Search Results (3,598)

The precise measuring and control of fiber tension are critically important for enhancing structural and mechanical properties in spinning processes, as tension directly influences orientation, crystallinity, and mechanical properties. However, current tension measurement methods primarily operate offline and lack real-time measuring capabilities. A non-contact fiber tension detection system is introduced to investigate the effects of draw tension and its uniformity on the structure and mechanical properties of polyester fibers. During experiments conducted at a spinning speed of 1200 m/min across different draw ratios, the non-contact system demonstrated strong agreement with the contact tension detector. The results showed that increasing the tension from 34 cN to 164 cN reduced the monofilament diameter from 39.61 µm to 20.35 µm. Simultaneously, the orientation factor nearly tripled, while crystallinity increased from 55.72% to 77.39%. Mechanical testing revealed a 50.96% improvement in breaking strength, rising from 1.57 to 2.37 cN/dtex, accompanied by a significant decrease in elongation at break from 275.55% to 34.95%. However, tension fluctuations, characterized by an average fluctuation coefficient increase from 4.51% to 18.18%, caused diameter inconsistency. These fluctuations also reduced the orientation factor by 10.78%, lowered crystallinity, and substantially deteriorated mechanical properties. These findings underscore the critical importance of real-time, online tension monitoring for ensuring polyester fiber quality and performance during production. Full article

Feed is an important source of antibiotic resistance genes (ARGs) in animals and products, posing significant potential risks to human health and the environment. Ensiling may present a feasible method for reducing ARGs in animal feed. This study involved the addition of four types of lactic acid bacteria (LAB) inoculants, Lactiplantibacillus plantarum (LP), Pediococcus acidilactici (P), Enterococcus faecium (E), and Ligilactobacillus salivarius (LS), to whole-crop corn silage to investigate changes in ARGs, mobile genetic elements (MGEs), and their transmission risks during ensiling. The results indicated that the addition of LAB significantly reduced the ammonia nitrogen content and pH value of whole-crop corn silage, inhibited the growth of harmful microorganisms, and increased the lactic acid content (p < 0.05). The improvement effect was particularly pronounced in the P treatment group. Natural fermentation plays a significant role in reducing ARG abundance, and the addition of different types of lactic acid bacteria helps reduce the abundance of both ARGs and MGEs. Specifically, the LS treatment group exhibited a significant decrease in MGE abundance, potentially reducing the horizontal transmission risk of ARGs. Furthermore, variations in ARG abundance within different LAB strains were detected, showing a consistent trend with that in silage. ARGs and MGEs were correlated with the fermentation parameters and microbial communities (p < 0.05). This suggests that adding LAB with low levels of ARGs to silage can effectively reduce ARG contamination. Bacterial community structure, MGEs, and fermentation quality may act as driving forces for the transfer and dissemination of ARGs in the silage ecosystem. Full article

Small ruminant production is increasingly affected by heat stress, with recent heat waves highlighting growing economic and welfare-related challenges. Chronic exposure to elevated temperatures disrupts thermoregulation, reduces feed intake, slows growth, compromises meat quality, and increases mortality. This study evaluated the effects of chromium-yeast supplementation at different doses and timepoints on physiological and molecular stress biomarkers in heat-stressed lambs. Forty-eight clinically healthy 6-month-old Katahdin lambs (average weight 20 ± 2.9 kg) were assigned to a 2 × 4 factorial design, with two ambient temperature conditions (heat stress [HS] and thermoneutral [TN]) and four levels of dietary Cr-yeast (0, 0.2, 0.4, and 0.8 mg/kg of dry matter intake). Lambs were housed individually in pens (1.2 × 2.5 m), with ad libitum access to water, and fed a 50:50 corn silage and concentrate diet (excluding mineral premix) twice daily. Blood samples were collected at days 0, 30, and 60 to evaluate plasma cortisol and the expression of hsp60 and hsp70. Chromium bioavailability was assessed by blood levels using absorption chromatography, and glucose clearance was measured at the end of the experiment. Significant reductions in cortisol and hsp70 expression were observed after 30 days of Cr-yeast supplementation under HS conditions (p < 0.05), particularly at the highest dose. For hsp60, a significant reduction was observed at the highest dose on day 30 under HS (p < 0.05). These effects were not sustained on day 60 (p > 0.05). No significant differences were detected under TN conditions (p > 0.05). These findings suggest that Cr-yeast may offer short-term physiological and cellular protection against chronic heat stress in lambs. Full article

Mastitis, an inflammatory disease caused by the invasion of various pathogenic microorganisms into mammary gland tissue, is a core health issue plaguing the global dairy industry. The consequences of this disease are manifold. In addition to directly compromising the health and welfare of dairy cows, it also precipitates a substantial decline in lactation function, a precipitous drop in raw milk production, and alterations in milk composition (e.g., increased somatic cell counts and imbalanced ratios of milk protein to fat). These changes result in a marked degradation of milk quality and safety, and in turn, engender significant economic losses for the livestock industry. Therefore, the establishment and implementation of a comprehensive prevention and control system is a key strategy to effectively curb the occurrence of mastitis, reduce its incidence rate, and minimise economic losses. This review systematically explores the complex etiological factors and pathogenic mechanisms of mastitis in dairy cows, and summarises various diagnostic methods, including milk apparent indicators monitoring, pathogen detection, physiological parameter monitoring, omics technologies, and emerging technologies. Furthermore, it undertakes an analysis of treatment protocols for mastitis in dairy cows, with a particular emphasis on the significance of rational antibiotic use and alternative therapies. Moreover, it delineates preventive measures encompassing both environmental and hygiene management, and dairy cow health management. The objective of this paper is to provide a comprehensive and scientific theoretical basis and practical guidance for dairy farming practices. This will help to improve the health of dairy cows, ensure a stable supply of high-quality dairy products, and promote the sustainable and healthy development of the dairy farming industry. Full article

Burned area (BA) mapping and fire severity assessment are essential for understanding fire occurrence patterns, formulating post-fire restoration strategies and evaluating vegetation recovery processes. However, existing BA datasets are primarily derived from coarse-resolution satellite imagery and often lack sufficient consideration of fire severity. To address these limitations, this study utilized dense time-series Landsat imagery available on the Google Earth Engine, applying the qualityMosaic method to generate annual composites of minimum normalized burn ratio values. These composites imagery enabled the rapid identification of fire sample points, which were subsequently used to train a random forest classifier for estimating per-pixel burn probability. Pixels with a burned probability greater than 0.9 were selected as the core of the BA, and used as candidate seeds for region growing to further expand the core and extract complete BA. This two-stage extraction method effectively balances omission and commission errors. To avoid the repeated detection of unrecovered BA, this study developed distinct correction rules based on the differing post-fire recovery characteristics of forests and grasslands. The extracted BA were further categorized into four fire severity levels using the delta normalized burn ratio. In addition, we conducted a quantitative validation of the BA mapping accuracy based on Sentinel-2 data between 2015 and 2023. The results indicated that the BA mapping achieved an overall accuracy of 93.90%, with a Dice coefficient of 82.04%, and omission and commission error rates of 26.32% and 5.25%, respectively. The BA dataset generated in this study exhibited good spatiotemporal consistency with existing products, including MCD64A1, FireCCI51, and GABAM. The BA fluctuated significantly between 1985 and 2010, with the highest value recorded in 1987 (13,315 km²). The overall trend of BA showed a decline, with annual burned areas remaining below 2000 km² after 2010 and reaching a minimum of 92.8 km² in 2020. There was no significant temporal variation across different fire severity levels. The area of high-severity burns showed a positive correlation with the annual total BA. High-severity fire-prone zones were primarily concentrated in the northeastern, southeastern, and western parts of the study area, predominantly within grasslands and forest–grassland ecotone regions. Full article

Background/Objectives: Drug products on the pharmaceutical market must meet a number of requirements that guarantee their quality, safety, and efficacy. Accordingly, periodic inspection of the content of active pharmaceutical ingredients (APIs) in marketed drug products is carried out, confirming that they meet all quality and quantity requirements for a given drug formulation before the expiration date. Therefore, the purpose of this study was to evaluate the suitability of the most commonly used thermal analysis methods, differential thermal analysis (DTA), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA), in the control of the composition of commercially available drug products. Results: Based on a review of the literature, it was shown that thermal methods can be useful in distinguishing drug products from different manufacturers, which guarantees their usefulness in quality control of finished drug products and detecting drug products from illegal manufacturers. They are also useful as tools for confirming the presence of APIs in dosage forms under investigation. The cited literature also indicates that DSC and TGA methods can be used in the quantification of APIs in marketed drug products and to detect non-compliant drug products. The use of chemometric techniques to interpret thermal data can eliminate the adverse effects of excipients on quantification results. Conclusions: Thermal methods are a good complement to chromatographic and spectroscopic methods, with the particular advantages of not needing any sample pretreatment, low sample weight, and short analysis time. Full article

Focusing on the characteristic tea resource Zijuan tea, this study addresses the difficulty of grading on production lines and the complexity of quality evaluation. On the basis of the fusion of near-infrared (NIR) spectroscopy and visual features, a novel method is proposed for classifying different tenderness levels and quantitatively assessing key anthocyanin components in Zijuan tea fresh leaves. First, NIR spectra and visual feature data were collected, and anthocyanin components were quantitatively analyzed using UHPLC-Q-Exactive/MS. Then, four preprocessing techniques and three wavelength selection methods were applied to both individual and fused datasets. Tenderness classification models were developed using Particle Swarm Optimization–Support Vector Machine (PSO-SVM), Random Forest (RF), and Convolutional Neural Networks (CNNs). Additionally, prediction models for key anthocyanin content were established using linear Partial Least Squares Regression (PLSR), nonlinear Support Vector Regression (SVR) and RF. The results revealed significant differences in NIR spectral characteristics across different tenderness levels. Model combinations such as TEX + Medfilt + RF and NIR + Medfilt + CNN achieved 100% accuracy in both training and testing sets, demonstrating robust classification performance. The optimal models for predicting key anthocyanin contents also exhibited excellent predictive accuracy, enabling the rapid and nondestructive detection of six major anthocyanin components. This study provides a reliable and efficient method for intelligent tenderness classification and the rapid, nondestructive detection of key anthocyanin compounds in Zijuan tea, holding promising potential for quality control and raw material grading in the specialty tea industry. Full article

With the rapid development of agricultural intelligence, nondestructive testing (NDT) has shown considerable promise for agricultural product inspection. Compared with traditional methods—which often suffer from subjectivity, low efficiency, and sample damage—NDT offers rapid, accurate, and non-invasive solutions that enable precise inspection without harming the products. These inherent advantages have promoted the increasing adoption of NDT technologies in agriculture. Meanwhile, rising quality standards for agricultural products have intensified the demand for more efficient and reliable detection methods, accelerating the replacement of conventional techniques by advanced NDT approaches. Nevertheless, selecting the most appropriate NDT method for a given agricultural inspection task remains challenging, due to the wide diversity in product structures, compositions, and inspection requirements. To address this challenge, this paper presents a review of recent advancements and applications of several widely adopted NDT techniques, including computer vision, near-infrared spectroscopy, hyperspectral imaging, computed tomography, and electronic noses, focusing specifically on their application in agricultural product evaluation. Furthermore, the strengths and limitations of each technology are discussed comprehensively, quantitative performance indicators and adoption trends are summarized, and practical recommendations are provided for selecting suitable NDT techniques according to various agricultural inspection tasks. By highlighting both technical progress and persisting challenges, this review provides actionable theoretical and technical guidance, aiming to support researchers and practitioners in advancing the effective and sustainable application of cutting-edge NDT methods in agriculture. Full article

Cryptosporidium is a major waterborne parasite that causes gastrointestinal illness. Conventional assays, including microscopy and immunological identification, often suffer from false positives or negatives due to non-specific binding or morphological differences between Cryptosporidium species. We developed a novel qPCR assay based on a Cryptosporidium-specific Conserved Signature Protein (CSP) to address the limitations of testing complex samples, including those from recreational waters. The CSP (hypothetical protein (cgd2_3830)) was identified as taxonomically unique to Cryptosporidium species. The CSP sequence and designed qPCR assay primers/probe demonstrated high specificity for the targeted Cryptosporidium species when tested against NCBI RefSeq databases. qPCR assay efficiency was determined as 95% and an R² value of 0.99, with a slope and intercept of −3.4 and 40.1, respectively. Additionally, the Lower Limit of Detection (ALLOD) was determined as three gene copies, suggesting the potential to detect even a single oocyst. No non-specific amplification products or primer dimers were observed when the qPCR assay was evaluated using recreational water, fecal solution, and wastewater, while spike-in-control tests indicated minimal interference with the sensitivity of the assay, highlighting application for testing complex environmental DNA extracts. These findings highlight the application of the novel CSP-based qPCR assay for the rapid and sensitive detection of Cryptosporidium sp., thereby circumventing the sequence variability and multi-copy limitations associated with existing molecular markers. This proof-of-concept study presents a diagnostic framework utilizing CSP-based markers for developing water quality monitoring strategies, with scope for expansion to other microbial pathogens and potential applications in clinical and food safety settings. Full article

To achieve dual functionality that can monitor both Al³⁺ levels in food and the freshness of fish, rice straw fibers (RSFs) were treated in NaOH solutions and then cationized with 2,3-epoxypropyltrimethylammonium chloride, onto which alizarin red S molecules were immobilized through electrostatic interaction to develop a smart felt-like label. An optimized treatment in 5 wt% NaOH solution effectively removed lignin and hemicellulose, facilitating quaternary ammonium group grafting and stable ARS anchoring. The ARS@BRSF-5NaOH exhibited high pH sensitivity, showing visually discernible color changes (ΔE > 5, perceptible to the naked eye) under acidic (pH ≤ 6) and strongly alkaline (pH > 12) conditions. During the storage of the fish, the label transformed from yellow to dark purple (ΔE increase) as TVB-N levels approached 20 mg/100 g, enabling real-time freshness monitoring for protein-rich products. Additionally, the label achieved a detection threshold of 1 × 10⁻⁵ mol·L⁻¹ for Al³⁺ through a coordination-induced chromatic transition (purple to pale pink). This research highlights the feasibility of utilizing an agricultural waste-derived material to develop cost-effective, visually responsive, dual-functional intelligent labels for food safety, offering significant advancements in on-site quality assessment. Full article

Background: Intramuscular fat (IMF) enhances marbling, improving meat quality and value. Transcriptome analysis enables the identification of genes and pathways involved in IMF deposition, supporting targeted breeding and nutritional strategies to improve beef quality. Methods: This study used RNA-Seq to compare gene expression in high- (Hereford; Her), moderate- (Holstein Friesian; Hf), and low-marbling (Limousine; Lim) Semitendinosus muscle. Using Illumina’s NovaSeqX Plus, sequencing data underwent quality control with FastQC to remove low-quality reads and adapters, followed by alignment to the bovine genome using HISAT2. Differential expression analysis was performed using DESeq2, and genes were filtered based on a threshold of p-value < 0.05 and |log2FC| > 0.5 to identify significantly regulated genes. Results: A total of 21,881 expressed genes were detected, with 3025 and 7407 significantly differentially expressed in Her and Hf vs. Lim, respectively (|log2FC| > 0.5, p < 0.05). Protein–protein interaction analysis revealed 20 hub genes, including SMAD3, SCD, PLIN2, SHH, SQLE, RXRA, NPPA, NR1H4, PRKCA, and IL10. Gene ontology and KEGG pathway analyses linked these genes to lipid metabolism and IMF-associated pathways, such as PPAR signaling, fatty acid metabolism, and PI3K–Akt signaling. Conclusions: These findings highlight RNA-Seq’s utility in uncovering the genetic basis of marbling and the importance of aligning beef production with consumer demands through genetic improvements. This study aimed to identify breed-independent molecular mechanisms of intramuscular fat deposition and shared metabolic processes in the Semitendinosus muscle to improve beef quality. Full article

It is critical to provide proper environmental conditions in poultry houses to maintain birds’ health, boost productivity, and improve the overall economic viability of the poultry industry. Among the myriad of environmental elements, indoor air quality has been a determining factor that directly affects poultry well-being. Elevated concentrations of harmful gases—in particular Carbon Dioxide ($C{O}_2$), Methane ($C{H}_4$), and Ammonia ($N{H}_3$)—decomposition products of poultry litter, feed wastage, and biological processes have draconian effects on bird health, feed efficiency, the growth rate, reproduction efficiency, and mortality rate. Despite their importance, traditional air quality monitoring systems are often operated manually, labor intensive, and cannot detect sudden environmental changes due to the lack of real-time sensing. To overcome these limitations, this paper presents an interdisciplinary approach combining cloud computing, Artificial Intelligence (AI), and Internet of Things (IoT) technologies to measure real-time poultry gas concentrations. Real-time sensor feeds are transmitted to a cloud-based platform, which stores, displays, and processes the data. Furthermore, a machine learning (ML) model was trained using historical sensory data to predict the next-day gas emission levels. A web-based platform has been developed to enable convenient user interaction and display the gas sensory readings on an interactive dashboard. Also, the developed system triggers automatic alerts when gas levels cross safe environmental thresholds. Experimental results of $C{O}_2$ concentrations showed a significant diurnal trend, peaking in the afternoon, followed by the evening, and reaching their lowest levels in the morning. In particular, $C{O}_2$ concentrations peaked at approximately 570 ppm during the afternoon, a value that was significantly elevated (p < 0.001) compared to those recorded in the evening (~560 ppm) and morning (~555 ppm). This finding indicates a distinct diurnal pattern in $C{O}_2$ accumulation, with peak concentrations occurring during the warmer afternoon hours. Full article

Background/Objectives: Dry Eye Disease (DED) significantly impacts quality of life due to the instability of the tear film and reduced tear production. The limited availability of eye care professionals, combined with traditional diagnostic methods that are invasive, non-portable, and time-consuming, results in delayed detection and hindered treatment. This proof-of-concept study aims to explore the feasibility of using smartphone-based infrared thermography (IRT) as a non-invasive, portable screening method for DED. Methods: This study included infrared thermography (IRT) images of 40 subjects (22 normal and 58 DED). Ocular surface temperature changes at three regions of interest (ROIs): nasal cornea, center cornea, and temporal cornea, were compared with Tear Film Break-up Time (TBUT) and Ocular Surface Disease Index (OSDI) scores. Statistical correlations and independent t-tests were performed, while machine learning (ML) models classified normal vs. DED eyes. Results: In these preliminary results, DED eyes exhibited a significantly faster cooling rate (p < 0.001). TBUT showed a negative correlation with OSDI (r = −0.802, p < 0.001) and positive correlations with cooling rates in the nasal cornea (r = 0.717, p < 0.001), center cornea (r = 0.764, p < 0.001), and temporal cornea (r = 0.669, p < 0.001) regions. Independent t-tests confirmed significant differences between normal and DED eyes across all parameters (p < 0.001). The Quadratic Support Vector Machine (SVM) achieved the highest accuracy among SVM models (90.54%), while the k-Nearest Neighbours (k-NN) model using Euclidean distance (k = 3) outperformed overall with 91.89% accuracy, demonstrating strong potential for DED classification. Conclusions: This study provides initial evidence supporting the use of smartphone-based infrared thermography (IRT) as a screening tool for DED. The promising classification performance highlights the potential of this approach, though further validation on larger and more diverse datasets is necessary to advance toward clinical application. Full article

In recent years, ultrasound has been integrated into fermentation technology due to its activating effect on microorganisms, and the possible effects of ultrasound-assisted fermentation on the fermentation process, yield and quality of the final product have also attracted attention. This study aimed to reveal the effects of sonication applied before the fermentation on the fermentation process and the quality of fermented black carrot juice. The samples were sonicated at a frequency of 35 kHz and an amplitude of 60% for 0, 5, 15 or 30 min before the fermentation. During the fermentation, the pH, acidity, organic acid profile, ethanol and soluble solid content (SSC), color, turbidity, total lactic acid bacteria (LAB), total mesophilic aerobic bacteria (TMAB) and yeast counts were determined. The amount of SSC in the samples increased at the beginning of fermentation as the sonication time increased. Lactic, acetic and propionic acids were detected in the samples. The amount of lactic acid in all the samples treated with ultrasound was higher than in the control sample and the amounts of acetic acid, propionic acid and ethanol were lower. Ultrasound application caused an increase in the TMAB and yeast counts. A five-minute ultrasound application caused a decrease in the number of LAB, while 15- and 30-min applications caused an increase. Thirty minutes of ultrasound treatment resulted in the reddest fermented black carrot juices with the highest level of color saturation. The most appreciated sample in terms of taste, aroma and general acceptability was the sample subjected to a five-minute ultrasound application. As a result, ultrasound application before fermentation positively supports different quality parameters of fermented black carrot juice and the use of sonication in production can be recommended. Full article

Accurate label claims are essential for consumer trust in probiotic efficacy, yet limited datasets are available for poultry formulations marketed in the United Kingdom. We quantified and identified the viable bacteria in twelve commercial probiotics, seven for poultry and five for human use, using selective plate counts and MALDI-TOF MS. Observed colony forming units (CFU) were compared with declared values using one-sample t-tests, adopting a practical acceptance range of ±0.5 log CFU. Poultry products largely met or exceeded their labels (e.g., P5: 1.4 × 10¹⁰ CFU g⁻¹ vs. 2 × 10⁹ CFU g⁻¹ declared), whereas human products delivered greater variability in both species composition and stated CFU count; one contained no detectable viable bacteria. All products deviated significantly from their label claims (p < 0.05); however, 11 of 12 met the ±0.5 log₁₀ CFU benchmark—10 within the range and 1 above its “≥” value—leaving only one probiotic below the threshold. MALDI-TOF MS confirmed the presence of most labelled species, though Bifidobacterium bifidum was absent from one human product and Bacillus isolates were re-assigned to B. velezensis/B. amyloliquefaciens. These findings indicate robust quality assurance in UK poultry probiotics, but substantial under-delivery in the human probiotics, underscoring the need for harmonized viability standards and tighter post-market surveillance. Full article