Multi-innovation stochastic gradient algorithms for dual-rate sampled systems with preload nonlinearity

AbstractSince the stochastic gradient algorithm has a slower convergence rate, this letter presents a multi-innovation stochastic gradient algorithm for a class of dual-rate sampled systems with preload nonlinearity. The basic idea is to transform the dual-rate system model into an identification model which can use dual-rate data by using the polynomial transformation technique. A simulation example is provided to verify the effectiveness of the proposed method

Two Identification Methods for Dual-Rate Sampled-Data Nonlinear Output-Error Systems

This paper presents two methods for dual-rate sampled-data nonlinear output-error systems. One method is the missing output estimation based stochastic gradient identification algorithm and the other method is the auxiliary model based stochastic gradient identification algorithm. Different from the polynomial transformation based identification methods, the two methods in this paper can estimate the unknown parameters directly. A numerical example is provided to confirm the effectiveness of the proposed methods

Parameter and State Estimator for State Space Models

This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective

Prognosis and pain dissection of novel signatures in kidney renal clear cell carcinoma based on fatty acid metabolism-related genes

Renal cell carcinoma (RCC) is a malignant tumor that is characterized by the accumulation of intracellular lipid droplets. The prognostic value of fatty acid metabolism-related genes (FMGs) in RCC remains unclear. Alongside this insight, we collected data from three RCC cohorts, namely, The Cancer Genome Atlas (TCGA), E-MTAB-1980, and GSE22541 cohorts, and identified a total of 309 FMGs that could be associated with RCC prognosis. First, we determined the copy number variation and expression levels of these FMGs, and identified 52 overall survival (OS)-related FMGs of the TCGA-KIRC and the E-MTAB-1980 cohort data. Next, 10 of these genes—FASN, ACOT9, MID1IP1, CYP2C9, ABCD1, CPT2, CRAT, TP53INP2, FAAH2, and PTPRG—were identified as pivotal OS-related FMGs based on least absolute shrinkage and selection operator and Cox regression analyses. The expression of some of these genes was confirmed in patients with RCC by immunohistochemical analyses. Kaplan–Meier analysis showed that the identified FMGs were effective in predicting the prognosis of RCC. Moreover, an optimal nomogram was constructed based on FMG-based risk scores and clinical factors, and its robustness was verified by time-dependent receiver operating characteristic analysis, calibration curve analysis, and decision curve analysis. We have also described the biological processes and the tumor immune microenvironment based on FMG-based risk score classification. Given the close association between fatty acid metabolism and cancer-related pain, our 10-FMG signature may also serve as a potential therapeutic target with dual effects on ccRCC prognosis and cancer pain and, therefore, warrants further investigation

Plasma concentrations of coffee polyphenols and plasma biomarkers of diabetes risk in healthy Japanese women

Coffee consumption has been reported to reduce the risk of type 2 diabetes in experimental and epidemiological studies. This anti-diabetic effect of coffee may be attributed to its high content in polyphenols especially caffeic acid and chlorogenic acid. However, the association between plasma coffee polyphenols and diabetic risks has never been investigated in the literature. In this study, fasting plasma samples were collected from 57 generally healthy females aged 38-73 (mean 52, s.d. 8) years recruited in Himeji, Japan. The concentrations of plasma coffee polyphenols were determined by liquid chromatography coupled with mass tandem spectrometer. Diabetes biomarkers in the plasma/serum samples were analysed by a commercial diagnostic laboratory. Statistical associations were assessed using Spearman's correlation coefficients. The results showed that plasma chlorogenic acid exhibited negative associations with fasting blood glucose, glycated hemoglobin and C-reactive protein, whereas plasma total coffee polyphenol and plasma caffeic acid were weakly associated with these biomarkers. Our preliminary data support previous findings that coffee polyphenols have anti-diabetic effects but further replications with large samples of both genders are recommended

Research on machine vision and deep learning based recognition of cotton seedling aphid infestation level

Aphis gossypii Glover is a major insect pest in cotton production, which can cause yield reduction in severe cases. In this paper, we proposed the A. gossypii infestation monitoring method, which identifies the infestation level of A. gossypii at the cotton seedling stage, and can improve the efficiency of early warning and forecasting of A. gossypii, and achieve precise prevention and cure according to the predicted infestation level. We used smartphones to collect A. gossypii infestation images and compiled an infestation image data set. And then constructed, trained, and tested three different A. gossypii infestation recognition models based on Faster Region-based Convolutional Neural Network (R-CNN), You Only Look Once (YOLO)v5 and single-shot detector (SSD) models. The results showed that the YOLOv5 model had the highest mean average precision (mAP) value (95.7%) and frames per second (FPS) value (61.73) for the same conditions. In studying the influence of different image resolutions on the performance of the YOLOv5 model, we found that YOLOv5s performed better than YOLOv5x in terms of overall performance, with the best performance at an image resolution of 640×640 (mAP of 96.8%, FPS of 71.43). And the comparison with the latest YOLOv8s showed that the YOLOv5s performed better than the YOLOv8s. Finally, the trained model was deployed to the Android mobile, and the results showed that mobile-side detection was the best when the image resolution was 256×256, with an accuracy of 81.0% and FPS of 6.98. The real-time recognition system established in this study can provide technical support for infestation forecasting and precise prevention of A. gossypii

Profiles of biliary microbiota in biliary obstruction patients with Clonorchis sinensis infection

BackgroundClonorchis sinensis (C. sinensis) is a epidemiologically significant food-borne parasite, causing several hepatobiliary diseases. Biliary microbiota community structure might be influenced by infection with pathogens. However, the biliary microbiome of biliary obstruction patients infected with C. sinensis is still an unexplored aspect.MethodsA total of 50 biliary obstruction patients were enrolled, including 24 infected with C. sinensis and 26 non-infected subjects. The bile samples were collected by Endoscopic Retrograde Cholangiopancretography. Biliary microbiota alteration was analyzed through high-throughput 16S ribosomal RNA (rRNA) gene sequencing.ResultsOur findings revealed that there was significant increase in both richness and diversity, as well as changes in the taxonomic composition of the biliary microbiota of C. sinensis infected patients. At the phylum level, C. sinensis infection induced Proteobacteria increased and Firmicutes reduced. At the genus level, the relative abundance of Pseudomonas and Staphylococcus increased significantly, while Enterococcus decreased prominently in infected groups (P < 0.05). The PICRUSt analysis further showed remarkably different metabolic pathways between the two groups.ConclusionC. sinensis infection could modify the biliary microbiota, increasing the abundance and changing the phylogenetic composition of bacterial in biliary obstruction patients. This study may help deepen the understanding of the host-biliary microbiota interplay with C. sinensis infection on the background of biliary obstruction and provide new insights into understanding the pathogenesis of clonorchiasis

A tau fragment links depressive-like behaviors and cognitive declines in Alzheimer’s disease mouse models through attenuating mitochondrial function

IntroductionAlzheimer’s disease (AD) is the most prevalent neurodegenerative disease characterized by extracellular senile plaques including amyloid-β peptides and intracellular neurofibrillary tangles consisting of abnormal Tau. Depression is one of the most common neuropsychiatric symptoms in AD, and clinical evidence demonstrates that depressive symptoms accelerate the cognitive deficit of AD patients. However, the underlying molecular mechanisms of depressive symptoms present in the process of AD remain unclear.MethodsDepressive-like behaviors and cognitive decline in hTau mice were induced by chronic restraint stress (CRS). Computational prediction and molecular experiments supported that an asparagine endopeptidase (AEP)-derived Tau fragment, Tau N368 interacts with peroxisome proliferator-activated receptor delta (PPAR-δ). Further behavioral studies investigated the role of Tau N368-PPAR-δ interaction in depressive-like behaviors and cognitive declines of AD models exposed to CRS.ResultsWe found that mitochondrial dysfunction was positively associated with depressive-like behaviors and cognitive deficits in hTau mice. Chronic stress increased Tau N368 and promoted the interaction of Tau N368 with PPAR-δ, repressing PPAR-δ–mediated transactivation in the hippocampus of mice. Then we predicted and identified the binding sites of PPAR-δ. Finally, inhibition of AEP, clearance of Tau N368 and pharmacological activation of PPAR-δ effectively alleviated CRS-induced depressive-like behaviors and cognitive decline in mice.ConclusionThese results demonstrate that Tau N368 in the hippocampus impairs mitochondrial function by suppressing PPAR-δ, facilitating the occurrence of depressive-like behaviors and cognitive decline. Therefore, our findings may provide new mechanistic insight in the pathophysiology of depression-like phenotype in mouse models of Alzheimer’s disease