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ABSTRACT Background Non–small cell lung cancer (NSCLC) is one of the cancers with the highest mortality and morbidity in the world. Circular RNAs (circRNAs) are newly identified players in carcinogenesis and development of various cancers. This study is aimed at exploring the functional effects and mechanism of circ_0028826 in the development of NSCLC. Methods Real‐time quantitative PCR (RT‐qPCR) was used to detect the expression levels of circ_0028826, IDH2 mRNA, and miR‐758‐3p. IDH2, Bcl2, Bax, and E‐cadherin protein levels were detected using a western blot. Cell Counting Kit‐8 (CCK‐8), 5‐ethynyl‐2′‐deoxyuridine (EdU), flow cytometry, wound healing, and transwell assays were used to assess the capacities of proliferation, apoptosis, migration, and invasion. Interaction between miR‐758‐3p and circ_0028826 or IDH2 was validated using a dual‐luciferase reporter assay. The role of circ_0028826 in vivo was checked based on a xenograft tumor model. Results Circ_0028826 was elevated in NSCLC, and its absence inhibited NSCLC cell proliferation, migration, invasion, and induced apoptosis. In terms of mechanism, circ_0028826 increased IDH2 expression by targeting miR‐758‐3p. In addition, circ_0028826 knockdown also regulated IDH2 by targeting miR‐758‐3p to inhibit tumor growth in vivo. Conclusion Circ_0028826 promoted the development of NSCLC via regulation of the miR‐758‐3p/IDH2 axis, providing a new strategy for NSCLC treatment.

Fibrous plants with higher biomass, particularly industrial hemp, have ability to withstand and accumulate significant quantities of heavy metals from contaminated environments. The present study aimed to evaluate the dynamics of different levels (ratios) of macronutrients nitrogen, phosphorus and potassium (NPK) viz., NPK1NPK (1:1:1); NPK2NPK (2:1:1); NPK3NPK (3:1:2); NPK4NPK (4:1:2) on hemp growth and Cu contents under various levels of Cu stress (100, 400 and 800 mg kg−1 on dry soil basis using CuSO4·5H2O). Results revealed that by increasing the Cu stress, growth and biomass decreased linearly and lipid per oxidation and enzymatic antioxidants increased. Balanced application of NPK improved the biomass and decreased the membrane damage by the modulation of malonaldehyde contents. Maximum concentration of Cu in roots (377.47 ± 4.90 mg kg−1), shoots (137.45 ± 5.60 mg kg−1) and (150.07 ± 3.57 mg kg−1) was recorded at Cu3NPK2 treatment as compared to control. Maximum translocation factor (TF) and bioaccumulation coefficients (BAC) in the shoots and leaves of hemp plant were noticed where Cu stress was applied at the rate of 100 mg kg−1. However, BAC and TF were below 1. The NPK2 treatment enhanced biomass and increase Cu content both in leaves and stems, rather than the roots. Our study suggests that balanced application of NPK is a practicable approach to alleviate Cu stress and improve biomass production of industrial hemp plant. These findings indicate that optimum nutrient supply, under Cu stress, can maximize the growth potential and overall health of industrial hemp, making it a viable option for phytoremediation and sustainable agriculture on contaminated soils.

Digital technologies are empowering economic and social development, which attracts scholars’ attention to the relationship between digitalization and economic resilience, However, the empirical analysis for different countries and stages of development are inconsistent, and the influencing mechanism need to be further explored. Using panel data for 284 prefecture-level cities in China from 2007 to 2020, this study examines the impact of urban digital development on economic resilience. The findings are as follows: (1) The increased digitalization significantly enhances the urban economic resilience, and this effect was more pronounced in eastern regions and large-scale cities. (2) The relationship between digitalization and economic resilience follows an inverted U-shape as population density increases. (3) The spatial effects show that increased digitalization has a significant positive effect on local economic resilience, but weakens the resilience of the surrounding areas. (4) The analysis of mechanism reveals that the positive impact of digitalization on the urban economic resilience is mainly achieved by improving the quality of the regional labor force and total factor productivity. The study provides theoretical and empirical evidence for accelerating the digital construction, fully releasing the digital dividend in order to strengthen economic resilience.

Imbalance of active oxygen metabolism can lead to oxidative stress in postharvest fruits and vegetables, which could accelerate their quality deterioration, and ruin their commercial value. In this review, the relationship between the antioxidant effect and storage quality of fruits and vegetables has been taken as the breakthrough point, and the effect of antioxidant enzyme activities on delaying postharvest quality deterioration of fruits and vegetables induced by low temperature has been discussed. Moreover, antioxidant effects in postharvest fruits and vegetables have been further analyzed from the perspective of transcriptional regulation, to explore the role of low temperature responsive transcription factors like bHLH, WRKY and NAC in the regulation of antioxidant enzyme activities during low temperature storage, and finally to reveal the possible mechanism of antioxidant transcription regulation that low temperature could delay the quality deterioration of fruits and vegetables. Therefore, this article was suggested to provide a new theoretical basis for carrying out basic research on postharvest quality of fruits and vegetables including physiology, biochemistry and molecular biology, and further elucidating the mechanism of ripening, senescence and quality deterioration in postharvest fruit and vegetables.

The traditional dynamic weapon target assignment model is a combination of multiple static weapon target assignment stages. The assignment of the next stage is carried out after the result of the previous static weapon target assignment is settled. However, between two static weapon target assignment stages, the threat ranking of multiple targets may change with time, and the traditional dynamic weapon target assignment model does not take this time issue into consideration. This paper proposes a “time sampling dynamic weapon assignment model”. This model divides the decision-making stage by setting the time interval of data collection, and it can capture the real-time changes in the target threat degree and make timely decisions. With this model, this study designed a dynamic weapon target assignment method based on the reinforcement learning algorithm. Additionally, according to this method, a comparative experiment with different sampling time divisions was designed, and a better sampling time division method was obtained. Finally, a comparative experiment between the reinforcement learning algorithm and the traditional heuristic algorithm was designed in this study. The simulation results show that, compared with the traditional heuristic algorithm, the proposed assignment model and the reinforcement learning algorithm are better in terms of decision-making timeliness and global considerations.

It is the key research object of electronic warfare to use UAV (Unmanned Aerial Vehicle) clusters to carry out electronic countermeasure tasks. The UAV carries loads such as reconnaissance and interference at the same time, which makes it necessary to simultaneously decide multiple types of actions—namely, compound actions—which poses a challenge to intelligent decision-making algorithms. Considering the problem of action-space dimensional complexity and weak collaboration between decisions in multi-agent scenarios with composite actions, this study proposed a decision algorithm involving a multi-agent reinforcement-learning sequence, which combined joint composite actions into sequential decision, reducing the difficulty of a single decision and enhancing the collaboration between various agents and their individual decisions. Because long decision sequences required better depth modeling and had high variance, a DeLighT module was added to the naïve transformer model to increase the depth and baseline techniques, which were used to reduce the variance in the value estimation. The simulated results verified the effectiveness of the proposed algorithm in the UAV cooperative combat scenario, where each agent had a composite action space and showed better performance than the existing algorithms.

With the deep integration of "AI + medicine", AI-assisted technology has been of great help to human beings in the medical field, especially in the area of predicting and diagnosing diseases based on big data, because it is faster and more accurate. However, concerns about data security seriously hinder data sharing among medical institutions. To fully exploit the value of medical data and realize data collaborative sharing, we developed a medical data security sharing scheme based on the C/S communication mode and constructed a federated learning architecture that uses homomorphic encryption technology to protect training parameters. Here, we chose the Paillier algorithm to realize the additive homomorphism to protect the training parameters. Clients do not need to share local data, but only upload the trained model parameters to the server. In the process of training, a distributed parameter update mechanism is introduced. The server is mainly responsible for issuing training commands and weights, aggregating the local model parameters from the clients and predicting the joint diagnostic results. The client mainly uses the stochastic gradient descent algorithm for gradient trimming, updating and transmitting the trained model parameters back to the server. In order to test the performance of this scheme, a series of experiments was conducted. From the simulation results, we can know that the model prediction accuracy is related to the global training rounds, learning rate, batch size, privacy budget parameters etc. The results show that this scheme realizes data sharing while protecting data privacy, completes the accurate prediction of diseases and has a good performance.

In this paper, we present an application of the renormalization group method developed by Chen, Goldenfeld and Oono for a stochastic differential equation in a space of Hilbert space-valued generalized random variables with multiplicative noise. The driving process is a real-valued fractional white noise with a Hurst parameter greater than 1/2. The stochastic integration is understood in the Wick–Itô–Skorohod sense. This article is a generalization of results of Glatt-Holtz and Ziane, which were for the systems with white noise. We firstly demonstrate the process of formulating the renormalization group equation and the asymptotic solution. Then, we give rigorous proof of the consistency of the approximate solution. In addition, some numerical comparisons are given to illustrate the validity of our results.

Summary: Enzyme immobilization into porous frameworks is an emerging strategy for enhancing the stability of dynamic conformation and prolonging the lifespan of enzymes. Here, we present a protocol for a de novo mechanochemistry-guided assembly strategy for enzyme encapsulation using covalent organic frameworks. We describe steps for mechanochemical synthesis, enzyme loading measurements, and material characterizations. We then detail evaluations of biocatalytic activity and recyclability.For complete details on the use and execution of this protocol, please refer to Gao et al. (2022).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

A 0.005% and 0.01% morin treatment was applied to treat mango fruits stored under ambient conditions (25 ± 1 °C) with 85–90% relative humidity, and the effects on quality indexes, enzyme activity related to antioxidation and cell wall degradation, and gene expressions involved in ripening and senescence were explored. The results indicate that a 0.01% morin application effectively delayed fruit softening and yellowing and sustained the nutritional quality. After 12 days of storage, the contents of soluble sugar and carotenoid in the treatment groups were 68.54 mg/g and 11.20 mg/100 g, respectively, lower than those in control, while the vitamin C content in the treatment groups was 0.58 mg/g, higher than that in control. Moreover, a morin application successively enhanced the activity of superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD), but reduced the activity of polygalacturonase (PG) and pectin lyase (PL). Finally, real-time PCR and correlation analysis suggested that morin downregulated the ethylene biosynthesis (ACS and, ACO) and signal transduction (ETR1, ERS1, EIN2, and ERF1) genes, which is positively associated with softening enzymes (LOX, EXP, βGal, and EG), carotenoid synthesis enzymes (PSY and, LCYB), sucrose phosphate synthase (SPS), and uncoupling protein (UCP) gene expressions. Therefore, a 0.01% morin treatment might efficiently retard mango fruit ripening and senescence to sustain external and nutritional quality through ethylene-related pathways, which indicates its preservation application.

Multi-stage feature fusion is pretty effective for deep Siamese trackers to promote tracking performance. Unfortunately, conventional fusion approaches, such as weighted average, are so simple that they are inappropriate to combine the features with diverse characteristics. In addition, the fusion module is generally optimized along with Siamese network module, which may result in the performance degradation of the whole tracker. In this paper, we propose a novel feature fusion network for Siamese tracker by exploiting the expression capacity of residual fusion learning (SiamRFL). Specifically, the network employs the deep-layer features as direct input to semantically recognize the object from background, and refines the object state with local detail patterns by exploring the shallow-layer features through residual channel. The classification and the regression features can be fused respectively by deploying multiple fusion units. To avoid the degradation problem, we also present an ensemble training framework for our tracker, in which different loss functions are introduced to individually optimize the Siamese and the fusion modules. Compared to the baseline SiamRPN++ tracker, the proposed tracker achieves favorable gains by $0.696\rightarrow 0.709$ , $0.285\rightarrow 0.308$ , $0.603\rightarrow 0.624$ , $0.496\rightarrow 0.520$ and $0.517\rightarrow 0.559$ on OTB100, VOT2019, UAV123, LaSOT and GOT10k datasets, outperforming other approaches by an obvious margin.

Images captured in low-brightness environments typically have low brightness, low contrast, and high noise levels, which significantly affect the overall image quality. To improve the image quality, a low-brightness image enhancement algorithm based on multi-scale fusion is proposed. First, a novel brightness transformation function is used for the generation of two images with different brightnesses. Then, the illumination estimation technique is used to construct a weight matrix, which facilitates the extraction of advantageous features from each image. Finally, the enhanced image is obtained by the fusion of two images using the weight matrix and the pyramid reconstruction algorithm. The proposed method has a better enhancement effect as shown by the experimental results. Compared to other image enhancement algorithms, it has lower evaluation values in the natural image quality evaluator (NIQE) and lightness order error (LOE) indices. The lowest average NIQE value of the proposed algorithm in each dataset is 2.836. This further demonstrates its superior performance.

Cinnamomum zelanicum is one of the popular medicinal plants. The plant has many pharmaceutical uses in traditional medicine. In this study, copper nanoparticles were synthesized using the aqueous extract of Cinnamomum zelanicum. The CuNPs were characterized using different techniques such as EDX, FE-SEM, XRD, and FT-IR. The FE-SEM results confirm spherical morphology for the nanoparticles with size of 19.55 to 69.70 nm. For investigating the antioxidant property of CuNPs, the DPPH test was used in the presence of butylated hydroxytoluene as the positive control. To survey the cytotoxicity and anti-human lung carcinoma effects of CuNPs, MTT assay was used on the NCI-H2126, NCI-H1437, NCI-H1573, and NCI-H661 cell lines. DPPH test revealed similar antioxidant potentials for Cinnamomum zelanicum aqueous extract, CuNPs, and butylated hydroxytoluene. Copper nanoparticles had very low cell viability and anti-lung carcinoma properties dose-dependently against NCI-H2126, NCI-H1437, NCI-H1573, and NCI-H66 cell lines without any cytotoxicity on the normal cell line. The best result of anti-lung carcinoma properties of CuNPs against the above cell lines was seen in the case of the NCI-H2126 cell line. According to the above findings, the copper nanoparticles containing Cinnamomum zelanicum can be administrated in humans for the treatment of several types of lung carcinoma.

This paper examines the relationship between local venture capital (VC) funds and portfolio enterprise internationalization from the perspective of limited partners (LPs). Based on a sample consist of 581 listed enterprises invested by local VC funds in China during 2009–2021, it’s found that, compared to the VC funds invested by local LPs completely (LLP_VC), the VC funds with foreign LP investment (FLP_VC) are more likely to help portfolio enterprises list overseas. If FLP_VC syndicates or act as a major investor, it will have a greater role in promoting the internationalization of enterprises.

In recent years, venture capital (VC) cross-border syndication has shown an obvious growth trend. Based on the existing studies, this paper explores the impact of VC cross-border syndication on corporate innovation. We also examine the mediating roles of cross-border quadratic relationship closure (CBQRC) formed by the strategic cooperation relationship between the respective portfolio companies of domestic and foreign VCs. This paper conducted an empirical analysis to test our hypotheses using a sample of first-round investments in domestic firms by domestic VC firms from 2014 to 2016. Results show that the more investment events of VC cross-border syndication or the more partners of VC cross-border syndication, the more likely it is to have a significant positive impact on the innovation of domestic portfolio companies. CBQRC plays a mediating role between VC cross-border syndication on corporate innovation. Results remain robust after removing endogeneity using the instrumental variables approach and removing sample selection bias using Heckman two-stage regression. Results deepen the understanding of the relationship between VC cross-border syndication and corporate innovation and provide essential guidance to domestic VC firms promoting corporate innovation in open partnerships.

In this study, a dynamic model of a solar array drive system that includes a pair of flexible solar arrays with a central rigid shaft and a permanent-magnet synchronous motor (PMSM) was developed, and a disturbance compensation sliding mode control (DCSMC) strategy was proposed to realize the speed smoothing and vibration suppression control of the system. The continuous nonlinear dynamic equation of the system was derived from Hamilton’s principle, and its linearized form was combined with the boundary conditions to obtain its natural frequency and global mode. The design of the DCSMC strategy was based on the solar array drive assembly (SADA) electromechanical dynamics model of the PMSM direct drive. An extended state observer (ESO) was used to estimate any system disturbances, and the signal was fed forward to sliding mode control (SMC) based on the varying gain saturation reaching law (VGSRL). To verify the validity of the model, its results were compared with those obtained using commercial finite element software. The numerical results showed that the SADA system with the DCSMC strategy outperformed the traditional proportional–integral (PI) control and SMC systems.

Abstract Background XP-Endo Finisher (XPF) and passive ultrasonic irrigation (PUI) are commonly used in intracanal medicament removal. The effectiveness of these two techniques needs to be compared, and evidence-based research should be conducted. Methods A comprehensive literature search was conducted in PubMed, Web of Science, Embase, Cochrane Library, and Google Scholar up to December 20th, 2020. The outcomes of the included trials were pooled into the Cochrane Collaboration’s Review Manager 5.3 software. Cochrane’s risk-of-bias tool 2.0 was applied to assess the risk of bias. Results Nine articles were included in this systematic review and processed for data extraction, and eight studies were identified for meta-analysis. In general, the use of PUI showed better medicament removal effectiveness than XPF (odds ratio [OR]: 3.09; 95% confidence interval [CI], 1.96–4.86; P

In subgingival plaque biofilms, Fusobacterium nucleatum is closely related to the occurrence and development of periodontitis. Streptococcus gordonii, as an accessory pathogen, can coaggregate with periodontal pathogens, facilitating the subgingival colonization of periodontal pathogens. Studies have shown that F. nucleatum can coaggregate with S. gordonii and colonize the subgingival plaque. However, most studies have focused on monocultures or coinfection of species and the potential impact of coaggregation between the two species on periodontal interactions to human gingival epithelial cells (hGECs) remains poorly understood. The present study explored the effect of coaggregation between F. nucleatum and S. gordonii on subgingival synergistic virulence to hGECs. The results showed that coaggregation inhibited the adhesion and invasion of F. nucleatum to hGECs compared with that in the F. nucleatum monoculture and coinfection group. Coaggregation and coinfection with F. nucleatum both enhanced S. gordonii adhesion to hGECs, but neither of the two groups affected S. gordonii invasion to hGECs compared with S. gordonii monoculture. The gene expression levels of TLR2 and TLR4 in hGECs in the coaggregation group were higher than those in the monoculture groups but lower than those in the coinfection group. Compared with coinfection, the coaggregation inhibited apoptosis of hGECs and promoted the secretion of the proinflammatory cytokines TNF-α and IL-6 by hGECs, showed a synergistic inflammatory effect, while coaggregation inhibited the secretion of the anti-inflammatory cytokine TGF-β1. Coaggregation enhanced the phosphorylation of p65, p38, and JNK proteins and therefore activated the NF-κB and MAPK signaling pathways. Pretreatment with a pathway antagonist/inhibitor decreased the phosphorylation levels of proteins and the secretion of TNF-α and IL-6. In conclusion, coaggregation inhibited the adhesion and invasion of F. nucleatum to hGECs. However, it enhanced the adhesion of S. gordonii to hGECs. Compared with coinfection, coaggregation inhibited the apoptosis of hGECs. The coaggregation coordinately promoted the secretion of TNF-α and IL-6 by hGECs through the TLR/NF-κB and TLR/MAPK signaling pathways while inhibiting the secretion of TGF-β1, thus aggravating the inflammatory response of hGECs.

Interspecies coaggregation promotes transcriptional changes in oral bacteria, affecting bacterial pathogenicity. Streptococcus gordonii (S. gordonii) and Fusobacterium nucleatum (F. nucleatum) are common oral inhabitants. The present study investigated the transcriptional profiling of S. gordonii and F. nucleatum subsp. polymorphum in response to the dual-species coaggregation using RNA-seq. Macrophages were infected with both species to explore the influence of bacterial coaggregation on both species’ abilities to survive within macrophages and induce inflammatory responses. Results indicated that, after the 30-min dual-species coaggregation, 116 genes were significantly up-regulated, and 151 genes were significantly down-regulated in S. gordonii; 97 genes were significantly down-regulated, and 114 genes were significantly up-regulated in F. nucleatum subsp. polymorphum. Multiple S. gordonii genes were involved in the biosynthesis and export of cell-wall proteins and carbohydrate metabolism. F. nucleatum subsp. polymorphum genes were mostly associated with translation and protein export. The coaggregation led to decreased expression levels of genes associated with lipopolysaccharide and peptidoglycan biosynthesis. Coaggregation between S. gordonii and F. nucleatum subsp. polymorphum significantly promoted both species’ intracellular survival within macrophages and attenuated the production of pro-inflammatory cytokines IL-6 and IL-1β. Physical interactions between these two species promoted a symbiotic lifestyle and repressed macrophage’s killing and pro-inflammatory responses.

With XML becoming a promising standard for data storage, describing, transferring and exchanging information on the Internet, data security and privacy protection of XML become the focus of research in recent years. In order to achieve the authorization of legitimate user and ensure the secure access to sensitive information, in this paper, in the context of cloud storage, with the purpose of sharing sensitive XML information, a polynomial authorization scheme with Kerberos authentication was proposed, which was based on the users' access purpose and privacy policy. In this scheme, first, Kerberos authentication was used to identify the user, and then the polynomial whose coefficients were from the leaf node address was used to complete the authorization of user. For the legitimate user, under the interaction of authorization polynomials and the global structure view, authorization matrix is generated dynamically, its temporary and dynamic characteristics greatly improves the security of the system. Finally, with the help of authorization matrix and auxiliary information tables, security queries were successfully completed. The experimental results show that the scheme not only effectively protects XML sensitive data, but also reduces the server's storage pressure, at the same time it is beneficial to the rapid search and information positioning.

In recent years, with the development of the unmanned aerial vehicle (UAV) and battlefield environments, the UAV swarm has attracted significant research attention. To solve problems regarding poor state consensus among swarm individuals due to a small number of individuals easily falling into local minima upon encountering an obstacle, this paper proposes a flocking obstacle avoidance algorithm with local interaction of obstacle information. To make the UAV swarm follow the desired trajectory with better state consensus, we improved the flocking control algorithm of agents according to the characteristics and requirements of the UAV swarm. The obstacle avoidance algorithm for the UAV swarm is based on Olfati-Saber's multi-agent obstacle avoidance algorithm. The proposed method has individuals in the swarm communicate obstacle information with their neighbors, and we present a simple analysis of this method. The method improves the cooperative obstacle avoidance capability of the flocking control algorithm. The simulation results showed that the proposed flocking control algorithm provides a better tracking effect and consensus for the UAV swarm when avoiding obstacles.

Taking Chinese startups backed by venture capital (VC) in 1997–2017 as the sample, this study investigates the impact of VC background on chief executive officer (CEO) replacement in portfolio companies. The results show that (1) compared to foreign VC, domestic VC is more likely to replace the CEO of the portfolio companies. (2) Syndicate with domestic VC can overcome the disadvantage of foreign VC geographically distant from the portfolio companies, and domestic VC as coinvestors can effectively monitor portfolio companies, increasing the possibility of CEO replacement. Heterogeneity analysis shows that the positive effect of VC background on CEO replacement exists in the subgroup of VC geographically proximate to the portfolio companies, indicating that geographic proximity to the portfolio companies helps VC more easily grasp the development of the portfolio companies and more likely to replace CEO. This paper reveals the differences in the behavior of VC in replacing CEO during the postinvestment management process, highlights the critical role of geographical proximity, and provides important management insights for VC and entrepreneurs.

The permutation flow shop scheduling problem (PFSSP) is a typical production scheduling problem and it has been proved to be a nondeterministic polynomial (NP-hard) problem when its scale is larger than 3. The whale optimization algorithm (WOA) is a new swarm intelligence algorithm which performs well for PFSSP. But the stability is still low, and the optimization results are not too good. On this basis, we optimize the parameters of WOA through chaos theory, and put forward a chaotic whale algorithm (CWA). Firstly, in this paper, the proposed CWA is combined with Nawaz–Ensco–Ham (NEH) and largest-rank-value (LRV) rule to initialize the population. Next, chaos theory is applied to WOA algorithm to improve its convergence speed and stability. On this basis, we also use cross operator and reversal-insertion operator to enhance the search ability of the algorithm. Finally, the improved local search algorithm is used to optimize the job sequence to find the minimum makespan. In several experiments, different benchmarks are used to investigate the performance of CWA. The experimental results show that CWA has better performance than other scheduling algorithms.

Mouth breathing induces a series of diseases, while the influence on microbiota of oral cavity and salivary proteins remains unknown. In this study, for the first time, profiles of oral–nasal–pharyngeal microbiota among mouth-breathing children (MB group, n = 10) were compared with paired nose-breathing children (NB group, n = 10) using 16S ribosomal DNA (rDNA) (V3–V4 region) high-throughput sequencing. The differentially expressed salivary proteins were revealed using label-free quantification (LFQ) method, and their associations with bacterial abundance were measured by canonical correspondence analysis (CCA). The overall bacterial profiles differed between the two groups, and the differences were related to the duration of mouth breathing. The diversity of oral–pharyngeal microbiota was significantly higher, and the nasal–pharyngeal species tended to be consistent (unweighted UniFrac, p = 0.38) in the MB group. Opportunistic pathogens were higher in relative abundance as follows: Acinetobacter in the anterior supragingival plaque, Neisseria in unstimulated saliva, Streptococcus pneumoniae in the pharynx, and Stenotrophomonas in the nostrils. The expression level of oxidative-stress-related salivary proteins (lactoylglutathione lyase and peroxiredoxin-5) were upregulated, while immune-related proteins (integrin alpha-M and proteasome subunit alpha type-1) were downregulated in MB group. The differentially expressed proteins were associated with specific bacteria, indicating their potentials as candidate biomarkers for the diagnosis, putatively early intervention, and therapeutic target of mouth breathing. This study showed that mouth breathing influences the oral–nasal–pharyngeal microbiota and enriches certain pathogens, accompanied with the alterations in the salivary environment. Further research on the pathological mechanisms and dynamic changes in longitudinal studies are warranted.

Stochastic parallel gradient descent (SPGD) algorithm is one of the most promising methods for effective coherent beam combination. However, the algorithm also has some disadvantages, such as slow convergence speed and local extremum. This paper proposes an AdaDelSPGD algorithm, which combines an AdaDelta algorithm with a SPGD algorithm, and improves the traditional AdaDelta algorithm with adaptive gain coefficient. It is worth noting that the adaptive gain coefficient can be adjusted in real time to improve the convergence rate. The effectiveness of the proposed algorithm is verified by relevant simulation experiments, and the results show that the proposed algorithm can significantly improve the convergence speed. Following the experiments with the fiber laser-phased array multiplex communication system, we can draw the conclusion that the addition of communication modulation reduces the beam quality, and the higher the modulation frequency, the worse the beam quality. However, adding the SPGD algorithm can improve the beam quality. The AdaDelSPGD algorithm proposed in this paper can further improve the beam quality, and the bit error rate of communication is also decreased after testing. This provides a foundation for further research on the fiber laser-phased array multiplex communication system.

Streptococcus mutans is an oral species closely associated with dental caries. As an early oral colonizer, S. mutans utilizes interspecies coaggregation to promote the colonization of subsequent species and affect polymicrobial pathogenesis. Previous studies have confirmed several adhering partner species of S. mutans, including Candida albicans and Fusobacterium nucleatum. In this study, we discovered new intergeneric co-adherence between S. mutans and the saliva isolate Streptococcus agalactiae (GBS-SI101). Research shows that GBS typically colonizes the human gastrointestinal and vaginal tracts. It is responsible for adverse pregnancy outcomes and life-threatening infections in neonates and immunocompromised people. Our results revealed that GtfB and GtfC of S. mutans, which contributed to extracellular polysaccharide synthesis, promoted coaggregation of S. mutans with GBS-SI101. In addition, oral streptococci, including Streptococcus sanguinis, Streptococcus gordonii and S. mutans, barely inhibited the growth of GBS-SI101. This study indicated that S. mutans could help GBS integrate into the Streptococcus-associated oral polymicrobial community and become a resident species in the oral cavity, increasing the risk of oral infections.

Capability assessment plays a crucial role in the demonstration and construction of equipment. To improve the accuracy and stability of capability assessment, we study the neural network learning algorithms in the field of capability assessment and index sensitivity. Aiming at the problem of overfitting and parameter optimization in neural network learning, the paper proposes an improved machine learning algorithm—the Ensemble Learning Based on Policy Optimization Neural Networks (ELPONN) with the policy optimization and ensemble learning. This algorithm presents an optimized neural network learning algorithm through different strategies evolution, and builds an ensemble learning model of multi-intelligent algorithms to assess the capability and analyze the sensitivity of the indexes. Through the assessment of capabilities, the algorithm effectively avoids parameter optimization from entering the minimum point in performance to improve the accuracy of equipment capability assessment, which is significantly better than previous neural network assessment methods. The experimental results show that the mean relative error is 4.10%, which is better than BP, GABP, and early stopping. The ELPONN algorithm has better accuracy and stability performance, and meets the requirements of capability assessment.

Abstract In this study, a novel 27-locus system (now known as the SureID PanGlobal system), including 24 autosomal STRs (D3S1358, TH01, D21S11, D18S51, Penta E, D12S391, D6S1043, D2S1338, D1S1656, D2S441, D5S818, D13S317, D7S820, D19S433, CSF1PO, Penta D, vWA, D8S1179, TPOX, FGA, D16S539, D22S1045, SE33, D10S1248), two Y-chromosome markers (DYS391 and Y-indel) and the sex determining marker, Amelogenin was developed with six fluorescent dyes labeling. The included STR loci belonged to the core loci in the Combined DNA Index System (CODIS) and the European Standard Set (ESS) as well as some additional loci commonly used in commercial kits and national DNA databases. This paper describes the validation studies conducted with the SureID PanGlobal system using Applied Biosystems 3500 XL Genetic Analyzer for fragment length detection that included the analysis of the following parameters and aspects: PCR conditions, sensitivity, species specificity, inhibition, precision, stutter, DNA mixtures, and stability studies with crime scene samples. The studies demonstrated that the SureID PanGlobal system is reproducible, accurate, sensitive and robust for forensic application and databasing. Additionally, the whole cycling time of the system can finish within 65 minutes, which was developed specifically for rapid and reliable generation of DNA profiles obtained from blood, buccal swabs and forensic stains.

Intelligent detection and recognition of ships from high-resolution remote sensing images is an extraordinarily useful task in civil and military reconnaissance. It is difficult to detect ships with high precision because various disturbances are present in the sea such as clouds, mist, islands, coastlines, ripples, and so on. To solve this problem, we propose a novel ship detection network based on multi-layer convolutional feature fusion (CFF-SDN). Our ship detection network consists of three parts. Firstly, the convolutional feature extraction network is used to extract ship features of different levels. Residual connection is introduced so that the model can be designed very deeply, and it is easy to train and converge. Secondly, the proposed network fuses fine-grained features from shallow layers with semantic features from deep layers, which is beneficial for detecting ship targets with different sizes. At the same time, it is helpful to improve the localization accuracy and detection accuracy of small objects. Finally, multiple fused feature maps are used for classification and regression, which can adapt to ships of multiple scales. Since the CFF-SDN model uses a pruning strategy, the detection speed is greatly improved. In the experiment, we create a dataset for ship detection in remote sensing images (DSDR), including actual satellite images from Google Earth and aerial images from electro-optical pod. The DSDR dataset contains not only visible light images, but also infrared images. To improve the robustness to various sea scenes, images under different scales, perspectives and illumination are obtained through data augmentation or affine transformation methods. To reduce the influence of atmospheric absorption and scattering, a dark channel prior is adopted to solve atmospheric correction on the sea scenes. Moreover, soft non-maximum suppression (NMS) is introduced to increase the recall rate for densely arranged ships. In addition, better detection performance is observed in comparison with the existing models in terms of precision rate and recall rate. The experimental results show that the proposed detection model can achieve the superior performance of ship detection in optical remote sensing image.

Abstract Background Trastuzumab, a humanized antibody targeting HER2, exhibits remarkable therapeutic efficacy against HER2-positive gastric cancer. However, recurrent therapeutic resistance presents revolutionary claims. Warburg effect and AKT signaling pathway was involved in the resistance to trastuzumab. Our previous studies have demonstrated that overexpression of metastasis associated with the colon cancer 1 (MACC1) predicted poor prognosis of GC and promoted tumor cells proliferation and invasion. In this study, we found that MACC1 was significantly upregulated in trastuzumab-resistant cell lines. Besides, downregulation of MACC1 reversed this resistance. Methods The effect of trastuzumab and glycolysis inhibitor combination on cell viability, apoptosis, and cell metabolism was investigated in vitro using established trastuzumab-resistant GC cell lines. We assessed the impact of trastuzumab combined with oxamate on tumor growth and metabolism in an established xenograft model of HER2-positive GC cell lines. Results Here, we found that MACC1 was significantly upregulated in trastuzumab-resistant cell lines. Besides, downregulation of MACC1 in trastuzumab-resistant cells reversed this resistance. Overexpression of MACC1-induced trastuzumab resistance, enhanced the Warburg effect, and activated the PI3K/AKT signaling pathway, while downregulation of MACC1 presented the opposite effects. Moreover, when the PI3K/AKT signaling pathway was inhibited, the effects of MACC1 on resistance and glycolysis were diminished. Our findings indicated that MACC1 promoted the Warburg effect mainly through the PI3K/AKT signaling pathway, which further enhanced GC cells trastuzumab resistance. Conclusions Our results indicate that co-targeting of HER2 and the Warburg effect reversed trastuzumab resistance in vitro and in vivo, suggesting that the combination might overcome trastuzumab resistance in MACC1-overexpressed, HER2-positive GC patients.

Multiple unmanned aerial vehicle (UAV) collaboration has great potential. To increase the intelligence and environmental adaptability of multi-UAV control, we study the application of deep reinforcement learning algorithms in the field of multi-UAV cooperative control. Aiming at the problem of a non-stationary environment caused by the change of learning agent strategy in reinforcement learning in a multi-agent environment, the paper presents an improved multiagent reinforcement learning algorithm—the multiagent joint proximal policy optimization (MAJPPO) algorithm with the centralized learning and decentralized execution. This algorithm uses the moving window averaging method to make each agent obtain a centralized state value function, so that the agents can achieve better collaboration. The improved algorithm enhances the collaboration and increases the sum of reward values obtained by the multiagent system. To evaluate the performance of the algorithm, we use the MAJPPO algorithm to complete the task of multi-UAV formation and the crossing of multiple-obstacle environments. To simplify the control complexity of the UAV, we use the six-degree of freedom and 12-state equations of the dynamics model of the UAV with an attitude control loop. The experimental results show that the MAJPPO algorithm has better performance and better environmental adaptability.

Efflux transporters have been implicated in regulating bacterial virulence properties such as resistance to antibiotics, biofilm formation and colonization. The pathogenicity of Streptococcus mutans, the primary etiologic agent of human dental caries, relies on the bacterium’s ability to form biofilms on tooth surface. However, the studies on efflux transporters in S. mutans are scare and the function of these transporters remained to be clarified. In this study, we identified an efflux transporter (LmrB) in S. mutans through cloning the lmrB gene into Escherichia coli. Introducing lmrB into E. coli conferred a multidrug-resistant phenotype and resulted in higher EtBr efflux activity which could be suppressed by efflux inhibitor. To explore whether LmrB was involved in S. mutans virulence properties regulation, we constructed the lmrB inactivation mutant and examined the phenotypes of the mutant. It was found that LmrB deficiency resulted in increased IPS storage and prolonged acid production. Enhanced biofilm formation characterized by increased extracellular polysaccharides (EPS) production and elevated resistance to hydrogen peroxide and antimicrobials were also observed in lmrB mutant. To gain a better understanding of the global role of LmrB, a transcriptome analysis was performed using lmrB mutant strain. The expression of 107 genes was up- or down-regulated in the lmrB mutant compared with the wild type. Notably, expression of genes in several genomic islands was differentially modulated, such as stress-related GroELS and scnRK, sugar metabolism associated glg operons and msmREFGK transporter. The results presented here indicate that LmrB plays a vital global role in the regulation of several important virulence properties in S. mutans.

Inspired by the behavior of elephants in nature, elephant herd optimization (EHO) was proposed recently for global optimization. Like most other metaheuristic algorithms, EHO does not use the previous individuals in the later updating process. If the useful information in the previous individuals were fully exploited and used in the later optimization process, the quality of solutions may be improved significantly. In this paper, we propose several new updating strategies for EHO, in which one, two, or three individuals are selected from the previous iterations, and their useful information is incorporated into the updating process. Accordingly, the final individual at this iteration is generated according to the elephant generated by the basic EHO, and the selected previous elephants through a weighted sum. The weights are determined by a random number and the fitness of the elephant individuals at the previous iteration. We incorporated each of the six individual updating strategies individually into the basic EHO, creating six improved variants of EHO. We benchmarked these proposed methods using sixteen test functions. Our experimental results demonstrated that the proposed improved methods significantly outperformed the basic EHO.

Electromechanical actuator (EMA) systems are widely employed in missiles. Due to the influence of the nonlinearities, there is a flat-top of about 64 ms when tracking the small-angle sinusoidal signals, which significantly reduces the performance of the EMA system and even causes the missile trajectory to oscillate. Aiming to solve these problems, this paper presents a hybrid control for flat-top situations. In contrast to the traditional PID or sliding mode controllers that missiles usually use, this paper utilizes improved sliding mode control based on a novel reaching law to eliminate the flat-top during the steering of the input signal, and utilizes the PID control to replace discontinuous control and improve the performance of EMA system. In addition, boundary layer and switching function are employed to solve the high-frequency chattering problem caused by traditional sliding mode control. Experiments indicate that the hybrid control can evidently reduce the flat-top time from 64 ms to 12 ms and eliminate the trajectory limit cycle oscillation. Compared with PID controllers, the proposed controller provides better performance—less chattering, less flat-top, higher precision, and no oscillation.

As the usage and development of wireless sensor networks increases, problems related to these networks are becoming apparent. Dynamic deployment is one of the main topics that directly affects the performance of the wireless sensor networks. In this paper, biogeography-based optimization is applied to the dynamic deployment of static and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A binary detection model is considered to obtain realistic results while computing the effectively covered area. Performance of the algorithm is compared with that of the artificial bee colony algorithm, Homo-H-VFCPSO and stud genetic algorithm that are also population-based optimization algorithms. Results show biogeography-based optimization can be preferable in the dynamic deployment of wireless sensor networks.

Tunable mid-infrared lasers are widely used in laser spectroscopy, gas sensing and many other related areas. In order to solve heat dissipation problems and improve the environmental temperature adaptability of solid-state laser sources, a tunable all-fiber laser pumped optical parametric oscillator (OPO) was established, and a compact thermal control system based on thermoelectric coolers, an automatic temperature control circuit, cooling fins, fans and heat pipes was integrated and designed for the laser. This system is compact, light and air-cooling which satisfies the demand for miniaturization of lasers. A mathematical model and method was established to estimate the cooling capacity of this thermal control system under different ambient environments. A finite-element model was built and simulated to analyze the thermal transfer process. Experiments in room and high temperature environments were carried out and showed that the substrate temperature of a pump module could be maintained at a stable value with controlled precision to 0.2 degrees, while the output power stability of the laser was within ±1%. The experimental results indicate that this compact air-cooling thermal control system could effectively solve the heat dissipation problem of mid-infrared solid-state lasers with a one hundred watts level pump module in room and high temperature environments.

Target threat assessment is a key issue in the collaborative attack. To improve the accuracy and usefulness of target threat assessment in the aerial combat, we propose a variant of wavelet neural networks, MWFWNN network, to solve threat assessment. How to select the appropriate wavelet function is difficult when constructing wavelet neural network. This paper proposes a wavelet mother function selection algorithm with minimum mean squared error and then constructs MWFWNN network using the above algorithm. Firstly, it needs to establish wavelet function library; secondly, wavelet neural network is constructed with each wavelet mother function in the library and wavelet function parameters and the network weights are updated according to the relevant modifying formula. The constructed wavelet neural network is detected with training set, and then optimal wavelet function with minimum mean squared error is chosen to build MWFWNN network. Experimental results show that the mean squared error is , which is better than WNN, BP, and PSO_SVM. Target threat assessment model based on the MWFWNN has a good predictive ability, so it can quickly and accurately complete target threat assessment.

A novel robust hybrid metaheuristic optimization approach, which can be considered as an improvement of the recently developed bat algorithm, is proposed to solve global numerical optimization problems. The improvement includes the addition of pitch adjustment operation in HS serving as a mutation operator during the process of the bat updating with the aim of speeding up convergence, thus making the approach more feasible for a wider range of real-world applications. The detailed implementation procedure for this improved metaheuristic method is also described. Fourteen standard benchmark functions are applied to verify the effects of these improvements, and it is demonstrated that, in most situations, the performance of this hybrid metaheuristic method (HS/BA) is superior to, or at least highly competitive with, the standard BA and other population-based optimization methods, such as ACO, BA, BBO, DE, ES, GA, HS, PSO, and SGA. The effect of the HS/BA parameters is also analyzed.

Acidogenicity and aciduricity are the main virulence factors of the cavity-causing bacterium Streptococcus mutans. Monitoring at the individual cell level the temporal and spatial distribution of acid produced by this important oral pathogen is central for our understanding of these key virulence factors especially when S. mutans resides in multi-species microbial communities. In this study, we explored the application of pH-sensitive green fluorescent proteins (pHluorins) to investigate these important features. Ecliptic pHluorin was functionally displayed on the cell surface of S. mutans as a fusion protein with SpaP. The resulting strain (O87) was used to monitor temporal and spatial pH changes in the microenvironment of S. mutans cells under both planktonic and biofilm conditions. Using strain O87, we revealed a rapid pH drop in the microenviroment of S. mutans microcolonies prior to the decrease in the macro-environment pH following sucrose fermentation. Meanwhile, a non-uniform pH distribution was observed within S. mutans biofilms, reflecting differences in microbial metabolic activity. Furthermore, strain O87 was successfully used to monitor the S. mutans acid production profiles within dual- and multispecies oral biofilms. Based on these findings, the ecliptic pHluorin allows us to investigate in vivo and in situ acid production and distribution by the cariogenic species S. mutans.

Recently, Gandomi and Alavi proposed a novel swarm intelligent method, called krill herd (KH), for global optimization. To enhance the performance of the KH method, in this paper, a new improved meta-heuristic simulated annealing-based krill herd (SKH) method is proposed for optimization tasks. A new krill selecting (KS) operator is used to refine krill behavior when updating krill’s position so as to enhance its reliability and robustness dealing with optimization problems. The introduced KS operator involves greedy strategy and accepting few not-so-good solutions with a low probability originally used in simulated annealing (SA). In addition, a kind of elitism scheme is used to save the best individuals in the population in the process of the krill updating. The merits of these improvements are verified by fourteen standard benchmarking functions and experimental results show that, in most cases, the performance of this improved meta-heuristic SKH method is superior to, or at least highly competitive with, the standard KH and other optimization methods.

A hybrid metaheuristic approach by hybridizing harmony search (HS) and firefly algorithm (FA), namely, HS/FA, is proposed to solve function optimization. In HS/FA, the exploration of HS and the exploitation of FA are fully exerted, so HS/FA has a faster convergence speed than HS and FA. Also, top fireflies scheme is introduced to reduce running time, and HS is utilized to mutate between fireflies when updating fireflies. The HS/FA method is verified by various benchmarks. From the experiments, the implementation of HS/FA is better than the standard FA and other eight optimization methods.

Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model.

Path planning for uninhabited combat air vehicle (UCAV) is a complicated high dimension optimization problem, which mainly centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. Original bat algorithm (BA) is used to solve the UCAV path planning problem. Furthermore, a new bat algorithm with mutation (BAM) is proposed to solve the UCAV path planning problem, and a modification is applied to mutate between bats during the process of the new solutions updating. Then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic BA. The realization procedure for original BA and this improved metaheuristic approach BAM is also presented. To prove the performance of this proposed metaheuristic method, BAM is compared with BA and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, PBIL, PSO, and SGA. The experiment shows that the proposed approach is more effective and feasible in UCAV path planning than the other models.