Agricultural Applications Research Articles

Evolution of unexpected diversity in a putative mating type locus and its correlation with genome variability reveals likely asexuality in the model mycorrhizal fungus Rhizophagus irregularis

BackgroundArbuscular mycorrhizal fungi (AMF) form mutualistic partnerships with approximately 80% of plant species. AMF, and their diversity, play a fundamental role in plant growth, driving plant diversity, and global carbon cycles. Knowing whether AMF are sexual or asexual has fundamental consequences for how they can be used in agricultural applications. Evidence for and against sexuality in the model AMF, Rhizophagus irregularis, has been proposed. The discovery of a putative mating-type locus (MAT locus) in R. irregularis, and the previously suggested recombination among nuclei of a dikaryon R. irregularis isolate, potentially suggested sexuality. Unless undergoing frequent sexual reproduction, evolution of MAT-locus diversity is expected to be very low. Additionally, in sexual species, MAT-locus evolution is decoupled from the evolution of arbitrary genome-wide loci.ResultsWe studied MAT-locus diversity of R. irregularis. This was then compared to diversification in a phosphate transporter gene (PTG), that is not involved in sex, and to genome-wide divergence, defined by 47,378 single nucleotide polymorphisms. Strikingly, we found unexpectedly high MAT-locus diversity indicating that either it is not involved in sex, or that AMF are highly active in sex. However, a strongly congruent evolutionary history of the MAT-locus, PTG and genome-wide arbitrary loci allows us to reject both the hypothesis that the MAT-locus is involved in mating and that the R. irregularis lineage is sexual.ConclusionOur finding shapes the approach to developing more effective AMF strains and is highly informative as it suggests that introduced strains applied in agriculture will not exchange DNA with native populations.

Automated Disease Detection in Silkworms Using Machine Learning Techniques

Silkworm diseases pose a major threat to the sericulture industry, with early detection remaining a challenge due to limited infrastructure. This study focuses on detecting Grasserie disease, which can rapidly spread in silkworm rearing units, leading to significant economic losses. A novel dataset of 668 healthy and 574 Grasserie-affected silkworm images forms the basis of this research. The study applies machine learning techniques, using the Histogram Oriented Gradient (HOG) feature descriptor combined with Kernel Principal Component Analysis (KPCA) and supervised classifiers. The integration of Support Vector Machines (SVM) with HOG and KPCA achieved high accuracy (93.16%), recall (93.38%), and precision (91.94%), offering a faster, more accurate alternative to manual detection methods. This approach holds great potential for developing real-time, IoT-based diagnostic tools that enable farmers to quickly identify infected silkworms, reducing disease spread and economic losses, and can be extended to other agricultural applications requiring early disease detection.

Recent Advances in Phenazine Natural Products: Biosynthesis and Metabolic Engineering.

Phenazine natural products are a class of nitrogen-containing heterocyclic compounds produced by microorganisms. The tricyclic ring molecules show various chemical structures and extensive pharmacological activities, such as antimicrobial, anticancer, antiparasitic, anti-inflammatory, and insecticidal activities, with low toxicity to the environment. Since phenazine-1-carboxylic acid has been developed as a registered biopesticide, the application of phenazine natural products will be promising in the field of agriculture pathogenic fungi control based on broad-spectrum antifungal activity, minimal toxicity to the environment, and improvement of crop production. Currently, there are still plenty of intriguing hidden biosynthetic pathways of phenazine natural products to be discovered, and the titer of naturally occurring phenazine natural products is insufficient for agricultural applications. In this review, we spotlight the progress regarding biosynthesis and metabolic engineering research of phenazine natural products in the past decade. The review provides useful insights concerning phenazine natural products production and more clues on new phenazine derivatives biosynthesis.

Class incremental learning with analytic learning for hyperspectral image classification

Hyperspectral image classification (HSIC) is crucial for applications in agriculture and environmental monitoring. As ground objects evolve and remote sensing technology progresses, there is a growing need for HSIC models that can adapt to new data classes without requiring to be retrained from scratch. Throughout the continual learning procedure, the model is expected to not only effectively extract spatial–spectral features from the hyperspectral image but also alleviate the issue of catastrophic forgetting, i.e., the model forgets the learned classes’ knowledge when accessing novel classes during the training process. In this paper, for the HSIC task, we propose a class incremental learning method (HSI-CIL) that is based on analytic learning, a technique that converts network training into linear problems. Specifically, The HSI-CIL model consists of a lightweight feature extractor, a Feature Processing Module (FPM) and an Analytic Linear Classifier (ALC). This model does not need data storage for old and new classes and has only one epoch in the incremental learning stage, so it has lower consumption of resources and training time than several attempts have been proposed for addressing catastrophic forgetting. We perform abundant experiments with the proposed HSI-CIL on three publicly available hyperspectral datasets including Indian Pines, Pavia University, and Salinas. The experiments demonstrate that our HSI-CIL exceeds the state-of-the-art class incremental learning (CIL) techniques applied in HSIC with a certain gap.

Bee Together: Joining Bee Audio Datasets for Hive Extrapolation in AI-Based Monitoring.

Beehive health monitoring has gained interest in the study of bees in biology, ecology, and agriculture. As audio sensors are less intrusive, a number of audio datasets (mainly labeled with the presence of a queen in the hive) have appeared in the literature, and interest in their classification has been raised. All studies have exhibited good accuracy, and a few have questioned and revealed that classification cannot be generalized to unseen hives. To increase the number of known hives, a review of open datasets is described, and a merger in the form of the "BeeTogether" dataset on the open Kaggle platform is proposed. This common framework standardizes the data format and features while providing data augmentation techniques and a methodology for measuring hives' extrapolation properties. A classical classifier is proposed to benchmark the whole dataset, achieving the same good accuracy and poor hive generalization as those found in the literature. Insight into the role of the frequency of the classification of the presence of a queen is provided, and it is shown that this frequency mostly depends on a colony's belonging. New classifiers inspired by contrastive learning are introduced to circumvent the effect of colony belonging and obtain both good accuracy and hive extrapolation abilities when learning changes in labels. A process for obtaining absolute labels was prototyped on an unsupervised dataset. Solving hive extrapolation with a common open platform and contrastive approach can result in effective applications in agriculture.

Chestnut quality classification by THz Time-Domain Hyperspectral Imaging combined with unsupervised learning analysis

Chestnut crops are threatened by fungal pathogens such as Gnomoniopsis castaneae, which cause significant degradation of quality. Early detection of such infections is crucial to maintain the quality of chestnuts in the food industry. This study explores the application of Terahertz Time-Domain Hyperspectral Imaging (THz-TDHIS) combined with unsupervised learning techniques to identify fungal infections in chestnuts. Unlike conventional methods that rely on light attenuation, this approach leverages the unique spectral signatures of infected tissues. By employing Principal Component Analysis, K-Means Clustering, and Agglomerative Clustering, we effectively differentiate between healthy and infected portions of chestnuts. Our findings indicate that spectral features, rather than just intensity variations, provide more reliable markers for infection. In addition, we demonstrate that these methods enable the quantification of the degree of infection in chestnuts. The robustness of these unsupervised learning methods in handling large and heterogeneous data sets further underscores their potential in agricultural applications. This integrated THz-TDHIS and machine learning approach presents a promising solution to ensure chestnut quality and safety.

Multifaceted Impact of Lipid Extraction on the Characteristics of Polymer-Based Sewage Sludge towards Sustainable Sludge Management.

Dewatered sludge (DS) is a sewage sludge with a unique property due to extracellular polymeric substances (EPSs) and polymer flocculants. These components form a stable 3D polymer network to increase dewatering efficiency, leaving behind valuable materials such as lipids. This article explored the influences of DS particle size on lipid yield and the effects of extraction on the chemical, morphological, and thermal properties of the residual dewatered sludge (RDS). Lipid yields with unimodal distribution were observed across the particle size ranges (<0.5, 0.5-1.0, 1.0-2.0, 2.0-4.0, and 4.0 mm). The highest lipid yield of 1.95% was extracted from 1.0-2.0 mm after 4 h at 70 °C and 0.1 g/mL sludge-to-solvent ratio. Efficiency was influenced by the DS's morphology, facilitating solvent infiltration and pore diffusion. The extraction process reduced water and organic fractions, resulting in higher thermal stability. Bibliometric analysis of "extraction*" and "sewage sludge" shows increasing research interest from 1973 to 2024. Five research clusters were observed: heavy metal speciation and stabilization, sludge and its bioavailability, extraction techniques and resource recovery, contaminants remediation, as well as phosphorus recovery and agricultural applications. These clusters highlight the diverse approaches to researching DS and RDS while promoting sustainable waste management.

Potential of Trichoderma strains to positively modulate plant growth processes and bulb yield in Rabi onion

The use of beneficial microbes is hitherto known and constantly increasing in agriculture due to their positive impact on crop growth and yield, and their minimal negative impact on the environment. The objective of this study was to evaluate the impact of eight Trichoderma strains of diverse origin on crop growth and yield of onion under field conditions. The identity of the strains used in the current study was confirmed by ITS and Tef1 gene sequencing. Field experiments were conducted in the Rabi season for 2 years (2020–21, and 2022–23) to evaluate the effect of the application of eight different Trichoderma strains that were applied individually and separately as eight different treatments (T1–T8) in experimental plots. In the plant growth promotion assay conducted in vitro, all strains showed the ability to produce IAA (indole-3-acetic acid), with levels ranging from 23.52 μg/mL (T6) to 45.54 μg/mL (T3). Our results revealed that Trichoderma treated experimental plots displayed better growth indices (plant height, pseudostem diameter), RWC (Relative water content), leaf chlorophyll content, and yield-attributing features like biomass (bulb and root dry mass), bulb diameter, and harvested bulb yield compared to the untreated control plants. In terms of yield, the T2 strain exhibited the highest bulb yield consistently for both the years (2020–21 and 2022–23) followed by T3 being statistically at par with T5. Among all the evaluated Trichoderma strains, the strain T2 (OGRDT2) and T3 (GRDT1), taxonomically identified as Trichoderma longibrachiatum, registered bulb yield of 32.24 t/ha and 30.76 t/ha, respectively while T5 (GRDT3), identified as Trichoderma asperellum, registered 30.55 t/ha average yield for 2 years compared to 24.08 t/ha average yield recorded for untreated control plants with an increase of 34, 28 and 27%, respectively. Based on our findings, it is concluded that the T. longibrachiatum strains OGRDT2 (T2) and GRDT1 (T3), T. asperellum strain GRDT3 (T5) are the best inducers of the onion crop growth and yield in the Rabi season and would be explored further for its commercial application in onion farming.

Capillary container array structures for efficient, energy-saving, and sustainable evaporative cooling and humidification

Evaporative cooling is a widely employed method for air conditioning and heat dissipation, utilizing the phase transition of water to achieve simultaneous cooling and humidification. It relies on high-surface-area solid materials to enhance air-liquid contact upon wetting. However, these water-absorbing materials have drawbacks such as increased air resistance, degradation of water quality, and limited water retention capabilities. This study introduces a novel structure called the capillary container array (CCA) for effective humidification and cooling. The CCA structure overcomes the limitations of traditional methods by incorporating interconnected capillary containers with adjustable spacing and utilizing capillary forces to capture liquid droplets while minimizing the solid component. Compared to commercial paper-based wet curtains, the CCA structure offers significant advantages including strong water retention capabilities, high humidification performance, low air resistance and high pollution resistance. It increases water capacity by 6 times, humidification capacity by 8 %, and temperature drop by 2.2 °C. Moreover, it exhibits a 5-fold longer in water retention duration, a 30 % reduction in air pressure drop and a 3-fold increase in the performance coefficient, and maintains excellent water quality (<1 NTU) over prolonged operation. These advancements make the CCA structure highly promising for various applications in agriculture, industry and environmental conservation.

First Investigation of Secondary Metabolites from Aspergillus xerophilus Reveals Compounds with Inhibitive Effects against Three Phytopathogenic Fungi of Agrarian Crops.

Fungal secondary metabolites play a highly significant role in crop protection, which is related to their antifungal activity against agriculturally important phytopathogens. In fact, plant diseases caused by fungi including species belonging to the genera of Alternaria, Botrytis, and Fusarium have become increasingly serious affecting crop yield and quality. Hence, there is increasing awareness by the scientific community of the importance of exploiting fungal products for finding new compounds able to inhibit phytopathogens. In this study several drimane-type sesquiterpenes have been detected for the first time as products of Aspergillus xerophilus by GC-MS analysis of the organic extracts obtained from the mycelia and culture filtrates of the fungus grown on two different substrates. Seven pure drimane-type sesquiterpenes were also isolated and identified by spectroscopic methods. The inhibitory effects of the pure compounds have been investigated against three phytopathogenic fungi of agrarian crops (i.e., Botrytis cinerea, Alternaria alternata, and Fusarium oxysporum f. sp. pisi). Among the drimane-type sesquiterpenes isolated in this study, 9,11-dihydroxy-6-oxodrim-7-ene is the most active against the three phytopathogens. Our findings also reveal the high sensitivity of A. alternata to the isolated compounds. These results pave the way for future applications in agriculture of both A. xerophilus and its metabolites.

Development of a bionic hexapod robot with adaptive gait and clearance for enhanced agricultural field scouting.

High agility, maneuverability, and payload capacity, combined with small footprints, make legged robots well-suited for precision agriculture applications. In this study, we introduce a novel bionic hexapod robot designed for agricultural applications to address the limitations of traditional wheeled and aerial robots. The robot features a terrain-adaptive gait and adjustable clearance to ensure stability and robustness over various terrains and obstacles. Equipped with a high-precision Inertial Measurement Unit (IMU), the robot is able to monitor its attitude in real time to maintain balance. To enhance obstacle detection and self-navigation capabilities, we have designed an advanced version of the robot equipped with an optional advanced sensing system. This advanced version includes LiDAR, stereo cameras, and distance sensors to enable obstacle detection and self-navigation capabilities. We have tested the standard version of the robot under different ground conditions, including hard concrete floors, rugged grass, slopes, and uneven field with obstacles. The robot maintains good stability with pitch angle fluctuations ranging from -11.5° to 8.6° in all conditions and can walk on slopes with gradients up to 17°. These trials demonstrated the robot's adaptability to complex field environments and validated its ability to maintain stability and efficiency. In addition, the terrain-adaptive algorithm is more energy efficient than traditional obstacle avoidance algorithms, reducing energy consumption by 14.4% for each obstacle crossed. Combined with its flexible and lightweight design, our robot shows significant potential in improving agricultural practices by increasing efficiency, lowering labor costs, and enhancing sustainability. In our future work, we will further develop the robot's energy efficiency, durability in various environmental conditions, and compatibility with different crops and farming methods.

Molecular hydrogen: a sustainable strategy for agricultural and food production challenges

The world is confronting numerous challenges, including global warming, health epidemics, and population growth, each presenting significant threats to the stability and sustainability of our planet’s ecosystems. Such issues have collectively contributed to a reduction in agricultural productivity, corresponding with an increase in demand and costs of essential commodities. This critical situation requires more sustainable environmental, social, and technological solutions. Molecular hydrogen (H2) has been suggested as a “green” solution for our energy needs and many health, agricultural, and food applications. H2 supplementation in agriculture may represent a novel and low-carbon biotechnological strategy applicable to the abundant production of crops, vegetables, and fruits in agri-food chains. H2 is a potential green alternative to conventional chemical fertilizers. The use of a hydrogen-rich water irrigation system may also provide other health-related advantages, i.e., decreasing the heavy metal accumulation in crops. By adopting a H2 strategy, crop producers, food processors, and decision-makers can contribute to sustainable solutions in the face of global challenges such as climate change, communicable disease epidemics, and a growing population. The versatile applications of H₂ in agriculture and the wider food industry position it as a uniquely suitable approach to address today’s significant challenges, potentially fostering better crop production and positively impacting the agri-food chain. The present review is timely in combining the latest knowledge about the potential applications of H2 in the agriculture and food industry, from farm to fork.

Camouflaged cotton bollworm instance segmentation based on PVT and Mask R-CNN

Many pests change their appearance color to seamlessly blend with the surrounding environment in agricultural ecosystems, thereby rendering themselves virtually invisible. When the pest’s color and texture resemble the background, accurately identifying and detecting it becomes challenging. In this study, we construct a new dataset focusing on the cotton bollworm and conduct in-depth optimization and improvement of the instance segmentation model based on the Pyramid Vision Transformer (PVT) and Mask R-CNN. To better capture the features of camouflaged organisms, the proposed model utilizes the PVT as a feature extraction network and Mask-RCNN for instance segmentation. We also introduce overlapping image embedding patch structure and further incorporate a feed-forward network with depthwise separable convolution. These improvements enhance the PVT’s capability to capture global and intricate features and significantly boost the accuracy of instance segmentation. Considering the computational efficiency demands in real-time agricultural applications, we introduce a linear spatial-reduction attention mechanism that effectively reduces computational complexity. The experimental results show that the model achieves the detection accuracy of 89.7% and the segmentation accuracy of 89.2% for camouflaged cotton bollworms.

Creating ultra-strong and recyclable green plastics from marine-sourced alginate-chitosan nanowhisker nanocomposites for controlled release urea fertilizer

In response to the pressing environmental challenge posed by petroleum-derived plastics, the development of green plastics derived from all-biomass nanocomposites offers promising solutions. However, conventional nanocomposites often prioritize enhanced stiffness at the expense of flexibility. We introduce sodium alginate (SA)/chitosan nanowhisker (CSW) nanocomposites, derived entirely from marine-sourced all-biomass, to create ultra-strong and flexible green plastics. Through the synergistic interaction between SA and CSW, these nanocomposites demonstrate simultaneous stiffening and toughening, overcoming the traditional trade-off. Two key mechanisms contribute: geometric reinforcement from the needle-like structure of CSW and electrostatic reinforcement at the interface between oppositely charged CSW and SA. Compared to control SA, the SA/CSW nanocomposites exhibit remarkable enhancements in tensile modulus, strength, and stretchability, by 49%, 85%, and 55%, respectively (7.6 GPa, 223.3 MPa, 14.7%). Cellulose nanocrystals, serving as a control, only stiffen the nanocomposites, adhering to the typical trade-off. Biodegradability in compost can be tailored based on the type of nanofillers. Due to the water resistance of CSW, SA/CSW nanocomposites are proven effective for the controlled release of urea fertilizer in agricultural applications. With recyclability and superior mechanical properties, these marine-sourced green plastics offer a sustainable alternative to conventional plastics, promising significant impact in the eco-plastic industry.

Bioactive Insecticides from Chemometric Diverse Ant-Associated Symbionts Streptomyces novaecaesareae and Streptomyces nojiriensis against the Fall Armyworm Larvae.

The Streptomyces genus has long been recognized as a prolific and valuable source of diverse secondary metabolites. These metabolites contribute significantly to the intricate chemical diversity exhibited by Streptomyces, making them an indispensable reservoir for drug discovery, agricultural applications, and industrial processes. Exploiting the potential of these natural compounds holds the promise of ushering in a new era in insect pest management, reducing reliance on synthetic chemicals and fostering ecologically sustainable solutions. This study dives into the realm of chemo diversity within isolates of Streptomyces nojiriensis and Streptomyces novaecaesareae, with a specific focus on the production of insecticidal compounds. We explored chromatographic techniques for the identification and isolation of insecticidal compounds, and two bioactive compounds were identified in extracts of S. novaecaesareae. Valinomycin was identified from hexanic extracts of strain Asp59, while naphthomycin from ethyl acetate extracts of strain Asp58. These compounds showed insecticidal activity against first instars of Spodoptera frugiperda (Asp59: LC50 = 10.82 µg/µL, LC90 = 26.25 µg/µL; Asp58: LC50 = 15.05 µg/µL, LC90 = 38.84 µg/µL). Notably, this is the first report of naphthomycin as an insecticidal compound. The present study suggests that valinomycin and naphthomycin may be a novel biological source for the control of Spodoptera frugiperda in early stages.

High-efficiency breeding of Bacillus siamensis with hyper macrolactins production using physical mutagenesis and a high-throughput culture system

Macrolactins have attracted considerable attention due to their value and application in medicine and agriculture. However, poor yields severely hinder their broader application in these fields. This study aimed to improve macrolactins production in Bacillus siamensis using a combined atmospheric and room-temperature plasma mutagenesis and a microbial microdroplet culture system. After 25 days of treatment, a desirable strain with macrolactins production 3.0-fold higher than that of the parental strain was successfully selected. The addition of 30 mg/L ZnSO4 further increased macrolactins production to 503 ± 37.6 μg/mL, representing a 30.9 % improvement in production compared to controls. Based on transcriptome analysis, the synthesis pathways of amino acids, fengycin, and surfactin were found to be downregulated in IMD4036. Further fermentation experiments confirmed that inhibition of the comparative fengycin synthesis pathway was potentially driving the increased production of macrolactins. The strategies and possible mechanisms detailed in this study can provide insight into enhancing the production of other secondary metabolites toxic to the producer strains.

Integrated Valorization of Fucus spiralis Alga: Polysaccharides and Bioactives for Edible Films and Residues as Biostimulants.

Fucus spp. seaweeds thrive in the cold temperate waters of the northern hemisphere, specifically in the littoral and sublittoral regions along rocky shorelines. Moreover, they are known to be a rich source of bioactive compounds. This study explored the valorization of Fucus spiralis through the extraction of bioactives and polysaccharides (PSs) for food applications and biostimulant use. The bioactives were extracted using microwave hydrodiffusion and gravity (MHG), where the condition of 300 W for 20 min resulted in the highest total phenolic content and antioxidant activity of the extract. Cellular assays confirmed that the extract, at 0.5 mg/mL, was non-cytotoxic to HaCat cells. Polysaccharides (PSs) were extracted from the remaining biomass. The residue from this second extraction contained 1.5% protein and 13.35% carbohydrates. Additionally, the free amino acids and minerals profiles of both solid residues were determined. An edible film was formulated using alginate (2%), PS-rich Fucus spiralis extract (0.5%), and F. spiralis bioactive-rich extract (0.25%). The film demonstrated significant antioxidant properties, with ABTS and DPPH values of 221.460 ± 10.389 and 186.889 ± 36.062 µM TE/mg film, respectively. It also exhibited notable physical characteristics, including high water vapor permeability (11.15 ± 1.55 g.mm.m-2.day-1.kPa-1) and 100% water solubility. The residues from both extractions of Fucus spiralis exhibited biostimulant (BS) effects on seed germination and seedling growth. BSs with PSs enhanced pea germination by 48%, while BSs without PSs increased the root dry weight of rice and tomato by 53% and up to 176%, respectively, as well as the shoot dry weight by up to 38% and up to 74%, respectively. These findings underscore the potential of Fucus spiralis within the framework of a circular economy, wherein both extracted bioactives and post-extraction by-products can be used for sustainable agriculture and food applications.

Role of Artificial Intelligence in Vegetable Production: A Review

Role of Artificial Intelligence (AI) in vegetable production, emphasizing its potential to address critical challenges such as climate change, population growth, and resource scarcity. AI technologies, including machine learning, computer vision, and robotics, are revolutionizing agricultural practices. AI-driven innovations in crop management, pest control, and soil analysis enhance productivity, reduce labour costs, and ensure sustainable farming practices. Notable advancements include precision spraying by Blue River Technology, significantly reducing herbicide use, and deploying autonomous tractors and drones for efficient farm management. AI applications, such as PEAT's Plantix and Trace Genomics, provide accurate diagnostics for soil health and pest management. Satellite-based solutions like Farm Shots and aWhere offer real-time crop monitoring and weather prediction, optimizing resource use and mitigating risks. The review highlights the importance of making AI technologies more affordable and accessible to farmers, particularly in developing regions. Collaboration between researchers, industry stakeholders, and policymakers is crucial to harness AI's full potential in agriculture. As AI continues to evolve, its integration into vegetable production promises a more efficient, resilient, and sustainable agricultural sector, contributing to global food security and environmental preservation. The aim of the study is to evaluate the impact and effectiveness of Artificial Intelligence (AI) in vegetable production, focusing on how AI technologies enhance productivity, efficiency, and sustainability. The objectives are to assess current AI applications, analyze their benefits and challenges, and provide recommendations for future improvements and wider adoption in the agricultural sector. The research methodology for the study on the role of Artificial Intelligence in vegetable production involves a comprehensive literature review of existing AI technologies and their applications in agriculture, coupled with the analysis of case studies to evaluate real-world implementations. Additionally, expert interviews and surveys with farmers and industry professionals will be conducted to gather insights on the benefits, challenges, and future potential of AI in this sector. The theoretical implications of the study on the role of Artificial Intelligence in vegetable production include advancing the understanding of AI's capabilities in agricultural optimization and contributing to the academic discourse on sustainable farming practices. Practically, the study provides actionable insights for farmers and agribusinesses on implementing AI technologies to enhance crop yields, reduce resource wastage, and improve overall farm management efficiency.

Infrared Thermography as a Diagnostic Tool for the Assessment of Mastitis in Dairy Ruminants.

Among the health issues of major concern in dairy ruminants, mastitis stands out as being associated with considerable losses in productivity and compromised animal health and welfare. Currently, the available methods for the early detection of mastitis are either inaccurate, requiring further validation, or expensive and labor intensive. Moreover, most of them cannot be applied at the point of care. Infrared thermography (IRT) is a rapid, non-invasive technology that can be used in situ to measure udder temperature and identify variations and inconsistencies thereof, serving as a benchmarking tool for the assessment of udders' physiological and/or health status. Despite the numerous applications in livestock farming, IRT is still underexploited due to the lack of standardized operation procedures and significant gaps regarding the optimum settings of the thermal cameras, which are currently exploited on a case-specific basis. Therefore, the objective of this review paper was twofold: first, to provide the state of knowledge on the applications of IRT for the assessment of udder health status in dairy ruminants, and second, to summarize and discuss the major strengths and weaknesses of IRT application at the point of care, as well as future challenges and opportunities of its extensive adoption for the diagnosis of udder health status and control of mastitis at the animal and herd levels.

Optimization of fermentation conditions for the production of recombinant feruloyl esterase BpFaeT132C-D143C.

Feruloyl esterases (FAEs) are a crucial component of the hemicellulose-degrading enzyme family that facilitates the degradation of lignocellulose while releasing hydroxycinnamic acids such as ferulic acid with high added value. Currently, the low enzyme yield of FAEs is one of the primary factors limiting its application. Therefore, in this paper, we optimized the fermentation conditions for the expression of FAEBpFaeT132C-D143C with excellent thermal stability in Escherichia coli by experimental design. Firstly, we explored the effects of 11 factors such as medium type, isopropyl-β-D-thiogalactopyranoside (IPTG) concentration, and inoculum size on BpFaeT132C-D143C activity separately by the single factor design. Then, the significance of the effects of seven factors, such as post-induction temperature, shaker rotational speed, and inoculum size on BpFaeT132C-D143C activity, was analyzed by Plackett-Burman design. We identified the main factors affecting the fermentation conditions of E. coli expressing BpFaeT132C-D143C as post-induction temperature, pre-induction period, and post-induction period. Finally, we used the steepest ascent path design and response surface method to optimize the levels of these three factors further. Under the optimal conditions, the activity of BpFaeT132C-D143C was 3.58 U/ml, which was a significant 6.6-fold increase compared to the pre-optimization (0.47 U/ml), demonstrating the effectiveness of this optimization process. Moreover, BpFaeT132C-D143C activity was 1.52 U/ml in a 3-l fermenter under the abovementioned optimal conditions. It was determined that the expression of BpFaeT132C-D143C in E. coli was predominantly intracellular in the cytoplasm. This study lays the foundation for further research on BpFaeT132C-D143C in degrading agricultural waste transformation applications.