<abstract> <p>Somatic cell count (SCC) is a fundamental approach for determining the quality of cattle and bovine milk. So far, different classification and recognition methods have been proposed, all with certain limitations. In this study, we introduced a new deep learning tool, i.e., an improved ResNet50 model constructed based on the residual network and fused with the position attention module and channel attention module to extract the feature information more effectively. In this paper, macrophages, lymphocytes, epithelial cells, and neutrophils were assessed. An image dataset for milk somatic cells was constructed by preprocessing to increase the diversity of samples. PolyLoss was selected as the loss function to solve the unbalanced category samples and difficult sample mining. The Adam optimization algorithm was used to update the gradient, while Warm-up was used to warm up the learning rate to alleviate the overfitting caused by small sample data sets and improve the model's generalization ability. The experimental results showed that the classification accuracy, precision rate, recall rate, and comprehensive evaluation index F value of the proposed model reached 97%, 94.5%, 90.75%, and 92.25%, respectively, indicating that the proposed model could effectively classify the milk somatic cell images, showing a better classification performance than five previous models (i.e., ResNet50, ResNet18, ResNet34, AlexNet andMobileNetv2). The accuracies of the ResNet18, ResNet34, ResNet50, AlexNet, MobileNetv2, and the new model were 95%, 93%, 93%, 56%, 37%, and 97%, respectively. In addition, the comprehensive evaluation index F1 showed the best effect, fully verifying the effectiveness of the proposed method in this paper. The proposed method overcame the limitations of image preprocessing and manual feature extraction by traditional machine learning methods and the limitations of manual feature selection, improving the classification accuracy and showing a strong generalization ability.</p> </abstract>
Abstract Objectives Nasopharyngeal carcinoma (NPC) is an aggressive malignancy with high rates of morbidity and mortality, largely because of its late diagnosis and metastatic potential. Lactate metabolism and protein lactylation are thought to play roles in NPC pathogenesis by modulating the tumor microenvironment and immune evasion. However, research specifically linking lactate-related mechanisms to NPC remains limited. This study aimed to identify lactate-associated biomarkers in NPC and explore their underlying mechanisms, with a particular focus on immune modulation and tumor progression. Methods To achieve these objectives, we utilized a bioinformatics approach in which publicly available gene expression datasets related to NPC were analysed. Differential expression analysis revealed differentially expressed genes (DEGs) between NPC and normal tissues. We performed weighted gene coexpression network analysis (WGCNA) to identify module genes significantly associated with NPC. Overlaps among DEGs, key module genes, and lactate-related genes (LRGs) were analysed to derive lactate-related differentially expressed genes (LR-DEGs). Machine learning algorithms can be used to predict potential biomarkers, and immune infiltration analysis can be used to examine the relationships between identified biomarkers and immune cell types, particularly M0 macrophages and B cells. Results A total of 1,058 DEGs were identified between the NPC and normal tissue groups. From this set, 372 key module genes associated with NPC were isolated. By intersecting the DEGs, key module genes, and lactate-related genes (LRGs), 17 lactate-related DEGs (LR-DEGs) were identified. Using three machine learning algorithms, this list was further refined, resulting in three primary lactate-related biomarkers: TPPP3, MUC4, and CLIC6. These biomarkers were significantly enriched in pathways related to "immune cell activation" and the "extracellular matrix environment." Additionally, M0 and B macrophages were found to be closely associated with these biomarkers, suggesting their involvement in shaping the NPC immune microenvironment. Conclusion In summary, this study identified TPPP3, MUC4, and CLIC6 as lactate-associated clinical modelling indicators linked to NPC. linked to NPC, providing a foundation for advancing diagnostic and therapeutic strategies for this malignancy.
<abstract> <p>Traditional laboratory microscopy for identifying bovine milk somatic cells is subjective, time-consuming, and labor-intensive. The accuracy of the recognition directly through a single classifier is low. In this paper, a novel algorithm that combined the feature extraction algorithm and fusion classification model was proposed to identify the somatic cells. First, 392 cell images from four types of bovine milk somatic cells dataset were trained and tested. Secondly, filtering and the K-means method were used to preprocess and segment the images. Thirdly, the color, morphological, and texture features of the four types of cells were extracted, totaling 100 features. Finally, the gradient boosting decision tree (GBDT)-AdaBoost fusion model was proposed. For the GBDT classifier, the light gradient boosting machine (LightGBM) was used as the weak classifier. The decision tree (DT) was used as the weak classifier of the AdaBoost classifier. The results showed that the average recognition accuracy of the GBDT-AdaBoost reached 98.0%. At the same time, that of random forest (RF), extremely randomized tree (ET), DT, and LightGBM was 79.9, 71.1, 67.3 and 77.2%, respectively. The recall rate of the GBDT-AdaBoost model was the best performance on all types of cells. The F1-Score of the GBDT-AdaBoost model was also better than the results of any single classifiers. The proposed algorithm can effectively recognize the image of bovine milk somatic cells. Moreover, it may provide a reference for recognizing bovine milk somatic cells with similar shape size characteristics and is difficult to distinguish.</p> </abstract>
Solving a large-scale Poisson system is computationally expensive for most of the Eulerian fluid simulation applications. We propose a novel machine learning-based approach to accelerate this process. At the heart of our approach is a deep convolutional neural network (CNN), with the capability of predicting the solution (pressure) of a Poisson system given the discretization structure and the intermediate velocities as input. Our system consists of four main components, namely, a deep neural network to solve the large linear equations, a geometric structure to describe the spatial hierarchies of the input vector, a Principal Component Analysis (PCA) process to reduce the dimension of input in training, and a novel loss function to control the incompressibility constraint. We have demonstrated the efficacy of our approach by simulating a variety of high-resolution smoke and liquid phenomena. In particular, we have shown that our approach accelerates the projection step in a conventional Eulerian fluid simulator by two orders of magnitude. In addition, we have also demonstrated the generality of our approach by producing a diversity of animations deviating from the original datasets.
e20033 Background: Circulating tumor DNA (ctDNA) is a form of cell-free DNA (cfDNA) found in blood that originates from tumor cells, which has emerged as a promising non-invasive biomarker in monitoring cancer prognosis and precision medicine with the advancement of deep sequencing. Here we compared the mutational landscape between solid tumor and cfDNA in metastasis-free and metastasized lung cancer patients using targeted NGS to investigate the feasibility of ctDNA sequencing in monitoring cancer prognosis. Methods: Blood-derived cfDNA samples and formalin-fixed paraffin embedded tissue were collected from 217 Chinese patients with metastatic lung cancer (n = 39) or metastasis-free lung cancer (n = 178). Blood samples were collected after surgery. Panel sequencing targeting 680 cancer-related genes was performed on both samples. Somatic single nucleotide variations and indels were analyzed. Results: Among the 178 metastasis-free patients, 89.3% of the patients have at least one mutation both detected in tumor and blood samples. On average, 50.4% of tumor mutations were detected in blood for each patients. EGFR, TP53, CSMD3, LRP1B and SYNE1 were the top five most frequently mutated genes, with the proportion of tumor mutation found in blood of 89.1%, 80.2%, 70.0%, 61.3% and 64.3% respectively. In the metastasized cohort, all 39 individuals have at least one tumor mutations found in blood, achieving a concordance of 100%. On average, 59.9% of tumor mutations were detected in blood for each patients. Notably, the proportion of tumor mutations detected in blood for each patients is significantly higher than the metastasis-free cohort (unpaired T-test, p = 0.0167). EGFR, TP53, RBM10, PIK3CA and CSMD3 were the top five most frequently mutated genes, with the proportion of tumor mutation found in blood of 93.5%, 80.8%, 57.1%, 83.3% and 42.9% respectively. Moreover, the proportion of patients having EGFR p.L858R mutation in metastasized cohort was higher than the metastasis-free cohort (54.3% vs 32.8%). While within the metastasized cohort, blood samples detected a higher proportion of EGFR p.T790M (12.9% vs 7.69%) but a lower proportion of EGFR exon19 deletion (p.E746_A750del, 19.4% vs 27.7%) comparing to tumor sample, which can indicate drug resistance. Conclusions: We demonstrated a high mutation detection concordance between tumor and blood using targeted NGS, with an exceptional performance especially in driver oncogenes like EGFR and TP53. Our findings showed that metastasized lung cancer patients exhibit a higher positive concordance rate compared to metastasis-free cohort using ctDNA. We also showed a varied mutational landscape between ctDNA and tumor sequencing in metastasized lung cancer patients. Overall, this study not only reaffirms the viability of using blood as an alternative of tissue biopsy, but also provides new insights on application of ctDNA monitoring lung cancer metastasis and its clinically targetable mutations.
The sheep body size measurement is primarily realized by human experts at a low efficiency, which does not meet the requirements of animal welfare. In this study, we propose a non-contact measurement method base on machine vision. Sheep images are first captured by the camera, and then processed to obtain the sheep contour which can provide the sheep contour for calculating the sequence while getting the effective determination of measurement points. The experimental results demonstrate the effectiveness of the no-stress measurement of the sheep body size parameters.
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