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ObjectiveThyroid hormones have actions on cartilage, whereas the association between thyroid hormone related diseases and osteoarthritis (OA) are unclear. This study aims to investigate the association between thyrotoxicosis and OA.MethodsSummary-level genetic data of thyrotoxicosis were obtained from FinnGen cohorts (nCase = 10,569, nControl = 762,037). Summary-level data of OA were obtained from a large-scale genome-wide association study of UK Biobank (nCase = 40,659, nControl = 756,338). Single nucleotide polymorphisms (SNPs) robustly associated with thyrotoxicosis or OA were used as genetic instruments. A two-sample bidirectional Mendelian randomization (MR) analysis was designed to assess the effect of genetic predisposition of thyrotoxicosis on OA risk, as well as the reverse their relationship. The causal effect was estimated by Inverse-variance weighted method, with weighted median and MR-Egger as supplementary methods.ResultsGenetic predisposition of thyrotoxicosis was associated with the onset of knee OA (autoimmune hyperthyroidism: odds ratio [OR]: 1.05, 95% confidence interval [CI]: 1.03-1.07, FDR < 0.001; thyrotoxicosis: OR: 1.05, 95% CI: 1.02-1.08, FDR = 0.016; thyrotoxicosis with diffuse goitre: OR: 1.04, 95% CI: 1.02-1.07, FDR = 0.003; other and/or unspecified thyrotoxicosis: OR: 1.05, 95% CI: 1.02-1.09, FDR = 0.003), whereas thyrotoxicosis was not associated with hip OA. In reverse MR analysis, genetic predisposition to OA was not associated with thyrotoxicosis. No pleiotropy was identified in the MR analyses. Sensitivity analyses indicated the robustness of the MR estimates.ConclusionThis study provides MR evidence supporting causal association of thyrotoxicosis with knee OA in European population, whereas OA may have no causal effects on thyrotoxicosis.

Abstract Background Emerging evidence has indicated the associations between subacromial impingement syndrome (SIS) of shoulder and lifestyle factors. However, whether unhealthy lifestyle factors causally increase SIS risk is not determined. This study aims to evaluate whether lifestyle factors are the risk factors of SIS. Methods A two-sample Mendelian randomization (MR) study was designed to evaluate the effect of 11 lifestyle factors on SIS risk. Causality was determined using the inverse-variance weighted method to calculate the odds ratio (OR) and establish a 95% confidence interval (CI). Weighted median method, MR-Egger method and MR-PRESSO method were conducted as sensitivity analysis. Results Four lifestyle factors were identified causally associated with an increased risk of SIS using the IVW method: insomnia (OR: 1.66 95% CI 1.38, 2.00; P = 8.86 × 10− 8), short sleep duration (OR: 1.53 95% CI 1.14, 2.05: P = 0.0043), mobile phone usage (OR: 4.65, 95% CI 1.59, 13.64; P = 0.0051), and heavy manual or physical work (OR: 4.24, 95% CI 2.17, 8.26; P = 2.20 × 10− 5). Another causal but weak association was found between smoking initiation on SIS (OR: 1.17, 95% CI 1.01, 1.35; P = 3.50 × 10− 2). Alcohol, coffee consumption, physical activity, sedentary behavior, sleep duration and computer usage were not found to be causally associated with an increased risk of SIS. Sensitivity analyses indicated that the MR estimates were robust and no heterogeneity and pleiotropy were identified in these MR analyses. Conclusion Sleep habits and shoulder usage were identified as causal factors for SIS. This evidence supports the development of strategies aimed at improving sleep behaviors and optimizing shoulder usage patterns as effective measures to prevent SIS.

Abstract Due to a rapidly aging global population, osteoporosis and the associated risk of bone fractures have become a wide-spread public health problem. However, osteoporosis is very heterogeneous, and the existing standard diagnostic measure is not sufficient to accurately identify all patients at risk of osteoporotic fractures and to guide therapy. Here, we constructed the first prospective multi-omics atlas of the largest osteoporosis cohort to date (longitudinal data from 366 participants at three time points), and also implemented an explainable data-intensive analysis framework (DLSF: Deep Latent Space Fusion) for an omnigenic model based on a multi-modal approach that can capture the multi-modal molecular signatures (M3S) as explicit functional representations of hidden genotypes. Accordingly, through DLSF, we identified two subtypes of the osteoporosis population in Chinese individuals with corresponding molecular phenotypes, i.e., clinical intervention relevant subtypes (CISs), in which bone mineral density benefits response to calcium supplements in 2-year follow-up samples. Many snpGenes associated with these molecular phenotypes reveal diverse candidate biological mechanisms underlying osteoporosis, with xQTL preferences of osteoporosis and its subtypes indicating an omnigenic effect on different biological domains. Finally, these two subtypes were found to have different relevance to prior fracture and different fracture risk according to 4-year follow-up data. Thus, in clinical application, M3S could help us further develop improved diagnostic and treatment strategies for osteoporosis and identify a new composite index for fracture prediction, which were remarkably validated in an independent cohort (166 participants).

ObjectiveTo improve the understanding, diagnosis and treatment of bladder large cell neuroendocrine carcinoma (LCNEC).MethodsA clinical case of bladder LCNEC admitted to our hospital was reported. The epidemiology, prognosis, diagnosis and treatment methods of large cell neuroendocrine carcinoma were reviewed. The diagnosis and treatment status and prognosis were discussed based on the literature.ResultsThe female patient was admitted to hospital for “more than 4 years after TURBT and intermittent hematuria for more than 2 years”. She was diagnosed as recurrent bladder cancer and underwent “radical cystotomy + hysterectomy”. The postoperative pathological findings were high-grade urothelial carcinoma of the bladder neck and large cell neuroendocrine carcinoma of the bladder. The patient recovered well after surgery, but refused radiotherapy and chemotherapy and is still under close follow-up.ConclusionBladder LCNEC is clinically rare, has unique pathological features, is more aggressive than traditional urothelial carcinoma, and has a poor prognosis. Surgery, chemotherapy and radiotherapy should be combined with multi-mode treatment.

With the increasing rise of the Internet, as well as the democratization of technology, people’s pursuit of entertainment and visual stimulation inspired countless new types of creativity. Social media fanatics have surged a myriad of content-creating works that brought this creativity to the forefront; this phenomenon has transformed everyday spaces into settings or backdrops for new narratives. The everyday domesticity we inhabit, such as bodies, activities, objects, furniture, and rooms, have all been involved in the process of creation and work, and these spaces can be described as Everyday Theater. The term scenography is of Greek origin, skēnē, meaning hut or tent, in the theater of ancient Greece, it is a light structure usually at the back of the stage, but over the course of time skēnē underwent fundamental change. The core concept of scenography is the narrative. This core remains even though it is now given a new meaning, as everyday life has become a stage for a new kind of spectacle. In other words, the conventional domestic spaces are re-invented as a setting for new personal narratives and commercial venues. In this context, the domestic spaces have been transformed into a platform, where the performers use their surrounding elements, objects, spaces, homes, and environments as settings for their own narrative. The goal is to create an immersive experience that captures the audience’s attention and transports them into a world that is both familiar and extraordinary.

Abstract Core genome multilocus sequence typing (cgMLST) is commonly used to classify bacterial strains into different types, for taxonomical and epidemiological applications. However, cgMLST schemes require central databases for the nomenclature of new alleles and sequence types, which must be synchronized worldwide and involve increasingly intensive calculation and storage demands. Here, we describe a distributed cgMLST (dcgMLST) scheme that does not require a central database of allelic sequences and apply it to study evolutionary patterns of epidemic and endemic strains of the genus Neisseria. We classify 69,994 worldwide Neisseria strains into multi-level clusters that assign species, lineages, and local disease outbreaks. We divide Neisseria meningitidis into 168 endemic lineages and three epidemic lineages responsible for at least 9 epidemics in the past century. According to our analyses, the epidemic and endemic lineages experienced very different population dynamics in the past 100 years. Epidemic lineages repetitively emerged from endemic lineages, disseminated worldwide, and apparently disappeared rapidly afterward. We propose a stepwise model for the evolutionary trajectory of epidemic lineages in Neisseria, and expect that the development of similar dcgMLST schemes will facilitate epidemiological studies of other bacterial pathogens.

Abstract Osteoarthritis (OA) is a common degenerative joint disease urgently needing effective treatments. Bone marrow mesenchymal stromal cell-derived exosomes (Exo) are considered good drug carriers whereas they have limitations such as fast clearance and low retention. This study aimed to overcome the limitations of Exo in drug delivery using multiple strategies. Novel photocrosslinking spherical gelatin methacryloyl hydrogel (GelMA)-encapsulated cartilage affinity WYRGRL (W) peptide-modified engineered Exo were developed for OA treatment and the performance of the engineered Exo (W-Exo@GelMA) loaded with a small inhibitor LRRK2-IN-1 (W-Exo-L@GelMA) was investigated in vitro and in vivo. The W-Exo-L@GelMA showed an effective targeting effect on chondrocytes and a pronounced action on suppressing catabolism and promoting anabolism in vitro. Moreover, W-Exo-L@GelMA remarkably inhibited OA-related inflammation and immune gene expression, rescuing the IL-1β-induced transcriptomic responses. With enhanced retention in the joint, W-Exo-L@GelMA demonstrated superior anti-OA activity and cartilage repair ability in the OA murine model. The therapeutic effect was validated in the cultured human OA cartilage. In conclusion, photocrosslinking spherical hydrogel-encapsulated targeting peptide-modified engineered Exo exhibit notable potential in OA therapy. Engineering Exo by a series of strategies enhanced the targeting ability and retention and cartilage-targeting and Exo-mediated drug delivery may offer a novel strategy for OA treatment. Clinical trial registration: Not applciable. Graphical Abstract

Based on the previous research on the energy absorption of foam metal materials with different structures, a composite blast-resistant energy-absorbing material with a flexible core layer was designed. The material is composed of three different fiber materials (carbon fiber, aramid fiber, and glass fiber) as the core layer and foamed iron–nickel metal as the front and rear panels. The energy absorption characteristics were tested using a self-built gas explosion tube network experimental platform, and the energy absorption effects of different combinations of blast-resistant materials were analyzed. The purpose of this paper is to evaluate the performance of blast-resistant materials designed with flexible fiber core layers. The experimental results show that the composite structure blast-resistant material with a flexible core layer has higher energy absorption performance. The work performed in this paper shows that the use of flexible core layer materials has great research potential and engineering research value for improving energy absorption performance, reducing the mass of blast-resistant materials, and reducing production costs. It also provides thoughts for the research of biomimetic energy-absorbing materials.

This paper focuses on a heterogeneous redundant inertial navigation system (INS) that consists of one three-axis rotation-modulation fiber optic gyroscope (FOG) INS and one strapdown ring laser gyroscope (RLG) INS. A novel co-calibration method is proposed to estimate the FOG scale factor instability and RLG bias instability error in real time, in which the geometric constraints of the FOG-INS rotating axis are deployed to establish the observation equation without external reference information. In addition, the proposed method has a global navigation capability, which is achieved by using the normal vector of the earth ellipsoid and the state transformation extended Kalman filter to predict positioning errors. An output position error compensation method is used without disturbing the original INSs. Simulation and physical experiment results show that the positioning error of the heterogeneous redundant marine INS is reduced by more than 30% over 300 h of navigation.

Rapid non-destructive testing technologies are effectively used to analyze and evaluate the linoleic acid content while processing fresh meat products. In current study, hyperspectral imaging (HSI) technology was combined with deep learning optimization algorithm to model and analyze the linoleic acid content in 252 mixed red meat samples. A comparative study was conducted by experimenting mixed sample data preprocessing methods and feature wavelength extraction methods depending on the distribution of linoleic acid content. Initially, convolutional neural network Bi-directional long short-term memory (CNN-Bi-LSTM) model was constructed to reduce the loss of the fully connected layer extracted feature information and optimize the prediction effect. In addition, the prediction process of overfitting phenomenon in the CNN-Bi-LSTM model was also targeted. The Bayesian-CNN-Bi-LSTM (Bayes-CNN-Bi-LSTM) model was proposed to improve the linoleic acid prediction in red meat through iterative optimization of Gaussian process acceleration function. Results showed that best preprocessing effect was achieved by using the detrending algorithm, while 11 feature wavelengths extracted by variable combination population analysis (VCPA) method effectively contained characteristic group information of linoleic acid. The Bi-directional LSTM (Bi-LSTM) model combined with the feature extraction data set of VCPA method predicted 0.860 Rp2 value of linoleic acid content in red meat. The CNN-Bi-LSTM model achieved an Rp2 of 0.889, and the optimized Bayes-CNN-Bi-LSTM model was constructed to achieve the best prediction with an Rp2 of 0.909. This study provided a reference for the rapid synchronous detection of mixed sample indicators, and a theoretical basis for the development of hyperspectral on-line detection equipment.

Aims: Osteoarthritis (OA) is a common degenerative joint disease. The osteocyte transcriptome is highly relevant to osteocyte biology. This study aimed to explore the osteocyte transcriptome in subchondral bone affected by OA. Methods: Gene expression profiles of OA subchondral bone were used to identify disease-relevant genes and signalling pathways. RNA-sequencing data of a bone loading model were used to identify the loading-responsive gene set. Weighted gene co-expression network analysis (WGCNA) was employed to develop the osteocyte mechanics-responsive gene signature. Results: A group of 77 persistent genes that are highly relevant to extracellular matrix (ECM) biology and bone remodelling signalling were identified in OA subchondral lesions. A loading responsive gene set, including 446 principal genes, was highly enriched in OA medial tibial plateaus compared to lateral tibial plateaus. Of this gene set, a total of 223 genes were identified as the main contributors that were strongly associated with osteocyte functions and signalling pathways, such as ECM modelling, axon guidance, Hippo, Wnt, and transforming growth factor beta (TGF-β) signalling pathways. We limited the loading-responsive genes obtained via the osteocyte transcriptome signature to identify a subgroup of genes that are highly relevant to osteocytes, as the mechanics-responsive osteocyte signature in OA. Based on WGCNA, we found that this signature was highly co-expressed and identified three clusters, including early, late, and persistently responsive genes. Conclusion: In this study, we identified the mechanics-responsive osteocyte signature in OA-lesioned subchondral bone. Cite this article: Bone Joint Res 2022;11(6):362–370.

With the development of urbanization, the demand for bricks continues to increase. However, traditional brick production methods result in significant energy consumption and environmental pollution. In Hebei Province, China, historical mineral extraction activities have left behind substantial iron ore tailings (IOT). With the objective of recycling IOT resources and promoting ecological restoration and sustainable development, the feasibility of producing alkali-activated bricks using iron ore tailings was explored. This study primarily utilized IOT supplemented with ground granulated blast-furnace slag (GGBS) to successfully synthesize sustainable eco-friendly alkali-activated high-content IOT bricks. Experimental investigations were conducted to explore the effects of the raw material mixing ratio, content, and modulus of the alkaline activator, molding pressure, and grain size distribution on the strength. The research demonstrated that the IOT: GGBS ratio of 85:15 met the requirements of the Chinese JC/T422-2007 MU25 standard, resulting in compressive and flexural strengths of 31.72 MPa and 2.83 MPa, respectively. Increasing the alkali activator content enhanced the brick strength, with an optimal alkali activator modulus of 1 M. Moreover, the molding pressure significantly improved brick strength and also enhanced the particle-to-particle contact density. Bricks prepared using finer particle size IOT exhibited higher compressive strength, whereas flexural strength remained relatively unaffected by particle size distribution. Furthermore, a comprehensive analysis of the microstructure and alkali activation mechanism of IOT-GGBS bricks was performed using XRD, SEM, FTIR, and AFM techniques. The results indicated that IOT primarily acted as an aggregate and partially participated in the reaction, whereas GGBS reacted extensively, generating C-S-H gel and C-A-H, providing robust bonding strength. Additionally, the increase in GGBS content led to the partial disintegration of some IOT particles, forming more stable aggregates under the influence of the C-S-H gel. This study offers theoretical guidance for the efficient utilization of IOT in construction materials, thereby contributing to the promotion of sustainable development and environmental conservation.

ObjectiveHyperuricemia and gout have become gradually more common. The effect of serum urate on organism aging and systematic inflammation is not determined. This study aims to evaluate whether serum urate is causally associated with cellular aging markers and serum inflammation markers.MethodsA Mendelian randomization study was performed on summary-level data from the largest published genome-wide association studies. Single nucleotide polymorphisms with a genome-wide significance level were selected as instrumental variables for leukocyte telomere length (LTL), and serum soluble makers of inflammation (CRP, IL-6, TNF-α, and IGF-1). Standard inverse variance weighted (IVW) method was used as the primary statistical method. The weighted median, MR-Egger regression, and MR-PRESSO methods were used for sensitivity analysis.ResultsAn inverse causal association of genetically predicted serum urate levels and LTL was found using IVW method (OR: 0.96, 95%CI 0.95, 0.97; β=-0.040; SE=0.0072; P=4.37×10-8). The association was also supported by MR results using MR-Egger method and weighted median method. The MR-PRESSO analysis and leave-one-out sensitivity analysis supported the robustness of the combined results. In terms of other aging-related serum biomarkers, there was no evidence supporting a causal effect of serum urate on CRP, IL-6, TNF-α, or IGF-1 levels.ConclusionsSerum urate levels are negatively associated with telomere length but are not associated with serum soluble indicators of inflammation. Telomere length may be a critical marker that reflects urate-related organismal aging and may be a mechanism in the age-related pathologies and mortality caused by hyperuricemia.

ObjectiveConstraint-induced movement therapy (CIMT) is a common treatment for upper extremity motor dysfunction after a stroke. However, whether it can effectively improve lower extremity motor function in stroke patients remains controversial. This systematic review comprehensively studies the current evidence and evaluates the effectiveness of CIMT in the treatment of post-stroke lower extremity motor dysfunction.MethodsWe comprehensively searched randomized controlled trials related to this study in eight electronic databases (PubMed, Embase, The Cochrane Library, Web of Science, CBM, CNKI, WAN FANG, and VIP). We evaluated CIMT effectiveness against post-stroke lower extremity motor dysfunction based on the mean difference and corresponding 95% confidence interval (95% CI). We assessed methodological quality based on the Cochrane Bias Risk Assessment Tool. After extracting the general information, mean, and standard deviation of the included studies, we conducted a meta-analysis using RevMan 5.3 and Stata 16.0. The primary indicator was the Fugl-Meyer Assessment scale on lower limbs (FMA-L). The secondary indicators were the Berg balance scale (BBS), 10-meter walk test (10MWT), gait speed (GS), 6-min walk test (6MWT), functional ambulation category scale (FAC), timed up and go test (TUGT), Brunnstrom stage of lower limb function, weight-bearing, modified Barthel index (MBI), functional independence measure (FIM), stroke-specific quality of life questionnaire (SSQOL), World Health Organization quality of life assessment (WHOQOL), and National Institute of Health stroke scale (NIHSS).ResultsWe initially identified 343 relevant studies. Among them, 34 (totaling 2,008 patients) met the inclusion criteria. We found that patients treated with CIMT had significantly better primary indicator (FMA-L) scores than those not treated with CIMT. The mean differences were 3.46 (95% CI 2.74–4.17, P < 0.01, I2 = 40%) between CIMT-treated and conventional physiotherapy-treated patients, 3.83 (95% CI 2.89–4.77, P < 0.01, I2 = 54%) between patients treated with CIMT plus conventional physiotherapy and patients treated only with conventional physiotherapy, and 3.50 (95% CI 1.08–5.92, P < 0.01) between patients treated with CIMT plus western medicine therapy and those treated only with western medicine therapy. The secondary indicators followed the same trend. The subgroup analysis showed that lower extremity CIMT with device seemed to yield a higher mean difference in FMA-L scores than lower extremity CIMT without device (4.52, 95% CI = 3.65–5.38, P < 0.01 and 3.37, 95% CI = 2.95–3.79, P < 0.01, respectively).ConclusionCIMT effectively improves lower extremity motor dysfunction in post-stroke patients; however, the eligible studies were highly heterogeneous.Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=277466.

Exercise is indispensable for maintaining cartilage integrity in healthy joints and remains a recommendation for knee osteoarthritis. Although the effects of exercise on cartilage have been implied, the detailed mechanisms, such as the effect of exercise time which is important for exercise prescription, remain elusive. In this study, bioinformatic analyses, including unbiased comparisons and modularization, were performed on the transcriptomic data of rat cartilage to identify the time-related genes and signaling pathways. We found that exercise had a notable effect on cartilage transcriptome. Exercise prominently suppressed the genes related to cell division, hypertrophy, catabolism, inflammation, and immune response. The downregulated genes were more prominent and stable over time than the upregulated genes. Although exercise time did not prominently contribute to the effects of exercise, it was a factor related to a batch of cellular functions and signaling pathways, such as extracellular matrix (ECM) homeostasis and cellular response to growth factors and stress. Two clusters of genes, including early and late response genes, were identified according to the expression pattern over time. ECM organization, BMP signaling, and PI3K-Akt signaling were early responsive in the exercise duration. Moreover, time-related signaling pathways, such as inositol phosphate metabolism, nicotinate/nicotinamide metabolism, cell cycle, and Fc epsilon RI signaling pathway, were identified by unbiased mapping and polarization of the highly time-correlated genes. Immunohistochemistry staining showed that Egfr was a late response gene that increased on day 15 of exercise. This study elucidated time-related transcriptomic reprogramming induced by exercise in cartilage, advancing the understanding of cartilage homeostasis.

Osteoarthritis (OA) is a multifactorial disease that affects the entire joint, often resulting in severe pain, disability, psychological distress, and a lower quality of life. Patient self-management is emphasized in OA clinical recommendations. Currently, the clinical treatment of OA mainly focuses on pain relief and the improvement of joint function, with few options for regenerating degenerative cartilage or slowing the progression of OA. Therefore, we first reviewed the current treatment of OA, and then summarized the research advances of nanotechnology in OA treatment, including nano drug delivery systems for small molecule drugs, nucleic acids and proteins, nano-scaffolds for cartilage regeneration, and nanoparticle lubricants. Finally, we discussed the opportunities and potential challenges of nanotechnology in OA treatment.

A method of local anode effect prediction is proposed for the problem that it is difficult to detect the local anode effect in large aluminum reduction cell in real time. Firstly, a fuzzy classification of local anode effect prediction in terms of fuzziness level is proposed considering various working conditions of anode current in the region. Secondly, a current volatility detection method based on time-sliding window density is designed from the problem of uneven current distribution in the region, and the anode currents in the region are classified and tracked for prediction according to the different current volatility. Thirdly, an improved Gated Recurrent Unit (GRU) neural network structure is proposed to improve the prediction accuracy of fluctuating currents. Finally, simulation experiments are conducted based on actual data, and compared with Long Short-Term Memory (LSTM) and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), the proposed method has certain advantages in both prediction time, training time, and the mean absolute error (MAE) and mean square error (MSE), which verifies the effectiveness of the proposed method.

Allergic asthma is a stubborn chronic inflammatory disease, and is considered a co-result of various immune cells, especially mast cells, eosinophils and T lymphocytes. At present, the treatment methods of allergic asthma are limited and the side effects are obvious. Traditional Chinese medicine has been used to treat diseases for thousands of years in China. One such example is the treatment of allergic asthma, which take the characteristics of less adverse reactions and obvious curative effect. Tuo-Min-Ding-Chuan Decoction (TMDCD) is a traditional Chinese medicine compound for the treatment of allergic asthma optimized from Ma-Xing-Gan-Shi Decoction (MXGSD), which was put forward in Treatise on Febrile Diseases by Zhang Zhongjing in the Eastern Han Dynasty. The compound shows a significant clinical effect, but the mechanism of its influence on the immune system is still unclear. The purpose of this study was to observe whether TMDCD could alleviate the symptoms of ovalbumin (OVA) challenged allergic asthma mice, and to explore its immune regulatory mechanism, especially on mast cell (MC) degranulation. The results showed TMDCD could not only reduce the airway hyperresponsiveness (AHR), inflammatory cell infiltration and mucus secretion in the lung tissue of OVA challenged mice, but also decrease the levels of total IgE, OVA-specific IgE, histamine and LTC4 in serum. We found that TMDCD can downregulate the expression of Fractalkine, Tryptase ε, IL-25, CCL19, MCP-1, OX40L, Axl, CCL22, CD30, G-CSF, E-selectin, OPN, CCL5, P-selectin, Gas6, TSLP in OVA challenged mice serum by using mouse cytokines antibody array. It has been reported in some literatures that these differentially expressed proteins are related to the occurrence of allergic asthma, such as tryptase ε, MCP-1, CCL5, etc. can be released by MC. And the results of in vitro experiments showed that TMDCD inhibited the degranulation of RBL-2H3 cells stimulated by DNP-IgE/BSA. Taken together, we made the conclusion that TMDCD could reduce the infiltration of inflammatory cells in lung tissue and alleviate airway remodeling in mice with allergic asthma, showed the effects of anti-inflammatory and antiasthmatic. TMDCD could also reduce the levels of IgE, histamine, LTC4, Tryptase ε, and other MC related proteins in the serum of allergic asthma mice, and the in vitro experiments showed that TMDCD could inhibit IgE mediated degranulation and histamine release of RBL-2H3 cells, proved its anti allergic effect.

Alanine (Ala), as the most important free amino acid, plays a significant role in food taste characteristics and human health regulation. The feasibility of using near–infrared hyperspectral imaging (NIR–HSI) combined with two–dimensional correlation spectroscopy (2D–COS) analysis to predict beef Ala content quickly and nondestructively is first proposed in this study. With Ala content as the external disturbance condition, the sequence of chemical bond changes caused by synchronous and asynchronous correlation spectrum changes in 2D–COS was analyzed, and local sensitive variables closely related to Ala content were obtained. On this basis, the simplified linear, nonlinear, and artificial neural network models developed by the weighted coefficient based on the feature wavelength extraction method were compared. The results show that with the change in Ala content in beef, the double-frequency absorption of the C-H bond of CH2 in the chemical bond sequence occurred prior to the third vibration of the C=O bond and the first stretching of O-H in COOH. Furthermore, the wavelength within the 1136–1478 nm spectrum range was obtained as the local study area of Ala content. The linear partial least squares regression (PLSR) model based on effective wavelengths was selected by competitive adaptive reweighted sampling (CARS) from 2D–COS analysis, and provided excellent results (R2C of 0.8141, R2P of 0.8458, and RPDp of 2.54). Finally, the visual distribution of Ala content in beef was produced by the optimal simplified combination model. The results show that 2D–COS combined with NIR–HSI could be used as an effective method to monitor Ala content in beef.

The Rat Sarcoma (RAS) family (NRAS, HRAS, and KRAS) is endowed with GTPase activity to regulate various signaling pathways in ubiquitous animal cells. As proto-oncogenes, RAS mutations can maintain activation, leading to the growth and proliferation of abnormal cells and the development of a variety of human cancers. For the fight against tumors, the discovery of RAS-targeted drugs is of high significance. On the one hand, the structural properties of the RAS protein make it difficult to find inhibitors specifically targeted to it. On the other hand, targeting other molecules in the RAS signaling pathway often leads to severe tissue toxicities due to the lack of disease specificity. However, computer-aided drug design (CADD) can help solve the above problems. As an interdisciplinary approach that combines computational biology with medicinal chemistry, CADD has brought a variety of advances and numerous benefits to drug design, such as the rapid identification of new targets and discovery of new drugs. Based on an overview of RAS features and the history of inhibitor discovery, this review provides insight into the application of mainstream CADD methods to RAS drug design.

Aiming at the problem of anomalous and non-independent distribution of the image errors in the feature-based visual pose estimation, a method of monocular visual pose estimation based on the uncertainty of noise error established by projection vector is proposed. First, by using the covariance matrix to describe the uncertainty of the feature point direction and integrating the uncertainty of the feature point direction into the pose estimation, characteristic point measurement error with different degrees of directional uncertainty can be adapted that can makes the algorithm robust. Then, by introducing the projection vector and combining the depth information of each feature point to represent the collinearity error, the model nonlinear problem caused by the camera perspective projection can be eliminated that can make the algorithm have global convergence. Finally, we use global convergence theorem to prove the global convergence of the proposed algorithm. The results show that the proposed method has good robustness and convergence while adapting to different degrees of error uncertainty, which can meet practical engineering applications.

This paper studies the problem of adaptive fault estimation and accommodation for a class of stochastic nonlinear systems with unknown time-varying faults. Different from existing fault estimation methods, a novel adaptive prescribed performance fault estimator is designed, which guarantees that the fault estimation error is confined within a pre-set region, and a better estimation accuracy is obtained. The designed fault-tolerant tracking controller is capable of attenuating the effect of faults through using back-stepping techniques. Furthermore, the proposed method guarantees that all the error signals of the closed-loop system be bounded in probability, the fault estimation error and output tracking error both converge to desired neighborhoods of origin in the sense of quadratic mean value. Finally, the simulation results are provided to verify the method proposed in this paper.

This paper studies the tracking control problem for nonlinear system with largely jumping parameters. To deal with the large uncertainties of system parameters, an RBF networks-based weighted multi-model adaptive control (WMMAC) strategy is designed, in which the model set can be constructed to cover and approximate the variation range of the plant parameters. Correspondingly, the RBF network controller set generates the appropriate global control signals when parameters jumping, and better transient performance is obtained. Different from existing neural network learning methods, two novel learning rules based on the difference value between the current value of an objective function and its optimal value for the RBF networks are developed to achieve a fast convergence rate. The stability of the overall closed-loop system is validated by the virtual equivalent system (VES) theory, and favorable performance of the proposed control strategy is demonstrated by the numerical simulations.

In order to the make the magnetic field synchronous with the actual current in the current interruption experiment, an external excitation of axial magnetic field for intermediate-frequency vacuum arc experiment and research is designed and tested in this paper. The excitation system is consisted of intermediate-frequency current transformers (CTs) and Helmholtz coil. CTs are used to induct current in main circuit and the Helmholtz coil can be excited by the secondary current. Thus the axial magnetic field (AMF), which is synchronous with the primary current, is generated inside the Helmholtz coil. And different from traditional magnetic field excitation system, the magnetic field generated by commercial AMF contact can be simulated. The test is implemented to test the excitation system. The phase difference between the primary side and secondary side of the CT is less than 10'. The steady state error of B is within 1% and the dynamic error of B/I is within 1.5% as well. The results turn out to be consistent with the theoretical value, which proves the effectiveness of this external excitation system.

Cartilage oligomeric matrix protein (COMP) is an extracellular matrix (ECM) glycoprotein that is critical for collagen assembly and ECM stability. Mutations of COMP cause endoplasmic reticulum stress and chondrocyte apoptosis, resulting in rare skeleton diseases. The bouquet-like structure of COMP allows it to act as a bridging molecule that regulates cellular phenotype and function. COMP is able to interact with many other ECM components and binds directly to a variety of cellular receptors and growth factors. The roles of COMP in other skeleton diseases, such as osteoarthritis, have been implied. As a well-established biochemical marker, COMP indicates cartilage turnover associated with destruction. Recent exciting achievements indicate its involvement in other diseases, such as malignancy, cardiovascular diseases, and tissue fibrosis. Here, we review the basic concepts of COMP and summarize its novel functions in the regulation of signaling events. These findings renew our understanding that COMP has a notable function in cell behavior and disease progression as a signaling regulator. Interestingly, COMP shows distinct functions in different diseases. Targeting COMP in malignancy may withdraw its beneficial effects on the vascular system and induce or aggravate cardiovascular diseases. COMP supplementation is a promising treatment for OA and aortic aneurysms while it may induce tissue fibrosis or cancer metastasis.

Ningxia wolfberry is the only wolfberry product with medicinal value in China. However, the nutritional elements, active ingredients, and economic value of the wolfberry vary considerably among different origins in Ningxia. It is difficult to determine the origin of wolfberry by traditional methods due to the same variety, similar origins, and external characteristics. In the study, we have for the first time used a multi-task residual fully convolutional network (MRes-FCN) under Bayesian optimized architecture for imaging from visible-near-infrared (Vis-NIR, 400–1000 nm) and near-infrared (NIR-1700 nm) hyperspectral imaging (HSI) technology to establish a classification model for near geographic origin of Ningxia wolfberries (Zhongning, Guyuan, Tongxin, and Huinong). The denoising auto-encoder (DAE) was used to generate augmented data, then principal component analysis (PCA) was combined with gray level co-occurrence matrix (GLCM) to extract the texture features. Finally, three datasets (HSI, DAE, and texture) were added to the multi-task model. The reshaped data were up-sampled using transposed convolution. After data-sparse processing, the backbone network was imported to train the model. The results showed that the MRes-FCN model exhibited excellent performance, with the accuracies of the full spectrum and optimum characteristic spectrum of 95.54% and 96.43%, respectively. This study has demonstrated that the MRes-FCN model based on Bayesian optimization and DAE data augmentation strategy may be used to identify the near geographical origin of wolfberries.

With the continuous development of large-scale aluminum reduction cells, the problem of the uniform distribution of alumina concentration in the cell has become more and more serious for the reduction process. In order to achieve the uniform distribution of the alumina concentration, a data-driven distributed subspace predictive control feeding strategy is proposed in this paper. Firstly, the aluminum reduction cell is divided into multiple sub-systems that affect each other according to the position of the feeding port. Based on the subspace method, the prediction model of the whole cell is identified, and the prediction output expression of each sub-system is deduced by decomposition. Secondly, the feeding controller is designed for each aluminum reduction cell subsystem, and the input and output information can be exchanged between each controller through the network. Thirdly, under consideration of the influence of other subsystems, each subsystem solves the Nash-optimal control feeding quantity, so that each subsystem realizes distributed feeding. Finally, the simulation results show that, compared with the traditional control method, the proposed distributed feeding control strategy can significantly improve the problem of the uniform distribution of alumina concentration and improve the current efficiency of the aluminum reduction cell.

Robots are coming to help us in different harsh environments such as deep sea or coal mine. Waste landfill is the place like these with casualty risk, gas poisoning, and explosion hazards. It is reasonable to use robots to fulfill tasks like burying operation, transportation, and inspection. In these assignments, one important issue is to obtain appropriate paths for robots especially in some complex applications. In this context, a novel hybrid swarm intelligence algorithm, ant colony optimization enhanced by chaos-based particle swarm optimization, is proposed in this article to deal with the path planning problem for landfill inspection robots in Asahikawa, Japan. In chaos-based particle swarm optimization, Chebyshev chaotic sequence is used to generate the random factors for particle swarm optimization updating formula so as to effectively adjust particle swarm optimization parameters. This improved model is applied to optimize and determine the hyper parameters for ant colony optimization. In addition, an improved pheromone updating strategy which combines the global asynchronous feature and “Elitist Strategy” is employed in ant colony optimization in order to use global information more appropriately. Therefore, the iteration number of ant colony optimization invoked by chaos-based particle swarm optimization can be reduced reasonably so as to decrease the search time effectively. Comparative simulation experiments show that the chaos-based particle swarm optimization-ant colony optimization has a rapid search speed and can obtain solutions with similar qualities.

An improved multi-objective particle swarm optimization with time-varying parameter and follower bee search is proposed in this article. In this algorithm, the weight of personal best solution decreases gradually as the iteration continues. This approach eliminates the effect caused by its poorer quality compared to global best solution so that the convergence ability of the algorithm is improved. Furthermore, the follower bee search in artificial bee colony algorithm is introduced to strengthen the randomness of the algorithm and discover more non-dominated solutions. A comparative simulation study is carried out using internal raw ore dispatching in an iron mining group that contains multiple stopes and concentrating mills. The results show that the proposed algorithm can significantly improve convergence and diversity.

Advanced technology for process monitoring and fault diagnosis is widely used in complex industrial processes. An important issue that needs to be considered is the ability to monitor key performance indicators (KPIs), which often cannot be measured sufficiently quickly or accurately. This paper proposes a data-driven approach based on maximizing the coefficient of determination for probabilistic soft sensor development when data are missing. Firstly, the problem of missing data in the training sample set is solved using the expectation maximization (EM) algorithm. Then, by maximizing the coefficient of determination, a probability model between secondary variables and the KPIs is developed. Finally, a Gaussian mixture model (GMM) is used to estimate the joint probability distribution in the probabilistic soft sensor model, whose parameters are estimated using the EM algorithm. An experimental case study on the alumina concentration in the aluminum electrolysis industry is investigated to demonstrate the advantages and the performance of the proposed approach.

Virus-induced gene silencing (VIGS) technique is an important means for rapid identification of plant gene function, and a stable VIGS system in taro can provide technical support for rapid and efficient gene function verification. A VIGS system was constructed with a tobacco rattle virus (TRV)-based vector, with phytoene desaturase (CePDS) as the indicator gene, then CeTCP14 was silenced to further verify the robustness of this system. Ganyu No.1 was used as the material to construct the taro VIGS system using the leaf injection method with CePDS at OD600 = 0.6, and the silencing plant rate was 12.23%, the expression level of CePDS was about 59.34%-77.18% compared to the control, and the chlorophyll content decreased 37.80%-56.11% in CePDS silencing plants. The silencing plant rate increased significantly by leaf injection at OD600 = 1.0, and reached 27.77%, but there was no significant difference in silencing plant rate between the leaf injection method and bulb vacuum treatment. CeTCP14 was further silenced with bacteria solution OD600 = 1.0 and bulb vacuum treatment in Ganyu No.2, the silencing plant rate was 20%, and the expression level of CeTCP14 was 43.94%-63.34% of the control. Meanwhile, the starch content in the corms decreased significantly to 70.88%-80.61% compared to the control. In conclusion, the results indicated that a TRV-based VIGS system is effective in taro. The concentration of bacterial solution is a key factor affecting the VIGS system, CeTCP14 can affect starch accumulation in taro bulbs. A robust VIGS system establishment in taro could lay a good foundation for the subsequent rapid gene function verification. [ABSTRACT FROM AUTHOR]