Environmental Adaptation Research Articles

The Optimization and Application Research of the RRT-APF-Based Path Planning Algorithm

To address the shortcomings of the original rapidly-exploring random tree (RRT) algorithm, such as long and non-smooth paths, slow convergence to the goal region, and limited adaptability in dynamic environments, this paper proposes a hybrid path planning method combining the artificial potential field (APF) approach with the RRT algorithm. This integrated approach leverages the strengths of both methods to achieve efficient, collision-free path planning in both two-dimensional and three-dimensional environments. The algorithm overcomes the local minima problem inherent in APF while maintaining the RRT’s efficiency in high-dimensional spaces and complex environments. A dynamic adjustment strategy is introduced to adapt to specific application scenarios and varying environmental complexity. Additionally, Bezier curve fitting is applied to smooth the resulting paths. Simulations conducted in various environments demonstrate the effectiveness of the proposed method, highlighting its efficiency and robustness in generating collision-free paths. Compared to the original RRT algorithm, the proposed method reduces path length by 13.4% to 24.9% and decreases search time by 9.8% to 56.5%, improving both path quality and planning efficiency.

Numerical Study on the Hydrodynamics of Fish Swimming with Different Morphologies in Oblique Flow

In confined and intricate aquatic environments, fish frequently encounter the need to propel themselves under oblique flow conditions. This study employs a self-developed ghost-cell immersed boundary method coupled with GPU acceleration technology to numerically simulate the propulsion dynamics of flexible biomimetic fish swimming in oblique flow environments. This research scrutinizes diverse biomimetic fish fin morphologies, with particular emphasis on variations in the Strouhal number and angle of attack, to elucidate hydrodynamic performance and wake evolution. The results demonstrate that as the fin thickness increases, the propulsion efficiency decreases within the Strouhal number range of St = 0.2, 0.4. Conversely, within the range of St = 0.6 to 1.0, the efficiency variations stabilize. For all three fin morphologies, an increase in the Strouhal number significantly augmented both the lift-to-drag ratio and thrust, concomitant with a transition in the wake structure from smaller vortices to a larger alternating vortex shedding pattern. Furthermore, within the Strouhal number range of St = 0.2 to 0.4, the propulsion efficiency exhibits an increase, whereas in the range of St = 0.6 to 1.0, the propulsion efficiency stabilizes. As the angle of attack increases, the drag coefficient increases significantly, while the lift coefficient exhibits a diminishing rate of increase. An increased fin thickness adversely affects the hydrodynamic performance. However, this effect attenuates at higher Strouhal numbers. Conversely, variations in the angle of attack manifest a more pronounced effect on hydrodynamic performance. A thorough investigation and implementation of the hydrodynamic mechanisms demonstrated by swimming fish in complex flow environments enables the development of bio-inspired propulsion systems that not only accurately replicate natural swimming patterns, but also achieve superior locomotion performance and robust environmental adaptability.

CsIREH1 phosphorylation regulates DELLA protein affecting plant height in cucumber (Cucumis sativus).

Plant height is a critical agronomic trait that affects crop yield, plant architecture, and environmental adaptability. Gibberellins (GAs) regulate plant height, with DELLA proteins acting as key repressors in the GA signaling pathway by inhibiting GA-induced growth. While DELLA phosphorylation is essential for regulating plant height, the precise mechanisms underlying this process remain incompletely understood. In this study, we identified a cucumber mutant with delayed growth, which exhibited reduced sensitivity to GA treatment. Through bulked segregant analysis (BSA-seq) combined with molecular marker linkage analysis, we successfully identified and cloned the gene responsible for the dwarf phenotype, CsIREH1 (INCOMPLETE ROOT HAIR ELONGATION 1), which encodes an AGC protein kinase. Further research revealed that CsIREH1 interacts with and phosphorylates DELLA proteins, specifically targeting CsGAIP and CsGAI2. We propose that IREH1-dependent phosphorylation of DELLA proteins prevents their excessive accumulation, thereby maintaining normal plant growth. Therefore, investigating the role of IREH1-mediated DELLA phosphorylation provides valuable insights and theoretical foundations for understanding how plants regulate growth mechanisms.

Active Perception in SLAM Exploration: Problem Formulation and Methods Review

Active SLAM (A-SLAM) aims to enable an agent’s autonomous exploration and mapping task, provide robustness and accuracy in both localization and mapping and enhance environmental adaptivity in applications. Its working principle, as a holistic probabilistic-based problem in model formulation, can be divided into three decoupled working stages and accordingly solved, these stages are brief action identification, cost and utility computation, and action execution. The identification stage needs reasoning on what and where to look at in order to diminish uncertainty in an efficient fashion, by employing sensory methods and performing searching or optimization tasks with appropriate environmental representations, to feed the following stages with a discretized set of planning goals. This is the active perception problem by definition. The activeness of active SLAM is mostly decided at the perception stage with appropriate quantifying measures. This paper revisits the formulation of the A-SLAM problem especially by the stage of active perception, reviews its environmental representations (maps) and related modeling measures for information and uncertainty, underlines a sample-based aim of maximizing the information gain from sensors with respect to corresponding maps, and make comments on some novel approaches.

A Seed Endophytic Bacterium Cronobacter dublinensis BC-14 Enhances the Growth and Drought Tolerance of Echinochloa crus-galli

Successful seed germination and plant seedling growth often require association with endophytic bacteria. Barnyard grass (Echinochloa crus-galli (L.) P. Beauv.) is a main weed during rice cultivation and has frequently been found in drought-prone fields such as cornfields in recent years. To determine whether endophytic bacteria enhance the survival chances of barnyard grass in dryland conditions, endophytic bacteria were collected from barnyard grass seeds. An endophytic bacterial strain, BC-14, was selected and confirmed as Cronobacter dublinensis based on its morphology, physiology, biochemistry, and genomic information. Moreover, C. dublinensis BC-14 secreted IAA in the Luria–Bertani broth up to 28.44 mg/L after 5 days; it could colonize the roots of barnyard grass. After the inoculation with seeds or the well-mixed planting soil, the bacterium can significantly increase the root length and plant height of barnyard grass under drought conditions. When comparing with the control group on the 28th day, it can be seen that the bacterium can significantly increase the contents of chlorophyll b (up to 7.58 times) and proline (37.21%); improve the activities of superoxide dismutase, catalase, and peroxidase (36.90%, 51.51%, and 12.09%, respectively); and reduce the content of malondialdehyde around 25.92%, which are correlated to the drought tolerance. The bacterial genomic annotation revealed that it contains growth-promoting and drought-resistant functional genes. In a word, C. dublinensis BC-14 can help barnyard grass suppress drought stress, promote plant growth, and enhance biomass accumulation, which is helpful to interpret the mechanism of weed adaptability in dry environments.

Geometric nonlinear analysis based on the generalized displacement control method and orthogonal iteration

Abstract In response to the shortcomings of insufficient efficiency in calculating geometric nonlinear features and high environmental impact in the current construction, this study explores the control of nonlinear geometric structures based on an updated Lagrangian model function in the construction. In this method, the change in the geometric nonlinear stiffness is analyzed using generalized stiffness parameters and a displacement increment method, and the iteration step size and loading/unloading directions are adjusted in the iteration process to achieve the convergence of the solution. In the simulation experiment, the proposed method took 126 s to calculate the incremental iteration steps 700 times in a conventional environment, which is 28.8% more efficient than the cross-sectional method. In the simulated disaster environment, the model took 1,615 s to calculate the ultimate load of 84 contact elements, which is 43.1% more efficient than the section method and 62.6% more efficient than the discrete analysis method. Experimental results showed that the displacement judgment calculation efficiency of the method proposed in this study is higher than that of other models under different loading and unloading conditions and even in geological disaster states. This method had high environmental adaptability in solving nonlinear building structures and could improve the efficiency of solving general nonlinear building results.

Microbial community structure and functional traits involved in the adaptation of culturable bacteria within the gut of amphipods from the deepest ocean.

Amphipods are widely distributed in the Hadal trenches, and the study of their gut microbes has garnered considerable scientific interest. Our research breaks away from traditional omics approaches, innovatively combining sequencing technologies with culture-dependent methods to analyze the gut microbiome structure of amphipods from the Mariana Trench. This not only complements the current omics-dominated field but also paves the way for future resource development of extreme microbes. Furthermore, by conducting genomic analyses and functional validations on a representative strain, we have uncovered its probiotic effects and strategies for adapting to extreme environments. This provides new insights into the theoretical study of the ecological functions of deep-sea bacteria. Overall, our findings offer a fresh perspective on the microbial community structure and environmental adaptation strategies of gut microorganisms in the Hadal Zone.

Identification of the LH2 Locus for Prostrate Hair Density in Grapevine

Prostrate hairs are one of the anatomic barriers for grapevine resistance to pests and diseases, as well as in environmental adaptability, making them valuable for breeding programs. This study investigates the genetic determinants underlying prostrate hair density in grapevine, a key trait associated with resistance to pests and pathogens. Using an F1 hybrid population derived from Vitis vinifera L. ‘Cabernet Sauvignon cv.’ and V. pseudoreticulata W.T.Wang ‘Huadong1058’, we performed a combination of quantitative trait locus (QTL) mapping and genome-wide association study (GWAS) to identify the genomic regions influencing the density of prostrate hair. We identified a major locus on 9.56–10.54 Mbp of chromosome 17, designated as ‘LH2’, which accounts for 43% of the phenotypic variation. This locus was delineated with high precision, and 92 candidate genes were identified within the region. Functional enrichment analysis suggested that these genes are potentially involved in binding, catalytic activity, and various cellular processes. In particular, the SNP markers ‘chr17_10130288’ and ‘chr17_10428273’ were significantly associated with prostrate hair density, providing valuable information for marker-assisted selection. These findings offer a reliable target for analyzing the hair development mechanism of grapevine leaves and breeding new cultivars rich in prostrate hair on the back of the leaves.

Research on 3D Localization of Indoor UAV Based on Wasserstein GAN and Pseudo Fingerprint Map

In addition to outdoor environments, unmanned aerial vehicles (UAVs) also have a wide range of applications in indoor environments. The complex and changeable indoor environment and relatively small space make indoor localization of UAVs more difficult and urgent. An innovative 3D localization method for indoor UAVs using a Wasserstein generative adversarial network (WGAN) and a pseudo fingerprint map (PFM) is proposed in this paper. The primary aim is to enhance the localization accuracy and robustness in complex indoor environments. The proposed method integrates four classic matching localization algorithms with WGAN and PFM, demonstrating significant improvements in localization precision. Simulation results show that both the WGAN and PFM algorithms significantly reduce localization errors and enhance environmental adaptability and robustness in both small and large simulated indoor environments. The findings confirm the robustness and efficiency of the proposed method in real-world indoor localization scenarios. In the inertial measurement unit (IMU)-based tracking algorithm, using the fingerprint database of initial coarse particles and the fingerprint database processed by the WGAN algorithm to locate the UAV, the localization error of the four algorithms is reduced by 30.3% on average. After using the PFM algorithm for matching localization, the localization error of the UAV is reduced by 28% on average.

Hybrid Soybean as Green Manure for Improving Soil Properties and Subsequent Crop Growth

The rapid increase in fertilizer use has led to the degradation of soil quality, nutrient imbalances, reduced biodiversity, and soil compaction. To address these challenges, hybrid soybeans with efficient biological nitrogen fixation capabilities and broad environmental adaptability were selected as green manure to reduce fertilizer application, thereby improving soil fertility and structure. This study utilized the varieties “Forage Soybean S001” (S001), “Neinong S002 Forage Soybean” (S002), “Mengnong S003 Forage Soybean” (S003), “Mengnong S004 Forage Soybean” (S004), “Mengnong S005 Forage Soybean” (S005), and “Mengnong S006 Forage Soybean” (S006) as green manure materials. The clean tillage (CK) treatment served as the control, ensuring a residue-free soil surface while maintaining consistent practices in soil preparation, irrigation, and field management across all treatments. Field planting of green manure and subsequent crops was conducted at the M-Grass Ecology and Environment (Group) Company’s experimental site in Hohhot in early May of 2023 and 2024. The plots each measured 20 m2, with three replications arranged in a randomized block design. A combination of field experiments and laboratory analyses was utilized to investigate the effects of incorporating various hybrid soybean varieties as green manure on soil nutrient levels, soil enzyme activity, soil microbial communities, and the subsequent growth of oats. The results indicated that incorporating various hybrid soybean varieties as green manure into the soil significantly improved soil nutrient levels and enzyme activity. The diversity and richness of soil bacterial communities increased significantly, accompanied by alterations in community structure and composition. These changes enhanced soil fertility and optimized the microbial community structure, promoting the growth of subsequent crops. Among all the treatments, S001 and S004 were particularly effective in enhancing the soil environment, indicating their potential as superior green manure resources for broader application.

Inter-year consistencies and discrepancies on intestinal microbiota for overwintering relict gulls: correlations with food composition and implications for environmental adaptation

The gut microbiota of migratory birds is influenced by their food choices, and exploring the potential relationship between diet composition and gut microbiota can help better protect related species. By integrating non-invasive sampling techniques, high-throughput sequencing technology, and microscopic examination technology, this study presents the first evidence on diet composition during overwintering periods as well as the potential relationship between diet composition and gut microbiota in wild relict gulls (Larus relictus). Thirty-five fecal samples from two consecutive overwintering periods (2021 and 2022 overwintering periods) in Tianjin coastal wetland were used to investigate inter-year consistencies and discrepancies on diet composition and gut microbiota in wild Larus relictus. It was found that the common dominant phyla of both 2021 and 2022 group included Firmicutes, Proteobacteria, Chloroflexi and Actinobacteriota. The common dominant genera were Catellicoccus and Ilumatobacter. The diversity of gut microbiome in 2022 group was higher, while the richness was not significantly different. Based on the high-throughput sequencing technology of 18S rDNA, the study found that the dominant classes within the diet components of Larus relictus included Polychaeta, Bivalvia, Malacostraca, Gastropoda, unclassified_p__Dinoflagellata, Dinophyceae, and Ostracoda. Among them, Bivalvia, Malacostraca, and Gastropoda were also found with microscopic examination technology from the same samples. The abundance of Fusobacteriota and Cetobacterium were positively correlated with the abundance of Bivalvia and Malacostraca; while the abundance of Psychrobacter and Breznakia were negatively correlated with the abundance of Malacostraca and Gastropoda. Findings from this study could provide scientific references for health monitoring and conservation of relict gulls.

Different Species and Cultivars of Broad Beans, Lupins, and Clovers Demonstrated Varying Environmental Adaptability and Nitrogen Fixation Potential When Cultivated as Green Manures in Northeastern Portugal

The success of growing legumes as green manure depends on their spatial and temporal integration within agroecosystems, which minimizes competition with cash crops, and on their nitrogen (N) fixation potential. This study evaluated seven legume species for biomass production, N fixation, and suitability for use in cropping systems in northern Portugal. Oats (Avena sativa L.) were grown to estimate the N fixation using the difference method, as a non-legume reference crop is required for this purpose, and oats are widely grown in the region. The study was conducted over four cropping cycles (2021–2024) in two climate zones across four land plots. The results indicated that the biomass production and N fixation varied by the species/cultivar and cropping cycle, which was significantly influenced by spring precipitation. Broad beans (Vicia faba L.) failed to develop in one cycle on highly acidic soil (pH 4.9), showing negative N fixation values when calculated by the difference method. Conversely, the lupins maintained a relatively high level of N fixation across all the conditions, demonstrating strong environmental adaptability. Thus, the N fixation values across the four cycles ranged from −5.4 to 419.4 kg ha−1 for broad bean (cv. Favel), while yellow lupin (Lupinus luteus L.) exhibited average values between 204.0 and 274.0 kg ha−1. The percentage of N derived from the atmosphere (%Ndfa) ranged from −13.3 to 91.6, −39.4 to 85.8, 83.8 to 94.7, 74.9 to 94.3, 72.8 to 92.2, 23.1 to 75.8, and 11.7 to 21.7 for these species/cultivars. Due to their environmental adaptability, biomass production, and N fixation capacity, these legumes could be used as green manure in inter-rows of woody crops or in summer annual crops like tomatoes and maize, grown in winter as an alternative to fallow land. The lupins showed strong promise due to their environmental resilience.

A compilation of histidine kinase genes in Enterobacter hormaechei strain HCF3 isolated from the North West Province of South Africa

This study investigates the histidine kinase (HK) gene repertoire of Enterobacter hormaechei strain HCF3, isolated from fresh cow dung in Mogosane Village, Northwest Province, South Africa. Histidine kinases are critical components of bacterial two-component signal transduction systems, enabling bacteria to sense and adapt to diverse environmental conditions. Given the growing concern over antimicrobial resistance (AMR) associated with E. hormaechei, this research elucidates the genetic components that facilitate its environmental adaptability. After isolating the strain, genomic sequencing using Illumina technology, resulting in high-quality sequence data, was conducted. The assembled genome was meticulously annotated and deposited in the National Center for Biotechnology Information (NCBI) under BioProject number PRJNA991313, with additional accession numbers for raw reads (JAUOLV000000000.1) and BioSample (SAMN36292742). Histidine kinase genes were identified based on conserved domains, particularly HisKA and HATPase. This led to compiling a comprehensive HK gene catalogue with locus tags, protein accession numbers, and functional annotations. To validate the HK gene set of E. hormaechei HCF3, we conducted a rigorous comparative analysis with other strains. This revealed that strain HCF1 contains 21 histidine kinase genes, HCF2 has 25, while HCF4 has 19. These findings underscore the diversity and conservation of HK genes across different Enterobacter species, providing a new perspective on their evolutionary significance. The assembled dataset provides valuable insights into the signalling pathways of E. hormaechei, highlighting the potential roles of HKs in environmental sensing, adaptation, and pathogenicity. Furthermore, this research lays the groundwork for future studies on the applications of these genes in agriculture and biotechnology, offering new avenues for understanding and managing E. hormaechei in various ecological contexts.

Whole-genome resequencing to investigate the genetic diversity and mechanisms of plateau adaptation in Tibetan sheep

IntroductionTibetan sheep, economically important animals on the Qinghai–Tibet Plateau, have diversified into numerous local breeds with unique characteristics through prolonged environmental adaptation and selective breeding. However, most current research focuses on one or two breeds, and lacks a comprehensive representation of the genetic diversity across multiple Tibetan sheep breeds. This study aims to fill this gap by investigating the genetic structure, diversity and high-altitude adaptation of 6 Tibetan sheep breeds using whole-genome resequencing data.ResultsSix Tibetan sheep breeds were investigated in this study, and whole-genome resequencing data were used to investigate their genetic structure and population diversity. The results showed that the 6 Tibetan sheep breeds exhibited distinct separation in the phylogenetic tree; however, the levels of differentiation among the breeds were minimal, with extensive gene flow observed. Population structure analysis broadly categorized the 6 breeds into 3 distinct ecological types: plateau-type, valley-type and Euler-type. Analysis of unique single-nucleotide polymorphisms (SNPs) and selective sweeps between Argali and Tibetan sheep revealed that Tibetan sheep domestication was associated primarily with sensory and signal transduction, nutrient absorption and metabolism, and growth and reproductive characteristics. Finally, comprehensive analysis of selective sweep and transcriptome data suggested that Tibetan sheep breeds inhabiting different altitudes on the Qinghai–Tibet Plateau adapt by enhancing cardiopulmonary function, regulating body fluid balance through renal reabsorption, and modifying nutrient digestion and absorption pathways.ConclusionIn this study, we investigated the genetic diversity and population structure of 6 Tibetan sheep breeds in Qinghai Province, China. Additionally, we analyzed the domestication traits and investigated the unique adaptation mechanisms residing varying altitudes in the plateau region of Tibetan sheep. This study provides valuable insights into the evolutionary processes of Tibetan sheep in extreme environments. These findings will also contribute to the preservation of genetic diversity and offer a foundation for Tibetan sheep diversity preservation and plateau animal environmental adaptation mechanisms.

Research and Experiments on Adaptive Root Cutting Using a Garlic Harvester Based on a Convolutional Neural Network

Aimed at the problems of a high leakage rate, a high cutting injury rate, and uneven root cutting in the existing combined garlic harvesting and root-cutting technology, we researched the key technologies used in a garlic harvester for adaptive root cutting based on machine vision. Firstly, research was carried out on the conveyor alignment and assembly of the garlic harvester to realize the adjustment of the garlic plant position and the alignment of the bulb’s upper surface before the roots were cut, to establish the parameter equations and to modify the structure of the conveyor to form the adaptive garlic root-cutting system. Then, a root-cutting test using the double-knife disk-type cutting device was carried out to examine the root-cutting ability of the cutting device. Finally, a bulb detector trained with the IRM-YOLO model was deployed on the Jetson Nano device (NVIDIA, Jetson Nano(4GB), Santa Clara, CA, USA) to conduct a harvester field trial study. The pass rate for the root cutting was 82.8%, and the cutting injury rate was 2.7%, which tested the root cutting performance of the adaptive root cutting system and its field environment adaptability, providing a reference for research into combined garlic harvesting technology.

Optimizing Personalized and Context-Aware Recommendations in Pervasive Computing Environments

The researchers in the current era provided many new recommendation methodologies. Though various recommendation techniques exist, there is a need to develop a unique technique for capturing latent factors and patterns from sparse and high-dimensional data in pervasive environments, specifically for optimizing dynamic recommendations. This study proposes a hybrid approach for optimizing dynamic recommendations in pervasive environments by combining Non-Negative Matrix Factorization (NMF) with deep learning and reinforcement learning techniques. The goal is to overcome the challenge of capturing latent factors and patterns from sparse and high-dimensional data. By leveraging NMF, meaningful latent factors are extracted, while deep learning, specifically Faster recurrent neural networks (FRNNs), learns complex feature representations. Reinforcement learning algorithms optimize the recommendation policy based on user feedback. This Hybrid Context-Aware Optimized Recommendation (HCOR) approach improves recommendation accuracy and relevance in pervasive environments, adapts to changing contexts, and enhances user experiences. The performance benefits are achieved by effectively capturing latent factors and patterns, resulting in improved accuracy and the ability to provide personalized and context-aware recommendations. The performance indicators to validate the research work include the recommendations' accuracy, relevance, and adaptability in pervasive environments. Additionally, metrics, such as precision, recall, and F1-score, are used to evaluate the effectiveness of the hybrid approach in capturing latent factors and patterns. User feedback and satisfaction are also measured to assess the impact on user experiences. The HCOR approach shows substantial performance gains, measuring a precision of 0.932, a recall of 0.922, and an F1-score of 0.943, which indicates the excellent ability of the approach to deliver accurate and personalized recommendations in a pervasive environment.

A Comparative Transcriptomic and Proteomic Analysis of the Pileus of Agaricus bisporus During Its Different Developmental Phases

The analysis of the developmental stages of Agaricus bisporus, a major edible and medicinal mushroom, has always been an important focus in this research area. The process of the growth and development of edible mushrooms is complex and involves the regulation of multiple genes and metabolic pathways. Less data exist on the mechanism of their growth and development at the overall level. In this study, RNA sequencing analyses (RNA-Seq) and data-independent acquisition (DIA) proteomic analyses were carried out at the button phase (BP), harvesting phase (HP), and opening phase (OP) stages of Agaricus bisporus ‘Shuangbao 106’ to reveal the changes in gene expression during the different growth periods of its substrates. The authors screened and explored 3351 differentially expressed genes (DEGs) with a difference factor of ≥2.0, including 2080 up-regulated and 1271 down-regulated genes. After proteome sequencing, 1156 differentially expressed proteins (DEPs) were screened, including 524 up-regulated and 632 down-regulated genes. The expression in TPM of glycoside hydrolase, catalytic core, and chitinase II decreased during both the HP and OP compared with the BP. This may be because mushrooms require higher levels of glycoside hydrolase, catalytic core, and chitinase II activity during the BP to cope with external threats and the need for cell wall remodeling. Conversely, the growth of mushrooms slowed down and the need for cell wall remodeling decreased during the HP and OP, leading to a decrease in the expression of glycoside hydrolase, catalytic core, and chitinase II. This change is related to the need for environmental adaptation, immune defense, and cell wall remodeling, and may regulate the post-growth process of A. bisporus via the hydrolysis of cell wall chitin and glycoside hydrolase. It may also inhibit the growth of mushroom pilei.

ZF-HD gene family in rapeseed (Brassica napus L.): genome-wide identification, phylogeny, evolutionary expansion and expression analyses

BackgroundThe zinc finger-homeodomain (ZF-HD) transcription factor family is widely involved in regulating plant growth and fruit filling, as well as responding to various abiotic stress. Rapeseed (Brassica napus L.), the second largest oil-producing crop in the world, is an annual or biennial herb of Brassica in Cruciferae. However, there is currently no systematic study on the evolutionary relationship and stress response of ZF-HD transcription factors in rapeseed.ResultsIn this study, 60 ZF-HD genes in B. napus (BnZHDs) were identified and named based on the chromosomal location. The evolutionary relationships, classifications, gene structures, motif compositions, chromosome localization, and gene replication events in these BnZHD genes were systematically analyzed. These 60 BnZHD members were divided into seven groups. According to the phylogenetic tree and repetitive events, subfamilies MIF, and V may have undergone stronger expansions during the evolutionary process. Interestingly, segmental duplications may have a more important contribution, which distinguishes them from other dicotyledon plants. To further investigate the evolutionary relationship of the ZF-HD family, we constructed eleven comparative genomic maps of homologous genes between rapeseed and different representative monocotyledonous and dicotyledonous plants. Finally, the gene expression pattern of 15 BnZHD genes from different subfamilies under different tissues, fruit developmental stages, and different abiotic stress were analyzed. The expression profile from real-time quantitative PCR analysis showed different expression patterns of BnZHD gene in B. napus. We found that certain BnZHD genes are preferentially expressed in specific tissues of B. napus, while most genes are expressed in multiple tissues. For example, BnZHD37, BnZHD53, and BnZHD55 may be sensitive to different hormones. Under different stresses, the expression of BnZHD3, BnZHD4, BnZHD7, BnZHD38, BnZD45, and BnZHD53 significantly increased in roots, stems, and leaves within 24 h. These genes may play important roles in the growth, development, and environmental adaptation of rapeseed.ConclusionsThese findings provide a basis for a comprehensive understanding of the ZF-HD family in rapeseed, which will provide information for further research on the functional characteristics of the BnZHD genes.

Response of soybean root exudates and related metabolic pathways to low phosphorus stress.

Phosphorus (P) is an essential elemental nutrient required in high abundance for robust soybean growth and development. Low P stress negatively impacts plant physiological and biochemical processes, such as photosynthesis, respiration, and energy transfer. Soybean roots play key roles in plant adaptive responses to P stress and other soil-related environmental stressors. Study the changes in soybean root exudates and differences in related metabolic pathways under low phosphorus stress, analyzing the response mechanism of soybean roots to phosphorus stress from the perspective of root exudates, which provide a theoretical basis for further analyzing the physiological mechanism of phosphorus stress on soybean. In this study, soybean roots were exposed to three phosphate levels: 1 mg/L (P stress), 11 mg/L (P stress) and 31 mg/L (Normal P) for 10 days and 20 days, then root exudates were analyzed via ultra-high-performance liquid chromatography-mass spectrometry to identify effects of P stress on root metabolite profiles and associated metabolic pathways. Our results revealed that with increasing P stress severity and/or duration, soybean roots produced altered types, quantities, and increased numbers of exudate metabolites (DMs in the P1 group were primarily upregulated, whereas those in the P11 group were predominately downregulated) caused by changes in regulation of activities of numerous metabolic pathways. These pathways had functions related to environmental adaptation, energy metabolism, and scavenging of reactive oxygen species and primarily included amino acid, flavonoid, and nicotinate and nicotinamide metabolic pathways and pathways related to isoquinoline alkaloid biosynthesis, sugar catabolism, and phospholipid metabolism. These metabolites and metabolic pathways lay a foundation to support further investigations of physiological mechanisms underlying the soybean root response to P deficiency.

Exploring the function of plant root diffusion barriers in sealing and shielding for environmental adaptation.

Plant roots serve as the primary interface between the plant and the soil, encountering numerous challenges ranging from water balance to nutrient uptake. One of the central mechanisms enabling plants to thrive in diverse ecosystems is the building of apoplastic diffusion barriers. These barriers control the flow of solutes into and out of the roots, maintaining water and nutrient homeostasis. In this Review, we summarize recent advances in understanding the establishment, function and ecological significance of root apoplastic diffusion barriers. We highlight the plasticity of apoplastic diffusion barriers under various abiotic stresses such as drought, salinity and nutrient deficiency. We also propose new frontiers by discussing the current bottlenecks in the study of plant apoplastic diffusion barriers.