Multiple Plant Research Articles

Ecological aspects and relationships of the emblematic Vachellia spp. exposed to anthropic pressures and parasitism in natural hyper-arid ecosystems: ethnobotanical elements, morphology, and biological nitrogen fixation

Main conclusionEmblematic Vachellia spp. naturally exposed to hyper-arid conditions, intensive grazing, and parasitism maintain a high nitrogen content and functional mutualistic nitrogen-fixing symbioses.AlUla region in Saudi Arabia has a rich history regarding mankind, local wildlife, and fertility islands suitable for leguminous species, such as the emblematic Vachellia spp. desert trees. In this region, we investigated the characteristics of desert legumes in two nature reserves (Sharaan and Madakhil), at one archaeological site (Hegra), and in open public domains at Al. Ward and Jabal Abu Oud. Biological nitrogen fixation (BNF), isotopes, and N and C contents were investigated through multiple lenses, including parasitism, plant tissues, species identification, plant maturity, health status, and plant growth. The average BNF rates of 19 Vachellia gerrardii and 21 Vachellia tortilis trees were respectively 39 and 67%, with low signs of inner N content fluctuations (2.10–2.63% N) compared to other co-occurring plants. The BNF of 23 R. raetam was just as high, with an average of 65% and steady inner N contents of 2.25 ± 0.30%. Regarding parasitism, infected Vachellia trees were unfazed compared to uninfected trees, thereby challenging the commonly accepted detrimental role of parasites. Overall, these results suggest that Vachellia trees and R. raetam shrubs exploit BNF in hyper-arid environments to maintain a high N content when exposed to parasitism and grazing. These findings underline the pivotal role of plant-bacteria mutualistic symbioses in desert environments. All ecological traits and relationships mentioned are further arguments in favor of these legumes serving as keystone species for ecological restoration and agro-silvo-pastoralism in the AlUla region.Graphical abstract

Potential of Drought Tolerant Rhizobacteria Amended with Biochar on Growth Promotion in Wheat.

Drought stress is the prime obstacle for worldwide agricultural production and necessitates innovative strategies for enhancing crop resilience. This study explores the efficacy of plant growth-promoting rhizobacteria (PGPR) and biochar (BC) as sustainable amendments for mitigating the effects of drought on wheat growth. Multiple experiments were carried out on isolated strains to assess their drought tolerance potential and multiple plant growth-promoting attributes. Experiments in the laboratory and natural environment were conducted to assess the impact of plant growth-promoting rhizobacteria, biochar, and their synergistic application on various growth parameters of wheat. The results revealed that the drought-tolerant PGPR strains (Bacillus subtilis and Bacillus tequilensis), alongside biochar (rice husk), alleviated the phytotoxic impact of drought by increasing the root length from 17.0% to 70.0% and shoot length from 30.0% to 82.0% as compared to un-inoculated stressed controls. The total chlorophyll and carotenoid contents of the plants were substantially increased to 477% and 423%, respectively, when biochar and PGPR were applied synergistically. Significant enhancements in membrane stability index, relative water content, proline, and sugar level were achieved by combining biochar and bacterial strains, resulting in increases of 19.5%, 37.9%, 219%, and 300%, respectively. The yield of wheat in terms of plant height, spike length, number of spikelets per spike, and number of grains per spike was enhanced from 26.7% to 44.6%, 23.5% to 62.7%, 91.5% to 154%, and 137% to 182%, respectively. It was concluded that the biochar-based application of PGPR induced drought tolerance in wheat under water deficit conditions, ultimately improving the production and yield of wheat.

Peer-to-peer electricity trading model for urban virtual power plants considering prosumer preferences and power demand heterogeneity

Peer-to-peer electricity trading has emerged as a critical way to meet the varied demands of prosumers within urban virtual power plants. To better support supply-demand balance and ensure the stable, low-carbon and efficient peer-to-peer electricity trading, both prosumer trading preferences and power demand heterogeneity need to be considered. This study proposes a peer-to-peer electricity trading model for urban virtual power plants that considers both preference of prosumers and power demand heterogeneity. First, to effectively meet the trading demands of diverse prosumers and enhance the penetration of renewable energy, prosumers are classified based on variations in financial returns, green electricity preferences and risk avoidance. Power demand is further classified based on the trading preferences of prosumers. Then, a peer-to-peer electricity trading model incorporating a game model and a market bidding mechanism is proposed to enable efficient and dynamic matching between supply and demand. Finally, to mitigate the impact of renewable energy uncertainty on peer-to-peer electricity trading, real-time auctions are implemented across multiple virtual power plants. The results show that the proposed model is capable of more effectively accommodating the diverse demands of prosumers, and facilitates a more stable, low-carbon and efficient peer-to-peer electricity trading.

Decreased irrigation volume, not irrigation placement, promotes accumulation of multiple hormones in cotton leaves during partial rootzone drying

While ABA is often assumed to mediate partial stomatal closure as the soil dries, other plant hormones and hydraulic signals may also be involved. We tested whether irrigation volume (% of crop evapotranspiration, ET) and placement (partial rootzone drying [PRD] or deficit irrigation [DI], which irrigate part or all of the rootzone respectively) affect this signalling by measuring stomatal conductance (gs), leaf and shoot water potential (Ψleaf, Ψshoot), shoot xylem sap ABA concentration ([X-ABA]shoot) and various foliar hormones (ABA, IAA, SA, JA, JA-Ile and cis-OPDA) in cotton plants exposed to different irrigation volumes (100 %ET or 50 %ET) and placements (DI or PRD). Partial rootzone drying caused stomatal closure coincident with sustained foliar ABA accumulation and minimal changes in Ψshoot, but continued soil drying of the dry compartment reversed partial stomatal closure (with gs of 100 %ET PRD plants sometimes greater than well-watered plants). With 100 %ET PRD, partial stomatal closure correlated with decreased soil moisture of the dry compartment and increased [ABA]leaf, but neither Ψleaf nor [X-ABA]shoot. Irrespective of irrigation placement, 50 %ET significantly decreased gs, Ψleaf and Ψshoot, but significantly increased [ABA]leaf, [X-ABA]shoot, [SA]leaf, [IAA]leaf and [cis-OPDA]leaf, with stomatal closure of 50 %ET PRD plants occurring earlier than 50 %ET DI plants. While stomatal closure at 50 %ET correlated with foliar accumulation of multiple plant hormones, foliar ABA dynamics best explained transient stomatal closure at 100 %ET PRD but not stomatal re-opening with prolonged soil drying. Thus, stomatal sensitivity to drying soil (and putative regulatory signals such as ABA) depended on irrigation volume and placement.

Performance and cost potential for direct-fired supercritical CO2 natural gas power plants

Direct-fired supercritical CO2 (sCO2) power cycles are being explored as an attractive alternative to natural gas combined cycle (NGCC) plants with carbon capture and storage (CCS). Therefore, understanding their performance and cost potential is important for the commercialization of the technology. This study presents detailed techno-economic optimization results of natural gas-fired, utility-scale power plants based on the direct sCO2 power cycle, which is lacking in public literature. The study considered multiple plant configurations with varying levels of thermal integration with the plant air separation unit (ASU) to shows the systematic impact of thermal integration on plant performance and economics. Several design variables for each power cycle configuration were identified and optimized to minimize the levelized cost of electricity (LCOE). The optimized direct sCO2 power plants achieved ∼48 % plant efficiency (HHV basis) which is similar or slightly higher plant efficiencies than state-of-the-art NGCC plants based on the F-class gas turbine with carbon capture and storage (CCS). The LCOE of the optimized direct sCO2 plants is 13–17 % higher than the reference NGCC plants with CCS due to high capital costs associated with the ASU and sCO2 power block, though there is significant room for improvement due to high uncertainty in component capital costs for these new plants. Based on the results, direct sCO2 power cycles have the potential to achieve >50 % efficiency (HHV basis) with >98 % CO2 capture rate which can lead to significant efficiency improvement and CO2 emissions reductions compared to NGCC plants with CCS.

Impacts of Invasive Plants on Native Vegetation Communities in Wetland and Stream Mitigation.

We sampled vegetation communities across plant invasion gradients at multiple wetland and stream mitigation sites in the Coastal Plain and Piedmont physiographic provinces of Virginia, USA. Impacts of invasion were evaluated by tracking changes in species composition and native vegetation community properties along the abundance gradients of multiple plant invaders. We found that native species richness, diversity, and floristic quality were consistently highest at moderate levels of invasion (ca. 5-10% relative abundance of invader), regardless of the identity of the invasive species or the type of mitigation (wetland or stream). Likewise, native species composition was similar between uninvaded and moderately invaded areas, and only diminished when invaders were present at higher abundance values. Currently, low thresholds for invasive species performance standards (e.g., below 5% relative abundance of invader) compel mitigation managers to use non-selective control methods such as herbicides to reduce invasive plant cover. Our results suggest that this could cause indiscriminate mortality of desirable native species at much higher levels of richness, diversity, and floristic quality than previously thought. From our data, we recommend an invasive species performance standard of 10% relative invader(s) abundance on wetland and stream mitigation sites, in combination with vigilant invasive plant mapping strategies. Based on our results, this slightly higher standard would strike a balance between proactive management and unnecessary loss of plant community functions at the hands of compulsory invasive species management.

Non-target screening and prioritization of organic contaminants in seawater desalination and their ecological risk assessment

The impact of desalination brine on the marine environment is a global concern. Regarding this, salinity is generally accepted as the major environmental factor in desalination concentrate. However, recent studies have shown that the influence of organic contaminants in brine cannot be ignored. Therefore, a non-targeted screening method based on comprehensive two-dimensional gas chromatography-quadrupole mass spectrometry (GC × GC-qMS) was developed for identifying organic contaminants in the desalination brine. A total of 404 compounds were tentatively identified from four seawater desalination plants (three reverse osmosis plants and one multiple effect distillation plant) in China. The identified compounds were prioritized based on their persistence, bioaccumulation, ecotoxicity, usage, and detection frequency. Twenty-one (21) compounds (seven phthalates, ten pesticides, four trihalomethanes) were then selected for further quantitative analysis and ecological risk assessment, including compounds from the priority list along with substances from the same chemical classes. Ecologically risky substances in brine include diisobutylphthalate and bis(2-Ethylhexyl) phthalate, atrazine and acetochlor, and bromoform. Most of the contaminants come from raw seawater, and no high risk contaminants introduced by the desalination process have been found except for disinfection by-products. In brine discharge management, people believed that all pollution in raw seawater was concentrated by desalination process. This study shows that not all pollutants are concentrated during the desalination process. In this study, the total concentration of pesticide in the brine increased by 58.42%. The concentration of ∑PAEs decreased by 13.65% in reverse osmosis desalination plants and increased by 10.96% in the multi-effect distillation plant. The concentration of trihalomethane increased significantly in the desalination concentrate. The change in the concentration of pollutants in the desalination concentrate was related to the pretreatment method and the chemical characteristics of the contaminants. The method and results given in this study hinted a new idea to identify and control the environmental impact factors of brine.

Chloroplast genomes of Caragana tibetica and Caragana turkestanica: structures and comparative analysis

BackgroundThe genus Caragana encompasses multiple plant species that possess medicinal and ecological value. However, some species of Caragana are quite similar in morphology, so identifying species in this genus based on their morphological characteristics is considerably complex. In our research, illumina paired-end sequencing was employed to investigate the genetic organization and structure of Caragana tibetica and Caragana turkestanica, including the previously published chloroplast genome sequence of 7 Caragana plants.ResultsThe lengths of C. tibetica and C. turkestanica chloroplast genomes were 128,433 bp and 129,453 bp, respectively. The absence of inverted repeat sequences in these two species categorizes them under the inverted repeat loss clade (IRLC). They encode 110 and 111 genes (4 /4 rRNA genes, 30 /31tRNA genes, and 76 /76 protein-coding genes), respectively. Comparison of the chloroplast genomes of C. tibetica and C. turkestanica with 7 other Caragana species revealed a high overall sequence similarity. However, some divergence was observed between certain intergenic regions (matK-rbcL, psbD-psbM, atpA-psbI, and etc.). Nucleotide diversity (π) analysis revealed the detection of five highly likely variable regions, namely rps2-atpI, accD-psaI-ycf4, cemA-petA, psbN-psbH and rpoA-rps11. Phylogenetic analysis revealed that C. tibetica’s sister species is Caragana jubata, whereas C. turkestanica’s closest relative is Caragana arborescens.ConclusionsThe present study provides worthwhile information about the chloroplast genomes of C. tibetica and C. turkestanica, which aids in the identification and classification of Caragana species.

An Effective Image Classification Method for Plant Diseases with Improved Channel Attention Mechanism aECAnet Based on Deep Learning

Since plant diseases occurring during the growth process are a significant factor leading to the decline in both yield and quality, the classification and detection of plant leaf diseases, followed by timely prevention and control measures, are crucial for safeguarding plant productivity and quality. As the traditional convolutional neural network structure cannot effectively recognize similar plant leaf diseases, in order to more accurately identify the diseases on plant leaves, this paper proposes an effective plant disease image recognition method aECA-ResNet34. This method is based on ResNet34, and in the first and the last layers of this network, respectively, we add this paper’s improved aECAnet with the symmetric structure. aECA-ResNet34 is compared with different plant disease classification models on the peanut dataset constructed in this paper and the open-source PlantVillage dataset. The experimental results show that the aECA-ResNet34 model proposed in this paper has higher accuracy, better performance, and better robustness. The results show that the aECA-ResNet34 model proposed in this paper is able to recognize diseases of multiple plant leaves very accurately.

Transcriptomic and Metabolomic Research on the Germination Process of Panax ginseng Overwintering Buds.

Ginseng (Panax ginseng C. A. Meyer) is a perennial plant with a long dormancy period. While some researchers employ gibberellin and other substances to stimulate premature germination, this method is limited to laboratory settings and cannot be applied to the field cultivation of ginseng. The mechanism underlying the germination of ginseng overwintering buds remains largely unexplored. Understanding the internal changes during the dormancy release process in the overwintering buds would facilitate the discovery of potential genes, metabolites, or regulatory pathways associated with it. In this study, we approximately determined the onset of dormancy release through morphological observations and investigated the process of dormancy release in ginseng overwintering buds using transcriptomic and metabolomic approaches. Our analyses revealed that the germination process of ginseng overwintering buds is regulated by multiple plant hormones, each acting at different times. Among these, abscisic acid (ABA) and gibberellic acid (GA) serve as classical signaling molecules regulating the dormancy process, while other hormones may promote the subsequent growth of overwintering buds. Additionally, metabolic pathways associated with arginine may be involved in the dormancy release process. Polyamines synthesized downstream may promote the growth of overwintering buds after dormancy release and participate in subsequent reproductive growth. This study provides insights into the germination process of ginseng overwintering buds at the molecular level and serves as a reference for further exploration of the detailed mechanism underlying ginseng overwintering germination in the future.

Cross-kingdom RNA interference mediated by insect salivary microRNAs may suppress plant immunity

Communication between insects and plants relies on the exchange of bioactive molecules that traverse the species interface. Although proteinic effectors have been extensively studied, our knowledge of other molecules involved in this process remains limited. In this study, we investigate the role of salivary microRNAs (miRNAs) from the rice planthopper Nilaparvata lugens in suppressing plant immunity. A total of three miRNAs were confirmed to be secreted into host plants during insect feeding. Notably, the sequence-conserved miR-7-5P is specifically expressed in the salivary glands of N. lugens and is secreted into saliva, distinguishing it significantly from homologues found in other insects. Silencing miR-7-5P negatively affects N. lugens feeding on rice plants, but not on artificial diets. The impaired feeding performance of miR-7-5P-silenced insects can be rescued by transgenic plants overexpressing miR-7-5P. Through target prediction and experimental testing, we demonstrate that miR-7-5P targets multiple plant genes, including the immune-associated bZIP transcription factor 43 (OsbZIP43). Infestation of rice plants by miR-7-5P-silenced insects leads to the increased expression of OsbZIP43, while the presence of miR-7-5P counteracts this upregulation effect. Furthermore, overexpressing OsbZIP43 confers plant resistance against insects which can be subverted by miR-7-5P. Our findings suggest a mechanism by which herbivorous insects have evolved salivary miRNAs to suppress plant immunity, expanding our understanding of cross-kingdom RNA interference between interacting organisms.

Camera-based plant growth monitoring for automated plant cultivation with controlled environment agriculture

Building integrated photovoltaic thermal (BIPVT) greenhouse technology exhibits great potential to enhance crop yielding, energy harvesting, and water management for highly efficient farming and enables controlled environment agriculture (CEA) for automated plant cultivation. The proposed smart greenhouse system uses BIPVT, geothermal, image-based sensing, and control technologies for high energy efficiency and high-fidelity plant growth control. A camera-based, computer-controlled system was demonstrated to successfully monitor single and multiple food plants' growth. The growth rate over the life-cycle of the plant was automatically recorded by cameras without human interference. A novel algorithm was developed to extract the plants from the background of the image and then to measure the length from the ground to the top of the longest plant with different shapes or profiles. The growth rate was automatically generated as fundamental data for farming management and control. Because the plant growth is slow and the errors caused by image noise can be comparable with the actual growth when the interval between images is not large enough, the noise of a camera is evaluated by comparing multiple images under the same exposure condition, which is used to provide the error range of the measurements. The plant growth monitoring system will be integrated into an ongoing greenhouse project for smart greenhouse operation.

Vom Chaos zur Klarheit. Die Neuordnung der Apfel TCP Gen-Benennung

Apple Proliferation (AP) is a widespread disease affecting apple orchards throughout Europe, including South Tyrol.The disease is associated with a phytoplasma, 'Candidatus Phytoplasma mali' ('Ca. P. mali'), which manipulates the plant's physiological processes through the secretion of small peptides called effectors. One well-studied phytoplasma effector is SAP11, which targets Teosinte Branched 1/Cycloidea/Proliferating Cell Factor 1 (TCP) genes in multiple plant species, including apple. TCP genes encode plant-specific transcription factors involved in various biological processes, such as growth, development, and responses to stimuli. The identification and naming of TCP genes have been inconsistent, leading to confusion and redundancy in sequence databases. In apple, 52 TCP genes (MdTCP) were identified in 2014 on the 2010 assembly of the apple genome, which is now considered outdated. To address this issue, a comprehensive investigation was conducted to identify and name TCP genes in apple using the high-quality genome assembly, GDDH13v1.1. Existing TCP sequences were aligned with the genome excluding redundant, fragmented, and non-TCP sequences. The revised set comprised 40 unique MdTCPs, including three novel genes. To establish a standardised nomenclature, each MdTCP was BLASTedagainst the Arabidopsis thaliana gene database, in order to identify the best hits for each MdTCP. The MdTCP genes were then renamed, incorporating the letter "a" or "b" to differentiate between MdTCPs showing homology to the same AtTCP, and also between the existing and proposed nomenclatures. The present study highlights the need for clarity and organisation in sequence databases, especially in respect of TCP genes. The presence of redundant and fragmented sequences in databases complicates accurate identification and annotation of genes. By providing a comprehensive and standardised nomenclature system, we aim to enhance the coherence and interoperability of future TCP gene research in plant and crop science. In our view, this work will serve as a valuable resource for researchers studying TCP genes in apple and will provide insights into the evolutionary dynamics and functional roles of TCP genes in plants.

Aptly chosen, effectively emphasizing the action and mechanism of antimycin A1.

Rhizoctonia solani Kühn, a plant pathogenic fungus that can cause diseases in multiple plant species is considered one of the common and destructive pathogens in many crops. This study investigated the action of antimycin A1, which was isolated from Streptomyces AHF-20 found in the rhizosphere soil of an ancient banyan tree, on Rhizoctonia solani and its mechanism. The inhibitory effect of antimycin A1 on R. solani was assessed using the comparative growth rate method. The results revealed that antimycin A1 exhibited a 92.55% inhibition rate against R. solani at a concentration of 26.66 μg/mL, with an EC50 value of 1.25 μg/mL. To observe the impact of antimycin A1 on mycelial morphology and ultrastructure, the fungal mycelium was treated with 6.66 μg/mL antimycin A1, and scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were employed. SEM analysis demonstrated that antimycin A1 caused mycelial morphology to become stripped, rough, and folded. The mycelium experienced severe distortion and breakage, with incomplete or locally enlarged ends, shortened branches, and reduced numbers. TEM observation revealed thickened cell walls, indistinct organelle boundaries, swollen mitochondria, exosmotic substances in vesicles, slow vesicle fusion, and cavitation. Real-time quantitative PCR and enzyme activity assays were conducted to further investigate the impact of antimycin A1 on mitochondria. The physiological and biochemical results indicated that antimycin A1 inhibited complexes III and IV of the mitochondrial electron transport chain. RT-PCR analysis demonstrated that antimycin A1 controlled the synthesis of relevant enzymes by suppressing the transcription levels of ATP6, ATP8, COX3, QCR6, CytB, ND1, and ND3 genes in mitochondria. Additionally, a metabolomic analysis revealed that antimycin A1 significantly impacted 12 metabolic pathways. These pathways likely experienced alterations in their metabolite profiles due to the inhibitory effects of antimycin A1. Consequently, the findings of this research contribute to the potential development of novel fungicides.

Effects of plant leaf traits, abundance and phylogeny on differentiation of herbivorous insect assemblages in Mediterranean mixed forest

AbstractHerbivorous insect assemblages are functionally diverse, with each species exploiting plant tissues in different ways. Availability and palatability of plant tissues influence the diversity and composition of herbivorous insect assemblages. However, few studies have compared herbivorous insect assemblages and their ecological correlates across multiple plant species within the same plant community. Here, we sampled insect assemblages from the canopies of 1060 plants belonging to 36 woody species in two mixed Mediterranean forest stands. 401 insect species were classified as herbivores and grouped into sucker or chewer guilds. We explored differences in the diversity and composition of each insect guild across plant species, and tested their relationships with plant leaf traits, abundance or phylogeny, and explored whether the structures of plant-herbivorous insect networks depended on any of the studied plant traits. Plant identity accounted for the highest proportion of variation in the composition of each insect guild. Plant species abundance showed a positive effect on both insect guilds’ diversity. Suckers’ diversity was higher in plant species with deciduous leaves and low SLA, while the composition was more similar between phylogenetically closer plant species. Chewers diversity increased with the leaf area, while plants with similar LA, leaf nitrogen, SLA and distinct leaf habit showed more similar assemblages. Similarly, closely related angiosperms showed similar chewer assemblages. Plant–insect interaction networks present a modular structure, in which plants belonging to the same module tend to be related and share more sucker species. We add to the evidence supporting the role of plant species features as filters for structuring their associated herbivore insect assemblages.

Root hydraulic properties: An exploration of their variability across scales.

Root hydraulic properties are key physiological traits that determine the capacity of root systems to take up water, at a specific evaporative demand. They can strongly vary among species, cultivars or even within the same genotype, but a systematic analysis of their variation across plant functional types (PFTs) is still missing. Here, we reviewed published empirical studies on root hydraulic properties at the segment-, individual root-, or root system scale and determined its variability and the main factors contributing to it. This corresponded to a total of 241 published studies, comprising 213 species, including woody and herbaceous vegetation. We observed an extremely large range of variation (of orders of magnitude) in root hydraulic properties, but this was not caused by systematic differences among PFTs. Rather, the (combined) effect of factors such as root system age, driving force used for measurement, or stress treatments shaped the results. We found a significant decrease in root hydraulic properties under stress conditions (drought and aquaporin inhibition, p < .001) and a significant effect of the driving force used for measurement (hydrostatic or osmotic gradients, p < .001). Furthermore, whole root system conductance increased significantly with root system age across several crop species (p < .01), causing very large variation in the data (>2 orders of magnitude). Interestingly, this relationship showed an asymptotic shape, with a steep increase during the first days of growth and a flattening out at later stages of development. We confirmed this dynamic through simulations using a state-of-the-art computational model of water flow in the root system for a variety of crop species, suggesting common patterns across studies and species. These findings provide better understanding of the main causes of root hydraulic properties variations observed across empirical studies. They also open the door to better representation of hydraulic processes across multiple plant functional types and at large scales. All data collected in our analysis has been aggregated into an open access database (https://roothydraulic-properties.shinyapps.io/database/), fostering scientific exchange.

Plant-Denoising-Net (PDN): A plant point cloud denoising network based on density gradient field learning

Effective point cloud denoising is critical in 3D plant phenotyping applications, which reduces interference in subsequent algorithms and improves the accuracy of plant phenotypes measurement. Deep learning-based point cloud denoising algorithms have shown excellent denoising performance on simple CAD models. However, these algorithms suffer from issues including over-smoothing or shrinkage and low efficiency when applied on density uneven, incomplete, various types of noise and complex plant point clouds. We proposed a plant point cloud denoising network (PDN) based on point cloud density gradient field learning, which can effectively address the challenges posed by plant point clouds. PDN consists of three main modules: point density feature (PDF) exception module, umbrella operator feature (UOF) computation module, and point density gradient (DG) estimation module. The performance of PDN was evaluated in experiments using point clouds of multiple plant species with noise of different types. Under different levels of Gaussian noise, our method achieved a relative performance improvement of 7.6%-19.3% compared to the state-of-the-art baseline methods, reaching state-of-the-art denoising performance. For noise of different types, the majority of our denoising results outperformed the baseline methods. In addition, our method was 0.5 and 8.6 times faster than the baseline methods when processing point clouds with low and high noise level, respectively. The good robustness, generalization, and computational efficacy of PDN are expected to facilitate the acquisition of high-precision 3D point clouds for various plant species, enhance the versatility of 3D phenotyping methods, improve the accuracy of the measurement of structural phenotypes, and increase the throughput of data processing, therefore facilitate the development of modern breeding research. The source code and the datasets used in this study is available on GitHub at https://github.com/suetme/PDN-plant-denoising-net.

Development of High Yielding Rice Varieties Tolerant to Phosphorus Deficiency

Improvement of nutrient-use efficiency (NUE) in rice is important for increasing productivity. Rice varieties with tolerance to phosphorus (P) deficiency increase the productivity in P-deficient fields. Therefore, the objective of this study was to develop high yielding, P-deficiency tolerant rice varieties to enhance the NUE. High yielding and popular rice varieties (Bg300 and Bg94-1) and known P-deficiency tolerant rice varieties (H4 and At353) were used as female and male parents, respectively, in a crossing program. Single seed descent approach was used for rapid generation advancement. Bg300/At353 and Bg94-1/H4 crosses were advanced from F2 to F6 in planthouse condition under minimum fertigation for restricted growth. In F7 generation, 310 recombinant inbred lines (RIL) in both crosses were space-planted and 40 RILs were selected from each cross. Selected RILs were screened in a hydroponic system at 10 μM P level to select the best lines for P-deficiency tolerance. Multiple plant traits were used for selection and the most promising 20 rice lines were selected as the best performers under P-deficient hydroponic conditions. Marker assisted selection was carried out to confirm the presence of PSTOL1 gene. Selected rice lines were evaluated for agronomic traits and yield parameters. Ten promising rice lines were identified as high yielding lines with accepted agronomic traits.

Pseudomonas sp. AMGC1 takes on rice blast: Broad-spectrum antimicrobial activity underpins plant growth and disease tolerance.

A diverse group of gram-negative bacteria called Pseudomonas have been extensively researched for their ability to promote plant growth and suppress pathogens. The aim of the study was to identify the strain AMGC1 as a biocontrol agent against rice blast disease and a plant growth-promoting agent. The activity of AMGC1 against phytopathogens and its plant growth promotion activity were tested in vitro. Molecular identification by 16S rRNA gene sequencing, detection of antifungal antibiotic genes, biochemical characterization and antibiotic resistance testing were performed. Moreover, an in-planta biocontrol experiment with a blast susceptible rice cultivar was conducted to assess the effectiveness in managing the disease. Thin-layer chromatography was used to verify the presence of antifungal compounds. The results showed that AMGC1 had strong antimicrobial activity and multiple plant growth promotion traits. The strain was identified as Pseudomonas sp. and it contained genes for phenazine-1-carboxamide, hydrogen cyanide, and 2-hexyl, 5-propyl resorcinol antifungals. In the in-planta suppression experiment, applying AMGC1 strain two days before pathogen inoculation reduced the disease severity by 28% and disease incidence by 29% compared to control. Applying AMGC1 two days after pathogen inoculation resulted in lower reductions in disease severity (17.10%) and disease incidence (18.5%). Two putative bioactive compounds, phenazine-1-carboxamide and 2-hexyl, 5-propyl resorcinol, were isolated and purified from the AMGC1 growth medium. AMGC1 showed varied carbohydrate utilization patterns and both resistance and susceptibility to different antibiotics. The strain had multiple features that enhance plant growth and resistance against various phytopathogens, making it a potential biocontrol agent for sustainable agriculture.

Phaseolus vulgaris STP13.1 is an H+-coupled monosaccharide transporter, present in source leaves and seed coats, with higher substrate affinity at depolarized potentials.

Sugar transport proteins (STPs) are high-affinity H+-coupled hexose symporters. Recently, the contribution of STP13 to bacterial and fungal pathogen resistance across multiple plant species has garnered significant interest. Quantitative PCR analysis of source leaves, developing embryos, and seed coats of Phaseolus vulgaris L. (common bean) revealed that PvSTP13.1 was expressed in source leaves and seed coats throughout seed development. In contrast, PvSTP13.1 transcripts were detected at exceedingly low levels in developing embryos. To characterize the transport mechanism, PvSTP13.1 was expressed in Xenopus laevis oocytes, and inward-directed currents were analyzed using two-electrode voltage clamping. PvSTP13.1 was shown to function as an H+-coupled monosaccharide symporter exhibiting a unique high affinity for hexoses and aldopentoses at depolarized membrane potentials. Specifically, of the 31 assessed substrates, which included aldohexoses, deoxyhexoses, fructose, 3-O-methyl-D-glucose, aldopentoses, polyols, glycosides, disaccharides, trisaccharides, and glucuronic acid, PvSTP13.1 displayed the highest affinity (K 0.5) for glucose (43 μM), mannose (92 μM), galactose (145 μM), fructose (224 μM), xylose (1.0mM), and fucose (3.7mM) at pH5.6 at a depolarized membrane potential of -40 mV. The results presented here suggest PvSTP13.1 contributes to retrieval of hexoses from the apoplasmic space in source leaves and coats of developing seeds.