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Abstract Background Amorphophallus konjac (A. konjac), a perennial tuberous plant, is widely cultivated for its high konjac glucomannan (KGM) content, a heteropolysaccharide with diverse applications. The cellulose synthase-like (CSL) gene family is known to be a group of processive glycan synthases involved in the synthesis of cell-wall polysaccharides and plays an important role in the biological process of KGM. However, in A. konjac the classification, structure, and function of the AkCSLA superfamily have been studied very little. Results Bioinformatics methods were used to identify the 11 AkCSLA genes from the whole genome of Amorphophallus konjac and to systematically analyze their characteristics, phylogenetic evolution, promoter cis-elements, expression patterns, and subcellular locations. Phylogenetic analysis revealed that the AkCSLA gene family can be divided into three subfamilies (Groups I- III), which have close relationships with Arabidopsis. The promoters of most AkCSLA family members contain MBS elements and ABA response elements. Analysis of expression patterns in different tissues showed that most AkCSLAs are highly expressed in the corms. Notably, PEG6000 induced down-regulation of the expression of most AkCSLAs, including AkCSLA11. Subcellular localization results showed that AkCSLA11 was localized to the plasma membrane, Golgi apparatus and endoplasmic reticulum. Transgenic Arabidopsis experiments demonstrated that overexpression of AkCSLA11 reduced the plant’s drought tolerance. This overexpression also inhibited the expression of drought response genes and altered the sugar components of the cell wall. These findings provide new insights into the response mechanisms of A. konjac to drought stress and may offer potential genetic resources for improving crop drought resistance. Conclusion In conclusion, the study reveals that the AkCSLA11 gene from A. konjac negatively impacts drought tolerance when overexpressed in Arabidopsis. This discovery provides valuable insights into the mechanisms of plant response to drought stress and may guide future research on crop improvement for enhanced resilience.

Abstract Monogalactosyldiacylglycerol (MGDG), a predominant photosynthetic membrane lipid derived from plants and microalgae, has important applications in feed additives, medicine, and other fields. The low content and various structural stereoselectivity differences of MGDG in plants limited the biological extraction or chemical synthesis of MGDG, resulting in a supply shortage of monogalactosyldiacylglycerol with a growing demand. Herein, we established Saccharomyces cerevisiae as a cell factory for efficient de novo production of monogalactosyldiacylglycerol for the first time. Heterologous production of monogalactosyldiacylglycerol was achieved by overexpression of codon-optimized monogalactosyldiacylglycerol synthase gene MGD1, the key Kennedy pathway genes (i.e. GAT1, ICT1, and PAH1), and multi-copy integration of the MGD1 expression cassette. The final engineered strain (MG-8) was capable of producing monogalactosyldiacylglycerol with titers as high as 16.58 nmol/mg DCW in a shake flask and 103.2 nmol/mg DCW in a 5 L fed-batch fermenter, respectively. This is the first report of heterologous biosynthesis of monogalactosyldiacylglycerol in microorganisms, which will provide a favorable reference for study on heterologous production of monogalactosyldiacylglycerol in yeasts. Graphical Abstract

Purpose: This study aims to translate and culturally adapt the revised The Teachers' Attitudes Towards Inclusion Scale Adjusted (TAISA) into Chinese and to evaluate its reliability and validity among Chinese preservice physical educators. Methodology: The English version of The Teachers' Attitudes Towards Inclusion Scale Adjusted (TAISA) was translated through a rigorous process that included a comprehensive literature review and consultations with subject matter experts. A survey was administered to 500 preservice physical educators in Chongqing. The adapted scale underwent thorough analysis, encompassing demographic frequency analysis, item analysis, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and evaluation of test-retest reliability. Results: The analysis revealed that the Chinese version of The Teacher Attitude toward Inclusive Education Scale for Preservice Physical Educators (TAISA-P) retained the original three-factor structure, comprising positive affect, training and perceived competence, and negative affect. After the removal of four items, the final scale consisted of 17 items, with each dimension containing 5 to 6 items. Factor loadings ranged from 0.442 to 0.705, and the model demonstrated a χ²/df ratio of 1.350, a root mean square error of approximation (RMSEA) of 0.066, a non-normed fit index (NNFI) of 0.825, a comparative fit index (CFI) of 0.946, an incremental fit index (IFI) of 0.948, and a goodness-of-fit index (GFI) of 0.834. The overall internal consistency reliability of TAISA-P was 0.759, with dimension-specific reliabilities of 0.835, 0.790, and 0.779. The test-retest reliability coefficient was 0.743 (P < 0.001). Conclusion: The Teacher Attitude toward Inclusive Education Scale for Preservice Physical Educators (TAISA-P) demonstrates strong reliability and validity, thereby establishing it as a suitable instrument for assessing the attitudes of Chinese preservice physical educators toward inclusive education.

MicroRNAs (miRNAs) are a class of small non-coding RNAs, typically 20–25 nucleotides in length, that play a crucial role in regulating gene expression post-transcriptionally. They are involved in various biological processes such as plant growth, development, stress response, and hormone signaling pathways. Plants interact with microbes through multiple mechanisms, including mutually beneficial symbiotic relationships and complex defense strategies against pathogen invasions. These defense strategies encompass physical barriers, biochemical defenses, signal recognition and transduction, as well as systemic acquired resistance. MiRNAs play a central role in regulating the plant’s innate immune response, activating or suppressing the transcription of specific genes that are directly involved in the plant’s defense mechanisms against pathogens. Notably, miRNAs respond to pathogen attacks by modulating the balance of plant hormones such as salicylic acid, jasmonic acid, and ethylene, which are key in activating plant defense mechanisms. Moreover, miRNAs can cross boundaries into fungal and bacterial cells, performing cross-kingdom RNA silencing that enhances the plant’s disease resistance. Despite the complex and diverse roles of miRNAs in plant defense, further research into their function in plant-pathogen interactions is essential. This review summarizes the critical role of miRNAs in plant defense against pathogens, which is crucial for elucidating how miRNAs control plant defense mechanisms.

The physiological activity facilitated by arbuscular mycorrhizal fungi (AMF) contributes to plants’ ability to tolerate drought. Nevertheless, it is unclear if AMF colonization affects the expression of genes in the host plant that encode antioxidant enzymes in the superoxide dismutase (SOD) family, which help alleviate drought stress in plants. Here, we conducted a pot trial to determine whether colonization by the AMF Rhizophagus irregularis improves drought resistance in Bombax ceiba. We comprehensively analyzed the SOD gene family and evaluated genome-wide expression patterns of SODs and SOD activity in AMF-colonized and non-mycorrhizal plants under simulated drought. We identified a total of 13 SODs in the genome of B. ceiba, including three FeSODs (BcFSDs), three MnSODs (BcMSDs), and seven Cu/ZnSODs (BcCSDs). Phylogenetic analysis based on binding domain revealed that SOD genes from B. ceiba and various other plant species can be divided into three separate groups, showing significant bootstrap values. Our examination of gene composition and patterns suggests that most BcSOD genes in these three subgroups are significantly conserved. Additionally, it was noted that hormones and stress-responsive cis-regulatory elements were found in all BcSOD promoters. Expression profiling by qRT-PCR demonstrated that AMF increased relative expression levels of Cu/Zn-SODs in both roots and shoots under drought stress, except for BcCSD3 in roots. Furthermore, AMF colonization increased the relative expression of BcMSD1a and BcMSD1b in roots, augmenting SOD activities and increasing ROS scavenging during drought. In general, this work offers molecular evidence in support of the beneficial effect of AMF colonization on drought tolerance in B. ceiba. It also elucidates the expression patterns of SOD genes, which will support efforts to optimize mycorrhizal seedling cultivation under stressful conditions.

Purpose: The development of athletics is pivotal to China's aspiration of becoming a modern competitive sports powerhouse. Spiritual culture, being its core component, also serves as an intrinsic driving force. A nuanced understanding of the formation of spiritual and cultural differences between Chinese and Western competitive sports is essential for the connotative construction of China's competitive sports strength. Methodology: This study employs qualitative text analysis to examine 46 texts in both Chinese and English. The aim is to investigate the mechanisms underlying the cultural differences between Chinese and Western sportsmanship and to construct a theoretical model. Findings: The study identifies five key dimensions—mindset, internal and external drive, traditional paradigm, code of conduct, and spiritual pillars—that collectively contribute to the cultural differences in Chinese and Western competitive sportsmanship. Among these, mindset, internal and external drive, and traditional paradigm are the causative factors, while codes of conduct and spiritual pillars represent the actions and outcomes that shape these cultural differences. Value: This study aims to elucidate the specific spiritual and cultural differences between Chinese and Western competitive sports, and the reasons behind these differences. By constructing a mechanism for the formation of these cultural differences, the study seeks to foster a conducive cultural environment for the development of competitive sports in China. This, in turn, is expected to accelerate China's transition from a major sports nation to a leading sports power on the global stage.

Cell plates are transient structures formed by the fusion of vesicles at the center of the dividing plane; furthermore, these are precursors to new cell walls and are essential for cytokinesis. Cell plate formation requires a highly coordinated process of cytoskeletal rearrangement, vesicle accumulation and fusion, and membrane maturation. Tethering factors have been shown to interact with the Ras superfamily of small GTP binding proteins (Rab GTPases) and soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs), which are essential for cell plate formation during cytokinesis and are fundamental for maintaining normal plant growth and development. In Arabidopsis thaliana, members of the Rab GTPases, tethers, and SNAREs are localized in cell plates, and mutations in the genes encoding these proteins result in typical cytokinesis-defective phenotypes, such as the formation of abnormal cell plates, multinucleated cells, and incomplete cell walls. This review highlights recent findings on vesicle trafficking during cell plate formation mediated by Rab GTPases, tethers, and SNAREs.

Knowledge payment is an emerging business mode that has developed in the era of social media. With the impact of Internet technology, the knowledge payment market is rapidly expanding, especially in China. However, piracy leads to more intense competition and affects the profits of knowledge providers and knowledge payment platforms. Government policies combined with the anti-piracy measures of these platforms have become effective methods to combat piracy. This paper investigates the impact of government subsidies and anti-piracy efforts on a knowledge provider’s and platform’s optimal decisions. We develop a two-stage knowledge payment product supply chain with a knowledge provider and a knowledge payment platform. The platform in the leader position of the supply chain has anti-piracy efforts and the government subsidizes the platform. Stackelberg game models are constructed and compared to examine the equilibrium solution in relation to three modes (no government subsidies and no anti-piracy efforts; only anti-piracy efforts; and both government subsidies and anti-piracy efforts). Our analysis shows that (1) both government subsidies and anti-piracy efforts increase the quality level and unit quality signing bonus of the knowledge payment product. Meanwhile, the product’s retail price increases with anti-piracy efforts but decreases within a certain subsidy range. (2) The knowledge provider’s profit always increases with the anti-piracy effort level, while the platform’s profit is an inverted U-shaped relationship with the anti-piracy effort level. (3) Government subsidy behavior can always increase a knowledge provider’s profit but may not necessarily increase the platform’s profit. Moreover, there exists a certain threshold: when subsidies are lower (higher) than this threshold, it is more beneficial to the knowledge provider (platform). In addition, we also find that the method of high subsidies combined with a low anti-piracy effort level benefits both parties and that the subsidized party will be more sensitive. The results will provide knowledge providers and platforms with new market management insights from the perspective of government subsidies and anti-piracy efforts and guide them to make optimal decisions.

SNARE (soluble N-ethylmaleimide-sensitive factor attachment protein receptor) proteins assemble to drive the final membrane fusion step of membrane trafficking. Thus, SNAREs are essential for membrane fusion and vesicular trafficking, which are fundamental mechanisms for maintaining cellular homeostasis. In plants, SNAREs have been demonstrated to be located in different subcellular compartments and involved in a variety of fundamental processes, such as cytokinesis, cytoskeleton organization, symbiosis, and biotic and abiotic stress responses. In addition, SNAREs can also contribute to the normal growth and development of Arabidopsis. Here, we review recent progress in understanding the biological functions and signaling network of SNAREs in vesicle trafficking and the regulation of root growth and development in Arabidopsis.

The trajectory optimization design of interceptor is very important in the defense combat against a class of high-speed and strong-maneuvering aircraft. At present, an important idea is to use hp pseudospectral method to offline optimizes the trajectory of interceptor, but the solving efficiency of this method needs to be further improved. Aiming at this issue, an improved adaptive hp pseudospectral method is proposed in this paper. In order to shorten the solving time of the algorithm, the proposed method has two main improvements on the basis of the traditional hp pseudospectral method: on one hand, by judging the positions of the control sudden change points, prerefine the mesh around them according to certain rules. On the other hand, the curvature of the system state curve is used as the criterion to segment the original mesh nonuniformly so that more mesh points can be allocated where the curvature is large. These two points together ensure that the collocation point resources can be used more efficiently in the mesh refinement process. The simulation results show that the proposed method can solve the optimized trajectory of interceptor effectively, and it also proves that this method has higher solving efficiency than the traditional adaptive hp method.

For traditional predictor-corrector guidance algorithm for reentry glide vehicle, it cost a lot of time to obtain predicted flight range with a slow speed to iterate. In this paper, according to residual network (ResNet)’s block and dynamic model of vehicle, through analyzing the characteristics of predicted flight range with constraints, the flight range prediction block and flight range prediction neural network are designed, which can obtain the predicted range accurately and quickly; then aiming at the separation between guidance logic and no-fly zone avoidance logic, which may lead to guidance failure and increasing of the sign variation number of the bank angle, the no-fly zone crossrange and the no-fly zone mapping crossrange are proposed in this paper. According the repulsion force of artificial potential field, an adaptive crossrange corridor combining guidance logic and no-fly zone avoidance logic is proposed, and the convergence of the corridor is analyzed theoretically. Through simulation, the block number of flight range prediction network is determined firstly. By this method, the efficiency of lateral guidance can be improved. Then, through the simulation with the different no-fly zones under different disturbed conditions, the stability and validity of the guidance method are verified. Finally, compared with other predictor-corrector algorithms, the proposed method can realize guidance with less sign variation number of bank angle and better avoidance for no-fly zones.

Optimal trajectory generation for the guidance of re-entry glide vehicles is of great significance. To realize a faster generation speed and consistency with the guidance mechanism, an improved convex optimization trajectory generation algorithm based on the flight range domain for the re-entry glide vehicles is proposed in this paper. Firstly, according to the definition of the range-to-go, the projected range-to-go of the re-entry glide vehicle is presented when the dynamic model is converted to the flight range domain. Then, the attack angle and bank angle are expanded to the state variables and the change rate, which is designed as a new control variable. When the dynamic models and constraints are convexificated and discretized, the vehicle trajectory discrete convex model in the flight range domain is proposed. In order to further improve the generation speed and accuracy, an initial trajectory generation method that is close to the guidance requirements is proposed by the landing points of different control laws. In addition, by analyzing the nonlinear illegal degree of grid points, the probability density of grid points and the adjustment strategy of grid points are proposed. Finally, the ablation experiment shows that the initial trajectory generation and distributed grid points method works. With different target points, different no-fly zones, different initial states, and the Monte Carlo experiment, our method can effectively and robustly complete the generation. The lateral and longitudinal generation error is less than 1 km. And compared with the Gaussian pseudo-spectral method, our method obtained comparable accuracy and faster speed.

Aimed at the problem of the insufficient utilization of intention information and flight data for the trajectory prediction of a reentry glide target, an intelligent trajectory prediction algorithm based on intention inference was proposed. Firstly, a control parameter prediction network was developed to predict the variation of the control parameter values. Secondly, based on the Gaussian mixture model and Dubins circle, the importance of strategic places was modeled, the functional relationship between the strategic places and trajectories was established, and the influence factors of target threat were analyzed. Subsequently, an intention inference network was designed, which can accurately identify the key point the target intends to attack and realize the nonlinear calculation of the target’s threat value. Finally, according to the common guidance law of reentry glide target, the lateral sign-variation rule was designed to carry out the target trajectory prediction based on intention inference. Simulation revealed that the parameter prediction network designed in this paper can realize the modification of filtering control parameters and effectively predict the value of these parameters. Moreover, taking the result of intention inference network, trajectory prediction in different prediction cases was accomplished. The maximal error of spatial distance (MESD) of the proposed method was less than 9 km when the prediction time was 100 s, and the best result was obtained in the long-term prediction. Compared with other mainstream prediction methods, the proposed method obtained the best prediction accuracy.

Low temperature is a major adverse environment that affects normal plant growth. Previous reports showed that the actin cytoskeleton plays an important role in the plant response to low-temperature stress, but the regulatory mechanism of the actin cytoskeleton in this process is not clear. C-repeat binding factors (CBFs) are the key molecular switches for plants to adapt to cold stress. However, whether CBFs are involved in the regulation of the actin cytoskeleton has not been reported. We found that Arabidopsis actin depolymerizing factor 5 (ADF5), an ADF that evolved F-actin bundling function, was up-regulated at low temperatures. We also demonstrated that CBFs bound to the ADF5 promoter directly in vivo and in vitro. The cold-induced expression of ADF5 was significantly inhibited in the cbfs triple mutant. The freezing resistance of the adf5 knockout mutant was weaker than that of wild type (WT) with or without cold acclimation. After low-temperature treatment, the actin cytoskeleton of WT was relatively stable, but the actin cytoskeletons of adf5, cbfs, and adf5 cbfs were disturbed to varying degrees. Compared to WT, the endocytosis rate of the amphiphilic styryl dye FM4-64 in adf5, cbfs, and adf5 cbfs at low temperature was significantly reduced. In conclusion, CBFs directly combine with the CRT/DRE DNA regulatory element of the ADF5 promoter after low-temperature stress to transcriptionally activate the expression of ADF5; ADF5 further regulates the actin cytoskeleton dynamics to participate in the regulation of plant adaptation to a low-temperature environment.

This paper presents a noncertainty equivalent adaptive backstepping control scheme for advanced fighter attitude tracking, in which unsteady effects, parameter uncertainties, and input constraints are all considered which increase the design difficulty to a large extent. Based on unsteady attitude dynamics and the noncertainty equivalent principle, a new observer is first developed to reconstruct the immeasurable and time-varying unsteady states. Afterwards, the unsteady aerodynamics is compensated in the backstepping controller where the command filter is introduced to impose physical constraints on actuators. In order to further enhance the robustness, the noncertainty equivalent adaptive approach is again used to estimate the uncertain constant parameters. Moreover, stability of the closed-loop system that includes the state observer, parameter estimator, and backstepping controller is proven by the Lyapunov theorem in a unified architecture. Finally, simulation results show that performance of the deterministic control system can be captured when attractive manifolds are achieved. The effectiveness and robustness of the proposed control scheme are verified by the Herbst maneuver.

An adaptive neural control method is proposed in this paper for the flexible air-breathing hypersonic vehicle (AHV) with constraints on actuators. This scheme firstly converts the original control problem with input constraints into a new control problem without input constraints based on the control input saturation function. Secondly, on the basis of the implicit function theorem, the radial basis function neural network (RBFNN) is introduced to approximate the uncertain items of the model. And the minimal-learning-parameter (MLP) technique is adopted to design the adaptive law for the norm of network weight vector, which significantly reduces calculations. Meanwhile, the finite-time convergence differentiator (FD) is introduced, through which the model state variables and their derivatives are accurately estimated to ensure the control effect. Finally, it is theoretically proved that the closed-loop control system is stable. And the effectiveness of the designed controller is verified by simulation.

Aiming at improving the tracking and stabilizing performance of two-axis optronic stabilized platform with Stribeck friction and uncertain velocity disturbance, a prescribed performance control strategy with unknown initial errors is designed. By designing a new performance function, the limit of traditional prescribed control that the initial error has to be known accurately is broken through. The strategy possesses strong robustness against unknown disturbance, and the state error is restrained to a predefined arbitrary small residual. It is guaranteed that the closed-loop system is uniformly ultimately bounded. The simulation results demonstrate the effectiveness of proposed strategy.