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Tumor necrosis factor alpha-induced protein 9 (TNFAIP9) is a crucial effector molecule that protects cells from inflammatory and metabolic damage. This study focuses on investigating the role and regulatory mechanisms of TNFAIP9 in the progression of chronic kidney disease (CKD). By analyzing CKD-related datasets from the GEO database, we discovered that TNFAIP9 was upregulated in CKD patients and CKD mice compared to their normal controls. To elucidate the functional role of TNFAIP9, we established a mouse model of CKD through a two-step 5/6 nephrectomy (Nx). The experimental mice were transduced with an adenoviral vector to express TNFAIP9. The results showed that mice undergoing 5/6-Nx developed evident renal impairment, inflammation, and fibrosis. Overexpression of TNFAIP9 resulted in the remission of renal impairment, a decreased inflammatory response, and a reduced expression of fibrotic markers. In vitro, human renal tubular epithelial human kidney-2 (HK-2) cells were exposed to tumor necrosis factor-alpha (TNF-α) or transforming growth factor-beta (TGF-β) to simulate inflammatory and fibrotic conditions, respectively. Then, the overexpression plasmid or small interfering RNA (siRNA) targeting TNFAIP9 was transfected into HK-2 cells to either overexpress or knock down the target protein. Overexpression of TNFAIP9 reduced the TNF-α-induced inflammatory response, while its knockdown amplified it. Likewise, overexpression of TNFAIP9 decreased the TGF-β-induced fibrosis, whereas its knockdown heightened it. In summary, it is suggested that TNFAIP9 plays a protective role against the early stage of CKD by suppressing renal inflammation and fibrosis. Therefore, targeting TNFAIP9 could be a promising therapeutic approach for CKD.

The setting of parameter values will directly affect the performance of the neural network, and the manual parameter tuning speed is slow, and it is difficult to find the optimal combination of parameters. Based on this, this paper applies the improved Hunger Games search algorithm to find the optimal value of neural network parameters adaptively, and proposes an ATHGS-GoogleNet model. Firstly, adaptive weights and chaos mapping were integrated into the hunger search algorithm to construct a new algorithm, ATHGS. Secondly, the improved ATHGS algorithm was used to optimize the parameters of GoogleNet to construct a new model, ATHGS-GoogleNet. Finally, in order to verify the effectiveness of the proposed algorithm ATHGS and the model ATHGS-GoogleNet, a comparative experiment was set up. Experimental results show that the proposed algorithm ATHGS shows the best optimization performance in the three engineering experimental designs, and the accuracy of the proposed model ATHGS-GoogleNet reaches 98.1%, the sensitivity reaches 100%, and the precision reaches 99.5%.

The parameter values of neural networks will directly affect the performance of the network, so it is very important to choose the appropriate parameter tuning method to improve the performance of the neural network. In this paper, the improved beluga whale optimization hyperparameter optimization ResNet model is used to construct a new model, EBWO-ResNet. Firstly, in order to solve the problem that the initial population of the original beluga whale optimization is not rich enough, the Tent chaotic map is introduced into the beluga whale optimization, and a new algorithm EBWO is constructed. Secondly, in order to solve the problems of low accuracy and difficult parameter tuning of ResNet, the EBWO algorithm was integrated into ResNet to construct a new model EBWO-ResNet. Finally, in order to verify the effectiveness of the EBWO algorithm, the EBWO algorithm was applied to three engineering problems and compared with other five swarm intelligent algorithms, and in order to verify the effectiveness of the EBWO-ResNet model, EBWO-ResNet was applied to maize disease identification,in order to improve the accuracy of corn identification and ensure corn yield,and the other seven models were compared based on three evaluation indexes. The experimental results show that the EBWO algorithm provides the best solutions in the three engineering problems, and the EBWO-ResNet has the best performance in identifying maize diseases, with an accuracy of 96.3%,which is 0.2-1.5 percentage points higher than that of other models.

In this paper, the author investigates Chemostat with Delay and Simplifified Holling Type-IV Response Function, which more match the actual meaning in the chemostat system. Using bifurcation theory, we discuss the hopf bifurcation stability in detail.

Achieving oriented and ordered remineralization on the surface of demineralized dental enamel, thereby restoring the satisfactory mechanical properties approaching those of sound enamel, is still a challenge for dentists. To mimic the natural biomineralization approach for enamel remineralization, the biological process of enamel development proteins, such as amelogenin, was simulated in this study. In this work, carboxymethyl chitosan (CMC) conjugated with alendronate (ALN) was applied to stabilize amorphous calcium phosphate (ACP) to form CMC/ACP nanoparticles. Sodium hypochlorite (NaClO) functioned as the protease which decompose amelogenin in vivo to degrade the CMC-ALN matrix and generate HAP@ACP core-shell nanoparticles. Finally, when guided by 10 mM glycine (Gly), HAP@ACP nanoparticles can arrange orderly and subsequently transform from an amorphous phase to well-ordered rod-like apatite crystals to achieve oriented and ordered biomimetic remineralization on acid-etched enamel surfaces. This biomimetic remineralization process is achieved through the oriented attachment (OA) of nanoparticles based on non-classical crystallization theory. These results indicate that finding and developing analogues of natural proteins such as amelogenin involved in the biomineralization by natural macromolecular polymers and imitating the process of biomineralization would be an effective strategy for enamel remineralization. Furthermore, this method represents a promising method for the management of early caries in minimal invasive dentistry (MID).

Currently, it is still a tough task for dentists to remineralize dentine in deep caries. The aim of this study was to remineralize demineralized dentine in a tooth model of deep caries using nanocomplexes of carboxymethyl chitosan/amorphous calcium phosphate (CMC/ACP) based on mimicking the stabilizing effect of dentine matrix protein 1 (DMP1) on ACP in the biomineralization of dentine. The experimental results indicate that CMC can stabilize ACP to form nanocomplexes of CMC/ACP, which is able to be processed into scaffolds by lyophilization. In the single-layer collagen model, ACP nanoparticles are released from scaffolds of CMC/ACP nanocomplexes dissolved and then infiltrate into collagen fibrils via the gap zones (40 nm) to accomplish intrafibrillar mineralization of collagen. With this method, the completely demineralized dentine was partially remineralized in the tooth mode. This is a bottom-up remineralizing strategy based on non-classical crystallization theory. Since nanocomplexes of CMC/ACP show a promising effect of remineralization on demineralized dentine via biomimetic strategy, thereby preserving dentinal tissue to the maximum extent possible, it would be a potential indirect pulp capping (IPC) material for the management of deep caries during vital pulp therapy based on the concept of minimally invasive dentistry (MID).

In this study, a hybrid method combining extreme learning machine (ELM) and particle swarm optimization (PSO) is proposed to forecast train arrival delays that can be used for later delay management and timetable optimization. First, nine characteristics (e.g., buffer time, the train number, and station code) associated with train arrival delays are chosen and analyzed using extra trees classifier. Next, an ELM with one hidden layer is developed to predict train arrival delays by considering these characteristics mentioned before as input features. Furthermore, the PSO algorithm is chosen to optimize the hyperparameter of the ELM compared to Bayesian optimization and genetic algorithm solving the arduousness problem of manual regulating. Finally, a case is studied to confirm the advantage of the proposed model. Contrasted to four baseline models (k-nearest neighbor, categorical boosting, Lasso, and gradient boosting decision tree) across different metrics, the proposed model is demonstrated to be proficient and achieve the highest prediction accuracy. In addition, through a detailed analysis of the prediction error, it is found that our model possesses good robustness and correctness.

Redundant manipulators are universally employed to save manpower and improve work efficiency in numerous areas. Nevertheless, the redundancy makes the inverse kinematics of manipulators hard to address, thus increasing the difficulty in instructing manipulators to perform a given task. To deal with this problem, an online learning fuzzy echo state network (OLFESN) is proposed in the first place, which is based upon an online learning echo state network and the Takagi–Sugeno–Kang fuzzy inference system (FIS). Then, an OLFESN-based control scheme is devised to implement the efficient control of redundant manipulators. Furthermore, simulations and experiments on redundant manipulators, covering UR5 and Franka Emika Panda manipulators, are carried out to verify the effectiveness of the proposed control scheme.

The wavefront reconstruction is a crucial step in determining the performance of wavefront detection instruments. The wavefront reconstruction algorithm is primarily evaluated in three dimensions: accuracy, speed, and noise immunity. In this paper, we propose a hybrid zonal reconstruction algorithm that introduces slope and curvature information in the diagonal, anti-diagonal, horizontal, and vertical directions by dividing the neighbor sampling points into subregions in groups of four. By canceling the same parameters in integration equations, an algorithm using multi-directional slope–curvature information is achieved with only two sets of integration equations in each subregion, reducing the processing time. Simulation experiments show that the relative root-mean-square reconstruction error of this algorithm is improved by about 4 orders of magnitude compared with existing algorithms that use multi-directional slope information or slope–curvature information alone. Compared with the hybrid multi-directional slope–curvature algorithm, the proposed algorithm can reduce computation time by about 50% as well as provide better noise immunity and reconstruction accuracy. Finally, the validity of the proposed algorithm is verified by the null test experiment.

Cascaded diffractive optical element (DOE), consisting of multiple DOE layers, is a type of multi-layer architecture that introduces additional design freedom, e.g. rotation angle, wavelength or polarization state, enabling more flexible and precise modulation of light field compared to a single-layer DOE. This enhanced modulation capability endows it with significant potential for applications in the field of information encryption. For this application, the fidelity of image reconstruction is critically important to the performance of the cascaded DOE. In this work, we propose a new cascaded DOE design framework with the integration of an optimized Harvey’s model, enabling larger modulation bandwidth compared to conventional angular spectrum method (ASM), thereby increasing the information capacity of cascaded DOE, as well as the accuracy of reconstructed images. To validate the proposed method, we design a cascaded DOE for four distinct images encryption. The correlation coefficient of decrypted images is improved by 37% compared to the result that used ASM-based design method. Future work includes fabricating the designed DOE using a two-photon polymerization (2PP) technique and verifying its performance experimentally.