Methodology of drug screening and target identification for new necroptosis inhibitors

Apoptosis has been considered as the only form of regulated cell death for a long time. However, a novel form of programmed cell death called necroptosis was recently reported. The process of necroptosis is regulated and plays a critical role in the occurrence and development of multiple human diseases. Thus,the study on the molecular mechanism of necroptosis and its effective inhibitors has been an attractive field for researchers. Herein, we introduce the molecular mechanism of necroptosis and focus on the literature about necroptosis drug screening in recent years. In addition, the identification of the critical drug targets of the necroptosis is also discussed.

Computational Identification of Conserved microRNAs and Their Targets in Tea (Camellia sinensis)

MicroRNAs (miRNAs) are a class of ~22 nucleotides long non coding RNA molecules which play an important role in gene regulation at the post transcriptional level. The conserved nature of miRNAs provides the basis of new miRNA identification through homology search. In an attempt to identify new conserved miRNAs in tea, previously known plant miRNAs were used for searching their homolog in a tea Expressed Sequence Tags and full length nucleotide sequence database. The sequences showing homolog no more than four mismatches were predicted for their fold back structures and passed through a series of filtration criteria, finally led us to identify 13 conserved miRNAs in tea belonging to 9 miRNA families. A total of 37 potential target genes in Arabidopsis were identified subsequently for 7 miRNA families based on their sequence complementarity which encode transcription factors (8%), enzymes (30%) and transporters (14%) as well as other proteins involved in physiological and metabolic processes (48%). Overall, our findings will accelerate the way for further researches of miRNAs and their functions in tea.

Identification of a dihydroorotate dehydrogenase inhibitor thatinhibits cancer cell growth by proteomic profiling

Dihydroorotate dehydrogenase(DHODH)is a central enzyme of the de novo pyrimidine biosynthesis pathway and is a promising drug target for the treatment of cancer and autoimmune diseases.This study presents the identification of a potent DHODH inhibitor by proteomic profiling.Cell-based screening revealed that NPD723,which is reduced to H-006 in cells,strongly induces myeloid differentiation and inhibits cell growth in HL-60 cells.H-006 also suppressed the growth of various cancer cells.Proteomic profiling of NPD723-treated cells in ChemProteoBase showed that NPD723 was clustered with DHODH inhibitors.H-006 potently inhibited human DHODH activity in vitro,whereas NPD723 was approximately 400 times less active than H-006.H-006-induced cell death was rescued by the addition of the DHODH product orotic acid.Moreover,metabolome analysis revealed that H-006 treatment promotes marked accumulation of the DHODH substrate dihydroorotic acid.These results suggest that NPD723 is reduced in cells to its active metabolite H-006,which then targets DHODH and suppresses cancer cell growth.Thus,H-006-related drugs represent a potentially powerful treatment for cancer and other diseases.

Target Ship Identification Algorithm Based on Comprehensive Correlation Discriminant and Information Entropy

Ship type identification is an important part of electronic reconnaissance. However, in the existing methods, such as statistical-based methods and fuzzy-mathematics-based methods, the information acquired by the passive sensor is not fully utilized, and there is a certain ambiguity in the assignment relationship of the emitters-ship. They can’t conclude the accurate and reliable assignment relationship of the emitters-ship. Therefore, this paper proposes a comprehensive correlation discriminant method to obtain a more reliable and comprehensive emitters-ship assignment, and then uses information entropy method to identify the type of the target ship on the basis of this association and assign the credibility. The simulation results show that this algorithm can effectively solve the problem of target ship type identification using the information of multi-passive sensors.

OPTIMAL APPROXIMATION ALGORITHM OF FEATURE COEFFICIENTS AND IDENTIFICATION OF TARGET

On the basis of target transfer function with the early time and late time response together, a method for solving the feature coefficients of target is investigated by utilizing approximation theory and method. Then, the feature coefficients are classified by the minimum distance criterion to identify targets automatically.

RADAR TARGET IDENTIFICATION BY ADAPTIVE DISCRIMINATION WAVEFORM SYNTHESIS AND NEAREST NEIGHBOR NEURAL NETWORK

In this paper,a new radar target identification scheme is presented based on adaptivediscrimination waveform synthesis and a nearest neighbor neural network.It can directly use theimpulse response of the target to synthesize discrimination waveform,so the poles extractionprocedure is not required.Particularly,it can successfully operate on the case that the poles ofthe target are weakly dependent on the aspect angle.

METHODS OF RADAR DATA COMPRESSION AND TARGET IDENTIFICATION BASED ON BIORTHOGONAL FDWT

In this paper, by using the biorthogonal quadrature filters, the biorthogonal mul-tiresolution analysis of finite dimension space equipped with inner product and the fast discrete wavelet transform (FDWT) are constructed. The dual transform method is proposed and the radar data storage is reduced by it. The method of choosing the wavelet coefficients, and the methods of correlation and nearest neighbor classification in wavelet domain based on the compressed data, are presented. The experimental results of the classification, using the high resolution range returns from six kinds of aircrafts, show that the methods of transform, compression and recognition are efficient.

GTD MODEL-BASED TIME-SHIFT INVARIANT TARGET IDENTIFICATION USING MATCHING PURSUITS AND LIKELIHOOD RATIO TESTS

In order to improve the identification capability of ultra wide-band radar,this paper in-troduces a step-variant multiresolution approach for the time-shift parameter estimation. Subsequently,combining with the approach,a Geometrical Theory of Diffraction(GTD) model-based time-shift Invariant method to target identification using Matching Pursuits and Likelihood Ratio Test(IMPLRT) is developed. Simulation results using measured scattering signatures of two targets in an ultra wide-band chamber are presented contrasting the performance of the IMPLRT to the Wang's MPLRT technique.

Traditional Chinese medicine target identification based on ＂Target Fishing＂ strategy

OBJECTIVE Our group mainly focuses on the target identification and pharmacological mechanism study of TCM.We deeply identified the direct targets of the active ingredients in TCM using molecule probe-'Target Fishing' technology in chemical biology,and explored the related signaling pathways to explain the traditional efficiency of TCM.METHODS We synthesized biotin-tagged mole.cule probe by connecting biotin tag to TCM active molecule using PGE as a linker.Then,the biotintagged molecule probe was bound to the surface of solid beads by strong biotin-avidin interaction.Thus,the molecule probe-bound beads were mixed with cell lysates to capture the potential targets and identified by MS.RESULTS Our study found that SA which was an anti-inflammatory compound.could selectively bind to IMPDH2 in microglial cells,and SA showed weaker anti-inflammatory effect on IMPDH2-knock down microglial cells,suggesting IMPDH2 as a key anti-inflammatory target for SA.Ad.ditionally,handelin was a key anti-inflammatory compound.We identified the target protein of handelin as Hsp70 from microglial cells using target pull-down technology.Moreover,handelin showed weaker anti-inflammatory effect on Hsp70-knock down microglial cells,revealing that Hsp70 was the direct antiinflammatory target of handelin.CONCLUSION Our study provided methodology references for TCM target identification in the future,and also showed a new insight for exploring the pharmacological mechanism of TCM active ingredients.More importantly,we can perform scientific annotation for TCM efficiency by clarifying the biological functions of each target protein,showing important significance on modernization and internationalization of TCM.

An Adjoint-Free CNOP–4DVar Hybrid Method for Identifying Sensitive Areas in Targeted Observations: Method Formulation and Preliminary Evaluation

This paper proposes a hybrid method, called CNOP–4 DVar, for the identification of sensitive areas in targeted observations, which takes the advantages of both the conditional nonlinear optimal perturbation(CNOP) and four-dimensional variational assimilation(4 DVar) methods. The proposed CNOP–4 DVar method is capable of capturing the most sensitive initial perturbation(IP), which causes the greatest perturbation growth at the time of verification;it can also identify sensitive areas by evaluating their assimilation effects for eliminating the most sensitive IP. To alleviate the dependence of the CNOP–4 DVar method on the adjoint model, which is inherited from the adjoint-based approach, we utilized two adjointfree methods, NLS-CNOP and NLS-4 DVar, to solve the CNOP and 4 DVar sub-problems, respectively. A comprehensive performance evaluation for the proposed CNOP–4 DVar method and its comparison with the CNOP and CNOP–ensemble transform Kalman filter(ETKF) methods based on 10 000 observing system simulation experiments on the shallow-water equation model are also provided. The experimental results show that the proposed CNOP–4 DVar method performs better than the CNOP–ETKF method and substantially better than the CNOP method.

A novel multi-sensor multiple model particle filter with correlated noises for maneuvering target tracking

Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is proposed.Combined with the kinetic evolution equation of target state,a multi-sensor multiple model particle filter is firstly constructed,which is also used as the basic framework of a new algorithm.In the new algorithm,in order to weaken the adverse influence from random measurement noises in the measuring process of particle weight,a weight optimization strategy is introduced to improve the reliability and stability of particle weight.In addition,considering the correlated noise existing in the practical engineering,a decoupling method of correlated noise is given by the rearrangement and transformation of the state transition equation and measurement equation.Since the weight optimization strategy and noise decoupling method adopt respectively the center fusion structure and the off-line way,it improves the adverse effect effectively on computational complexity for increasing state dimension and sensor number.Finally,the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

Isolation and Identification of Porcine Circovirus Type 2 in Taizhou

In September 2011, an infectious disease suspected to be postweaning multisystemic wasting syndrome （PMWS） broke out in some pig farm in Taizhou. The inguinal lymph node, liver and lung tissues were collected and grinded into tissue suspension. The suspension was subjected to PCR detection, and the positive product was sequenced. The suspension of positive samples was filtered with 0.22 μm filter membrane, and the filtrate was inoculated onto PK15 cells. After five generations of blind passages, the cell viral liquid was collected and extracted for DNA, which was subjected to PCR detection and indirect immunofluorescence. The results showed that the isolate was porcine circovirus type 2 （PCV2） and designated as TAIZ110926. The target sequence was committed to NCBI with a serial number： KF039888.

Electronic Throttle Control System: Modeling, Identification and Model-Based Control Designs

Electronic throttle control (ETC) system has worked its way to becoming a standard subsystem in most of the current automobiles as it has contributed much to the improvement of fuel economy, emissions, drivability and safety. Precision control of the subsystem, which consists of a dc motor driving a throttle plate, a pre-loaded return spring and a set of gear train to regulate airflow into the engine, seems rather straightforward and yet complex. The difficulties lie in the unknown system parameters, hard nonlinearity of the pre-loaded spring that pulls the throttle plate to its default position, and friction, among others. In this paper, we extend our previous results obtained for the modeling, unknown system parameters identification and control of a commercially available Bosch’s DV-E5 ETC system. Details of modeling and parameters identification based on laboratory experiments, data analysis, and knowledge of the system are provided. The parameters identification results were verified and validated by a real-time PID control implemented with an xPC Target. A nonlinear control design was then proposed utilizing the input-output feedback linearization approach and technique. In view of a recent massive auto recalls due to the controversial uncontrollable engine accelerations, the results of this paper may inspire further research interest on the drive-by-wire technology.

基于改进YOLOv5-LITE轻量级的配电组件缺陷识别

为对配电组件缺陷进行精确快速的定位和识别,提出一种基于改进YOLOv5-LITE轻量级的配电组件缺陷识别方法。为使模型便于部署至移动设备终端,该方法使用ShuffleNetV2作为骨干网提取特征构建YOLOv5-LITE轻量级神经网络模型,并摘除ShuffleNetV2的1024卷积和5×5池化,采用全局平均池化操作替代,降低网络参数量,提升模型检测速度;通过引入有利于细粒度目标检测的152×152特征层,实现了对大、中、小尺度的缺陷检测;在PANet架构中采用深度可分离卷积代替下采样使得网络更加轻量化。实验结果表明:该方法能够识别电缆脱离垫片、电缆与绝缘子脱落、无环绝缘子3种缺陷,其检测精度分别达到92%、95%、95%,网络参数量约为YOLOv5的1/4,检测速度达到2 ms/张。所提出的方法具有实时性、准确率高、轻量化等特点。

An innovative inertial parameters identification method for non-cooperative space targets based on electrostatic interaction

Inertial characteristics of non-cooperative targets are crucial for space capture and sub-sequent on-orbit servicing.Previous methods for identifying inertial parameters involve proximity operations,which are associated with the risk of collision with non-cooperative targets.This paper introduces a long-range,contactless method for identifying the inertial parameters of a non-cooperative target during the pre-capture phase.Specifically,electrostatic interaction is used as an external excitation to alter the target's motion.A force estimation algorithm that uses measure-ments from visual and potential sensors is proposed to estimate the electrostatic interaction and eliminate the need for force sensors.Furthermore,a recursive estimation-identification framework is presented to concurrently estimate the coupled motion state,weak electrostatic interaction,and inertial parameters of the target.The simulation results show that the proposed method extends the identification distance to 170 times that of the previous method while maintaining high identifica-tion precision forall parameters.

基于改进YOLO v5的复杂环境下桑树枝干识别定位方法

为实现复杂自然环境下对桑树嫩叶处枝干的识别检测,改变当前桑叶采摘设备作业过程中依赖人工辅助定位的现状,解决识别目标姿态多样和环境复杂导致的低识别率问题,提出一种基于改进YOLO v5模型的桑树枝干识别模型(YOLO v5-mulberry),并结合深度相机构建定位系统。首先,在YOLO v5的骨干网络中加入CBAM(Convolutional block attention module)注意力机制,提高神经网络对桑树枝干的关注度;并增加小目标层使模型可检测4像素×4像素的目标,提高了模型检测小目标的性能;同时使用GIoU损失函数替换原始网络中的IoU损失函数,有效防止了预测框和真实框尺寸较小时无法正确反映预测框及真实框之间位置关系的情况;随后,完成深度图和彩色图的像素对齐,通过坐标系转换获取桑树枝干三维坐标。试验结果表明:YOLO v5-mulberry检测模型的平均精度均值为94.2%,较原模型提高16.9个百分点,置信度也提高12.1%;模型室外检测时应检测目标数53,实际检测目标数为48,检测率为90.57%;桑树嫩叶处枝干三维坐标识别定位系统的定位误差为(9.4985 mm,11.285 mm,19.11 mm),满足使用要求。该研究可实现桑树嫩叶处枝干的识别与定位,有助于推动桑叶智能化采摘机器人研究。

基于改进YOLO v5的复杂环境下花椒簇识别与定位方法

花椒树产果量大,枝干纵横交错,树叶茂密,给花椒的自动化采摘带来了困难。因此,本文设计一种基于改进YOLO v5的复杂环境下花椒簇的快速识别与定位方法。通过在主干提取网络CSPDarknet的CSPLayer层和Neck的上采样之后增加高效通道注意力ECA(Efficient channel attention)来简化CSPLayer层的计算量,提升了特征提取能力。同时在下采样层增加协同注意力机制CA(Coordinate attention),减少下采样过程中信息的损失,强化特征空间信息,配合热力图(Grad-CAM)和点云深度图,来完成花椒簇的空间定位。测试结果表明,与原YOLO v5相比较,改进的网络将残差计算减少至1次,保证了模型轻量化,提升了效率。同帧数区间下,改进后的网络精度为96.27%,对比3个同类特征提取网络YOLO v5、YOLO v5-tiny、Faster R-CNN,改进后网络精确度P分别提升5.37、3.35、15.37个百分点,连株花椒簇的分离识别能力也有较大提升。实验结果表明,自然环境下系统平均识别率为81.60%、漏检率为18.39%,能够满足花椒簇识别要求,为移动端部署创造了条件。

多特征融合的无人艇视觉目标长时相关鲁棒跟踪

[目的]针对显著海浪遮挡、相机剧烈晃动引起的无人艇视觉目标跟踪脱靶问题,提出一种基于多特征融合的长时相关鲁棒跟踪算法。[方法]首先,采用多特征融合技术,增强目标特征表达,提高目标模型鲁棒性;其次,利用高维特征降维和响应图子网格插值,提高目标跟踪的效率与精度;然后,设计水面目标重识别机制,解决目标完全脱离视野时的稳定跟踪问题;最后,采用多个代表性视频数据集进行验证和比较分析。[结果]实验结果表明,相较于传统的长时相关跟踪算法,平均成功率提升15.7%,平均距离精度指标提升30.3%,F-Score指标提升7.0%。[结论]所提算法能够处理恶劣海况下的目标脱靶问题,对于提升无人船艇及海洋机器人智能感知能力,具有重要技术支撑意义。

D-S证据理论在空中目标识别中的应用现状与展望

D-S证据理论作为一种多源信息融合工具,在空中目标识别领域中得到了广泛应用。对D-S证据理论进行了概述;简要梳理了D-S证据理论在空中目标识别领域中的发展脉络,并提出应用中需要解决的三类关键问题;围绕上述问题,重点对该领域中的BPA获取、证据冲突度量、证据融合的应用现状进行综述;最后,基于空域控制视角,对D-S证据理论在该领域中的应用进行了展望。研究可为空中目标识别领域的理论发展和工程应用提供参考。

基于旋转不变性的高分辨率遥感影像船舶检测

近年来,随着高分辨率遥感影像和船舶智能化的发展,通过遥感技术在大范围内对船舶目标进行检测识别已在海洋监管和安全等领域发挥出重要的现实意义。考虑到人类的视觉回路系统中对外界特定目标有很强的方向选择性,借鉴视觉的方向选择性机制,将有助于提升舰船检测识别任务的性能。从3个方面来模拟这种视觉的方向性选择机制:对卷积层采用Gabor卷积核分解的方法来模拟视觉回路的方向性,使深度卷积网络具有方向不变性;通过采用方向回归的方式估计舰船目标的主方向,模拟方向性选择机制;结合方向性目标来提升舰船检测识别任务的性能。试验结果表明:与快速区域卷积神经网络(Faster R-CNN)、单步多框检测(SSD)和定向响应网络(ORN)方法相比,该方法能取得较好的效果,表现出潜在的优势,均值平均精度(mAP)可达到约98%。