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With the increasing number of international contacts and the rapid development of global trades, the company’s workforce is becoming more diverse. The problem of Stereotypes and Attitudes has jeopardized the relationship between employees, which increases the phenomenon of inequality and discrimination in the workplace. In order to find possible solutions for such problems, this paper reviewed multiple articles and tried to look at Stereotypes and explicit and implicit attitudes from a different angle. While there is relatively less literature regarding the topic of possible relationship between stereotypes and attitudes, a few research articles helped to infer possible connections between them: explicit attitudes can exacerbate the contradiction of stereotypes; and implicit attitudes also affect the formation of stereotypes unconsciously. Because implicit attitudes affect people’s behaviour unconsciously, diving into it with more details, few articles have suggested it is possible to change individuals’ implicit attitudes, and the duration is able to last in the long term. Based on these reviewed articles, it has come to a conclusion that Attitudes play an important role in impacting the formation of Stereotypes. This paper intends to help reduce stereotype issues and offer possible solutions to decrease workplace inequality and discrimination.

Traditional visual simultaneous localization and mapping (SLAM) systems rely on point features to estimate camera trajectories. However, feature-based systems are usually not robust in complex environments such as weak textures or obvious brightness changes. To solve this problem, we used more environmental structure information by introducing line segments features and designed a monocular visual SLAM system. This system combines points and line segments to effectively make up for the shortcomings of traditional positioning based only on point features. First, ORB algorithm based on local adaptive threshold was proposed. Subsequently, we not only optimized the extracted line features, but also added a screening step before the traditional descriptor matching to combine the point features matching results with the line features matching. Finally, the weighting idea was introduced. When constructing the optimized cost function, we allocated weights reasonably according to the richness and dispersion of features. Our evaluation on publicly available datasets demonstrated that the improved point-line feature method is competitive with the state-of-the-art methods. In addition, the trajectory graph significantly reduced drift and loss, which proves that our system increases the robustness of SLAM.

As a typical bulb flower, lily is widely cultivated worldwide because of its high ornamental, medicinal and edible value. Although breeding efforts evolved over the last 10000 years, there are still many problems in the face of increasing consumer demand. The approach of biotechnological methods would help to solve this problem and incorporate traits impossible by conventional breeding. Target traits are dormancy, development, color, floral fragrance and resistances against various biotic and abiotic stresses, so as to improve the quality of bulbs and cut flowers in planting, cultivation, postharvest, plant protection and marketing. Genetic transformation technology is an important method for varietal improvement and has become the foundation and core of plant functional genomics research, greatly assisting various plant improvement programs. However, achieving stable and efficient genetic transformation of lily has been difficult worldwide. Many gene function verification studies depend on the use of model plants, which greatly limits the pace of directed breeding and germplasm improvement in lily. Although significant progress has been made in the development and optimization of genetic transformation systems, shortcomings remain. Agrobacterium -mediated genetic transformation has been widely used in lily. However, severe genotypic dependence is the main bottleneck limiting the genetic transformation of lily. This review will summarizes the research progress in the genetic transformation of lily over the past 30 years to generate the material including a section how genome engineering using stable genetic transformation system, and give an overview about recent and future applications of lily transformation. The information provided in this paper includes ideas for optimizing and improving the efficiency of existing genetic transformation methods and for innovation, provides technical support for mining and identifying regulatory genes for key traits, and lays a foundation for genetic improvement and innovative germplasm development in lily.

Object detection plays a vital role in autonomous driving systems, and the accurate detection of surrounding objects can ensure the safe driving of vehicles. This paper proposes a category-assisted transformer object detector called DetectFormer for autonomous driving. The proposed object detector can achieve better accuracy compared with the baseline. Specifically, ClassDecoder is assisted by proposal categories and global information from the Global Extract Encoder (GEE) to improve the category sensitivity and detection performance. This fits the distribution of object categories in specific scene backgrounds and the connection between objects and the image context. Data augmentation is used to improve robustness and attention mechanism added in backbone network to extract channel-wise spatial features and direction information. The results obtained by benchmark experiment reveal that the proposed method can achieve higher real-time detection performance in traffic scenes compared with RetinaNet and FCOS. The proposed method achieved a detection performance of 97.6% and 91.4% in AP50 and AP75 on the BCTSDB dataset, respectively.

Advanced plant disease management strategies are essential to sustainable agriculture and global food security. Advances in plant immunity have given rise to a variety of innovative disease control strategies, such as NLR gene transfer, RNA silencing technology, and CRISPR/Cas9-based gene disruption, as well as the use of immunity inducers. Recently, several novel resistance strategies, including the bioengineering of immunoreceptors, protease-triggered resistance design, and the sentinel approach, have enabled the customized development of disease resistance traits. These new approaches envisage a new paradigm of precision-targeted, artificially engineered resistance to enhance crop protection.

What are the main findings? * Developed MAIENet, a single-backbone multimodal SAR object detection framework built upon YOLOv11m, integrating three dedicated modules: BSCC (batch-wise splitting and channel-wise concatenation), MAIE (modality-aware adaptive interaction enhancement), and MF (multi-directional focus). These modules jointly exploit complementary SAR-optical features, delivering 90.8% mAP50 on the OGSOD-1.0 dataset. * Compared with leading multimodal benchmarks such as DEYOLO and CoLD, MAIENet achieves superior detection accuracy while retaining fewer parameters than dual-backbone designs, despite necessarily introducing additional parameters relative to the YOLOv11m baseline. Developed MAIENet, a single-backbone multimodal SAR object detection framework built upon YOLOv11m, integrating three dedicated modules: BSCC (batch-wise splitting and channel-wise concatenation), MAIE (modality-aware adaptive interaction enhancement), and MF (multi-directional focus). These modules jointly exploit complementary SAR-optical features, delivering 90.8% mAP50 on the OGSOD-1.0 dataset. Compared with leading multimodal benchmarks such as DEYOLO and CoLD, MAIENet achieves superior detection accuracy while retaining fewer parameters than dual-backbone designs, despite necessarily introducing additional parameters relative to the YOLOv11m baseline. What is the implication of the main finding? * Demonstrates that carefully designed single-backbone multimodal fusion can outperform both unimodal and dual-backbone multimodal detectors, achieving a better balance between accuracy gains and parameter increments. * Validates the efficacy of BSCC, MAIE, and MF modules in enhancing cross-modal feature interaction and receptive field coverage, showing their potential for improving detection of diverse target scales (bridges, harbors, oil tanks) in complex SAR-optical remote sensing scenarios. Demonstrates that carefully designed single-backbone multimodal fusion can outperform both unimodal and dual-backbone multimodal detectors, achieving a better balance between accuracy gains and parameter increments. Validates the efficacy of BSCC, MAIE, and MF modules in enhancing cross-modal feature interaction and receptive field coverage, showing their potential for improving detection of diverse target scales (bridges, harbors, oil tanks) in complex SAR-optical remote sensing scenarios. Syntheticaperture radar (SAR) object detection offers significant advantages in remote sensing applications, particularly under adverse weather conditions or low-light environments. However, single-modal SAR image object detection encounters numerous challenges, including speckle noise, limited texture information, and interference from complex backgrounds. To address these issues, we present Modality-Aware Adaptive Interaction Enhancement Network (MAIENet), a multimodal detection framework designed to effectively extract complementary information from both SAR and optical images, thereby enhancing object detection performance. MAIENet comprises three primary components: batch-wise splitting and channel-wise concatenation (BSCC) module, modality-aware adaptive interaction enhancement (MAIE) module, and multi-directional focus (MF) module. The BSCC module extracts and reorganizes features from each modality to preserve their distinct characteristics. The MAIE module component facilitates deeper cross-modal fusion through channel reweighting, deformable convolutions, atrous convolution, and attention mechanisms, enabling the network to emphasize critical modal information while reducing interference. By integrating features from various spatial directions, the MF module expands the receptive field, allowing the model to adapt more effectively to complex scenes. The MAIENet framework is end-to-end trainable and can be seamlessly integrated into existing detection networks with minimal modifications. Experimental results on the publicly available OGSOD-1.0 dataset demonstrate that MAIENet achieves superior performance compared with existing methods, achieving 90.8% mAP[sub.50].

Geological hazards caused by strong earthquakes have caused continuous social and economic losses and destruction of the ecological environment in the hazard area, and are mostly manifested in the areas with frequent occurrence of geological hazards or the clustering of geological hazards. Considering the long-term nature of earthquakes and geological disasters in this region, this paper takes ten earthquake-stricken areas in Wenchuan earthquake zone as examples to collect shallow landslide data in 2010, combined with the spatial location of landslides and other factors. Kernel density estimation (KDE) method is used to analyze the spatial characteristics of shallow landslide. Taking the space of shallow landslide as the characteristic variable and fully considering the regulating factors of earthquake-induced landslide: terrain complexity, distance to river, distance to fault, distance to road, lithology, normalized vegetation difference index (NDVI) and ground peak acceleration (PGA) as independent variables, based on KDE and polynomial logistic regression (MLR), A quantitative model of shallow landslide in the earthquake area is constructed. The results show that: (1) PGA has the greatest impact on landslide in the study area. (2) Compared with the two-category logistic regression (two-category LR) model, the susceptibility map of landslide prediction results based on the KDE-MLR landslide susceptibility prediction model is more consistent with the actual situation. (3) The prediction accuracy of the model validation set is 70.7%, indicating that the landslide susceptibility prediction model based on KDE-MLR can effectively highlight the spatial characteristics of shallow landslides in 10 extreme disaster areas. The research results can provide decision-making basis for shallow landslide warning and post-disaster reconstruction in earthquake-stricken areas.

The mucosal system serves as the primary barrier against respiratory diseases and plays a crucial role in combating viral infections through mucosal immunity. The resident microbial community constitutes the main component of the mucosal system and exerts a significant inhibitory impact on the invasion of exogenous agents. However, the precise relationship between resident microbiota, mucosal immunity, and viral infections remains incomplete. This review aims to summarize the regulatory interactions between the resident microbiota of the mucosal system and innate immune components such as mucosal immunity and trained immunity. By clarifying these complex relationships, this review seeks to identify potential targets for augmenting respiratory disease prevention strategies and developing novel vaccine formulations. Furthermore, we propose the possibility of integrating the fields of microbiome-based therapeutics and vaccine development to create multifunctional vaccine formulations capable of targeting mucosal immunity induction. Such an approach holds great potential in offering novel pathways and strategies for the prevention and treatment of respiratory diseases.

Virulence factor genes (VFGs) play pivotal roles in bacterial infections and have been identified within the human gut microbiota. However, their involvement in chronic diseases remains poorly understood. Here, we establish an expanded VFG database (VFDB 2.0) consisting of 62,332 nonredundant orthologues and alleles of VFGs using species-specific average nucleotide identity ( https://github.com/Wanting-Dong/MetaVF_toolkit/tree/main/databases ). We further develop the MetaVF toolkit, facilitating the precise identification of pathobiont-carried VFGs at the species level. A thorough characterization of VFGs for 5452 commensal isolates from healthy individuals reveals that only 11 of 301 species harbour these factors. Further analyses of VFGs within the gut microbiomes of nine chronic diseases reveal both common and disease-specific VFG features. Notably, in type 2 diabetes patients, long HiFi sequencing confirms that shared VF features are carried by pathobiont strains of Escherichia coli and Klebsiella pneumoniae . These findings underscore the critical importance of identifying and understanding VFGs in microbiome-associated diseases. Here, by mining 18,521 complete bacterial genomes, the authors construct VFDB 2.0, an expanded database of virulence factor genes, consisting of 62,332 nonredundant orthologues and alleles with annotated host taxa using species-specific average nucleotide identity, and present MetaVF, a toolkit that facilitates precise identification and quantification of virulence factor genes carried by specific pathobionts in human gut metagenomes.