, facilitating oxygen-vacancy-mediated interfacial resistive switching with excellent COMS compatibility and low switching variability (1.03% for C2C and 3.96% for D2D variability). Moreover, this dynamic memristor exhibits favorable nonlinearity and short-term memory (STM) effects, which are crucial properties for the development of memristor-based reservoir computing (RC) systems. Compared with the conventional neural network, the RC system requires fewer trainable weights to achieve an accuracy of 96.7% for the Iris classification task. By the addition of virtual nodes to the reservoir with masks, the accuracy can be further increased to 100%. A simple digit recognition task is also employed, and noisy images that are not included in the original training set can be recognized correctly. These results highlight the great potential of our device for constructing reservoir computing hardware. [ABSTRACT FROM AUTHOR]

Background: The suitability of hepatectomy among patients with multinodular hepatocellular carcinoma (MHCC) beyond the Milan criteria remains controversial. There is a need for a reliable risk stratification tool among these patients for the selection of ideal candidates of curative resection. Purpose: To determine the clinicoradiological prognostic factors for patients with MHCC beyond the Milan criteria to further develop a stratification system. Study Type: Retrospective. Subjects: 176 patients with pathologically confirmed MHCC beyond the Milan criteria. Field Strength/Sequence: The 1.5 T scanner, including T1‐, T2‐, diffusion‐weighted imaging, in/out‐phase imaging, and dynamic contrast‐enhanced imaging. Assessment: Conventional MRI features and preoperative laboratory data including aspartate aminotransferase (AST) and α‐fetoprotein (AFP) were collected and analyzed. Two nomograms incorporating clinicoradiological variables were independently constructed to predict recurrence‐free survival (RFS) and overall survival (OS) with Cox regression analyses and verified with 5‐fold cross validation. Based on the nomograms, two prognostic stratification systems for RFS and OS were further developed. Statistical Tests: The Cohen's kappa/intraclass correlation coefficient, C‐index, calibration curve, Kaplan–Meier curve, log‐rank test. A P value <0.05 was considered statistically significant. Results: AST > 40 U/L, increased tumor burden score, radiological liver cirrhosis and nonsmooth tumor margin were independent predictors for poor RFS, while AST > 40 U/L, AFP > 400 ng/mL and radiological liver cirrhosis were independent predictors for poor OS. The two nomograms demonstrated good discrimination performance with C‐index of 0.653 (95% confidence interval [CI], 0.602–0.794) and 0.685 (95% CI, 0.623–0.747) for RFS and OS, respectively. The 5‐fold cross validation further validated the discrimination capability of the nomograms. Based on the nomogram models, MHCC patients beyond the Milan criteria were stratified into low−/medium−/high‐risk groups with significantly different RFS and OS. Data Conclusion: The proposed MRI‐based prognostic stratification system facilitates the refinement and further subclassification of patients with MHCC beyond the Milan criteria. Evidence Level: 4. Technical Efficacy: 2. [ABSTRACT FROM AUTHOR]

We show that there is an intrinsic link between the use of Walsh aberration modes in adaptive optics (AO) and the mathematics of lattices. The discrete and binary nature of these modes means that there are infinite combinations of Walsh mode coefficients that can optimally correct the same aberration. Finding such a correction is hence a poorly conditioned optimisation problem that can be difficult to solve. This can be mitigated by confining the AO correction space defined in Walsh mode coefficients to the fundamental Voronoi cell of a lattice. By restricting the correction space in this way, one can ensure there is only one set of Walsh coefficients that corresponds to the optimum correction aberration. This property is used to enable the design of efficient estimation algorithms to solve the inverse problem of finding correction aberrations from a sequence of measurements in a wavefront sensorless AO system. The benefit of this approach is illustrated using a neural-network-based estimator. [ABSTRACT FROM AUTHOR]

Dynamic vision sensor (DVS) is a new type of image sensor, which has application prospects in the fields of automobiles and robots. Dynamic vision sensors are very different from traditional image sensors in terms of pixel principle and output data. Background activity (BA) in the data will affect image quality, but there is currently no unified indicator to evaluate the image quality of event streams. This paper proposes a method to eliminate background activity, and proposes a method and performance index for evaluating filter performance: noise in real (NIR) and real in noise (RIN). The lower the value, the better the filter. This evaluation method does not require fixed pattern generation equipment, and can also evaluate filter performance using natural images. Through comparative experiments of the three filters, the comprehensive performance of the method in this paper is optimal. This method reduces the bandwidth required for DVS data transmission, reduces the computational cost of target extraction, and provides the possibility for the application of DVS in more fields. [ABSTRACT FROM AUTHOR]