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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.

Rapid advances in DNA synthesis techniques have allowed the assembly and engineering of viral and microbial genomes. Multicellular eukaryotic organisms, with their larger genomes, abundant transposons, and prevalent epigenetic regulation, present a new frontier to synthetic genomics. Plant synthetic genomics have long been proposed, and exciting progress has been made using the top-down approach. In this perspective, we propose applying bottom-up genome synthesis in multicellular plants, starting from the model moss Physcomitrium patens, in which homologous recombination, DNA delivery, and regeneration are possible, although further optimizations are necessary. We then discuss technical barriers, including genome assembly and plant transformation, associated with synthetic genomics in seed plants.

The shoot apical meristem (SAM) maintains stem cells and generates new leaves and flowers from its periphery. Here via spatially resolved translatome profiling, Tian et al. define distinct molecular signatures of different SAM and leaf domains and identify regulators of axillary meristem initiation.

Summary: Cortical microtubules (CMTs) play pivotal roles during plant cell growth and division. The organization of CMTs undergoes important changes during different cellular and developmental processes. Here, we describe two methods for the visualization of CMT organization in plant cells using confocal laser scanning microscopy. CMT networks in the outer tissue layers can be directly visualized by live imaging of a fluorescent reporter line, and a protocol combining sectioning and immunostaining is applied for visualization of CMTs throughout tissues.For complete details on the use and execution of this protocol, please refer to Zhao et al. (2020).

Quantitative plant biology is an interdisciplinary field that builds on a long history of biomathematics and biophysics. Today, thanks to high spatiotemporal resolution tools and computational modelling, it sets a new standard in plant science. Acquired data, whether molecular, geometric or mechanical, are quantified, statistically assessed and integrated at multiple scales and across fields. They feed testable predictions that, in turn, guide further experimental tests. Quantitative features such as variability, noise, robustness, delays or feedback loops are included to account for the inner dynamics of plants and their interactions with the environment. Here, we present the main features of this ongoing revolution, through new questions around signalling networks, tissue topology, shape plasticity, biomechanics, bioenergetics, ecology and engineering. In the end, quantitative plant biology allows us to question and better understand our interactions with plants. In turn, this field opens the door to transdisciplinary projects with the society, notably through citizen science.

Abstract Gene regulatory networks (GRNs) control development via cell type‐specific gene expression and interactions between transcription factors (TFs) and regulatory promoter regions. Plant organ boundaries separate lateral organs from the apical meristem and harbor axillary meristems (AMs). AMs, as stem cell niches, make the shoot a ramifying system. Although AMs have important functions in plant development, our knowledge of organ boundary and AM formation remains rudimentary. Here, we generated a cellular‐resolution genomewide gene expression map for low‐abundance Arabidopsis thaliana organ boundary cells and constructed a genomewide protein–DNA interaction map focusing on genes affecting boundary and AM formation. The resulting GRN uncovers transcriptional signatures, predicts cellular functions, and identifies promoter hub regions that are bound by many TFs. Importantly, further experimental studies determined the regulatory effects of many TFs on their targets, identifying regulators and regulatory relationships in AM initiation. This systems biology approach thus enhances our understanding of a key developmental process.

Leaves, the primary plant organs that function in photosynthesis and respiration, have highly organized, flat structures that vary within and among species. In recent years, it has become evident that auxin plays central roles in leaf development, including leaf initiation, blade formation, and compound leaf patterning. In this review, we discuss how auxin maxima form to define leaf primordium formation. We summarize recent progress in understanding of how spatial auxin signaling promotes leaf blade formation. Finally, we discuss how spatial auxin transport and signaling regulate the patterning of compound leaves and leaf serration.

Shoot branching requires the establishment of new meristems harboring stem cells; this phenomenon raises questions about the precise regulation of meristematic fate. In seed plants, these new meristems initiate in leaf axils to enable lateral shoot branching. Using live-cell imaging of leaf axil cells, we show that the initiation of axillary meristems requires a meristematic cell population continuously expressing the meristem marker SHOOT MERISTEMLESS (STM). The maintenance of STM expression depends on the leaf axil auxin minimum. Ectopic expression of STM is insufficient to activate axillary buds formation from plants that have lost leaf axil STM expressing cells. This suggests that some cells undergo irreversible commitment to a developmental fate. In more mature leaves, REVOLUTA (REV) directly up-regulates STM expression in leaf axil meristematic cells, but not in differentiated cells, to establish axillary meristems. Cell type-specific binding of REV to the STM region correlates with epigenetic modifications. Our data favor a threshold model for axillary meristem initiation, in which low levels of STM maintain meristematic competence and high levels of STM lead to meristem initiation.

Enhanced root hair production, which increases the root surface area for nutrient uptake, is a typical adaptive response of plants to phosphate (Pi) starvation. Although previous studies have shown that ethylene plays an important role in root hair development induced by Pi starvation, the underlying molecular mechanism is not understood. In this work, we characterized an Arabidopsis mutant, hps5, that displays constitutive ethylene responses and increased sensitivity to Pi starvation due to a mutation in the ethylene receptor ERS1. hps5 accumulates high levels of EIN3 protein, a key transcription factor involved in the ethylene signaling pathway, under both Pi sufficiency and deficiency. Pi starvation also increases the accumulation of EIN3 protein. Combined molecular, genetic, and genomic analyses identified a group of genes that affect root hair development by regulating cell wall modifications. The expression of these genes is induced by Pi starvation and is enhanced in the EIN3-overexpressing line. In contrast, the induction of these genes by Pi starvation is suppressed in ein3 and ein3eil1 mutants. EIN3 protein can directly bind to the promoter of these genes, some of which are also the immediate targets of RSL4, a key transcription factor that regulates root hair development. Based on these results, we propose that under normal growth conditions, the level of ethylene is low in root cells; a group of key transcription factors, including RSL4 and its homologs, trigger the transcription of their target genes to promote root hair development; Pi starvation increases the levels of the protein EIN3, which directly binds to the promoters of the genes targeted by RSL4 and its homologs and further increase their transcription, resulting in the enhanced production of root hairs. This model not only explains how ethylene mediates root hair responses to Pi starvation, but may provide a general mechanism for how ethylene regulates root hair development under both stress and non-stress conditions.

Abstract Determining both the expression levels of mRNA and the regulation of its translation is important in understanding specialized cell functions. In this study, we describe both the expression profiles of cells within spatiotemporal domains of the Arabidopsis thaliana flower and the post‐transcriptional regulation of these mRNAs, at nucleotide resolution. We express a tagged ribosomal protein under the promoters of three master regulators of flower development. By precipitating tagged polysomes, we isolated cell type‐specific mRNAs that are probably translating, and quantified those mRNAs through deep sequencing. Cell type comparisons identified known cell‐specific transcripts and uncovered many new ones, from which we inferred cell type‐specific hormone responses, promoter motifs and coexpressed cognate binding factor candidates, and splicing isoforms. By comparing translating mRNAs with steady‐state overall transcripts, we found evidence for widespread post‐transcriptional regulation at both the intron splicing and translational stages. Sequence analyses identified structural features associated with each step. Finally, we identified a new class of noncoding RNAs associated with polysomes. Findings from our profiling lead to new hypotheses in the understanding of flower development.

We study the problem of finding global minimizers of V(x):Rd→R via sampling from a probability distribution μσ with density pσ(x)=exp(-V(x)/σ)/∫Rdexp(-V(y)/σ)dy with respect to the Lebesgue measure for σ∈(0,1] small enough. We analyze a sampler based on the Euler-Maruyama discretization of the Schrödinger-Föllmer diffusion processes with stochastic approximation under appropriate assumptions on the step size s and the potential V. We prove that the output of the proposed sampler is an approximate global minimizer of V(x) with high probability at cost of sampling Ϭ(d³) standard normal random variables. Numerical studies illustrate the effectiveness of the proposed method and its superiority to the Langevin method. [ABSTRACT FROM AUTHOR]

In this paper, we construct neural networks with ReLU, sine, and 2x as activation functions. For a general continuous f defined on [0, 1]d with continuity modulus ωf (·), we construct ReLU-sine-2x networks that enjoy an approximation rate... where M,N ∊ N+ are the hyperparameters related to widths of the networks. As a consequence, we can construct ReLU-sine-2x network with the depth 6 and width max... that approximates... within a given tolerance ∊ > 0 measured in the Lp norm with... denotes the Hölder continuous function class defined on [0, 1]d with order α ∊ (0, 1] and constant μ > 0. Therefore, the ReLU-sine-2x networks overcome the curse of dimensionality in an approximation on Hαμ ([0, 1]d). In addition to its super expressive power, functions implemented by ReLU-sine-2x networks are (generalized) differentiable, enabling us to apply stochastic gradient descent to train. [ABSTRACT FROM AUTHOR]

In modern economic studies, the population heterogeneity of multiple strata and high dimensionality of predictors pose major challenges. In this study, we introduce an integrative procedure that can be used to explore group and sparsity structures of high-dimensional and heterogeneous stratified models. Furthermore, we propose K-regression modelling as a hybrid of complex and simple models that exhibits arbitrary dependence on the stratum features, but linear dependence on the other variables. K-regression models exhibit the following features:(i) they are essentially nonparametric with respect to the stratified feature, and have parametric linear effects in the other variables with a potentially integrative pattern, because the effects and the corresponding sparsity structures can be the same for the strata in common groups, but vary across different groups; (ii) the devised K-regression algorithm automatically integrates the strata pertaining to a common regression model, and simultaneously estimates the corresponding effects; (iii) the proposed method quickly recovers subpopulation and sparsity structure of the K-regression models within massive high-dimensional strata; and (iv) the resulting estimators exhibit two-layer oracle properties, that is, the oracle estimator obtained using the known group and sparsity structures is the local minimizer of the objective function, with high probability. The stratum-specific bootstrap sampling scheme improves the integration accuracy. The results of simulation show that the proposed method performs appropriately for finite samples, and we demonstrate the usefulness of the method using real data. [ABSTRACT FROM AUTHOR]