Hysteresis Modeling and Compensation of Piezoelectric Actuators Using Gaussian Process with High-Dimensional Input

Rate-dependent hysteresis seriously deteriorates the positioning accuracy of the piezoelectric actuators, especially when tracking high-frequency signals. As a widely-used nonparametric Bayesian method, the Gaussian process (GP) has proven its effectiveness in nonlinear hysteresis modeling. In this paper, the dimension of the input to the GP model is extended to consider more dynamic features of the tracking signal so as to improve the rate-dependent hysteresis modeling accuracy. In contrast with the traditional training set containing only the position and speed information, the acceleration and jerk information, as well as their temporal distribution information, is also included in the input of the model. An inverse hysteresis compensator (IHC) is established in the same way, and open-loop and closed-loop controllers are developed by using the IHC. Experimental results on a PEA stage show that with the increase in the input dimension, the hysteresis modeling accuracy improves greatly and, thus, the controllers based on IHC can achieve a better tracking performance

Feature Enhancement Pyramid and Shallow Feature Reconstruction Network for SAR Ship Detection

Recently, convolutional neural network based methods have been studied for ship detection in optical remote sensing images. However, it is challenging to apply them to microwave synthetic aperture radar (SAR) images. First, most of the regions in the inshore scene include scattered spots and noises, which dramatically interfere with ship detection. Besides, SAR ship images contain ship targets of different sizes, especially small ships with dense distribution. Unfortunately, small ships have fewer distinguishing features making it difficult to be detected. In this article, we propose a novel SAR ship detection network called feature enhanced pyramid and shallow feature reconstruction network (FEPS-Net) to solve the above problems. We design a feature enhancement pyramid, which includes a spatial enhancement module to enhance spatial position information and suppress background noise, and the feature alignment module to solve the problem of feature misalignment during feature fusion. Additionally, to solve the problem of small ship detection in SAR ship images, we design a shallow feature reconstruction module to extract semantic information from small ships. The effectiveness of the proposed network for SAR ship detection is demonstrated by experiments on two publicly available datasets: SAR ship detection dataset and high-resolution SAR images dataset. The experimental results show that the proposed FEPS-Net has advantages in SAR ship detection over the current state-of-the-art methods

Advance of SOX Transcription Factors in Hepatocellular Carcinoma: From Role, Tumor Immune Relevance to Targeted Therapy

Sex determining region Y (SRY)-related high-mobility group (HMG) box (SOX) factors belong to an evolutionarily conserved family of transcription factors that play essential roles in cell fate decisions involving numerous developmental processes. In recent years, the significance of SOX factors in the initiation and progression of cancers has been gradually revealed, and they act as potential therapeutic targets for cancer. However, the research involving SOX factors is still preliminary, given that their effects in some leading-edge fields such as tumor immune microenvironment (TIME) remain obscure. More importantly, as a class of ‘undruggable’ molecules, targeting SOX factors still face considerable challenges in achieving clinical translation. Here, we mainly focus on the roles and regulatory mechanisms of SOX family members in hepatocellular carcinoma (HCC), one of the fatal human health burdens worldwide. We then detail the role of SOX members in remodeling TIME and analyze the association between SOX members and immune components in HCC for the first time. In addition, we emphasize several alternative strategies involved in the translational advances of SOX members in cancer. Finally, we discuss the alternative strategies of targeting SOX family for cancer and propose the opportunities and challenges they face based on the current accumulated studies and our understanding

The Effects of <i>n</i>-3 PUFA Supplementation on Bone Metabolism Markers and Body Bone Mineral Density in Adults: A Systematic Review and Meta-Analysis of RCTs

Supplemental n-3 polyunsaturated fatty acids (PUFA) on bone metabolism have yielded inconsistent results. This study aimed to examine the effects of n-3 PUFA supplementation on bone metabolism markers and bone mineral density through a meta-analysis of randomized controlled trials. A systematic literature search was conducted using the PubMed, Web of Science, and EBSCO databases, updated to 1 March 2023. The intervention effects were measured as standard mean differences (SMD) and mean differences (MD). Additionally, n-3 PUFA with the untreated control, placebo control, or lower-dose n-3 PUFA supplements were compared, respectively. Further, 19 randomized controlled trials (RCTs) (22 comparisons, n = 2546) showed that n-3 PUFA supplementation significantly increased blood n-3 PUFA (SMD: 2.612; 95% CI: 1.649 to 3.575). However, no significant effects were found on BMD, CTx-1, NTx-1, BAP, serum calcium, 25(OH)D, PTH, CRP, and IL-6. Subgroup analyses showed significant increases in femoral neck BMD in females (0.01, 95% CI: 0.01 to 0.02), people aged n-3 PUFA only (0.36, 95% CI: 0.06 to 0.66), and in studies lasting ≤6 months (0.29, 95% CI: 0.11 to 0.47). NTx-1 decreased in both genders (−9.66, 95% CI: −15.60 to −3.71), and serum calcium reduction was found in studies lasting >6 months (−0.19, 95% CI: −0.37 to −0.01). The present study demonstrated that n-3 PUFA supplementation might not have a significant effect on bone mineral density or bone metabolism markers, but have some potential benefits for younger postmenopausal subjects in the short term. Therefore, additional high-quality, long-term randomized controlled trials (RCTs) are warranted to fully elucidate the potential benefits of n-3 PUFA supplementation, as well as the combined supplementation of n-3 PUFA, on bone health

Mechanistic Insight into Royal Protein Inhibiting the Gram-Positive Bacteria

Royal jelly (RJ), a natural honeybee product, has a wide range of antibacterial activities. N-glycosylated major royal jelly protein 2 (N-MRJP2), purified from RJ, can inhibit the growth of Paenibacillus larvae (P. larvae, Gram-positive), a contagious etiological agent of the American foulbrood disease of honeybees. However, the inhibitory mechanism is largely unknown. Antibacterial assay and membrane proteome were conducted to investigate the inhibition capacity of RJ from different instar larvae and P. larvae treated by N-MRJP2, respectively. The similar antibacterial efficiency of RJ from different larval instar indicates that RJ is vital for the adaptive immune defense of small larvae. The killing of P. larvae by N-MRJP2 is achieved by disturbing the cell wall biosynthesis, increasing the permeability of cell membrane, hindering aerobic respiration, restraining cell division and inducing cell death. This demonstrates that RJ is critical for the passive immunity of immature larvae and N-MRJP2 can be used as natural antibiotic substance to resist P. larvae, even for other gram-positive bacteria. This constitutes solid evidence that RJ and N-MRJP2 have potentials as novel antibacterial agents

SET8 suppression mediates high glucose-induced vascular endothelial inflammation via the upregulation of PTEN

Diabetes: Protein modulators of vascular inflammation High glucose levels in patients with diabetes trigger vascular inflammation by affecting the expression of key proteins in blood vessel linings. Elevated glucose causes inflammation of the endothelium, a thin layer of cells that lines blood and lymph vessels, leading to cardiovascular complications. The phosphatase and tensin homolog protein (PTEN) contributes to endothelial inflammation, but the precise mechanisms are unclear. Xuefang Shen at Fudan University in Shanghai, China, and co-workers demonstrated that elevated glucose increases PTEN expression, with increased levels of the protein found in peripheral blood cells of diabetic patients and aortic tissues of diabetic rats. In further experiments on rats, the researchers found that glucose also suppressed another protein called SET8, which contributed to increased PTEN levels. This suggests that SET8 is involved in PTEN modulation, and that both proteins influence vascular inflammation

Additional file 1 of HGF-mediated elevation of ETV1 facilitates hepatocellular carcinoma metastasis through upregulating PTK2 and c-MET

Additional file 1: Supplementary materials. FigureS1. (A) ETV1expression in LIHC and correlation of ETV1 expression with overallsurvival were analyzed in LIHC according to the data of The Cancer Genome Atlas(TCGA). (B) CellCounting Kit-8 (CCK8) assay assessing the cell proliferation of theETV1-overexpressing PLC/PRF/5 cells and ETV1-knockdown MHCC97H cells. (C) Colony formation assay showing the proliferationof the indicated HCC cells. Therepresentative photos were shown and the cell numbers were quantified. (D-F) Tumorgrowth of the indicated HCC cells was assessed by subcutaneous xenograft tumormodels. The tumor volume and weight were shown in (D) and (E), the representative images of Ki67 were shownin (F). n = 5 in each group. (G) The correlation between ETV1 expression and PTK2 or MET expression inTCGA-LIHC and GEO database. *p < 0.05, ****p < 0.0001. Data were shown as Mean ± SD. Figure S2. ETV1 binding sites withinthe promoter regions of PTK2. Thesequences highlighted in yellow represent the three binding sites of ETV1 onthe PTK2 promoter, and the arrow represents the transcription initiation sites.The   mutagenesis of the promoter sequencewere annotated. Figure S3. ETV1binding sites within the promoter regions of MET. The sequenceshighlighted in yellow represent the four binding sites of ETV1 onthe MET promoter, and the arrow represents the transcription initiation sites.The mutagenesis of the promoter sequence were annotated. Figure S4. (A-C) Western blotverifying PTK2 and MET knockdown effect in PLC/PRF/5-ETV1 cells and ELK1knockdown effect in PLC/PRF/5 cells. FigureS5. (A) The expression levels of MTDH, RHOA, TCF4 and MCL1 were determined in the indicated cells by real-time PCR. (B) Westernblotting assays of MTDH, RHOA, TCF4 and MCL1 in the indicated cells transfected with lentivirus. (C) The migratingand invasive capability of the indicated cells was determined via transwellassay. Figure S6. (A) The level of ETV1 in the PLC/PRF/5 cells upon HGFtreatment with/without ERK1/2 knockdown. Figure S7. Transcription factors binding siteswithin the promoter regions of ETV1. The sequences highlighted in blue represent the four binding sites ofELK1 on the ETV1 promoter. The yellow highlighted sequences representthe one binding site of ETS1 onthe ETV1 promoter. The sequenceshighlighted in grey represent the binding site of SP1 on the ETV1promoter. The red highlighted sequences represent the binding site of NF-ΚB1 onthe ETV1 promoter. The pink highlighted sequences represent the bindingsequence of STAT3 on the ETV1 promoter. The arrows representtranscription start sites. The mutagenesis of the promoter sequence wereannotated. Figure S8. (A)Representative IHC staining of ETV1 is shown. (B) Pearsoncorrelation analyses between ETV1 IHC score and the levels of serum HGF in HCCpatients. n=30. (C) The correlation between ETV1 expression and HGF expression inTCGA-LIHC and GEO database.(D) The correlation between ETV1expression and ELK1 expression in TCGA-LIHC and GEO database. Figure S9. Effectof ERK1/2 inhibitor on HGF-mediated HCC cell migration and invasion. (A)Transwell assays displayed the migratory and invasive capacity of theindicated cells upon HGF treatment. (B) Transwell assays displayed themigratory and invasive capacity of the indicated cells. Supplementary Table S1. List of genesdifferentially expressed in PLC/PRF/5-ETV1 versus PLC/PRF/5-Control cells usinga human liver cancer PCR array. Supplementary Table S2. List of genes differentially expressed in MHCC97H-shETV1 versusMHCC97H-shControl cells using a human liver cancer PCR array. Supplementary Table S3. Primer sequences usedin the study. Supplementary Table S4. Knockdown shRNA sequencesused in this study. Supplementary Table S5. Correlation between PTK2 expression and clinicopathologicalcharacteristics of HCCs in two independent cohorts of human HCC tissues. Supplementary Table S6. Correlationbetween c-MET expression and clinicopathological characteristics of HCCs in twoindependent cohorts of human HCC tissues