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In the early stage of this study, three strains of Lactobacillus with anti-soybean allergy potential were screened: Lactobacillus acidophilus CICC 6081, Lactobacillus delbrueckii subsp. Bulgaricus CICC 6103 and Lactobacillus plantarum subsp. Plantarum CICC 20988. The aim of this study was to analyze the desensitization effect of three strains of Lactobacillus administered by gavage to soybean-allergic mice through the differentiation of immune cells in intestinal lymph nodes and the changes to gut microbiota. The results showed that the three strains of Lactobacillus could stimulate the proliferation of dendritic cells (DCs) and regulate the balance of Th1/Th2 differentiation in the MLNs and PPs of soybean-allergic mice. Furthermore, the Th17/Tregs cell-differentiation ratio in the MLNs of the Lactobacillus-treated mice was significantly lower than that of the allergic mice (p < 0.05). Compared to the control group, the Shannon, Sobs and Ace indexes of intestinal microbiota in the allergic mice were significantly increased (p < 0.05), and the proportion of Clostridiales was significantly higher (p < 0.05), which was reversed by Lactobacillus gavage. In conclusion, the three strains of Lactobacillus can inhibit the intestinal mucosal immune response and regulate gut microbiota balance in soybean-allergic mice.

Cervical carcinoma (CC) is the second most prevalent gynecologic cancer in females across the world. To obtain a better understanding of the mechanisms underlying the development of CC, high-resolution label-free mass spectrometry was performed on CC and adjacent normal tissues from eight patients. A total of 2631 proteins were identified, and 46 significant differently expressed proteins (DEPs) were found between CC and normal tissues (p < 0.01, fold change >10 or

Age-related hearing loss (ARHL) is associated with cognitive dysfunction; however, the detailed underlying mechanisms remain unclear. The aim of this study is to investigate the potential underlying mechanism with a system genetics approach. A transcriptome-wide association study was performed on aged (12–32 months old) BXD mice strains. The hippocampus gene expression was obtained from 56 BXD strains, and the hearing acuity was assessed from 54 BXD strains. Further correlation analysis identified a total of 1,435 hearing-related genes in the hippocampus (p < 0.05). Pathway analysis of these genes indicated that the impaired glutamatergic synapse pathway is involved in ARHL (p = 0.0038). Further gene co-expression analysis showed that the expression level of glutamine synthetase (Gls), which is significantly correlated with ARHL (n = 26, r = −0.46, p = 0.0193), is a crucial regulator in glutamatergic synapse pathway and associated with learning and memory behavior. In this study, we present the first systematic evaluation of hippocampus gene expression pattern associated with ARHL, learning, and memory behavior. Our results provide novel potential molecular mechanisms involved in ARHL and cognitive dysfunction association.

In industrial processes, the composition of raw material and the production environment are complex and changeable, which makes the production process have multiple steady states. In this situation, it is difficult for the traditional single-mode monitoring methods to accurately detect the process abnormalities. To this end, a multimode monitoring method based on the factor dynamic autoregressive hidden variable model (FDALM) for industrial processes is proposed in this paper. First, an improved affine propagation clustering algorithm to learn the model modal factors is adopted, and the FDALM is constructed by combining multiple high-order hidden state Markov chains through the factor modeling technology. Secondly, a fusion algorithm based on Bayesian filtering, smoothing, and expectation-maximization is adopted to identify model parameters. The Lagrange multiplier formula is additionally constructed to update the factor coefficients by using the factor constraints in the solving. Moreover, the online Bayesian inference is adopted to fuse the information of different factor modes and obtain the fault posterior probability, which can improve the overall monitoring effect of the model. Finally, the proposed method is applied in the sintering process of ternary cathode material. The results show that the fault detection rate and false alarm rate of this method are improved obviously compared with the traditional methods.

Abstract Background Esophageal cancer (EC) is one of the malignant tumors with a poor prognosis. The early stage of EC is asymptomatic, so identification of cancer biomarkers is important for early detection and clinical practice. Methods In this study, we compared the protein expression profiles in esophageal squamous cell carcinoma (ESCC) tissues and adjacent normal esophageal tissues from five patients through high-resolution label-free mass spectrometry. Through bioinformatics analysis, we found the differentially expressed proteins of ESCC. To perform the rapid identification of biomarkers, we adopted a high-throughput protein identification technique of Quantitative Dot Blot (QDB). Meanwhile, the QDB results were verified by classical immunohistochemistry. Results In total 2297 proteins were identified, out of which 308 proteins were differentially expressed between ESCC tissues and normal tissues. By bioinformatics analysis, the four up-regulated proteins (PTMA, PAK2, PPP1CA, HMGB2) and the five down-regulated proteins (Caveolin, Integrin beta-1, Collagen alpha-2(VI), Leiomodin-1 and Vinculin) were selected and validated in ESCC by Western Blot. Furthermore, we performed the QDB and IHC analysis in 64 patients and 117 patients, respectively. The PTMA expression was up-regulated gradually along the progression of ESCC, and the PTMA expression ratio between tumor and adjacent normal tissue was significantly increased along with the progression. Therefore, we suggest that PTMA might be a potential candidate biomarker for ESCC. Conclusion In this study, label-free quantitative proteomics combined with QDB revealed that PTMA expression was up-regulated in ESCC tissues, and PTMA might be a potential candidate for ESCC. Since Western Blot cannot achieve rapid and high-throughput screening of mass spectrometry results, the emergence of QDB meets this demand and provides an effective method for the identification of biomarkers.

The fine control of endpoint temperatures of the dome and exhaust gas is the key to improving the heat storage efficiency and capacity of the pebble stove combustion process. However, owing to nonlinearity, time delay, uncertain endpoint time, and strong coupling issues of the combustion process, it is difficult to achieve the desired dome and exhaust gas endpoint temperatures, simultaneously. Therefore, a two-stage (fast heating stage and heat storage stage) control system for the endpoint temperature is developed for the combustion process of the pebble stove. According to the heat transfer theory and the principle of conservation of heat, a transient heat transfer model between the flue gas and the regenerator is developed for the combustion process of the pebble stove. Based on the transient heat transfer model, prediction models of the dome and exhaust gas endpoint temperatures are obtained by a finite-difference method. Moreover, to solve the strong coupling between the dome temperature in the fast heating period and the exhaust gas temperature in the thermal storage period, a two-stage intelligent proportion-integral-derivative decoupling control algorithm is developed based on a self-refreshing recurrent neural network to realize the decoupling control of the dome and exhaust gas endpoint temperatures. The performance of the developed system is demonstrated with simulation and industrial experiments. The results show that compared to the conventional control system, the developed system effectively shortens the adjusting time of the dome endpoint temperature by 13 min with a reduced temperature fluctuation from ±14 °C to ±4 °C and increases the exhaust gas endpoint temperature from 328.3 °C to 348.0 °C. The enhanced temperature control performance demonstrates the validity of the established system.

In practical wireless sensor networks (WSNs)-based applications, often only a few nodes are active while most of the others are activated occasionally due to limited resource of the WSNs. Thus most sensors only provide a few observations and only a few sensors make many observations, which often cause long-tail issue to undermine information fusion performance. So we present a confidence-aware information fusion scheme named CAIF to solve such a problem. In particular, we first make a quantitative study on the long-tail data phenomenon and the relationship between node-target distance and node sensing capability, which can provide a guideline to improve node weight estimation error caused by long-tail data. Then, we propose a truth discovery-based method in WSNs via incorporating node-target distance into the truth discovery optimization solution framework to infer the sensor node's fusion weight. In order to adapt to the distribution characteristic of the WSNs, we propose a distributed implementation to estimate the sensor nodes' weights via confidence level and node-target distance to further improve the fusion performance. Besides, the iterative process shown in CAIF converges to a stationary point of the optimization problem and its time complexity is linear with respect to the total number of observations. Finally, we conduct extensive experiments on real data to validate and evaluate CAIF. The experimental results demonstrate the superior performance of our method over existing solutions in terms of root-mean-square error and accuracy.

Generally, the main task of the optimal operation in the industrial process is to satisfy the process technical requirements, and the PID controller is a well-known controller in industrial control applications. Nevertheless, due to the complicated mechanisms and the dynamic characteristics of a complex industrial process, the conventional PID controller fails to provide effective control to such systems. In this paper, we develop a novel discrete-time fractional order PID (DFOPID) control strategy to achieve the technical requirements of the complex industrial process. The proposed work is conducted through a combination of three novel interdependent efforts. First, on the basis of the widely used Tustin operator and its Taylor series, a digital structure of the DFOPID is proposed. Second, in order to solve the stability problem of the complex industrial process, an optimal setting of the approximation function's order (N) and five parameters (λ, μ, Kp, Ki, Kd) is necessary. Hence, an integral time absolute error (ITAE) criterion is applied to convert the optimal setting problem to a nonconvex optimization problem. Finally, a novel intelligent optimization search algorithm called state transition algorithm is employed to carry out the aforementioned design procedure. Furthermore, the performance of the DFOPID control strategy in some practical industrial control systems, including the copper removal process and the electrochemical process of zinc are also investigated.

In this paper, the containment control was studied for high-order discrete-time multi-agent systems. A distributed algorithm is proposed for all followers to be gathered in the convex area formed by multiple stationary leaders. It is revealed that the algorithm can solve the containment problem with communication delays and switching networks provided that each follower has at least a directed path from some leaders to itself in the union of graphs. The main analysis approaches include model transformation techniques, Lyapunov function, and convexity analysis. Finally, a simulation is also given to show the theoretical result.

Developments in defect descriptors and computer vision-based algorithms for automatic optical inspection (AOI) allows for further development in image-based measurements. Defect classification is a vital part of an optical-imaging-based surface quality measuring instrument. The high-speed production rhythm of hot continuous rolling requires an ultra-rapid response to every component as well as algorithms in AOI instrument. In this paper, a simple, fast, yet robust texture descriptor, namely selectively dominant local binary patterns (SDLBPs), is proposed for defect classification. First, an intelligent searching algorithm with a quantitative thresholding mechanism is built to excavate the dominant non-uniform patterns (DNUPs). Second, two convertible schemes of pattern code mapping are developed for binary encoding of all uniform patterns and DNUPs. Third, feature extraction is carried out under SDLBP framework. Finally, an adaptive region weighting method is built for further strengthening the original nearest neighbor classifier in the feature matching stage. The extensive experiments carried out on an open texture database (Outex) and an actual surface defect database (Dragon) indicates that our proposed SDLBP yields promising performance on both classification accuracy and time efficiency.

This paper generates an augmented hyperchaotic system from the famous Lorenz system. The hyperchaotic system has complex dynamic properties, including stability, periodicity, multiple coexisting attractors, period-doubling and Hopf bifurcations, and hyperchaos for different parameter conditions and all these dynamic properties are presented by detailed theoretical and numerical analysis. Moreover, the finite-time synchronization of the hyperchaotic system is considered by using the state-error controller. Both the sufficient conditions for finite-time synchronization and the corresponding finite time are strictly established.

The existing model-free adaptive control encounters problems, such as too many parameters that need to be determined, some of which with unclear physical significance and whose selection depend entirely on trial and error. Aiming at this problem, a new dynamic linearized model is established by using Taylor series expansion of discrete-time nonlinear systems and the differential mean value theorem. Then, a new data-driven model-free adaptive control is proposed, which reduces the required parameters from six in the existing model-free adaptive control to four in the new model-free adaptive control. All the parameters have clear physical significance, and the selections of the initial values of the parameters are based on the stability conditions of the closed-loop system. Therefore, the selection of the parameters in the new model-free adaptive control does not depend entirely on trial and error but on regularity. By introducing the idea of internal model control in the new model-free adaptive control, the anti-disturbance performance of the closed-loop system is enhanced. Finally, simulation results for three complicated nonlinear systems show that the proposed model-free adaptive control is superior to the existing model-free adaptive control.

The real-time, continuity, and accuracy of blast furnace stockline information are of great significance in reducing energy consumption and improving smelting efficiency. However, the traditional mechanical measurement method has the problem of measuring point discontinuity, while the radar measurement method exhibits problems such as weak anti-interference ability, low accuracy, and poor stability. Therefore, a high-dimensional, spatial feature stockline detection method based on the maximum likelihood radial basis function model (MLRBFM) and structural dynamic self-optimization RBF neural network (SDSO-RBFNN) is proposed. Firstly, the discrete time series joint partition method is used to extract the time dimension periodic features of the blast furnace stockline. Based on MLRBFM, the high-dimensional spatial features of the stockline are then obtained. Finally, an SDSO-RBFNN is constructed based on an eigen orthogonal matrix and a right triangular matrix decomposition (QR) direct clustering algorithm with spatial–temporal features as input, so as to obtain continuous, high-precision stockline information. Both the simulation results and industrial validation indicate that the proposed method can provide real-time and accurate stockline information, and has great practical value for industrial production.

Real-time, continuous and accurate blast furnace burden level information is of great significance for controlling the charging process, ensuring a smooth operation of a blast furnace, reducing energy consumption and emissions and improving blast furnace output. However, the burden level information measured by conventional mechanical stock rods and radar probes exhibit problems of weak anti-interference ability, large fluctuations in accuracy, poor stability and discontinuity. Therefore, a space-time fusion prediction and detection method of burden level based on a long-term focus memory network (LFMN) and an efficient structure self-tuning RBF neural network (ESST-RBFNN) is proposed. First, the space dimensional features are extracted by the space regression model based on radar data. Then, the LFMN is designed to predict the burden level and extract the time dimensional features. Finally, the ESST-RBFNN based on a proposed fast eigenvector space clustering algorithm (ESC) is constructed to obtain reliable and continuous burden level information with high accuracy. Both the simulation results and industrial verification indicate that the proposed method can provide real-time and continuous burden level information in real-time, which has great practical value for industrial production.

Diagnosis and prognosis of inflammatory bowel disease (IBD)—a chronic inflammation that affects the gastrointestinal tract of patients—are challenging, as most clinical symptoms are not specific to IBD, and are often seen in other inflammatory diseases, such as intestinal infections, drug-induced colitis, and monogenic diseases. To date, there is no gold-standard test for monitoring IBD. Endoscopy and imaging are essential diagnostic tools that provide information about the disease’s state, location, and severity. However, the invasive nature and high cost of endoscopy make it unsuitable for frequent monitoring of disease activity in IBD patients, and even when it is possible to replace endoscopy with imaging, high cost remains a concern. Laboratory testing of blood or feces has the advantage of being non-invasive, rapid, cost-effective, and standardizable. Although the specificity and accuracy of laboratory testing alone need to be improved, it is increasingly used to monitor disease activity or to diagnose suspected IBD cases in combination with endoscopy and/or imaging. The literature survey indicates a dearth of summarization of biomarkers for IBD testing. This review introduces currently available non-invasive biomarkers of clinical importance in laboratory testing for IBD, and discusses the trends and challenges in the IBD biomarker studies.

Modulating the gut microbiota composition is a potent approach to treat various chronic diseases, including obesity, metabolic syndrome, and ulcerative colitis (UC). However, the current methods, such as fecal microbiota transplantation, carry a risk of serious infections due to the transmission of multi-drug-resistant organisms. Here, we developed an organism-free strategy in which the gut microbiota is modulated ex vivo and microbiota-secreted metabolites are transferred back to the host. Using feces collected from the interleukin-10 (IL-10) knockout mouse model of chronic UC, we found that a drug candidate (M13)-loaded natural-lipid nanoparticle (M13/nLNP) modified the composition of the ex vivo-cultured inflamed gut microbiota and its secreted metabolites. Principal coordinate analysis (PCoA) showed that M13/nLNP shifted the inflamed microbiota composition toward the non-inflamed direction. This compositional modification induced significant changes in the chemical profiles of secreted metabolites, which proved to be anti-inflammatory against in vitro-cultured NF-κβ reporter cells. Further, when these metabolites were orally administered to mice, they established strong protection against the formation of chronic inflammation. Our study demonstrates that ex vivo modulation of microbiota using M13/nLNP effectively reshaped the microbial secreted metabolites and that oral transfer of these metabolites might be an effective and safe therapeutic approach for preventing chronic UC.

Abstract Background Gastric cancer is one of the most common malignant tumors. Cyclin G2 has been shown to be associated with the development of multiple types of tumors, but its underlying mechanisms in gastric tumors is not well-understood. The aim of this study is to investigate the role and the underlying mechanisms of cyclin G2 on Wnt/β-catenin signaling in gastric cancer. Methods Real-time PCR, immunohistochemistry and in silico assay were used to determine the expression of cyclin G2 in gastric cancer. TCGA datasets were used to evaluate the association between cyclin G2 expression and the prognostic landscape of gastric cancers. The effects of ectopic and endogenous cyclin G2 on the proliferation and migration of gastric cancer cells were assessed using the MTS assay, colony formation assay, cell cycle assay, wound healing assay and transwell assay. Moreover, a xenograft model and a metastasis model of nude mice was used to determine the influence of cyclin G2 on gastric tumor growth and migration in vivo. The effects of cyclin G2 expression on Wnt/β-catenin signaling were explored using a TOPFlash luciferase reporter assay, and the molecular mechanisms involved were investigated using immunoblots assay, yeast two-hybrid screening, immunoprecipitation and Duolink in situ PLA. Ccng2 −/− mice were generated to further confirm the inhibitory effect of cyclin G2 on Wnt/β-catenin signaling in vivo. Furthermore, GSK-3β inhibitors were utilized to explore the role of Wnt/β-catenin signaling in the suppression effect of cyclin G2 on gastric cancer cell proliferation and migration. Results We found that cyclin G2 levels were decreased in gastric cancer tissues and were associated with tumor size, migration and poor differentiation status. Moreover, overexpression of cyclin G2 attenuated tumor growth and metastasis both in vitro and in vivo. Dpr1 was identified as a cyclin G2-interacting protein which was required for the cyclin G2-mediated inhibition of β-catenin expression. Mechanically, cyclin G2 impacted the activity of CKI to phosphorylate Dpr1, which has been proved to be a protein that acts as a suppressor of Wnt/β-catenin signaling when unphosphorylated. Furthermore, GSK-3β inhibitors abolished the cyclin G2-induced suppression of cell proliferation and migration. Conclusions This study demonstrates that cyclin G2 suppresses Wnt/β-catenin signaling and inhibits gastric cancer cell growth and migration through Dapper1.

We have demonstrated that a specific population of ginger-derived nanoparticles (GDNP-2) could effectively target the colon, reduce colitis, and alleviate colitis-associated colon cancer. Naturally occurring GDNP-2 contains complex bioactive components, including lipids, proteins, miRNAs, and ginger secondary metabolites (gingerols and shogaols). To construct a nanocarrier that is more clearly defined than GDNP-2, we isolated lipids from GDNP-2 and demonstrated that they could self-assemble into ginger lipid-derived nanoparticles (GLDNP) in an aqueous solution. GLDNP can be used as a nanocarrier to deliver drug candidates such as 6-shogaol or its metabolites (M2 and M13) to the colon. To characterize the nanostructure of GLDNP, our lab extensively used atomic force microscopy (AFM) technique as a tool for visualizing the morphology of the drug-loaded GLDNP. Herein, we provide a detailed protocol for demonstrating such a process.

Urban rivers represent a unique ecosystem in which pollution occurs regularly, altering the biogeochemical characteristics of waterbodies and sediments. However, little is presently known about the spatiotemporal patterns of planktonic and sediment bacterial community diversities and compositions in urban rivers. Herein, Illumina MiSeq high-throughput sequencing was performed to reveal the spatiotemporal dynamics of bacterial populations in Liangtan River, a heavily polluted urban river in Chongqing City (China). The results showed the richness and diversity of sediment bacteria were significantly higher than those of planktonic bacteria, whereas a strong overlap (46.7%) in OTUs was identified between water and sediment samples. Bacterial community composition remarkably differed in waters and sediments. Planktonic bacterial communities were dominated by Proteobacteria, Bacteroidetes, Cyanobacteria and Actinobacteria, while sediment bacterial communities mainly included Proteobacteria, Actinobacteria, Chloroflexi and Bacteroidetes. Additionally, several taxonomic groups of potential bacterial pathogens showed an increasing trend in water and sediment samples from residential and industrial areas (RI). Variation partition analysis (VPA) indicated that temperature and nutrient were identified as the main drivers determining the planktonic and sediment bacterial assemblages. These results highlight that bacterial communities in the polluted urban river exhibit spatiotemporal variation due to the combined influence of environmental factors associated with sewage discharge and hydropower dams.

The traction converter is one of the key components of high-speed trains. Current and voltage sensor faults in the converter may lead to feedback values deviation and system degradation, which will bring security risks to the train. This paper proposes a real-time fault diagnosis method for grid current, DC-link voltage and stator current sensor faults in the traction converter with two stator current sensors, which can not only detect and locate faults but also identify the types of faults. Moreover, the faults considered in this paper are incipient. First, the DC-link model is established, and the fault is detected by the residual of the DC-link voltage. Next, the differential of DC-link voltage residual is calculated, which is applied to fault location. Then, according to the change of the differential values, different fault types are determined. Finally, the hardware-in-the-loop (HIL) platform is built and the effectiveness and accuracy of the proposed method are verified by the HIL tests.

Background Older adults have been reported to be a population with high-risk of death in the COVID-19 outbreak. Rapid detection of high-risk patients is crucial to reduce mortality in this population. The aim of this study was to evaluate the prognositc accuracy of the Modified Early Warning Score (MEWS) for in-hospital mortality in older adults with COVID-19. Methods A retrospective cohort study was conducted in Wuhan Hankou Hospital in China from 1 January 2020 to 29 February 2020. Receiver operating characteristic (ROC) analysis was used to evaluate the predictive value of MEWS, Acute Physiology and Chronic Health Evaluation II (APACHE II), Sequential Organ Function Assessment (SOFA), quick Sequential Organ Function Assessment (qSOFA), Pneumonia Severity Index (PSI), Combination of Confusion, Urea, Respiratory Rate, Blood Pressure, and Age ≥65 (CURB-65), and the Systemic Inflammatory Response Syndrome Criteria (SIRS) for in-hospital mortality. Logistic regression models were performed to detect the high-risk older adults with COVID-19. Results Among the 235 patients included in this study, 37 (15.74%) died and 131 (55.74%) were male, with an average age of 70.61 years (SD 8.02). ROC analysis suggested that the capacity of MEWS in predicting in-hospital mortality was as good as the APACHE II, SOFA, PSI and qSOFA (Difference in AUROC: MEWS vs. APACHE II, −0.025 (95% CI [−0.075 to 0.026]); MEWS vs. SOFA, −0.013 (95% CI [−0.049 to 0.024]); MEWS vs. PSI, −0.015 (95% CI [−0.065 to 0.035]); MEWS vs. qSOFA, 0.024 (95% CI [−0.029 to 0.076]), all P > 0.05), but was significantly higher than SIRS and CURB-65 (Difference in AUROC: MEWS vs. SIRS, 0.218 (95% CI [0.156–0.279]); MEWS vs. CURB-65, 0.064 (95% CI [0.002–0.125]), all P < 0.05). Logistic regression models implied that the male patients (≥75 years) had higher risk of death than the other older adults (estimated coefficients: 1.16, P = 0.044). Our analysis further suggests that the cut-off points of the MEWS score for the male patients (≥75 years) subpopulation and the other elderly patients should be 2.5 and 3.5, respectively. Conclusions MEWS is an efficient tool for rapid assessment of elderly COVID-19 patients. MEWS has promising performance in predicting in-hospital mortality and identifying the high-risk group in elderly patients with COVID-19.

Synthetic nanoparticle-based drug delivery system is widely known for its ability to increase the efficacy and specificity of loaded drugs, but it often suffers from relatively higher immunotoxicity and higher costs as compared to traditional drug formulations. Contrarily, plant-derived nanoparticles appear to be free from these limitations of synthetic nanoparticles; they are naturally occurring biocompatible vesicles that do not generate immunotoxicity and are easy to obtain. Additionally, lipids isolated from plant-derived nanoparticles have shown the capability of assembling themselves to spherical nano-sized liposomal particles. Herein, we employ lipids extracted from ginger-derived nanoparticles and load them with therapeutic siRNA (CD98-siRNA) to create CD98-siRNA/ginger-lipid nanoparticles. Characterization of the CD98-siRNA/ginger-lipid nanoparticles showed that they present a spherical shape, with a diameter of around 189.5 nm. The surface zeta potential of the nanoparticles varies from -18.1 to -18.4 mV. Furthermore, in recent research, the CD98-siRNA/ginger-lipid nanoparticles have shown specific colon targeting capability and excellent anti-inflammatory efficacy in a Dextran Sodium Sulfate (DSS) induced mouse model of colitis.

Exosomes secreted by colonic epithelial cells are present in feces and contain valuable epigenetic information, such as miRNAs, proteins, and metabolites. An in-depth study of this information is conducive to the diagnosis or treatment of relevant diseases. A crucial prerequisite of such a study is to establish an efficient isolation method, through which we can obtain a relatively more significant amount of exosomes from feces. This protocol is designed to effectively isolate a large number of exosomes from contaminants and other particles in feces by a combined method with fast filtration and sucrose density gradient ultracentrifugation. Exosomes generated by this method are suitable for further RNA, protein, and lipid analysis.

Prostate cancer is a common malignant tumor and the second leading cause of cancer-related death in men. Radiation therapy is a curative treatment for localized prostate cancer and has a limited effect for castration-resistant prostate cancer (CRPC). Interleukin 24 (IL-24) has a radiosensitizing effect in cancer cells. Our previous studies showed that ZD55-IL-24, an oncolytic adenovirus harboring IL-24, had better anti-tumor effect with no toxicity to normal cells. In this study, we evaluated the synergistic anti-tumor effect of oncolytic adenovirus ZD55-IL-24 combined with radiotherapy in prostate cancer. In Vitro and In Vivo experiments showed that the combined therapy significantly inhibited the growth of prostate cancer and provoked apoptosis of prostate cancer cells. In conclusion, the combination of ionizing radiation and oncolytic adenovirus expressing IL24 could achieve synergistic anti-tumor effect on prostate cancer, and is a promising strategy for prostate cancer therapy.

The goethite process is complicated since its chemical reactions interact with each other, making its control and optimization in industrial production difficult. The goal of the goethite process is to make the outlet ion concentration satisfy the technical requirements with minimal process consumption. To simplify the difficulties of optimization in the goethite process, an optimization method based on a setpoint tracking strategy is proposed. The set-point tracking strategy is used to transform the complex state constraints into an additional objective. Therefore, the single optimization control problem for the goethite process is transformed into a bi-objective optimization control problem. Furthermore, PID controllers are adopted to control the addition amounts of zinc oxide and oxygen in the goethite process. The optimal parameters of the PID controllers are obtained via a multi-objective state transition algorithm (MOSTA). The performance of MOSTA is verified by several benchmark test functions with performance matrices. The control performance reveals that the proposed method is an effective way to control the process and can not only reduce the zinc oxide and oxygen addition amounts compared with manual operation and traditional PID control but also reject disturbances. The proposed method can satisfy the industrial requirements with less energy consumption.

Steel strip acts as a fundamental material for the steel industry. Surface defects threaten the steel quality and cause substantial economic and reputation losses. Roll marks, always occurring periodically in a large area, are put on the top of the list of the most serious defects by steel mills. Essentially, the online roll mark detection is a tiny target inspection task in high-resolution images captured under harsh environment. In this paper, a novel method—namely, Smoothing Complete Feature Pyramid Networks (SCFPN)—is proposed for the above focused task. In particular, the concept of complete intersection over union (CIoU) is applied in feature pyramid networks to obtain faster fitting speed and higher prediction accuracy by suppressing the vanishing gradient in training process. Furthermore, label smoothing is employed to promote the generalization ability of model. In view of lack of public surface image database of steel strips, a raw defect database of hot-rolled steel strip surface, CSU_STEEL, is opened for the first time. Experiments on two public databases (DeepPCB and NEU) and one fresh texture database (CSU_STEEL) indicate that our SCFPN yields more competitive results than several prestigious networks—including Faster R-CNN, SSD, YOLOv3, YOLOv4, FPN, DIN, DDN, and CFPN.

The past decade has seen increasing interest in microbiota-targeting therapeutic strategies that aim to modulate the gut microbiota’s composition and/or function to treat chronic diseases, such as inflammatory bowel disease (IBD), metabolic symptoms, and obesity. While targeting the gut microbiota is an innovative means for treating IBD, it typically requires an extended treatment time, hampering its potential application. Herein, using an established natural-lipid nanoparticle (nLNP) platform, we demonstrate that nLNPs encapsulated with the drug candidate 6-shogaol (6S/nLNP) distinctly altered microbiota composition within one day of treatment, significantly accelerating a process that usually requires five days using free 6-shogaol (6S). In addition, the change in the composition of the microbiota induced by five-day treatment with 6S/nLNP was maintained for at least 15 days (from day five to day 20). The consequent alteration in the fecal metabolic profile stemming from this compositional change manifested as functional changes that enhanced the in vitro anti-inflammatory and wound-healing efficacy of macrophage cells (Raw 264.7) and epithelial cells (Caco-2 BBE1), respectively. Further, this metabolic compositional change, as reflected in an altered metabolic profile, promoted a robust anti-inflammatory effect in a DSS-induced mouse model of acute colitis. Our study demonstrates that, by near-instantly modulating microbiota composition and function, an nLNP-based drug-delivery platform might be a powerful tool for treating ulcerative colitis.

This paper investigates the problem of guaranteed cost control of an interval type-2 Takagi-Sugeno fuzzy descriptor systems with time-varying delays satisfying probabilistic characteristics. In order to fully consider the delay distributions, the delay segmentation approach is introduced. Then, an interval type-2 fuzzy controller is designed such that the closed-loop fuzzy descriptor system is regular, causal, mean square asymptotically stable, and the corresponding quadratic cost function is guaranteed which lower than a certain upper bound. Finally, one simulation example is given to demonstrate the effectiveness of the proposed method.

This paper is concerned with the optimal control problem for the zinc electrowinning (EW) process during the current switching period. A mathematical model is developed to reveal the dynamic characteristics of the whole plant of the zinc EW process and an energy consumption model is established to set the expected set points of the concentrations of the zinc ion and the sulfuric acid under different current. Furthermore, an optimal control problem is constructed in the light of free initial time, free terminal time, and fixed system switching time during the zinc EW process. A novel time-scaling transformation-based control parametrization method is introduced to transform the optimal control problem into a multiple parameters optimization selection problem, which can be effectively solved by the optimization algorithm. The applications on the EW process of a zinc hydrometallurgy plant demonstrate the validity of proposed method.

In complex industrial processes, the knowledge has properties of multi-source heterogeneity, polymorphism, and uncertainty. When the conventional knowledge representation methods are used to represent this type of knowledge, they often result in misunderstanding, inexplicability, and ambiguity. To solve this problem, a semantic network based on intuitionistic fuzzy directed hyper-graphs (SN-IFDHGs) model is proposed. First, qualitative knowledge is transformed to quantitative knowledge using an intuitionistic fuzzy algorithm. In the SN-IFDHG model, an edge set can connect multiple vertexes, which mean multi-source knowledge elements. Meanwhile, to present uncertain knowledge, the weights between semantic nodes are characterized by simultaneously containing both membership and non-membership. Then, to reduce the space complexity and facilitate the reconstruction of the SN-IFDHG model, a novel storage structure based on in-degree index list is proposed. Finally, a knowledge reasoning method based on entropy weight of SN-IFDHG is proposed and applied to aluminum electrolysis cell condition identification. The experimental results show that the proposed knowledge reasoning method is more effective and accurate than other existing algorithms.

Factors implicated in the pathophysiology of intestinal inflammation include defects in intestinal epithelial barrier function, abnormal immune responses, and activities of the gut microbiota. Current agents used to treat human Inflammatory Bowels Disease (IBD), chronic inflammation of digestive tract, have serious side effects. In addition, most of these treatments target the damaging factors while not providing pro-healing factors that repair the damaged intestine. Here we provide a method to isolate, purify and characterize a specific population from ginger (ginger-derived nanoparticles: GDNPs 2) with anti-inflammatory activities. GDNPs 2 as a drug vehicle are a novel natural, nontoxic delivery system, which target the inflamed intestinal mucosa, blocks damaging factors while promoting pro-healing factors and could easily be developed for large-scale production aimed at the treatment of IBD.

Toxicity studies for conventional oral drug formulations are standardized and well documented, as required by the guidelines of administrative agencies such as the US Food & Drug Administration (FDA), the European Medicines Agency (EMA) or European Medicines Evaluation Agency (EMEA), and the Japanese Pharmaceuticals and Medical Devices Agency (PMDA). Researchers tend to extrapolate these standardized protocols to evaluate nanoformulations (NFs) because standard nanotoxicity protocols are still lacking in nonclinical studies for testing orally delivered NFs. However, such strategies have generated many inconsistent results because they do not account for the specific physicochemical properties of nanomedicines. Due to their tiny size, accumulated surface charge and tension, sizeable surface-area-to-volume ratio, and high chemical/structural complexity, orally delivered NFs may generate severe topical toxicities to the gastrointestinal tract and metabolic organs, including the liver and kidney. Such toxicities involve immune responses that reflect different mechanisms than those triggered by conventional formulations. Herein, we briefly analyze the potential oral toxicity mechanisms of NFs and describe recently reported in vitro and in vivo models that attempt to address the specific oral toxicity of nanomedicines. We also discuss approaches that may be used to develop nontoxic NFs for oral drug delivery.

Excessive discharge of heavy metal ions will aggravate environment pollution and threaten human health. Thus, it is of significance to real-time detect metal ions and control discharge in the metallurgical wastewater. We developed an accurate and rapid approach based on the singular perturbation spectrum estimator and extreme gradient boosting (SPSE-XGBoost) algorithms to simultaneously determine multi-metal ion concentrations by UV–vis spectrometry. In the approach, the spectral data is expanded by multi-order derivative preprocessing, and then, the sensitive feature bands in each spectrum are extracted by feature importance (VI score) ranking. Subsequently, the SPSE-XGBoost model are trained to combine multi-derivative features and to predict ion concentrations. The experimental results indicate that the developed “Expand-Extract-Combine” strategy can not only overcome problems of background noise and spectral overlapping but also mine the deeper spectrum information by integrating important features. Moreover, the SPSE-XGBoost strategy utilizes the selected feature subset instead of the full-spectrum for calculation, which effectively improves the computing speed. The comparisons of different data processing methods are conducted. It outcomes that the proposed strategy outperforms other routine methods and can profoundly determine the concentrations of zinc, copper, cobalt, and nickel with the lowest RMSEP. Therefore, our developed approach can be implemented as a promising mean for real-time and on-line determination of multi-metal ion concentrations in zinc hydrometallurgy.

Colorectal cancer (CRC) is a prevalent disease worldwide, and patients at late stages of CRC often suffer from a high mortality rate after surgery. Adjuvant chemotherapeutics (ACs) have been extensively developed to improve the survival rate of such patients, but conventionally formulated ACs inevitably distribute toxic chemotherapeutic drugs to healthy organs and thus often trigger severe side effects. CRC cells may also develop drug resistance following repeat dosing of conventional ACs, limiting their effectiveness. Given these limitations, researchers have sought to use targeted drug delivery systems (DDSs), specifically the nanotechnology-based DDSs, to deliver the ACs. As lipid-based nanoplatforms have shown the potential to improve the efficacy and safety of various cytotoxic drugs (such as paclitaxel and vincristine) in the clinical treatment of gastric cancer and leukemia, the preclinical progress of lipid-based nanoplatforms has attracted increasing interest. The lipid-based nanoplatforms might be the most promising DDSs to succeed in entering a clinical trial for CRC treatment. This review will briefly examine the history of preclinical research on lipid-based nanoplatforms, summarize the current progress, and discuss the challenges and prospects of using such approaches in the treatment of CRC.

BACKGROUND:The severity of ischemic stroke symptoms varies among patients and is a critical determinant of patient outcome. To date, the association between the number of stenotic intracranial arteries and stroke severity remains unclear. AIMS:We aimed to investigate the association between the number of stenotic major intracranial arteries (NSMIA) and ischemic stroke severity, as well as the degree of stenosis and common stroke risk factors. METHODS:We performed a retrospective analysis of patients with digital subtraction angiography (DSA)-confirmed ischemic stroke. Clinical stroke severity was measured using the National Institutes of Health Stroke Scale (NIHSS). The number of stenotic vessels was counted from the internal carotid arteries and vertebral arteries, bilaterally. RESULTS:Eighty three patients were recruited from a single center and included in the study. NSMIA was significantly correlated with stroke severity (Pearson Correlation Coefficient = 0.485, P < 0.001), but not with the degree of stenosis (Pearson Correlation Coefficient = 0.01, P = 0.90). Multivariate regression analysis revealed that NSMIA was significantly associated with the NIHSS score after adjusting for stroke risk factors. The adjusted odds ratio (per lateral) was 2.092 (95% CI, 0.865 to 3.308, P = 0.001). The degree of stenosis was also significantly associated with the NIHSS score after adjusting for common risk factors. The odds ratio (per 10%) was 0.712 (95% CI, 0.202 to 1.223, P = 0.007). CONCLUSIONS:The number of stenotic intracranial major arteries is associated with the severity of ischemic stroke independent of the degree of stenosis and other stroke risk factors. To the best of our knowledge, this has not been previosuly studied in great detail using DSA. Our data highlight the importance of examining all major arteries in stroke patients.

The temperature measurement of blast furnace (BF) molten iron is a mandatory requirement in the ironmaking process, and the molten iron temperature is significant in estimating the molten iron quality and control blast furnace condition. However, it is not easy to realize real-time measurement of molten iron temperature because of the harsh environment in the blast furnace casthouse and the high-temperature characteristics of molten iron. To achieve continuous detection of the molten iron temperature of the blast furnace, this paper proposes a temperature measurement method based on infrared thermography and a temperature reduction model. Firstly, an infrared thermal imager is applied to capture the infrared thermal image of the molten iron flow after the skimmer. Then, based on the temperature distribution of the molten iron flow region, a temperature mapping model is established to measure the molten iron temperature after the skimmer. Finally, a temperature reduction model is developed to describe the relationship between the molten iron temperature at the taphole and skimmer, and the molten iron temperature at the taphole is calculated according to the temperature reduction model and the molten iron temperature after the skimmer. Industrial experiment results illustrate that the proposed method can achieve simultaneous measurement of molten iron temperature at the skimmer and taphole and provide reliable temperature data for regulating the blast furnace.

Viscoelastic polymers can be used as damping materials to control unexpected vibration and noise through energy dissipation. To investigate the effect of an inorganic filler on damping property, a series of inorganic particles, Ferriferous oxide(Fe3O4), Graphene/Fe3O4(GF), and Fe3O4 of demagnetization(α-Fe2O3) were incorporated into poly(butyl-methacrylate) (PBMA). The effects of the dispersion of particles, as well as the interaction between particles and the PBMA matrix on the damping property of composites, were systematically studied. Results revealed that the addition of three types of particles can effectively improve the damping properties and broaden the effective damping temperature range. Dispersion of α-Fe2O3 in the PBMA matrix is better than that of Fe3O4. As a result, the damping peak can be increased more. The interaction between GF and the PBMA matrix is stronger than that between Fe3O4 and the PBMA. The damping peak of the composites can be suppressed by GF, which is opposite to Fe3O4 and α-Fe2O3. In addition, glass transition temperature (Tg) of all composites in the study shifted to low temperatures.

Natural and complementary therapies in conjunction with mainstream cancer care are steadily gaining popularity. Ginger extract (GE) confers significant health-promoting benefits owing to complex additive and/or synergistic interactions between its bioactive constituents. Recently, we showed that preservation of natural "milieu" confers superior anticancer activity on GE over its constituent phytochemicals, 6-gingerol (6G), 8-gingerol (8 G), 10-gingerol (10 G) and 6-shogaol (6S), through enterohepatic recirculation. Here we further evaluate and compare the effects of GE and its major bioactive constituents on cytochrome P450 (CYP) enzyme activity in human liver microsomes by monitoring metabolites of CYP-specific substrates using LC/MS/MS detection methods. Our data demonstrate that individual gingerols are potent inhibitors of CYP isozymes, whereas GE exhibits a much higher half-maximal inhibition value, indicating no possible herb-drug interactions. However, GE's inhibition of CYP1A2 and CYP2C8 reflects additive interactions among the constituents. In addition, studies performed to evaluate transporter-mediated intestinal efflux using Caco-2 cells revealed that GE and its phenolics are not substrates of P-glycoprotein (Pgp). Intriguingly, however, 10 G and 6S were not detected in the receiver compartment, indicating possible biotransformation across the Caco-2 monolayer. These data strengthen the notion that an interplay of complex interactions among ginger phytochemicals when fed as whole extract dictates its bioactivity highlighting the importance of consuming whole foods over single agents. Our study substantiates the need for an in-depth analysis of hepatic biotransformation events and distribution profiles of GE and its active phenolics for the design of safe regimens.

Cost-sensitive support vector machine is one of the most popular tools to deal with class-imbalanced problem such as fault diagnosis. However, such data appear with a huge number of examples as well as features. Aiming at class-imbalanced problem on big data, a cost-sensitive support vector machine using randomized dual coordinate descent method (CSVM-RDCD) is proposed in this paper. The solution of concerned subproblem at each iteration is derived in closed form and the computational cost is decreased through the accelerating strategy and cheap computation. The four constrained conditions of CSVM-RDCD are derived. Experimental results illustrate that the proposed method increases recognition rates of positive class and reduces average misclassification costs on real big class-imbalanced data.

The grinding-classification is the prerequisite process for full recovery of the nonrenewable minerals with both production quality and quantity objectives concerned. Its natural formulation is a constrained multiobjective optimization problem of complex expression since the process is composed of one grinding machine and two classification machines. In this paper, a hybrid differential evolution (DE) algorithm with multi-population is proposed. Some infeasible solutions with better performance are allowed to be saved, and they participate randomly in the evolution. In order to exploit the meaningful infeasible solutions, a functionally partitioned multi-population mechanism is designed to find an optimal solution from all possible directions. Meanwhile, a simplex method for local search is inserted into the evolution process to enhance the searching strategy in the optimization process. Simulation results from the test of some benchmark problems indicate that the proposed algorithm tends to converge quickly and effectively to the Pareto frontier with better distribution. Finally, the proposed algorithm is applied to solve a multiobjective optimization model of a grinding and classification process. Based on the technique for order performance by similarity to ideal solution (TOPSIS), the satisfactory solution is obtained by using a decision-making method for multiple attributes.

This paper presents an improved model for improving headway-based bus route service reliability at bus stops using real-time preventive operation control, taking into account dynamic interaction among random passenger demand, stochastic driving conditions of route segments, and vehicle load capacity constraint. In this model, the real-time information of passenger demand and vehicle operation is involved to predict the imminent unacceptable headway deviation, in the case of which some in-time preventive control strategies are deployed according to the given control rules. As a case study, a single fixed bus route with high-frequency services was simulated and different scenarios of real-time preventive operation control were performed. Headway adherence and average passenger wait time were used to measure bus service reliability. The results show that the improved model is closer to the real bus route service, and using real-time information to predict potential service unreliability and trigger in-time preventive control can reduce bus bunching and avoid big gap.

In this paper, an adaptive segmentation based image processing method is presented for mineral flotation processes so as to obtain an optimal quality profile measure. An optimal structural element (SE) is firstly provided for initial image segmentation where fuzzy c-means (FCM) algorithm is combined with a watershed algorithm. Then, the extracted fuzzy texture spectrum feature is discriminated as either under-segmentation or over-segmentation regions. Finally, the segmentation results are subsequently processed to estimate the output probability density function (PDF) of bubble size distribution, which is the basis of the fault detection and real-time control for flotation processes. Experimental results demonstrate that the proposed method avoids both over-segmentation and under segmentation, and reveal the reliability of bubble size distribution nonparametric density estimation.

Differentiated embryo-chondrocyte expressed gene 1 (DEC1, also known as sharp2, stra13, or BHLHB2) is a mammalian basic helix-loop-helix protein that is involved in many aspects of gene regulation through acting as a transcription factor. Changes in DEC1 expression levels have been implicated in the development of cancers. Using COS-7 cell, we showed that DEC1 can be modified by the small ubiquitin-like modifiers, SUMO1, 2 and 3. Two major SUMOylation sites (K(159) and K(279)) were identified in the C-terminal domain of DEC1. Substitution of either K(159) or K(279) with arginine reduced DEC1 SUMOylation, but substitution of both K(159) and K(279) abolished SUMOylation, and more protein appeared to be retained in the cytoplasm compared to wild-type DEC1. The expression of DEC1 was up-regulated after serum starvation as previously reported, but at the same time, serum starvation also led to more SUMOylation of DEC1. In MCF-7 cells SUMOylation also stabilized DEC1 through inhibiting its ubiquitination. Moreover, SUMOylation of DEC1 promoted its repression of CLOCK/BMAL1-mediated transcriptional activity through recruitment of histone deacetylase1. These findings suggested that posttranslational modification of DEC1 in the form of SUMOylation may serve as a key factor that regulates the function of DEC1 in vivo.