儿童肺炎支原体肺炎肺泡灌洗液CARDS TX、HMGB1、TLR2表达及临床意义

目的研究肺炎支原体肺炎(MPP)患儿肺泡灌洗液中社区获得性呼吸窘迫综合征毒素(CARDS TX)、高迁移率族蛋白1(HMGB1)、Toll样受体2(TLR2)表达,探讨其在MPP发病中的临床意义。方法回顾性分析55例MPP病例组及20例对照组临床资料,同时检测两组支气管肺泡灌洗液(BALF)中CARDS TX、HMGB1、TLR2、TLR4、晚期糖基化终末产物受体(RAGE)及髓样分化因子88(MyD88)表达,体外检测不同浓度CARDS TX刺激下HMGB1、TLR2的表达并进行比较。结果与对照组相比,MPP组LYM绝对值计数、PA、CD3^(+)、CD3^(+)CD4^(+)、CD3^(+)CD8^(+)、CD3-CD19^(+)、NK cell、CD19^(+)CD23^(+)明显下降,CRP、LDH、D-二聚体、IgA、IgG、IgM、CARDS TX、HMGB1、TLR2、MyD88均升高,差异有统计学意义(P<0.05或0.001)。CARDS TX刺激后HMGB1、TLR2的表达升高,且呈正相关,差异有统计学意义(P<0.001)。结论CARDS TX可能通过HMGB1-TLR2-MyD88途径参与肺炎支原体(MP)的致病。

CARD6在脓毒症心肌损伤中的机制研究

目的:探讨CARD6在脓毒症心肌损伤中的调节机制。方法:将C57BL/6小鼠随机分为对照组与模型组。采用LPS腹腔注射法构建脓毒症心肌损伤小鼠模型,RT-qPCR检测小鼠心肌组织中CARD6、caspase-1的mRNA水平。体外实验则采用LPS刺激转染CARD6过表达质粒的H9C2细胞,检测caspase-1、细胞活力、CK-MB水平。结果:与对照组相比,模型组小鼠心肌组织中LDH、CK-MB含量,caspase-1表达水平上升,提示脓毒症导致小鼠心肌损伤,而CARD6表达水平明显降低。体外实验发现CARD6表达水平呈下降趋势,转染含有CARD6的过表达质粒后细胞释放CK-MB的水平降低、细胞活力增加、caspase-1水平降低。结论:CARD6可改善脓毒症所致心肌损伤,其机制可能与caspase-1介导的细胞焦亡相关。

肺炎支原体CARDS毒素依赖KELED序列调控炎性反应

目的细胞内逆向运输系统是微生物毒素蛋白导致细胞毒性的关键。KELED序列是肺炎支原体CARDS毒素逆向转运至内质网,而后发挥细胞毒性作用的关键氨基酸基序。在小鼠模型中,体外重组的CARDS蛋白可以重现肺炎支原体肺炎患者的特征性症状。然而,CARDS是否依赖于KELED序列调控肺组织炎性反应仍不清楚。方法在大肠杆菌中表达并纯化CARDS及其KELED序列突变蛋白;随后在细胞水平验证其致空泡化活性;最后,在小鼠模型中评价KELED序列突变的CARDS蛋白是否影响炎性因子的表达以及是否造成肺组织病理学损伤。结果KELED序列氨基酸发生突变,减弱CARDS蛋白致细胞空泡化效应,下调肺组织炎性因子mRNA表达,减轻肺组织的病理学损伤。结论KELED序列对CARDS蛋白致细胞空泡化及炎性损伤活性的维持至关重要。

Credit Card Fraud Detection Using Improved Deep Learning Models

Fraud of credit cards is a major issue for financial organizations and individuals.As fraudulent actions become more complex,a demand for better fraud detection systems is rising.Deep learning approaches have shown promise in several fields,including detecting credit card fraud.However,the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters.This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data,thereby improving fraud detection.Three deep learning models:AutoEncoder(AE),Convolution Neural Network(CNN),and Long Short-Term Memory(LSTM)are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card fraud.The experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision,leading these models to be effective in accurately predicting credit card fraud.The results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy(99.2%),detection rate(93.3%),and area under the curve(96.3%).These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.

A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

CARD11 serves as a therapeutic biomarker for the drug therapies of ccRCC

Background:The incidence of clear cell renal cell carcinoma(ccRCC)is globally high;however,despite the introduction of innovative drug therapies,there remains a lack of effective biomarkers for evaluating treatment response.Recently,Caspase recruiting domain-containing protein 11(CARD11)has garnered attention due to its significant association with tumor development and the immune system.Methods:The expression of CARD11 mRNA and protein in ccRCC were analyzed by public database and immunohistochemistry.The focus of this study is on the epigenomic modifications of CARD11,its expression of ccRCC immunophenotype,and its correlation with response to immunotherapy and targeted therapy.Furthermore,to investigate the mechanism of this molecule’s influence on different biological behaviors of cells,cell tests in vitro have been conducted to observe the impact of its expression level.Results:CARD11 expression was upregulated which may be mainly modified by body methylation and was correlated with poor prognosis in ccRCC.In the tumor microenvironment of ccRCC,CARD11 expression was positively correlated with increased T lymphocyte infiltration and increased expression of inhibitory immune checkpoints.Moreover,ccRCC patients with high CARD11 expression had a better response to immunotherapy and targeted therapy.The knockdown of CARD11 ultimately suppressed the proliferation,migration,and invasion capabilities of ccRCC cells while simultaneously enhancing tumor cell apoptosis.Conclusion:We identified CARD11 as a novel therapeutic biomarker for immunotherapy and targeted therapy in ccRCC.

A Study on the Problem of Employee Punch Card Substitution in Enterprise Management:The Case of Jingdong Enterprise

With economic progress and the continuous advancement of science and technology,the issue of employees substituting punch cards has gradually become a significant challenge in enterprise management.The purpose of this paper is to discuss the causes,effects,and countermeasures of the employee punch card phenomenon,with the aim of providing effective management recommendations for Chinese enterprises.In practice,enterprises should flexibly apply the countermeasures proposed in this paper according to their specific circumstances to prevent substitute punch card incidents and improve overall management efficiency.

CARD8基因新发P367L失功能突变患者自身炎症性疾病的临床及免疫学特征

目的报道1例CARD8基因新发突变导致自身炎症性疾病患儿及其家系的临床特征、致病性鉴定与免疫致病机制。方法收集并分析1例CARD8基因突变患儿的突变位点以及临床资料,随访患儿治疗及预后;采用全外显子测序发现可疑致病基因并利用Sanger测序验证家系突变位点;利用体外实验进行致病性验证及免疫学分析。结果先证者为1名男性患儿,主要临床表现为极早发的食物蛋白过敏性肠炎、特应性皮炎及反复发作的上消化道出血。实验室检查提示血清IgE升高。全外显子测序发现CARD8基因新发杂合突变c.1100C>T(p.P367L),且该突变位点经Sanger测序验证证实来源于其父亲。该突变致病性不明确,且既往未见报道。实验室检查提示患儿血浆中多种促炎细胞因子水平显著升高。患儿原代外周血单个核细胞(PBMCs)及转染相同突变质粒的293T细胞CARD8蛋白表达均显著降低。患儿PBMCs中NLRP3炎性小体过度激活,而NF-κB通路无异常。以上研究均提示患儿的CARD8基因p.P367L突变为失功能性致病突变。结论本文报道全球第2例,中国第1例CARD8基因杂合突变患儿及家系,并明确CRAD8基因c.1100C>T的突变致病性。该CARD8基因失功能突变可导致NLRP3炎性小体的过度激活而诱发患儿发生以消化道症状起病的自身炎症性疾病。

DS-CDMA system outer loop power control and improvement for multi-service

When a new user accesses the CDMA system, the load will change drastically, and therefore, the advanced outer loop power control （OLPC） technology has to be adopted to enrich the target signal interference ratio （Silt） and improve the system performance. The existing problems about DS-CDMA outer loop power control for multi-service are introduced and the power control theoretical model is analyzed. System simulation is adopted on how to obtain the theoretical performance and parameter optimization of the power control algorithm. The OLPC algorithm is improved and the performance comparisons between the old algorithm and the improved algorithm are given. The results show good performance of the improved OLPC algorithm and prove the validity of the improved method for multi-service.

SERVICE-AWARE BASED FUZZY ADMISSION CONTROL SCHEME IN MULTI-SERVICE NETWORKS

Multi-service aggregated transmission is the direction of IP network. Providing different Quality of Service (QoS) assurance for different services has become a crucial problem in future network. Admission control is a vital function for multi-service IP network. This paper proposes a novel fuzzy admission control scheme based on coarse granularity service-aware technique. Different service has discriminative sensitivity to the same QoS characteristic parameter in general. The traffic class can be perceived by the service request parameter and the proposed QoS function. And requirements of dif- ferent applications can be met by maintaining the life parameter. From simulation results, the proposed scheme shows a better QoS provisioning than those traditional fuzzy logic based methods under the same admission probability.

携带罕见突变的CARD14相关丘疹鳞屑性疾病一例

患者,男,41岁。因“全身红斑鳞屑40天”来我院就诊,临床、组织病理学具有毛发红糠疹和银屑病的交叉表现,无家族史,首次发病年龄为41岁。初步诊断为I型毛发红糠疹,口服阿维A有显著改善。经过全外显子测序和Sanger测序发现患者携带CARD14基因的罕见致病性突变位点c.1799 C>T(p.Pro600Leu),综合临床、组织病理学表现,以及基因检测的结果,最终诊断为CARD14相关的丘疹鳞屑性疾病。

Study on Task Assignment of Two-stage Multi-service Capability Port Logistics Service Providers

This paper mainly studies the problem of multi-task assignment of providers in port logistics service supply chain.As a core enterprise,port plays the role of logistics service integrator.With the continuous development of industrial integration,logistics service providers not only provide one kind of logistics service,but also develop into composite suppliers who capable of providing a variety of logistics services.This paper studies the task assignment problem of multi-service capability providers in the port logistics service supply chain.The two-stage logistics service provider task assignment model was built,which is based on the mixed evaluation method(including MOORA and FMEA)and the multi-objective planning method.Eventually,the effectiveness of the model method was verified by combining with an example.

COMBINED ADMISSION CONTROL AND SCHEDULING IN MULTI-SERVICE NETWORKS

Providing the required metrics for different service respectively is a basic characteristic in multi-service networks. The different service can be accessed and forwarded differently to provide the different transmission performance. The state information between admission control and scheduling can be exchanged each other by the defined correlation coefficient to adjust the flow distribution in progress. The priority queue length measured by scheduler implicitly can describe the priority flows load. And the fair rate can describe the non-priority flows load. Different admission decision will be made according to the state of scheduler to assure the time-delay upper threshold for the priority flows under heavy load and the fairness for elastic flows in light load, respectively. The stability condition was conduced and proved. Simulation results show the policy can ensure both the delay for the priority flows and the minimal throughput for non-priority flows.

Study on Licker-In and Flat Speeds of Carding Machine and Its Effects on Quality of Cotton Spinning Process

Spinning has a significant influence on all textile processes. Combinations of all the capital equipment display the process’ critical condition. By transforming unprocessed fibers into carded sliver and yarn, the carding machine serves a critical role in the textile industry. The carding machine’s licker-in and flat speeds are crucial operational factors that have a big influence on the finished goods’ quality. The purpose of this study is to examine the link between licker-in and flat speeds and how they affect the yarn and carded sliver quality. A thorough experimental examination on a carding machine was carried out to accomplish this. The carded sliver and yarn produced after experimenting with different licker-in and flat speed combinations were assessed for important quality factors including evenness, strength, and flaws. To account for changes in material qualities and machine settings, the study also took into consideration the impact of various fiber kinds and processing circumstances. The findings of the investigation showed a direct relationship between the quality of the carded sliver and yarn and the licker-in and flat speeds. Within a limited range, greater licker-in speeds were shown to increase carding efficiency and decrease fiber tangling. On the other hand, extremely high speeds led to more fiber breakage and neps. Higher flat speeds, on the other hand, helped to enhance fiber alignment, which increased the evenness and strength of the carded sliver and yarn. Additionally, it was discovered that the ideal blend of licker-in and flat rates varied based on the fiber type and processing circumstances. When being carded, various fibers displayed distinctive behaviors that necessitated adjusting the operating settings in order to provide the necessary quality results. The study also determined the crucial speed ratios between the licker-in and flat speeds that reduced fiber breakage and increased the caliber of the finished goods. The results of this study offer useful information for textile producers and process engineers to improve the quality of carded sliver and yarn while maximizing the performance of carding machines. Operators may choose machine settings and parameter adjustments wisely by knowing the impacts of licker-in and flat speeds, which will increase textile industry efficiency, productivity, and product quality.

A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost

With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detection has started to use this method in large numbers, but thetraditional Adaboost is prone to overfitting in the presence of noisy samples.Therefore, in order to alleviate this phenomenon, this paper proposes a newidea: using the number of consecutive sample misclassifications to determinethe noisy samples, while constructing a penalty factor to reconstruct thesample weight assignment. Firstly, the theoretical analysis shows that thetraditional Adaboost method is overfitting in a noisy training set, which leadsto the degradation of classification accuracy. To this end, the penalty factorconstructed by the number of consecutive misclassifications of samples isused to reconstruct the sample weight assignment to prevent the classifierfrom over-focusing on noisy samples, and its reasonableness is demonstrated.Then, by comparing the penalty strength of the three different penalty factorsproposed in this paper, a more reasonable penalty factor is selected.Meanwhile, in order to make the constructed model more in line with theactual requirements on training time consumption, the Adaboost algorithmwith adaptive weight trimming (AWTAdaboost) is used in this paper, so thepenalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally obtained.Finally, PF_AWTAdaboost is experimentally validated against other traditionalmachine learning algorithms on credit card fraud datasets and otherdatasets. The results show that the PF_AWTAdaboost method has betterperformance, including detection accuracy, model recall and robustness, thanother methods on the credit card fraud dataset. And the PF_AWTAdaboostmethod also shows excellent generalization performance on other datasets.From the experimental results, it is shown that the PF_AWTAdaboost algorithmhas better classification performance.

THE STUDY OF SCP OVERLOAD CONTROL IN MULTI-SERVICE ENVIRONMENT

This paper first gives a SCP abstract model, then SCP’s overload detection and maximum processing capability are discussed quantitatively. Based upon dynamic adjustment, a new two-level SCP overload control algorithm is proposed. Theoretical analysis and simulation prove the algorithm’s effectiveness and fairness.

DESIGN OF MULTI-SERVICE SDH NETWORKS: FORMULATION AND HEURISTICS

Multi-service SDH networks support both packet- and circuit-switched traffic. Optimal design of such a network means to guarantee the circuit connections and configure a logical packet-switched topology with lowest congestion. This letter first formulates the problem as a mixed integer linear programming, which achieves optimal solution but has high computation. Then a heuristic algorithm is proposed to yield near-optimal solution efficiently. Performance of the algorithm is verified by an example.

A Credit Card Fraud Detection Model Based on Multi-Feature Fusion and Generative Adversarial Network

Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses.

STUDY ON THE ACCOUNT-SHARING STRATEGY FOR MULTI-SERVICES IN IMS ONLINE CHARGING MECHANISM

A prepaid subscriber is allowed to simultaneously implement multiple services in online charging mechanism of IP Multimedia Subsystem (IMS). It is a noteworthy discussion to effectively distribute the limited account resources among concurrent services. An account-sharing algorithm is proposed for multi-services,which introduces resource reclamation and redistribution processes based on the resource reservation of standard specifications and dynamically adjusts them according to the changes of Quality of Service (QoS). Three performance indexes are investigated in the simulation experiments, which are average number of accommodated sessions, average number of completed ses- sions, and average number of iterations per accommodated session. The results show that in the normal QoS level, the three indexes of the proposed algorithm averagely increase by 18.7%, 5.4%, and 3.1% compared with the Prepaid Credit Distribution (PCD) algorithm, and by 2.1%, 1.0%, and 1.8% compared with the Prepaid Credit Reclaim (PCR) algorithm. In the poor QoS level, the performance advantages are greater, which averagely increase by 29.1%, 7.1%, and 2.8% compared with PCD, and by 9.4%, 4.1%, and 3.6% compared with PCR.

Echo-state-network classification based multi-services awareness in high-speed optical passive networks

With the challenge of great growing of services diversity,service-oriented supporting ability is required by current high-speed passive optical network( PON). Aimed at enhancing the quality of service( Qo S) brought by diversified-services,this study proposes an echo state network( ESN)based multi-service awareness mechanism in 10-Gigabite ethernet passive optical network( 10GEPON). In the proposed approach,distributed architecture is adopted to realize this ESN based multi-service awareness. According to the network architecture of 10G-EPON,where a main ESN is running in OLT and a number of ESN agents works in ONUs. The main-ESN plays the main function of service-awareness from the total view of various kinds of services in 10G-EPON system,by full ESN training. Then,the reservoir information of well-trained ESN in OLT will be broadcasted to all ONUs and those ESN agents working in ONUs are allowed to conduct independent service-awareness function. Thus,resources allocation and transport policy are both determined only in ONUs. Simulation results show that the proposed mechanism is able to better support the ability of multiple services.