Composite Disturbance Filtering: A Novel State Estimation Scheme for Systems With Multi-Source, Heterogeneous, and Isomeric Disturbances

State estimation has long been a fundamental problem in signal processing and control areas. The main challenge is to design filters with ability to reject or attenuate various disturbances. With the arrival of big data era, the disturbances of complicated systems are physically multi-source, mathematically heterogenous, affecting the system dynamics via isomeric (additive, multiplicative and recessive) channels, and deeply coupled with each other. In traditional filtering schemes, the multi-source heterogenous disturbances are usually simplified as a lumped one so that the "single" disturbance can be either rejected or attenuated. Since the pioneering work in 2012, a novel state estimation methodology called {\it composite disturbance filtering} (CDF) has been proposed, which deals with the multi-source, heterogenous, and isomeric disturbances based on their specific characteristics. With the CDF, enhanced anti-disturbance capability can be achieved via refined quantification, effective separation, and simultaneous rejection and attenuation of the disturbances. In this paper, an overview of the CDF scheme is provided, which includes the basic principle, general design procedure, application scenarios (e.g. alignment, localization and navigation), and future research directions. In summary, it is expected that the CDF offers an effective tool for state estimation, especially in the presence of multi-source heterogeneous disturbances

SUBP: Soft Uniform Block Pruning for 1xN Sparse CNNs Multithreading Acceleration

The study of sparsity in Convolutional Neural Networks (CNNs) has become widespread to compress and accelerate models in environments with limited resources. By constraining N consecutive weights along the output channel to be group-wise non-zero, the recent network with 1$\times$N sparsity has received tremendous popularity for its three outstanding advantages: 1) A large amount of storage space saving by a \emph{Block Sparse Row} matrix. 2) Excellent performance at a high sparsity. 3) Significant speedups on CPUs with Advanced Vector Extensions. Recent work requires selecting and fine-tuning 1$\times$N sparse weights based on dense pre-trained weights, leading to the problems such as expensive training cost and memory access, sub-optimal model quality, as well as unbalanced workload across threads (different sparsity across output channels). To overcome them, this paper proposes a novel \emph{\textbf{S}oft \textbf{U}niform \textbf{B}lock \textbf{P}runing} (SUBP) approach to train a uniform 1$\times$N sparse structured network from scratch. Specifically, our approach tends to repeatedly allow pruned blocks to regrow to the network based on block angular redundancy and importance sampling in a uniform manner throughout the training process. It not only makes the model less dependent on pre-training, reduces the model redundancy and the risk of pruning the important blocks permanently but also achieves balanced workload. Empirically, on ImageNet, comprehensive experiments across various CNN architectures show that our SUBP consistently outperforms existing 1$\times$N and structured sparsity methods based on pre-trained models or training from scratch. Source codes and models are available at \url{https://github.com/JingyangXiang/SUBP}.Comment: 14 pages, 4 figures, Accepted by 37th Conference on Neural Information Processing Systems (NeurIPS 2023

Temperature characteristics modeling of Preisach theory

This paper proposes a modeling method of the temperature characteristics of Preisach theory. On the basis of the classical Preisach hysteresis model, the Curie temperature, the critical exponent and the ambient temperature are introduced after which the effect of temperature on the magnetic properties of ferromagnetic materials can be accurately reflected. A simulation analysis and a temperature characteristic experiment with silicon steel was carried out. The results are basically the same which proves the validity and the accuracy of the method

Temperature characteristics modeling of Preisach theory

This paper proposes a modeling method of the temperature characteristics of Preisach theory. On the basis of the classical Preisach hysteresis model, the Curie temperature, the critical exponent and the ambient temperature are introduced after which the effect of temperature on the magnetic properties of ferromagnetic materials can be accurately reflected. A simulation analysis and a temperature characteristic experiment with silicon steel was carried out. The results are basically the same which proves the validity and the accuracy of the method

Composite Disturbance Filtering: A Novel State Estimation Scheme for Systems With Multisource, Heterogeneous, and Isomeric Disturbances

State estimation has long been a fundamental problem in signal processing and control areas. The main challenge is to design filters with ability to reject or attenuate various disturbances. With the arrival of Big Data era, the disturbances of complicated systems are physically multisource, mathematically heterogenous, affecting the system dynamics via isomeric (additive, multiplicative, and recessive) channels, and deeply coupled with each other. In traditional filtering schemes, the multisource heterogenous disturbances are usually simplified as a lumped one so that the “single” disturbance can be either rejected or attenuated. Since the pioneering work in 2012 (Guo and Cao, 2012), a novel state estimation methodology called composite disturbance filtering (CDF) has been proposed, which deals with the multisource, heterogenous, and isomeric disturbances based on their specific characteristics. With CDF, enhanced antidisturbance capability can be achieved via refined quantification, effective separation, and simultaneous rejection and attenuation of the disturbances. In this article, an overview of the CDF scheme is provided, which includes the basic principle, general design procedure, application scenarios (e.g., alignment, localization, and navigation), and future research directions. In summary, it is expected that CDF offers an effective tool for state estimation, especially in the presence of multisource heterogeneous disturbances

High Glucose-Induced ROS Production Stimulates Proliferation of Pancreatic Cancer via Inactivating the JNK Pathway

Aberrant glucose metabolism of diabetes mellitus or hyperglycemia stimulates pancreatic tumorigenesis and progression. Hyperglycemic environment can increase the ROS level of tumors, but the role of upregulation of ROS levels in pancreatic cancer (PC) still remains controversial. Here, the same as other reports, we demonstrate that high glucose promoted pancreatic cancer cell growth and resulted in an increase in the level of ROS. However, it is interesting that the phosphorylation of JNK was reduced. When treating PC cells with N-acetyl-L-cysteine (NAC), the intracellular ROS generation is repressed, but the expression of phosphorylation of JNK and c-Jun increased. Moreover, the JNK inhibitor SP600125 significantly promoted cell proliferation and suppressed cell apoptosis of pancreatic cancer cells under high glucose conditions. Collectively, high levels of ROS induced by high glucose conditions stimulated the proliferation of pancreatic cancer cells, and it may be achieved by inactivating the JNK pathway

Rh₂(Ph₃COO)₃(OAc)/Chiral Phosphoric Acid Cocatalyzed <i>N</i>‑Alkyl Imines-Involved Multicomponent Reactions Yielding <i>N</i>‑(Anthrancen-9-ylmethyl) Isoserines as Drug Intermediates

N-(Anthrancen-9-ylmethyl) isoserines are useful drug intermediates but short for efficient synthesis. We herein report the synthesis of N-(anthrancen-9-ylmethyl) isoserines via a Rh2(Ph3COO)3(OAc) and chiral phosphoric acid (CPA) synergistically catalyzed multicomponent reaction (MCR) of N-alkyl imines, alcohols, and diazoesters. The method representing the first example of N-alkyl imines-involved MCR is featured by high atom-economy, high diastereo- and enantioselectivities, and broad substrate scope. DFT calculations on the mechanism of the MCR reveals that the hydrophobic interactions and π–π stackings between N-(anthrancen-9-ylmethyl) imines and Rh2(Ph3COO)3(OAc)/CPA cocatalyst is essential to the reactivity and stereocontrol. The synthetic applications of the MCR products include the semisynthesis of paclitaxel, its alkyne-tagged derivative, and β-lactam as an anticancer agent overcoming paclitaxel-resistance. We expect this work to shed light on the development of new N-alkyl imines-involved reactions and on the synthesis of drugs with isoserines as intermediates

Inhibition of growth of hepatocellular carcinoma by co-delivery of anti-PD-1 antibody and sorafenib using biomimetic nano-platelets

Abstract Background Traditional nanodrug delivery systems have some limitations, such as eliciting immune responses and inaccuracy in targeting tumor microenvironments. Materials and methods Targeted drugs (Sorafenib, Sora) nanometers (hollow mesoporous silicon, HMSN) were designed, and then coated with platelet membranes to form aPD-1-PLTM-HMSNs@Sora to enhance the precision of drug delivery systems to the tumor microenvironment, so that more effective immunotherapy was achieved. Results These biomimetic nanoparticles were validated to have the same abilities as platelet membranes (PLTM), including evading the immune system. The successful coating of HMSNs@Sora with PLTM was corroborated by transmission electron microscopy (TEM), western blot and confocal laser microscopy. The affinity of aPD-1-PLTM-HMSNs@Sora to tumor cells was stronger than that of HMSNs@Sora. After drug-loaded particles were intravenously injected into hepatocellular carcinoma model mice, they were demonstrated to not only directly activate toxic T cells, but also increase the triggering release of Sora. The combination of targeted therapy and immunotherapy was found to be of gratifying antineoplastic function on inhibiting primary tumor growth. Conclusions The aPD-1-PLTM-HMSNs@Sora nanocarriers that co-delivery of aPD-1 and Sorafenib integrates unique biomimetic properties and excellent targeting performance, and provides a neoteric idea for drug delivery of personalized therapy for primary hepatocellular carcinoma (HCC)

The DNA methylation profile of non-coding RNAs improves prognosis prediction for pancreatic adenocarcinoma

Abstract Background Compelling lines of evidence indicate that DNA methylation of non-coding RNAs (ncRNAs) plays critical roles in various tumour progression. In addition, the differential methylation of ncRNAs can predict prognosis of patients. However, little is known about the clear relationship between DNA methylation profile of ncRNAs and the prognosis of pancreatic adenocarcinoma (PAC) patients. Methods The data of DNA methylation, RNA-seq, miRNA-seq and clinical features of PAC patients were collected from TCGA database. The DNA methylation profile was obtained using the Infinium HumanMethylation450 BeadChip array. LASSO regression was performed to construct two methylation-based classifiers. The risk score of methylation-based classifiers was calculated for each patient, and the accuracy of the classifiers in predicting overall survival (OS) was examined by ROC curve analysis. In addition, Cox regression models were utilized to assess whether clinical variables and the classifiers were independent prognostic factors for OS. The targets of miRNA and the genes co-expressed with lncRNA were identified with DIANA microT-CDS and the Multi-Experiment Matrix (MEM), respectively. Moreover, DAVID Bioinformatics Resources were applied to analyse the functional enrichment of these targets and co-expressed genes. Results A total of 4004 CpG sites of miRNA and 11,259 CpG sites of lncRNA were screened. Among these CpG sites, 8 CpG sites of miRNA and 7 CpG sites of lncRNA were found with regression coefficients. By multiplying the sum of methylation degrees of the selected CpGs with these coefficients, two methylation-based classifiers were constructed. The classifiers have shown good performance in predicting the survival rate of PAC patients at varying follow-up times. Interestingly, both of these two classifiers were predominant and independent factors for OS. Furthermore, functional enrichment analysis demonstrated that aberrantly methylated miRNAs and lncRNAs are related to calcium ion transmembrane transport and MAPK, Ras and calcium signalling pathways. Conclusion In the present study, we identified two methylation-based classifiers of ncRNA associated with OS in PAC patients through a comprehensive analysis of miRNA and lncRNA profiles. We are the first group to demonstrate a relationship between the aberrant DNA methylation of ncRNAs and the prognosis of PAC, and this relationship would contribute to individualized PAC therapy