Defective phagocytosis leads to neurodegeneration through systemic increased innate immune signaling.

In nervous system development, disease, and injury, neurons undergo programmed cell death, leaving behind cell corpses that are removed by phagocytic glia. Altered glial phagocytosis has been implicated in several neurological diseases including Alzheimer's disease. To untangle the links between glial phagocytosis and neurodegeneration, we investigated Drosophila mutants lacking the phagocytic receptor Draper. Loss of Draper leads to persistent neuronal cell corpses and age-dependent neurodegeneration. Here we investigate whether the phagocytic defects observed in draper mutants lead to chronic increased immune activation that promotes neurodegeneration. We found that the antimicrobial peptide Attacin-A is highly upregulated in the fat body of aged draper mutants and that the inhibition of the Immune deficiency (Imd) pathway in the glia and fat body of draper mutants led to reduced neurodegeneration. Taken together, these findings indicate that phagocytic defects lead to neurodegeneration via increased immune signaling, both systemically and locally in the brain.

Truck model recognition for an automatic overload detection system based on the improved MMAL-Net.

Efficient and reliable transportation of goods through trucks is crucial for road logistics. However, the overloading of trucks poses serious challenges to road infrastructure and traffic safety. Detecting and preventing truck overloading is of utmost importance for maintaining road conditions and ensuring the safety of both road users and goods transported. This paper introduces a novel method for detecting truck overloading. The method utilizes the improved MMAL-Net for truck model recognition. Vehicle identification involves using frontal and side truck images, while APPM is applied for local segmentation of the side image to recognize individual parts. The proposed method analyzes the captured images to precisely identify the models of trucks passing through automatic weighing stations on the highway. The improved MMAL-Net achieved an accuracy of 95.03% on the competitive benchmark dataset, Stanford Cars, demonstrating its superiority over other established methods. Furthermore, our method also demonstrated outstanding performance on a small-scale dataset. In our experimental evaluation, our method achieved a recognition accuracy of 85% when the training set consisted of 20 sets of photos, and it reached 100% as the training set gradually increased to 50 sets of samples. Through the integration of this recognition system with weight data obtained from weighing stations and license plates information, the method enables real-time assessment of truck overloading. The implementation of the proposed method is of vital importance for multiple aspects related to road traffic safety.

Federated Transfer Learning Strategy: A Novel Cross-Device Fault Diagnosis Method Based on Repaired Data.

Federated learning has attracted much attention in fault diagnosis since it can effectively protect data privacy. However, efficient fault diagnosis performance relies on the uninterrupted training of model parameters with massive amounts of perfect data. To solve the problems of model training difficulty and parameter negative transfer caused by data corruption, a novel cross-device fault diagnosis method based on repaired data is proposed. Specifically, the local model training link in each source client performs random forest regression fitting on the fault samples with missing fragments, and then the repaired data is used for network training. To avoid inpainting fragments to produce the wrong characteristics of faulty samples, joint domain discrepancy loss is introduced to correct the phenomenon of parameter bias during local model training. Considering the randomness of the overall performance change brought about by the local model update, an adaptive update is proposed for each round of global model download and local model update. Finally, the experimental verification was carried out in various industrial scenarios established by three sets of bearing data sets, and the effectiveness of the proposed method in terms of fault diagnosis performance and data privacy protection was verified by comparison with various currently popular federated transfer learning methods.

An Approach to Integrated Scheduling of Flexible Job-Shop Considering Conflict-Free Routing Problems.

This study proposes an approach to minimize the maximum makespan of the integrated scheduling problem in flexible job-shop environments, taking into account conflict-free routing problems. A hybrid genetic algorithm is developed for production scheduling, and the optimal ranges of crossover and mutation probabilities are also discussed. The study applies the proposed algorithm to 82 test problems and demonstrates its superior performance over the Sliding Time Window (STW) heuristic proposed by Bilge and the Genetic Algorithm proposed by Ulusoy (UGA). For conflict-free routing problems of Automated Guided Vehicles (AGVs), the genetic algorithm based on AGV coding is used to study the AGV scheduling problem, and specific solutions are proposed to solve different conflicts. In addition, sensors on the AGVs provide real-time data to ensure that the AGVs can navigate through the environment safely and efficiently without causing any conflicts or collisions with other AGVs or objects in the environment. The Dijkstra algorithm based on a time window is used to calculate the shortest paths for all AGVs. Empirical evidence on the feasibility of the proposed approach is presented in a study of a real flexible job-shop. This approach can provide a highly efficient and accurate scheduling method for manufacturing enterprises.

Polyethylene glycol diacrylate scaffold filled with cell-laden methacrylamide gelatin/alginate hydrogels used for cartilage repair.

Natural cartilage tissue has excellent mechanical properties and has certain cellular components. At this stage, it is a great challenge to produce cartilage scaffolds with excellent mechanical properties, biocompatibility, and biodegradability. Hydrogels are commonly used in tissue engineering because of their excellent biocompatibility; however, the mechanical properties of commonly used hydrogels are difficult to meet the requirements of making cartilage scaffolds. The mechanical properties of high concentration polyethylene glycol diacrylate (PEGDA) hydrogel are similar to those of natural cartilage, but its biocompatibility is poor. Low concentration hydrogel has better biocompatibility, but its mechanical properties are poor. In this study, two different hydrogels were combined to produce cartilage scaffolds with good mechanical properties and strong biocompatibility. First, the PEGDA grid scaffold was printed with light curing 3D printing technology, and then the low concentration GelMA/Alginate hydrogel with chondral cells was filled into the PEGDA grid scaffold. After a series of cell experiments, the filling hydrogel with the best biocompatibility was screened out, and finally the filled hydrogel with cells and excellent biocompatibility was obtained. Cartilage tissue engineering scaffolds with certain mechanical properties were found to have a tendency of cartilage formation in in vitro culture. Compared with the scaffold obtained by using a single hydrogel, this molding method can produce a tissue engineering scaffold with excellent mechanical properties on the premise of ensuring biocompatibility, which has a certain potential application value in the field of cartilage tissue engineering.

Drug release evaluation of Paclitaxel/Poly-L-Lactic acid nanoparticles based on a microfluidic chip.

Paclitaxel is a commonly used drug in the medical field because of its strong anticancer effect. However, it may produce relatively severe side effects (i.e., allergic reactions). A major characteristic of paclitaxel is low solubility in water. Special solvents are used for dissolving paclitaxel and preparing the paclitaxel drugs, while the solvents themselves will cause certain effects. Polyoxyethylene castor oil, for example, can cause severe allergic reactions in some people, and the clinical use is limited. In this study, we developed a new Paclitaxel/Poly-L-Lactic Acid (PLLA) nanoparticle drug, which is greatly soluble in water, and carried out in vitro drug sustained release research on it and the original paclitaxel drug. However, because the traditional polymer drug carrier usually uses dialysis bag and thermostatic oscillation system to measure the drug release degree in vitro, the results obtained are greatly different from the actual drug release results in human body. Therefore, this paper adopts the microfluidic chip we previously developed to mimic the human blood vessels microenvironment to study the sustained-release of Paclitaxel/PLLA nanoparticles to make the results closer to the release value in human body. The experimental results showed that compared with the original paclitaxel drug, Paclitaxel/PLLA nanoparticles have a long-sustained release time and a slow drug release, realizing the sustained low-dose release of paclitaxel, a cell cycle-specific anticancer drug, and provided certain reference significance and theoretical basis for the research and development of anticancer drugs.

A New Universal Domain Adaptive Method for Diagnosing Unknown Bearing Faults.

The domain adaptation problem in transfer learning has received extensive attention in recent years. The existing transfer model for solving domain alignment always assumes that the label space is completely shared between domains. However, this assumption is untrue in the actual industry and limits the application scope of the transfer model. Therefore, a universal domain method is proposed, which not only effectively reduces the problem of network failure caused by unknown fault types in the target domain but also breaks the premise of sharing the label space. The proposed framework takes into account the discrepancy of the fault features shown by different fault types and forms the feature center for fault diagnosis by extracting the features of samples of each fault type. Three optimization functions are added to solve the negative transfer problem when the model solves samples of unknown fault types. This study verifies the performance advantages of the framework for variable speed through experiments of multiple datasets. It can be seen from the experimental results that the proposed method has better fault diagnosis performance than related transfer methods for solving unknown mechanical faults.