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Unmanned aerial vehicles (UAVs) with cameras offer extensive monitoring capabilities and exceptional maneuverability, making them ideal for real-time ship detection and effective ship management. However, ship detection by camera-equipped UAVs faces challenges when it comes to multi-viewpoints, multi-scales, environmental variability, and dataset scarcity. To overcome these challenges, we proposed a data augmentation method based on stable diffusion to generate new images for expanding the dataset. Additionally, we improve the YOLOv8n OBB model by incorporating the BiFPN structure and EMA module, enhancing its ability to detect multi-viewpoint and multi-scale ship instances. Through multiple comparative experiments, we evaluated the effectiveness of our proposed data augmentation method and the improved model. The results indicated that our proposed data augmentation method is effective for low-volume datasets with complex object features. The YOLOv8n-BiFPN-EMA OBB model we proposed performed well in detecting multi-viewpoint and multi-scale ship instances, achieving the mAP (@0.5) of 92.3%, the mAP (@0.5:0.95) of 77.5%, a reduction of 0.8 million in model parameters, and a detection speed that satisfies real-time ship detection requirements.

In order to enhance the precision of UAV (unmanned aerial vehicle) landings and realize the convenient and rapid deployment of the model to the mobile terminal, this study proposes a Land-YOLO lightweight UAV-guided landing algorithm based on the YOLOv8 n model. Firstly, GhostConv replaces standard convolutions in the backbone network, leveraging existing feature maps to create additional “ghost” feature maps via low-cost linear transformations, thereby lightening the network structure. Additionally, the CSP structure of the neck network is enhanced by incorporating the PartialConv structure. This integration allows for the transmission of certain channel characteristics through identity mapping, effectively reducing both the number of parameters and the computational load of the model. Finally, the bidirectional feature pyramid network (BiFPN) module is introduced, and the accuracy and average accuracy of the model recognition landing mark are improved through the bidirectional feature fusion and weighted fusion mechanism. The experimental results show that for the landing-sign data sets collected in real and virtual environments, the Land-YOLO algorithm in this paper is 1.4% higher in precision and 0.91% higher in mAP0.5 than the original YOLOv8n baseline, which can meet the detection requirements of landing signs. The model’s memory usage and floating-point operations per second (FLOPs) have been reduced by 42.8% and 32.4%, respectively. This makes it more suitable for deployment on the mobile terminal of a UAV.

Silicosis is an interstitial lung disease (ILD) caused by prolonged inhalation of silica particles. Acute lung injury (ALI) is a critical clinical syndrome involving bilateral lung infiltration and acute hypoxic respiratory failure. However, there is currently no effective treatment for these two diseases. Previous research has established that cyclic adenosine monophosphate (cAMP) is pivotal in the pathogenesis of silicosis and acute lung injury. Phosphodiesterase 4 (PDE4) is a hydrolase enzyme of cAMP, and BI 1015550, as an inhibitor of PDE4B, is expected to be a candidate drug for treating both. BI 1015550 has shown certain anti-inflammatory and anti-fibrotic properties in systemic sclerosis-associated interstitial lung disease (SSc-ILD) and idiopathic pulmonary fibrosis (IPF), but there is a lack of research on silicosis and acute lung injury. In this research, we successfully synthesized BI 1015550 autonomously and demonstrated that it could significantly improve lung fibrosis and inflammation in a silica-induced silicosis mouse model. Furthermore, we found that BI 1015550 could also alleviate lung inflammation in a Lipopolysaccharide (LPS)-induced acute lung injury mouse model. The mechanism of action may involve the regulation of cAMP-related signaling pathways.

Glycolysis is a complex metabolic process that occurs to convert glucose into pyruvate to produce energy for living cells. Normal cells oxidized pyruvate into adenosine triphosphate and carbon dioxide in the presence of oxygen in mitochondria while cancer cells preferentially metabolize pyruvate to lactate even in the presence of oxygen in order to maintain a slightly acidic micro-environment of PH 6.5 and 6.9, which is beneficial for cancer cell growth and metastasis. Therefore targeting glycolytic signaling pathways provided new strategy for anti-cancer therapy. Natural products are important sources for the treatment of diseases with a variety of pharmacologic activities. Accumulated studies suggested that natural products exhibited remarkable anti-cancer properties both in vitro and in vivo . Plenty of studies suggested natural products like flavonoids, terpenoids and quinones played anti-cancer properties via inhibiting glucose metabolism targets in glycolytic pathways. This study provided an updated overview of natural products controlling glycolytic pathways, which also provide insight into druggable mediators discovery targeting cancer glucose metabolism.

Visual Simultaneous Localization and Mapping (V-SLAM) plays a crucial role in the development of intelligent robotics and autonomous navigation systems. However, it still faces significant challenges in handling highly dynamic environments. The prevalent method currently used for dynamic object recognition in the environment is deep learning. However, models such as Yolov5 and Mask R-CNN require significant computational resources, which limits their potential in real-time applications due to hardware and time constraints. To overcome this limitation, this paper proposes ADM-SLAM, a visual SLAM system designed for dynamic environments that builds upon the ORB-SLAM2. This system integrates efficient adaptive feature point homogenization extraction, lightweight deep learning semantic segmentation based on an improved DeepLabv3, and multi-view geometric segmentation. It optimizes keyframe extraction, segments potential dynamic objects using contextual information with the semantic segmentation network, and detects the motion states of dynamic objects using multi-view geometric methods, thereby eliminating dynamic interference points. The results indicate that ADM-SLAM outperforms ORB-SLAM2 in dynamic environments, especially in high-dynamic scenes, where it achieves up to a 97% reduction in Absolute Trajectory Error (ATE). In various highly dynamic test sequences, ADM-SLAM outperforms DS-SLAM and DynaSLAM in terms of real-time performance and accuracy, proving its excellent adaptability.

Background Interleukin-17 (IL-17), the characteristic cytokine secreted by T helper 17 lymphocytes (Th17 cells), plays a pivotal role in host defense and many inflammatory or autoimmune diseases. The aim of this study was to obtain purified protein caprine IL-17A (cIL-17A) as an antigen for preparing an IL-17A-specific monoclonal antibody (mAb). Results The coding sequence (CDS) region of cIL-17A was cloned from the peripheral blood mononuclear cells (PBMCs) of dairy goats and then inserted into the expression vector PET 32a and transformed into competent TransB (DE3) cells. Recombinant fusion protein obtained under optimized conditions was used to immunize BALB/c mice for preparing monoclonal antibodies. Finally, the supernatants of two hybridoma cell lines showing positive reaction with the recombinant fusion protein and negative reaction with fusion tags of PET 32a were collected for western blot, immunofluorescence (IF) and immunohistochemistry (IHC) analysis. Our results showed that the maximum amount of soluble protein could be obtained directly in the supernatant when the recombinant expression cells were induced by isopropyl-β-d-thiogalactoside (IPTG) at a concentration of 0.3 mmol/L at 16 °C for 42 h. Western blot analysis showed that the mAb H8 could recognize the eukaryotically expressed cIL-17A in the supernatant of transfected HEK293T cells. Immunofluorescence and immunohistochemistry assays showed that mAb H8 could strongly recognize both the eukaryotically expressed and natural cIL-17A. Conclusions The monoclonal antibody mAb H8 prepared in this study may be a potential tool for the detection of cIL-17A and beneficial for investigating the pathogenesis of various IL-17-associated diseases.

Background Episodic memory (EM) deteriorates as a result of normal aging as well as Alzheimer’s disease. The neural underpinnings of such age-related memory impairments in older individuals are not well-understood. Although previous research has unveiled the association between gray matter volume (GMV) and EM in the elderly population, such findings exhibit variances across distinct age cohorts. Consequently, an investigation into the dynamic evolution of this relationship with advancing age is imperative. Result The present study utilized a sliding window approach to examine how the correlation between EM and GMV varied with age in a cross-sectional sample of 926 Chinese older adults. We found that both verbal EM (VEM) and spatial EM (SEM) exhibited positive correlations with GMV in extensive areas primarily in the temporal and frontal lobes and that these correlations typically became stronger with older age. Moreover, there were variations in the strength of the correlation between EM and GMV with age, which differed based on sex and the specific type of EM. Specifically, the association between VEM and GMVs in the insula and parietal regions became stronger with age for females but not for males, whereas the association between SEM and GMVs in the parietal and occipital regions became stronger for males but not for females. At the brain system level, there is a significant age-related increase in the correlations between both types of EM and the GMV of both the anterior temporal (AT) system and the posterior medial (PM) system in male group. In females, both types of EM show stronger age-related correlations with the GMV of the AT system compared to males. Conclusions Our study revealed a significant positive correlation between GMV in most regions associated with EM and age, particularly in the frontal and temporal lobes. This discovery offers new insights into the connection between brain structure and the diminishing episodic memory function among older individuals.

Traditional Chinese medicine (TCM) has been practiced for thousands of years, although concerns about the efficacy, legality, and safety of TCM continue to be raised. Chromatographic studies have detected the presence of heavy metals and plant toxins within some TCM preparations. However, chromatography is not able to identify all of the compounds of TCM, particularly those items that are not clearly labeled on the packaging. The present study aimed to establish a supplemental method that better assesses the ingredient components of TCM preparations.We established an effective approach to screen the biological and toxical composition of TCM based on high-throughput sequencing (HTS), as well as fast detection and validation of the toxical species by real-time PCR, based on ITS2 DNA barcoding. Ruyi jinhuang powder (RHP), a classical herbal prescription containing the toxical herb Arisaematis rhizoma, was chosen to test the method. This method could determine whether the Arisaematis Rhizoma had been replaced by Pinellia pedatisecta in the RHP. The results were validated by real-time PCR. 90% compositions of RHP were identified by ITS2 DNA barcoding, suggesting that more DNA barcoding markers are needed for TCM identification. The strategy of high-throughput sequencing has the potential for comprehensive ingredient profiling for TCM preparations. Real-time PCR provides a expeditious metehod for monitoring the safety and legality of TCM preparations.

Background. Biejiajian pill (BJJP), a classical traditional Chinese formula, has been reported that it has an effective treatment for diabetic atherosclerosis in recent years, but its underlying mechanisms remain elusive. The study aimed to explore the potential mechanisms of BJJP on diabetic atherosclerosis by integrating network pharmacology, molecular docking, and molecular dynamics simulation. Methods. The active components of BJJP were collected by TCMSP and TCMID, and then the potential targets were obtained from the SwissTargetPrediction database. The targets related to diabetic atherosclerosis were identified from the GeneCards and OMIM databases. The intersection of the potential targets regulated by active components of BJJP and the targets of diabetic atherosclerosis were common targets, which were visualized by the Venn diagram. The common targets were imported into the STRING database to construct a protein-protein interaction (PPI) network. The network of “Medicine-Compound-Target” was constructed with Cytoscape 3.7.1 software. GO functional enrichment analysis and KEGG pathway enrichment analysis were performed using the DAVID database and visualized through bioinformatics. The intersecting targets were input into Cytoscape 3.7.1 software, and the Network Analyzer tool was employed to screen out the key targets. Then molecular docking was used to verify the binding affinity between the active compounds and the key targets, and molecular dynamics simulation was used to investigate the stability of the binding models. Results. A total of 81 active components, 186 targets of BJJP, and 4041 targets of diabetic atherosclerosis were obtained. Furthermore, 121 overlapping targets were identified. GO functional enrichment analysis revealed that these targets were correlated with the oxidation-reduction process, negative regulation of apoptotic process, inflammatory response, and other biological processes. The results of the KEGG pathway enrichment analysis showed that the common targets mainly participated in proteoglycans in cancer, PPAR signaling pathway, adherens junction, insulin resistance, HIF-1 signaling pathway, PI3K-Akt signaling pathway, etc. The results of molecular docking confirmed that the core active components in BJJP could bind well to the key targets. Results from molecular dynamics simulation showed that the binding energies of AKT1-Luteolin, MMP9-quercetin, and MMP9-luteolin complexes were −28.93 kJ·mol−1, −37.12 kJ·mol−1, and −62.91 kJ·mol−1, respectively. Conclusion. The study revealed that BJJP is characterized as multicomponent, multitarget, and multipathway to treat diabetic atherosclerosis, which is helpful to provide ideas and a basis for pharmacological research and clinical application in the future.

Background Cognitive decline with age has heterogeneous, which might be related to the accumulation of protective factors called cognitive reserve, especially intellectual engagement factors over the life course. However, how lifetime intellectual cognitive reserve (LICR) protects cognitive function in the elderly remains unclear. We aimed to examine the relationship between LICR and cognition and the mild cognitive impairment (MCI) risk, as well as the neural mechanism of LICR on cognition. Methods A total of 5126 participants completed extensive neuropsychological tests, with LICR indicator encompassing early education, midlife occupational complexity, and mental leisure activities after retirement. Confirmatory factor analysis was performed to derive LICR score and cognitive function scores, then the hierarchical regression analysis was used to explore the relationship between LICR and cognitive functions and the risk of MCI. We further explored the macro- and micro-structural preservation underly LICR in 1117 participants. Multiple regressions and tract-based spatial statistics were used to explore the relationship between LICR and gray matter volume and white matter microstructure (FA value). Finally, using the mediation model to explore the relationship of “LICR-brain-cognition”. Result The new LICR index, which was more protective than its single indexes, could protect widespread cognitive functions and was associated with a reduction in MCI risk (Odds Ratio, 0.52; 95% CI, 0.47–0.57). For the structure basis of LICR, the higher LICR score was associated with the greater gray matter volume in right fusiform gyrus ( t  = 4.62, FDR corrected, p  < 0.05) and left orbital superior frontal gyrus ( t  = 4.56, FDR corrected, p  < 0.05), and the higher FA values in the frontotemporal related white matter fiber tracts. Furthermore, the right fusiform gyrus partially mediated the relationship between LICR and executive processing ability ( β  = 0.01, p  = 0.02) and general cognitive ability ( β  = 0.01, p  = 0.03). Conclusions The new comprehensive cognitive reserve index could promote the temporal macro-structural preservation and thus contribute to maintain better cognitive function. These findings highlight the importance of intellectual CR accumulation over the life course in successful cognitive aging and MCI prevention, thereby contributing to improve the quality of life in the elderly.