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Introduction: Flower characteristics are crucial ornamental and reproductive traits in Chimonanthus praecox. Over its long cultivation history, variations have been observed in the floral organs, primarily in the petals, with limited reports on stamen traits. Stamen variation, integral to the mating system, can enhance the plant's ornamental value and directly impact its reproductive success. Methods: This study is the first to report the phenomenon of red coloration in C. praecox stamens. Using UPLC-MS/MS, we analyzed the types and quantities of major metabolites in stamens of different colors. Results: Our results indicated that the red coloration was primarily due to the accumulation 42 on of high levels of anthocyanins, specifically cyanidin 3-O-rutinoside and cyanidin 3-O-glucoside. Transcriptomic sequencing identified 63 differentially expressed genes (DEGs) related to the anthocyanin biosynthetic pathway, most showing peak expression during the bud stage. The results of the metabolite analysis and transcriptomic sequencing were similar to those of previous studies on petal reddening, suggesting a close relationship between the mechanisms of stamen and petal reddening. Discussion: This study elucidated the mechanism of stamen reddening in C. praecox , expanding the species' genetic resources and offering insights into color changes across floral tissue.. [ABSTRACT FROM AUTHOR]

Camellia (Camellia japonica) is a semi-shaded plant that is highly vulnerable to heat stress. To investigate the mechanisms underlying heat stress in C. japonica, two C. japonica cultivars, "Xiaotaohong" and "Zhuapolian", which exhibit significant differences in heat tolerance, were selected from four common cultivars. The selection methods included phenotypic observations and physiological index detection, including relative electric conductivity (REC), malondialdehyde (MDA) content, superoxide dismutase (SOD) enzyme activity, relative water content (RWC), and chlorophyll content. RNA-seq analysis yielded 980 million reads and identified 68,455 differentially expressed genes (DEGs) in the two C. japonica cultivars during heat stress compared to the control samples. Totals of 12,565 and 16,046 DEGs were differentially expressed at 16 h and 32 h, respectively, in "Xiaotaohong" during heat stress. In "Zhuapolian", 40,280 and 37,539 DEGs were found at 16 h and 32 h, respectively. KEGG enrichment analysis revealed that both cultivars were enriched in the "plant hormone signal transduction" and "circadian rhythm" pathways at two stages, indicating the critical role these pathways play in the heat stress response. The differences in the tolerance between the two cultivars are likely linked to pathways such as "plant hormone signal transduction", "photosynthesis", and "circadian rhythm". Some members of heat shock proteins (HSPs) are associated with the heat stress response. It is speculated that transcription factor families contributing to the tolerance differences include AP2/ERF, C3H, bHLH, bZIP, and MYB-related with a small number of heat shock factors (HSFs) also induced by the stress. In conclusion, these results reveal the changes in the physiological indices and molecular networks of two C. japonica cultivars under heat stress. This study lays the foundation for the breeding of superior heat-resistant C. japonica cultivars and for further molecular research. [ABSTRACT FROM AUTHOR]

Chimonanthus praecox (Calycanthaceae family) is a unique ornamental and economic flowering tree in China, and after thousands of years of cultivation, it has produced several varieties and varietal types. Notably, male sterility is common in flowering plants and is an important tool for the genetic improvement in plants and optimization using hybrid plant technology; however, there have been no reports on male-sterile material or related studies on C. praecox. To our knowledge, this is the first time that C. praecox male sterility is dissected unveiling the involvement of key metabolic pathways. Notably, male sterility in C. praecox was observed during the budding period and likely occurred during the premature stage of pollen cell maturation. Additionally, differentially expressed genes in the starch and sucrose metabolism pathway and the plant hormone signal transduction pathway showed regular expression trends. This study reports on significant genetic differences that contribute to male sterility in C. praecox and provides a basis for further research and breeding strategies. [ABSTRACT FROM AUTHOR]

Due to disparities in tolerance, resource availability, and acquisition of labeled training data between satellite-terrestrial integrated networks (STINs) and terrestrial networks, the application of traditional terrestrial network intrusion detection techniques to satellite networks poses significant challenges. This paper presents a satellite network intrusion detection system named Bi-LSTM sparse selfencoder (BLSAE-SNIDS) to address this issue. Through the development of an innovative unsupervised training Bi-LSTM stacked self-encoder, BLSAE-SNIDS facilitates feature extraction from satellite network traffic, diminishes dimensionality, considerably reduces training and testing durations, and enhances the attack prediction accuracy of the classifier. To assess the efficacy of the proposed model, we conduct comprehensive experiments utilizing STIN and UNSW-NB15 datasets. The results obtained from the STIN dataset demonstrate that BLSAE-SNIDS achieves 99.99% accuracy with reduced computational and transmission overheads alongside enhanced flexibility. Furthermore, results from the UNSW-NB15 dataset exhibit BLSAE-SNIDS’ proficiency in detecting various network intrusion attacks efficiently. These findings indicate that BLSAE-SNIDS suits general satellite security networks and offers a novel approach to designing security systems for polar satellite networks, thus exhibiting practical utility. [ABSTRACT FROM AUTHOR]

Fast and accurate detection of ship objects in remote sensing images must overcome two critical problems: the complex content of remote sensing images and the large number of small objects reduce ship detection efficiency. In addition, most existing deep learning-based object detection models require vast amounts of computation for training and prediction, making them difficult to deploy on mobile devices. This paper focuses on an efficient and lightweight ship detection model. A new efficient ship detection model based on device–cloud collaboration is proposed, which achieves joint optimization by fusing the semantic segmentation module and the object detection module. We migrate model training, image storage, and semantic segmentation, which require a lot of computational power, to the cloud. For the front end, we design a mask-based detection module that ignores the computation of nonwater regions and reduces the generation and postprocessing time of candidate bounding boxes. In addition, the coordinate attention module and confluence algorithm are introduced to better adapt to the environment with dense small objects and substantial occlusion. Experimental results show that our device–cloud collaborative approach reduces the computational effort while improving the detection speed by 42.6% and also outperforms other methods in terms of detection accuracy and number of parameters. [ABSTRACT FROM AUTHOR]

Based on the mean sea level pressure field and 10-meter wind field across eastern China, weather patterns were classified using principal component analysis in the T-model (T-PCA), and four weather conditions were identified. Weather conditions and meteorological factors affecting the winter PM2.5 concentration in the Beijing–Tianjin–Hebei (BTH) region and the Yangtze River Delta (YRD) were also analyzed. The results showed that there were significant differences in the PM2.5 distribution between BTH and the YRD under different weather conditions. The intensity and path of cold air played important roles in regulating the PM2.5 concentration distribution in eastern China. For the BTH region, under type 2 and type 4 conditions, the weather was stable, and heavy pollution frequently occurred; however, under type 1 and type 3 conditions, cold air was active, and the air quality improved. For the YRD, both type 1 and type 4 conditions lead to high PM2.5 concentrations in this region. Type 1 cold air flows southward along the northwestern path and is beneficial for transporting pollutants from BTH to the YRD, causing a high probability of PM2.5 pollution. Conversely, the southward movement of type 3 cold air along the eastern path was beneficial for pollutant diffusion in the YRD. [ABSTRACT FROM AUTHOR]

A well-performing data-driven sparse sensor deployment strategy is critical for marine monitoring systems, as it enables the optimal reconstruction of marine physical quantities with fewer sensors. However, ocean data typically contain substantial amounts of noise, including outliers (incomplete data) and inherent measurement noise, which heightens the complexity of sensor deployment. Therefore, this study optimizes the sparse sensor placement model by establishing noise indicators, including small noise weight and large noise weight, which are measured by entropy to minimize the prediction bias. Building on this, a robust sparse sensor placement algorithm is proposed, which utilizes the block coordinate update (BCU) iteration method to solve the function. During the iterative updating process, the proposed algorithm simultaneously updates the selection matrix, reconstruction matrix, and noise matrix. This allows for effective identification and mitigation of noise in the data through evaluation. Consequently, the deployed sensors achieve superior reconstruction performance compared to other deployment methods that do not incorporate noise evaluation. Experiments are also conducted on datasets of sea surface temperature (SST) and global ocean salinity, which demonstrate that our strategy significantly outperforms several other considered methods in terms of reconstruction accuracy while enabling autonomous sensor deployment under noisy conditions. [ABSTRACT FROM AUTHOR]

Underwater localization is considered a critical technique in the Internet of Underwater Things (IoUTs). However, acquiring accurate location information is challenging due to the heterogeneous underwater environment and the hostile propagation of acoustic signals, especially when using received signal strength (RSS)-based techniques. Additionally, most current solutions rely on strict mathematical expressions, which limits their effectiveness in certain scenarios. To address these challenges, this study develops a quantum-behaved meta-heuristic algorithm, called quantum enhanced Harris hawks optimization (QEHHO), to solve the localization problem without requiring strict mathematical assumptions. The algorithm builds on the original Harris hawks optimization (HHO) by integrating four strategies into various phases to avoid local minima. The initiation phase incorporates good point set theory and quantum computing to enhance the population quality, while a random nonlinear technique is introduced in the transition phase to expand the exploration region in the early stages. A correction mechanism and exploration enhancement combining the slime mold algorithm (SMA) and quasi-oppositional learning (QOL) are further developed to find an optimal solution. Furthermore, the RSS-based Cramér–Raolower bound (CRLB) is derived to evaluate the effectiveness of QEHHO. Simulation results demonstrate the superior performance of QEHHO under various conditions compared to other state-of-the-art closed-form-expression- and meta-heuristic-based solutions. [ABSTRACT FROM AUTHOR]