A novel method for clustering cellular data to improve classification

Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.

含裂纹压电材料的Cell-Based光滑有限元法

裂纹缺陷限制了压电材料更广泛的应用和相关器件性能的进一步提升.为提高求解压电材料断裂参数的精度和效率,将复势函数法和Cell-based光滑有限元法引入到含裂纹平面压电材料问题中,提出了含裂纹压电材料的Cell-based光滑有限元法.以含中心裂纹压电平板的问题为例,对不同材料、裂纹长度、网格和光滑子元数下的正则强度因子进行了讨论,并与FEM作了对比,数值算例结果表明,Cell-based光滑有限元法改善了有限元法刚度"偏硬"的缺点,具有高精度、高效率的优点.

基于随机场正交展开理论的Cell-based随机光滑有限元方法

为解决工程结构中随机参数大变异问题,提出基于正交展开理论的Cell-based随机光滑有限元方法.该方法采用Karhunen-Loève级数将随机场分解为不相关随机变量,再使用混沌多项式将位移随机响应展开,将展开后的随机场及位移响应引入到Cell-based光滑有限元中.详细推导了基于正交展开理论的Cell-based随机光滑有限元平衡方程,进一步给出了结构位移均值与协方差矩阵的计算公式,具有对网格要求低、计算精度高的优点,并对材料特性具有随机性的带孔方板的随机响应问题进行了分析,数值算例结果表明该方法是正确有效的.

复合材料层合板的摄动随机Cell-Based光滑有限元

随机性是实际工程结构的固有特性,如何更真实地描述含随机参数结构的随机响应及统计特性,对工程结构的可靠性设计具有非常重要的意义。本文基于Cell-Based光滑有限元,采用四边形单元,推导了基于一阶剪切变形理论的复合材料层合板的光滑有限元公式,降低了网格划分要求,适应不规则网格,并采用离散剪切间隙有效地消除了剪切自锁;结合摄动法和随机场理论,导出了复合材料层合板的摄动随机光滑有限元平衡方程,并给出了结构随机响应数字特征的计算公式,求解了材料属性含随机性的复合材料层合板的随机响应问题,数值算例结果表明了本方法的有效性和准确性。

Drug-and cell-based therapies for targeting neuroinflammation in traumatic brain injury

TBI pathology： Traumatic brain injury （TBI） is caused by an external force to the head, resulting in trauma to the brain. Approximately 1.7 million Americans suffer from TBI every year. Out of the 1.7 million suffering from TBI, an estimated 52,000 injuries result in death, leaving a mass amount of peo- ple with symptoms that could last a few days, a few years, or their entire life （Faul et al., 2010）. TBI can be classified as mild, moderate and severe. Depending on the classification and the extent of the injury, TBI can cause both physical symptoms and cognitive disorders （Lozano et al., 2015）.

Fortuitous benefits of activity-based rehabilitation in stem cell-based therapy for spinal cord repair: enhancing graft survival

Traumatic injuries to spinal cord elicit diverse signaling pathways leading to unselective and complex pathological outcomes：death of multiple classes of neural cells,formation of cystic cavities and glial scars,disruption of axonal connections,and demyelination of spared axons,all of which can contribute more or less to debilitating functional impairments found in patients with spinal cord injury.

Seropositivity rates of water channel protein 4 antibodies compared between a cell-based immunofluorescence assay and an enzyme-linked immunosorbent assay in neuromyelitis optica patients

A total of 66 samples （from 27 cases with neuromyelitis optica, 26 cases with multiple sclerosis, aa 13 cases with optic neuritis） were tested for aquaporin-4 antibody by a cell-based immunofluorescence assay and an enzyme-linked immunosorbent assay. The sensitivities and specificities of the two assays were similar. We further analyzed an additional 68 patients and 93 healthy controls using the enzyme-linked immunosorbent assay. A Kappa test showed good consistency between the two methods in terms of detection of anti-aquaporin-4 antibody in the se of neuromyelitis optica patients. No significant correlations were identified with onset age or disea duration, suggesting that aquaporin-4 antibody is a good marker for neuromyelitis optica. The enzyme-linked immunosorbent assay can be used for quantifying aquaporin-4 antibody concentrations and may be useful to dynamically monitor changes in the levels of aquaporin-4 antibody during disease duration.

The potential of stem cell-based therapy for retinal repair

Like injured neurons in the brain or spinal cord, neurons in the retina are incapable to regenerate following injury and ultimately would lead to irreversible neuronal loss and vision impairment. Over decades, extensive effort has been made to develop strategies to protect retinal neurons from death; however, the outcome is limited （Pettmann and Henderson, 1998; Bahr, 2000; Lagali and Picketts, 2011）. Replacing the degenerated retinal neurons by newly generated and functional neurons would be an ideal scenario. The rapid development of stem cell biology has recently demonstrated that stem cells could be a potential source of cells for cell replace- ment therapy because these cells have the self-renewal capacity and could be differentiated into many cell types. This review will dis- cuss the therapeutic potential of stem cell-based therapy to retinal degenerative diseases.

Potential of transposon-mediated cellular reprogramming towards cell-based therapies

Induced pluripotent stem(iPS)cells present a seminal discovery in cell biology and promise to support innovative treatments of so far incurable diseases.To translate iPS technology into clinical trials,the safety and stability of these reprogrammed cells needs to be shown.In recent years,different non-viral transposon systems have been developed for the induction of cellular pluripotency,and for the directed differentiation into desired cell types.In this review,we summarize the current state of the art of different transposon systems in iPS-based cell therapies.

基于Blending-Clustering集成学习的大坝变形预测模型

【目的】变形是反映大坝结构性态最直观的效应量,构建科学合理的变形预测模型是保障大坝安全健康运行的重要手段。针对传统大坝变形预测模型预测精度低、误报率高等问题导致的错误报警现象,【方法】选取不同预测模型和聚类算法集成,构建了一种Blending-Clustering集成学习的大坝变形预测模型,该模型以Blending对单一预测模型集成提升预测精度为核心,并通过Clustering聚类优选预测值改善模型稳定性。以新疆某面板堆石坝变形监测数据为实例分析,通过多模型预测性能比较,对所提出模型的预测精度和稳定性进行全面评估。【结果】结果显示:Blending-Clustering模型将预测模型和聚类算法集成,均方根误差(RMSE)和归一化平均百分比误差(nMAPE)明显降低,模型的预测精度得到显著提高;回归相关系数(R~2)得到提升,模型具备更强的拟合能力;在面板堆石坝上22个测点变形数据集上的预测评价指标波动范围更小,模型的泛化性和稳定性得到有效增强。【结论】结果表明:Blending-Clustering集成预测模型对于预测精度、泛化性和稳定性均有明显提升,在实际工程具有一定的应用价值。

Development of PDMS-based Microfluidic Device for Cell-based Assays

In a single step photolithography, muhi-level microfluidic device is fabricated by printing novel architectures on a film photomasks. The whole fabrication process is executed by classical PCB technology without the need to access clean room facilities. Different levels of protruding features on PCB master are produced by exposing a photomask with specifically arranged "windows and rims" architectures, followed by chemical wet etching. Poly(dimethylsiloxane)(PDMS) is then molded against the positive relief master to generate microfluidic device featured with multi-level sandbag structure and peripheral microchannels. This sandbag structure is an analog to traditional dam or weir for particle entrapment. The microstructure does not collapse when subjected to applied pressure, which is suitable for operation on elastic PDMS substrate.Typical immunocytochemcial staining assays were performed in the microdevice to demonstrate the applicability of the sandbag structure for cellular analysis. This simplified microfabrication process employs low-cost materials and minimal specialized equipment and can reproducibly produce mask lines with about 20 μm in width, which is sufficient for most microfluidic applications.

Cell-Based Biosensors and Bio-MEMS for Its New Applications

This paper reviews a novel cell-based biosensor and Bio-MEMS which incorporate living cells as sensing elements that convert a change in immediate environment to signals conducive for processing.It is characterized with high sensitivity,excellent selectivity and fast response and have been implemented for a number of applications ranging from pharmaceutical screening to environmental pollutant detection.This paper also introduces our recent work about Light-Addressable Potentiometric Sensors (LAPS),Field Effect Transistor (FET),Micro-Electrode Array Sensors (MEAS) and Bio-MEMS for detecting the changes of concentration of extracellular ions and the action potential of living cell under effect of drugs and environmental parameters.Finely, the paper gives some prospects of cell-based biosensors in the future.

DESIGN OF MICROPHYSIOMETER BASED ON MULTIPARAMETER CELL-BASED BIOSENSORS FOR QUICK DRUG ANALYSIS

Cellular metabolism arouses the changes of substance in extracellular physiological microenvironment,and the metabolic level reflects the physiological state of cells.This paper developed a novel microphysiometer automatic analysis instrument based on multiparameter cell-based biosensors for quick drug analysis.This study included the multiparameter cell-based biosensors,cell culture chamber,drug auto-injection detection and analysis.The analysis instrument was capable of real-time detection for the acidic product and other chemical parameters generated by the cellular metabolism in the micro-volume.Finally,the paper employs human breast cancer cell line MCF-7 and drug experiments to verify the performance of microphysiometer,and study effects of different drugs on cell metabolism.Further,the research explores drug analysis method of the multiparameter microphysiometer.The results showed that the cell-based microphysiometer system provides a utility platform for rapid,long-term and automatic cell physiological environment detection and drug analysis.

A Novel Structure of LAPS Array for Cell-Based Biosensor

The light-addressable potentiometric sensor (LAPS) is a semiconductor-based cellular biosensor with an electrolyte-insulator-semiconductor (EIS) structure.By depositing biocompatible layers on the sensing surface for cell culture, it can be used to detect bioelectrical parameters of cells.The characteristic curve for photocurrent versus applied bias voltage to the system shows a current-voltage curve (Ⅰ-Ⅴcurve).This technique can be used to detect the action potential changes towards different drugs based on the bias voltage dependence of an optical current,and provides a dynamic system by scanning light beam at the very cell on the sensor device.The LAPS overcomes the limitation of recording sites,but high spatial resolution and sensitivity are also paramount.This paper discussed a novel structure of LAPS array for extracellular monitoring to decrease potential noise level.Both characteristics of active recording array areas and cell culture conditions are measured.

An efficient 3D cell-based discrete fracture-matrix flow model for digitally captured fracture networks

Complex hydraulic fracture networks are critical for enhancing permeability in unconventional reservoirs and mining indus-tries.However,accurately simulating the fluid flow in realistic fracture networks(compared to the statistical fracture net-works)is still challenging due to the fracture complexity and computational burden.This work proposes a simple yet efficient numerical framework for the flow simulation in fractured porous media obtained by 3D high-resolution images,aiming at both computational accuracy and efficiency.The fractured rock with complex fracture geometries is numerically constructed with a cell-based discrete fracture-matrix model(DFM)having implicit fracture apertures.The flow in the complex fractured porous media(including matrix flow,fracture flow,as well as exchange flow)is simulated with a pipe-based cell-centered finite volume method.The performance of this model is validated against analytical/numerical solutions.Then a lab-scale true triaxial hydraulically fractured shale sample is reconstructed,and the fluid flow in this realistic fracture network is simu-lated.Results suggest that the proposed method achieves a good balance between computational efficiency and accuracy.The complex fracture networks control the fluid flow process,and the opened natural fractures behave as primary fuid pathways.Heterogeneous and anisotropic features of fluid flow are well captured with the present model.

Clustered张拉整体结构的动力学建模

针对clustered张拉整体结构,提出了一种基于任意拉格朗日-欧拉(arbitrary Lagrangian-Eulerian, ALE)法的多体动力学模型。相较于传统的拉格朗日法模型,该研究中的模型具有更为简单的运动学约束。首先,引入了一种ALE时变长度索单元,其网格节点与物质点可以独立运动,提供了一种自然的方式描述结构中运动的滑轮和滑动绳索;其次,利用达朗贝尔原理,推导了该单元的广义力向量并计算了相应的雅可比矩阵;然后,建立了张拉整体系统的动力学方程,并利用广义α算法对其进行求解,选取节点的全局位置坐标和物质坐标作为广义坐标,其中全局位置坐标可以被不同物体共享,以减少动力学方程的自由度数和消除物体间的约束;最后,展示了一个数值算例,10层可折叠张拉整体塔架,对其折叠过程进行了准静态和动力学仿真,验证了模型的有效性。所提出的模型和算法可为clustered张拉整体结构的设计提供理论指导,具有工程意义。

Identification of HCV Inhibitors from a Cell-Based Sub-Genomic Replicon Screen

A high throughput screen of the Pfizer compound collection was carried out using a hepatitis C virus (HCV) genotype 1b subgenomic replicon cell line. Those confirmed hits that demonstrated broad spectrum activity without overt cytotoxicity were further evaluated, leading to the identification of a series of pyrrolopyridines with excellent antiviral activity in a fully infectious HCV cell-based assay and pharmacokinetic properties.

基于Windows Server Failover Cluster的Apache故障转移集群应用实践

在当前互联网时代,Web应用程序的高可用性和可靠性是确保业务持续运行的关键因素。针对Apache Web服务器,介绍了利用Windows Server 2019 Failover Cluster构建Apache故障转移集群的实践方案。通过配置两个节点的故障转移集群,当某个节点发生故障时,Apache服务将自动在另一个节点上重新启动,从而最大程度地减少系统中断的时间,提高Web应用的可用性。详细阐述了集群搭建的具体步骤、配置要点、集群验证方法以及运行时的监控与维护方法,为构建高可用Apache Web服务器提供一种实用的解决方案。

Formation and transformation of metastable LPSO building blocks clusters in Mg-Gd-Y-Zn-Zr alloys by spinodal decomposition and heterogeneous nucleation

To study the formation and transformation mechanism of long-period stacked ordered(LPSO)structures,a systematic atomic scale analysis was conducted for the structural evolution of long-period stacked ordered(LPSO)structures in the Mg-Gd-Y-Zn-Zr alloy annealed at 300℃~500℃.Various types of metastable LPSO building block clusters were found to exist in alloy structures at different temperatures,which precipitate during the solidification and homogenization process.The stability of Zn/Y clusters is explained by the first principles of density functional theory.The LPSO structure is distinguished by the arrangement of its different Zn/Y enriched LPSO structural units,which comprises local fcc stacking sequences upon a tightly packed plane.The presence of solute atoms causes local lattice distortion,thereby enabling the rearrangement of Mg atoms in the different configurations in the local lattice,and local HCP-FCC transitions occur between Mg and Zn atoms occupying the nearest neighbor positions.This finding indicates that LPSO structures can generate necessary Schockley partial dislocations on specific slip surfaces,providing direct evidence of the transition from 18R to 14H.Growth of the LPSO,devoid of any defects and non-coherent interfaces,was observed separately from other precipitated phases.As a result,the precipitation sequence of LPSO in the solidification stage was as follows:Zn/Ycluster+Mg layers→various metastable LPSO building block clusters→18R/24R LPSO;whereas the precipitation sequence of LPSO during homogenization treatment was observed to be as follows:18R LPSO→various metastable LPSO building block clusters→14H LPSO.Of these,14H LPSO was found to be the most thermodynamically stable structure.

Deep Learning and Tensor-Based Multiple Clustering Approaches for Cyber-Physical-Social Applications

The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Instagram. While these platforms offer avenues for self-expression and community support, they concurrently harbor negative impacts, fostering antisocial behaviors like phishing, impersonation, hate speech, cyberbullying, cyberstalking, cyberterrorism, fake news propagation, spamming, and fraud. Notably, individuals also leverage these platforms to connect with authorities and seek aid during disasters. The overarching objective of this research is to address the dual nature of network platforms by proposing innovative methodologies aimed at enhancing their positive aspects and mitigating their negative repercussions. To achieve this, the study introduces a weight learning method grounded in multi-linear attribute ranking. This approach serves to evaluate the significance of attribute combinations across all feature spaces. Additionally, a novel clustering method based on tensors is proposed to elevate the quality of clustering while effectively distinguishing selected features. The methodology incorporates a weighted average similarity matrix and optionally integrates weighted Euclidean distance, contributing to a more nuanced understanding of attribute importance. The analysis of the proposed methods yields significant findings. The weight learning method proves instrumental in discerning the importance of attribute combinations, shedding light on key aspects within feature spaces. Simultaneously, the clustering method based on tensors exhibits improved efficacy in enhancing clustering quality and feature distinction. This not only advances our understanding of attribute importance but also paves the way for more nuanced data analysis methodologies. In conclusion, this research underscores the pivotal role of network platforms in contemporary society, emphasizing their potential for both positive contributions and adverse consequences. The proposed methodologies offer novel approaches to address these dualities, providing a foundation for future research and practical applications. Ultimately, this study contributes to the ongoing discourse on optimizing the utility of network platforms while minimizing their negative impacts.