外周血CTC、PD-L1及S100A6BP水平对胃癌患者诊断及预后价值

目的 探讨外周血循环肿瘤细胞(CTC)、程序性死亡因子配体-1(PD-L1)、及钙周期素结合蛋白(S100A6BP)水平对胃癌患者诊断及预后价值研究。方法 选取2020年5月至2022年5月南阳市第一人民医院收治的182例胃癌患者为观察组,根据预后情况分为预后良好组(124例)及预后不良组(58例),选取同期90名健康志愿者作为对照组。比较所有受试者外周血CTC、PD-L1、S100A6BP水平,评估CTC、PD-L1、S100A6BP单独及联合检测在胃癌患者诊断及预后中的应用价值。结果 观察组外周血CTC、PD-L1及S100A6BP水平均高于对照组,差异有统计学意义(P<0.05);CTC、PD-L1及S100A6BP三者联合检测对胃癌患者诊断灵敏度、特异度均较单一检测高(P<0.05);预后良好者肿瘤直径、CTC、PD-L1、S100A6BP、TNM(Ⅲ~Ⅳ)、中高分化、淋巴结转移、浸润深度(T3-T4)均较预后不良组低,差异有统计学意义(P<0.05),TNM(Ⅰ~Ⅱ)、低度分化、浸润深度(T1-T2)水平较预后不良组高,差异有统计学意义(P<0.05);多因素Logistic回归分析显示,肿瘤直径、TNM分期、分化程度、浸润深度、淋巴结转移、CTC、PD-L1、S100A6BP是影响胃癌预后不良的独立危险因素(P<0.05);ROC曲线分析显示,CTC、PD-L1、S100A6BP单一及联合检测预测胃癌患者预后AUC分别为0.808、0.845、0.874、0.955(P<0.05)。结论 CTC、PD-L1、S100A6BP在胃癌患者外周血中呈高表达,与患者病理特征及预后有密切关系,三者联合检测对胃癌患者诊断及预后具有较高的预测价值。

CTCS-2+ATO列控系统对短站坪长度需求适应性研究

研究目的:对于有地下线路的铁路工程,由于开挖土方等方面的问题,要求站坪长度尽量缩短,以节省工程投资。现有CTCS-2+ATO列控系统因其安全防护距离设置位置、车载设备安装位置、适应的运行速度较高等原因,对站坪长度要求较CBTC列控系统长,成为在市域铁路运用时的不利因素,需要对其进行短站坪长度需求的适应性分析并提出适宜的解决方法,提升系统的竞争力。研究结论:(1)侧线股道增设安全线,基于列车停稳信息及时解锁过走防护区段、利用应答器报文增加过走防护区段信息、及时回缩列控移动授权范围等配套方法,将安全距离、附加距离设于目标点信号机的内方,利用安全线及咽喉区线路缩短股道区线路长度;(2)合理约束车载应答器接收天线安装位置,根据市域列车运行速度计算、确定可靠解析出报文所需要的应答器距离信号机距离,以及应答器组间最小距离要求,最终确定股道出站应答器安装位置最小长度需求;(3)经折返线道岔侧向的进路不发低频码,轨道电路长度仅考虑轨道占用检查时间,进一步缩短折返道岔区段长度要求;(4)本研究成果在市域铁路中有广阔的运用前景。

CTC、RON及c-Met在早期三阴性乳腺癌预后预测中的作用

目的 分析循环肿瘤细胞(CTC)、受体酪氨酸激酶(RON)及间质表皮转化因子(c-Met在早期三阴性乳腺癌(TNBC)预后预测中的作用。方法 分析2018年1月至2021年1月于郑州大学第一附属医院进行诊治的1 225例乳腺癌患者资料,根据纳入标准最终选取168例TNBC患者设为研究组,选取患者癌旁3 cm的组织为对照组,另选取同时期在本院进行健康体检者168名为健康组。根据患者治疗后的预后情况将研究组分为预后良好组(n=142)以及预后不良组(n=26)。比较研究组与健康组CTC的表达情况,比较研究组与对照组c-Met与RON的表达情况,采用多元Logistic回归分析影响TNBC预后的独立危险因素;并通过受试者工作特征曲线(ROC)分析c-Met、RON、CTC对TNBC患者预后的预测价值。结果 研究组CTC水平高于健康组,差异有统计学意义(P<0.05);研究组c-Met与RON阳性表达高于对照组,差异有统计学意义(P<0.05);预后良好组、预后不良组年龄、肿瘤直径比较差异无统计学意义(P>0.05),预后良好组、预后不良组腋窝淋巴结、体重指数、糖尿病、RON、c-Met、CTC比较,差异有统计学意义(P<0.05),经非条件多因素logistic回归模型分析显示,腋窝淋巴结转移、CTC阳性、RON阳性、c-Met阳性为TNBC患者预后的危险因素(P<0.05)。CTC、RON及c-Met单独检测以及三者联合检测AUC分别为0.764、0.778、0.776、0.857,其中三者联合检测AUC值最大。结论 联合检测RON、c-MET、CTC水平对TNBC患者预后具有一定的预测价值。

引入预训练表示混合矢量量化和CTC的语音转换

预训练模型通过自监督学习表示在非平行语料语音转换(VC)取得了重大突破。随着自监督预训练表示(SSPR)的广泛使用,预训练模型提取的特征中被证实包含更多的内容信息。提出一种基于SSPR同时结合矢量量化(VQ)和联结时序分类(CTC)的VC模型。将预训练模型提取的SSPR作为端到端模型的输入,用于提高单次语音转换质量。如何有效地解耦内容表示和说话人表示成为语音转换中的关键问题。使用SSPR作为初步的内容信息,采用VQ从语音中解耦内容和说话人表示。然而,仅使用VQ只能将内容信息离散化,很难将纯粹的内容表示从语音中分离出来,为了进一步消除内容信息中说话人的不变信息,提出CTC损失指导内容编码器。CTC不仅作为辅助网络加快模型收敛,同时其额外的文本监督可以与VQ联合优化,实现性能互补,学习纯内容表示。说话人表示采用风格嵌入学习,2种表示作为系统的输入进行语音转换。在开源的CMU数据集和VCTK语料库对所提的方法进行评估,实验结果表明,该方法在客观上的梅尔倒谱失真(MCD)达到8.896 d B,在主观上的语音自然度平均意见分数(MOS)和说话人相似度MOS分别为3.29和3.22,均优于基线模型,此方法在语音转换的质量和说话人相似度上能够获得最佳性能。

基于智能CTC的智慧客运枢纽站方案及应用

铁路客运枢纽站因作业组织复杂、系统间联动性不足及智能化程度低等,面临作业效率不高、应急响应时间长等问题。运用北斗、5G、GIS、数字孪生等先进技术,以智能CTC3.0为基础,构建“智能CTC3.0平台、综合共享平台、5G+北斗应用平台”三大平台,通过三大平台完成行车指挥、设备监控、作业管控、旅客司乘、地理信息等重要数据的有机融合和安全运用,实现行车组织、调机作业、客运作业、运维施工、应急处置5类业务的智能化升级。以白云站枢纽工程为契机,建设“三个平台、五个智能化”架构的智慧车站,有效提升了客运枢纽站的运营效率和安全管控能力,可为其他客运枢纽站智慧化建设提供参考。

高速铁路智能CTC多区段列车运行协同调整方法研究

为破解调度集中系统(CTC)在多区段调整方面的技术瓶颈,支撑智能高铁2.0时代多线成网运营的智能化行车调度运用场景,梳理并分析现阶段CTC智能调整方面的研究成果及问题,明确了调度管理边界及CTC系统边界将长期存在的客观性,从模型构建、动态协同联盟定界机制、求解算法等方面对多区段调整方法进行探讨。立足于CTC系统现状及规划实施的智能化技术路线,从多区段调整业务主体、信息存储、相关信息综合利用技术、动态信道分配等方面,分析CTC承载多区段调整业务的可行性及技术研究路线。分析研究结果表明:基于智能CTC的多区段列车运行协同调整方法研究具有较强的可行性和紧迫性。分析过程及相关结论对大范围智能行车调度的深入研究具有参考价值。

CTCS-3线路GSM-R网络运维质量评价体系研究

针对高速铁路GSM-R网络承载行车通信业务的特点,分析CTCS-3线路GSM-R网络运维质量评价思路,提出CTCS-3线路GSM-R网络运维质量评价体系,通过GSM-R网络运行数据进行计算得出评价结果,综合反映出CTCS-3线路GSM-R网络运维质量状况。

Classification of Sailboat Tell Tail Based on Deep Learning

The tell tail is usually placed on the triangular sail to display the running state of the air flow on the sail surface.It is of great significance to make accurate judgement on the drift of the tell tail of the sailboat during sailing for the best sailing effect.Normally it is difficult for sailors to keep an eye for a long time on the tell sail for accurate judging its changes,affected by strong sunlight and visual fatigue.In this case,we adopt computer vision technology in hope of helping the sailors judge the changes of the tell tail in ease with ease.This paper proposes for the first time a method to classify sailboat tell tails based on deep learning and an expert guidance system,supported by a sailboat tell tail classification data set on the expert guidance system of interpreting the tell tails states in different sea wind conditions,including the feature extraction performance.Considering the expression capabilities that vary with the computational features in different visual tasks,the paper focuses on five tell tail computing features,which are recoded by an automatic encoder and classified by a SVM classifier.All experimental samples were randomly divided into five groups,and four groups were selected from each group as the training set to train the classifier.The remaining one group was used as the test set for testing.The highest resolution value of the ResNet network was 80.26%.To achieve better operational results on the basis of deep computing features obtained through the ResNet network in the experiments.The method can be used to assist the sailors in making better judgement about the tell tail changes during sailing.

CTC系统与SMIS调车数据交互服务平台设计与实现

为解决调度集中(CTC,Centralized Traffic Control)系统无法获取调车作业计划数据、仍使用人工排列调车进路方式的问题,在现有CTC系统与车站综合管理信息系统(SMIS,Synthesized Management Information System)应用基础上,构建调车数据交互服务平台,并对该平台的总体架构、功能应用及平台支撑下的流程设计展开研究。通过基于边缘计算的数据采集与处理、调车作业计划数据安全防护等关键技术,高效地实现了调车作业计划的数据采集、数据分发、数据集成及平台监控等功能。调车数据交互服务平台已在中国铁路武汉局集团有限公司部分车站投入使用,显著提升了作业人员的工作效率,保障了调车作业进路排列的准确性和安全性。

基于α-截集三角模糊数和攻击树的CTCS网络安全风险评估方法

针对工业控制系统网络安全风险评估影响因素的不确定性和指标量化困难问题,提出一种基于模糊理论和攻击树的方法评估工业控制系统风险,并将它应用于中国列车控制系统(CTCS)的风险评估。首先,基于CTCS可能面临的网络安全威胁和系统自身的脆弱性建立攻击树模型,使用α-截集三角模糊数(TFN)计算攻击树叶节点和攻击路径的区间概率;其次,利用层次分析法(AHP)建立安全事件损失数学模型,最终得出风险评估值。实验结果表明,所提方法可以有效评估系统风险,预测攻击路径,降低主观因素对风险评估过程的影响,使评估结果更契合实际,为安全防护策略的选择提供参考和依据。

Depression Intensity Classification from Tweets Using Fast Text Based Weighted Soft Voting Ensemble

Predicting depression intensity from microblogs and social media posts has numerous benefits and applications,including predicting early psychological disorders and stress in individuals or the general public.A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media text.This study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces(APIs).A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the corpus.Furthermore,an algorithm is developed to annotate the data into three depression classes:‘Mild,’‘Moderate,’and‘Severe,’based on International Classification of Diseases-10(ICD-10)depression diagnostic criteria.Different baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the corpus.Further FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model,which significantly increases the depression classification performance to an 84%F1 score and 90%accuracy compared to baselines.Finally,a FastText-based weighted soft voting ensemble(WSVE)is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual performances.The proposed WSVE outperformed all baselines as well as FastText alone,with an F1 of 89%,5%higher than FastText alone,and an accuracy of 93%,3%higher than FastText alone.The proposed model better captures the contextual features of the relatively small sample class and aids in the detection of early depression intensity prediction from tweets with impactful performances.

Classification of congenital cataracts based on multidimensional phenotypes and its association with visual outcomes

●AIM:To establish a classification for congenital cataracts that can facilitate individualized treatment and help identify individuals with a high likelihood of different visual outcomes.●METHODS:Consecutive patients diagnosed with congenital cataracts and undergoing surgery between January 2005 and November 2021 were recruited.Data on visual outcomes and the phenotypic characteristics of ocular biometry and the anterior and posterior segments were extracted from the patients’medical records.A hierarchical cluster analysis was performed.The main outcome measure was the identification of distinct clusters of eyes with congenital cataracts.●RESULTS:A total of 164 children(299 eyes)were divided into two clusters based on their ocular features.Cluster 1(96 eyes)had a shorter axial length(mean±SD,19.44±1.68 mm),a low prevalence of macular abnormalities(1.04%),and no retinal abnormalities or posterior cataracts.Cluster 2(203 eyes)had a greater axial length(mean±SD,20.42±2.10 mm)and a higher prevalence of macular abnormalities(8.37%),retinal abnormalities(98.52%),and posterior cataracts(4.93%).Compared with the eyes in Cluster 2(57.14%),those in Cluster 1(71.88%)had a 2.2 times higher chance of good best-corrected visual acuity[<0.7 logMAR;OR(95%CI),2.20(1.25–3.81);P=0.006].●CONCLUSION:This retrospective study categorizes congenital cataracts into two distinct clusters,each associated with a different likelihood of visual outcomes.This innovative classification may enable the personalization and prioritization of early interventions for patients who may gain the greatest benefit,thereby making strides toward precision medicine in the field of congenital cataracts.

A Bitcoin Address Multi-Classification Mechanism Based on Bipartite Graph-Based Maximization Consensus

Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment projects.Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly.Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way.In this paper,we address the problem of identifying multiple classes of Bitcoin services,and for the poor classification of individual addresses that do not have significant features,we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and entities.The innovation of the method is to(1)Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task.(2)Unlike the general supervised model approach,this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping relationships.Specifically,after obtaining the overall features,the address classification and entity clustering tasks are performed separately,and the results are subjected to graph-basedmaximization consensus.The final result ismade to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as possible.By testing and evaluating over 26,000 Bitcoin addresses,our feature extraction method captures more useful features.In addition,the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.

Pathological and Clinical Correlation European Union-Thyroid Imaging Reporting and Data System (EU-TIRADS) Classification of Thyroid Nodules in Two University Hospitals in Cotonou

Introduction: Since its creation in 2017 by the European community, the EU-TIRADS classification has enjoyed an excellent reputation in several countries around the world. Indeed, several studies conducted in these countries testify to the effectiveness of this tool for the management of nodular thyroid pathology. However, in Benin, the contribution of this classification has not yet been evaluated. It is therefore to overcome this inadequacy that we undertook this study. Objective: Participate in improving the diagnostic and therapeutic management of thyroid nodules at the CNHU HKM in Cotonou and at the CHUZ in Suru-Léré. Methods: This is a cross-sectional study with retrospective data collection spread over a period of 3 years 5 months, from January 2019 to May 2022 and carried out jointly in the Endocrinology Metabolism Nutrition and ORL-CCF departments of the CNHU HKM of Cotonou and in the ORL-CCF department of the CHUZ of Suru-Léré. The study population consisted of patients who consulted the University Clinic of Endocrinology Metabolism Nutrition, the University Clinic of ORL-CCF of the CNHU-HKM and the University Clinic of ORL-CCF of the CHUZ of Suru-Léré for thyroid nodule and who have had surgery. The study data was collected from patients hospitalization records using a survey form. Results: On ultrasound, according to the EU-TIRADS classification, 56.8% of nodules presented a low risk of malignancy (EU-TIRADS 3) compared to respectively 19.8%;23% and 2.5% of nodules with zero (EU-TIRADS 2), intermediate (EU-TIRADS 4) and high (EU-TIRADS 5) risk of malignancy. Regarding the performance of this classification, it is sensitive in 37.5% of cases and has a specificity of 78.5% with a PPV (Positive Predictive Value) and a NPV (Negative Predictive Value) respectively of 6.6 % and 91.6%. Furthermore, the bivariate correlations revealed that the size of the nodule was significantly associated with the malignancy of the nodule (p = 0.014) and the calculated value of the Yule’s Q coefficient (0.375) reflects a moderate intensity of the connection between the EU-TIRADS and histology. Conclusion: the EU-TIRADS classification, due to its excellent NPV, is of great interest for the management of thyroid nodules at the CNHU-HKM of Cotonou and at the CHUZ of Suru-Léré. In view of this, particular emphasis must be placed on its regular and rigorous use.

Orchard Sports Injury and Illness Classification System (OSIICS) Version 15

Background:Sports medicine(injury and illnesses)requires distinct coding systems because the International Classification of Diseases is insuf-ficient for sports medicine coding.The Orchard Sports Injury and Illness Classification System(OSIICS)is one of two sports medicine coding systems recommended by the International Olympic Committee.Regular updates of coding systems are required.Methods:For Version 15,updates for mental health conditions in athletes,sports cardiology,concussion sub-types,infectious diseases,and skin and eye conditions were considered particularly important.Results:Recommended codes were added from a recent International Olympic Committee consensus statement on mental health conditions in athletes.Two landmark sports cardiology papers were used to update a more comprehensive list of sports cardiology codes.Rugby union protocols on head injury assessment were used to create additional concussion codes.Conclusion:It is planned that OSIICS Version 15 will be translated into multiple new languages in a timely fashion to facilitate international accessibility.The large number of recently published sport-specific and discipline-specific consensus statements on athlete surveillance warrant regular updating of OSIICS.

Monitoring Surface Water Change in Northeast China in 1999–2020:Evidence from Satellite Observation and Refined Classification

As a typical region with high water demand for agricultural production,understanding the spatiotemporal surface water changes in Northeast China is critical for water resources management and sustainable development.However,the long-term variation characteristics of surface water of different water body types in Northeast China remain rarely explored.This study investigated how surface water bodies of different types(e.g.,lake,reservoir,river,coastal aquaculture,marsh wetland,ephemeral water) changed during1999–2020 in Northeast China based on various remote sensing-based datasets.The results showed that surface water in Northeast China grew dramatically in the past two decades,with an equivalent area increasing from 24 394 km^(2) in 1999 to 34 595 km^(2) in 2020.The surge of ephemeral water is the primary driver of surface water expansion,which could ascribe to shifted precipitation pattern.Marsh wetlands,rivers,and reservoirs experienced a similar trend,with an approximate 20% increase at the interdecadal scale.By contrast,coastal aquacultures and natural lakes remain relatively stable.This study is expected to provide a more comprehensive investigation of the surface water variability in Northeast China and has important practical significance for the scientific management of different types of surface water.

Diffraction deep neural network-based classification for vector vortex beams

The vector vortex beam(VVB)has attracted significant attention due to its intrinsic diversity of information and has found great applications in both classical and quantum communications.However,a VVB is unavoidably affected by atmospheric turbulence(AT)when it propagates through the free-space optical communication environment,which results in detection errors at the receiver.In this paper,we propose a VVB classification scheme to detect VVBs with continuously changing polarization states under AT,where a diffractive deep neural network(DDNN)is designed and trained to classify the intensity distribution of the input distorted VVBs,and the horizontal direction of polarization of the input distorted beam is adopted as the feature for the classification through the DDNN.The numerical simulations and experimental results demonstrate that the proposed scheme has high accuracy in classification tasks.The energy distribution percentage remains above 95%from weak to medium AT,and the classification accuracy can remain above 95%for various strengths of turbulence.It has a faster convergence and better accuracy than that based on a convolutional neural network.

Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://github.com/yahuiliu99/PointC onT.

Data-driven casting defect prediction model for sand casting based on random forest classification algorithm

The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality,resulting in a high scrap rate.A strategy based on a data-driven model was proposed to reduce casting defects and improve production efficiency,which includes the random forest(RF)classification model,the feature importance analysis,and the process parameters optimization with Monte Carlo simulation.The collected data includes four types of defects and corresponding process parameters were used to construct the RF model.Classification results show a recall rate above 90% for all categories.The Gini Index was used to assess the importance of the process parameters in the formation of various defects in the RF model.Finally,the classification model was applied to different production conditions for quality prediction.In the case of process parameters optimization for gas porosity defects,this model serves as an experimental process in the Monte Carlo method to estimate a better temperature distribution.The prediction model,when applied to the factory,greatly improved the efficiency of defect detection.Results show that the scrap rate decreased from 10.16% to 6.68%.

TEAM:Transformer Encoder Attention Module for Video Classification

Much like humans focus solely on object movement to understand actions,directing a deep learning model’s attention to the core contexts within videos is crucial for improving video comprehension.In the recent study,Video Masked Auto-Encoder(VideoMAE)employs a pre-training approach with a high ratio of tube masking and reconstruction,effectively mitigating spatial bias due to temporal redundancy in full video frames.This steers the model’s focus toward detailed temporal contexts.However,as the VideoMAE still relies on full video frames during the action recognition stage,it may exhibit a progressive shift in attention towards spatial contexts,deteriorating its ability to capture the main spatio-temporal contexts.To address this issue,we propose an attention-directing module named Transformer Encoder Attention Module(TEAM).This proposed module effectively directs the model’s attention to the core characteristics within each video,inherently mitigating spatial bias.The TEAM first figures out the core features among the overall extracted features from each video.After that,it discerns the specific parts of the video where those features are located,encouraging the model to focus more on these informative parts.Consequently,during the action recognition stage,the proposed TEAM effectively shifts the VideoMAE’s attention from spatial contexts towards the core spatio-temporal contexts.This attention-shift manner alleviates the spatial bias in the model and simultaneously enhances its ability to capture precise video contexts.We conduct extensive experiments to explore the optimal configuration that enables the TEAM to fulfill its intended design purpose and facilitates its seamless integration with the VideoMAE framework.The integrated model,i.e.,VideoMAE+TEAM,outperforms the existing VideoMAE by a significant margin on Something-Something-V2(71.3%vs.70.3%).Moreover,the qualitative comparisons demonstrate that the TEAM encourages the model to disregard insignificant features and focus more on the essential video features,capturing more detailed spatio-temporal contexts within the video.