Search Results (35)

During growth and metabolism, the budding yeast Saccharomyces cerevisiae is continuously exposed to diverse environmental stresses, and conserved mitogen-activated protein kinase (MAPK) cascades execute core functions in stress sensing and adaptive regulation. The high-osmolarity glycerol (HOG) pathway, a classical MAPK cascade in S. cerevisiae, was initially identified as the core regulator of hyperosmotic stress responses. Over recent decades, accumulating evidence has revealed that the HOG pathway is not merely an osmoregulatory module but a versatile signaling hub that integrates multiple stress inputs and orchestrates a broad spectrum of adaptive responses. This review systematically summarizes the core architecture of the HOG pathway, including its upstream sensing branches (Sln1 and Sho1) and the conserved three-tiered MAPK cascade, with an emphasis on how different stressors engage distinct branches and lead to differential Hog1 phosphorylation kinetics. This review further discusses the multifaceted roles of the HOG pathway in stress adaptation, covering transcriptional reprogramming, cell cycle arrest, metabolic reprogramming centered on glycerol synthesis, and emerging functions such as cell wall remodeling, flocculation, mitophagy, and cross-talk with other MAPK pathways. By integrating classical and contemporary findings, this review presents a comprehensive view of the HOG pathway in S. cerevisiae and provides a reference for future research on stress signaling and engineering of this model organism. Full article

Background/Objectives: Azole resistance in fungal pathogens is a growing clinical and environmental concern. However, short-term cellular responses during the first hours of azole exposure remain insufficiently characterised, particularly in filamentous fungi. This study used Neurospora crassa as a genetically tractable model to investigate early cellular responses to azole stress. Methods: Exponentially growing mycelia were exposed to conidium-derived MIC₈₀ reference concentrations of fluconazole, voriconazole, ravuconazole, and ketoconazole. Early responses were assessed by monitoring radial growth, intracellular glycerol, H₂DCF-DA microscopy, antioxidant enzyme activities, Calcofluor White staining, and RT-qPCR analysis of genes associated with osmoregulation, sterol homeostasis, oxidative stress, and cell-wall remodelling. Results: Azole exposure was associated with reduced net post-transfer radial growth and rapid treatment- and time-dependent changes in intracellular glycerol. Induction of hog1 together with treatment-dependent changes in gpd1 and glycerol accumulation was consistent with an early osmoregulatory response. Sterol-homeostasis genes showed selective feedback regulation, and several cell-wall-remodelling genes underwent treatment-dependent transcriptional changes. Qualitative H₂DCF-DA microscopy showed limited oxidant-associated fluorescence during azole challenge. Catalase activity remained close to control levels, whereas representative SOD-activity experiments showed larger fold changes after voriconazole and ravuconazole exposure. Representative Calcofluor White micrographs showed irregular septation and localised regions of enhanced cell-wall-associated staining. Conclusions: Neurospora crassa shows multiple concurrent early responses to azole stress involving osmotic adjustment, sterol-pathway feedback, antioxidant responses, and cell-wall remodelling. These findings provide a framework for future studies examining how such stress responses contribute to recovery, tolerance, or longer-term adaptation. Full article

First homesteaded in 1870, Longdale Farm (289 ha) in the Driftless Area hill country of west central Wisconsin, USA, has remained under the same family ownership since 1888 (138 years and five generations). Because such long-term family farm ownership and operation is very rare, our objective was to document and describe how the history and operational changes that took place on this family farm allowed it to continue under management of the same family, while so many other family farms were being sold off or transferred to commercial entities. We examined available farm records, diaries, and documents, governmental records, and historical photographs, and questioned living family members to gather data that allowed us to examine expected shifts away from self-sustaining farming, decreasing farm diversification, and changes in family interest in active farming. Hand-written entries in a multi-volume farm book or diary spanning 1913 through 2007 and information provided by living family members allowed for a generation-by-generation reconstruction of farm activities and assessments of changing practices. The farm has remained as a diversified operation throughout its history, including crop production, livestock (dairy and beef cattle, hogs, chickens) rearing, timber harvest, and wildlife and fish harvest. Farming priorities have shifted significantly across the generations, moving away from being largely self-sustaining but not becoming specialized in one particular area (e.g., dairy production) as have neighboring farms, instead remaining as a diversified operation. Multiple generations of family-owners still reside on and manage the farm with assistance from governmental land management professionals, but the farm’s croplands and pastures are now leased to neighboring farmers as a major source of farm income. Although the latest generations of farm owners have shifted away from active crop and livestock farming, Longdale Farm continues as a sustainable, revenue-generating operation available to future generations. Full article

Clothing classification by occasion is an important area in computer vision and artificial intelligence (AI). This task is particularly challenging because of the subtle visual similarities among clothing categories such as formal, party, and casual attire. Variations in color, fabric, patterns, and lighting further increase the complexity of this task. To address this challenge, we used the Fashionpedia dataset to create a balanced subset of 15,000 images. Specifically, we adopted two different methods for labeling these images: automated classification, which relies on category identifications (IDs) and components, and manual labeling performed by human annotators. We then implemented our preprocessing pipeline, which includes several steps: resizing, image normalization, background removal using segmentation masks, and class balancing. We benchmarked traditional models, including artificial neural networks (ANNs), support vector machines (SVMs), and k-nearest neighbors (KNNs), which use a histogram of oriented gradient (HOG) features, as well as deep learning models such as convolutional neural networks (CNNs), the Visual Geometry Group 16 (VGG16) model utilizing transfer learning, and the vision transformer (ViT) model, all evaluated using identical data splits and preprocessing procedures. The traditional models achieved moderate accuracy, ranging from 54% to 66%. In contrast, the ViT model achieved an accuracy of 81.78% with automated classification and 98.09% with manual labeling. This indicates that a higher label accuracy, along with the preprocessing steps used, significantly enhances the performance. Together, these factors improve the effectiveness of ViT in context-aware apparel classification and establish a reliable baseline for future research. Full article

The integrated crop–livestock farming model not only enhances resource utilization efficiency and reduces environmental pollution but also serves as a vital pathway for promoting sustainable agricultural development. System dynamics models enable in-depth analysis of dynamic system changes. Therefore, in this study, we constructed a comprehensive system dynamics model for the combination of farming and raising in Jilin Province, and used Vensim-PLE software(Version 6.3) to simulate and predict the dynamic changes of the agricultural structure and the future development trend based on the data of the planting and raising industry from 2006 to 2021. The results of the study show that: (1) It is expected that by 2035, the grain crop production, cash crop production, hog and beef cattle output, as well as the utilization of straw and livestock and poultry manure in Jilin Province will increase significantly. And the livestock and poultry manure load warning value reaches 0.35, which does not pose a threat to the environment. (2) By setting the simulation structure optimization analysis, it is found that the integrated crop-livestock systems performs the best in terms of economic, social, ecological and environmental benefits. (3) In 2035, Jilin Province should control the total crop planting area within 7250 thousand hectares, the number of hog output should not exceed 25 million, and the number of beef cattle slaughtered should not exceed 4.25 million, so as to ensure that the early warning value of livestock and poultry fecal matter loading is lower than 0.4, and to achieve the balance of farming and maximize economic benefits. Finally, this paper proposes policy recommendations to optimize crop structures, develop precision farming and aquaculture technologies, establish resource recycling projects, and strengthen policy support and technology promotion. Full article

Background: Dental disorders, such as cavities, periodontal disease, and periapical infections, remain major global health issues, often resulting in pain, tooth loss, and systemic complications if not identified early. Traditional diagnostic methods rely heavily on visual inspection and manual interpretation of panoramic X-ray images by dental professionals, making them time-consuming, subjective, and less accessible in resource-limited settings. Objectives: Accurate and timely diagnosis is vital for effective treatment and prevention of disease progression, reducing healthcare costs and patient discomfort. Recent advances in deep learning (DL) have demonstrated remarkable potential to automate and improve the precision of dental diagnostics by objectively analyzing panoramic, periapical, and bitewing X-rays. Methods: In this research, a hybrid feature-fusion framework is proposed. It integrates handcrafted Histogram of Oriented Gradients (HOG) features with deep representations from DenseNet-201 and the Shifted Window (Swin) Transformer models. Sequential dependencies among the fused features were learned utilizing the Long Short-Term Memory (LSTM) classifier. The framework was evaluated on the Dental Radiography Analysis and Diagnosis (DRAD) dataset following preprocessing steps, including resizing, normalization, Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement, and image cropping. Results: The proposed LSTM-based hybrid model achieved 96.47% accuracy, 91.76% specificity, 94.92% precision, 91.76% recall, and 93.14% F1-score. Conclusions: The proposed framework offers flexibility, interpretability, and strong empirical performance, making it suitable for various image-based recognition applications and serving as a reproducible framework for future research on hybrid feature fusion and sequence-based classification. Full article

With the rapid development of artificial intelligence technology, facial expression recognition (FER) has gained increasingly widespread applications in digital human generation, humanoid robotics, mental health, and human–computer dialogue. Typical FER algorithms based on machine learning have been widely studied over the past few decades, which motivated our survey. In this study, we have surveyed the state of the art in FER across two categories: traditional machine learning-based (ML-based) and deep learning-based (DL-based) approaches. Each category is analyzed based on six subcategories. Then, twelve methods, including four ML-based models and eight DL-based models, are compared to evaluate FER performance across four datasets. The experimental results show that in validation sets, the average accuracy of HOG-SVM is 50.12%, which is the best performance for the four ML-based methods; in contrast, Poster has an average accuracy of 75.98%, which is the best result obtained among the eight DL-based methods. The most difficult expression to recognize is contempt, with recognition accuracies of 10.00% and 40.06% for ML-based and DL-based methods, respectively. The accuracy of the ML-based method for identifying neutral expression is the highest at 35.25%; the DL-based method has the highest accuracy in identifying surprise at 69.56%. From the theoretical analysis and comparative experimental results of existing methods, we can see that FER faces challenges, including inaccurate recognition in complex environments and unbalanced data categories, highlighting several future research directions, especially those involving the latest applications of digital humans and large language models. Full article

Ceftiofur sodium (CFS) is a clinically significant cephalosporin widely used in the livestock and poultry industries. However, CFS that is not absorbed by animals is excreted in feces, entering the environment and contributing to the emergence of antibiotic-resistant bacteria (ARB) and antibiotic-resistant genes (ARGs). This situation poses substantial challenges to both environmental integrity and public health. Currently, research on the biodegradation of CFS is limited. In this study, we isolated a strain of Escherichia coli, designated E. coli CS-1, a Gram-negative, rod-shaped bacterium capable of utilizing CFS as its sole carbon source, from fecal samples collected from hog farms. We investigated the effects of initial CFS concentration, pH, temperature, and inoculum size on the degradation of CFS by E. coli CS-1 through a series of single-factor experiments conducted under aerobic conditions. The results indicated that E. coli CS-1 achieved the highest CFS degradation rate under the following optimal conditions: an initial CFS concentration of 50 mg/L, a pH of 7.0, a temperature of 37 °C, and an inoculum size of 6% (volume fraction). Under these conditions, E. coli CS-1 was able to completely degrade CFS within 60 h. Additionally, E. coli CS-1 exhibited significant capabilities for CFS degradation. In this study, six major degradation products of (CFS) were identified by UPLC–MS/MS: desfuroyl ceftiofur, 5-hydroxymethyl-2-furaldehyde, 7-aminodesacetoxycephalosporanic acid, 5-hydroxy-2-furoic acid, 2-furoic acid, and CEF-aldehyde. Based on these findings, two degradation pathways are proposed. Pathway I: CFS is hydrolyzed to break the sulfur–carbon (S–C) bond, generating two products. These products undergo subsequent hydrolysis and redox reactions for gradual transformation. Pathway II: The β-lactam bond of CFS is enzymatically cleaved, forming CEF-aldehyde as the primary degradation product, which is consistent with the biodegradation mechanism of most β-lactam antibiotics via β-lactam ring cleavage. Transcriptome sequencing revealed that 758 genes essential for degradation were upregulated in response to the hydrolysis and redox processes associated with CFS. Furthermore, the differentially expressed genes (DEGs) of E. coli CS-1 were functionally annotated using a combination of genomics and bioinformatics approaches. This study highlights the potential of E. coli CS-1 to degrade CFS in the environment and proposes hypotheses regarding the possible biodegradation mechanisms of CFS for future research. Full article

Knee osteoarthritis (KOA) is a leading cause of disability, particularly affecting older adults due to the deterioration of articular cartilage within the knee joint. This condition is characterized by pain, stiffness, and impaired movement, posing a significant challenge in medical diagnostics and treatment planning, especially due to the current inability for early and accurate detection or monitoring of disease progression. This research introduces a multifaceted approach employing feature extraction and machine learning (ML) to improve the accuracy of diagnosing and classifying KOA stages from radiographic images. Utilizing a dataset of 3154 knee X-ray images, this study implemented feature extraction methods such as Histogram of Oriented Gradients (HOG) with Linear Discriminant Analysis (LDA) and Min–Max scaling to prepare the data for classification. The study evaluates six ML classifiers—K Nearest Neighbors classifier, Support Vector Machine (SVM), Gaussian Naive Bayes, Decision Tree, Random Forest, and XGBoost—optimized via GridSearchCV for hyperparameter tuning within a 10-fold Stratified K-Fold cross-validation framework. An ensemble model has also been made for the already high-accuracy models to explore the possibility of enhancing the accuracy and reducing the risk of overfitting. The XGBoost classifier and the ensemble model emerged as the most efficient for multiclass classification, with an accuracy of 98.90%, distinguishing between healthy and unhealthy knees. These results underscore the potential of integrating advanced ML methodologies for the nuanced and accurate diagnosis and classification of KOA, offering new avenues for clinical application and future research in medical imaging diagnostics. Full article

Recent technological developments have enabled computers to identify and categorize facial expressions to determine a person’s emotional state in an image or a video. This process, called “Facial Expression Recognition (FER)”, has become one of the most popular research areas in computer vision. In recent times, deep FER systems have primarily concentrated on addressing two significant challenges: the problem of overfitting due to limited training data availability, and the presence of expression-unrelated variations, including illumination, head pose, image resolution, and identity bias. In this paper, a comprehensive survey is provided on deep FER, encompassing algorithms and datasets that offer insights into these intrinsic problems. Initially, this paper presents a detailed timeline showcasing the evolution of methods and datasets in deep facial expression recognition (FER). This timeline illustrates the progression and development of the techniques and data resources used in FER. Then, a comprehensive review of FER methods is introduced, including the basic principles of FER (components such as preprocessing, feature extraction and classification, and methods, etc.) from the pro-deep learning era (traditional methods using handcrafted features, i.e., SVM and HOG, etc.) to the deep learning era. Moreover, a brief introduction is provided related to the benchmark datasets (there are two categories: controlled environments (lab) and uncontrolled environments (in the wild)) used to evaluate different FER methods and a comparison of different FER models. Existing deep neural networks and related training strategies designed for FER, based on static images and dynamic image sequences, are discussed. The remaining challenges and corresponding opportunities in FER and the future directions for designing robust deep FER systems are also pinpointed. Full article

China’s large-scale hog farmers are playing an increasingly important role in promoting the stable development of the hog industry. Taking large-scale hog enterprises as samples, based on hog sales data from January 2019 to July 2022, this paper adopts a two-way fixed-effects model to test the impact, mechanism, and heterogeneity of hog futures on the production stability of large-scale hog farmers. The study found that hog futures help promote stable production of large-scale farmers. This finding still holds after a series of robustness tests. The mechanism analysis found that, first, hog futures help large-scale farmers expand their risk management factor inputs. Second, hog futures help reduce the impact of hog price risk on production. Finally, hog futures help stabilize farmers’ production expectations. The moderating effects analysis found that the stabilizing effect of hog futures will enhance as farmers’ share of hog farming operations increases. Heterogeneity analysis found that when hog prices fluctuate negatively, hog futures help promote the stable production of large-scale farmers. When hog prices fluctuate positively, the production stabilization effect of hog futures is not obvious. Therefore, hog enterprises should be encouraged to participate in hog futures hedging transactions to promote stable hog production. Full article

In the Suidae family, warthogs show significant survival adaptability and trait specificity. This study offers a comparative genomic analysis between the warthog and other Suidae species, including the Luchuan pig, Duroc pig, and Red River hog. By integrating the four genomes with sequences from the other four species, we identified 8868 single-copy orthologous genes. Based on 8868 orthologous protein sequences, phylogenetic assessments highlighted divergence timelines and unique evolutionary branches within suid species. Warthogs exist on different evolutionary branches compared to DRCs and LCs, with a divergence time preceding that of DRC and LC. Contraction and expansion analyses of warthog gene families have been conducted to elucidate the mechanisms of their evolutionary adaptations. Using GO, KEGG, and MGI databases, warthogs showed a preference for expansion in sensory genes and contraction in metabolic genes, underscoring phenotypic diversity and adaptive evolution direction. Associating genes with the QTLdb-pigSS11 database revealed links between gene families and immunity traits. The overlap of olfactory genes in immune-related QTL regions highlighted their importance in evolutionary adaptations. This work highlights the unique evolutionary strategies and adaptive mechanisms of warthogs, guiding future research into the distinct adaptability and disease resistance in pigs, particularly focusing on traits such as resistance to African Swine Fever Virus. Full article

Lateral distortional buckling (LDB) is an instability phenomenon characteristic of steel-concrete composite beams (SCCB) that occurs in the presence of hogging moments in regions close to internal supports. The LDB behavior in SCCB is not yet fully understood. The procedures for determining the LDB strength are based on the classic lateral torsional buckling theory or on the inverted U-frame model. In addition, the standard procedures make use of the classic design curves of the SSRC (Structural Stability Research Council) and ECCS (European Convention for Constructional Steelwork) developed to analyze the stability behavior of steel elements. However, studies indicate that the use of the same empirical curves obtained for the analysis of steel elements leads to the conservative results of the LDB strength in SCCB. Therefore, this article aims to assess the LDB strength in SCCB through the development of post-buckling numerical analysis using the ABAQUS software. In the parametric study, four types of steel with different mechanical properties were analyzed. In addition, the I-section, the unrestrained length, and the reinforcement rate in the concrete slab were varied. The results showed the influence of the steel type on the LDB strength and deviations from the standard procedures. A small influence of the longitudinal reinforcement area variation was verified in the LDB strength in the FE analyses; however, this factor is significantly important in the standard procedures, causing considerable divergences. These results can provide a reference for future research and specification reviews. Full article

China, the largest hog producer and consumer globally, has long experienced significant fluctuations in hog prices, partly due to the lack of rational expectations for future hog spot prices. However, on 8 January 2021, China’s first futures in animal husbandry, the live hog futures, were listed on the Dalian Commodity Exchange. To investigate the forecasting performance of the new live hog futures on forthcoming hog spot prices, we developed six futures-based forecasting models and utilized data on daily hog spot and futures prices from January 2021 to March 2023. Our results show that all six models consistently generate more accurate forecasts than the no-change model across six prediction horizons and four accuracy measures, indicating that China’s new live hog futures prices help forecast forthcoming hog spot prices. Among the futures-based forecasting models, futures spread-based models generally produce the best forecasts for one-, two-, three-, and four-month-ahead forecasting, while the simple linear regression using both spot and futures prices is the best for five- and six-month-ahead forecasting. Our results suggest that live hog futures are a promising and practical tool for various stakeholders in China’s hog industry to develop rational expectations for future hog spot prices. Full article

Uncertainty remains over composting time and maturity degree for compost production. The objectives of this study were to establish maturity indicators for composting based on spectral and chemical components and to provide a reference for future composting management. Several indicators of composting were assessed for three commercial composts at 0, 7, 15, 30, 45, and 60 days during the germination of Chinese cabbage, including (1) central temperature, (2) moisture content, (3) pH, (4) electrical conductivity, (5) C/N ratio, (6) E4/E6 ratio, (7) fluorescence humification index (HIX), and (8) germination index (GI). We evaluated the optimal composting time using these indicators, reflecting the changes in hog manure, chicken manure, and agricultural by-product composts throughout their composting process to provide a basis for maturity time. The results showed that the E4/E6 ratio, C/N ratio, humic acid (HA), fulvic acid (FA), and germination rate, which reached a stable status after 30 days of composting, could be the indicators of “early-stage” maturity. In contrast, central temperature, electrical conductivity, HIX, and GI reached stable values after 45 days of composting and thus could be more suitable indicators of full maturity. Based on our results, we recommend a minimum composting time of 30 days to achieve primary maturity, while fully matured compost may be obtained after 45 days. Full article