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Drought stress is a critical environmental factor that significantly impacts plant growth and productivity. However, the transcriptome analysis of differentially expressed genes in response to drought stress in Camellia oleifera Abel. is still unclear. This study analyzed the transcriptome sequencing data of C. oleifera under drought treatments. A total of 20,674 differentially expressed genes (DEGs) were identified under drought stress, with the number of DEGs increasing with the duration of drought. Specifically, 11,793 and 18,046 DEGs were detected after 8 and 15 days of drought treatment, respectively, including numerous upregulated and downregulated genes. Gene Ontology (GO) enrichment analysis showed that the DEGs were primarily involved in various biological processes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that carbon metabolism, glyoxylate and dicarboxylate metabolism, proteasome, glycine, serine, and threonine metabolism were the main affected pathways. Among the DEGs, 376 protein kinases, 42 proteases, 168 transcription factor (TF) genes, and 152 other potential functional genes were identified, which may play significant roles in the drought response of C. oleifera. The expression of relevant functional genes was further validated using quantitative real-time PCR (qRT-PCR). These findings contribute to the comprehension of drought tolerance mechanisms in C. oleifera and bolster the identification of drought-resistant genes for molecular breeding purposes.

Soil nutrient transformation and the microbial metabolism are primarily regulated by soil microorganisms, including fungi and bacteria, which exhibit distinct growth patterns, energy substrate utilization, and survival strategies. Despite their significance, our understanding of the key microorganisms governing the soil microbial metabolism and multifunctionality in subtropical woodlands remains limited. To address this knowledge gap, we conducted a large-scale investigation and assessment of the soil microbial metabolic limitation and soil multifunctionality in Camellia oleifera Abel and Pinus massoniana Lamb. woodlands in subtropical China. Our results reveal that the microbial phosphorus limitation was more severe in C. oleifera compared to P. massoniana woodlands. Nonetheless, the pattern of carbon metabolic limitation for microbes and soil multifunctionality was similar in both types of woodland. Specifically, the microbial carbon limitation was positively associated with both bacterial and fungal richness, while the microbial phosphorus limitation was significantly correlated with fungi including the richness and community structure in the P. massoniana woodland. By contrast, we did not observe significant correlations between microbial metabolic limitation indices and microbial parameters in C. oleifera woodlands. Regarding soil multifunctionality, the results reveal a strong positive correlation between the soil multifunctionality and fungal community in both P. massoniana and C. oleifera woodlands. Furthermore, our structural equation modeling revealed that the soil fungal community, rather than the bacterial community, had a significant effect on the microbial metabolic limitation and soil multifunctionality. Overall, our study provides profound insights into the relative importance of bacterial and fungal communities in shaping the soil microbial metabolic limitation and soil multifunctionality in subtropical woodlands. The findings of our study have important implications for the management and conservation of subtropical woodlands.

High temperature and drought are the main factors restricting the increase in Camellia oleifera Abel production that can seriously harm its physiological and biochemical functions. This study aimed to explore the effect by which Ca2+ enhances the drought resistance of C. oleifera. Four-year-old potted plants of the ‘Xianglin 210’ variety were treated with four levels of CaCl2 solution (0, 10, 20, and 40 mmol/L) and subjected to natural drought stress. The drought levels were categorized as CS (non-drought), LD (light drought), MD (moderate drought), and SD (severe drought). The effects of drought stress on the growth and physiological-biochemical characteristics of C. oleifera were measured. Under drought stress, spraying CaCl2 solution on leaves could significantly increase the net photosynthetic rate (Pn), transpiration rate (Tr), stomatal conductance (Gs), endogenous hormone indoleacetic acid (IAA) content, abscisic acid (ABA) content, and gibberellic acid (GA) content in organs and tissues; enhance the superoxide dismutase (SOD) activity; and decrease the concentration of intercellular carbon dioxide (Ci) and number of dehiscence fruits. Spraying 10 mmol/L CaCl2 effectively alleviates the damage of drought stress to tung oil trees. The study provides references for alleviating drought stress and increasing economic benefits in C. oleifera production.

Arbuscular mycorrhizal fungi (AMF) symbiosis has been shown to improve the ability to obtain nutrients and resist adverse environmental conditions. However, there are few studies on the functions of AMF in the absorption and accumulation of boron (B). Moreover, it is still unclear whether the root colonization rates of AMF are limited by B deficiency. In this study, Camellia oleifera seedlings were planted in normal and boron-deficient substrates, and the seedlings were inoculated with Funneliformis mosseae or left uninoculated. The growth and physiological indices of C. oleifera seedlings were determined. The results of this experiment indicate that AMF inoculation increased the plant biomass, B content, B accumulation, and antioxidant enzyme activity in both normal and boron-deficient C. oleifera seedlings. Furthermore, boron deficiency resulted in a decrease in the AMF root colonization efficiency and the inhibition of C. oleifera seedlings’ growth and physiological activity. These findings suggest that AMF inoculation could improve the resistance to B-deficiency stress. Additionally, the colonization efficiency of AMF was adversely affected by B deficiency; thus, AMF play a cooperative role with B in the growth and physiological functions of plants. The results provide a theoretical basis for taking measures to solve B-deficiency stress in C. oleifera and other plants’ cultivation.

Camellia oleifera Abel. is one of the world’s four famous woody oil trees and has drawn increasing attention because of its high commercial value. Endophytes are microorganisms inhabiting inside plant tissues, and their functions vary with the change of host status and environmental parameters. To deepen our understanding of the interactions between C. oleifera and their endophytic actinobacteria, the present study investigated the four endophytic actinobacterial composition-residing high-yield C. oleifera trees. A total of 156 endophytic actinobacterial isolates were obtained distributed in 17 genera. Among them, Streptomyces was the dominant endophytic actinobacteria, followed by Nocardia, Amycolatopsis, Microbiospora, Micromonospora and other rare actinobacteria genera. Soil characteristics including soil pH and organic matter were found to play crucial roles in shaping the endophytic actinobacterial community composition. Furthermore, all isolates were studied to determine their plant growth-promotion traits, 86.54% could produce Indole 3-Acetic Acid, 16.03% showed nitrogen-fixing, 21.15% showed phosphorus solubilizing, and 35.26% produced siderophore. Under the glasshouse condition, some isolates exhibited growth promotion effects on C. oleifera seedlings with significant increase in spring shoot length and ground diameter. Altogether, this study demonstrated that C. oleifera plants harbored a high diversity and novelty of culturable endophytic actinobacteria, which represent important potential as natural biofertilizers for the high production of C. oleifera.

Camellia oleifera Abel., is an important woody plant, and its fruit contains high-quality edible oil. Production of C. oleifera has significantly expanded over the last 20 years in China. Due to the lack of appropriate information on nutrient management, its production has encountered low yield and low oil quality problems. As nitrogen (N) is an essential nutrient and the most abundant in C. oleifera tissues, the present study investigated effects of different ratios of nitrate (NO3−) and ammonium (NH4+) on the growth of a cultivar Xianglin 27 at the seedling stage. Uniform seedlings were grown in a soil-based substrate in containers and fertigated with solutions composed of six ratios of NO3− and NH4+, respectively for five months. Results showed that C. oleifera prefers both NO3− and NH4+ at a ratio of 1:1. Seedlings receiving this solution had the highest total N in leaves and total dry weight; elevated chrolophyll, soluble saccharide and protein contents as well as higher activities of peroxidase (POD), superoxide dismutase (SOD), nitrate reductase (NR), glutamine synthetase (GS), and glutamate synthase (GOGAT). Our study shows for the first time that N supply for producing C. oleifera should be an equal ratio of NO3− and NH4+.

Oil tea Camellia, as a major cash and oil crop, has a high status in the forestry cultivation systems in China. To meet the current market demand for oil tea Camellia, its potential distribution and suitable soil condition was researched, to instruct its cultivation and popularization. The potential distribution of oil tea Camellia in China was predicted by the maximum entropy model, using global environmental and soil databases. Then, we collected 10-year literature data about oil tea Camellia soil and applied multiple imputation and factor modeling for an in-depth analysis of soil suitability for growing of oil tea Camellia. The prediction indicated that oil tea Camellia was mainly distributed in Hunan, Jiangxi, Zhejiang, Hainan, East Hubei, Southwest Anhui and most of Guangdong. Climatic factors were more influential than soil factors. The minimum temperature of the coldest month, mean temperature of the coldest quarter and annual precipitation were the most significant contributors to the habitat suitability distribution. In the cultivated area of oil tea Camellia, soil fertility was poor, organic matter was the most significant factor for the soil conditions. Based on climatic and soil factor analyses, our data suggest there is a great potential to spread the oil tea Camellia cultivation industry.

Entropy measures the randomness or uncertainty of a stochastic process, and the entropy rate refers to the limit of the time average of entropy. The generalized entropy rate in the form of delayed averages can overcome the redundancy of initial information while ensuring stationarity. Therefore, it has better practical value. A Hidden Markov Model (HMM) contains two stochastic processes, a stochastic process in which all states can be observed and a Markov chain in which all states cannot be observed. The entropy rate is an important characteristic of HMMs. The transition matrix of a homogeneous HMM is unique, while a Nonhomogeneous Hidden Markov Model (NHMM) requires the transition matrices to be dependent on time variables. From the perspective of model structure, NHMMs are novel extensions of homogeneous HMMs. In this paper, the concepts of the generalized entropy rate and NHMMs are defined and fully explained, a strong limit theorem and limit properties of a norm are presented, and then generalized entropy ergodic theorems with an almost surely convergence for NHMMs are obtained. These results provide concise formulas for the computation and estimation of the generalized entropy rate for NHMMs.

Portfolio management has long been one of the most significant challenges in large- and small-scale investments alike. The primary objective of portfolio management is to make investments with the most favorable rate of return and the lowest amount of risk. On the other hand, time series prediction has garnered significant attention in recent years for predicting the trend of stock prices in the future. The combination of these two approaches, i.e., predicting the future stock price and adopting portfolio management methods in the forecasted time series, has turned out to be a novel research line in the past few years. That is, to have a better understanding of the future, various researchers have attempted to predict the future behavior of stocks and subsequently implement portfolio management techniques on them. However, due to the uncertainty in predicting the future, the reliability of these methodologies is in question, and it is unclear to what extent their results can be relied upon. Therefore, probabilistic approaches have also entered the research arena, and attempts have been made to incorporate uncertainty into future forecasting and portfolio management. This issue has led to the development of probabilistic portfolio management for future data. This review paper begins with a discussion of various time-series prediction methods for stock market data. Next, a classification and evaluation of portfolio management approaches are provided. Afterwards, the Monte Carlo sampling method is discussed as the most prevalent technique for probabilistic analysis of stock market data. The probabilistic portfolio management method is applied to future Shanghai Stock Exchange data in the form of a case study to measure the applicability of this method to real-world projects. The results of this research can serve as a benchmark example for the analysis of other stock market data.