Genome-Wide Identification, Characterization, and Expression Analysis of the HD-Zip Gene Family in Lagerstroemia for Regulating Plant Height.

The Homeodomain leucine zipper (HD-Zip) family of transcription factors is crucial in helping plants adapt to environmental changes and promoting their growth and development. Despite research on the HD-Zip family in various plants, studies in Lagerstroemia (crape myrtle) have not been reported. This study aimed to address this gap by comprehensively analyzing the HD-Zip gene family in crape myrtle. This study identified 52 HD-Zip genes in the genome of Lagerstroemia indica, designated as LinHDZ1-LinHDZ52. These genes were distributed across 22 chromosomes and grouped into 4 clusters (HD-Zip I-IV) based on their phylogenetic relationships. Most gene structures and motifs within each cluster were conserved. Analysis of protein properties, gene structure, conserved motifs, and cis-acting regulatory elements revealed diverse roles of LinHDZs in various biological contexts. Examining the expression patterns of these 52 genes in 6 tissues (shoot apical meristem, tender shoot, and mature shoot) of non-dwarf and dwarf crape myrtles revealed that 2 LinHDZs (LinHDZ24 and LinHDZ14) and 2 LinHDZs (LinHDZ9 and LinHDZ35) were respectively upregulated in tender shoot of non-dwarf crape myrtles and tender and mature shoots of dwarf crape myrtles, which suggested the important roles of these genes in regulate the shoot development of Lagerstroemia. In addition, the expression levels of 2 LinHDZs (LinHDZ23 and LinHDZ34) were significantly upregulated in the shoot apical meristem of non-dwarf crape myrtle. These genes were identified as key candidates for regulating Lagerstroemia plant height. This study enhanced the understanding of the functions of HD-Zip family members in the growth and development processes of woody plants and provided a theoretical basis for further studies on the molecular mechanisms underlying Lagerstroemia plant height.

The state of science on severe air pollution episodes: Quantitative and qualitative analysis.

Severe episodic air pollution blankets entire cities and regions and have a profound impact on humans and their activities. We compiled daily fine particle (PM2.5) data from 100 cities in five continents, investigated the trends of number, frequency, and duration of pollution episodes, and compared these with the baseline trend in air pollution. We showed that the factors contributing to these events are complex; however, long-term measures to abate emissions from all anthropogenic sources at all times is also the most efficient way to reduce the occurrence of severe air pollution events. In the short term, accurate forecasting systems of such events based on the meteorological conditions favouring their occurrence, together with effective emergency mitigation of anthropogenic sources, may lessen their magnitude and/or duration. However, there is no clear way of preventing events caused by natural sources affected by climate change, such as wildfires and desert dust outbreaks.

The Relationship between Social Development and Ambient Particulate Matter Pollution: Can We Predict The Turning Points?

Much research has been conducted to find evidence of the environmental Kuznets curve (EKC) in the relationship between air-pollutant concentration and economic development. A major focus of EKC-related research has so far been to identify the turning point illustrated by EKC theory or to predict the moment when the turning point will occur. In our research, via analyzing the relationship between an aggregate social-development-representative variable (the Socio-demographic Index or SDI) and the population-weighted concentration of PM2.5, we propose that the overall relationship takes the form of a tilted-S shape with two types of turning points rather than one. Additionally, our research shows that the EKC is highly country-specific, making it extremely difficult to predict the positions of both turning points based on the historical development or trajectories of other countries. Therefore, we conclude that EKC theory is not a useful tool to predict the location of the turning points. However, for short-term prediction of the relationship, we advocate the use of support vector regression, which can forecast the evolution, unless rapid changes are occurring. We suggest that policy makers should not anchor their hopes on predicting turning points from previous studies, but should put more effort into dealing with present particulate matter pollution.