Search Results (3,264)

Heavy metal pollution is one of the most devastating abiotic factors, significantly damaging crops and human health. One of the serious problems it causes is a rise in cadmium (Cd) toxicity. Cd is a highly toxic metal with a negative biological role, and it enters plants via the soil–plant system. Cd stress induces a series of disorders in plants’ morphological, physiological, and biochemical processes and initiates the inhibition of seed germination, ultimately resulting in reduced growth. Fiber crops such as kenaf, jute, hemp, cotton, and flax have high industrial importance and often face the issue of Cd toxicity. Various techniques have been introduced to counter the rising threats of Cd toxicity, including reducing Cd content in the soil, mitigating the effects of Cd stress, and genetic improvements in plant tolerance against this stress. For decades, plant breeders have been trying to develop Cd-tolerant fiber crops through the identification and transformation of novel genes. Still, the complex mechanism of Cd tolerance has hindered the progress of genetic breeding. These crops are ideal candidates for the phytoremediation of heavy metals in contaminated soils. Hence, increased Cd uptake, accumulation, and translocation in below-ground parts (roots) and above-ground parts (shoots, leaves, and stems) can help clean agricultural lands for safe use for food crops. Earlier studies indicated that reducing Cd uptake, detoxification, reducing the effects of Cd stress, and developing plant tolerance to these stresses through the identification of novel genes are fruitful approaches. This review aims to highlight the role of some conventional and molecular techniques in reducing the threats of Cd stress in some key fiber crops. Molecular techniques mainly involve QTL mapping and GWAS. However, more focus has been given to the use of transcriptome and TFs analysis to explore the potential genomic regions involved in Cd tolerance in these crops. This review will serve as a source of valuable genetic information on key fiber crops, allowing for further in-depth analyses of Cd tolerance to identify the critical genes for molecular breeding, like genetic engineering and CRISPR/Cas9. Full article

WD40 proteins play important roles in the synthesis and regulation of anthocyanin, the regulation of plant morphology and development, and the response to various abiotic stresses. However, the role of WD40 in Fragaria vesca (F. vesca) has not been studied. In this study, a total of 216 FvWD40 family members were identified, which were divided into four subfamilies based on evolutionary tree analysis. Subcellular localization predictions show that FvWD40 family members are mainly localized in chloroplasts, nuclei, and cytoplasm. An analysis of collinearity revealed a total of eight pairs of intraspecific collinearity of the FvWD40 gene family, and interspecific collinearity showed that the FvWD40 gene family covaried more gene pairs with Arabidopsis thaliana (Arabidopsis) than with rice (Oryza sativa). Promoter cis-acting elements revealed that the FvWD40 gene family contains predominantly light, hormone, and abiotic stress response elements. Tissue-specific expression analysis showed that a number of members including FvWD40-111 and FvWD40-137 were highly expressed in all tissues, and a number or members including FvWD40-97 and FvWD40-102 were lowly expressed in all tissues. The FvWD40 gene family was found to be expressed at all four different coloring stages of F. vesca by qRT-PCR, with lower expression at the 50% coloring stage (S3). FvWD40-24, FvWD40-50, and FvWD40-60 showed the highest expression during the white fruit stage (S1) period, suggesting that these genes play a potential regulatory role in the pre-fruit coloring stage. FvWD40-62, FvWD40-88 and FvWD40-103 had the highest expression at the 20% coloration stage (S2), and FvWD40-115, FvWD40-170, FvWD40-184 and FvWD40-195 had the highest expression at the full coloration stage (S4). These results suggest a potential role for these genes during fruit coloration. This study lays a foundation for further research on the function of the WD40 gene family. Full article

Osteoporosis (OP) is a widespread age-related disorder marked by decreased bone density and increased fracture risk, presenting a significant public health challenge. Central to the development and progression of OP is the dysregulation of the mechanistic target of the rapamycin (mTOR)-signaling pathway, which plays a critical role in cellular processes including autophagy, growth, and proliferation. The mTOR-autophagy axis is emerging as a promising therapeutic target due to its regulatory capacity in bone metabolism and homeostasis. This review aims to (1) elucidate the role of mTOR signaling in bone metabolism and its dysregulation in OP, (2) explore the interplay between mTOR and autophagy in the context of bone cell activity, and (3) assess the therapeutic potential of targeting the mTOR pathway with modulators as innovative strategies for OP treatment. By examining the interactions among autophagy, mTOR, and OP, including insights from various types of OP and the impact on different bone cells, this review underscores the complexity of mTOR’s role in bone health. Despite advances, significant gaps remain in understanding the detailed mechanisms of mTOR’s effects on autophagy and bone cell function, highlighting the need for comprehensive clinical trials to establish the efficacy and safety of mTOR inhibitors in OP management. Future research directions include clarifying mTOR’s molecular interactions with bone metabolism and investigating the combined benefits of mTOR modulation with other therapeutic approaches. Addressing these challenges is crucial for developing more effective treatments and improving outcomes for individuals with OP, thereby unveiling the therapeutic potentials of targeting the mTOR-autophagy axis in this prevalent disease. Full article

Currently, photovoltaic (PV) resources have been widely applied in the agricultural sector. However, due to the unreasonable configuration of multi-energy collaboration, issues such as unstable power supply and high investment costs still persist. Therefore, this study proposes a solution to reasonably determine the area and capacity of PV panels for irrigation machines, addressing the fluctuations in power generation of solar sprinkler PV systems under different regional and meteorological conditions. The aim is to more accurately predict photovoltaic power generation (PVPG) to optimize the configuration of the solar sprinkler power supply system, ensuring reliability while reducing investment costs. This paper first establishes a PVPG prediction model based on four forecasting models and conducts a comparative analysis to identify the optimal model. Next, annual, seasonal, and solar term scale models are developed and further studied in conjunction with the optimal model, using evaluation metrics to assess and compare the models. Finally, a mathematical model is established based on the optimal combination and solved to optimize the configuration of the power supply system in the irrigation machines. The results indicate that among the four PVPG prediction models, the SARIMAX model performs the best, as the R² index reached 0.948, which was 19.4% higher than the others, while the MAE index was 10% lower than the others. The solar term scale model exhibited the highest accuracy among the three time scale models, the RMSE index was 4.8% lower than the others, and the MAE index was 1.1% lower than the others. After optimizing the configuration of the power supply system for the irrigation machine using the SARIMAX model based on the solar term scale, it is verified that the model can ensure both power supply reliability and manage energy overflow effectively. Full article

Nitrous oxide (N₂O) ranks as the third most significant greenhouse gas, capable of depleting the ozone layer and posing threats to terrestrial ecosystems. Climate change alters precipitation variability, notably in terms of frequency and magnitude. However, the implications of precipitation variability on N₂O emissions and the underlying mechanisms remain inadequately understood. In this study, employing laboratory incubation methods on three representative sandy soil types (sandy soil, shrub soil, and crust soil), we examined the impacts of diverse precipitation levels (5 mm and 10 mm) and frequencies (7 days and 14 days) on N₂O emissions from these soil types. This study aims to clarify the complex connections between soil N₂O emission fluxes and soil physicochemical properties in the soil environment. Our findings reveal that the N₂O emission flux exhibits heightened responsiveness to 5 mm precipitation events and a 14-day precipitation frequency, and compared to other treatments, the 5 mm precipitation and 14-day precipitation frequency treatment resulted in a 20% increase in cumulative nitrous oxide emissions. Consequently, cumulative N₂O emissions were notably elevated under the 5 mm precipitation and 14-day precipitation frequency treatments compared to the other experimental conditions. The N₂O emission flux in sandy soil displayed a positive correlation with available phosphorus (AP) and a negative correlation with pH, primarily attributed to the exceedingly low AP content in sandy soil. In shrub soil, the soil N₂O emission flux exhibited a significant positive correlation with NH₄⁺-N and a negative correlation with NO₃⁻-N. Conversely, no significant correlations were observed between soil N₂O emission flux and soil physicochemical properties in crust soil, underscoring the importance of considering plant–soil microbial interactions. Our findings suggest that soil nitrous oxide emissions in arid and semi-arid regions will be particularly responsive to small and frequent rainfall events as precipitation patterns change in the future, primarily due to their soil physicochemical characteristics. Full article

Drought adversely affects plant growth, which leads to reduced crop yields and exacerbates food insecurity. Late embryogenesis abundant (LEA) proteins are crucial for plants’ responses to abiotic stresses. This research further investigates the role of SiLEA5 by utilizing transgenic tomatoes under drought stress. The expression of SiLEA5 was upregulated under drought and abscisic acid (ABA) treatment, resulting in decreased electrolyte leakage and malondialdehyde content, alongside increased levels of osmotic regulators and antioxidant enzyme activity. These biochemical alterations reduce oxidative damage and enhance drought resistance. qRT-PCR analysis revealed the upregulation of ABA signaling genes and key enzymes involved in proline biosynthesis (P5CS) and dehydrin (DHN) synthesis under drought stress. Additionally, overexpression of SiLEA5 increased the net photosynthetic rate (Pn) and fruit yield of tomatoes by regulating stomatal density and aperture. These findings suggest that SiLEA5 may be a potential target for improving drought tolerance in tomatoes and other crops. Full article

Volatile compounds are important components of the flavor quality of Chinese chives, but the distribution of flavor components in different organs of Chinese chives is still unclear. In this experiment, two Chinese chive varieties, ‘Fu Jiu Bao F1’ and ‘Jiu Xing 22’, were taken as test materials, and the contents of volatile compounds in different stages and organs of Chinese chive were determined by HS-SPME/GC-MS technology. A total of 70 and 85 volatile organic compounds (VOCs) were detected in various organs of two varieties at the commodity harvesting stage and physiological maturity stage, respectively. The total volatile compound content of Fu Jiu Bao F1 in the stage of commodity harvesting was higher than that of the physiological maturity stage, but Jiu Xing 22 showed the opposite trend. The organ distribution pattern of total volatile compounds in Fu Jiu Bao F1 and Jiu Xing 22 at the commodity harvesting stage was consistent, as follows: leaf > pseudostem > root. However, at the physiological maturity stage, the distribution pattern of Fu Jiu Bao F1 was different from that of Jiu Xing 22. Further, sulfur-containing compounds at different stages showed different organic distributions. Comprehensive analysis indicated that organic-common and organic-specific compounds varied from different cultivars and growth stages of Chinese chive, and organ differences in VOC distribution were greater than the varieties’ differences based on PCA analysis. The results of this study clarify the composition and organ distribution of volatile compounds in Chinese chive and provide a direction for the study of Chinese chive flavor quality. Full article

This study was conducted in order to explore the pathogens that cause stem rot of fresh onions during postharvest storage, identify the incidence of stem rot, investigate the influence of pathogen infection on the active components of onion, and provide a theoretical basis for disease control during the postharvest storage of fresh onions. The pathogens were isolated and purified from the junction between the rotten and healthy tissues of onion stem rot that occurred naturally during storage at room temperature by tissue separation; then, the pathogens were identified by morphological and molecular biological techniques, the biological characteristics of the pathogens were analyzed, and finally, the influence of pathogen infection on the active ingredients of onion was studied. The results suggested that the main pathogens causing stem rot of fresh onions during postharvest storage were Talaromyces pinophilus, Trichoderma simmonsii, and Talaromyces minioluteus. The optimum colony growth conditions for T. pinophilus were as follows: a temperature of 30 °C, a pH of 7, light for 24 h, maltose as a carbon source, and peptone as a nitrogen source; the lethal temperature was 65 °C for 15 min. For T. simmonsii, the lethal temperature was 60 °C for 15 min, and the optimum sporulation conditions were a temperature of 25 °C, a pH of 5–7, light for 24 h, a carbon source of sucrose, and a nitrogen source of yeast powder. For T. minioluteus, the lethal condition was 65 °C for 15 min; the optimum colony growth conditions were a temperature of 25 °C, a pH of 8–9, 24 h of darkness, a carbon source of maltose, and a nitrogen source of peptone. The relative content of sulfur compounds, as the active components of onions, was much lower in the infected onions than in the healthy onions due to infection by the pathogens T. pinophilus, T.simmonsii, and T.minioluteus. This study will provide a theoretical basis for further effective control of the occurrence of postharvest stem rot diseases of onions. Full article

This thesis proposes an improved droop control strategy design based on active disturbance rejection control and LSTM. This strategy uses the droop control method to coordinately control the distributed generation units (DGs) in a microgrid to achieve stable operation of the microgrid system. Linear-Auto Disturbance Rejection Control (LADRC) is introduced and an improved LADRC is designed based on the error principle. A disturbance compensation link is introduced on the basis of traditional LADRC to form ILADRC and a droop control strategy is used. Instead of improving the PD controller in LADRC, an improved droop control strategy is formed, which not only achieves natural decoupling between powers, but also improves the system’s immunity and transient operation capabilities. At the same time, in order to achieve adaptive parameter tuning in the improved droop control strategy, this article introduces long short-term memory (LSTM) to form an adaptive improved droop control strategy which further improves the system’s immunity and robustness. This article builds a simulation model through the MATLAB/Simulink simulation experiment platform and tests PI control and traditional droop control. The strategy and the improved droop control strategy designed in this thesis are experimentally compared and verified, and simulation analysis and verification are conducted on the two working conditions. The simulation results clearly demonstrate the superiority of the improved droop control strategy over PI control and traditional droop control, indicating that the correctness and reliability under various working conditions are verified. Full article

Conventional methods for tumor diagnosis suffer from two inherent limitations: they are time-consuming and subjective. Computer-aided diagnosis (CAD) is an important approach for addressing these limitations. Pathology whole-slide images (WSIs) are high-resolution tissue images that have made significant contributions to cancer diagnosis and prognosis assessment. Due to the complexity of WSIs and the availability of only slide-level labels, multiple instance learning (MIL) has become the primary framework for WSI classification. However, most MIL methods fail to capture the interdependence among image patches within a WSI, which is crucial for accurate classification prediction. Moreover, due to the weak supervision of slide-level labels, overfitting may occur during the training process. To address these issues, this paper proposes a dual-attention-based multiple instance learning framework (DAMIL). DAMIL leverages the spatial relationships and channel information between WSI patches for classification prediction, without detailed pixel-level tumor annotations. The output of the model preserves the semantic variations in the latent space, enhances semantic disturbance invariance, and provides reliable class identification for the final slide-level representation. We validate the effectiveness of DAMIL on the most commonly used public dataset, Camelyon16. The results demonstrate that DAMIL outperforms the state-of-the-art methods in terms of classification accuracy (ACC), area under the curve (AUC), and F1-Score. Our model also allows for the examination of its interpretability by visualizing the dual-attention weights. To the best of our knowledge, this is the first attempt to use a dual-attention mechanism, considering both spatial and channel information, for whole-slide image classification. Full article

To investigate the soil-specific metabolites of Codonopsis pilosula under different stubble management practices, this study analyzed differentially abundant metabolites in the rhizosphere soils of rotational (DS) and continuous (LS) cropping systems via liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based metabolomic approaches. The results revealed that 66 metabolites, including amino acids and their derivatives, nucleic acids, alcohols, organic acids, amines, fatty acids, purines, and sugars, were significantly different (p < 0.05) between the DS and LS groups. Under continuous cropping, the levels of amines, fatty acids, organic acids, and sugars in the rhizosphere soil were significantly greater (p < 0.05) than those under rotational cropping, whereas the levels of amino acids and their derivatives, nucleic acids, and purines and pyrimidines were significantly lower (p < 0.05). KEGG pathway enrichment analysis revealed that these differentially abundant metabolites were enriched in metabolic pathways such as amino acid metabolism (e.g., alanine, aspartate, and glutamate metabolism), carbon metabolism, the cAMP signaling pathway, ABC transporter proteins, phenylalanine metabolism, and the biosynthesis of plant secondary metabolites. These metabolic pathways were involved in osmoregulation, energy supply, and resilience in plants. In conclusion, inter-root soil metabolites in rotational and continuous cropping of Codonopsis pilosula were able to influence soil physicochemical properties and microbial populations by participating in various biological processes. Full article

While investigating macrofungi diversity in Gansu province, northwestern China, five fresh and fleshy specimens were collected, which are characterized by nearly white to buff hemispherical pileus with waved margins, a disc depressed with coral to brownish red fibrillose scales, adnate to sub-decurrent lamellae with four relatively regular rows of lamellulae, a stipe that is central, hollow, frequently straight to curved; basidiospores that are globose to subglobose, 5.0–6.0 × (3.5−) 4.0–5.0 (−5.5) μm, narrowly clavate cheilocystidia predominantly, pleurocystidia and caulocystidia not observed; and a cutis pileipellis, with hyphae slightly inflated in the KOH. The results of phylogeny analysis indicated that the species forms an independent lineage in Phyllotopsidaceae based on the ITS (ITS5/ITS4) and nLSU (LR0R/LR7) dataset. Molecular clock analyses suggested the common ancestor of Neotricholomopsis emerged later than upper Cretaceous with a mean crown age of 229.36 Mya (95% highest posterior density of 129.63–343.08 Mya). These five specimens were described as an unreported taxon based on the phylogeny analysis combined with morphological examination and ecological and geographical distribution. Detailed descriptions, illustrations, and phylogenetic trees to demonstrate the placement of this species and discussions with its related species are provided. Full article

High-quality grape raw materials are fundamental for producing premium wine. Ensuring the quality of grape raw materials, particularly enhancing their phenolic profiles, significantly improves wine flavor. Therefore, this study focused on ‘Cabernet Franc’ grapes, where a 0.1% chitooligosaccharide (COS) solution was foliar sprayed during the green pea stage, the onset of veraison stage, and the mid-ripening stage to investigate the impact of exogenous COS treatment on the accumulation of phenolic compounds in grape berries. The results revealed that COS treatment during the green pea and the onset of veraison stages significantly increased the levels of total phenolic, total flavonoid, and total anthocyanin in grapes, with distinct effects on flavanols, phenolic acids, flavonols, and stilbenes, respectively. Eight key compounds most significantly influenced by the treatment were identified through orthogonal partial least squares discriminant analysis (OPLS-DA) and machine learning screening. Specifically, treatment during the green pea stage had a significant impact on total soluble solids, proanthocyanidin B1, catechin, and vanillic acid, while veraison treatment notably affected petunidin-3-O-(6″-O-p-coumaryl)-glucoside, cyanidin-3-O-(6″-O-p-coumaryl)-glucoside, cyanidin-3-O-glucoside and isorhamnetin. This study could provide valuable data references and theoretical support for applying COS in wine grapes and regulating high-quality raw materials. Full article

The spatial distribution and trend of carbon emissions in the Yellow River Basin—an important ecological barrier and economic belt in China—directly affect the stability of the ecosystem and the sustainable development of the regional economy. Based on the data for carbon emissions in China’s counties from 1997 to 2017, this paper utilizes standard deviation ellipses, Theil index nested decomposition, and geographic detector models to make a comprehensive description of the spatial and temporal distribution and dynamic evolution characteristics of carbon emissions in the Yellow River Basin. Factors influencing carbon emissions are also analyzed from multiple dimensions. According to the findings, (1) carbon emissions at the county level show a clear upward trend without reaching a peak, exhibiting a spatial distribution of higher emissions in the east and lower in the west and higher in the south and lower in the north, with the mid-lower reaches being the center. The junction of the Shandong, Shaanxi, and Gansu provinces further exhibits a significant expansion, forming two core areas of carbon emissions. (2) Carbon emissions at the county level in the Yellow River Basin are influenced by both economic and geographic factors, exhibiting a significant high carbon spillover effect and a low carbon lock-in effect. The gravity center of the distribution has shifted towards the mid-lower reaches, with the upper reaches displaying dispersion tendencies. (3) Intra-regional disparities are the main source of the overall spatial differences in carbon emissions, with the largest disparities being observed in the upper reaches, followed by the middle reaches, and the smallest disparities being observed in the lower reaches. Further analysis shows that the level of economic development is the primary factor influencing the spatial variation of carbon emissions, and the combined effects of population size and industrial agglomeration are the key drivers of the annual growth in carbon emissions. Full article

As coal mining depths continue to rise, consideration of WFFZ elevations is becoming increasingly important to mine safety. The goal was to accurately predict the height of the WFFZ to effectively prevent and manage possible roof water catastrophes and ensure the ongoing safety of the mine. To achieve this goal, we combined the particle swarm optimisation (PSO) algorithm with a backpropagation neural network (BPNN) in order to enhance the accuracy of the forecast. The present study draws upon the capacity of the PSO algorithm to conduct global searches and the nonlinear mapping capability of the BPNN. Through grey relational analysis (GRA), the order of the correlation degree was as follows: mining thickness > mining depth > overburden structure > mining width > mining dip. GRA has identified the degree of correlation between five influencing factors and the height of the WFFZ, among these, mining thickness, mining depth, overburden structure and mining width all show strong correlations, and the mining dip of the coal seam shows a good correlation. The weight ranking obtained by the PSO-BPNN method was the same as that obtained by the GRA method. Based on two actual cases, the relative errors of the obtained prediction results after PSO implementation were 2.97% and 3.47%, while the relative errors of the BPNN before optimisation were 18.46% and 4.34%, respectively, indicating that the PSO-BPNN method provides satisfactory prediction results and demonstrating that the PSO-optimised BPNN is easy to use and yields reliable results. In this paper, the height of the WFFZ model under the influence of five factors is only established for the Northwest Mining Area. With the continuous progress of technology and research, the neural network can consider more factors affecting the height of hydraulic fracturing development zones in the future to improve the comprehensiveness and accuracy of prediction. Full article