Proximal Alternating-Direction-Method-of-Multipliers-Incorporated Nonnegative Latent Factor Analysis

High-dimensional and incomplete(HDI)data subject to the nonnegativity constraints are commonly encountered in a big data-related application concerning the interactions among numerous nodes.A nonnegative latent factor analysis(NLFA)model can perform representation learning to HDI data efficiently.However,existing NLFA models suffer from either slow convergence rate or representation accuracy loss.To address this issue,this paper proposes a proximal alternating-directionmethod-of-multipliers-based nonnegative latent factor analysis(PAN)model with two-fold ideas:(1)adopting the principle of alternating-direction-method-of-multipliers to implement an efficient learning scheme for fast convergence and high computational efficiency;and(2)incorporating the proximal regularization into the learning scheme to suppress the optimization fluctuation for high representation learning accuracy to HDI data.Theoretical studies verify that PAN converges to a Karush-KuhnTucker(KKT)stationary point of its nonnegativity-constrained learning objective with its learning scheme.Experimental results on eight HDI matrices from real applications demonstrate that the proposed PAN model outperforms several state-of-the-art models in both estimation accuracy for missing data of an HDI matrix and computational efficiency.

气管镜联合肺泡灌洗液NGS检查在肺部感染的早期诊断及治疗中的应用

目的 探讨气管镜联合肺泡灌洗液NGS检查在肺部感染的早期诊断及治疗中的应用。方法 选取2022年1月—2023年6月本院收治的75例肺部感染患者作为研究对象,随机将患者分为对照组与观察组,对照组(37例)采用气管镜检查,观察组(38例)采用气管镜联合肺泡灌洗液NGS检查,比较两种检查方法的诊断准确度,并观察两组患者的临床症状消失时间及炎症因子水平。结果 气管镜联合肺泡灌洗液NGS检测的诊断准确度高于气管镜检测;观察组的发热消失时间、咳嗽消失时间、咯痰消失时间、肺部啰音消失时间、住院时间显著低于对照组(P<0.05)。治疗前,两组的WBC、CRP、PCT水平相比无显著差异(P>0.05);治疗后,观察组患者的WBC、CRP、PCT水平显著低于对照组,差异具有统计学意义(P<0.05)。结论 气管镜联合肺泡灌洗液NGS检测在肺部感染的早期诊断中具有较高的准确度,能够缩短患者的临床症状消失时间和住院时间,并有助于更快速有效地降低炎症因子水平。

Vertex centrality of complex networks based on joint nonnegative matrix factorization and graph embedding

Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlation among each attribute or the heterogeneity between attribute and structure. To overcome these problems, a novel vertex centrality approach, called VCJG, is proposed based on joint nonnegative matrix factorization and graph embedding. The potential attributes with linearly independent and the structure information are captured automatically in light of nonnegative matrix factorization for factorizing the weighted adjacent matrix and the structure matrix, which is generated by graph embedding. And the smoothness strategy is applied to eliminate the heterogeneity between attributes and structure by joint nonnegative matrix factorization. Then VCJG integrates the above steps to formulate an overall objective function, and obtain the ultimately potential attributes fused the structure information of network through optimizing the objective function. Finally, the attributes are combined with neighborhood rules to evaluate vertex's importance. Through comparative analyses with experiments on nine real-world networks, we demonstrate that the proposed approach outperforms nine state-of-the-art algorithms for identification of vital vertices with respect to correlation, monotonicity and accuracy of top-10 vertices ranking.

Ng-Perron单位根检验在径流序列非平稳性分析中的应用

在全球气候变化和高强度人类活动的共同影响下,许多流域天然水循环过程受到破坏。径流序列呈现明显的非平稳特性,给水资源规划、管理、预测和调控带来一定的挑战。揭示径流序列的非平稳特性可以有效应对全球气候变化下的复杂水问题,对降低水文分析难度和提高径流预测精度具有十分重要的意义。研究以汾河上游兰村站为研究对象,分析该站1958-2016年年径流和月径流序列是否平稳。首先从随机水文学角度,采用Mann-Kendall检验法和小波分析法识别径流序列的趋势、突变和周期特征。在此基础上,从统计水文学角度引入Ng-Perron单位根检验方法。通过Mann-Kendall趋势检验和散点图法选择合适的检验方程,对径流序列进行广义最小二乘法(Generalized Least Squares,GLS)退势,并利用修正的信息准则(Modified information criterion,MIC)计算最优时间滞后阶数,判别径流序列是否具有非平稳性。结果显示,径流序列存在趋势、突变和周期成分,为非平稳径流序列。同时Ng-Perron单位根检验表明,该站年、月径流序列在1%显著性水平上具有非平稳特性。相较传统单位根检验方法,Ng-Perron单位根检验采用更为稳健的修正检验统计量,显著调整小样本情况下水平扭曲的现象,具有更好检验水平和功效,因而可以得到更合理的检验结果。研究成果为径流序列非平稳性检验理论的进一步改进及径流预测模型发展与应用提供参考。

质粒DNA Fast NGS测序方法的开发和应用

质粒DNA是最常用的基因运载工具,在基因合成技术中扮演着至关重要的角色。如何实现合成质粒DNA的准确且快速检测,是确保基因组完整性和提高基因合成效率的关键。尽管基于一代DNA的测序方法,其准确性已成为行业标准,但在检测通量、检测速度和检测成本等方面仍然存在局限性,这促使科学家们不断寻求新的解决方案。基于生物酶库,开发了DNA建库酶TN5,建立了高通量质粒DNA检测方案——Fast NGS。利用不同长度、不同质量的质粒DNA样本评估了Fast NGS的可行性,并对质粒DNA样本进行了高通量测序,最后对比了Fast NGS与Sanger测序的效率。结果表明,DNA建库酶TN5蛋白的纯度和质量符合二代测序要求。Fast NGS适用于3~8 kb基因合成质粒的测序检测,其检测通量高达2500个·12 h-1,测序成功率超过95%,测序准确性与一代测序相当,并且无明显序列偏好性。Fast NGS实现了质粒DNA的高通量、快速且低成本检测,为基因合成技术的发展提供了新的方向。

THE LOGARITHMIC SOBOLEV INEQUALITY FOR A SUBMANIFOLD IN MANIFOLDS WITH ASYMPTOTICALLY NONNEGATIVE SECTIONAL CURVATURE

In this note,we prove a logarithmic Sobolev inequality which holds for compact submanifolds without a boundary in manifolds with asymptotically nonnegative sectional curvature.Like the Michale-Simon Sobolev inequality,this inequality contains a term involving the mean curvature.

新一代单对线以太网NG-SPE技术及在流程工业数据通信中的应用

回顾了流程工业两线制通信技术的发展历程,包括4~20mA,HART,RS-485和新兴的APL/10BASE-T1L单对线以太网通信技术,以及各自的优缺点、面临的问题和后续的演进方向;重点介绍了新一代单对线以太网NG-SPE技术,以及该技术在带宽、距离、节点数和供电四个方面的均衡表现。通过与其他主流单对线技术对比后表明,新一代单对线以太网NG-SPE技术可实现在1km内,通信速率为100Mibit/s的数据传输,15节点接入,点到多点供电,并支持多种线缆和拓扑结构,可应用于流程工业、物联等领域,取代低速总线,利用已有线缆,实现高速远距离总线通信。

某肿瘤医院NGS、PCR实验室空调系统设计浅析

NGS、PCR实验室已成为医院建设的重要部分,而空调、通风系统的合理设计和布局对这两个实验室安全、稳定、高效的运行起到重要的作用。以某肿瘤医院实验室为例,对NGS、PCR实验室各区域平面布局作了简要介绍,并结合其使用要求,对室内参数、压力梯度、送风、排风系统等要求作了简要阐述。

Neurogenic potential of NG2 in neurotrauma:a systematic review

Regenerative approaches towards neuronal loss following traumatic brain or spinal cord injury have long been considered a dogma in neuroscience and remain a cutting-edge area of research.This is reflected in a large disparity between the number of studies investigating primary and secondary injury as therapeutic to rgets in spinal co rd and traumatic brain injuries.Significant advances in biotechnology may have the potential to reshape the current state-of-the-art and bring focus to primary injury neurotrauma research.Recent studies using neural-glial factor/antigen 2(NG2)cells indicate that they may differentiate into neurons even in the developed brain.As these cells show great potential to play a regenerative role,studies have been conducted to test various manipulations in neurotrauma models aimed at eliciting a neurogenic response from them.In the present study,we systematically reviewed the experimental protocols and findings described in the scientific literature,which were peer-reviewed original research articles(1)describing preclinical experimental studies,(2)investigating NG2 cells,(3)associated with neurogenesis and neurotrauma,and(4)in vitro and/or in vivo,available in PubMed/MEDLINE,Web of Science or SCOPUS,from 1998 to 2022.Here,we have reviewed a total of 1504 papers,and summarized findings that ultimately suggest that NG2 cells possess an inducible neurogenic potential in animal models and in vitro.We also discriminate findings of NG2 neurogenesis promoted by different pharmacological and genetic approaches over functional and biochemical outcomes of traumatic brain injury and spinal co rd injury models,and provide mounting evidence for the potential benefits of manipulated NG2 cell ex vivo transplantation in primary injury treatment.These findings indicate the feasibility of NG2 cell neurogenesis strategies and add new players in the development of therapeutic alternatives for neurotrauma.

DA42NG飞机涡轮增压系统典型故障分析

涡轮增压系统对于飞机的飞行性能和经济性都有着至关重要的影响,它能够提高发动机效率、减少燃油消耗和排放、提升飞行的操作灵活性和安全可靠性。对于装配有涡轮增压系统的飞机而言,涡轮增压系统故障是发生率和维护成本较高的故障之一。文章以DA42NG飞机的涡轮增压系统为研究对象,首先分析系统的组成和工作原理,其次剖析典型故障的原因,最后提出维护建议,以期为该机型及类似机型相关故障的排除提供参考。

B737NG前缘缝翼指示故障保留对签派放行影响的研究

波音737NG经常会遇到飞机前缘装置位置指示故障保留,该保留会影响到飞机的运行限制、机组操作、管制指挥及应急程序等,评估不当容易出现超标准放行情况,发生不安全事件。本文从系统原理、MEL保留放行、放行依据三个方面进行论述,分析了该保留的衍生风险,总结该保留对运行的影响,对航班的安全、正常、高效运行具有重要意义。

基于OSU技术的全光网络弹性NG-OTN研究

随着5G商用部署、国家战略推进数字化转型,党政军、金融和大企业等高端政企客户对高品质专线提出了更高的要求。基于OSU技术的全光网络弹性NG-OTN具备物理隔离、颗粒灵活、泛在连接、超低时延、极速传输、极简调整等技术优势,能满足高端政企专线市场的弹性提速、超低时延接入、按需入云、差异化SLA服务等需求。

不同播种密度和灌溉管理下冬小麦APSIM NG模型的敏感性研究与模型校准验证

以中国农业大学吴桥试验站2015—2018年度不同灌溉管理和播种密度下冬小麦为研究对象,应用全局敏感性分析方法扩展傅里叶幅度检验法(EFAST)分析新一代的APSIM模型(APSIM Next-generation)中的品种参数对冬小麦生长的敏感性,并实现模型参数的本地化调试与验证,为该模型未来开展各项研究提供基础支持。结果表明,影响冬小麦开花期和成熟期最敏感的参数是叶热间距、最小叶片数量、光周期敏感度;影响产量最敏感的参数是开花期单位茎秆干重可孕花数、单籽粒库容潜力和叶热间距。根据敏感性分析的结果,对以上参数进行优先校准后,提高了APSIM NG模型对小麦生育时期和产量模拟的准确性,校准后的模型可以解释生育时期超过98%的变异,产量实测值和模拟值的均方根误差(RMSE)为508 kg·hm^(-2)。

基于RNA-based NGS检测非小细胞肺癌融合基因临床实践中国专家共识

基于RNA水平的二代测序(RNA-based next-generation sequencing, RNA-based NGS)技术已被非小细胞肺癌(non-small cell lung cancer, NSCLC)临床实践指南和专家共识推荐为融合基因的检测方法之一。NSCLC可用药靶点主要包括基因突变和融合,用于评估靶向治疗可行性的基因突变和融合基因检测均不可或缺。目前,基于DNA水平的NGS(DNA-based NGS)结合RNA-based NGS一次性同步检测基因突变和融合的技术已部分应用于临床实践。然而,RNA-based NGS检测融合基因的应用时机、应用场景和质控方面在我国仍缺乏规范和标准。本共识将进一步明确RNA-based NGS在融合基因检测中的应用时机、应用场景和质控,并给予指导性建议,推动RNA-based NGS在NSCLC临床诊疗中的应用,使患者能够最大程度地从融合基因检测中获益。

STRUCTURE OF NONNEGATIVE NONTRIVIAL AND POSITIVE SOLUTIONS OF SINGULARLY PERTURBED p-LAPLACE EQUATIONS

Structure of nonnegative nontrivial and positive solutions was precisely studied for some singularly perturbed p-Laplace equations. By virtue of sub- and supersolution method, it is shown that there are many nonnegative nontrivial spike-layer solutions and positive intermediate spike-layer solutions. Moreover, the upper and lower bound on the measure of each spike-layer were estimated when the parameter is sufficiently small.

多种驱替方式后储层物性及剩余油变化规律研究——以GD油田中一区Ng3单元11-检11井区为例

目前GD油田中一区Ng3单元11-检11井区经历了水驱、聚合物驱、后续水驱及非均相复合驱等多种驱替方式开发。探索多种驱替方式后的储层物性及剩余油分布变化规律对高含水期油藏进一步挖潜,特别是GD油田中一区Ng3单元的进一步稳产、增产具有重要意义。以中一区Ng3单元中11-检11井区为研究对象,综合利用密闭取芯井及开发动态资料,对注水、注聚及非均相复合驱等多种驱替方式后的油藏物性变化规律及剩余油分布进行了研究。结果表明:与聚合物驱前相比,聚合物驱后的储层非均质性得到了一定改善;对正韵律储层,聚合物驱后底部层位储层非均质性在聚合物驱后进一步增强,而其他部分层位的储层非均质性减弱;对于复合正韵律储层,分段水洗明显,各韵律段中下部水洗较强,各韵律段上部水洗弱、剩余油富集。非均相复合驱前后的储层孔隙度分布变化差异小,但非均相复合驱后储层的非均质性变强。研究成果对GD油田中一区及类似油藏的开发具有重要意义。

Area-Correlated Spectral Unmixing Based on Bayesian Nonnegative Matrix Factorization

To solve the problem of the spatial correlation for adjacent areas in traditional spectral unmixing methods, we propose an area-correlated spectral unmixing method based on Bayesian nonnegative matrix factorization. In the proposed me-thod, the spatial correlation property between two adjacent areas is expressed by a priori probability density function, and the endmembers extracted from one of the adjacent areas are used to estimate the priori probability density func-tions of the endmembers in the current area, which works as a type of constraint in the iterative spectral unmixing process. Experimental results demonstrate the effectivity and efficiency of the proposed method both for synthetic and real hyperspectral images, and it can provide a useful tool for spatial correlation and comparation analysis between ad-jacent or similar areas.

Orthogonal nonnegative matrix factorization based local hidden Markov model for multimode process monitoring

Traditional data driven fault detection methods assume that the process operates in a single mode so that they cannot perform well in processes with multiple operating modes. To monitor multimode processes effectively,this paper proposes a novel process monitoring scheme based on orthogonal nonnegative matrix factorization(ONMF) and hidden Markov model(HMM). The new clustering technique ONMF is employed to separate data from different process modes. The multiple HMMs for various operating modes lead to higher modeling accuracy.The proposed approach does not presume the distribution of data in each mode because the process uncertainty and dynamics can be well interpreted through the hidden Markov estimation. The HMM-based monitoring indication named negative log likelihood probability is utilized for fault detection. In order to assess the proposed monitoring strategy, a numerical example and the Tennessee Eastman process are used. The results demonstrate that this method provides efficient fault detection performance.

Unsupervised hyperspectral unmixing based on robust nonnegative dictionary learning

Considering the sparsity of hyperspectral images(HSIs),dictionary learning frameworks have been widely used in the field of unsupervised spectral unmixing.However,it is worth mentioning here that existing dictionary learning method-based unmixing methods are found to be short of robustness in noisy contexts.To improve the performance,this study specifically puts forward a new unsupervised spectral unmixing solution.For the reason that the solution only functions in a condition that both endmembers and the abundances meet non-negative con-straints,a model is built to solve the unsupervised spectral un-mixing problem on the account of the dictionary learning me-thod.To raise the screening accuracy of final members,a new form of the target function is introduced into dictionary learning practice,which is conducive to the growing robustness of noisy HSI statistics.Then,by introducing the total variation(TV)terms into the proposed spectral unmixing based on robust nonnega-tive dictionary learning(RNDLSU),the context information under HSI space is to be cited as prior knowledge to compute the abundances when performing sparse unmixing operations.Ac-cording to the final results of the experiment,this method makes favorable performance under varying noise conditions,which is especially true under low signal to noise conditions.

Extracting Sub-Networks from Brain Functional Network Using Graph Regularized Nonnegative Matrix Factorization

Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the disease may affect some local connectivity in the brain functional network.That is,there are functional abnormalities in the sub-network.Therefore,it is crucial to accurately identify them in pathological diagnosis.To solve these problems,we proposed a sub-network extraction method based on graph regularization nonnegative matrix factorization(GNMF).The dynamic functional networks of normal subjects and early mild cognitive impairment(eMCI)subjects were vectorized and the functional connection vectors(FCV)were assembled to aggregation matrices.Then GNMF was applied to factorize the aggregation matrix to get the base matrix,in which the column vectors were restored to a common sub-network and a distinctive sub-network,and visualization and statistical analysis were conducted on the two sub-networks,respectively.Experimental results demonstrated that,compared with other matrix factorization methods,the proposed method can more obviously reflect the similarity between the common subnetwork of eMCI subjects and normal subjects,as well as the difference between the distinctive sub-network of eMCI subjects and normal subjects,Therefore,the high-dimensional features in brain functional networks can be best represented locally in the lowdimensional space,which provides a new idea for studying brain functional connectomes.