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To enhance the flattening quality and efficiency of plates and realize intelligent flattening, in this work, machine vision is utilized in place of manual recognition to realize accurate recognition of the three-dimensional (3D) contour of warped plates. Modeling research on the relevant processes of point cloud recognition, surface fitting, and flattening process is conducted. A structured light camera is utilized to obtain the point cloud data of the 3D contour of warped plates, and the recognition accuracy is found to meet the requirements of the flattening of the warped plate. Preprocessing of the point cloud data is conducted. The ground point cloud is segmented from the point cloud of the warped plate based on a shape model method. The inner outline of the ground point cloud is extracted, and afterward, the warped plate at any position is converted into a new pose, with the plate center serving as the origin of the camera coordinate system and the plate edge being parallel to the coordinate axis. It provides a foundation for acquiring the warped plate distribution in the coordinate system of the flattening machine, but it also facilitates subsequent point cloud denoising, which is conducted using conditional filters and distance-based techniques. Finally, the point cloud is simplified to enhance the computational efficiency using the principle of sparse sampling points. The 3D surface of the warped plate is reconstructed using the least-squares method. The curvature of the warped plate is computed using the surface theory in differential geometry, and the positions of the warped plate’s pad and pressing points are optimized based on the 3D surface features. Based on the three-point bending leveling theory, the screw-down force and displacement of warped plate are obtained. In comparison to the conventional modeling research of the flattening process based on the two-dimensional warping contour, we establish a reconstruction method of the warped plate’ 3D contour and a curvature model and determine the optimization principles for the positions of the pad plate and pressing point thereby providing theoretical models for intelligent flattening of plates. A finite element model of the flattening of the warped plate demonstrates that the deviation between the simulation and theoretical results for the flattening force is less than 5.4%, and the residual warping height is about 1 mm, indicating the reliability of the theoretical model. The deviation between the simulation and theoretical screw-down force is about 2.21%, and the unevenness decreased from 17.2 to 3.28 mm·m−1, demonstrating that the models and methods are relatively accurate and feasible.

ABSTRACTStroke is the second leading cause of death and a major cause of disability worldwide. Ischemic stroke accounts for approximately >60% of all stroke, and >80% of strokes can be prevented. Middle cerebral artery occlusion (MCAO) is a common cause of stroke in humans. MCAO is associated with blood vessel-related problems, such as reduced vascular plasticity and hypertension. These problems can be attenuated by the use of nutraceuticals such as laminarin. Laminarin is a storage glucan commonly found in brown algae. Accumulating evidence and studies have revealed that laminarin is a promising candidate drug for treating ischaemic stroke. However, details on pharmaceutical targets and molecular mechanisms underlying laminarin’s beneficial effects for treating ischaemic stroke remains largely unknown. Herein, we applied network pharmacology, bioinformatic analysis, and middle cerebral artery occlusion model to delineate the protective role of laminarin. By comparing the laminarin- and MCAO-associated genes, we identified 23 potential targets including tumour necrosis factor (TNF), vascular endothelial growth factor A, selectin P, presenilin 1, fibroblast growth factor 2, microtubule-associated protein tau, caspase 3, matrix metallopeptidase 1, 5-hydroxytryptamine receptor 2A, telomerase reverse transcriptase, interleukin 2, signal transducer and activator of transcription 3 (STAT3), ATP binding cassette subfamily B member 1, catalase, superoxide dismutase 2, adenosine A1 receptor, adenosine A2a receptor, 5-hydroxytryptamine receptor 1B, heat shock protein 90 alpha family class A member 1, matrix metallopeptidase 8, BCL2-like 1, galectin 3, and epoxide hydrolase 2 of laminarin against ischaemic stroke. The gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes enrichment analyses highlighted the importance of this gene cluster in the development of blood vessels, neuronal cell death, brain functions, and neuroinflammation. Furthermore, molecular docking analysis suggested a direct binding of laminarin to its target proteins STAT3 and TNF. Our results provide the pharmaceutical targets and delineate the details regarding the molecular mechanisms underlying the beneficial effects of laminarin against ischaemic stroke. Moreover, our findings support those of previous studies suggesting laminarin as a promising drug for treating ischaemic stroke.

Abstract The stomata on the leaf surface are mainly responsible for the material exchange between the internal and external environments of the plant, a large number of methods have been proposed to automatically measure the distribution position and number of stomatal, but few methods could achieve both stomatal count and open/closed‐state judgment. Therefore, this study proposes an automatic detection method for leaf stomatal morphology analysis based on an attention mechanism and deep learning. In order to obtain more stomatal feature information and send it to the network for learning, the proposed method adds a coordinate attention (CA) mechanism to the YOLOV5 backbone part. At the same time, in order to avoid the overfitting of the model during the training process, the authors added the training trick of label smoothing. Finally, the detection ability of the proposed method for stomata is verified on the broad bean leaves stomata dataset. The experimental results show that our method achieves a detection accuracy of 0.934 and an mAP of 0.968. By comparing with other state‐of‐the‐art algorithms, the detection capability of our method has been significantly improved. The generalization of the model is verified on the wheat leaf stomatal dataset. The experimental results show that our method can achieve a detection accuracy of 0.894 and an mAP of 0.907.

Diabetic retinopathy (DR), the most common microvascular complication of diabetes and leading cause of visual impairment in adults worldwide, is suggested to be linked to abnormal lipid metabolism. The present study aims to comprehensively investigate the relationship between n-6 polyunsaturated fatty acids (PUFAs) and DR. This was a propensity score matching based case–control study, including 69 pairs of DR patients and type 2 diabetic patients without DR with mean age of 56.7 ± 9.2 years. Five n-6 PUFAs were determined by UPLC-ESI-MS/MS system. Principle component regression (PCR) and multiple conditional logistic regression models were used to investigate the association of DR risk with n-6 PUFAs depending on independent training and testing sets, respectively. According to locally weighted regression model, we observed obvious negative correlation between levels of five n-6 PUFAs (linoleic acid, γ-linolenic acid, eicosadienoic acid, dihomo-γ- linolenic acid and arachidonicacid) and DR. Based on multiple PCR model, we also observed significant negative association between the five n-6 PUFAs and DR with adjusted OR (95% CI) as 0.62 (0.43,0.87). When being evaluated depending on the testing set, the association was still existed, and PCR model had excellent classification performance, in which area under the curve (AUC) was 0.88 (95% CI: 0.78, 0.99). In additio n, the model also had valid calibration with a non-significant Hosmer–Lemeshow Chi-square of 9.44 (P = 0.307) in the testing set. n-6 PUFAs were inversely associated with the presence of DR, and the principle component could be potential indicator in distinguishing DR from other T2D patients.

Introduction Despite advances in diabetic retinopathy (DR) medications, early identification is vitally important for DR administration and remains a major challenge. This study aims to develop a novel system of multidimensional network biomarkers (MDNBs) based on a widely targeted metabolomics approach to detect DR among patients with type 2 diabetes mellitus (T2DM) efficiently.Research design and methods In this propensity score matching-based case-control study, we used ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry system for serum metabolites assessment of 69 pairs of patients with T2DM with DR (cases) and without DR (controls). Comprehensive analysis, including principal component analysis, orthogonal partial least squares discriminant analysis, generalized linear regression models and a 1000-times permutation test on metabolomics characteristics were conducted to detect candidate MDNBs depending on the discovery set. Receiver operating characteristic analysis was applied for the validation of capability and feasibility of MDNBs based on a separate validation set.Results We detected 613 features (318 in positive and 295 in negative ESI modes) in which 63 metabolites were highly relevant to the presence of DR. A panel of MDNBs containing linoleic acid, nicotinuric acid, ornithine and phenylacetylglutamine was determined based on the discovery set. Depending on the separate validation set, the area under the curve (95% CI), sensitivity and specificity of this MDNBs system were 0.92 (0.84 to 1.0), 96% and 78%, respectively.Conclusions This study demonstrates that metabolomics-based MDNBs are associated with the presence of DR and capable of distinguishing DR from T2DM efficiently. Our data also provide new insights into the mechanisms of DR and the potential value for new treatment targets development. Additional studies are needed to confirm our findings.

Objective To investigate the validity and feasibility of a self-administered home vision examination programme in China.Design Cross-sectional study.Setting Yueqing, China.Participants A two-stage convenience sampling procedure was used to randomly select 600 households from 30 communities participating in the Yueqing Eye Study (YES). The aim of YES is to encourage home-based vision screening, reporting of visual acuity (VA) annually through social media and encouraging people to attend follow-up clinic appointments as a way to improve eye care access for adults with VA ≤+0.5 log of the minimum angle of resolution (logMAR).Interventions Household screeners (one per household) who tested other family members’ VA completed a questionnaire on family structure, demographic information and knowledge about screening procedures. Other family members then underwent confirmatory VA testing by researchers.Outcome measures The completion rate of home-based VA screening, its sensitivity and specificity were used to evaluate validity. Factors that determined whether families participated in the self-VA screening were used to evaluate feasibility.Results 345 (66%) of the 523 (87.2%) households with valid data form their home-based vision examinations also were retested by researchers. There was no statistically significant difference in scores on the family-administerd or researcher-administerd VA test (VA≤+0.5 logMAR, p=0.607; VA >+0.5 logMAR, p=0.612). The sensitivity and specificity of home-based vision screening were 80.5% (95% CI 70.2% to 86.9%) and 95.1% (95% CI 92.6% to 96.8%), respectively. 14.7% (77/523) of tested respondents had VA ≤+0.5 logMAR. Predictors of performing home screening for VA remaining in regression models included higher economic status (‘fair and above’ vs ‘poor’: OR 1.74; 95% CI 1.08 to 2.76; p=0.022), age (

Background and Objectives. Diabetic kidney disease is a leading cause of chronic kidney disease and end-stage renal disease across the world. Early identification of DKD is vitally important for the effective prevention and control of it. However, the available indicators are doubtful in the early diagnosis of DKD. This study is aimed at determining novel sensitive and specific biomarkers to distinguish DKD from their counterparts effectively based on the widely targeted metabolomics approach. Materials and Method. This case-control study involved 44 T2DM patients. Among them, 24 participants with DKD were defined as the cases and another 20 without DKD were defined as the controls. The ultraperformance liquid chromatography-electrospray ionization-tandem mass spectrometry system was applied for the assessment of the serum metabolic profiles. Comprehensive analysis of metabolomics characteristics was conducted to detect the candidate metabolic biomarkers and assess their capability and feasibility. Result. A total of 11 differential metabolites, including Hexadecanoic Acid (C16:0), Linolelaidic Acid (C18:2N6T), Linoleic Acid (C18:2N6C), Trans-4-Hydroxy-L-Proline, 6-Aminocaproic Acid, L-Dihydroorotic Acid, 6-Methylmercaptopurine, Piperidine, Azoxystrobin Acid, Lysopc 20:4, and Cuminaldehyde, were determined as the potential biomarkers for the DKD early identification, based on the multivariable generalized linear regression model and receiver operating characteristic analysis. Conclusion. Serum metabolites might act as sensitive and specific biomarkers for DKD early detection. Further longitudinal studies are needed to confirm our findings.

Objectives We investigated the association of specific serum amino acids (AAs) with the odds of arsenic-induced skin lesions (AISL) and their ability to distinguish patients with AISL from people chronically exposed to arsenic.Design Case–control study.Setting Three arsenic-exposed villages in Wuyuan County, Hetao Plain, Inner Mongolia, China were evaluated.Participants Among the 450 residents aged 18–79 years, who were chronically exposed to arsenic via drinking water, 56 were diagnosed as having AISL (defined as cases). Another 56 participants without AISL, matched by gender and age (±1 year) from the same population, were examined as controls.Main outcome measures and methods AA levels were determined by ultra-high-performance liquid chromatography–quadrupole time-of-flight mass spectrometry-based metabolomics analysis. Potential confounding variables were identified via a standardised questionnaire and clinical examination. Multivariable conditional logistic regression model and receiver operating characteristic curve analyses were performed to investigate the relationship between specific AAs and AISL.Results Tryptophan and phenylalanine levels were negatively associated with AISL (p

In this article, a method for two-dimensional scanning path planning based on robot is proposed. In this method, a section division algorithm based on neighborhood search method for scanning orientation determination is firstly produced. The scanning paths which meet constraints of the system are then generated. Finally, the experiment is carried out on robot-based scanning platform. The two-dimensional data from scanner and the robot position are combined to form three-dimensional surface data of measured workpiece. The experiment results verify the effectiveness of proposed method.

For the traditional reciprocating pumping unit, it generally starts at the bottom dead point with full load and is accelerated when starting and decelerated when stopping, which not only requires great energy from the motor but also impact on the output torque of motor. In order to tackle the above problems, we propose an energy-saving smooth reversing pumping system, which could store the energy in deceleration by making use of springs, and the stored energy could be reused in acceleration after reversion. This novel system targets to make the motor starting with light load, which could reduce the fluctuation in motor torque, decrease the starting time. In this article, a dynamic model and an efficiency model are established to compute the polished rod load dynamometer card and efficiency of the pumping unit. It is shown that the installed springs could help to reduce the impact on the system when reversing, and significantly decrease the energy consumption. In addition, we set up the real pumping system with load test system to verify the effectiveness of the model establishment. Through the experiment, the proposed pumping system could save energy up to 9.204% as compared to the traditional reciprocating pumping system.

Abstract Background Mucosal squamous cell carcinoma of the head and neck is a disease of high mortality and morbidity. Interactions between the squamous cell carcinoma and the host's local immunity, and how the latter contributes to the biological behavior of the tumor are unclear. In vivo studies have demonstrated sequential mast cell infiltration and degranulation during squamous cell carcinogenesis. The degree of mast cell activation correlates closely with distinct phases of hyperkeratosis, dysplasia, carcinoma in-situ and invasive carcinoma. However, the role of mast cells in carcinogenesis is unclear. Aim This study explores the effects of mast cells on the proliferation and gene expression profile of mucosal squamous cell carcinoma using human mast cell line (HMC-1) and human glossal squamous cell carcinoma cell line (SCC25). Methods HMC-1 and SCC25 were co-cultured in a two-compartment chamber, separated by a polycarbonate membrane. HMC-1 was stimulated to degranulate with calcium ionophore A23187. The experiments were done in quadruplicate. Negative controls were established where SCC25 were cultured alone without HMC-1. At 12, 24, 48 and 72 hours, proliferation and viability of SCC25 were assessed with MTT colorimetric assay. cDNA microarray was employed to study differential gene expression between co-cultured and control SCC25. Results HMC-1/SCC25 co-culture resulted in suppression of growth rate for SCC-25 (34% compared with 110% for the control by 72 hours, p < 0.001), and dysregulation of genes TRAIL, BIRC4, CDK6, Cyclin G2 and CDC6 in SCC25. Conclusion We show that mast cells have a direct inhibitory effect on the proliferation of mucosal squamous cell carcinoma in vitro by dysregulating key genes in apoptosis and cell cycle control.

Ferroptosis is a novel form of cell death characterized by unlimited accumulation of iron-dependent lipid peroxides. It is often accompanied by disease, and the relationship between ferroptosis of immune cells and immune regulation has been attracting increasing attention. Initially, it was found in cancer research that the inhibition of regulatory T cell (Treg) ferroptosis and the promotion of CD8+ T cell ferroptosis jointly promoted the formation of an immune-tolerant environment in tumors. T-cell ferroptosis has subsequently been found to have immunoregulatory effects in other diseases. As an autoimmune disease characterized by immune imbalance, T-cell ferroptosis has attracted attention for its potential in regulating immune balance in lupus nephritis. This article reviews the metabolic processes within different T-cell subsets in lupus nephritis (LN), including T follicular helper (TFH) cells, T helper (Th)17 cells, Th1 cells, Th2 cells, and Treg cells, and reveals that these cellular metabolisms not only facilitate the formation of a T-cell immune imbalance but are also closely associated with the occurrence of ferroptosis. Consequently, we hypothesize that targeting the metabolic pathways of ferroptosis could become a novel research direction for effectively treating the immune imbalance in lupus nephritis by altering T-cell differentiation and the incidence of ferroptosis. [ABSTRACT FROM AUTHOR]

A novel C3N5-poly(triazine imide) (PTI) heterojunction was designed and constructed using a thermal polymerization process, and featured an intimate S-scheme interface coupling and a particularly good performance for H2O2 production. This work provides a new perspective for constructing metal-free C3N5-based heterojunctions to be used for selective molecular oxygen activation. [ABSTRACT FROM AUTHOR]