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.

Multi-scale data joint inversion of minerals and porosity in altered igneous reservoirs—A case study in the South China Sea

There are abundant igneous gas reservoirs in the South China Sea with significant value of research,and lithology classification,mineral analysis and porosity inversion are important links in reservoir evaluation.However,affected by the diverse lithology,complicated mineral and widespread alteration,conventional logging lithology classification and mineral inversion become considerably difficult.At the same time,owing to the limitation of the wireline log response equation,the quantity and accuracy of minerals can hardly meet the exploration requirements of igneous formations.To overcome those issues,this study takes the South China Sea as an example,and combines multi-scale data such as micro rock slices,petrophysical experiments,wireline log and element cutting log to establish a set of joint inversion methods for minerals and porosity of altered igneous rocks.Specifically,we define the lithology and mineral characteristics through core slices and mineral data,and establish an igneous multi-mineral volumetric model.Then we determine element cutting log correction method based on core element data,and combine wireline log and corrected element cutting log to perform the lithology classification and joint inversion of minerals and porosity.However,it is always difficult to determine the elemental eigenvalues of different minerals in inversion.This paper uses multiple linear regression methods to solve this problem.Finally,an integrated inversion technique for altered igneous formations was developed.The results show that the corrected element cutting log are in good agreement with the core element data,and the mineral and porosity results obtained from the joint inversion based on the wireline log and corrected element cutting log are also in good agreement with the core data from X-ray diffraction.The results demonstrate that the inversion technique is applicable and this study provides a new direction for the mineral inversion research of altered igneous formations.

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.

Identification Method of Gas-Liquid Two-phase Flow Regime Based on Image Multi-feature Fusion and Support Vector Machine

流动政体的知识为确定是很重要的二阶段的流动系统的压力落下，稳定性和安全。基于图象多特征熔化和支持向量机器，在二阶段的流动识别流动政体的一个新方法被介绍。第一，煤气液体的二阶段的流动想象包括冒泡的流动，塞子流动，蛞蝓流动，成层的流动，起浪的流动，环形的流动和薄雾流动被数字高速度录像系统在水平试管捕获。图象时刻 invariants 和灰色的水平同现矩阵质地特征用处理技术的图象被提取。改进一个多重分类器系统的表演，不平的集合理论被用于减少无关紧要的因素。而且，支持向量机器被使用这些特徵向量作为流动政体样品，和流动政体减少尺寸训练聪明的鉴定被认识到。测试结果证明与不平的集合理论被减少的图象特征能最优地反映支持向量机器能快速并且精确地识别的七典型流动政体，并且成功的训练之间的差别在水平试管的煤气液体的二阶段的流动的七典型流动政体。图象多特征熔化方法提供了一个新方法识别煤气液体的二阶段的流动，并且比单个特征的完成了更高的鉴定能力。全面鉴定精确性是 100% ，并且处理时间的图象的估计是为联机流动政体鉴定的 8 ms。

Multi-Innovation Gradient Iterative Locally Weighted Learning Identification for A Nonlinear Ship Maneuvering System

This paper explores a highly accurate identification modeling approach for the ship maneuvering motion with fullscale trial. A multi-innovation gradient iterative(MIGI) approach is proposed to optimize the distance metric of locally weighted learning(LWL), and a novel non-parametric modeling technique is developed for a nonlinear ship maneuvering system. This proposed method’s advantages are as follows: first, it can avoid the unmodeled dynamics and multicollinearity inherent to the conventional parametric model; second, it eliminates the over-learning or underlearning and obtains the optimal distance metric; and third, the MIGI is not sensitive to the initial parameter value and requires less time during the training phase. These advantages result in a highly accurate mathematical modeling technique that can be conveniently implemented in applications. To verify the characteristics of this mathematical model, two examples are used as the model platforms to study the ship maneuvering.

Modal identification of multi-degree-of-freedom structures based on intrinsic chirp component decomposition method

Modal parameter identification is a mature technology.However,there are some challenges in its practical applications such as the identification of vibration systems involving closely spaced modes and intensive noise contamination.This paper proposes a new time-frequency method based on intrinsic chirp component decomposition(ICCD)to address these issues.In this method,a redundant Fourier model is used to ameliorate border distortions and improve the accuracy of signal reconstruction.The effectiveness and accuracy of the proposed method are illustrated using three examples:a cantilever beam structure with intensive noise contamination or environmental interference,a four-degree-of-freedom structure with two closely spaced modes,and an impact test on a cantilever rectangular plate.By comparison with the identification method based on the empirical wavelet transform(EWT),it is shown that the presented method is effective,even in a high-noise environment,and the dynamic characteristics of closely spaced modes are accurately determined.

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.

Inversion method for multi-point source pollution identification:Sensitivity analysis and application to European Tracer Experiment data

快速并且准确地识别未知污染源,在大气污染应急响应和源头控制过程中起着至关重要的作用.本文利用敏感性试验及欧洲示踪物测场试验(ETEX-1)数据研究了新提出的两步反演算法的实用性,并将其反演结果与现有的两种算法进行了对比分析.敏感试验表明,在观测数据较为丰富的情况下,三种算法对观测误差和风场误差的敏感性均较低;而当观测数据较为稀疏时,所有算法的估计精度都将随着误差的增加而下降,但与其他两种算法相比,两步反演算法具有更高的估计精度以及更稳定的估计性能.此外,欧洲示踪物测场试验的源项估计结果也表明,在三个算法中,两步反演算法具有最小的位置估计误差.

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.

Identification and Analysis of Multi-tasking Product Information Search Sessions with Query Logs

Purpose： This research aims to identify product search tasks in online shopplng ana analyze the characteristics of consumer multi-tasking search sessions. Design/methodology/approach： The experimental dataset contains 8,949 queries of 582 users from 3,483 search sessions. A sequential comparison of the Jaccard similarity coefficient between two adjacent search queries and hierarchical clustering of queries is used to identify search tasks. Findings： （1） Users issued a similar number of queries （1.43 to 1.47） with similar lengths （7.3-7.6 characters） per task in mono-tasking and multi-tasking sessions, and （2） Users spent more time on average in sessions with more tasks, but spent less time for each task when the number of tasks increased in a session. Research limitations： The task identification method that relies only on query terms does not completely reflect the complex nature of consumer shopping behavior.Practical implications： These results provide an exploratory understanding of the relationships among multiple shopping tasks, and can be useful for product recommendation and shopping task prediction. Originality/value： The originality of this research is its use of query clustering with online shopping task identification and analysis, and the analysis of product search session characteristics.

Numerical-Experimental Updating Identification of Elastic Behavior of a Composite Plate Using New Multi-Objective Optimization Procedure

This work focuses on the updating-based identification of the three-dimensional orthotropic elastic behavior of a thin carbon fiber reinforced plastic multilayer composite plate. This consists in identifying the engineering constants that minimize the relative deviations between the first eight experimental and three-dimensional finite element frequencies of the vibrating free plate. For this purpose, a multi-objective optimization procedure is applied;it exploits a Particle Swarm Optimizer algorithm (PSO) that is coupled to a metamodeling by the new response surfaces method procedure (NRSMP);the latter is based on numerical design experiments. The conducted sensitivity analyses indicate that the four engineering constants of the two-dimensional elasticity are the most influent.

A CNN-Based Single-Stage Occlusion Real-Time Target Detection Method

Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.

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.

Obstacle Avoidance and Multitarget Tracking of a Super Redundant Modular Manipulator Based on Bezier Curve and Particle Swarm Optimization

A super redundant serpentine manipulator has slender structure and multiple degrees of freedom.It can travel through narrow spaces and move in complex spaces.This manipulator is composed of many modules that can form different lengths of robot arms for different application sites.The increase in degrees of freedom causes the inverse kinematics of redundant manipulator to be typical and immensely increases the calculation load in the joint space.This paper presents an integrated optimization method to solve the path planning for obstacle avoidance and discrete trajectory tracking of a super redundant manipulator.In this integrated optimization,path planning is established on a Bezier curve,and particle swarm optimization is adopted to adjust the control points of the Bezier curve with the kinematic constraints of manipulator.A feasible obstacle avoidance path is obtained along with a discrete trajectory tracking by using a follow-the-leader strategy.The relative distance between each two discrete path points is limited to reduce the fitting error of the connecting rigid links to the smooth curve.Simulation results show that this integrated optimization method can rapidly search for the appropriate trajectory to guide the manipulator in obtaining the target while achieving obstacle avoidance and meeting joint constraints.The proposed algorithm is suitable for 3D space obstacle avoidance and multitarget path tracking.

Modified joint probabilistic data association with classification-aided for multitarget tracking

Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are similar for different closely spaced targets, there is ambiguity in using the kinematic information alone; the correct association probability will decrease in conventional joint probabilistic data association algorithm and track coalescence will occur easily. A modified algorithm of joint probabilistic data association with classification-aided is presented, which avoids track coalescence when tracking multiple neighboring targets. Firstly, an identification matrix is defined, which is used to simplify validation matrix to decrease computational complexity. Then, target class information is integrated into the data association process. Performance comparisons with and without the use of class information in JPDA are presented on multiple closely spaced maneuvering targets tracking problem. Simulation results quantify the benefits of classification-aided JPDA for improved multiple targets tracking, especially in the presence of association uncertainty in the kinematic measurement and target maneuvering. Simulation results indicate that the algorithm is valid.