Search Results (117)

Cultured meat, a sustainable alternative to conventional meat, addresses ethical and environmental challenges in livestock production. Its production relies on bovine muscle stem cells from adult muscle or fetal tissue, whose proliferation and differentiation vary with age and developmental stage. However, the molecular mechanisms underlying these variations remain unclear. RNA sequencing was performed to characterize the transcriptomic landscape of bovine muscle stem cells across developmental stages, including myogenic maturation. Differentially expressed genes and key signaling pathways regulating myogenesis were identified, and the functional impact of modulating the AKT-autophagy pathway on differentiation was assessed. Transcriptomic analysis revealed distinct age-dependent gene expression patterns. It was possible to classify cells into three categories: young undifferentiated, young differentiated, and old differentiated. Young undifferentiated-like cells exhibited upregulation of genes associated with active states during the transitions from quiescence to activation and, ultimately, to commitment, indicating that they had robust differentiation potential. In contrast, aged myogenic samples displayed gene expression profiles that acted as barriers to efficient myogenic differentiation. Notably, modulation of the AKT-autophagy pathway both facilitated the production of very mature myogenic cells and prevented spontaneous differentiation, thereby preserving differentiation capacity in vitro. These findings provide insights into age-dependent muscle stem cell differentiation and suggest strategies to optimize cultured meat production. The appropriate modulation of key signaling pathways may help us to overcome major challenges in achieving scalable and efficient cultured meat manufacturing. Full article

The reproductive efficiency of breeding boars substantially influences swine industry productivity. Sperm viability during ambient-temperature storage is critically affected by environmental factors, including microbial activity. This study aimed to elucidate the dynamics and interactions between the seminal microbiome and metabolome during boar semen storage at 17 °C. Using integrated 16S rRNA sequencing and untargeted metabolomics, we analyzed semen samples from six healthy boars (31–33 months old) collected at day 0 (control), 2, 4, and 6 of storage. Our results demonstrate that storage leads to a marked decline in microbial diversity, progressive enrichment of the opportunistic genus Proteus, depletion of key antioxidant and cofactor metabolites such as vitamin B6, and extensive metabolic reprogramming—including alterations in short-chain fatty acid, purine, and lipid oxidation pathways. Multi-omics correlation analysis further revealed strong associations between microbial succession and metabolic shifts, highlighting their combined role in driving sperm functional decline. These findings provide a mechanistic basis for improving semen preservation strategies through microbiome and metabolite-targeted interventions. Full article

Surface defects in industrial production are complex and diverse. Therefore, deep learning-based defect detection models must consistently adapt to newly emerging defect categories. The trained models generally suffer from catastrophic forgetting as they learn new defect categories. To address this issue, we propose a selective distillation for incremental defect detection (SD-IDD) model based on GFLv1. Specifically, three selective distillation strategies are proposed, including high-confidence classification distillation, dual-stage cascaded regression distillation, and Intersection over Union (IoU)-driven difficulty-aware feature distillation. The high-confidence classification distillation aims to preserve critical discriminative knowledge of old categories within semantic confusion regions of the classification head, reducing interference from low-value regions. Dual-stage cascaded regression distillation focuses on high-quality anchors through geometric prior coarse filtering and statistical fine filtering, utilizing IoU-weighted KL divergence distillation loss to accurately transfer localization knowledge. IoU-driven difficulty-aware feature distillation adaptively allocates distillation resources, prioritizing features of high-difficulty targets. These selective distillation strategies significantly mitigate catastrophic forgetting while enhancing the detection accuracy of new classes, without requiring access to old training samples. Experimental results demonstrate that SD-IDD achieves superior performance, with mAP_old of 58.2% and 99.3%, mAP_new of 69.0% and 97.3%, and mAP_all of 63.6% and 98.3% on the NEU-DET and DeepPCB datasets, respectively, surpassing existing incremental detection methods. Full article

Rock matrices, as low-permeability media, play a critical role in controlling the persistence and fate of groundwater contaminants. Accurately quantifying contaminant mass stored in these matrices is therefore essential for understanding contamination transport processes. In this study, a microwave-assisted extraction (MAE) method was developed to accelerate the complete extraction of trichloroethylene (TCE) from rock samples. Because microwave–sample interactions depend on multiple factors, extraction conditions, including solvent type, temperature, and extraction time, were optimized using dolostone samples collected from industrial sites with decades-old contamination in Guelph, Canada. Method performance was evaluated through extensive comparison of the newly developed MAE procedure with a conventional shake-flask extraction method used as a reference. In addition, the necessity of field preservation was assessed, given its importance in the overall analytical workflow and accuracy of total mass concentrations and mass stored. The MAE method provided recoveries comparable to or greater than those obtained with the reference method, while avoiding several drawbacks of the shake-flask approach, such as sample cross-contamination during prolonged extraction times over several weeks. Its shorter processing time and faster turnaround support rapid, field-based decision-making. Field preservation was determined to be essential, as non-preserved samples consistently yielded lower measured concentrations than preserved samples. Full article

Mucus plugs are airway-obstructing accumulations of inspissated secretions frequently observed in obstructive lung diseases (OLDs), including chronic obstructive pulmonary disease (COPD), severe asthma, and cystic fibrosis. Their presence on chest CT is strongly associated with airflow limitation, reduced lung function, and increased mortality, making them emerging imaging biomarkers of disease burden and treatment response. However, manual delineation of mucus plugs is labor-intensive, subjective, and impractical for large cohorts, leading most prior studies to rely on coarse segment-level scoring systems that overlook lesion-level characteristics such as size, extent, and location. Automated plug-level quantification remains challenging due to substantial heterogeneity in plug morphology, overlap in attenuation with adjacent vessels and airway walls on non-contrast CT, and pronounced size imbalance in clinical datasets, which are typically dominated by small distal plugs. To address these challenges, we developed and validated a simulation-driven, annotation-free deep learning framework for automated detection and segmentation of airway mucus plugs on non-contrast chest CT. A total of 200 COPD CT scans were analyzed (98 plug-positive, 83 plug-negative, and 19 uncertain). Synthetic mucus plugs were generated within segmented airways by transferring voxel-intensity statistics from adjacent intrapulmonary vessels, preserving realistic morphology and texture while enabling controlled sampling of plug phenotypes. An nnU-Net trained exclusively on synthetic data (S-Model) was evaluated on an independent, expert-annotated test set and compared with an nnU-Net trained on manual annotations using 10-fold cross-validation (M-Model). The S-Model achieved significantly higher detection performance than the M-Model (sensitivity 0.837 [95% CI: 0.818–0.854] vs. 0.757 [95% CI: 0.737–0.776]; 1.91 false positives per scan vs. 3.68; p < 0.001), with performance gains most pronounced for medium-to-large plugs (≥6 mm). This simulation-driven approach enables accurate, scalable quantification of mucus plugs without voxel-wise manual annotation in thin-slice (<1.5 mm) non-contrast chest CT scans. While the framework could, in principle, be extended to other annotation-limited medical imaging tasks, its generalizability beyond this COPD cohort and imaging protocol has not yet been established, and future work is required to validate performance across diverse populations and scanning conditions. Full article

Background and Objectives: Secondary bacterial infection drives poor outcomes in older adults with COVID-19, but age-specific microbiology and its interaction with severity scores are not well defined. We characterized respiratory and pleural pathogens, resistance profiles, and their impact on day-5 SOFA/APACHE II in octogenarians versus younger adults. Methods: We performed a retrospective cohort study of adults with RT-PCR-confirmed coronavirus disease 2019 (COVID-19) at a tertiary infectious diseases center (≥80 years, n = 152; <65 years, n = 327). Respiratory and pleural samples were processed according to EUCAST standards. Identification employed matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Pathogen distributions, susceptibilities, and rates of superimposed pneumonia, empyema, and bacteremia were compared by age, and associations between secondary pneumonia, day-5 SOFA/APACHE II, and 28-day mortality were analyzed. Results: Sputum was obtained in 67.1% of older and 65.7% of younger adults, with numerically higher culture positivity in older patients (73.5% vs. 65.1%). Pathogen spectra were similar, dominated by Streptococcus pneumoniae (24.0% vs. 24.3%), methicillin-susceptible Staphylococcus aureus (MSSA) (18.7% vs. 20.7%), methicillin-resistant Staphylococcus aureus (MRSA) (9.3% vs. 6.4%), and Klebsiella pneumoniae, including extended-spectrum β-lactamase (ESBL)-producing strains. Empyema was more frequent in octogenarians (7.9% vs. 3.1%), and pleural cultures were usually positive. Meropenem retained 100% activity against ESBL-producing K. pneumoniae and Pseudomonas in both strata. In ≥80-year-olds, superimposed pneumonia was associated with higher day-5 SOFA (6.6 vs. 5.5) and APACHE II (24.3 vs. 21.0) scores and markedly increased 28-day mortality (37.5% vs. 9.8%). Conclusions: In octogenarians with COVID-19, secondary bacterial pneumonia and empyema are frequent, microbiologically similar to younger adults, and strongly amplify organ dysfunction and mortality even with largely preserved carbapenem susceptibility. Full article

In nuclear power plant fault diagnosis, traditional machine learning models (e.g., SVM and KNN) require full retraining on the entire dataset whenever new fault categories are introduced, resulting in prohibitive computational overhead. Deep learning models, on the other hand, are prone to catastrophic forgetting under incremental learning settings, making it difficult to simultaneously preserve recognition performance on both old and newly added classes. In addition, nuclear power plant fault data typically exhibit significant class imbalance, further constraining model performance. To address these issues, this study employs SHAP-XGBoost to construct a feature evaluation system, enabling feature extraction and interpretable analysis on the NPPAD simulation dataset, thereby enhancing the model’s capability to learn new features. To mitigate insufficient temporal feature capture and sample imbalance among incremental classes, we propose a cascaded spatiotemporal feature extraction network: LSTM is used to capture local dependencies, and its hidden states are passed as position-aware inputs to a Transformer for modeling global relationships, thus alleviating Transformer overfitting on short sequences. By further integrating frequency-domain analysis, an improved Adaptive Time–Frequency Network (ATFNet) is developed to enhance the robustness of discriminating complex fault patterns. Experimental results show that the proposed method achieves an average accuracy of 91.36% across five incremental learning stages, representing an improvement of approximately 20.7% over baseline models, effectively mitigating the problem of catastrophic forgetting. Full article

Background: RT-qPCR is the gold standard for quantitative gene expression analysis, but it requires homogenized tissue and thus loses spatial information. RNA in situ hybridization (ISH) preserves tissue localization but is technically challenging, especially in calcified tissues such as bone and teeth, where decalcification can damage RNA. RNAscope, an advanced ISH method with high sensitivity and specificity, has been applied successfully to bone, but its use in dental pulp remains largely unexplored despite the pulp’s crucial role in tooth function and health. Our goal was to identify the optimal decalcification process of mouse tooth samples for RNAscope ISH, which preserves RNA integrity in mouse tooth pulp. Methods: We tested five different decalcification procedures (EDTA, Plank-Rychlo solution, 5% formic acid, ACD decalcification buffer and Morse solution) on tooth samples from 3-month-old male C57BL/6J mice. Micro-CT and hematoxylin-eosin (HE) staining was performed to evaluate the decalcification, the quality and the microstructure of the sections. RNAscope ISH was used to examine mRNA integrity by analyzing the expression patterns of three housekeeping genes with different expression levels (low, medium and high). Results: All five decalcification methods demonstrated well-preserved tissue structure based on HE staining, but RNA integrity was only preserved in the case of mouse dental pulp using the ACD decalcification buffer and Morse’s solution. Conclusions: We successfully identified the optimal decalcification procedures preserving RNA integrity in mouse tooth samples, which may be useful for any target RNA examinations by RNAscope ISH in the future. Full article

Background and Clinical Significance: Vesicovaginal leiomyomas are an exceedingly rare form of extrauterine fibroids. They represent less than 1% of all leiomyomas and have been reported in less than 300 cases worldwide since 1733. These benign smooth muscle tumors typically occur in perimenopausal women aged 35–50 years, presenting in young adults extraordinarily uncommonly. The rarity in younger patients creates significant diagnostic challenges, as clinical presentation often mimics malignant entities, particularly embryonal rhabdomyosarcoma. Case Presentation: This paper presents a 20-year-old nulliparous female who developed progressive dyspareunia and urinary dysfunction over 12 months due to a large vesicovaginal mass. Physical examination revealed a 6–7 cm smooth, firm mass obstructing the vaginal canal. Transvaginal ultrasound demonstrated a well-circumscribed, hypoechoic solid lesion measuring 6.9 cm in the vesicovaginal space. Magnetic resonance imaging showed a characteristic T2-hypointense signal with restricted diffusion consistent with leiomyoma, revealing an incidental septate uterus. Ultrasound-guided core needle biopsy confirmed benign leiomyoma with bland spindle cells, absent atypia, and minimal mitotic activity. The patient underwent successful transvaginal enucleation with complete symptom resolution. Conclusion: This case highlights diagnostic challenges posed by benign leiomyomas in young women presenting with solid pelvic masses. Systematic diagnostic approaches incorporating multimodal imaging and guided tissue sampling are essential to avoid misdiagnosis and unnecessary radical surgery. When malignancy is confidently excluded, management should prioritize fertility preservation in young patients. Full article

Bone density is considered one of the many factors influencing bone structure and DNA preservation. For this reason, it is of interest in fields such as anthropology, palaeontology, and genetics. This study describes a method for bone density assessment by gradient centrifugation in Sodium Poly-Tungstate (SPT) solutions (from 2.1 to 2.6 g/cm³). Fifty milligrams of bone powder (size range of 20–50 µm) were used, with an average recovery of 89.9 (IC = 3.3% at 95% of probability). In the first phase of the experiment, the protocol was applied to ten femurs: three exhumed from the WWII mass grave of Ossero, three aged (43–50 years old) femurs from a museum collection and four fresh controls. In the subsequent phase, the analysis was extended to three petrous bones, three metacarpals, and three metatarsals exhumed from the WWII mass grave. The SPT density gradient profiles revealed marked differences among the three femur sample sets: more than 80% of the powder from control femurs was recovered in fractions with a density ≤ 2.2 g/cm³, whereas approximately 45% of the femurs from the mass grave showed a density > 2.6 g/cm³. The remaining three aged femurs displayed peculiar density patterns. Among the other bone types, metatarsals showed the lowest density values, followed by petrous bones and metacarpals. To detect degradation signatures, all nineteen bone powders were also analysed by ATR-FTIR. The femurs from the mass grave exhibited spectral features consistent with mineral recrystallisation and degradation of the organic phase, whereas the other three aged femurs showed peculiar spectral profiles; metacarpals, petrous bones and metatarsals showed intermediate spectra. PCA was applied to SPT and ATR-FTIR data, revealing correlations that support the SPT method as a novel tool for bone quality assessment. Although based on a limited sample size, this preliminary work demonstrates that SPT gradient analysis is an effective, low-cost, rapid and reliable method for assessing bone density, with potential applications in different disciplines studying aged bone samples. Lastly, principal component analysis (PCA) revealed a correlation between bone density and the yield of DNA recovered from the ten femoral specimens. Full article

Symmetry and asymmetry between past and future knowledge are at the heart of continual learning. Deep neural networks typically lose the temporal symmetry that would preserve earlier knowledge when the network is trained sequentially, a phenomenon known as catastrophic forgetting. Dynamically expandable networks (DENs) attempt to restore symmetry by allocating a dedicated module—such as a feature extractor or a task token—for every new task while freezing all previously learned modules. Although this strategy yields high average accuracy, we observe a pronounced asymmetry: earlier tasks still degrade over time, indicating that frozen modules alone do not guarantee knowledge conservation. Moreover, feature bias, arising from the imbalance between old and new samples, further exacerbates the forgetting issue. This raises a fundamental challenge: how can multiple feature extractors be coordinated more effectively to mitigate catastrophic forgetting while enabling the robust acquisition of new tasks? To address this challenge, we propose two asymmetric, contrastive auxiliary losses that exploit rich information from previous tasks to guide new task learning. Specifically, our approach integrates features extracted by both frozen and current modules to reinforce task boundaries while facilitating the learning process. In addition, we introduce a feature adjustment mechanism to alleviate the bias caused by class imbalance. Extensive experiments on benchmarks, including DyTox and MCG, demonstrate that our approach reduces catastrophic forgetting and achieves state-of-the-art performance on ImageNet-100. Full article

In real-world applications, user demands for new functionalities and activities constantly evolve, requiring action recognition systems to incrementally incorporate new action classes without retraining from scratch. This class-incremental learning (CIL) paradigm is essential for enabling adaptive and scalable systems that can grow over time. However, Wi-Fi-based indoor action recognition under incremental learning faces two major challenges: catastrophic forgetting of previously learned knowledge and uncontrolled model expansion as new classes are added. To address these issues, we propose CAREC, a class-incremental framework that balances dynamic model expansion with efficient compression. CAREC adopts a multi-branch architecture to incorporate new classes without compromising previously learned features and leverages balanced knowledge distillation to compress the model by 80% while preserving performance. A data replay strategy retains representative samples of old classes, and a super-feature extractor enhances inter-class discrimination. Evaluated on the large-scale XRF55 dataset, CAREC reduces performance degradation by 51.82% over four incremental stages and achieves 67.84% accuracy with only 21.08 M parameters, 20% parameters compared to conventional approaches. Full article

Autonomous Underwater Vehicles (AUVs) are gradually becoming some of the most important equipment in deep-sea exploration. However, in the dynamic nature of the deep-sea environment, any unplanned fault of AUVs would cause serious accidents. Traditional fault diagnosis models are trained in static and fixed datasets, making them difficult to adopt in new and unknown deep-sea environments. To address these issues, this study explores incremental learning to enable AUVs to continuously adapt to new fault scenarios while preserving previously learned diagnostic knowledge, named the RM-MFKAN model. First, the approach begins by employing the Rainbow Memory (RM) framework to analyze data characteristics and temporal sequences, thereby delineating boundaries between old and new tasks. Second, the model evaluates data importance to select and store key samples encapsulating critical information from prior tasks. Third, the RM is combined with the enhanced KAN network, and the stored samples are then combined with new task training data and fed into a multi-branch feature fusion neural network. The proposed RM-MFKAN model was conducted on the “Haizhe” dataset, and the experimental results have demonstrated that the proposed model achieves superior performance in fault diagnosis for AUVs. Full article

Forests in the Eastern and Midwestern U.S. have been profoundly affected by human use over the last 150 years, with few old growth forests remaining. Such mature forests may harbor distinct communities and high biodiversity, particularly detritivores and their associated food webs. These communities, however, have been surveyed only rarely in comparisons of diversity and community composition between old and young forests. Here, we compare the mycophilous beetle communities of young and old deciduous forest stands in Southwestern Ohio (U.S.A.). We assess how the abundance and diversity of beetles associated with fungal sporocarps varies with forest age, downed woody debris, and invasive honeysuckle density. We surveyed fungus-associated beetles with baited traps at eight wooded parklands centered around Dayton, Ohio, conducting sampling three times over a growing season. In contrast to expectation, we found no clear effect of forest age on mycophilous beetle communities, but infestation by invasive honeysuckle (Lonicera maackii) negatively affected beetle abundance and diversity. Beetle abundance, richness, and community composition also strongly varied across seasonal sampling periods. Our surveys of mycophilous beetles in a Midwestern U.S. forest represent an initial step toward understanding how these communities are shaped by forest age and invasive species. Such information is crucial in managing forests to preserve biodiversity and ecosystem services. Full article

Ancient tea plantations possess extremely important economic and cultivation value. In China, ancient tea plantations with trees over 100 years old have been preserved. However, the status of soil microorganisms, soil fertility, and soil heavy metal pollution in these ancient tea plantations remains unclear. This study took four Dancong ancient tea plantations in Fenghuang, Chaozhou City, and Guangdong Province as the research objects. Soil samples were collected from the surface layer (0–20 cm) and subsurface layer (20–40 cm) of the ancient tea trees. The rhizosphere soil microbial diversity and soil nutrients were determined. On this basis, the soil fertility was evaluated by referring to the soil environmental quality standards so as to conduct a comprehensive evaluation of the soil in the Dancong ancient tea plantations. This study found that Proteobacteria, Acidobacteriota, Chloroflexi, and Actinobacteria were the dominant bacteria in the rhizosphere soil of the Dancong ancient tree tea plantation. Ascomycota and Mortierellomycota are the dominant fungal phyla. Subgroup_2, AD3, Acidothermus, and Acidibacter were the dominant bacterial genera. Saitozyma, Mortierella, and Fusarium are the dominant fungal genera. The redundancy analysis (RDA) revealed that at the bacterial phylum level, Verrucomicrobia showed positive correlations with alkali-hydrolyzable nitrogen (AN), available potassium (AK), and total nitrogen (TN); Proteobacteria exhibited a positive correlation with available phosphorus (AP); and Gemmatimonadetes was positively correlated with total potassium (TK). At the fungal phylum level, Ascomycota demonstrated a positive correlation with TK. TN, AN, and TK were identified as key physicochemical indicators influencing soil bacterial diversity, while TN, AN, AP, and AK were the key physicochemical indicators affecting soil fungal diversity. This study revealed that the soil of Dancong ancient tea plantations has reached Level I fertility in terms of TN, TP, SOM, and AP. TK and AN show Level I or near-Level I fertility, but AK only meets Level III fertility for tea planting, serving as the main limiting factor for soil fertility quality. Considering the relatively abundant TK content in the tea plantations, potassium-solubilizing bacteria should be prioritized over blind potassium fertilizer application. Meanwhile, it is particularly noteworthy that AN and SOM are at extremely high levels. Sustained excess of AN and SOM may lead to over-proliferation of dominant microorganisms, inhibition of other functional microbial communities, and disruption of ecological balance. Therefore, optimizing nutrient input methods during fertilization is recommended. Full article