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Oral delivery of insulin exhibits low bioavailability due to the hydrolysis in acidic gastric juice, biodegradation of enzymes, and inefficient penetration through the intestinal mucus and epithelial cell layer. Here, we report a micelle platform to enhance the oral delivery of insulin. Insulin was precipitated by zinc ions to form hydrophobic nanoparticles and subsequently coated with a surfactant polysorbate-80 (Tween-80) to form nanosized micelles (TW-Zn-rhINS). Tween-80 protects the insulin from the degradation of enzymes, meanwhile facilitating the diffusion within mucus and the epithelial cell layer by opening the tight junctions. The micelles were then lyophilized and encapsulated in enteric capsules to overcome acidic hydrolysis in gastric juice. The micelles significantly increased transcellular insulin transport and uptake. The in vivo experiments demonstrated that oral TW-Zn-rhINS micelle capsules (30 IU/kg) decreased the blood glucose of diabetic mice by 58.74% after administration for 6 h, while the postprandial blood glucose dropped by 51.1%. Pharmacokinetics data indicated that the relative oral bioavailability of TW-Zn-rhINS was 7.88%, which was 7.73 times higher than that of insulin. The micelles present a promising platform to enhance the oral bioavailability of insulin, also indicating a potential for oral delivery of protein. [ABSTRACT FROM AUTHOR]

In order to accurately obtain the seedling emergence rate of breeding sunflower and to assess the quality of sowing as well as the merit of sunflower varieties, a method of extracting the sunflower seedling emergence rate using multi-source remote sensing information from unmanned aerial vehicles is proposed. Visible and multispectral images of sunflower seedlings were acquired using a UAV. The thresholding method was used to segment the excess green image of the visible image into vegetation and non-vegetation, to obtain the center point of the vegetation to generate a buffer, and to mask the visible image to achieve weed removal. The components of color models such as the hue–saturation value (HSV), green-relative color space (YCbCr), cyan-magenta-yellow-black (CMYK), and CIELAB color space (L*A*B) models were compared and analyzed. The A component of the L*A*B model was preferred for the optimization of K-means clustering to segment sunflower seedlings and mulch using the genetic algorithm, and the segmentation accuracy was improved by 4.6% compared with the K-means clustering algorithm. All told, 10 geometric features of sunflower seedlings were extracted using segmented images, and 10 vegetation indices and 48 texture features of sunflower seedlings were calculated based on multispectral images. The Pearson's correlation coefficient method was used to filter the three types of features, and the geometric feature set, the vegetation index set, the texture feature set, and the preferred feature set were constructed. The construction of a sunflower plant number estimation model using the crested porcupine optimizer–support vector machine is proposed and compared with the sunflower plant number estimation models constructed based on decision tree regression, BP neural network, and support vector machine regression. The results show that the accuracy of the model based on the preferred feature set is higher than that of the other three feature sets, indicating that feature screening can improve the accuracy and stability of models; assessed using the CPO-SVR model, the accuracy of the preferred feature set was the highest, with an R² of 0.94, an RMSE of 5.16, and an MAE of 3.03. Compared to the SVR model, the value of the R2 is improved by 3.3%, the RMSE decreased by 18.3%, and the MAE decreased by 18.1%. The results of the study can be cost-effective, accurate, and reliable in terms of obtaining the seedling emergence rate of sunflower field breeding. [ABSTRACT FROM AUTHOR]

To identify the mechanism underlying macrosteatosis (MaS)‐related graft failure (GF) in liver transplantation (LT) by multi‐omics network analysis. The transcriptome and metabolome were assayed in graft and recipient plasma in discovery (n = 68) and validation (n = 89) cohorts. Differentially expressed molecules were identified by MaS and GF status. Transcriptional regulatory networks were generated to explore the mechanism for MaS‐related inferior post‐transplant prognosis. The differentially expressed molecules associated with MaS and GF were enriched in ferroptosis and peroxisome‐related pathways. Core features of MaS‐related GF were presented on decreased transferrin and impaired anti‐oxidative capacity dependent upon dysregulation of transcription factors hepatocyte nuclear factor 4A (HNF4A) and hypoxia‐inducible factor 1A (HIF1A). Furthermore, miR‐362‐3p and miR‐299‐5p inhibited transferrin and HIF1A expression, respectively. Lower M2 macrophages but higher memory CD4 T cells were observed in MaS‐related GF cases. These results were validated in clinical specimens and cellular models. Systemic analysis of multi‐omics data depicted a panorama of biological pathways deregulated in MaS‐related GF. Transcriptional regulatory networks centered on transferrin and anti‐oxidant responses were associated with poor MaS graft quality, qualifying as potential targets to improve prognosis of patients after LT. [ABSTRACT FROM AUTHOR]

In this article, a prescribed-time state-feedback stabilization design strategy is proposed for a class of p-norm stochastic nonlinear strict feedback systems. In previous work on prescribed-time stabilization of stochastic systems, only stochastic nonlinear systems with fractional power less than or equal to one are considered. To overcome this problem, we improve the existing method and discuss the issue of prescribed-time stabilization of stochastic nonlinear systems with fractional power is arbitrary positive odd rational number. First, a prescribed-time controller is designed by combining the Lyapunov function with adding a power integrator technique. It should be pointed out that the homogeneous domination approach is adopted when dealing with the nonlinear terms of the system. Then, according to the stochastic prescribed-time stability theorem, it is proved that the designed controller can ensure the closed-loop system is prescribed-time mean-square stable. Finally, three simulation examples are given to investigate the validity of the presented method, in which the last one is an electromechanical system example. [ABSTRACT FROM AUTHOR]

Summary: This article focuses on the fast finite‐time adaptive tracking control for non‐strict feedback stochastic nonlinear systems. Random disturbances with constraints have rarely been considered in previous studies of fast finite‐time stabilization problems. The corresponding extension of the adaptive fast finite‐time stabilization mechanism to stochastic nonlinear systems with full‐state constraints is one of the innovations of this article. First, neural networks and barrier Lyapunov functions are utilized to handle the problems arising from the unknown nonlinear terms and the state constraints, respectively. Second, an adaptive fast finite‐time tracking control scheme for stochastic systems is designed based on the backstepping technique and a fast finite‐time control strategy, which allows all signals of the closed‐loop system to be bounded. Meanwhile, the designed controller guarantees a fast convergence of the tracking error to a small neighborhood of the origin under full‐state constraints. Finally, two examples are given to illustrate the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: Anthocyanin accumulations in the flowers can improve seed production of hybrid lines, and produce higher commodity value in cotton fibre. However, the genetic mechanism underlying the anthocyanin pigmentation in cotton petals is poorly understood. Here, we showed that the red petal phenotype was introgressed from Gossypium bickii through recombination with the segment containing the R3bic region in the A07 chromosome of Gossypium hirsutum variety LR compared with the near‐isogenic line of LW with white flower petals. The cyanidin‐3‐O‐glucoside (Cy3G) was the major anthocyanin in red petals of cotton. A GhTT19 encoding a TT19‐like GST was mapped to the R3bic site associated with red petals via map‐based cloning, but GhTT19 homologue gene from the D genome was not expressed in G. hirsutum. Intriguingly, allelic variations in the promoters between GhTT19LW and GhTT19LR, rather than genic regions, were found as genetic causal of petal colour variations. GhTT19‐GFP was found localized in both the endoplasmic reticulum and tonoplast for facilitating anthocyanin transport. An additional MYB binding element found only in the promoter of GhTT19LR, but not in that of GhTT19LW, enhanced its transactivation by the MYB activator GhPAP1. The transgenic analysis confirmed the function of GhTT19 in regulating the red flower phenotype in cotton. The essential light signalling component GhHY5 bonded to and activated the promoter of GhPAP1, and the GhHY5‐GhPAP1 module together regulated GhTT19 expression to mediate the light‐activation of petal anthocyanin pigmentation in cotton. This study provides new insights into the molecular mechanisms for anthocyanin accumulation and may lay a foundation for faster genetic improvement of cotton. [ABSTRACT FROM AUTHOR]

This paper investigates the global fixed-time tracking control problem of nonlinear cascade systems with unknown high powers. In the process of control design, a upper bound and a lower bound of high powers are introduced to compensate the unknown system powers, and a state feedback controller is designed under any initial system conditions. Based on the Lyapunov stability analysis method and the fixed-time stability theory, it is verified that the proposed method can regulate the output tracking error to a disc region of the origin within a fixed-time and all the closed-loop signals are bounded. At last, the effectiveness of the proposed scheme is verified by some simulation results. [ABSTRACT FROM AUTHOR]

In this article, a new design method for an adaptive fast finite-time controller (FTC) is proposed for the finite-time stability (FTS) issue of a class of high-order stochastic nonlinear systems (HOSNSs) with unknown parameters. Using a power integrator technology and Lyapunov function approach, an adaptive state feedback controller is derived to ensure fast FTS of HOSNSs. The developed adaptive fast FTC is equipped with less settling time to obtain better steady-state accuracy compared with the traditional FTC. The effectiveness of the proposed adaptive control scheme is demonstrated by a numerical example. [ABSTRACT FROM AUTHOR]