Multiple sensors system has been adopted in most existing gait phase research. In this paper, we present a novel method to detect and discriminate gait phase based on single Inertial Measurement Unit (IMU). At first, we built a data acquisition system consisting of single IMU measuring the kinematic data of shank and foot switches labeling the data to collect the gait samples. And then we developed a gait phase recognition algorithm based on long short-term memory (LSTM) and trained it with phase-labeled data. The experimental results show that the algorithm has 91.4% accuracy on the testing set. Compared with existing approaches, the proposed method ensures the accuracy of gait phase detection and avoids the complex sensor system.

In the past decade many countries have launched programs to deploy wind turbines as alternative sources of electrical energy. When deployed in wind farms this technology has raised concerns from both air traffic control (ATC) and military authorities. This is due to the fact that the turbine blades return radar echoes that have the potential to distract and confuse the air traffic picture by creating false detections that can effectively mask genuine aircraft returns. Most of the mitigation solutions offered today are either primarily based on such measures as range-azimuth gating or inhibiting track initiation in the vicinity of wind farms. These draconian measures can result in a significant degradation in radar performance and potential air traffic control disruption. They may also require costly redesign of the existing radars. This paper presents a set of 'clean' solutions that mitigate, and in some cases completely eliminate the effect of wind turbine returns. The solution is based on the combination of discrimination techniques applied at the pre-detection, detection and post detection stages of the radar signal processing chain. The suit of mitigation solutions developed does not adversely affect aircraft detection, and can be readily retrofitted to the existing ATC primary surveillance radars (PSR).

Licorice (Glycyrrhiza L.) is a globally popular medicinal and edible plant, with nearly 30 species distributed across all continents. The usable part is primarily the root. To understand the metabolic differences among different Glycyrrhiza species, we selected four species and performed comprehensive analyses of their roots. Metabolomic profiling was conducted using UPLC-MS/MS and GC-MS, while transcriptomic analysis was carried out using RNA-sequencing. A total of 2716 metabolites were identified, including flavonoids (527 types) and terpenoids (251 types), among various other components. Subsequently, network pharmacology was employed to explore the medicinal value and potential pharmacological ingredients of these metabolites. Joint analysis of transcriptomic and metabolomic data revealed significant differences in differentially accumulated metabolites (DAMs) and differentially expressed genes (DEGs) in pairwise comparisons among the four species. These differences were primarily enriched in the isoflavone pathway. Further investigation into the regulatory mechanisms of isoflavone biosynthesis in different Glycyrrhiza species identified key genes and metabolites involved in isoflavone biosynthesis. Finally, we made reasonable predictions of the potential suitable habitats for the four Glycyrrhiza species, aiming to provide new insights for the development and utilization of licorice resources. The results of this study can serve as a basis for the development and utilization of licorice and for in-depth research on the regulation of isoflavone biosynthesis in licorice.

The objective of this study is to identify the active components of Crocus alatavicus and potential targets through a combination of network pharmacology, molecular docking technology combined with molecular dynamics simulation, and binding free energy analyses. A total of 253 active ingredients from C. alatavicus were screened, and 1360 associated targets were predicted through systematic searches conducted using TCMSP, SwissDrugDesign, and SymMap, which were integrated to construct a pharmacological network to dissect the relationships among active components, targets, diseases, and pathways; we found prostate cancer-related genes were significantly enriched among the targets. Subsequently, the core prostate cancer-related targets were identified in the network, and the binding interactions between protein targets and active components were evaluated using molecular docking technology. Furthermore, molecular dynamics simulation and binding free energy analyses were performed to verify the binding stability of the most promising complex. Then, protein–protein interaction network analysis was conducted to evaluate the core target sites, leading to the identification of nine target proteins with significant correlations, providing potential targets for cancer treatment. Furthermore, these targets were found to be associated with 20 signaling pathways, including neuroactive ligand–receptor interactions, prostate cancer, lipid metabolism and atherosclerosis, as well as calcium signaling pathways. The active component–target–disease–pathway network diagram suggests that Capillarisin, Eugenol, 1-(4-Methoxyphenyl)-1-propanol, 2,4,2′,4′-tetrahydroxy-3′-prenylchalcone, and 4-Hydroxymandelonitrile may serve as key components targeting prostate cancer. Molecular docking analyses demonstrated that Capillarisin has a high affinity for the androgen receptor (AR), and molecular dynamics simulation was performed to further verify the binding stability, indicating that Capillarisin may exert its pharmacological effects in prostate cancer. Based on the integrated strategies of network pharmacology, molecular docking, molecular dynamics simulation, and binding free energy analysis, this study generated novel insights into the active components of C. alatavicus and potential targets related to prostate cancer, thus providing valuable biological resources for future drug research and development.

The evolution of robust DNA repair mechanisms was a prerequisite for the conquest of land by plants, a transition that exposed them to harsh new environmental stressors. The poly (ADP-ribose) polymerase (PARP) family is central to this adaptation, as it orchestrates DNA repair and stress signaling pathways essential for coping with the elevated UV radiation and desiccation of terrestrial environments. Yet its early evolutionary origins are unknown. Here, we present a comprehensive reconstruction of the PARP family’s history across the plant kingdom. Our phylogenomic analysis reveals that PARP evolution ignited during the bryophyte radiation, expanding from a single ancestral algal gene into three distinct subfamilies (PARP1, PARP2, and PARP3). This diversification was driven by structural innovations in DNA-binding domains and a rewiring of transcriptional networks to respond to terrestrial challenges. We provide direct experimental support for this hypothesis through functional analysis of PARPs from the extremotolerant moss Syntrichia caninervis. We show that its PARP proteins provide multifaceted protection against UV radiation, heat, and genotoxic agents, and that recently duplicated PARP2 genes are already diverging in function. Our work pinpoints the molecular adaptations in a key DNA repair family that enabled the greening of Earth and uncovers novel genetic targets for enhancing crop resilience.