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Abstract Anxiety disorder, a prevalent mental health issue, is one of the leading causes of disability worldwide. Damage to the blood–brain barrier (BBB) is implicated in anxiety, but its regulatory mechanisms remain unclear. Herein, we show that adrenomedullin 2 (ADM2), a novel angiogenic growth factor, alleviates autistic and anxiety-like behaviors in mice. Based on transcriptome analysis and biochemical analyses, we found that ADM2 facilitates the expression of insulin-like growth factor 2 (IGF-II), which then triggers the activation of the AKT-GSK3β-mTOR signaling pathway via the IGF-II receptor (IGF-IIR), rather than the IGF-I receptor (IGF-IR). Furthermore, as evidenced by increased Evans blue staining and decreased VE-cadherin levels, the BBB exhibited dysfunction in ADM2 knockout mice with anxiety-like behaviors. In in vitro studies, ADM2 administration promoted the expression of VE-cadherin and decreased IGF-II leakage through the endothelial barrier in a BBB model. Taken together, ADM2 may alleviate anxiety-like behavior and social deficits by enhancing BBB integrity and increasing IGF-II levels in the brain. These findings highlight the potential of ADM2 as a therapeutic target for anxiety and related mental disorders.

This paper focuses on investigating the initial-boundary value problem of incompressible heat conducting Navier-Stokes equations with variable coefficients over bounded domains in $ \mathbb{R}^3 $, where the viscosity coefficient and heat conduction coefficient are powers of temperature. We obtain the global well-posedness of a strong solution under the assumption that the initial data and the measure of the initial vacuum region are sufficiently small. It is worth mentioning that the initial density is allowed to contain vacuum, and there are no restrictions on the power index of the temperature-dependent viscosity coefficient and heat conductivity coefficient. At the same time, the exponential decay-in-time results are also obtained.

Photosynthetic pigments such as phycobiliproteins and chlorophyll a are important quality indicators of seaweeds. In this study, multivariate nonlinear regression (MNLR) models were developed and validated for the rapid determination of photosynthetic pigments in Porphyra haitanensis based on colorimetric parameters (L*, a*, b*). The contents of phycoerythrin, phycocyanin, allophycocyanin and chlorophyll a in P. haitanensis were within 1.499–8.882 mg/g, 1.402–7.634 mg/g, 0.315–1.623 mg/g, and 0.340–2.160 mg/g, respectively. The L*, a*, and b* values were within 13.47–32.97, −1.88 to 2.74, and 0.23–4.61, respectively. This study indicated that the pigment contents of P. haitanensis, especially phycoerythrin and phycocyanin, could be effectively predicted based on color parameters with R2 of 0.901 and 0.701, respectively. The MNLR model also showed that the relative errors of phycoerythrin and phycocyanin content prediction were less than 10 and 20%, respectively. However, the prediction of allophycocyanin and chlorophyll a proved to be more challenging and the model showed limited predictive power. This discovery may make it easier to employ non-destructive techniques to evaluate the phycoerythrin and phycocyanin content of P. haitanensis and other seaweeds, which is important for the expanding Porphyra industry as it may enable a rapid assessment of Porphyra quality. This finding demonstrates the potential of visual analysis for quality assessment of Porphyra, as well as the convenience and non-destructive nature of the method. Future research should focus on improving the model and developing accurate and rapid quality control methods for the industrialization and scientific application of Porphyra.

The BRI1-EMS-SUPPRESSOR 1 (BES1) family comprises plant-specific transcription factors, which are distinguished by atypical bHLH domains. Over the past two decades, genetic and biochemical studies have established that members of the BRI1-EMS-SUPPRESSOR 1 (BES1) family are crucial for regulating the expression of genes involved in brassinosteroid (BR) response in rapeseed. Due to the significance of the BES1 gene family, extensive research has been conducted to investigate its functional properties. This study presents a comprehensive identification and computational analysis of BES1 genes in ‘Tieguanyin’ (TGY) tea (Camellia sinensis). A total of 10 BES1 genes were initially identified in the TGY genome. Through phylogenetic tree analysis, this study uniquely revealed that CsBES1.2 and CsBES1.5 cluster with SlBES1.8 from Solanum lycopersicum, indicating their critical roles in fruit growth and development. Synteny analysis identified 20 syntenic genes, suggesting the conservation of their evolutionary functions. Analysis of the promoter regions revealed two types of light-responsive cis-elements, with CsBES1.4 exhibiting the highest number of light-related cis-elements (13), followed by CsBES1.9 and CsBES1.10. Additional validation via qRT-PCR experiments showed that CsBES1.9 and CsBES1.10 were significantly upregulated under light exposure, with CsBES1.10 reaching approximately six times the expression level of the control after 4 h. These results suggest that CsBES1.9 and CsBES1.4 could play crucial roles in responding to abiotic stress. This study offers novel insights into the functional roles of the BES1 gene family in ‘Tieguanyin’ tea and establishes a significant foundation for future research, especially in exploring the roles of these genes in response to abiotic stresses, such as light exposure.

Abstract Background Chronic obstructive pulmonary disease (COPD) contributes to the incidence and prognosis of lung cancer. The presence of COPD significantly increases the risk of lung squamous cell carcinoma (LSCC). COPD may promote an immunosuppressive microenvironment in LSCC by regulating the expression of immune‐inhibitory factors in T cells, although the mechanisms remain unclear. In this study, we aimed to decipher the tumour microenvironment signature for LSCC with COPD at a single‐cell level. Methods We performed single‐cell RNA sequencing on tumour tissues from LSCC with or without COPD, then investigated the features of the immune and tumour cells. We employed multiple techniques, including multispectral imaging, flow cytometry, tissue microarray analysis, survival analysis, co‐culture systems and in vitro and in vivo treatment experiments, to validate the findings obtained from single‐cell analyses. Results LSCC with COPD showed increased proportions of tumour‐associated macrophages (TAMs) and higher levels of CD8+ T cell exhaustion molecules, which contributed to an immunosuppressive microenvironment. Further analysis revealed a critical cluster of CD74+ tumour cells that expressed both epithelial and immune cell signatures, exhibited a stronger capacity for tumorigenesis and predicted worse overall survival. Notably, migration inhibitory factor (MIF) secreted by TAMs from LSCC with COPD may promote the activation of CD74. MIF‐CD74 may interact with CD8+ T cells and impair their anti‐tumour activity by regulating the PI3K‐STAT3‐programmed cell death‐1 ligand 1 signalling pathway, facilitating tumour proliferation and immune evasion. Conclusions Our comprehensive picture of the tumour ecosystem in LSCC with COPD provides deeper insights into relevant immune evasion mechanisms and potential targets for immunotherapy. Highlight Our results demonstrated higher proportions of tumour‐associated macrophages (TAMs) and higher levels of exhaustion molecules in CD8+ T cells in the microenvironment of LSCC with COPD. CD74+tumour cells were associated with poor disease prognosis. Migration inhibitory factor (MIF)‐CD74 may interact with CD8+ T cells and impair their anti‐tumour activity by regulating the PI3K‐STAT3‐PD‐L1 signalling pathway, facilitating immune evasion.

The exploration and implementation of brain-computer interfaces (BCIs) utilizing electro- encephalography (EEG) are becoming increasingly widespread. However, their safety considerations have received scant attention. Recent studies have shown that EEG-based BCIs are vulnerable to adversarial attacks. Remarkably, only a limited amount of literature has addressed adversarial defense strategies against EEG-based BCIs. This study introduces a defense approach based on autoencoders, termed the Denoising Convolutional Autoencoder (DCAE), which serves as a preprocessing unit preceding the classification model. The DCAE aims to mitigate adversarial disturbances prior to inputting samples into the classifier, thereby preserving the classifier’s original structure. Experiments were conducted using two different EEG datasets and three convolutional neural network (CNN) models to evaluate the effectiveness of DCAE. The experimental results show that the proposed method can achieve better defense effect in most cases against various adversarial attack methods. Additionally, the sensitivity of the DCAE to different magnitudes of perturbation was evaluated. The findings indicate that the robustness of DCAE is not affected by the variation of attack intensity, a characteristic not observed in existing defense strategies for EEG-based BCIs. It is our aspiration that these results will advance the frontier of research on defending EEG-based BCIs against adversarial threats.

Gas-fired units provide great flexibility to accommodate the intermittent nature of renewable energy sources and are also considered to be an ideal substitute for retiring coal-fired generations. However, the increasing deployment of gas-fired units deepens interdependency between electric power and natural gas, calling for efficient modeling of the heterogeneous energy flow in the integrated electricity–gas systems (IEGS). Plenty of research works show the necessity of considering gas flow transients in the IEGS, which can be described by a set of partial differential equations (PDEs). To ensure a tractable optimization, the PDEs are often discretized using the finite/infinite difference method, which may result in a large computational scale for achieving good precision. To this end, this paper proposes an efficient model for capturing gas flow transients in the IEGS, which can be taken as the operational constraints in the optimal energy flow model for IEGS coordinated planning, scheduling, and control. For improving the computational efficiency, the PDE-based transient gas flow model is discretized using a space–time orthogonal collocation (OC) method, which provides high-quality results using fewer spatial–temporal discrete points than differential methods. Further, aiming at the difficulty in the determination of the number of space–time collocation points, an adaptive strategy is proposed and embedded in the proposed model utilizing the local fitting errors of state variables related to pipelines. Case studies show the superiority in computational accuracy and efficiency of the proposed model over the existing models based on Wendorff difference and global OC. And an illustrative case based on the modified IEGS benchmark system composed of the IEEE 118-bus power system and the Belgian 20-node gas network exhibits the application value of the proposed model in the optimization and control of the IEGS.

When asynchronous motors, especially double-fed asynchronous motors in large capacity pump storage are the main loads in the high voltage direct current (HVDC) receiving end power grid, the increase of the equivalent slip of asynchronous motor load may cause transient voltage instability. In order to recover the voltage rapidly in the grid, the emergency reactive power support needs to be quick and accurate. A method for transient voltage stability emergency control by temporarily reducing DC current is proposed, the inverter station is used as emergency reactive power source for the HVDC receiving end power grid. In detail, firstly, aiming at the quantitative calculation of DC current, a nonlinear optimization model with the optimization variable of DC current and the objective of minimizing energy transmission reduction of HVDC is established. Further, in order to achieve fast solution and meet the accuracy requirements, global orthogonal collocation (GOC) is incorporated into the optimization model to transform the differential equations of both objective function and constraints into algebraic equations, thus the optimization is transformed into a nonlinear programming (NLP) problem, by which the emergency control strategy, in specific, the optimal DC current control scheme is obtained. Finally, the modified IEEE 14 benchmark is used to verify the effectiveness and superiority of the proposed strategy.

Objective: To investigate the evolution of inflammation under conditions and the effects of ginsenosides on macrophages subjected to the simulated weightlessness, with the aim of mitigating the inflammation. Methods: Initially, genes related to weightlessness, inflammation, and immunity were identified in the GeneCards database. Then, Search Tool for the Retrieval of Interaction Gene/Proteins (STRING) protein network analysis was conducted to determine the core targets involved in the weightlessness-induced inflammation. Subsequently, Label-Free Quantitative (LFQ) proteomics was carried out to discern the distinctive genes within ginsenoside-treated Tohoku Hospital Pediatrics-1 (THP-1) cells. Next, utilizing the outcomes of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, the biological processes and signaling pathways in which ginsenosides predominately engaged were scrutinized, and the primary targets of ginsenosides in combating weightlessness-induced inflammation were examined. Finally, enzyme-linked immunosorbent assay (ELISA) was performed to detect the secretion levels of interleukin (IL)-1β, IL-6, IL-8, and tumor necrosis factor (TNF)-α from lipopolysaccharide (LPS)-induced THP-1 cells under simulated weightlessness conditions, as well as during the weightlessness recovery period following treatment with ginsenosides. Results: A total of 2 933 genes associated with inflammation, 425 genes linked to weightlessness, and 4 564 genes connected to immunity were retrieved from the GeneCards database. Protein-protein interaction (PPI) networks were generated to identify pivotal targets associated with weightlessness-induced inflammation such as IL-1β, IL-6, TNF, and albumin (ALB). It was found that ginsenosides primarily participated in the regulation of various inflammation-related signaling pathways and pathways related to pathogenic microorganism infections. Moreover, it has a significant impact on the expression of proteins such as cluster of differentiation 40 (CD40), IL-1β, and poly ADP-ribose polymerase 1 (PARP1). As revealed in the simulated weightlessness cell test, ginsenosides exhibited a remarkable capacity to attenuate the secretion of inflammatory factors, specifically IL-6 and TNF-α (P < 0.000 1), in THP-1 macrophages following induction by LPS under simulated weightlessness conditions. In addition, it reduced the secretion of IL-1β, IL-6, IL-8, and TNF-α (P < 0.000 1) during the weightlessness recovery phase. Conclusion: Weightlessness can disrupt several inflammation-related signaling pathways, but ginsenosides were shown to mitigate the release of various inflammatory factors in macrophages subjected to simulated weightlessness, thereby exerting a protective role against inflammation. This study has laid a theoretical groundwork for further exploring the potential application of ginsenosides in safeguarding against LPS induced inflammation in a weightlessness environment.

This study aimed to determine the integrated freshness index (IFI) of eggs using Vis-NIR spectroscopy and optimized support vector regression, which gave the first insight into the freshness quality of eggs from the biochemical essence of quality changes. In this work, Vis-NIR transmission spectra of brown-shell and pink-shell egg samples were analyzed between 500 nm and 900 nm. Standard normal variables (SNV) were used to normalize the spectral data, and the Shuffled Frog Leaping Algorithm (SFLA) and Competitive Adaptive Reweighted Sampling (CARS) were used to choose the optimal wavelengths. The quantitative analysis model of IFI was developed using a support vector regression (SVR) that was optimized using Grid Search (GS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). After conducting a comparative analysis, it was determined that the GA-SVR model based on 63 wavelengths screened by the SFLA best predicted IFI with a training set coefficient of determination (Rc2) of 0.900, root means square error (RMSEC) of 0.005, a prediction set coefficient of determination (Rp2) of 0.816, root mean square error (RMSEP) of 0.012 and relative analysis error (RPD) of 2.077. The results demonstrate that the model can be used to simultaneously perform nondestructive detection of two distinct egg IFI variants, suggesting broader applicability and enhanced model reliability.

Abstract Mental time travel (MTT) ability allows people to project themselves mentally into the past and future. It is associated with people’s mental representation of events and objects. Using text analysis methods, we explore the linguistic representation and emotional expression of people with various MTT abilities. In Study 1, we assessed the users’ MTT distances, text lengths, visual perspectives, priming effects of temporal words, and emotional valences by analyzing 2973 users’ microblog texts. From our statistical analysis findings, users with far MTT incorporated longer text length and more third-person pronouns in their microblogs and are more likely to relate the future and past with the present than people with near MTT. However, the study showed no significant difference in emotional valence between people with different MTT distances. In Study 2, we explored the relationship between emotional valence and MTT ability by analyzing the comments of 1112 users on “procrastination.” We found the users with far MTT more positive toward procrastination than those with near MTT. By analyzing users’ social media platform data, this study re-examined and verified previous findings indicating that users who mentally travel different temporal distances represent events and emotional expressions differently. This study serves as an important reference for MTT studies.

Intermedin (IMD) is a member of the calcitonin gene-related peptide (CGRP)/calcitonin (CT) superfamily, and it is expressed extensively throughout the body. The typical receptors for IMD are complexes composed of calcitonin receptor-like receptor (CLR) and receptor activity-modifying protein (RAMP), which leads to a biased activation towards Gαs. As a diagnostic and prognostic biomarker, IMD regulates the initiation and metastasis of multiple tumors. Additionally, IMD functions as a proangiogenic factor that can restrain excessive vascular budding and facilitate the expansion of blood vessel lumen, ultimately resulting in the fusion of blood vessels. IMD has protective roles in various diseases, including ischemia-reperfusion injury, metabolic disease, cardiovascular diseases and inflammatory diseases. This review systematically elucidates IMD’s expression, structure, related receptors and signal pathway, as well as its comprehensive functions in the context of acute kidney injury, obesity, diabetes, heart failure and sepsis. However, the precise formation process of IMD short peptides in vivo and their downstream signaling pathway have not been fully elucidated yet. Further in-depth studies are need to translate IMD research into clinical applications.

Lidar has the advantages of high accuracy, high resolution, and is not affected by sunlight. It has been widely used in many fields, such as autonomous driving, remote sensing detection, and intelligent robots. However, the current lidar detection system belongs to weak signal detection and generally uses avalanche photoelectric detector units as detectors. Limited by the current technology, the photosensitive surface is small, the receiving field of view is limited, and it is easy to cause false alarms due to background light. This paper proposes a method based on a combination of image-side telecentric lenses, microlens arrays, and interference filters. The small-area element detector achieves the high-concentration reception of echo beams in a large field of view while overcoming the interference of ambient background light. The image-side telecentric lens realizes that the center lines of the echo beams at different angles are parallel to the central axis, and the focus points converge on the same focal plane. The microlens array collimates the converged light beams one by one into parallel light beams. Finally, a high-quality aspherical focusing lens is used to focus the light on the small-area element detector to achieve high-concentration light reception over a large field of view. The system achieves a receiving field of view greater than 40° for a photosensitive surface detector with a diameter of 75 μm and is resistant to background light interference.

Abstract Colorectal cancer (CRC) is one of the most commonly diagnosed cancer types worldwide. Despite significant advances in prevention and diagnosis, CRC is still one of the leading causes of cancer-related mortality globally. RAB27A, the member of RAB27 family of small GTPases, is the critical protein for intracellular secretion and has been reported to promote tumor progression. However, it is controversial for the role of RAB27A in CRC progression, so we explored the exact function of RAB27A in CRC development in this study. Based on the stable colon cancer cell lines of RAB27A knockdown and ectopic expression, we found that RAB27A knockdown inhibited proliferation and clone formation of SW480 colon cancer cells, whereas ectopic expression of RAB27A in RKO colon cancer cells facilitated cell proliferation and clone formation, indicating that RAB27A is critical for colon cancer cell growth. In addition, our data demonstrated that the migration and invasion of colon cancer cells were suppressed by RAB27A knockdown, but promoted by RAB27A ectopic expression. Therefore, RAB27A is identified as an onco-protein in mediating CRC development, which may be a valuable prognostic indicator and potential therapeutic target for CRC.

Distributed optimization (DO) for solving optimal power flow (OPF) problems is a fundamental task for coordinated planning, scheduling, and operation of interconnected power grids. Among various distributed OPF (D-OPF) studies, the method based on the auxiliary problem principle (APP), has the advantage of fully parallel and decentralized implementation, which only involves neighbor-to-neighbor communication and no need for a central coordinator. However, due to it taking a fixed quadratic core as the estimator of boundary information, the convergent rate is slow when solving the ill-conditioned D-OPF problems, since the search direction simply relies on the first-order gradient information. Besides, for the non-convexity of AC-OPF, only empirical criteria for the APP’s parameter settings have been provided. Thus, convergence is not always guaranteed when the operation state changes without the criterion to select and readjust algorithm parameters. In this paper, to further exploit the potentialities of the APP during online coordinated scheduling and operation of interconnected power grids, the D-OPF method based on an adaptive core APP (AC-APP) is proposed, in which second-order cone programming (SOCP) is adopted to guarantee the finite convergence. The adaptive core is updated according to the Hessians of the boundary variables at each iterative step to correct the search path with second-order information. Compared to the existing fixed core APP (FC-APP) based D-OPF method, the proposed AC-APP based D-OPF method can obtain branch flow results at a higher accuracy with fewer iterations, which facilitates the online application. On the other hand, a boundary equivalence approach for the D-OPF with SOCP convexification is developed, guaranteeing the convergence prerequisite of the AC-APP. Case studies based on the IEEE-118 bus system verified the effectiveness and superiority of the proposed method.

Increased glycolysis is a key characteristic of malignant cells that contributes to their high proliferation rates and ability to develop drug resistance. The glycolysis rate-limiting enzyme hexokinase II (HK II) is overexpressed in most tumor cells and significantly affects tumor development. This paper examines the structure of HK II and the specific biological factors that influence its role in tumor development, as well as the potential of HK II inhibitors in antitumor therapy. Furthermore, we identify and discuss the inhibitors of HK II that have been reported in the literature.

Abstract Background The bifunctional enzyme β-carotene hydroxylase (CrtZ) catalyzes the hydroxylation of carotenoid β-ionone rings at the 3, 3’ position regardless of the presence of keto group at 4, 4’ position, which is an important step in the synthesis of astaxanthin. The level and substrate preference of CrtZ may have great effect on the amount of astaxanthin and the accumulation of intermediates. Results In this study, the substrate preference of PCcrtZ from Paracoccus sp. PC1 and PAcrtZ from Pantoea Agglomerans were certified and were combined utilization for increase astaxanthin production. Firstly, PCcrtZ from Paracoccus sp. PC1 and PAcrtZ from P. Agglomerans were expressed in platform strains CAR032 (β-carotene producing strain) and Can004 (canthaxanthin producing strain) separately to identify their substrate preference for carotenoids with keto groups at 4,4’ position or not. The results showed that PCcrtZ led to a lower zeaxanthin yield in CAR032 compared to that of PAcrtZ. On the contrary, higher astaxanthin production was obtained in Can004 by PCcrtZ than that of PAcrtZ. This demonstrated that PCCrtZ has higher canthaxanthin to astaxanthin conversion ability than PACrtZ, while PACrtZ prefer using β-carotene as substrate. Finally, Ast010, which has two copies of PAcrtZ and one copy of PCcrtZ produced 1.82 g/L of astaxanthin after 70 h of fed-batch fermentation. Conclusions Combined utilization of crtZ genes, which have β-carotene and canthaxanthin substrate preference respectively, can greatly enhance the production of astaxanthin and increase the ratio of astaxanthin among total carotenoids.

The tissue-engineered epidermal (TEE), composed of biocompatible vectors and autogenous functional cells, is a novel strategy to solve the problem of shortage of donor skin sources. The human primary keratinocyte (HPK), the major skin components, are self-evident vital in wound healing and was considered as one of the preferred seed cells for TEEs. Since the process of separating HPKs from the skin triggers a stress state of the cells, achieving its rapid adhesion and proliferation on biomaterials remains challenging. The key to the clinical application is to ensure the normal function of cells while improving the proliferation ability in vitro, and to complete the complex mesenchymal epithelialization to achieve tissue remodeling after vivo implantation. Herein, in order to aid HPKs adhesion and proliferation in vitro and promoting wound healing, we developed a three dimensional collagen scaffold with Y-27632 sustainedly released from the nanoplatform, hollow mesoporous organosilica nanoparticles (HMON). The results showed that the porous structure within the TEE supports the implanted HPKs expanding in a three-dimensional mode to jointly construct the tissue-engineered epidermis in vitro and inhibited the mitochondria-mediated cell apoptosis. It was confirmed that the TEEs with suitable degradation rate could maintain drug release after implantation and could accelerate vascularization of wound base and further revealed the involvement of mesenchymal transformation of transplanted HPKs during skin regeneration in a nude mouse model with full-thickness skin resection. In conclusion, our study highlights the great potential of constructing TEE using a nanoparticle platform for the treatment of large-area skin defects.

Abstract It has been acknowledged that dominance-resistant solutions (DRSs) extensively exist in the feasible region of multi-objective optimization problems. Recent studies show that DRSs can cause serious performance degradation of many multi-objective evolutionary algorithms (MOEAs). Thereafter, various strategies (e.g., the $$\epsilon $$ ϵ -dominance and the modified objective calculation) to eliminate DRSs have been proposed. However, these strategies may in turn cause algorithm inefficiency in other aspects. We argue that these coping strategies prevent the algorithm from obtaining some boundary solutions of an extremely convex Pareto front (ECPF). That is, there is a dilemma between eliminating DRSs and preserving boundary solutions of the ECPF. To illustrate such a dilemma, we propose a new multi-objective optimization test problem with the ECPF as well as DRSs. Using this test problem, we investigate the performance of six representative MOEAs in terms of boundary solutions preservation and DRS elimination. The results reveal that it is quite challenging to distinguish between DRSs and boundary solutions of the ECPF.

Resistive random-access memory (RRAM) is a new memory technology that can not only realize high-density storage, but also can simulate the neural synapse for use in artificial intelligence applications. In this study, we propose an RRAM device that shows competitive resistive memory characteristics and can similarly be used as a synapse in simulation of the human visual perception nervous system. First, we demonstrate that the polyvinyl alcohol-graphene oxide (PVA@GO) hybrid material-based RRAM device offers competitive resistive memory characteristics, including long retention capability, high durability, repeatability, and mechanical flexibility. Second, we integrate the RRAM (as the artificial synapse) with a light-sensitive electronic component (a photoreceptor cell) to construct an artificial visual perception system, and realize effective emulation of light perception and conversion of light signals into synaptic signals. Under light irradiation at 532 nm, a range of versatile synaptic functions, including short-term plasticity (STP), long-term plasticity (LTP), and paired pulse facilitation (PPF), was imitated. This work provides valuable insight into the development path for next-generation high-density data storage technology, and also offers a new way to imitate the human visual neural network for multi-functional humanoid robots.

Microsporidia are obligate intracellular parasites and possess a unique way of invading hosts, namely germination. Microsporidia are able to infect almost all animal cells by germination. During the process, the polar tube extrudes from the spores within, thus injecting infectious sporoplasm into the host cells. Previous studies indicated that subtilisin-like protease 1 (NbSLP1) of microsporidia Nosema bombycis were located at the polar cap of germinated spores where the polar tube extrusion. We hypothesized that NbSLP1 is an essential player in the germination process. Normally, SLP need to be activated by autoproteolysis under conditions. In this study, we found that the signal peptide of NbSLP1 affected the activation of protease, two self-cleavage sites were involved in NbSLP1 maturation between Ala104Asp105 and Ala124Asp125 respectively. Mutants at catalytic triad of NbSLP1 confirmed the decreasing of autoproteolysis. This study demonstrates that intramolecular proteolysis is required for NbSLP1 maturation. The protease undergoes a series of sequential N-terminal cleavage events to generate the mature enzyme. Like other subtilisin-like enzymes, catalytic triad of NbSLP1 are significant for the self-activation of NbSLP1. In conclusion, clarifying the maturation of NbSLP1 will be valuable for understanding the polar tube ejection mechanism of germination.

Reversible data hiding of encrypted images (RDHEI) is a technology where after the original image is encrypted with a key, secret data can be embedded in the encrypted image without knowing the information of the original image. In recent years, RDHEI has attracted much attention due to its security and effectiveness, and many RDHEI methods have been proposed. However, the capacity of embedded data is still not high enough. In this paper, a high-capacity RDHEI algorithm based on block-wise multi-predictor, and improved Huffman coding is proposed. First, the original image is divided into non-overlapping blocks. To better exploit the spatial correlation of the original image, 16 prediction models are used to predict the current pixel value from the neighboring pixels. All the pixels in one block use the same prediction model. The improved Huffman encoding is used to compress the prediction error image and the optimal prediction model determined by the sum of the absolute prediction errors, which makes more room for data embedding. The experimental results show that our method is superior to some of the latest algorithms in terms of achieving a higher embedding capacity.

Abstract Background Deactivated Cas9 (dCas9) led to significant improvement of CRISPR/Cas9-based techniques because it can be fused with a variety of functional groups to form diverse molecular devices, which can manipulate or modify target DNA cassettes. One important metabolic engineering strategy is to localize the enzymes in proximity of their substrates for improved catalytic efficiency. In this work, we developed a novel molecular device to manipulate the cellular location of specific DNA cassettes either on plasmids or on the chromosome, by fusing location tags to dCas9 (Cas9-Lag), and applied the technique for synthetic biology applications. Carotenoids like β-carotene serve as common intermediates for the synthesis of derivative compounds, which are hydrophobic and usually accumulate in the membrane compartment. Results Carotenoids like β-carotene serve as common intermediates for the synthesis of derivative compounds, which are hydrophobic and usually accumulate in the membrane components. To improve the functional expression of membrane-bound enzymes and localize them in proximity to the substrates, Cas9-Lag was used to pull plasmids or chromosomal DNA expressing carotenoid enzymes onto the cell membrane. For this purpose, dCas9 was fused to the E. coli membrane docking tag GlpF, and gRNA was designed to direct this fusion protein to the DNA expression cassettes. With Cas9-Lag, the zeaxanthin and astaxanthin titer increased by 29.0% and 26.7% respectively. Due to experimental limitations, the electron microscopy images of cells expressing Cas9-Lag vaguely indicated that GlpF-Cas9 might have pulled the target DNA cassettes in close proximity to membrane. Similarly, protein mass spectrometry analysis of membrane proteins suggested an increased expression of carotenoid-converting enzymes in the membrane components. Conclusion This work therefore provides a novel molecular device, Cas9-Lag, which was proved to increase zeaxanthin and astaxanthin production and might be used to manipulate DNA cassette location.

With the ongoing transformation of electricity generation from large thermal power plants to smaller renewable energy sources (RESs), such as wind and solar, modern renewable power systems need to address the new challenge of the increasing uncertainty and complexity caused by the deployment of electricity generation from RESs and the integration of flexible loads and new technologies. At present, a high volume of available data is provided by smart grid technologies, energy management systems (EMSs), and wide-area measurement systems (WAMSs), bringing more opportunities for data-driven methods. Deep reinforcement learning (DRL), as one of the state-of-the-art data-driven methods, is applied to learn optimal or near-optimal control policy by formulating the power system as a Markov decision process (MDP). This paper reviews the recent DRL algorithms and the existing work of operational control or emergency control based on DRL algorithms for modern renewable power systems and control-related problems for small signal stability. The fundamentals of DRL and several commonly used DRL algorithms are briefly introduced. Current issues and expected future directions are discussed.

Plant diseases and pests may seriously affect the yield of crops and even threaten the survival of human beings. The characteristics of plant diseases and insect pests are mainly reflected in the occurrence of lesions on crop leaves. Machine vision disease detection is of great significance for the early detection and prevention of plant diseases and insect pests. A fast detection method for lesions based on a single-channel gravitational kernel density clustering algorithm was designed to examine the complexity and ambiguity of diseased leaf images. Firstly, a polynomial was used to fit the R-channel feature histogram curve of a diseased leaf image in the RGB color space, and then the peak point and peak area of the fitted feature histogram curve were determined according to the derivative attribute. Secondly, the cluster numbers and the initial cluster center of the diseased leaf images were determined according to the peak area and peak point. Thirdly, according to the clustering center of the preliminarily determined diseased leaf images, the single-channel gravity kernel density clustering algorithm in this paper was used to achieve the rapid segmentation of the diseased leaf lesions. Finally, the experimental results showed that our method could segment the lesions quickly and accurately.

In this research, a new intermediate band (IB) material Sn-doped CuGaSe2 was synthesized for light absorbing layers of high-efficiency solar cells via ball milling. The experimental investigation indicated that element Sn can be successfully doped in the chalcopyrite CuGaSe2 sample, which enhanced the absorption spectrum significantly in the range of visible and near-infrared light wavelength (500 nm–900 nm). With the increase in the content of Sn, the optical bandgap of CuGa1−xSnxSe2 thin films was tuned from 1.65 eV to 1.41 eV for the doping content x from 0.00 to 0.06. The above results proved that the IB was introduced into the CuGa1−xSnxSe2 thin films, and due to the IB existence, this material leads to lower-energy photo absorption (with energy hν ≤ 1.68 eV). Moreover, the presence of Sn4+ in the host material was testified by x-ray photoelectron spectroscopy. Element composition and mapping analysis further confirmed that the fabricated film is composed of Cu, Ga, Sn, and S, and all elements have a homogeneous distribution without partial aggregation. Photoelectric investigations of the Sn–CuGaSe2 indicated that it is a desirable and promising IB material, which could be another candidate for light absorption layers of high-efficiency solar cells.

Imitation of memory and learning behaviors of nervous system by nanoscale photoelectric devices is highly desirable for building neuromorphic systems or even artificial neural networks. In this work, artificial synapses with photoelectric plasticity and memory behaviors based on a charge trapping memristive system was fabricated. Versatile synaptic functions, such as photoelectric excitatory postsynaptic current behavior, short-term memory, long-term memory, short - to long-term memory transition, and photonic learning and forgetting behaviors, were all mimicked by applied pulses of light and electricity. Moreover, the device also has the potential to be used in flexible applications. The photoelectric plasticity and memory phenomenon can be attributed to charge trapping and detrapping, since the used CdSe/ZnS quantum dots with a quantum well structure that act as trapping centers. This work provides a cost-effective method to develop artificial synapse devices, neural networks, and computers with photoelectric operations. Keywords: Photoelectric memristor, Artificial synapse, Space charge limited current, Excitatory postsynaptic current

Walking error has been problematic for pulsed LiDAR based on a single threshold comparator. Traditionally, walk error must be suppressed by some time discrimination methods with extremely complex electronic circuits and high costs. In this paper, we propose a compact and flexible method for reducing walk error and achieving distance-intensity imaging. A single threshold comparator and commercial time digital converter chip are designed to measure the laser pulse’s time of flight and pulse width. In order to obtain first-class measurement accuracy, we designed a specific pulse width correction method based on the Kalman filter to correct the laser recording time, significantly reducing the ranging walk error by echo intensity fluctuation. In addition, the pulse width obtained by our method, which is a recording of the laser intensity, is conducive to target identification. The experiment results verified plane point clouds of various targets obtained by the proposed method with a plane flatness less than 0.34. The novel contribution of the study is to provide a highly integrated and cost-effective solution for the realization of high-precision ranging and multi-dimensional detection by pulsed LiDAR. It is valuable for realizing multi-dimension, outstanding performance, and low-cost LiDAR.

Optical phased arrays (OPAs) are essential optical elements in applications that require the ability to manipulate the light-wavefront, such as beam focusing and light steering. To miniaturize the optical components, active metasurfaces, especially graphene metasurfaces, are used as competent alternatives. However, the metasurface cannot achieve strong resonance effect and phase control function in the mid-infrared region only relying on a single-layer graphene. Here we present a graphene-metal hybrid metasurface that can generate a specific phase or a continuous sweep in the range of a 275°-based single-layer graphene structure. A key feature of our design is that the phase adjustment mainly depends on the combination mechanism of resonance intensity and frequency modulation. An all-solid-state, electrically tunable, and reflective OPA is designed by applying the bias voltage to a different pixel metasurface. The simulation results show that the maximum deflection angle of the OPA can reach 42.716°, and the angular resolution can reach 0.62°. This design can be widely applied to mid-infrared imaging, optical sensing, and optical communication systems.

Active imaging technology can perceive the surrounding environment and obtain three-dimensional information of the target. Among them, light detection and ranging (LiDAR) imaging systems are one of the hottest topics in the field of photoelectric active imaging. Due to the small size, fast scanning speed, low power consumption, low price and strong anti-interference, a micro-electro-mechanical system (MEMS) based micro-scanning LiDAR is widely used in LiDAR imaging systems. However, the imaging point cloud will be distorted, which affects the accurate acquisition of target information. Therefore, in this article, we analyzed the causes of distortion initially, and then introduced a novel coordinate correction method, which can correct the point cloud distortion of the micro-scanning LiDAR system based on MEMS. We implemented our coordinate correction method in a two-dimensional MEMS LiDAR system to verify the feasibility. Experiments show that the point cloud distortion is basically corrected and the distortion is reduced by almost 72.5%. This method can provide an effective reference for the correction of point cloud distortion.

The invasive pest Spodoptera frugiperda first emerged in China in January 2019 and has, to date, migrated to 29 provinces and municipalities in China, causing heavy crop damage in large areas. As a response to this invasive species from the environment, some indigenous natural enemies have been discovered and reported after S. frugiperda invasion. In this paper, parasitic flies were collected and identified from S. frugiperda collected in the Yunnan, Guangxi, and Henan provinces and the Chongqing municipality in China. By using both conventional and molecular approaches, we were able to show that all the parasitic flies of S. frugiperda identified in the four regions were Megaselia. scalaris, and that they attacked the pest larvae and pupae. This is the first report on an indigenous Chinese Megaselia species that has parasitic ability against the invasive pest S. frugiperda, potentially providing new ideas for pest control in China.

We report on a self-adaptive waveform centroid algorithm that combines the selection of double-scale data and the intensity-weighted (DSIW) method for accurate LiDAR distance–intensity imaging. A time window is set to adaptively select the effective data. At the same time, the intensity-weighted method can reduce the influence of sharp noise on the calculation. The horizontal and vertical coordinates of the centroid point obtained by the proposed algorithm are utilized to record the distance and echo intensity information, respectively. The proposed algorithm was experimentally tested, achieving an average ranging error of less than 0.3 ns under the various noise conditions in the listed tests, thus exerting better precision compared to the digital constant fraction discriminator (DCFD) algorithm, peak (PK) algorithm, Gauss fitting (GF) algorithm, and traditional waveform centroid (TC) algorithm. Furthermore, the proposed algorithm is fairly robust, with remarkably successful ranging rates of above 97% in all tests in this paper. Furthermore, the laser echo intensity measured by the proposed algorithm was proved to be robust to noise and to work in accordance with the transmission characteristics of LiDAR. Finally, we provide a distance–intensity point cloud image calibrated by our algorithm. The empirical findings in this study provide a new understanding of using LiDAR to draw multi-dimensional point cloud images.

Since the development of renewable power generation, Sub/Super-Synchronous Control Interac-tion (SSCI), has attracted wide attention. Sub/super-synchronous oscillation (SSO) belongs to the category of small signal stability, and its characteristics are closely related to the operation mode and controller parameters in power grid. The operation mode of renewable power system changes frequently, a method to construct controller parameter stability region (PSR) is proposed for online assessment of the matching degree of controller parameters in power grid under current operation mode. Based on Gerschgorin disk theorem (GDT), eigenvalue distribution range of the system state matrix is estimated, thus the feasible value set of the controller parameter is deduced through small signal stability criterion. The stability margin evaluation index is proposed for guiding the prevention and control of SSO. Specifically, the preconditions of the application of the GDT on the PSR construction are discussed, and a construction method of transition matrix is proposed for the PSR construction. Furthermore, to reduce the conservativeness of PSR, an extension method for PSR is given. Finally, the validity of the proposed method is verified by a realistic benchmark. The efficiency of the proposed method is highlighted by comparing with the point-wise eigenvalue calculation of the state matrix.

Microwave power as directly controlled parameter determines the absorption of microwave energy inside extraction vial and the yield of objective component in microwave-assisted extraction (MAE). The aim of this study was to elucidate the effects of microwave powers on the yield of anthocyanin from blueberry (Vaccinium spp.) powder based on the absorption of microwave energy in extracts under MAE. Rising microwave powers have little effect on the distribution of microwave energy in extraction vial, but increase its temperature. The simulation results indicated that strength of electrical field tends to decay trend along with microwave irradiation; however, temperatures have the highest level in center location in an extraction vial. High microwave power strongly breaks cell wall of blueberry to open diffusion route of interior anthocyanin toward extraction solvent. A critical extraction temperature of 50.75±0.88°C is obtained with the highest anthocyanin yield under MAE. Three monomers of anthocyanin including pelargonidin, cyanidin, and delphinidin are, respectively, of the highest content of 1.02 μg/mL, 0.66 μg/mL, and 0.31 μg/mL. The research results contribute to the improvement of efficiency of microwave energy and yield of anthocyanin.

Humidity sensors allow electronic devices to convert the water content in the environment into electronical signals by utilizing material properties and transduction techniques. Three-dimensional graphene foam (3DGF) can be exploited in humidity sensors due to its convenient features including low-mass density, large specific surface area, and excellent electrical. In this paper, 3DGF with super permeability to water enables humidity sensors to exhibit a broad relative humidities (RH) range, from 0% to 85.9%, with a fast response speed (response time: ~89 ms, recovery time: ~189 ms). To interpret the physical mechanism behind this, we constructed a 3DGF model decorated with water to calculate the energy structure and we carried out the CASTEP as implemented in Materials Studio 8.0. This can be ascribed to the donor effect, namely, the electronic donation of chemically adsorbed water molecules to the 3DGF surface. Furthermore, this device can be used for user interaction (UI) with unprecedented performance. These high performances support 3DGF as a promising material for humidity sensitive material.

This study was conducted to investigate the effectiveness and possible mechanisms of an herbal prescription, Huangqi Maxingshigan decoction (Radix Astragali, Herba Ephedrae, Almond, Gypsum Fibrosum, Radix Glycytthizae) on infectious laryngotracheitis (ILT) in chickens. One hundred and sixty one-day-old chicks were randomly allocated into four groups, housed in isolated cages of 40 birds each: groupⅠwas Huangqi Maxingshigan decoction treatment group, group II as Ding Chuan San control group, group III as the model group, and group IV as the blank control group. Except for the blank control group, other groups were challenged with infectious laryngotracheitis virus via the intratracheal route on age day 52. Birds in group I were administered Huangqi Maxingshigan decoction at a concentration of 0.4 mL, or with Ding Chuan San in group II for comparison at 48 h post virus challenge (day 54) when they showed clinical signs, 2 times daily for 5 days consecutively. Then, the superoxide dismutases (SOD) activity and malondialdehyde (MDA) contents in serum, the expression of IFN-γ, IL-4 mRNA in spleen, the sIgA contents in tracheal fluid, the sIgA secreting cells in trachea were determined on day 5, day 15, day 25 post infection. The results showed that Huangqi Maxingshigan decoction could correct Th2-dominant Th1/th2 imbalance through up-regulation of IFN-γ, and down-regulation of IL-4 at the transcriptional level, enhance cell-mediated immunity, ease inflammatory responses caused by ILTV. The results also showed that Huangqi Maxingshigan decoction provide an important antioxidant defense in the process of anti-ILT. Furthermore, it could induce the production of sIgA to enhance mucosal immunity.

Band selection (BS) work for hyperspectral remote sensing images (HRSIs) has attracted increasing attention from researchers. In the HRSI, the salient features of each band always include the more important discriminative features. However, most existing BS methods always have difficulty in simultaneously performing local-global consistency analysis on salient features in bands of HRSIs. In addition, these methods also ignore the consideration of the diversity of band information to some extent when selecting a band subset. To tackle these problems, a novel unsupervised hyperspectral BS method combining the variation degree of salient features (UBSF) is proposed in this article. UBSF first promotes the singular value decomposition method to analyse the salient features in bands and develops a new low-dimensional local-global salient feature representation (NLR) method for discriminative feature representations of the bands. The NLR method combines the variation degree of the features of the significant representative patches of each band in their corresponding significant feature subspaces to achieve a localglobal consistency analysis of salient features of each band of HRSIs. Next, UBSF proposes a new two-stage strategy (TSS) to preserve the diversity of BS results as much as possible. In TSS, we propose a novel similarity criterion, which can make the highly similar band subsets with near information amount in the overall bands adaptively compressed according to the obtained similarity results of bands further. Then, the band subset with the most information is selected as the final BS results in the obtained reduceddimensional HRSI. Finally, the validity of the UBSF method is verified based on three measured HRSI datasets. The experimental results show that the proposed UBSF is superior to other state-of-the-art BS methods. [ABSTRACT FROM AUTHOR]

Abstract Aims The purpose of this study was to investigate the prevalence of sexual harassment among operating room nurses, and to analyze the influencing factors. Background Sexual harassment in the healthcare field has a high incidence and nurses are the main victims; sexual harassment not only leads to physical and mental problems, but also undermines nurses’ performance and affects the quality of medical care. Methods A multi-center cross-sectional study was conducted using an online survey to investigate the sexual harassment experiences of 483 operating room nurses. The survey included demographic information, personality traits (Ten-Item Personality Inventory), sexual harassment experiences (Workplace Sexual Harassment Questionnaire), negative consequences, coping strategies, and social support status. Results 63% of operating room nurses experienced sexual harassment in the past 12 months, with gender harassment predominating (56.7%), followed by unwanted sexual attention (28.6%) and sexual coercion (13.6%). Surgeons (81.20%) and married ones (66.7%) were the most common perpetrators. Most victims possess limited knowledge regarding sexual harassment laws and exhibit weak awareness of their rights protection. 47.8% remained silent after being sexually harassed; most of nurse employed passive coping strategies. Sexual harassment had the most prominent impact on the work of operating room nurses, mainly in the form of decreased work quality and efficiency (87.3%), as well as decreased work engagement (77.0%) and negative workplace pressure experience (72.2%); emotional disturbances, such as anxiety (76.6%), stigma (71.8%), and fear (71.5%); and was accompanied by sleep disorders (74.9%), headaches (56.0%) and nausea and vomiting (54.3%); as well as reduced trust in the opposite sex (83.8%) and disruption of family life (82.1%).Only 39.2% of the hospitals had sexual harassment reporting channels or methods, 30.7% had departments to deal with sexual harassment in the workplace, and 46.8% had communication discussions about psychological safety after sexual harassment. Personality traits, drinking behaviour, age, marriage, and social support were associated with sexual harassment behavior in the operating room. Conclusion The high incidence of sexual harassment in the operating room affects nurses’ productivity and physical and mental health. Operating room nurses had insufficient legal knowledge about sexual harassment and lacked hospital organizational support. agreeableness and openness personality traits, alcohol consumption, age, being married, and lack of social support were influential factors of sexual harassment in the operating room. Implications for nursing management Healthcare organizations should encourage reporting, care for victims, and join with the police department, judiciary, and women’s federation to develop reporting procedures and training programs on sexual harassment knowledge to create a safe and comfortable professional environment.