An optimal adaptive H-infinity tracking control design via wavelet network

In this paper, an optimal adaptive H-infinity tracking control design method via wavelet network for a class of uncertain nonlinear systems with external disturbances is proposed to achieve H-infinity tracking performance. First, an alternate tracking error and a performance index with respect to the tracking error and the control effort are introduced in order to obtain better performance, especially, in reducing the cost of the control effort in the case of small attenuation levels. Next, H-infinity tracking performance, which attenuates the influence of both wavelet network approximation error and external disturbances on the modified tracking error, is formulated. Our results indicate that a small attenuation level does not lead to a large control signal. The proposed method insures an optimal trade-off between the amplitude of control signals and the performance of tracking errors. An example is given to illustrate the design efficiency.

Complicated target recognition by archaeal box C/D guide RNAs

Box C/D RNAs guide the site-specific formation of 2′-O-methylated nucleotides(Nm)of RNAs in eukaryotes and archaea.Although C/D RNAs have been profiled in several archaea,their targets have not been experimentally determined.Here,we mapped Nm in r RNAs,t RNAs,and abundant small RNAs(s RNAs)and profiled C/D RNAs in the crenarchaeon Sulfolobus islandicus.The targets of C/D RNAs were assigned by analysis of base-pairing interactions,in vitro modification assays,and gene deletion experiments,revealing a complicated landscape of C/D RNA-target interactions.C/D RNAs widely use dual antisense elements to target adjacent sites in r RNAs,enhancing modification at weakly bound sites.Two consecutive sites can be guided with the same antisense element upstream of box D or D′,a phenomenon known as doublespecificity that is exclusive to internal box D′in eukaryotic C/D RNAs.Several C/D RNAs guide modification at a single non-canonical site.This study reveals the global landscape of RNA-guided 2′-O-methylation in an archaeon and unexpected targeting rules employed by C/D RNA.

SnoRNP is essential for thermospermine-mediated development in Arabidopsis thaliana

Polyamines have been discovered for hundreds of years and once considered as a class of phytohormones.Polyamines play critical roles in a range of developmental processes.However,the molecular mechanisms of polyamine signaling pathways remain poorly understood.Here,we measured the contents of main types of polyamines,and found that endogenous level of thermospermine(T-Spm)in Arabidopsis thaliana is comparable to those of classic phytohormones and is significantly lower than those of putrescine(Put),spermidine(Spd),and spermine(Spm).We further found a nodule-like structure around the junction area connecting the shoot and root of the T-Spm biosynthetic mutant acl5 and obtained more than 50 suppressors of acl5 nodule structure(san)through suppressor screening.An in-depth study of two san suppressors revealed that NAP57 and NOP56,core components of box H/ACA and C/D snoRNPs,were essential for T-Spm-mediated nodule-like structure formation and plant height.Furthermore,analyses of rRNA modifications showed that the overall levels of pseudouridylation and 2′-O-methylation were compromised in san1 and san2 respectively.Taken together,these results establish a strong genetic relationship between rRNA modification and T-Spm-mediated growth and development,which was previously undiscovered in all organisms.

STABILIZATION OF THE QUADRUPLE INVERTED PENDULUM BY VARIABLE UNIVERSE ADAPTIVE FUZZY CONTROLLER BASED ON VARIABLE GAIN H_∞ REGULATOR

在这份报纸，可变宇宙适应模糊控制器基于可变获得 H 管理者(VGHR ) 被设计稳定一只四倍的转换摆钟。VGHR 是一条新奇柔韧的安排获得的途径。由利用 VGHR 技术，一个更精确的即时反馈获得矩阵，随着状态正在变化，被获得。经由可变获得矩阵，四倍的转换摆钟的 10 个州的变量在采样时间被转变成一种合成错误(E) 和错误(EC ) 的变化的合成率。因此， multivariable 系统的尺寸被减少并且可变宇宙适应模糊控制器被造。实验说明建议控制计划的有效性。

A structural variation genotyping algorithm enhanced by CNV quantitative transfer

Genotyping of structural variations considering copy number variations(CNVs)is an infancy and challenging problem.CNVs,a prevalent form of critical genetic variations that cause abnormal copy numbers of large genomic regions in cells,often affect transcription and contribute to a variety of diseases.The characteristics of CNVs often lead to the ambiguity and confusion of existing genotyping features and algorithms,which may cause heterozygous variations to be erroneously genotyped as homozygous variations and seriously affect the accuracy of downstream analysis.As the allelic copy number increases,the error rate of genotyping increases sharply.Some instances with different copy numbers play an auxiliary role in the genotyping classification problem,but some will seriously interfere with the accuracy of the model.Motivated by these,we propose a transfer learning-based method to genotype structural variations accurately considering CNVs.The method first divides the instances with different allelic copy numbers and trains the basic machine learning framework with different genotype datasets.It maximizes the weights of the instances that contribute to classification and minimizes the weights of the instances that hinder correct genotyping.By adjusting the weights of the instances with different allelic copy numbers,the contribution of all the instances to genotyping can be maximized,and the genotyping errors of heterozygote variations caused by CNVs can be minimized.We applied the proposed method to both the simulated and real datasets,and compared it to some popular algorithms including GATK,Facets and Gindel.The experimental results demonstrate that the proposed method outperforms the others in terms of accuracy,stability and efficiency.The source codes have been uploaded at github/TrinaZ/CNVtransfer for academic use only.