Characterization of Intestinal Flora in Osteoporosis Patients Based on 16S rDNA Sequencing.

Aim: This study investigated differences in gut flora between osteoporosis (OP) patients and healthy individuals using 16S rDNA sequencing. The correlation between differential flora abundance and bone mineral density (BMD) was analyzed, and key flora and potential mechanisms associated with OP were explored.

Methods: Forty-three OP patients and twenty-four healthy volunteers were recruited.

Model Fusion and Multiscale Feature Learning for Fault Diagnosis of Industrial Processes.

The data generated by modern industrial processes often exhibit high-dimensional, nonlinear, timing, and multiscale characteristics. Presently, most of the fault diagnosis methods based on deep learning only consider the part of the characteristics of industrial data, which will cause the loss of part of the feature information during training, thereby affecting the final diagnosis effect. In order to solve the above problems, this article proposes an end-to-end multiscale feature learning method based on model fusion, which can simultaneously extract multiscale spatial features and temporal features of data, effectively reducing the loss of feature information.

Glycyrrhizic acid promotes sciatic nerves recovery in type 1 diabetic rats and protects Schwann cells from high glucose-induced cytotoxicity.

The present study aims to investigate the therapeutic effect and mechanism of glycyrrhizic acid (GA) in diabetic peripheral neuropathy (DPN). GA significantly mitigated nerve conduction velocity (NCV) deficit and morphological abnormality and reduced high-mobility group box-1 (HMGB1) expression in the sciatic nerves of diabetic rats independent of blood glucose and body weight. Notably, GA alleviated the increase of HMGB1 and the decrease of cell viability in high glucose-stimulated RSC96 cells.

Sirt1 Mediates Vitamin D Deficiency-Driven Gluconeogenesis in the Liver via mTorc2/Akt Signaling.

As an active form of vitamin D (VD), 1,25-dihydroxyvitamin D (1,25(OH)D) is involved in the development of many metabolic diseases, such as diabetes, autoimmune diseases, and tumours. While prospective epidemiological studies have consistently implicated VD deficiency in the regulation of glucose metabolism and insulin sensitivity, the specific mechanism remains unclear. Here, we generated 1(OH)ase-null mice (targeted ablation of the 25-hydroxyvitamin D 1 hydroxylase enzyme) and found that these mice developed hepatic glucose overproduction, glucose intolerance, and hepatic insulin resistance accompanied by reduced Sirtuin 1 (Sirt1) expression.

Serum myeloid-related protein 8/14 is a potential predictor of diabetic kidney disease.

Background: Myeloid-related protein 8/14 (MRP8/14) is secreted by macrophages and formed by MRP8 and MRP14, which is closely related to vascular inflammation. Chronic vascular inflammation plays a significant role in the development and progression of diabetic kidney disease (DKD). This study aims to investigate the relationship between MRP8/14 and DKD.

Intelligent Fault Diagnosis for Chemical Processes Using Deep Learning Multimodel Fusion.

Deep learning technology has been widely used in fault diagnosis for chemical processes. However, most deep learning technologies currently adopted only use a single network stack or a certain network stack with multilayer perceptron (MLP) behind it. Compared with traditional fault diagnosis technologies, this method has made progress in both the diagnosis accuracy and speed, but due to the limited performance of a single network, the accuracy or speed cannot meet the requirements to the greatest extent.

A 2D-FM model-based robust iterative learning model predictive control for batch processes.

The work deals with composite iterative learning model predictive control (CILMPC) for uncertain batch processes via a two dimensional Fornasini-Marchesini (2D-FM) model. A novel equivalent error system is first presented which is composed of state error and tracking error. Then an iterative learning predictive updating law is constructed by 2D state feedback control and the 'worst' case linear quadratic function is also designed.

Keap1-Nrf2 signaling activation by Bardoxolone-methyl ameliorates high glucose-induced oxidative injury in human umbilical vein endothelial cells.

In cultured human umbilical vein endothelial cells (HUVECs) high glucose (HG) stimulation will lead to significant cell death. Bardoxolone-methyl (BARD) is a NF-E2 p45-related factor 2 (Nrf2) agonist. In this study we show that BARD, at only nM concentrations, activated Nrf2 signaling in HUVECs.

Improved state space model predictive fault-tolerant control for injection molding batch processes with partial actuator faults using GA optimization.

A novel model predictive fault-tolerant control (MPFTC) strategy adopting genetic algorithm (GA) is proposed for batch processes under the case of disturbances and partial actuator faults. Based on the extended state space model in which the tracking error is contained, there are more degrees of freedom provided for the controller design and better control performance is obtained. In order to enhance the control performance further, the GA is introduced to optimize the relevant weighting matrices in the cost function.

Suppression of TLR4 by miR-448 is involved in Diabetic development via regulating Macrophage polarization.

Objectives: Lipopolysaccharide (LPS) contributed to the development and progression of type 2 diabetes mellitus (T2D), while TLR4 is reported to mediate the LPS-induced inflammation in macrophages. However, the potential molecular mechanisms for TLR4-mediated macrophages activation in T2D have not yet to be fully clarified.

Methods: Type 2 diabetes models in C57BL/6J mice were generated by a combination administration of streptozotocin (STZ) and a high-fat diet (HFD).

GLP‑1R agonists ameliorate peripheral nerve dysfunction and inflammation via p38 MAPK/NF‑κB signaling pathways in streptozotocin‑induced diabetic rats.

The present study aimed to investigate the mechanism of glucagon‑like peptide‑1 receptor (GLP‑1R) agonists in the progression of diabetic peripheral neuropathy (DPN) in streptozotocin (STZ)‑induced diabetic rats, through inflammatory signaling pathways. The DPN rat model was generated by intraperitoneal injection of STZ and then treated with the GLP‑1R agonist liraglutide or saline for 8 weeks. These animals were randomly divided into 4 groups (10 rats in each): The normal control + saline group, the normal control + liraglutide group, the diabetic + saline (DM) group and the diabetic + liraglutide (DML) group.

Improved fuzzy PID controller design using predictive functional control structure.

In conventional PID scheme, the ensemble control performance may be unsatisfactory due to limited degrees of freedom under various kinds of uncertainty. To overcome this disadvantage, a novel PID control method that inherits the advantages of fuzzy PID control and the predictive functional control (PFC) is presented and further verified on the temperature model of a coke furnace. Based on the framework of PFC, the prediction of the future process behavior is first obtained using the current process input signal.

Improved design of constrained model predictive tracking control for batch processes against unknown uncertainties.

In this paper, an improved constrained tracking control design is proposed for batch processes under uncertainties. A new process model that facilitates process state and tracking error augmentation with further additional tuning is first proposed. Then a subsequent controller design is formulated using robust stable constrained MPC optimization.

Application of multi-model switching predictive functional control on the temperature system of an electric heating furnace.

A method of multi-model switching based predictive functional control is proposed and applied to the temperature control system of an electric heating furnace. The control strategies provide the effective and independent control modes of the electric heating furnace temperature in order to obtain improved control performance. The method depends on conventional implementation of the multi-model switching state, which requires some endeavors to tune the switching model in the model predictive control and allows a reduction of the calculation compared with the weighted multiple model algorithms.

Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems.

Modeling of distributed parameter systems is difficult because of their nonlinearity and infinite-dimensional characteristics. Based on principal component analysis (PCA), a hybrid modeling strategy that consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear radial basis function (RBF) neural network model are proposed. The spatial-temporal output is first divided into a few dominant spatial basis functions and finite-dimensional temporal series by PCA.

Multivariable design of improved linear quadratic regulation control for MIMO industrial processes.

In this study, a multivariable linear quadratic control system using a new state space structure was developed for the chamber pressure in the industrial coke furnace. Such processes typically have complex and nonlinear dynamic behavior, which causes the performance of controllers using conventional design and tuning to be poor or to require significant effort in practice. The process model is first treated into a new state space form and the implementation of linear quadratic control is designed using this new model structure.

Enhancing in vitro bioactivity and in vivo osteogenesis of organic-inorganic nanofibrous biocomposites with novel bioceramics.

Fabricating bioactive nanofibrous scaffolds from biodegradable polymers to mimic native tissue is an important approach in repairing bony defects. Silk fibroin (SF) may contribute to bone regeneration because of its excellent mechanical properties, slow degradability, and low osteoconductivity. A combination of bioceramic-polymer materials is generally used to provide an improved osteoconductive environment for bone healing.

Adaptive extended state space predictive control for a kind of nonlinear systems.

This paper presents an adaptive nonlinear predictive control design strategy for a kind of nonlinear systems with output feedback coupling and results in the improvement of regulatory capacity for reference tracking, robustness and disturbance rejection. The nonlinear system is first transformed into an equal time-variant system by analyzing the nonlinear part. Then an extended state space predictive controller with a similar structure of a PI optimal regulator and with P-step setpoint feedforward control is designed.