Growth Cycle Research Articles

Poultry farms as a One health priority for Aspergillus section Fumigati surveillance

Abstract In poultry production environments, particulate matter (PM) is considered one of the harmful air pollutants [2]. Considering potential biological pathogens, the genus Aspergillus in particular, Aspergillus fumigatus, is a widely recognized human and animal pathogen [3]. Hot-humid farm environments could contribute to the proliferation of these fungi [4]. Therefore, poultry production workers, besides animals [4], may be at a higher risk of inhaling fungi spores [4,5]. This study aimed to determine particulate matter (PM) contamination and analyze the prevalence of Aspergillus section Fumigati in air samples from poultry pavilions. Indoor and outdoor air samples were taken in the 1st, 2nd and 3rd weeks of birds growth cycle (n = 58). Air was collected through the impaction method (MAS-100) onto polycarbonate filters, set at 100 L/min, for 5 min. Followed by DNA extraction, and real-time PCR detection of Aspergillus section Fumigati. For PM evaluation, portable direct-reading equipment (Lighthouse, model 3016 IAQ) was used. Particle concentrations were measured (n = 67) at five distinct fractions during 5 min. Considering particles fractions, PM10 and PM5 where prevalent in 1st (58% PM10; 28% PM5), 2nd (63% PM10; 26% PM5) and 3rd (64% PM10; 25% PM5) weeks. These results evidence that coarse particles (between 2.5 and 10 µm) are the most relevant PM present in this setting. Regarding fungal detection, Aspergillus section Fumigati was widespread inside poultry pavilions (62%, 26 out of 42), highlighting this environment has a potential reservoir of microbial pathogens [7]. Furthermore, PM can act as a host of biological fragments [6]. Overall, in addition to molecular analysis, future studies should consider using culture-based methods to assess the viability of pathogenic microorganisms and their infection potential [3]. From a One Health perspective, quantitative and qualitative research is required to fully understand this environment. Key messages • This study evidence that coarse particles (between 2.5 and 10 µm) are the most relevant PM present and Aspergillus section Fumigati is widespread in poultry pavilions. • A One health intervention should be considered in future studies.

Varying levels of natural light intensity affect the phyto-biochemical compounds, antioxidant indices and genes involved in the monoterpene biosynthetic pathway of Origanum majorana L.

BackgroundLight is a critical environmental factor in plants, encompassing two vital aspects: intensity and quality. To assess the influence of different light intensities on Origanum majorana L., pots containing the herb were subjected to four levels of light intensity: 20, 50, 70, and 100% natural light. After a 60-day treatment period, the plants were evaluated for metabolite production, including total sugar content, protein, dry weight, antioxidant indices, expression of monoterpenes biosynthesis genes, and essential oil compounds. The experimental design followed a randomized complete blocks format, and statistical analysis of variance was conducted.ResultsThe results indicated a correlation between increased light intensity and elevated total sugar and protein content, which contributed to improved plant dry weight. The highest levels of hydrogen peroxide and malondialdehyde (MDA) were observed under 100% light intensity. Catalase and superoxide dismutase enzymes exhibited increased activity, with a 4.23-fold and 2.14-fold increase, respectively, under full light. In contrast, peroxidase and polyphenol oxidase enzyme activities decreased by 3.29-fold and 3.24-fold, respectively. As light intensity increases, the expression level of the 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR) gene increases. However, beyond a light intensity of 70%, the DXR gene expression level decreased. Furthermore, the expression levels of the cytochrome P450 genes CYP71D178 and CYP71D179 exhibited an increasing trend in response to elevated light intensity. Essential oil content increased from 0.02 to 0.5% until reaching 70% light intensity. However, with further increases in light intensity, the essential oil content decreased by 54 to 0.23%.ConclusionsThese findings emphasize the importance of balancing plant growth promotion and stress management under different light conditions. The research suggests that sweet marjoram plants thrive best in unshaded open spaces, resulting in maximum biomass. However, essential oil production decreases under the same conditions. For farmers in areas with an average light intensity of approximately 1700 µmol m−2s−1, it is recommended to cultivate sweet marjoram in shade-free fields to optimize biomass and essential oil production. Towards the end of the growth cycle, it is advisable to use shades that allow 70% of light to pass through. The specific duration of shade implementation can be further explored in future research.

The Genomic Variation in Textured Hair: Implications in Developing a Holistic Hair Care Routine

Human hair is characterised by variability, determined by genetic and macromolecular factors. Whilst the European hair type has been a focus of extensive research, Afro-textured hair care faces challenges created by insufficient knowledge of its properties. Applications of hair care products that are incompatible with Afro-textured hair frequently have detrimental effects on the scalp. This highlights the need for partnerships to bridge the gap between research and hair care practices and address challenges related to Afro-textured hair. In this review, we performed data mining of the existing literature and in silico network analysis of the biomarkers relevant to Afro-textured hair. The approaches to hair maintenance are highlighted in the context of hair anatomy and growth cycles, organisation of keratins, surface lipids, and chemical bonds. We discuss a range of biomarkers affecting hair fibre’s shape and mechanical strength, with the gene interactive network pointing to the hierarchical organisation of important traits, notably hair shaft diameter, keratinization, and hair follicle patterning, which likely contribute to the increased sensitivity of hair to extrinsic factors. We propose that a better understanding of the genetic traits, molecular structure, and biomechanics of Afro-textured hair is required to initiate more effective hair care solutions that would benefit the wider population.

Influence of soil microplastic contamination on maize (Zea mays) development and microbial dynamics

BackgroundMicroplastics are contaminants in soil ecosystems that alter biophysical processes, affect plant growth, ecosystems and humans in the long run. This study was conducted to investigate the influence of soil microplastic contamination on the growth, development, and microbial dynamics of maize (Zea mays).Composite soil samples were collected from the farm from 0 to 10 cm depth. Physicochemical properties were assessed before maize seed sowing. Three soil weight categories were treated with 30 g, 20 g and 10 g microplastics. Soil physicochemical properties and heavy metal content in maize plants were analyzed. Morphological parameters, microbial counts, and microorganism identification were conducted. FTIR analysis was carried out in selected plant parts.ResultsThe study revealed a pH range of 5.00–6.70, electrical conductivity of 70–158 µS/cm, organic carbon and organic matter, total nitrogen, average phosphorus content, clay and silt content. The concentrations of heavy metals in plant parts were within WHO standard limits of 425.5 and 500 mg/kg for iron and manganese, but above the limits of 40, 60, and 350 mg/kg for copper, zinc and magnesium. The plant had various leaf sizes and heights, with chlorosis and necrotic activity ranging from 0.33 to 2.67, and a range of 1.33 to 6.33, respectively. Soil samples showed a total bacterial count (TBC) of 2.00 × 103 cfu/g to 7.60 × 104 cfu/g and a total fungal count (TFC) of 4.00 × 103 cfu/g to 2.20 × 104 cfu/g. Pseudomonas aeruginosa, Bacillus subtilis, Escherichia coli, Staphylococcus epidermidis, Proteus mirabilis and Bacillus cereus had percentage occurrences of 44.31, 21.17, 9.12, and 3.26%, respectively. Penicillium notatum was the most prevalent, while Fusarium spp. was the least prevalent. The FTIR analysis of maize seeds from soil samples A, B, and C revealed various organic compounds, with opium powder and streptomycin sulphate being the most abundant. The study revealed that pot B had the highest microplastic concentration at 1 WAS, while pot C had the lowest at 0.10 mg/kg, indicating a differential soil microplastic accumulation.ConclusionMicroplastics impact soil health, plant growth, and nutrient cycling, necessitating sustainable agriculture. Long-term research is needed to understand ecological and physiological impacts and explore remediation techniques.

The impact of weather patterns on inter-annual crop yield variability

Inter-annual variations in crop production have significant implications for global food security, economic stability, and environmental sustainability. Existing crop yield prediction models primarily using meteorological variables may not adequately encapsulate the full breadth of weather influences on crop development processes, such as compound or extreme events. Incorporating weather patterns into crop models could provide a more comprehensive understanding of the environmental conditions affecting growth, enabling more accurate and earlier yield predictions.Our study examines 30 distinct UK Met Office weather patterns (MO30) based on mean sea level pressure. We investigate their association with weather conditions that limit winter wheat yield in the UK (1990–2020). Blocked, negative North Atlantic Oscillation (NAO) patterns create the highest risk of temperatures that are below optimal for crop yield. However, the connection between weather patterns and yield is complex, with differing effects at a regional scale and even at which point in the growth cycle they appear. It was found that anticyclonic weather patterns during sowing, emergence, vernalisation, anthesis, and grain filling exhibit a relationship with good crop yields with a Spearman correlation coefficient of up to 0.55 for a single weather pattern (WP3 during vernalisation in South East England), whilst cyclonic patterns can help during the terminal spikelet phenological phase. The strongest positive correlations were during sowing, emergence, and vernalisation, whilst the largest negatives were observed in anthesis and grain filling.The potential of combining weather patterns with existing crop simulation models to produce earlier and more accurate yield predictions is shown. This would enable effective crop management and climate mitigation strategies, critical to strengthening food security. Projected changes in weather pattern occurrences in the late 21st century will likely reduce crop yields. This is due to increased cyclonic weather patterns, which bring warmer, wetter conditions during the wheat's vernalisation stage, followed by warmer, drier conditions during the anthesis and grain-filling phases.

Modeling of Carbon Footprint Emissions in Sugarcane Production Using System Dynamics

Objective: This study proposes a model aimed at estimating and reducing carbon emissions in sugarcane cultivation and juice extraction, designed for potential application in real-world systems. Method: The model grounded in a hypothetical case study of a 60-hectare sugarcane plantation in a warm climate with a 7-month growth cycle and a three-year simulation period, focuses on estimate and evaluate mitigation scenarios to decrease emissions from fuel and electricity usage in activities such as plowing, sowing, harvesting, and irrigation. Utilizing Vensim PLE, a System Dynamics model that combine event and dynamic simulation. Results and Discussion: Utilizing Vensim PLE, a System Dynamics model that combine event and dynamic simulation estimated a 3060.81 tons CO2 equivalent carbon footprint. Two scenarios aimed at footprint reduction were tested: substituting electrical energy with solar power reduced the footprint by 86% to 450.092 tons, demonstrating clean energy's efficacy. Conversely, replacing an industrial mill with the traditional trapiche technique for juice extraction yielded a mere 1% reduction, indicating its ineffectiveness for real-world application. Research Implications: This study emphasizes the development of a simulation model based on system dynamics to estimate the carbon footprint emissions in sugar cane production considering its cultivation and juice extraction. The results support that through simulations, it is possible to determine the factors that influence the generation of carbon dioxide, offering a broader vision to establish and implement mitigation measures such as adopting clean energies and reducing fuel consumption. Originality/Value: This research contributes to the understanding of the application of system dynamics by using software such as Vensim PLE to estimate the carbon footprint emissions in sugar cane production, considering its cultivation and juice extraction. The results found through simulations suggest the implementation of mitigation measures such as the adoption of clean energies and the reduction of fuel consumption.

Nutrient Stoichiometry and Tree Development: Insights from a 5-Year Study on Catalpa bungei Fertilization

Short-term fertilization may provide limited improvements in tree growth and demonstrate suboptimal fertilizer efficiency; however, its benefits often fall short of expectations. Unfortunately, research addressing the sustained impacts of prolonged fertilization (e.g., beyond five years) on trees’ developmental dynamics and productivity remains relatively scarce. This study focused on a 7-year-old Catalpa bungei plantation located in Jinan City, Shandong Province, China. The study employed two fertilization techniques: hole fertilization (HF) and integrated water and fertilizer application (WF), with a no-fertilization treatment serving as the control (CK). The findings revealed that the WF significantly enhanced stand productivity. When comparing the different treatments, the productivity of WF stands demonstrated a remarkable increase of 39.7% compared to HF stands and 55.1% compared to CK stands. After five years of fertilization, the stands treated with WF exhibited a significant increase in volume accumulation, reaching 112.36 m3·hm−2. Additionally, the productivity of these WF-fertilized stands achieved an impressive 41.75 m3·hm−2·a−1. Fertilization notably enhanced the nitrogen content in the leaves and fine roots of C. bungei, as well as the potassium content in the coarse roots. These nutrients were found to be more concentrated in the corresponding organs within the WF stands. Over the entire growth cycle, there was a substantial consumption of key nutrients, with leaf nitrogen, phosphorus, and potassium contents decreasing by 30.5%, 18.8%, and 47.3%, respectively. Similarly, the coarse root potassium and fine root phosphorus content decreased by 24.7% and 24.4%, respectively. The enhancement in leaf nitrogen content following fertilization significantly contributed to increases in tree height, breast height diameter (DBH), and individual tree volume. Similarly, the enrichment of potassium in the branches and coarse roots was associated with improvements in DBH and tree volume. To maximize forest stand productivity, the WF fertilization method demonstrated superior results compared to HF. Therefore, WF should be prioritized in future fertilization experiments for C. bungei.

Phenological trends and associated climate drivers of a tree community in lowland dipterocarp forest, Western Ghats, India.

Understanding phenological responses of tropical forest plant communities is crucial for identifying climate-induced changes in ecosystem dynamics. Monitoring phenology across diverse species in natural habitats provides cost-effective insights for conserving both species and forests. We studied tree phenology in a lowland evergreen dipterocarp forest in the Western Ghats, India. About 719 tree individuals representing 95 species were monitored for their vegetative and reproductive phenology from April 2021 to September 2023. Circular statistics detected seasonality in phenological events and Generalized Linear Mixed Modelling (GLMM) identified influence of climate variables on the phenological responses of the tree community. We also assessed how the activity and intensity of phenophases vary over the study period. Our results showed that leaf flushing and flowering peaked during the dry season, with mass flowering observed in two dominant dipterocarps. Fruit production peaked before the monsoon. We also observed diversity in vegetative and reproductive phenodynamics across species groups (forest strata, sexual system, and seed size). Leaf flushing was positively correlated with maximum relative humidity and negatively correlated with maximum temperature and the number of rainy days. Flowering had negative correlations with maximum relative humidity, rainfall days, and maximum temperature but showed a positive correlation with minimum temperature. Fruiting was positively correlated with maximum temperature and negatively correlated with rainy days. This detailed phenological information provides critical knowledge on resource availability and insights into how climate and seasonal changes affect plant growth cycles thereby aiding reforestation and biodiversity conservation strategies in vulnerable forest areas.

Dermal Papilla Cells: From Basic Research to Translational Applications.

As an appendage of the skin, hair protects against ultraviolet radiation and mechanical damage and regulates body temperature. It also reflects an individual's health status and serves as an important method of expressing personality. Hair loss and graying are significant psychosocial burdens for many people. Hair is produced from hair follicles, which are exclusively controlled by the dermal papilla (DP) at their base. The dermal papilla cells (DPCs) comprise a cluster of specialized mesenchymal cells that induce the formation of hair follicles during early embryonic development through interaction with epithelial precursor cells. They continue to regulate the growth cycle, color, size, and type of hair after the hair follicle matures by secreting various factors. DPCs possess stem cell characteristics and can be cultured and expanded in vitro. DPCs express numerous stemness-related factors, enabling them to be reprogrammed into induced pluripotent stem cells (iPSCs) using only two, or even one, Yamanaka factor. DPCs are an important source of skin-derived precursors (SKPs). When combined with epithelial stem cells, they can reconstitute skin and hair follicles, participating in the regeneration of the dermis, including the DP and dermal sheath. When implanted between the epidermis and dermis, DPCs can induce the formation of new hair follicles on hairless skin. Subcutaneous injection of DPCs and their exosomes can promote hair growth. This review summarizes the in vivo functions of the DP; highlights the potential of DPCs in cell therapy, particularly for the treatment of hair loss; and discusses the challenges and recent advances in the field, from basic research to translational applications.

Transferability of models for predicting potato plant nitrogen content from remote sensing data and environmental variables across years and regions

The use of remote sensing technologies to monitor the nitrogen nutrient status of crops is gradually becoming a more sensible choice, as traditional methods are time-consuming, labor-intensive, and destructive. However, most predictive models utilizing remote sensing data are statistical rather than mechanistic, making them difficult to extend at interannual and regional scales. This study explored the interannual and regional transferability of the potato plant nitrogen content (PNC) prediction models, which combined environmental variables (EVs, e.g. temperature, precipitation, etc.) with proximal hyperspectral vegetation indices (VIs). Two methodologies were implemented to fuse EVs and VIs. The first involved a multiple regression analysis utilizing a multivariate linear model and a random forest approach, with VIs and EVs treated as independent variables, respectively. The second, a hierarchical linear model (HLM), employed EVs to dynamically adjust the relationship between VIs and PNC for different experimental sites. The predictive outcomes demonstrated that (i) the conventional method relying solely on optical VIs exhibited limited accuracy and stability in interannual and regional PNC forecasting; (ii) albeit the multivariate regression approach significantly enhanced model accuracy within the calibration set, its scalability across years and regions remained suboptimal; (iii) the HLM method exhibited high precision and scalability across years and regions, with R2, RMSE, and NRMSE values of 0.68, 0.50 %, and 19.68 % in the validation set, respectively. Those findings corroborate that using a two-tier HLM method can automatically adjust for discrepancies in VIs response to PNC through EVs, thereby enhancing the model's stability. Provided that remote sensing data and EVs are sustainably acquired over the potato growth cycle, it will provide a particularly promising approach to potato nitrogen diagnostics as a decision-making tool for regional application of nitrogen fertilizer.

Machine learning-based analysis of nutrient and water uptake in hydroponically grown soybeans

Recent advancements in sustainable agriculture have spurred interest in hydroponics as an alternative to conventional farming methods. However, the lack of data-driven approaches in hydroponic growth presents a significant challenge. This study addresses this gap by varying nitrogen, magnesium, and potassium concentrations in hydroponically grown soybeans and conducting essential nutrient profiling across the growth cycle. Statistical techniques like Linear Interpolation are employed to interpolate nutrient data and a feature selection pipeline consisting of chi-squared testing methods, Linear Regression with Recursive Feature Elimination (RFE) and ExtraTreesClassifier have been used to select important nutrients for predicting water uptake using non-parametric regression methods. For different nutrient growth media, i.e. for soybeans grown in Hoagland + Nitrogen and Hoagland + Magnesium media, the Random Forest regressor outperformed other methods in predicting water uptake, achieving testing Mean Squared Error (MSE) scores of 24.55 (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}^{2}$$\\end{document} score 0.63) and 8.23 (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}^{2}$$\\end{document} score 0.81), respectively. Similarly, for soybeans grown in Hoagland + Potassium media, Support Vector Regression demonstrated superior performance with a testing MSE of 4.37 and R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}^{2}$$\\end{document} score of 0.85. SHapley Additive exPlanations (SHAP) values are examined in each case to elucidate the contributions of individual nutrients to water uptake predictions. This research aims to provide data-driven insights to optimize hydroponic practices for sustainable food production.

Application of microorganisms in Panax ginseng: cultivation of plants, and biotransformation and bioactivity of key component ginsenosides.

Panax ginseng is a precious Chinese medicinal plant with a long growth cycle and high medicinal value. Therefore, it is of great significance to explore effective ways to increase its yield and main active substance content to reduce the cost of ginseng, which is widely used in food and clinical applications. Here, we review the key roles of microorganisms in the biological control of ginseng diseases, enhancement of ginseng yield, biotransformation of ginsenosides, and augmentation of ginsenoside bioactivity. The application of microorganisms in P. ginseng faces multiple challenges, including the need for further exploration of efficient microbial strain resources used in the cultivation of ginseng and biotransformation of ginsenosides, lack of microbial application in large-scale field cultivation of ginseng, and unclear mechanism of microbial transformation of ginsenosides. This review provides a deeper understanding of the applications of microorganisms in P. ginseng.

Screening and expression validation of key proteins for secondary hair follicle growth in cashmere goats based on iTRAQ quantitative proteomics technology.

The growth of secondary hair follicles (SHFs) in cashmere goats has periodic changes, including telogen, anagen, and catagen, during which proteins play important roles as the executor of life activities. In this study, the skin tissues of cashmere goats at three different growth stages of SHFs were collected for proteome sequencing and validation experiments. Through protein differential expression analysis and time series analysis, FKBP prolyl isomerase 10 (FKBP10) and fibrillin 2 (FBN2) were screened as the key proteins for SHF cycle growth of cashmere goats, and albumin (ALB), collagen type I alpha 1 chain (COL1A1) and elastin (ELN) were predicted to be their interacting proteins. The results of quantitative real-time PCR (qRT-PCR), western blot, and immunohistochemistry experiments showed that the mRNA and protein expression levels of FKBP10, FBN2, COL1A1, ELN and ALB were higher in anagen and lower in telogen. They were all highly expressed in the outer root sheath of SHFs in anagen. FKBP10, FBN2, COL1A1, ELN, and ALB can promote the growth of SHFs in cashmere goats. This study lays the foundation for analyzing the growth cycle regulatory mechanism of SHFs in cashmere goats, and provides new ideas for further improving cashmere yield and quality.

From Spectral Characteristics to Index Bands: Utilizing UAV Hyperspectral Index Optimization on Algorithms for Estimating Canopy Nitrogen Concentration in Carya Cathayensis Sarg

Employing drones and hyperspectral imagers for large-scale, precise evaluation of nitrogen (N) concentration in Carya cathayensis Sarg canopies is crucial for accurately managing nitrogen fertilization in C. cathayensis Sarg cultivation. This study gathered five sets of hyperspectral imagery data from C. cathayensis Sarg plantations across four distinct locations with varying environmental stresses using drones. The research assessed the canopy nitrogen concentration of C. cathayensis Sarg trees both during singular growth periods and throughout their entire growth cycles. The objective was to explore the influence of band combinations and spectral index formula configurations on the predictive capability of the hyperspectral indices (HIs) for canopy N concentration (CNC), optimize the performance between HIs and machine learning approaches, and validate the efficacy of optimized HI algorithms. The findings revealed the following: (i) Optimized HIs demonstrated optimal predictive performance during both singular growth periods and the full growth cycles of C. cathayensis Sarg. The most effective HI model for singular growth periods was the optimized–modified–normalized difference vegetation index (opt-mNDVI), achieving an adjusted coefficient of determination (R2) of 0.96 and a root mean square error (RMSE) of 0.71. For the entire growth cycle, the HI model, also opt-mNDVI, attained an R2 of 0.75 and an RMSE of 2.11; (ii) optimized band combinations substantially enhanced HIs’ predictive performance by 16% to 71%, while the choice between three-band and two-band combinations influenced the predictive capacity of optimized HIs by 4% to 46%. Hence, utilizing optimized HIs combined with Unmanned Aerial Vehicle (UAV) hyperspectral imaging to evaluate nitrogen concentration in C. cathayensis Sarg trees under complex field conditions offers significant practical value.

Transcriptomic profiling reveals the effect of amino acid metabolism on the production capacity of Volvariella volvacea during repeated subculture

Tissue separation is commonly employed for the purification and rejuvenation of edible mushroom species. However, repeated or continuous tissue separation can result in strain degradation. In this study, cultivation experiments were conducted in Volvariella volvacea V844 (T0). Tissue separation subculture was repeated 16 times to obtain the succession strains T1–T16. Of these, T4, T8, T12, and T16 were selected as experimental strains, and their production traits were evaluated. Additionally, transcriptome sequencing technology was used to analyze the transcription spectrum of these strains. The results showed that with the increase in subculture cycles, the colony diameter, mycelial growth rate, mycelial biomass, and biological efficiency of V. volvacea first increased and then decreased, and the growth cycle was gradually prolonged. Transcriptome sequencing revealed the presence of 1375, 1015, 651, and 1648 differentially expressed genes (DEGs) in T4, T8, T12, and T16, respectively, versus T0. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis demonstrated that these DEGs were mainly involved in amino acid metabolic pathways. Further annotation analysis revealed that in V. volvacea subculture strains, the number of DEGs involved in histidine metabolism (Ko00340) and arginine and proline metabolism (Ko00330) was the highest. Amino acid analysis demonstrated that the proline and arginine content first rose and then fell across successive subculture strains, whereas the histidine content first decreased and then increased. The results indicated that the change in the production capacity of V. volvacea during repeated subculture may be due to the altered expression of DEGs involved in amino acid metabolism, leading to changes in the amino acid content of V. volvacea and further affecting the accumulation of reactive oxygen species, free radical scavenging, energy acquisition, nutrient absorption, and redox balance. These changes could result in the rejuvenation (<8) of the V. volvacea strain or cause its degradation (>12). The results of this study provide a theoretical framework for studying the mechanisms of rejuvenation and degradation in V. volvacea and also offer insights valuable for studying the molecular functions of genes involved in the growth of V. volvacea and other edible fungi.

Assessing the Synergistic Effects of Deficit Irrigation and Growth Retardants on Insitu Sprouting and Quality Parameters of Groundnut VRI 8

Background: Groundnut is a crucial oilseed crop cultivated year-round, with seed dormancy controlled by genetic and environmental factors. Preventing pre-harvest sprouting is essential, as it is a common issue. To study the effect of deficit irrigation management and foliar application of growth retardant chemicals on pre-harvest sprouting control efficiency to achieve higher yield of groundnut. Methodology: In a field experiment conducted during early summer 2024 at the Agricultural College and Research Institute, Madurai, the effectiveness of different growth retardant chemicals and deficit irrigation management on reducing in-situ sprouting of groundnut kernels was investigated. The experiment included three irrigation management strategies: conventional irrigation and two deficit irrigation treatments where irrigation was withheld from 90 to 105 DAS (Days After Sowing) and 85 to 100 DAS. Additionally, foliar sprays of growth retardant chemicals were applied at 75 and 90 DAS. The chemicals tested were maleic hydrazide (MH) @ 1250 ppm, cycocel (CCC) @ 1000 ppm, abscisic acid (ABA) @ 750 ppm, and salicylic acid (SA) @ 750 ppm. Proper agronomic practices were followed throughout the crop growth cycle. Results: Among the growth retardant chemicals tested, MH @ 1250 ppm proved effective in inducing dormancy. Withdrawal of irrigation from 90 to 105 DAS also significantly contributed to dormancy induction, extending dormancy by more than 5 days post-harvest as well as reduced the pod loss which significantly increased pod yield. It was because of the reduction in soil moisture content during harvest stage and alteration in the hormonal activities. Specifically, the combined treatment of foliar application of MH @ 1250 ppm and no irrigation from 90 to 105 DAS reduced germination percentage by 12.6%, 36.8% and 60.5% immediately 5, 10 and 15 days after harvest respectively. Conclusion: The reduction in soil moisture content during the harvest stage, coupled with changes in hormonal activities, significantly impacts seed sprouting. These factors can lead to stress conditions that hinder germination, ultimately affecting crop yields. Addressing these issues is crucial for ensuring optimal seed development and enhancing agricultural productivity.

A new model for tracing interest: the growth cycle of interest framework

AbstractMotivation is an interdisciplinary concept which encompasses various theories, concepts and frameworks. This paper aims to bring various motivational theories together, with interest development at its core, to develop a more streamlined framework of interest development. Interest is believed to play a vital role in student learning, and this “Growth cycle of interest” framework aims to provide a deeper understanding of interest development in the context of education. This paper begins with a brief overview of major motivational theories, synthesising them into the Growth Cycle of Interest framework, which is then exemplified through case studies.

Enhanced Blue Band Vegetation Index (The Re-Modified Anthocyanin Reflectance Index (RMARI)) for Accurate Farmland Shelterbelt Extraction

Farmland shelterbelts are aimed at farmland protection and productivity improvement, environmental protection and ecological balance, as well as land use planning and management. Farmland shelterbelts play a vital role in determining the structural integrity and overall effectiveness of farmland, and assessing the dynamic changes within these protective forests accurately and swiftly is essential to maintaining their protective functions as well as for policy formulation and effectiveness evaluation in relevant departments. Traditional methods for extracting farmland shelterbelt information have faced significant challenges due to the large workload required and the inconsistencies in the accuracy of existing methods. For example, the existing vegetation index extraction methods often have significant errors, which remain unresolved. Therefore, developing a more efficient extraction method with greater accuracy is imperative. This study focused on Youyi Farm in Heilongjiang Province, China, utilizing satellite data with spatial resolutions ranging from 0.8 m (GF-7) to 30 m (Landsat). By taking into account the growth cycles of farmland shelterbelts and variations in crop types, the optimal temporal window for extraction is identified based on phenological analysis. The study introduced a new index—the Re-Modified Anthocyanin Reflectance Index (RMARI)—which is an improvement on existing vegetation indexes, such as the NDVI and the improved original ARI. Both the accuracy and extraction results showed significant improvements, and the feasibility of the RMARI was confirmed. The study proposed four extraction schemes for farmland shelterbelts: (1) spectral feature extraction, (2) extraction using vegetation indexes, (3) random forest extraction, and (4) RF combined with characteristic index bands. The extraction process was implemented on the GEE platform, and results from different spatial resolutions were compared. Results showed that (1) the bare soil period in May is the optimal time period for extracting farmland shelterbelts; (2) the RF method combined with characteristic index bands produces the best extraction results, effectively distinguishing shelterbelts from other land features; (3) the RMARI reduces background noise more effectively than the NDVI and ARI, resulting in more comprehensive extraction outcomes; and (4) among the satellite images analyzed—GF-7, Planet, Sentinel-2, and Landsat OLI 8—GF-7 achieves the highest extraction accuracy (with a Kappa coefficient of 0.95 and an OA of 0.97), providing the most detailed textural information. However, comprehensive analysis suggests that Sentinel-2 is more suitable for large-scale farmland shelterbelt information extraction. This study provides new approaches and technical support for periodic dynamic forestry surveys, providing valuable reference points for agricultural ecological research.

Identification and Biological Characterization of Green Alga on Oil-Tea Camellia Leaves

Between 2023 and 2024, a type of green alga was observed for the first time settling on Oil-tea Camellia leaves and branches in the eastern Oil-tea Camellia planting area of Hainan Island, forming a layer of gray-green moss with a rough surface that seriously interfered with the leaves’ normal photosynthesis. To further research the issue, this study used the plant photosynthesis measurement system and the paraffin sectioning technique to compare and analyze the changes in photosynthetic characteristics and anatomical structure of healthy and green algal-covered Oil-tea Camellia leaves. At the same time, the algal strain was effectively separated and purified using the plate delineation method, and its species classification was determined by combining morphological observation and molecular identification based on SSU-ITS sequences. The results of the study demonstrated that the coating of green alga facilitated the lignification of the leaf’s epidermal cell walls. After being covered by the green alga, the intercellular CO2 concentration (Ci) increased significantly by 21.5%, while the net photosynthetic rate (Pn), transpiration rate (Tr), and stomatal conductance (Gs) all significantly decreased by 72.8%, 30.4%, and 24.9%, respectively. More specifically, the green alga that covers the leaves of Oil-tea Camellia was identified as Desmodesmus armatus of Chlorophyta. Notably, the green alga had a long growth cycle, did not undergo a decline phase within one month, had an optimal growth pH of 11.0, and could flourish in excessively alkaline conditions. In conclusion, this study not only reported for the first time the phenomena of D. armatus infesting Oil-tea Camellia leave, but also showed its unique physiological and ecological properties, providing a foundation for future research on relevant prevention and control approaches.

Study on the Microbial Mechanisms of Enhancing Agaricus bisporus Growth through Inoculation with Pseudomonas putida

Introducing Pseudomonas putida optimizes the microbial community within the casing layer, thereby enhancing Agaricus bisporus growth. Nevertheless, the underlying mechanisms governing microbial community succession within the casing layer following microbial agent introduction remained elusive. This study delineated the interplay among microbial populations in the casing layer across the growth cycle of A. bisporus, comparing cohorts treated with P. putida (AP) against untreated controls (CK). The inoculation with Pseudomonas promoted the growth of A. bisporus, shortened its growth cycle, and linearly enhanced the relative abundance of key bacterial genera integral to its growth, including Flavobacterium, Rhodoferax, Pseudarthrobacter, and Vogesella (adjusted R2=0.238-0.866). Importantly, a positive correlation was identified between the proliferation of these beneficial bacteria and positive community cohesion (adjusted R2=0.353-0.838). Additionally, analysis of the symbiotic network topology within the AP group indicated a more stable community following inoculation. The niche results (AP=11.7672±1.8938, CK=11.0481±3.1273, P<0.05) underscored the positive impact of Pseudomonas inoculation on ecosystem dynamics. Concentrations of essential nutrients for A. bisporus growth, notably readily available phosphorus (On day 17, the AP group was 78.8333±1.26623 mg/kg, while the CK group was 30.0333±0.98658 mg/kg) and readily available potassium (On day 17, the AP group was 2693.9±14.79561 mg/kg, while the CK group was 1646.9333±10.71276mg/kg), were significantly elevated in the AP group. These enhancements strongly correlated with the increased abundance of pivotal microbial populations. Our research provided insights into the application of microbial inoculants to enhance the production of edible fungi.