We propose an uplink rate splitting (RS) scheme for real-time health monitoring in the Internet of Medical Things (IoMT). To minimize total time cost, we jointly optimize biosensor grouping, decoding order, power allocation, receiver beamforming, and computation resources allocation under the constraints of the transmit power and computation resources. This process results in a discrete non-convex problem, which we decouple into three independent subproblems. Reduce co-channel interference to ease the transmit time cost. We solve this with a low-complexity biosensor grouping algorithm. Optimize decoding order, power allocation, and receiver beamforming to reduce the forwarding time cost. We thus develop an alternating optimization algorithm. Specifically, we propose a decoding order update algorithm to optimize ordering, which can converge to the global optimum. We construct accurate surrogates via a quadratic transform approach and use surrogate optimization to attack other variables. Allocate computation resources to minimize the processing time cost. Here, we derive the optimal solution with closed-form expressions. Simulation results indicate that the proposed overall scheme and algorithms present significant performance gains over several existing benchmarks.

Despite significant progress in existing methods for predicting drug-target binding affinity, there is still room for improvement in better utilizing molecular sequences and designing feature fusion strategies. Addressing these two points, we propose a novel computational model, Secondary Sequence and Cross-attention Block based Drug-Target binding Affinity prediction (SSCBDTA). The model is composed of sequence encoding, feature extraction, modal fusion and a decoder, with three innovations: (i) applying the byte pair encoding algorithm to process vast unlabeled data for obtaining molecular secondary sequences; (ii) extracting features from two perspectives: the primary and secondary sequences of molecules; (iii) combining cross-attention and criss-cross attention to fuse the extracted features of drugs and proteins. In two benchmark datasets, SSCBDTA outperforms ten state-of-the-art models on nearly all evaluation metrics. By conducting four different ablation experiments, we separately validated the effectiveness of molecular secondary sequences and multiple cross-attention in improving the prediction accuracy and stability of SSCBDTA. We also utilized SSCBDTA to predict binding affinities between 3,137 FDA-approved drugs and 6 SARS-CoV-2 replication-related proteins, identifying a number of promising molecules that could be further developed as anti-COVID drugs.

早产儿视网膜病变(retinopathy of prematurity,ROP)相关高危因素分析通常采用传统医学统计方法,分析效果依赖于数据集样本和维度的数量,很难挖掘深层次的高危因素.针对上述问题,提出基于机器学习LightGBM的ROP分析模型,通过该模型适合处理高维数据集的特点挖掘ROP高危因素.首先对原始数据集进行基本信息分析和数据预处理,然后对预处理后的数据集进行LightGBM模型搭建、训练、验证与特征优化,通过对比验证,证明特征优化后得出的高危因素更加准确.最终通过LightGBM特征优化分析得到的ROP相关高危因素为Px、氧时、无创、胎龄、Apgar1、母亲年龄、Apgar5、胎膜早破.

《算法设计与分析》是计算机科学与技术专业的核心修课程.现有实验教学模式过多关注书本知识,严重忽略了创新意识能力培养.该文以创新能力培养为主线,以学生自主学习为主,以教师为主导,探索算法设计与分析课程的实验教学改革模式.通过融入新算法、新问题,鼓励学生参与教师科研课题,设置层次化实验案例,融入思政元素等手段提高学生学习兴趣,培养学生的创新意识.同时,设置多样化考核方式,强化过程性评价,进而达到提高学生创意能力培养的目的.

Hyaluronic acid-mediated motility receptor (HMMR), a tumor-related gene, plays a vital role in the occurrence and progression of various cancers. This research is aimed to reveal the effect of HMMR in lung adenocarcinoma (LUAD). We first obtained the gene expression profiles and clinical data of patients with LUAD from The Cancer Genome Atlas (TCGA) database. Then, based on the TCGA cohort, the HMMR expression difference between LUAD tissues and nontumor tissues was detected and verified with public tissue microarrays (TMAs), clinical LUAD specimen cohort, and Gene Expression Omnibus (GEO) cohort. Logistic regression analysis and chi-square test were adopted to study the correlation between HMMR expression and clinicopathological parameters. The effect of HMMR expression on survival was evaluated by Kaplan–Meier survival analysis and using the Cox regression model. Furthermore, Gene Set Enrichment Analysis (GSEA) was utilized to screen out signaling pathways related to LUAD and the co-expression analysis was employed to build the protein–protein interaction (PPI) network. The HMMR expression level in LUAD tissues was dramatically higher than that in nontumor tissues. Logistic regression analysis and chi-square test demonstrated that the high HMMR expression in LUAD has relation with gender, pathological stage, T classification, lymph node metastasis, and distant metastasis. The Kaplan–Meier curve suggested a poor prognosis for LUAD patients with high HMMR expression. Multivariate analysis implied that the high HMMR expression was a vital independent predictor of poor overall survival (OS). GSEA indicated that a total of 15 signaling pathways were enriched in samples with the high HMMR expression phenotype. The PPI network gave 10 genes co-expressed with HMMR. HMMR may be an oncogene in LUAD and is expected to become a potential prognostic indicator and therapeutic target for LUAD.

目前医学信息工程专业课程实验内容存在知识体系不连贯、与行业背景脱节等问题.该文结合医疗信息化应用场景需求及课程综合技能训练要求,利用知识图谱、数据库处理以及数据可视化等技术,设计医疗健康知识百科查询系统的综合实验.系统包括用户注册、用户登录、快速链接、详细介绍以及知识图谱模块,能够实现常见疾病的查询、关联以及知识图谱的可视化等功能.通过教学实践发现,该实验有助于帮助学生通过各环节快速理解系统框架结构,增强学生理解所学专业课程之间的关联性,并培养智能医学方向的学生面向实际场景进行医疗信息系统开发的能力.

The growing evidence suggests that circular RNAs (circRNAs) have significant associations with tumor occurrence and progression, yet the regulatory mechanism of circRNAs in lung adenocarcinoma (LUAD) remains unclear. This study clarified the potentially regulatory network and functional mechanism of circRNAs in LUAD. The expression data of circRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs) were obtained from the Gene Expression Omnibus (GEO) database. Relying on GSE101586, GSE101684, and GSE112214, we identified differentially expressed circRNAs (DEcircRNAs). Depending on GSE135918 and GSE32863, we screened out differentially expressed miRNAs (DEmiRNAs) and mRNAs (DEmRNAs), respectively. Then, a novel competing endogenous RNA (ceRNA) regulatory network related to LUAD was constructed. We also revealed biological processes and signal pathways regulated by these DEcircRNAs. Based on gene expression data and survival information of LUAD patients in The Cancer Genome Atlas (TCGA) and GEO, we implemented survival analysis to select DEmRNAs related to prognosis and build a novel circRNA–miRNA–mRNA hub regulatory network. Meanwhile, quantitative real-time PCR (qRT-PCR) was utilized to validate DEcircRNAs in the ceRNA hub regulatory network. As a result, a total of 8 DEcircRNAs, 19 DEmiRNAs, and 85 DEmRNAs were identified. The novel ceRNA regulatory network included 5 circRNAs, 8 miRNAs, and 22 mRNAs. The final ceRNA hub regulatory network contained two circRNAs, two miRNAs, and two mRNAs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that the five DEcircRNAs may affect LUAD onset and progression through Wnt signaling pathway and Hippo signaling pathway. All in all, this study revealed the regulatory network and functional mechanism of circRNA-related ceRNAs in LUAD.

医器械管理是医疗体系的重要组成部分.现有医疗器械公司管理平台存在运行模式不统一、信息采集不全和开发成本高等缺点.针对以上不足,论文遵循系统开发和数据库设计原则,使用.NET技术、B/S架构和MVC模式,运用G#语言设计医疗器械公司管理平台,主要包含首页模块、商品交易管理模块和用户管理模块.测试结果表明所构建平台运行状态良好,可以减少传统销售模式带来的人工失误,对统一运行模式,降低开发和运行的成本,加快商品信息化进程有重要意义.

住院病房管理系统是医院信息系统的基础部分,是临床服务的重要载体,现有住院病房管理系统尚存在管理功能不全面、界面布局设计不够简洁等问题.笔者从住院病房实际需求出发,基于MyEclipse,借助Tomcat服务器、MySQL数据库,利用HTML以及JSP开发了一款住院病房管理系统,包含病房管理、医生管理、护士管理、病人基本信息管理、检查记录管理以及就诊记录管理等模块.测试结果表明所构建系统运行状态良好,有效提高了住院病房的管理效率.

In real industrial scenarios, with the use of conventional machine learning techniques, data-driven diagnosis models have a limitation that it is difficult to achieve the desirable fault diagnosis performance, and the reason is that the training and testing datasets are assumed to have the same feature distributions. To address this problem, a novel bearing fault diagnosis framework based on domain adaptation and preferred feature selection is proposed, in that the model trained by the labeled data collected from a working condition can be applied to diagnose a new but similar target data collected from other working conditions. In this framework, an improved domain adaptation method, transfer component analysis with preserving local manifold structure (TCAPLMS), is proposed to reduce the differences in the data distributions between different domain datasets and, at the same time, take the label information of feature dataset and the local manifold structure of feature data into consideration. Furthermore, preferred feature selection by fault sensitivity and feature correlation (PSFFC) is embedded into this framework for selecting features which are more beneficial to fault pattern recognition and reduce the redundancy of feature set. Finally, vibration datasets collected from two test platforms are used for experimental analysis. The experimental results validate that the proposed method can obviously improve diagnosis accuracy and has significant potential benefits towards actual industrial scenarios.

采用.net进行医疗文档共享服务系统设计,并将其分为网络结构设计和系统架构设计,网络结构采用了B/S模式.系统部署在网页,用户仅需安装浏览器即可登录系统,且客户端无需跟随系统进行同步更新,方便使用.系统采用了 3层架构,将数据、业务逻辑、显示页面分离,并对其中的数据和业务逻辑进行了封装处理,确保层次分明,便于后期功能添加与维护.在功能方面,实现了用户管理以及医疗文档的上传和下载等基本功能,提供对系统用户和已上传文档两方面的检索服务.考虑医院各科室文档的差异化,本系统还设计了文档查阅权限设定功能,将上传的文档转换为统一格式提供在线预览,避免发生越权查看的情况,为医院的数据信息安全提供保障.所设计系统能够作为医疗辅助系统,帮助医护人员快速整合处理病患信息,及时释放被占用的医疗资源,实现整个体系的高效率运转.

医工结合是医科、工科的交叉和融合.培养具有医学背景的复合型高素质技术应用型人才是学校计算机科学与技术专业的重要培养目标.毕业论文设计作为高校教学计划的重要环节,是锻炼学生工科与医科结合能力的良好机会.目前,计算机科学与技术专业毕业论文设计存在时间紧张、选题陈旧、创新不足等问题,为此,以医工结合为契机,以毕业论文设计为抓手,充分利用学校医学专业特色优势,激发学生创新意识,引导学生理性选择毕业论文设计题目,并不断优化指导教师的指导方式,切实推动学生毕业论文设计质量有所提高.结果表明,学生的多学科交叉融合思维能力显著提高,毕业论文设计质量显著提升.

面向医工交叉学科数字信号处理课程,设计医学信号处理实验.以心电信号处理为例,首先进行原始信号分析,确定干扰信号类型及频段,然后按需设计数字滤波器,并在MATLAB环境下进行干扰滤波实验.另外,添加基于小波变换的心电信号滤波实验,扩展学生知识体系,提升实际应用意识与操作能力.两组实验均表明,滤波后结果能够为医生分析心电信号提供有效信息.本实验难易适中,知识综合,满足医工交叉学科的数字信号处理教学实验需求.

随着互联网及大数据时代的到来,医院每天面临着海量的数据管理工作,而仅依靠人工对医院住院档案进行管理效率低下且不能充分挖掘档案价值.针对该问题,本文设计并实现了一个基于WEB的远程住院档案信息查询系统.

Circular RNA (CircRNA) plays an important role in tumorigenesis and progression of non-small cell lung cancer (NSCLC), but the pathogenesis of NSCLC caused by circRNA has not been fully elucidated. This study aimed to investigate differentially expressed circRNAs and identify the underlying pathogenesis hub genes of NSCLC by comprehensive bioinformatics analysis. Data of gene expression microarrays (GSE101586, GSE101684, and GSE112214) were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed circRNAs (DECs) were obtained by the "limma" package of R programs and the overlapping operation was implemented of DECs. CircBase database and Cancer-Specific CircRNA database (CSCD) were used to find miRNAs binding to DECs. Target genes of the found miRNAs were identified utilizing Perl programs based on miRDB, miRTarBase, and TargetScan databases. Functional and enrichment analyses of selected target genes were performing using the "cluster profiler" package. Protein-protein interaction (PPI) network was constructed by the Search Tool for the STRING database and module analysis of selected hub genes was performed by Cytoscape 3.7.1. Survival analysis of hub genes were performed by Gene Expression Profiling Interactive Analysis (GEPIA). Respectively, 1 DEC, 249 DECs, and 101 DECs were identified in GSE101586, GSE101684, and GSE112214. A total of eight overlapped circRNAs, 43 miRNAs and 427 target genes were identified. Gene Ontology (GO) enrichment analysis showed these target genes were enriched in biological processes of regulation of histone methylation, Ras protein signal transduction and covalent chromatin modification etc. Pathway enrichment analysis showed these target genes are mainly involved in AMPK signaling pathway, signaling pathways regulating pluripotency of stem cells and insulin signaling pathway etc. A PPI network was constructed based on 427 target genes of the 43 miRNAs. Ten hub genes were found, of which the expression of MYLIP, GAN, and CDC27 were significantly related to NSCLC patient prognosis. Our study provide a deeper understanding the circRNAs-miRNAs-target genes by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of NSCLC. MYLIP, GAN, and CDC27 genes might serve as novel biomarker for precise treatment and prognosis of NSCLC in the future.

目的:探讨结直肠癌组织与癌旁组织的差异表达circRNAs,阐明hsa_circ_0043278对结直肠癌抑制作用的潜在机制.方法:从GEO数据库下载数据,GSE126094芯片数据包含10例结直肠癌组织样本与10例癌旁组织样本.采用R软件筛选出差异表达circRNAs;在CSCD数据库中找到与差异表达circRNAs结合的miRNAs;采用Perl软件预测miRNAs的靶基因;对靶基因进行GO生物学功能富集分析与KEGG信号通路富集分析.结果:与癌旁组织样本比较,结直肠癌组织样本中表达水平升高的circRNAs有23个(logFC>2且P<0.05),表达水平降低的circRNAs有36个(logFC<-2且P<0.05).hsa_circ_0043278表达水平降低最明显(logFC=-7.481且P<0.05).共有66个miRNAs与hsa_circ_0043278结合,并预测其靶基因.GO富集分析,靶基因主要参与Wnt介导的细胞信号转导.KEGG富集分析,靶基因主要富集在PI3K-AKT信号通路.结论:hsa_circ_0043278表达水平在结直肠癌组织样本中明显降低,间接影响靶基因UTP18、CLIP4与STC2等的功能,进而影响其对Wnt介导的细胞信号转导与PI3K-AKT信号通路的调控作用,最终减弱其对结直肠癌的抑制作用.

In-band full-duplex wireless communication, which supports a node to transmit and receive simultaneously in the same frequency band, is receiving growing interest. The latency of packets in an in-band full-duplex wireless network can be significantly lowered by employing effective medium access control. However, queue delay is not considered in current delay performance analysis works, which do not describe the delay of a packet from generation to reception or analyze the ability to support real-time traffic. In this article, an easily expandable three-way handshaking in-band full-duplex medium access control mechanism is proposed, and both queueing delay and access delay are analyzed. Based on an M/G/1 queuing model, the system delay of in-band full-duplex access point from the packet coming to the queue, to its successful reception by the destination user, is provided. Simulation results show that compared with half-duplex access point, in-band full-duplex access point performs excellent low latency.

非极大值抑制(Non-Maximum Suppression,NMS)算法作为Faster R-CNN(region-based convolutional neural net-work,R-CNN)的后置处理算法,从物理空间判定检测框的重叠比例,忽略内在特征联系,造成漏检和误检问题.因此提出联合特征相似性度量和交并比的检测框优选方法(Optimized box Based on IoU and Feature similarity,OBIF).该方法首先计算两个检测框的交并比(Intersection over Union,IoU),判断检测框之间的重叠比例;然后计算闵式距离,表示重叠的检测框之间的特征相近性,进行深层次判断;最后联合闵氏距离和交并比实现检测框优选.当运行效率一致和时间复杂度相同时,将Faster R-CNN+OBIF应用到PASCAL VOC 2007数据集和结直肠腺癌数据集,比较传统NMS算法,平均识别准确率分别提高了1.4％和1.1％,方法检测精度得到显著的提升.

The realization of full-duplex wireless communication is predictable. And asymmetric transmission is a practical and low-cost application scenario, where full-duplex access point (FD_AP) can communicate with two users simultaneously to receive and send packets. While, in an asymmetric transmission, the transmit power of uplink sender decides the uplink and downlink rates because of the inter-client interference, which accordingly restricts the throughput. Besides, the size of packets in uplink and downlink is generally unequal. Therefore, a WIFI network with a FD_AP and half-duplex users is studied in this paper, and a medium access control (MAC) protocol based on power control and rate selection (PCRS) is proposed. PCRS MAC employs a received signal strength based rate selection strategy to select different rates and power for uplink and downlink transmission. Then, FD_AP can establish efficient and reliable full-duplex asymmetric transmission. Simulation results show that PCRS can improve the throughput and the probability of successful asymmetric communication as compared to the distributed coordination function (DCF) and a simple full-duplex MAC protocol without PCRS. Besides, PCRS MAC also maintains a high level of fairness.

面向慢性病等患者,设计了日常体检健康管理系统.该系统基于物联网技术和无线Mesh网络技术,实现用户体征信息的采集及软硬件的数据交互,患者通过Android端一键体验,实时查体征数据,便于自身健康管理;医生通过PC端对患者实时跟踪监测,便于医生对病人健康进行管理.该系统可靠性高,稳定性强,新颖有效且易于扩展和维护,能够满足患者日常体检的功能需求,改变了传统的健康管理模式,提高了健康管理服务的质量.