Search Results (2,195)

Mushrooms are eukaryotic organisms with absorptive heterotrophic nutrition, capable of feeding on organic matter rich in cellulose and lignocellulose. Since ancient times, they have been considered allies and, in certain cultures, they were seen as magical beings or food of the gods. Of the great variety of edible mushrooms identified worldwide, less than 2% are traded on the market. Although mushrooms have been valued for their multiple nutritional and healing benefits, some cultures perceive them as toxic and do not accept them in their culinary practices. Despite the existing skepticism, several researchers are promoting the potential of edible mushrooms. There are two main methods of mushroom cultivation: solid-state fermentation and submerged fermentation. The former is the most widely used and simplest, since the fungus grows in its natural environment; in the latter, the fungus grows suspended without developing a fruiting body. In addition, submerged fermentation is easily monitored and scalable. Both systems are important and have their limitations. This article discusses the main methods used to increase the performance of submerged fermentation with emphasis on the modes of operation used, types of bioreactors and application of morphological bioengineering of filamentous fungi, and especially the use of intelligent automatic control technologies and the use of non-invasive monitoring in fermentation systems thanks to the development of machine learning (ML), neural networks, and the use of big data, which will allow more accurate decisions to be made in the fermentation of filamentous fungi in submerged environments with improvements in production yields. Full article

Microplastic (MP) pollution is an emerging concern in ruminant production, as animals are exposed to MPs through air, water, and feeds. Ruminants play a key role in MP transmission to humans via animal products and contribute to MP return to agricultural soil through excreta. Identifying effective strategies to mitigate MP pollution in the ruminant sector is crucial. A promising yet understudied approach involves the potential ability of rumen microbiota to degrade MPs. This study investigated the in vitro ruminal degradation of three widely distributed MPs—low-density polyethylene (LDPE), polyethylene terephthalate (PET), and polyamide (PA)—over 24, 48, and 72 h. PET MP exhibited the highest degradation rates (24 h: 0.50 ± 0.070%; 48 h: 0.73 ± 0.057%; and 72 h: 0.96 ± 0.082%), followed by LDPE MP (24 h: 0.03 ± 0.020%; 48 h: 0.25 ± 0.053%; and 72 h: 0.56 ± 0.066%) and PA MP (24 h: 0.10 ± 0.045%; 48 h: 0.02 ± 0.015%; and 72 h: 0.14 ± 0.067%). These findings suggest that the ruminal environment could serve as a promising tool for LDPE, PET, and PA MPs degradation. Further research is needed to elucidate the mechanisms involved, potentially enhancing ruminants’ natural capacity to degrade MPs. Full article

Computer vision is of wide interest to perform the phenotyping of horticultural crops such as apple trees at high throughput. In orchards specially constructed for variety testing or breeding programs, computer vision tools should be able to extract phenotypical information form each tree separately. We focus on segmenting individual apple trees as the main task in this context. Segmenting individual apple trees in dense orchard rows is challenging because of the complexity of outdoor illumination and intertwined branches. Traditional methods rely on supervised learning, which requires a large amount of annotated data. In this study, we explore an alternative approach using prompt engineering with the Segment Anything Model and its variants in a zero-shot setting. Specifically, we first detect the trunk and then position a prompt (five points in a diamond shape) located above the detected trunk to feed to the Segment Anything Model. We evaluate our method on the apple REFPOP, a new large-scale European apple tree dataset and on another publicly available dataset. On these datasets, our trunk detector, which utilizes a trained YOLOv11 model, achieves a good detection rate of $97\%$ based on the prompt located above the detected trunk, achieving a Dice score of 70% without training on the REFPOP dataset and 84% without training on the publicly available dataset.We demonstrate that our method equals or even outperforms purely supervised segmentation approaches or non-prompted foundation models. These results underscore the potential of foundational models guided by well-designed prompts as scalable and annotation-efficient solutions for plant segmentation in complex agricultural environments. Full article

Accurate evaluation of fish feeding intensity is crucial for optimizing aquaculture efficiency and the healthy growth of fish. Previous methods mainly rely on single-modal approaches (e.g., audio or visual). However, the complex underwater environment makes single-modal monitoring methods face significant challenges: visual systems are severely affected by water turbidity, lighting conditions, and fish occlusion, while acoustic systems suffer from background noise. Although existing studies have attempted to combine acoustic and visual information, most adopt simple feature-level fusion strategies, which fail to fully explore the complementary advantages of the two modalities under different environmental conditions and lack dynamic evaluation mechanisms for modal reliability. To address these problems, we propose the Adaptive Cross-modal Attention Fusion Network (ACAF-Net), a cross-modal complementarity learning framework with a two-stage attention fusion mechanism: (1) a cross-modal enhancement stage that enriches individual representations through Low-rank Bilinear Pooling and learnable fusion weights; (2) an adaptive attention fusion stage that dynamically weights acoustic and visual features based on complementarity and environmental reliability. Our framework incorporates dimension alignment strategies and attention mechanisms to capture temporal–spatial complementarity between acoustic feeding signals and visual behavioral patterns. Extensive experiments demonstrate superior performance compared to single-modal and conventional fusion approaches, with 6.4% accuracy improvement. The results validate the effectiveness of exploiting cross-modal complementarity for underwater behavioral analysis and establish a foundation for intelligent aquaculture monitoring systems. Full article

Understanding how habitat conditions drive morphological adaptations in animals is critical in ecology, yet amphibian studies remain limited. This study investigated intraspecific variation in ecomorphological traits of three montane stream-dwelling frogs (Quasipaa boulengeri, Amolops sinensis, and Odorrana margaratae) across elevation gradients in Tianping Mountain, China. Using morphological measurements and environmental variables collected from ten transects, we analyzed functional traits related to feeding and locomotion and assessed their associations with microhabitat variables. Significant trait differences between low- and high-elevation groups were detected only in Q. boulengeri, with high-elevation individuals exhibiting greater body mass and shorter hindlimbs. Redundancy analysis demonstrated that microhabitat variables, particularly air humidity, flow rate, and rock coverage, were linked to trait variations. For example, air humidity and flow rate significantly influenced Q. boulengeri’s body and limb proportions, while flow rate affected A. sinensis’s snout and limb morphology. In addition, sex and seasonal effects were also associated with trait variations. These results underscore amphibians’ phenotypic plasticity in response to the environment and highlight the role of microhabitat complexity in shaping traits. By linking habitat heterogeneity to eco-morphology, this study advocates for conservation strategies that preserve varied stream environments to support amphibian resilience amid environmental changes. Full article

Healthy food service guidelines (HFSG) comprise food, nutrition, behavioral design, and other standards to guide the purchasing, preparation, and offering of foods and beverages in worksites and institutional food service. To date, there have been few attempts to synthesize evidence for HFSG effectiveness in non-K-12 or early childhood education sectors, particularly at worksites and institutional food services. We conducted a scoping review to achieve the following: (1) characterize the existing literature on the effectiveness of HFSG for improving the institution’s food environment, financial outcomes, and consumers’ diet quality and health, and (2) identify gaps in the literature. The initial search in PubMed and Web of Science retrieved 10,358 articles; after screening and snowball searching, 68 articles were included for analysis. Studies varied in terms of HFSG implementation settings, venues, and outcomes in both U.S. (n = 34) and non-U.S. (n = 34) contexts. The majority of HFSG interventions occurred in venues where food is sold (e.g., worksite cafeterias, vending machines). A diversity of HFSG terminology and measurement tools demonstrates the literature’s breadth. Literature gaps include quasi-experimental study designs, as well as interventions in settings that serve dependent populations (e.g., universities, elderly feeding programs, and prisons). Full article

The first few days following the birth are a vulnerable time for the neonate. Sick infants experience various interventions during their stay in the neonatal unit in order to stay alive and grow. Acquisition of gut microbes is critical for the short- and long-term health of the neonate. At a time when the gut microbiome is starting to take shape, crucial interventions directed at improving the growth, development and survival of the neonate impact its development. Events prior to and after the birth of the neonate, such as maternal conditions, antibiotic exposure, type of feeds, supplemental probiotics, and neonatal intensive care environment, contribute significantly to shaping the gut microbiome over the first few weeks and maintain its healthy balance crucial for long-term health. In this comprehensive review, we address common interventions the neonate is exposed to in its journey and their impact on gut microbiome, and discuss various interventions that minimize the dysbiosis of the gut. Full article

Superb Fairy-wren Optimization Algorithm (SFOA) is an animal-based meta-heuristic algorithm derived from Fairy-wren’s behavior of growing, feeding, and avoiding natural enemies. The SFOA has some shortcomings when facing complex environments. Its switching mechanism is not enough to adapt to complex optimization problems, and it faces a weakening of population diversity in the late stage of optimization, leading to a higher possibility of falling into local optima. In addition, its global search ability needs to be improved. To address the above deficiencies, this paper proposes an Adaptive Superb Fairy-wren Optimization Algorithm (ASFOA). To assess the ability of the proposed ASFOA, three groups of experiments are conducted in this paper. Firstly, the effectiveness of the proposed improved strategies is checked on the CEC2018 test set. Second, the ASFOA is compared with eight classical/highly cited/newly proposed metaheuristics on the CEC2018 test set, in which the ASFOA performed the best overall, with average rankings of 1.621, 1.138, 1.483, and 1.966 in the four-dimensional cases, respectively. Then the convergence and robustness of ASFOA is verified on the CEC2022 test set. The experimental results indicate that the proposed ASFOA is a competitive metaheuristic algorithm variant with excellent performance in terms of convergence and distribution of solutions. In addition, we further validate the ability of ASFOA to solve real optimization problems. The average ranking of the proposed ASFOA on 10 engineering constrained optimization problems is 1.500. In summary, ASFOA is a promising variant of metaheuristic algorithms. Full article

This study investigated the physiological regulatory mechanisms by which exogenous trehalose intake enhances the adaptation of the global stored-grain pest T. castaneum to high-concentration carbon dioxide (CO₂) stress. By supplementing exogenous trehalose under high-CO₂ controlled atmosphere stress, we measured the activities of key detoxification enzymes (e.g., carboxylesterase and cytochrome P450) and the levels of carbohydrate substances (e.g., glycogen, glucose, and trehalose). The results demonstrated that trehalose feeding significantly alleviated CO₂ induced mortality in T. castaneum and prolonged their survival time. In terms of detoxification metabolism, a trehalose-rich diet significantly reduced the activities of cytochrome P450 and carboxylesterase, while the glucose content in the beetles decreased markedly. These findings indicate that trehalose accumulation mitigates physiological damage caused by high-CO₂ stress in T. castaneum. Furthermore, exogenous trehalose intake did not disrupt carbohydrate metabolic homeostasis in the beetles, as trehalase activity and the levels of various carbohydrates remained relatively stable. This study elucidates the role of trehalose metabolism in T. castaneum’s adaptation to high-CO₂ environments, providing a theoretical foundation for optimizing controlled atmosphere grain storage technology and developing novel pest control strategies. Full article

Euphorbia hirta L. is traditionally used to treat tumors and has demonstrated anticancer effects. This study evaluated the phytochemical composition, toxicity, and antitumor activity of saline extract (SE) from E. hirta leaves in mice. Phytochemical analysis included thin layer chromatography, high-performance liquid chromatography, and quantification of phenols, flavonoids, and proteins. Acute toxicity (2000 mg/kg) assessed mortality, hematological, biochemical, histological parameters, water/feed intake, and body weight. Genotoxicity was evaluated via comet and micronucleus assays. Antitumor activity was tested in vitro and in vivo on sarcoma 180. SE contained 107.3 mg GAE/g phenolics and 22.9 mg QE/g flavonoids; the presence of gallic and ellagic acids was detected. Protein concentration was 12.16 mg/mL with lectin activity present. No mortality, organ damage, or genotoxic effects occurred in toxicity tests. SE demonstrated in vitro cytotoxicity against sarcoma cells (IC₅₀: 10 µg/mL). In vivo, SE (50–200 mg/kg) reduced tumor weight by 70.2–72.3%. SE modulated IL-2, IL-4, IL-6, IL-17, IFN-γ, and TNF-α in tumor environment. Tumors showed inflammatory infiltrate, necrosis, and fibrosis after treatment. These findings position the extract as a promising candidate for further development as a safe, plant-based antitumor agent. Full article

Micro- and Nanoplastic (MNP) pollution is an emerging challenge globally, posing a significant threat to both aquatic and terrestrial ecosystems worldwide. This review critically examines the sources, exposure routes, and impact of plastics, with particular focus on implications for the livestock sector. MNPs enter animals’ bodies primarily through ingestion of contaminated feed and water, inhalation, and dermal exposure, subsequently accumulating in various organs, disrupting physiological functions. Notably, MNPs facilitate the horizontal transfer of antimicrobial resistance genes (ARGs), exacerbating the global challenge of antimicrobial resistance (AMR). In agricultural environments, sources such as organic fertilizers, wastewater irrigation systems, surface runoff, and littering contribute to soil contamination, adversely affecting plant growth and soil health, which in turn compromises feed quality and ultimately animals’ productivity. This review synthesizes current evidence demonstrating how MNP exposure impairs animal production, reproduction, and survival, and highlights the interconnected risks to food safety and ecosystem health. The findings call for the urgent need for comprehensive research under controlled conditions to underscore the fine details regarding mechanisms of MNP toxicity and to inform effective mitigation strategies. Addressing MNP pollution is crucial for safeguarding animal health, ensuring sustainable livestock production, and promoting environmental sustainability and integrity. Full article

Stope dilution is a major hidden cost driver for the underground operation, especially in terms of reducing ore quality, increasing the amount of processing feed, and effects on operational cost. Accurate calculation and consideration of planned and unplanned dilution and mining loss amounts are essential during mine planning. The user interface named D–Loss has been developed with MATLAB R2023b, which provides a multiparadigm numerical computing environment for faster and more practical calculation of these dilution amounts to address these challenges by quantifying dilution and linking them directly to economic and CO₂ emissions indicators. By determination and analysis of the stope overall dilution amounts, it helps us understand greenhouse gas emissions and ensures the efficient use of underground equipment. Calculation of stope dilution in a practical and rapid manner allows for stope design and operational improvements, which can help reduce dilution in underground operations. This progress is tracked through the D–Loss interface within the short- and long-term production planning. Moreover, by quantifying dilution impacts on comminution and haulage costs, D–Loss becomes a critical software for tracking economic losses and optimizing financial outcomes in the mining industry. D–Loss helps users iteratively assess the efficiency of updates and provides support in mine design, scheduling, and environmental impact control by comparing planning and operational improvements before and after. Full article

Aquaculture is a key food production sector responsible for meeting the nutritional needs of a rapidly growing global population. However, the emergence of disease outbreaks has become a major challenge for the aquaculture industry, resulting in significant economic losses. The use of costly and toxic antibiotics for treatment has a negative impact on the aquatic environment. Consequently, there has been a growing interest in probiotics as a non-antibiotic approach to manage disease outbreaks and improve fish performance. The use of the yeast Saccharomyces cerevisiae (SC) has shown remarkable benefits in aquaculture. In February 2025, a systematic search was conducted based on the Web of Science (WoS) database for the period 2015–2025 to identify relevant studies investigating the beneficial effects of SC in aquaculture. After searching on WoS, 466 documents were found and analyzed using R-bibliometric package for comprehensive analysis to identify research gap, trends, and distribution of global literature that focuses on SC in aquaculture. The most relevant and recent articles were reviewed, summarized and discussed. The yeast SC have shown a wide range of benefits, including improved growth performance, feed efficiency, enhanced diversity of the gut microbiome and immune response. The implementation of SC is becoming a recent trend and its efficacy in aquatic environments has been thoroughly investigated. This review aims to provide a valuable insight into SC as one of the most important aquaculture probiotics. It also emphasizes the need for further research to fully understand its benefits and the way it works. Full article

Cruciferous vegetables are popular in Asian dishes. However, striped flea beetles prefer to feed on leaves, which can damage the appearance of crops and reduce their economic value. Due to the lack of pest monitoring, the occurrence of pests is often irregular and unpredictable. Regular and quantitative spraying of pesticides for pest control is an alternative method. Nevertheless, this requires manual execution and is inefficient. This paper presents a system powered by solar energy, utilizing batteries and supercapacitors for energy storage to support the implementation of edge AI devices in outdoor environments. Raspberry Pi is utilized for artificial intelligence image recognition and the Internet of Things (IoT). YOLOv5 is implemented on the edge device, Raspberry Pi, for detecting striped flea beetles, and StyleGAN3 is also utilized for data augmentation in the proposed system. The recognition accuracy reaches 85.4%, and the results are transmitted to the server through a 4G network. The experimental results indicate that the system can operate effectively for an extended period. This system enhances sustainability and reliability and greatly improves the practicality of deploying smart pest detection technology in remote or resource-limited agricultural areas. In subsequent applications, drones can plan routes for pesticide spraying based on the distribution of pests. Full article

To address the challenges of low recognition accuracy and substantial model complexity in crop disease identification models operating in complex field environments, this study proposed a novel hybrid model named ConvTransNet-S, which integrates Convolutional Neural Networks (CNNs) and transformers for crop disease identification tasks. Unlike existing hybrid approaches, ConvTransNet-S uniquely introduces three key innovations: First, a Local Perception Unit (LPU) and Lightweight Multi-Head Self-Attention (LMHSA) modules were introduced to synergistically enhance the extraction of fine-grained plant disease details and model global dependency relationships, respectively. Second, an Inverted Residual Feed-Forward Network (IRFFN) was employed to optimize the feature propagation path, thereby enhancing the model’s robustness against interferences such as lighting variations and leaf occlusions. This novel combination of a LPU, LMHSA, and an IRFFN achieves a dynamic equilibrium between local texture perception and global context modeling—effectively resolving the trade-offs inherent in standalone CNNs or transformers. Finally, through a phased architecture design, efficient fusion of multi-scale disease features is achieved, which enhances feature discriminability while reducing model complexity. The experimental results indicated that ConvTransNet-S achieved a recognition accuracy of 98.85% on the PlantVillage public dataset. This model operates with only 25.14 million parameters, a computational load of 3.762 GFLOPs, and an inference time of 7.56 ms. Testing on a self-built in-field complex scene dataset comprising 10,441 images revealed that ConvTransNet-S achieved an accuracy of 88.53%, which represents improvements of 14.22%, 2.75%, and 0.34% over EfficientNetV2, Vision Transformer, and Swin Transformer, respectively. Furthermore, the ConvTransNet-S model achieved up to 14.22% higher disease recognition accuracy under complex background conditions while reducing the parameter count by 46.8%. This confirms that its unique multi-scale feature mechanism can effectively distinguish disease from background features, providing a novel technical approach for disease diagnosis in complex agricultural scenarios and demonstrating significant application value for intelligent agricultural management. Full article