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Periodontitis is a chronic inflammatory disease caused by plaque that destroys the alveolar bone tissues, resulting in tooth loss. Poor eradication of pathogenic microorganisms, persistent malignant inflammation and impaired osteo-/angiogenesis are currently the primary challenges to control disease progression and rebuild damaged alveolar bone. However, existing treatments for periodontitis fail to comprehensively address these issues. Herein, an injectable composite hydrogel (SFD/CS/ZIF-8@QCT) encapsulating quercetin-modified zeolitic imidazolate framework-8 (ZIF-8@QCT) is developed. This hydrogel possesses thermo-sensitive and adhesive properties, which can provide excellent flowability and post-injection stability, resist oral fluid washout as well as achieve effective tissue adhesion. Inspirationally, it is observed that SFD/CS/ZIF-8@QCT exhibits a rapid localized hemostatic effect following implantation, and then by virtue of the sustained release of zinc ions and quercetin exerts excellent collective functions including antibacterial, immunomodulation, pro-osteo-/angiogenesis and pro-recruitment, ultimately facilitating excellent alveolar bone regeneration. Notably, our study also demonstrates that the inhibition of osteo-/angiogenesis of PDLSCs under the periodontitis is due to the strong inhibition of energy metabolism as well as the powerful activation of oxidative stress and autophagy, whereas the synergistic effects of quercetin and zinc ions released by SFD/CS/ZIF-8@QCT are effective in reversing these biological processes. Overall, our study presents innovative insights into the advancement of biomaterials to regenerate alveolar bone in periodontitis.

During the engineering structure's operation, the mechanical structure's performance and loading will change with time, so the parameter uncertainty and structural reliability will also have dynamic characteristics. The time-varying reliability analysis method can more accurately evaluate structural reliability by fully using this dynamic uncertainty. However, the time-varying reliability analysis was mainly based on the spanning rate method, which was complex and difficult to obtain the final result. Therefore, this study proposed an enhanced dung beetle optimization (EDBO) assisted time-varying reliability analysis method based on the adaptive Kriging model. With the help of the adaptive Kriging model and the EDBO optimization algorithm, the efficiency of the time-varying reliability analysis method was improved. At the same time, to prevent prematurely falling into the local search trap, the method improved the uniformity of the sample by initializing the sample through improved tent chaotic mapping (ITCM). Next, the Gaussian random walk strategy was used to search the updated position, which further improved the accuracy of the reliability analysis results. Finally, the accuracy and effectiveness of the proposed time-varying reliability analysis method were verified by four mechanical structure model examples. From the calculation results, it can be seen that with the help of the new DBO optimization algorithm, the relative error of the proposed reliability analysis results was about 20%~30% lower than that of the traditional reliability analysis method. What's more, the calculation efficiency was higher than that of other reliability analysis methods.

Abstract Background Studies have shown that mitochondrial function and macrophages may play a role in the development of idiopathic pulmonary fibrosis (IPF). However, the understanding of the interactions and specific mechanisms between mitochondrial function and macrophages in pulmonary fibrosis is still very limited. Methods To construct a prognostic model for IPF based on Macrophage- related genes (MaRGs) and Mitochondria-related genes (MitoRGs), differential analysis was performed to achieve differentially expressed genes (DEGs) between IPF and Control groups in the GSE28042 dataset. Then, MitoRGs, MaRGs and DEGs were overlapped to screen out the signature genes. The univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) algorithm were implemented to achieve key genes. Furthermore, the independent prognostic analysis was employed. The ingenuity pathway analysis (IPA) was employed to further understand the molecular mechanisms of key genes.Next, the immune infiltration analysis was implemented to identify differential immune cells between two risk subgroups. Results There were 4791 DEGs between IPF and Control groups. Furthermore, 26 signature genes were achieved by the intersection processing. Three key genes including ALDH2, MCL1, and BCL2A1 were achieved, and the risk model based on the key genes was created. In addition, a nomogram for survival forecasting of IPF patients was created based on riskScore, Age, and Gender, and we found that key genes were associated with classical pathways including ‘Apoptosis Signaling’, ‘PI3K/AKT Signaling’, and so on. Next, two differential immune cells including Monocytes and CD8 T cells were identified between two risk subgroups. Moreover, we found that MIR29B2CHG and hsa-mir-1-3p could regulate the expression of ALDH2. Conclusion We achieved 3 key genes including ALDH2, MCL1,, and BCL2A1 associated with IPF, providing a new theoretical basis for clinical treatment of IPF.

Abstract Periodontal disease ranks third among noncommunicable illnesses, behind cancer and cardiovascular disease, and is closely related to the occurrence and progression of various systemic diseases. However, elucidating the processes of periodontal disease and promoting periodontal bone regeneration remains a challenge. Here, quercetin is demonstrated to reduce the oxidative stress state of orofacial mesenchymal stem cells (OMSCs) in vitro and to affect the osteogenic growth of OMSCs through molecular mechanisms that mediate the m6A change in Per1. Nevertheless, the limited therapeutic efficacy of systemic medication and the limitations of local medication resulting from the small, moist, and highly dynamic periodontal environment make it challenging to treat periodontal tissues with medication. Herein, a biosafe injectable hydrogel drug‐controlled delivery system is constructed as a bone‐enhancing factory and loaded with quercetin to treat oxidative stress injury in periodontal tissues. This drug‐carrying system made up of nanoscale bioglass microspheres and a light‐cured injectable hydrogel, allows effective drug particle loading and cementation in the dynamic and moist periodontal environment. Furthermore, the system demonstrates the ability to stimulate OMSCs osteogenic differentiation in a Per1‐dependent manner, which ultimately promotes periodontal bone repair, suggesting that this system has potential for clinical periodontal therapy.

Abstract The Circulatory System Based Optimisation (CSBO) stands as a nascent metaheuristic optimisation algorithm known for its proficiency in tackling global optimisation problems. The authors introduce the Multi‐strategy Enhanced CSBO (MECSBO), an algorithm designed for global optimisation and Reliability‐based Design Optimisation (RBDO). MECSBO integrates adaptive inertia weight, golden sine operator and chaos strategy to augment the convergence capacity and efficiency of the original CSBO. Furthermore, MECSBO‐based RBDO algorithm is presented to address RBDO problem. The comparative analysis utilising standard real‐world benchmark functions has been carried out to validate the effectiveness of the proposed MECSBO. Several RBDO problems, including three typical numerical examples and three engineering cases, are used to show abilities of the proposed MECSBO‐based RBDO algorithm. The results demonstrated that MECSBO is outperformed comparing to the state‐of‐the‐art algorithms in terms of accuracy, efficiency, and robustness in RBDO problems.

This research proposes a novel hybrid reliability analysis method for rare failure events, which integrates the coupled Adaptive Kriging model and Generalized Subset Simulation (AK-GSS). In the proposed method, the adaptive Kriging model is applied to approximate the actual Performance Function (PF) to reduce the number of PF calls. A newly updated strategy is proposed to look for samples on the limit state surface to achieve active learning of the Kriging model. This updated strategy avoids the limitations of most current learning functions based on the prediction variance of Kriging models. The advantages of AK-GSS are illustrated through five examples, including two engineering applications of aircraft wings and hydraulic turbine rotor brackets. The results show that the proposed method is more efficient and accurate for rare failure events.

ABSTRACT: Objectives: The study aimed to characterize the quinolone resistance of Salmonella enterica serovar Typhimurium and its monophasic variant (Salmonella enterica serovar 1,4,[5],12:i:-) isolated from food and patients in China. Methods: All of the isolates were assessed for quinolone susceptibility via the broth microdilution method. Then, the isolates were checked for mutations within quinolone resistance-determining regions of gyrA, gyrB, parC, and parE and were examined for plasmid-mediated quinolone resistance genes. Results: High rates of resistance to nalidixic acid in the S. Typhimurium (70.7%) and S. 1,4,[5],12:i:- (41.9%) isolates were observed, and a considerable proportion of isolates with reduced susceptibility to ciprofloxacin and levofloxacin were also detected. The high frequency of mutations in GyrA (60.8%) and a variety of genes (aac[6′]-Ib-cr [23.2%], oqxAB [19.2%], qnrS [13.6%], and qnrA [3.2%]) conferring quinolone resistance in these Salmonella isolates were noteworthy. Lastly, the isolates carrying qnrS for transferability and transmission of the quinolone resistance were analysed by conjugation. Multiple locus variable-number tandem repeat analysis profiles indicated that some qnrS-positive isolates were clonally related, whilst the other isolates were genetically divergent. This suggested that both clonal spread of resistant strains and horizontal transmission of the plasmid-mediated resistance genes contributed to the dissemination of qnrS-positive Salmonella isolates. Conclusion: This study highlights the prevalence of quinolone-resistant S. Typhimurium and S. 1,4,[5],12:i:- in China, posing a threat to public health.

Ramulus mori oligosaccharides were used as the raw material to evaluate its hypoglycemic effect on high-fat diet/streptozotocin-induced diabetic mice through in vivo experiments. The diabetic mice model was established by high-fat diet combined with injection of streptozotocin, and it was given low-dose (200.00 mg/(kg·bw)), medium-dose (400.00 mg/(kg·bw)) and high-dose (800.00 mg/(kg·bw)) of Ramulus mori oligosaccharides by gavage intervention. In order to evaluate the effects of Ramulus mori oligosaccharides on biochemical indicators and intestinal flora in diabetic mice, the body weight and fasting blood glucose value of the mice were regularly measured. After 40 days, the oral glucose tolerance, insulin level, oxidative stress level, intestinal flora composition, kidney-related enzymes of the mice were measured, and the pathological morphology of the mice colon was observed. The results showed that Ramulus mori oligosaccharides alleviated the weight loss of the diabetic mice, decreased fasting blood glucose and glucose tolerance to a certain extent compared with the model group. After treatment with Ramulus mori oligosaccharides, the insulin level of diabetic mice was significantly decreased (P

As engineering systems become increasingly complex, reliability-based design optimization (RBDO) has been extensively studied in recent years and has made great progress. In order to achieve better optimization results, the mathematical model used needs to consider a large number of uncertain factors. Especially when considering mixed uncertainty factors, the contradiction between the large computational cost and the efficiency of the optimization algorithm becomes increasingly fierce. How to quickly find the optimal most probable point (MPP) will be an important research direction of RBDO. To solve this problem, this paper constructs a new RBDO method framework by combining an improved particle swarm algorithm (PSO) with excellent global optimization capabilities and a decoupling strategy using a simulated annealing algorithm (SA). This study improves the efficiency of the RBDO solution by quickly solving MPP points and decoupling optimization strategies. At the same time, the accuracy of RBDO results is ensured by enhancing global optimization capabilities. Finally, this article illustrates the superiority and feasibility of this method through three calculation examples.

Water target recognition is a critical challenge for the perception technology of unmanned surface vessels (USVs). In the application of USV, detection accuracy and the inference time both matter, while it is tough to strike a balance and single-frame water target detection behaves unstable in the video detection. To solve these problems, many strategies are applied to increase YOLOv4’s performance, including network pruning, the focal loss function, blank label training, and preprocessing with histogram normalization. The optimized detection method achieves a mean average precision (mAP) of 81.74% and a prediction speed of 26.77 frames per second (FPS), which meets the USV navigation requirements. To build the integrated USV-based system for water target recognition, a water target dataset containing 9936 images is created from offshore USV experiments in which the human-in-the-loop annotation and mosaic data augmentation methods are used. The issues of miss detection and false alarm can be considerably mitigated by cascading the Siamese-RPN tracking network, and the major color of a water target can be retrieved using a local contrast saliency color detection scheme. The system being tested is called “ME120” includes an embedded edge computing platform (Nvidia Jetson AGX Xavier). Finally, online dataset learning demonstrates the improved YOLOv4 achieves an increase of 66.98% in FPS at the cost of a decrease of 0.79% in mAP when compared with the original YOLOv4 and offline navigation experiments validate that our system achieves high recognition capability while maintaining a high degree of robustness.

Based on the stress interaction theory, this research constructed a model to study the joint moderating effects of the perception reduction of employment opportunities under the COVID-19 epidemic on the employment pressure of college students. With two moderating variables introduced, employment policy support and job-searching self-efficacy, this research studied the mechanism and boundary conditions of perception reduction of employment opportunities on employment pressure of college students from both individual and environmental aspects. The study found that during the epidemic if college students perceived fewer employment opportunities, they could have greater employment pressure from themselves, schools, and families; and that under the joint moderation of employment policy support and job-searching self-efficacy, the perception reduction of employment opportunities under the COVID-19 epidemic, the employment pressure of college students, universities, and families were connected, with different adjustment mechanisms. Based on empirical data, this research can provide theoretical enlightenment and practical guidance for the government, universities, and families to alleviate the employment pressure on college students during the epidemic.

Electric stimulation has been used in changing the morphology, status, membrane permeability, and life cycle of cells to treat certain diseases such as trauma, degenerative disease, tumor, and infection. To minimize the side effects of invasive electric stimulation, recent studies attempt to apply ultrasound to control the piezoelectric effect of nano piezoelectric material. This method not only generates an electric field but also utilizes the benefits of ultrasound such as non-invasive and mechanical effects. In this review, important elements in the system, piezoelectricity nanomaterial and ultrasound, are first analyzed. Then, we summarize recent studies categorized into five kinds, nervous system diseases treatment, musculoskeletal tissues treatment, cancer treatment, anti-bacteria therapy, and others, to prove two main mechanics under activated piezoelectricity: one is biological change on a cellular level, the other is a piezo-chemical reaction. However, there are still technical problems to be solved and regulation processes to be completed before widespread use. The core problems include how to accurately measure piezoelectricity properties, how to concisely control electricity release through complex energy transfer processes, and a deeper understanding of related bioeffects. If these problems are conquered in the future, piezoelectric nanomaterials activated by ultrasound will provide a new pathway and realize application in disease treatment.

With the rise of latency-sensitive and computationally intensive applications in mobile edge computing (MEC) environments, the computation offloading strategy has been widely studied to meet the low-latency demands of these applications. However, the uncertainty of various tasks and the time-varying conditions of wireless networks make it difficult for mobile devices to make efficient decisions. The existing methods also face the problems of long-delay decisions and user data privacy disclosures. In this paper, we present the FDRT, a federated learning and deep reinforcement learning-based method with two types of agents for computation offload, to minimize the system latency. FDRT uses a multi-agent collaborative computation offloading strategy, namely, DRT. DRT divides the offloading decision into whether to compute tasks locally and whether to offload tasks to MEC servers. The designed DDQN agent considers the task information, its own resources, and the network status conditions of mobile devices, and the designed D3QN agent considers these conditions of all MEC servers in the collaborative cloud-side end MEC system; both jointly learn the optimal decision. FDRT also applies federated learning to reduce communication overhead and optimize the model training of DRT by designing a new parameter aggregation method, while protecting user data privacy. The simulation results showed that DRT effectively reduced the average task execution delay by up to 50% compared with several baselines and state-of-the-art offloading strategies. FRDT also accelerates the convergence rate of multi-agent training and reduces the training time of DRT by 61.7%.

Based on microRNA (miR) microarray analysis, we previously found that miR22-5p expression is decreased in the mid-luteal endometrium of women with minimal/mild endometriosis. Bioinformatics analysis predicted that miR22-5p targets ten-eleven translocation (TET2) 3'-untranslated region. This study aimed to determine the regulation and roles of miR22-5p in the pathogenesis of minimal/mild endometriosis-associated infertility. MiR22-5p and TET2 expression in the mid-luteal endometrium from women with or without minimal/mild endometriosis was analyzed. After transfection with miR22-5p mimics or inhibitor, TET2 expression was analyzed by quantitative reverse transcription (RT-q) PCR, western blotting and immunohistochemistry. 5-Hydroxymethylcytosine was determined by immunofluorescence and dot blotting. Expression and promoter methylation of estrogen receptor 2 (ESR2) was measured by RT-qPCR and western blotting, and by bisulfite sequencing, respectively. We first established that miR22-5p expression decreased and TET2 expression increased in minimal/mild endometriosis during implantation window. TET2 was found to be a direct target of miR22-5p. MiR22-5p regulated the expression of ESR2, but did not directly affect methylation of its promoter region (-197/+359). Our results suggest that an imbalance in miR22-5p expression in the mid-luteal endometrium may be involved in minimal/mild endometriosis-associated infertility.

Patch-based image inpainting methods iteratively fill the missing region via searching the best sample patch from the source region. However, most of the existing approaches basically use the fixed size of patch regardless of content features nearby, which may lead to inpainting defects. Also, global match is needed for searching the best sample patch, but only to fill one target patch in each iteration, resulting in low efficiency. To handle the issues above, we first evaluate the nonuniformity in an image, by which the patch size is adaptively determined. Moreover, we divide the source region into multiple non-overlapping subregions with different nonuniformity levels, and the patch match proceeds in every subregion, respectively. This strategy not only saves the match time for single target patch, but also reduces the mismatch, and enables the simultaneous filling of multiple target patches in a single iteration. Experimental results show that in comparison to previous patch-based works, our method has achieved further improvement both in quality and efficiency. We believe our method could provide a new way for patch match with better accuracy and efficiency in image inpainting tasks.

A water-soluble oligosaccharide termed EMOS-1a was prepared by enzymatic hydrolysis of polysaccharides purified from mulberries by column chromatography. The chemical structure of the purified fraction was investigated by ultraviolet spectroscopy, Fourier-transform infrared spectroscopy, and gas chromatography−mass spectrometry, which indicated that galactose was the main constituent of EMOS-1a. Chemical analyses showed that the uronic acid and sulfate content of EMOS-1a were 5.6% and 8.35%, respectively, while gel permeation chromatography showed that EMOS-1a had an average molecular weight of 987 Da. The antioxidant activities of EMOS-1a were next investigated, and EMOS-1a exhibited concentration-dependent 1,1-diphenyl-2-picrylhydrazyl radical scavenging activity, Trolox equivalent antioxidant capacity, and ferric reducing antioxidant power. The level of proliferation of Lactobacillus rhamnosus reached 1420 ± 16% when 4% (w/v) EMOS-1a was added, where the number of colonies in MRS (de Man, Rogosa, and Sharpe) medium with no added oligosaccharide was defined as 100% proliferation. These results indicate that the oligosaccharide EMOS-1a could be used as a natural antioxidant in prebiotic preparations.

The escalating need for reliability analysis (RA) and reliability-based design optimization (RBDO) within engineering challenges has prompted the advancement of saddlepoint approximationmethods (SAM) tailored for such problems. This article offers a detailed overview of the general SAM and summarizes the method characteristics first. Subsequently, recent enhancements in the SAM theoretical framework are assessed. Notably, the mean value first-order saddlepoint approximation (MVFOSA) bears resemblance to the conceptual framework of the mean value second-order saddlepoint approximation (MVSOSA); the latter serves as an auxiliary approach to the former. Their distinctionis rootedinthe varyingexpansionorders of theperformance functionas implementedthrough the Taylor method. Both the saddlepoint approximation and third-moment (SATM) and saddlepoint approximation and fourth-moment (SAFM) strategies model the cumulant generating function (CGF) by leveraging the initial random moments of the function. Although their optimal application domains diverge, each method consistently ensures superior relative precision, enhanced efficiency, and sustained stability. Every method elucidated is exemplified through pertinent RA or RBDO scenarios. By juxtaposing them against alternative strategies, the efficacy of these methods becomes evident. The outcomes proffered are subsequently employed as a foundation for contemplating prospective theoretical and practical research endeavors concerning SAMs. The main purpose and value of this article is to review the SAM and reliability-related issues, which can provide some reference and inspiration for future research scholars in this field. [ABSTRACT FROM AUTHOR]