工业工程最新文献

Noise, as an unavoidable stress (pressure) source in the modern life, affects animals in many ways, both behaviorally and physiologically. Behavioral changes may be driven by changes in hormone secretion in animals. When animals face with noise stress, the neuroendocrine systems, mainly the hypothalamic-pituitary-adrenal (HPA) axis, are activated, which promotes the secretion and release of stress hormones, and then leads to a series of behavioral changes. The behavioral changes can be easily observed, but the changes in physiological indicators such as hormone levels need to be accurately measured. Currently, many studies have measured the variations of stress hormone levels in animals under different noise conditions. Taking glucocorticoid as an example, this paper summarizes the different measurement methods of stress hormones, especially the non-invasive measurement methods, and compares the advantages and shortcomings of them. It provides a variety of measurement choices for the study of related issues, and also helps us to further understand the sources of animal stress, in order to provide a better habitat for animals.

The role of the aryl hydrocarbon receptor (AhR) in regulating oxidative stress and immune responses has been increasingly recognized. However, its involvement in depression and the underlying mechanisms remain poorly understood. This study aimed to investigate the effect of 6-formylindolo[3,2-b]carbazole (FICZ), an endogenous AhR ligand, on a lipopolysaccharide (LPS)-induced depression model and the underlying mechanism. After being treated with FICZ (50 mg/kg), male C57BL/6J mice received intraperitoneal injection of LPS and underwent behavioral tests 24 h later. The levels of inflammatory cytokines, including IL-1β, IL-6, and TNF-α, were measured in the hippocampus and serum using enzyme-linked immunosorbent assay (ELISA). The expression levels of CYP1A1, AhR and NLRP3 were analyzed using qPCR and Western blot. The results showed that, compared with control group, LPS alone significantly down-regulated the expression levels of CYP1A1 mRNA and AhR protein in the hippocampus of mice, reduced glucose preference, prolonged immobility time in forced swimming test, increased IL-6 and IL-1β levels in the hippocampus, increased serum IL-1β level, and up-regulated NLRP3 mRNA and protein expression levels in mouse hippocampus, while FICZ significantly reversed the aforementioned effects of LPS. These findings suggest that AhR activation attenuates the inflammatory response associated with depression and modulates the expression of NLRP3. The present study provides novel insights into the role of AhR in the development of depression, and presents AhR as a potential therapeutic target for the treatment of depression.

The incidence of diabetes mellitus is increasing, and the sleep quality of patients with diabetes mellitus is often affected. Baduanjin may act on biological rhythm of the body, skeletal muscle glucose metabolism, skeletal muscle fibers and suprachiasmatic nucleus (SCN) by regulating the expression of Bmal1 gene, thus regulating the blood glucose level and circadian rhythm of patients with type 2 diabetes mellitus (T2DM) and improving their physiological functions. This article reviews the regulatory effect and mechanism of Baduanjin on Bmal1 gene expression in diabetes patients, and discusses the possibility of Baduanjin to improve the sleep quality of T2DM patients by regulating Bmal1 gene expression. This review can provide a new field for the clinical application of traditional Chinese Qigong Baduanjin, and provide a new scientific basis for exercise therapy of diabetes.

The rapid development of high-throughput chromatin conformation capture (Hi-C) technology provides rich genomic interaction data between chromosomal loci for chromatin structure analysis. However, existing methods for identifying topologically associated domains (TADs) based on Hi-C data suffer from low accuracy and sensitivity to parameters. In this context, a TAD identification method based on spatial density clustering was designed and implemented in this paper. The method preprocessed the raw Hi-C data to obtain normalized Hi-C contact matrix data. Then, it computed the distance matrix between loci, generated a reachability graph based on the core distance and reachability distance of loci, and extracted clustering clusters. Finally, it extracted TAD boundaries based on clustering results. This method could identify TAD structures with higher coherence, and TAD boundaries were enriched with more ChIP-seq factors. Experimental results demonstrate that our method has advantages such as higher accuracy and practical significance in TAD identification.

Electrical impedance tomography (EIT) is a non-radiation, non-invasive visual diagnostic technique. In order to improve the imaging resolution and the removing artifacts capability of the reconstruction algorithms for electrical impedance imaging in human-chest models, the HMANN algorithm was proposed using the Hadamard product to optimize multilayer artificial neural networks (MANN). The reconstructed images of the HMANN algorithm were compared with those of the generalized vector sampled pattern matching (GVSPM) algorithm, truncated singular value decomposition (TSVD) algorithm, backpropagation (BP) neural network algorithm, and traditional MANN algorithm. The simulation results showed that the correlation coefficient of the reconstructed images obtained by the HMANN algorithm was increased by 17.30% in the circular cross-section models compared with the MANN algorithm. It was increased by 13.98% in the lung cross-section models. In the lung cross-section models, some of the correlation coefficients obtained by the HMANN algorithm would decrease. Nevertheless, the HMANN algorithm retained the image information of the MANN algorithm in all models, and the HMANN algorithm had fewer artifacts in the reconstructed images. The distinguishability between the objects and the background was better compared with the traditional MANN algorithm. The algorithm could improve the correlation coefficient of the reconstructed images, and effectively remove the artifacts, which provides a new direction to effectively improve the quality of the reconstructed images for EIT.

This paper investigates the variation of lung tissue dielectric properties with tidal volume under in vivo conditions to provide reliable and valid a priori information for techniques such as microwave imaging. In this study, the dielectric properties of the lung tissue of 30 rabbits were measured in vivo using the open-end coaxial probe method in the frequency band of 100 MHz to 1 GHz, and 6 different sets of tidal volumes (30, 40, 50, 60, 70, 80 mL) were set up to study the trends of the dielectric properties, and the data at 2 specific frequency points (433 and 915 MHz) were analyzed statistically. It was found that the dielectric coefficient and conductivity of lung tissue tended to decrease with increasing tidal volume in the frequency range of 100 MHz to 1 GHz, and the differences in the dielectric properties of lung tissue for the 6 groups of tidal volumes at 2 specific frequency points were statistically significant. This paper showed that the dielectric properties of lung tissue tend to vary non-linearly with increasing tidal volume. Based on this, more accurate biological tissue parameters can be provided for bioelectromagnetic imaging techniques such as microwave imaging, which could provide a scientific basis and experimental data support for the improvement of diagnostic methods and equipment for lung diseases.

There are some problems in positron emission tomography/ computed tomography (PET/CT) lung images, such as little information of feature pixels in lesion regions, complex and diverse shapes, and blurred boundaries between lesions and surrounding tissues, which lead to inadequate extraction of tumor lesion features by the model. To solve the above problems, this paper proposes a dense interactive feature fusion Mask RCNN (DIF-Mask RCNN) model. Firstly, a feature extraction network with cross-scale backbone and auxiliary structures was designed to extract the features of lesions at different scales. Then, a dense interactive feature enhancement network was designed to enhance the lesion detail information in the deep feature map by interactively fusing the shallowest lesion features with neighboring features and current features in the form of dense connections. Finally, a dense interactive feature fusion feature pyramid network (FPN) network was constructed, and the shallow information was added to the deep features one by one in the bottom-up path with dense connections to further enhance the model's perception of weak features in the lesion region. The ablation and comparison experiments were conducted on the clinical PET/CT lung image dataset. The results showed that the APdet, APseg, APdet_s and APseg_s indexes of the proposed model were 67.16%, 68.12%, 34.97% and 37.68%, respectively. Compared with Mask RCNN (ResNet50), APdet and APseg indexes increased by 7.11% and 5.14%, respectively. DIF-Mask RCNN model can effectively detect and segment tumor lesions. It provides important reference value and evaluation basis for computer-aided diagnosis of lung cancer.

The segmentation of dental models is a crucial step in computer-aided diagnosis and treatment systems for oral healthcare. To address the issues of poor universality and under-segmentation in tooth segmentation techniques, an intelligent tooth segmentation method combining multiple seed region growth and boundary extension is proposed. This method utilized the distribution characteristics of negative curvature meshes in teeth to obtain new seed points and effectively adapted to the structural differences between the top and sides of teeth through differential region growth. Additionally, the boundaries of the initial segmentation were extended based on geometric features, which was effectively compensated for under-segmentation issues in region growth. Ablation experiments and comparative experiments with current state-of-the-art algorithms demonstrated that the proposed method achieved better segmentation of crowded dental models and exhibited strong algorithm universality, thus possessing the capability to meet the practical segmentation needs in oral healthcare.

Motor imagery is often used in the fields of sports training and neurorehabilitation for its advantages of being highly targeted, easy to learn, and requiring no special equipment, and has become a major research paradigm in cognitive neuroscience. Transcranial direct current stimulation (tDCS), an emerging neuromodulation technique, modulates cortical excitability, which in turn affects functions such as locomotion. However, it is unclear whether tDCS has a positive effect on motor imagery task states. In this paper, 16 young healthy subjects were included, and the electroencephalogram (EEG) signals and near-infrared spectrum (NIRS) signals of the subjects were collected when they were performing motor imagery tasks before and after receiving tDCS, and the changes in multiscale sample entropy (MSE) and haemoglobin concentration were calculated and analyzed during the different tasks. The results found that MSE of task-related brain regions increased, oxygenated haemoglobin concentration increased, and total haemoglobin concentration rose after tDCS stimulation, indicating that tDCS increased the activation of task-related brain regions and had a positive effect on motor imagery. This study may provide some reference value for the clinical study of tDCS combined with motor imagery.

Recently, there has been increasing interest in the use of bacteria for cancer therapy due to their ability to selectively target tumor sites and inhibit tumor growth. However, the complexity of the interaction between bacteria and tumor cells evokes unpredictable therapeutic risk, which induces inflammation, stimulates the up-regulation of cyclooxygenase II (COX-2) protein, and stimulates downstream antiapoptotic gene expression in the tumor microenvironment to reduce the antitumor efficacy of chemotherapy and immunotherapy. In this study, we encapsulated celecoxib (CXB), a specific COX-2 inhibitor, in liposomes anchored to the surface of Escherichia coli Nissle 1917 (ECN) through electrostatic absorption (C@ECN) to suppress ECN-induced COX-2 up-regulation and enhance the synergistic antitumor effect of doxorubicin (DOX). C@ECN improved the antitumor effect of DOX by restraining COX-2 expression. In addition, local T lymphocyte infiltration was induced by the ECN to enhance immunotherapy efficacy in the tumor microenvironment. Considering the biosafety of C@ECN, a hypoxia-induced lysis circuit, pGEX-Pvhb-Lysis, was introduced into the ECN to limit the number of ECNs in vivo. Our results indicate that this system has the potential to enhance the synergistic effect of ECN with chemical drugs to inhibit tumor progression in medical oncology.