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The cuticular wax that covers the surfaces of plants is the first barrier against environmental stresses and increasingly accumulates with light exposure. However, the molecular basis of light-responsive wax biosynthesis remains elusive. In grape (Vitis vinifera), light exposure resulted in higher wax terpenoid content and lower decay and abscission rates than controls kept in darkness. Assay for transposase-accessible chromatin with high-throughput sequencing and RNA-seq data were integrated to draw the chromatin accessibility and cis-elements regulatory map to identify the potential action sites. Terpenoid synthase 12 (VvTPS12) and 3-hydroxy-3-methylglutaryl-CoA reductase 2 (VvHMGR2) were identified as grape wax biosynthesis targets, while VvHYH and VvGATA24 were identified as terpenoid biosynthesis activators, as more abundant wax crystals and higher wax terpenoid content were observed in transiently overexpressed grape berries and Nicotiana benthamiana leaves. The interaction between VvHYH and the open chromatin of VvTPS12 was confirmed qualitatively using a dual luciferase assay and quantitatively using surface plasma resonance, with an equilibrium dissociation constant of 2.81 nm identified via the latter approach. Molecular docking simulation implied the structural nature of this interaction, indicating that 24 amino acid residues of VvHYH, including Arg106A, could bind to the VvTPS12 G-box cis-element. VvGATA24 directly bound to the open chromatin of VvHMGR2, with an equilibrium dissociation constant of 8.59 nm. Twelve amino acid residues of VvGATA24, including Pro218B, interacted with the VvHMGR2 GATA-box cis-element. Our work characterizes the mechanism underlying light-mediated wax terpenoid biosynthesis and provides gene targets for future molecular breeding. Two light-responsive transcription factors modulate wax terpenoid biosynthesis in grape by interacting with 2 wax biosynthesis target genes. [ABSTRACT FROM AUTHOR]

The pathogenesis of ankylosing spondylitis (AS) remains unclear, and while recent studies have implicated necroptosis in various autoimmune diseases, an investigation of its relationship with AS has not been reported. In this study, we utilized the Gene Expression Omnibus database to compare gene expressions between AS patients and healthy controls, identifying 18 differentially expressed necroptosis‐related genes (DENRGs), with 8 upregulated and 10 downregulated. Through the application of three machine learning algorithms—least absolute shrinkage and selection operation, support vector machine‐recursive feature elimination and random forest—two hub genes, FASLG and TARDBP, were pinpointed. These genes demonstrated high specificity and sensitivity for AS diagnosis, as evidenced by receiver operating characteristic curve analysis. These findings were further supported by external datasets and cellular experiments, which confirmed the downregulation of FASLG and upregulation of TARDBP in AS patients. Immune cell infiltration analysis suggested that CD4+ T cells, CD8+ T cells, NK cells and neutrophils may be associated with the development of AS. Notably, in the group with high FASLG expression, there was a significant infiltration of CD8+ T cells, memory‐activated CD4+ T cells and resting NK cells, with relatively less infiltration of memory‐resting CD4+ T cells and neutrophils. Conversely, in the group with high TARDBP expression, there was enhanced infiltration of naïve CD4+ T cells and M0 macrophages, with a reduced presence of memory‐resting CD4+ T cells. In summary, FASLG and TARDBP may contribute to AS pathogenesis by regulating the immune microenvironment and immune‐related signalling pathways. These findings offer new insights into the molecular mechanisms of AS and suggest potential new targets for therapeutic strategies. [ABSTRACT FROM AUTHOR]

This study aimed to elucidate the causal genetic relationships between iron, copper, zinc, magnesium, and rheumatoid arthritis (RA). A two-sample Mendelian randomization (MR) analysis was conducted using the "TwoSampleMR" and "MendelianRandomization" packages in R. The random-effects inverse variance-weighted (IVW) method was used as the primary approach. We performed sensitivity analyses to test the reliability of the results. The random-effects IVW analysis revealed that there was no genetic causal relationship between iron (P = 0.429, odds ratio [OR] 95% confidence interval [CI] = 0.919 [0.746–1.133]), copper (P = 0.313, OR 95% CI = 0.973 [0.921–1.027]), zinc (P = 0.633, OR 95% CI = 0.978 [0.891–1.073]), or magnesium (P = 0.218, OR 95% CI = 0.792 [0.546–1.148]) and RA. Sensitivity analysis verified the reliability of the results. Therefore, there is no evidence to support a causal relationship between iron, copper, zinc, and magnesium intake at the genetic level and the development of RA. [ABSTRACT FROM AUTHOR]

The gait rehabilitation knee exoskeleton is an advanced rehabilitative assistive device designed to help patients with knee joint dysfunction regain normal gait through training and activity support. This paper introduces a design framework based on the process knowledge representation method to optimize the design and control efficiency of the knee exoskeleton. This framework integrates knowledge of design objects and processes, specifically including requirements, functions, principle work areas, and the representation and multi-dimensional dynamic mapping of the Behavior–Structure (RFPBS) matrix, achieving multi-dimensional dynamic mapping of the knee exoskeleton. This method incorporates biomechanical and physiological knowledge from the rehabilitation process to more effectively simulate and support gait movements during rehabilitation. Research results indicate that the knee rehabilitation exoskeleton design, based on the RFPBS process knowledge representation model, accomplishes multi-dimensional dynamic mapping, providing a scientific basis and effective support for the rehabilitation of patients with knee joint dysfunction. [ABSTRACT FROM AUTHOR]

Finger technique is a crucial aspect of piano learning, and hand exoskeleton mechanisms effectively assist novice piano players in maintaining correct finger technique consistently. Addressing current issues with exoskeleton robots, such as the inability to provide continuous correction of finger technique and their considerable weight, a novel hand exoskeleton robot has been developed to enhance finger technique through continuous correction and reduced weight. Initial data are gathered using finger joint angle sensors to analyze movements during piano playing, focusing on the trajectory and angular velocity of key strikes. This analysis informs the design of a 6-bar double-closed-loop mechanism with an end equivalent sliding pair, using analytical methods to establish the relationship between motor extension and input rod rotation. Simulation studies assess the exoskeleton's motion space and dynamics, confirming its capability to meet structural and functional demands for accurate key striking. Prototype testing validates the exoskeleton's ability to maintain correct finger positioning and mimic natural strike speeds, thus improving playing technique while ensuring comfort and safety. [ABSTRACT FROM AUTHOR]

In batch processes, existing soft sensing methodologies encounter substantial challenges when confronted with nonlinearity and multi‐phase issues. In response to these challenges, an innovative soft sensing framework known as the multi‐phase stacking ensemble model with self‐selected primary learner is proposed. The main innovation of this framework lies in the solution to the primary learner selection issue within the stacking model. To commence, the batch process is divided into multiple phases employing a Gaussian mixture model, thereby establishing local ensemble models for each phase. Subsequently, the iterative self‐selection of primary learners strategy is proposed, which iteratively selects suitable primary learners for these models, optimizing their combination of primary learners for each local model. This primary learner selection strategy effectively enhances the accuracy of predictions in the stacking ensemble model. To further enhance the performance, Bayesian optimization is utilized to tune the hyperparameters of each local ensemble model. This step guarantees optimal performance of the model across diverse phases. Extensive simulation experiments are conducted on an industrial penicillin fermentation process to validate the effectiveness of the proposed framework. According to the findings, the model demonstrated superior performance compared to existing single‐learner soft sensing methods and commonly utilized ensemble‐based soft sensing methods in terms of both R2 score (0.97584) and RMSE (0.0513). Overall, this framework offers a novel approach for selecting primary learners in stacking ensemble models and enhancing the predictive performance in batch processes for soft sensing. [ABSTRACT FROM AUTHOR]