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The visibility of languages in the linguistic landscape reflects various aspects of linguistic diversity and dynamics in a given territory, signifying social interactions, identities, and power relationships among ethnic groups. However, postcolonial acontexts with widespread use of ethnic minority languages and the coexistence of state-sponsored national languages with precolonial official languages in the era of globalization have received limited research. This paper investigates the visibility of language in three neighbourhoods in George Town, Malaysia, a former administrative city under British governance, focusing on language visibility within three distinct neighborhoods: Chinatown, Little India, and the Beach Street. The study reveals a complex interplay of languages, highlighting the predominance of English as an information carrier and a symbol of modernity, alongside the increasing prominence of Chinese and the lesser visibility of Tamil and underrepresentation of other minority languages. The research underscores the ethnic boundaries between the vibrant enclaves of Little India and Chinatown, while Beach Street serves as a multicultural space reflecting a blend of local and global influences.

Hazardous material transport accidents are events with a low probability and high consequence risk. With an increase in the proportion of hazardous materials transported on domestic roads, an increasing number of scholars have begun to study this field. In this study, a multi–objective hazardous materials transport route planning model considering road traffic resilience and low carbon, which considers the uncertainty of demand and time and is under the limit of the time window. It transports many types of hazardous materials from multiple suppliers to multiple retails with three goals (transportation cost, risk, and carbon emission). This model fills the gap in the research on hazardous material transportation in the field of low carbon, and this is the first time that road traffic resilience is considered in the transport of hazardous materials as one of the weight factors of risk calculation. We designed a improved ant colony optimization algorithm (ACO) to obtain the pareto optimal solution set. We compared the improved ACO with genetic algorithm and simulated annealing algorithm. The results show that the improved ACO has better solution quality and space, which verifies the validity and reliability of the improved ACO.

Abstract Spatially resolved transcriptomics involves a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them are for single-cell RNA-seq datasets without consideration of spatial information. Thus, methods that can integrate spatial transcriptomics data from multiple tissue slides, possibly from multiple individuals, are needed. Here, we present PRECAST, a data integration method for multiple spatial transcriptomics datasets with complex batch effects and/or biological effects between slides. PRECAST unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, while requiring only partially shared cell/domain clusters across datasets. Using both simulated and four real datasets, we show improved cell/domain detection with outstanding visualization, and the estimated aligned embeddings and cell/domain labels facilitate many downstream analyses. We demonstrate that PRECAST is computationally scalable and applicable to spatial transcriptomics datasets from different platforms.

Triticeae, the wheatgrass tribe, includes several major cereal crops and their wild relatives. Major crops within the Triticeae are wheat, barley and rye which are important for human consumption, animal feed, and rangeland protection. Species within this tribe are known for their large genomes and complex genetic histories. Powered by recent advances in sequencing technology, researchers worldwide have made progress in elucidating the genomes of Triticeae crops. In addition to assemblies of high-quality reference genomes, pan-genome studies have just started to capture the genomic diversities of these species, shedding light on our understanding of the genetic basis of domestication and environmental adaptation of Triticeae crops. In this review, we focus on recent signs of progress in genome sequencing, pan-genome analyses, and resequencing analysis of Triticeae crops. We also propose future research avenues in Triticeae crop genomes, including identifying genome structure variations, the association of genomic regions with desired traits, mining functions of the non-coding area, introgression of high-quality genes from wild Triticeae resources, genome editing, and integration of genomic resources.

Summary: Live imaging through confocal laser scanning microscopy enables the recording, analysis, and comparison of the dynamics of shapes and gene expression patterns of plant shoot apical meristems (SAMs) or primordia. Here, we provide a protocol to describe the preparation process of imaging Arabidopsis SAMs and primordia using a confocal microscope. We describe steps for dissection, visualization of meristems using dyes and fluorescent proteins, and gain 3D morphology of meristems. We then detail analysis of shoot meristems using time-lapse imaging.For complete details on the use and execution of this protocol, please refer to Peng et al. (2022).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.