术前免疫炎症指数与胃癌患者预后关系的研究

目的:探讨术前免疫炎症指数(systemic immune-inflammation index,SII)与胃癌患者预后的关系。方法:回顾性分析2010年6月至2015年6月青海大学附属医院收治的接受手术治疗的胃癌患者临床资料,计算其SII数值。相关性分析采用Spearman相关系数。应用受试者工作特征曲线(receiver operating characteristic curve,ROC)确定SII的最佳临界值,χ2检验评价SII与各项临床病理特征之间的关系。Kaplan-Meier法绘制生存曲线,并计算总生存时间(overall survival,OS),显著性分析采用Logrank检验。应用Cox比例风险模型进行预后因素的单因素和多因素分析。结果:共纳入771例患者,其中男性606例、女性165例,年龄25~81(56.91±9.546)岁。Spearmen相关性分析结果显示,SII与胃癌患者术后生存率呈正相关(r=0.399,P<0.001)。ROC曲线确定SII的最佳临界值为489.52,将771例胃癌患者分为低SII(L-SII)组(SII≤489.52)和高SII(H-SII)组(SII>489.52)。Kaplan-Meier分析显示L-SII组胃癌患者生存率明显优于H-SII组,两组总体生存率差异具有统计学意义(P<0.05)。Ⅰ、Ⅱ、Ⅲ期中L-SII组的5年生存率明显高于H-SII组(分别为81.8%vs.24.8%、56.5%vs.21.2%、62.5%vs.24.6%),差异具有统计学意义(P<0.05)。单因素分析显示H-SII是胃癌患者预后的危险因素(HR=3.919,95%CI:3.087~4.977,P<0.001),多因素分析提示术前外周血SII是胃癌患者预后的独立危险因素(HR=2.707,95%CI:2.074~3.533,P<0.001)。再次通过ROC曲线比较SII与其他指标预测胃癌患者预后的能力,显示SII具有更准确的预测能力。将SII联合Alb、CEA、CA125、WBC较单独预测具有更优的预测效能(AUC=0.786)。结论:SII是胃癌患者预后的独立危险因素。术前外周血SII的升高提示着更差的预后。

Optical resonance in inhomogeneous parity-time symmetric systems

We show that inhomogeneous waveguides of slowly varied parity-time(PT) symmetry support localized optical resonances.The resonance is closely related to the formation of exceptional points separating exact and broken PT phases.Salient features of this kind of non-Hermitian resonance, including the formation of half-vortex flux and the discrete nature,are discussed.This investigation highlights the unprecedented uniqueness of field dynamics in non-Hermitian systems with many potential adaptive applications.

Single-scan polarization-resolved degenerate four-wave mixing spectroscopy using a vector optical field

We report on a new method to achieve the single-scan polarization-resolved degenerate four-wave mixing(DFWM)spectroscopy in a Rb atomic medium using a vector optical field,in which two pump beams are kept linearly polarized and a vector beam is employed as the probe beam.As the polarization and intensity of the DFWM signal are closely dependent on the polarization state of the probe beam,a vector probe beam with space-variant states of polarization is able to generate a DFWM signal with space-variant states of polarization and intensity across the DFWM image.Accordingly,the polarization-resolved spectra can be retrieved from a single DFWM image.To the best of our knowledge,this is the first time that the single-scan polarizationresolved spectrum detection has been realized experimentally with a vector beam.This work provides a simple but efficient single-scan polarization-resolved spectroscopic method,which would be of great utility for the samples of poor light stability and fast optical processes.

SceGAN:A method for generating autonomous vehicle cut-in scenarios on highways based on deep learning

With the increasing level of automation of autonomous vehicles,it is important to conduct comprehensive and extensive testing before releasing autonomous vehicles into the market.Traditional public road and closed-field testing failed to meet the requirements of high testing efficiency and scenario coverage.Therefore,scenario-based autonomous vehicle simulation testing has emerged.Many scenarios form the basis of simulation testing.Generating additional scenarios from an existing scenario library is a significant problem.Taking the scenarios of a proceeding vehicle cutting into an adjacent lane on highways as an example,based on an autoencoder and a generative adversarial network(GAN),a method that combines Transformer to capture the features of a long-time series,called SceGAN,is proposed to model and generate scenarios of autonomous vehicles on highways.An evaluation system is established to analyze the reliability of SceGAN using discriminative and predictive scores and further evaluate the effect of scenario generation in terms of similarity and coverage.Experiments showed that compared with TimeGAN and AEGAN,SceGAN is superior in data fidelity and availability,and their similarity increased by 27.22%and 21.39%,respectively.The coverage increased from 79.84%to 93.98%as generated scenarios increased from 2,547 to 50,000,indicating that the proposed method has a strong generalization capability for generating multiple trajectories,providing a basis for generating test scenarios and promoting autonomous vehicle testing.

A deep learning method for traffic light status recognition

Real-time and accurate traffic light status recognition can provide reliable data support for autonomous vehicle decision-making and control systems.To address potential problems such as the minor component of traffic lights in the perceptual domain of visual sensors and the complexity of recognition scenarios,we propose an end-to-end traffic light status recognition method,ResNeSt50-CBAM-DINO(RC-DINO).First,we performed data cleaning on the Tsinghua-Tencent traffic lights(TTTL)and fused it with the Shanghai Jiao Tong University’s traffic light dataset(S2TLD)to form a Chinese urban traffic light dataset(CUTLD).Second,we combined residual network with split-attention module-50(ResNeSt50)and the convolutional block attention module(CBAM)to extract more significant traffic light features.Finally,the proposed RC-DINO and mainstream recognition algorithms were trained and analyzed using CUTLD.The experimental results show that,compared to the original DINO,RC-DINO improved the average precision(AP),AP at intersection over union(IOU)=0.5(AP50),AP for small objects(APs),average recall(AR),and balanced F score(F1-Score)by 3.1%,1.6%,3.4%,0.9%,and 0.9%,respectively,and had a certain capability to recognize the partially covered traffic light status.The above results indicate that the proposed RC-DINO improved recognition performance and robustness,making it more suitable for traffic light status recognition tasks.