Multi-UAV Cooperative Trajectory Planning Based on the Modified Cheetah Optimization Algorithm.

The capacity for autonomous functionality serves as the fundamental ability and driving force for the cross-generational upgrading of unmanned aerial vehicles (UAVs). With the disruptive transformation of artificial intelligence technology, autonomous trajectory planning based on intelligent algorithms has emerged as a key technique for enhancing UAVs' capacity for autonomous behavior, thus holding significant research value. To address the challenges of UAV trajectory planning in complex 3D environments, this paper proposes a multi-UAV cooperative trajectory-planning method based on a Modified Cheetah Optimization (MCO) algorithm. Firstly, a spatiotemporal cooperative trajectory planning model is established, incorporating UAV-cooperative constraints and performance constraints. Evaluation criteria, including fuel consumption, altitude, and threat distribution field cost functions, are introduced. Then, based on its parent Cheetah Optimization (CO) algorithm, the MCO algorithm incorporates a logistic chaotic mapping strategy and an adaptive search agent strategy, thereby improving the home-returning mechanism. Finally, extensive simulation experiments are conducted using a considerably large test dataset containing functions with the following four characteristics: unimodal, multimodal, separable, and inseparable. Meanwhile, a strategy for dimensionality reduction searching is employed to solve the problem of autonomous trajectory planning in real-world scenarios. The results of a conducted simulation demonstrate that the MCO algorithm outperforms several other related algorithms, showcasing smaller trajectory costs, a faster convergence speed, and stabler performance. The proposed algorithm exhibits a certain degree of correctness, effectiveness, and advancement in solving the problem of multi-UAV cooperative trajectory planning.

Oral Porphyromonas gingivalis Infections Increase the Risk of Alzheimer's Disease: A Review.

Periodontal disease (PD) and Alzheimer's disease (AD) are inflammatory diseases affecting the adult population of the world. PD is mainly caused by infection with Porphyromonas gingivalis (P. gingivalis) and by the synergistic action of various microorganisms. These microorganisms penetrate into the subgingival tissue and cause bacteremia, leading to disruption of the homeostasis of the internal environment of the body. Virulence factors known as gingipains, which are cysteine proteases and other toxins, including fimbria and lipopolysaccharides (LPS), are strongly associated with periodontitis and other systemic inflammation. PD has a known polymicrobial aetiology, and patients who eventually develop sporadic AD tend to have recurrent infections before a clinical diagnosis of dementia. AD, the most common neurodegenerative disease, is characterised by poor memory and specific hallmark proteins. An increasing number of studies have shown that periodontal pathogens are increasingly associated with this form of dementia. Many articles have shown that P. gingivalis infections directly increase the risk of PD and may indirectly lead to the development of AD. However, these links and probable pathogenesis remain to be explored. The aim of this review was to explore whether P. gingivalis periodontal infection is associated with AD and to provide possible mechanisms of association.

Rhizoctonia solani transcriptional activator interacts with rice WRKY53 and grassy tiller 1 to activate SWEET transporters for nutrition.

INTRODUCTION: Rhizoctonia solani, the causative agent of the sheath blight disease (ShB), invades rice to obtain nutrients, especially sugars; however, the molecular mechanism via which R. solani hijacks sugars from rice remains unclear. OBJECTIVES: In this study, rice-R. solani interaction model was used to explore whether pathogen effector proteins affect plant sugar absorption during infection. METHODS: Yeast one-hybrid assay was used to identify Activator of SWEET2a (AOS2) from R. solani. Localization and invertase secretion assays showed that nuclear localization and secreted function of AOS2. Hexose transport assays verified the hexose transporter activity of SWEET2a and SWEET3a. Yeast two-hybrid assays, Bimolecular fluorescence complementation (BiFC) and transactivation assay were conducted to verify the AOS2-WRKY53-Grassy tiller 1 (GT1) transcriptional complex and its activation of SWEET2a and SWEET3a. Genetic analysis is used to detect the response of GT1, WRKY53, SWEET2a, and SWEET3a to ShB infestation. Also, the soluble sugar contents were measured in the mutants and overexpression plants before and after the inoculation of R. solani. RESULTS: The present study found that R. solani protein AOS2 activates rice SWEET2a and localized in the nucleus of tobacco cells and secreted in yeast. AOS2 interacts with rice transcription factor WRKY53 and GT1 to form a complex that activates the hexose transporter gene SWEET2a and SWEET3a and negatively regulate rice resistance to ShB. CONCLUSION: These data collectively suggest that AOS2 secreted by R. solani interacts with rice WRKY53 and GT1 to form a transcriptional complex that activates SWEETs to efflux sugars to apoplast; R. solani acquires more sugars and subsequently accelerates host invasion.

Extraction of Step-Feature Lines in Open-Pit Mines Based on UAV Point-Cloud Data.

Step-feature lines are one of the important geometrical elements for drawing the status quo maps of open-pit mines, and the efficient and accurate automatic extraction and updating of step-feature lines is of great significance for open-pit-mine stripping planning and analysis. In this study, an automatic extraction method of step-feature lines in an open-pit mine based on unmanned-aerial-vehicle (UAV) point-cloud data is proposed. The method is mainly used to solve the key problems, such as low accuracy, local-feature-line loss, and the discontinuity of the step-feature-line extraction method. The method first performs the regular raster resampling of the open-pit-mine cloud based on the MLS algorithm, then extracts the step-feature point set by detecting the elevation-gradient change in the resampled point cloud, further traces the step-feature control nodes by the seed-growth tracking algorithm, and finally generates smooth step-feature lines by fitting the space curve to the step-feature control nodes. The results show that the method effectively improves the accuracy of step-feature-line extraction and solves the problems of local-feature-line loss and discontinuity.

Hyperspectral imaging for skin assessment in systemic sclerosis: a pilot study.

OBJECTIVE: Hyperspectral imaging (HSI) is a novel technology for obtaining quantitative measurements from transcutaneous spatial and spectral information. In patients with SSc, the severity of skin tightness is associated with internal organ involvement. However, clinical assessment using the modified Rodnan skin score is highly variable and there are currently no universal standardized protocols. This study aimed to compare the ability to differentiate between SSc patients and healthy controls using skin scores, ultrasound and HSI. METHODS: Short-wave infrared light was utilized to detect the spectral angle mapper (SAM) of HSI. In addition, skin severity was evaluated by skin scores, ultrasound to detect dermal thickness and strain elastography. Spearman's correlation was used for assessing skin scores, strain ratio, thickness and SAM. Comparisons of various assessment tools were performed by receiver operating characteristic curves. RESULTS: In total, 31 SSc patients were enrolled. SAM was positively correlated with skin scores and dermal thickness. In SSc patients with normal skin scores, SAM values were still significantly higher than in healthy controls. SAM exhibited the highest area under the curve (AUC: 0.812, P < 0.001) in detecting SSc compared with skin scores (AUC: 0.712, P < 0.001), thickness (AUC: 0.585, P = 0.009) and strain ratio by elastography (AUC: 0.522, P = 0.510). Moreover, the severity of skin tightness was reflected by the incremental changes of waveforms in the spectral diagrams. CONCLUSION: SAM was correlated with skin scores and sufficiently sensitive to detect subclinical disease. HSI can be used as a novel, non-invasive method for assessing skin changes in SSc.