Adversarial Masked Image Inpainting for Robust Detection of Mpox and Non-Mpox

Due to the lack of efficient mpox diagnostic technology, mpox cases continue to increase. Recently, the great potential of deep learning models in detecting mpox and non-mpox has been proven. However, existing models learn image representations via image classification, which results in they may be easily susceptible to interference from real-world noise, require diverse non-mpox images, and fail to detect abnormal input. These drawbacks make classification models inapplicable in real-world settings. To address these challenges, we propose "Mask, Inpainting, and Measure" (MIM). In MIM's pipeline, a generative adversarial network only learns mpox image representations by inpainting the masked mpox images. Then, MIM determines whether the input belongs to mpox by measuring the similarity between the inpainted image and the original image. The underlying intuition is that since MIM solely models mpox images, it struggles to accurately inpaint non-mpox images in real-world settings. Without utilizing any non-mpox images, MIM cleverly detects mpox and non-mpox and can handle abnormal inputs. We used the recognized mpox dataset (MSLD) and images of eighteen non-mpox skin diseases to verify the effectiveness and robustness of MIM. Experimental results show that the average AUROC of MIM achieves 0.8237. In addition, we demonstrated the drawbacks of classification models and buttressed the potential of MIM through clinical validation. Finally, we developed an online smartphone app to provide free testing to the public in affected areas. This work first employs generative models to improve mpox detection and provides new insights into binary decision-making tasks in medical images

U-SEANNet: A Simple, Efficient and Applied U-Shaped Network for Diagnosis of Nasal Diseases on Nasal Endoscopic Images

Numerous studies have affirmed that deep learning models can facilitate early diagnosis of lesions in endoscopic images. However, the lack of available datasets stymies advancements in research on nasal endoscopy, and existing models fail to strike a good trade-off between model diagnosis performance, model complexity and parameters size, rendering them unsuitable for real-world application. To bridge these gaps, we created the first large-scale nasal endoscopy dataset, named 7-NasalEID, comprising 11,352 images that contain six common nasal diseases and normal samples. Subsequently, we proposed U-SEANNet, an innovative U-shaped architecture, underpinned by depth-wise separable convolution. Moreover, to enhance its capacity for detecting nuanced discrepancies in input images, U-SEANNet employs the Global-Local Channel Feature Fusion module, enabling it to utilize salient channel features from both global and local contexts. To demonstrate U-SEANNet's potential, we benchmarked U-SEANNet against seventeen modern architectures through five-fold cross-validation. The experimental results show that U-SEANNet achieves a commendable accuracy of 93.58%. Notably, U-SEANNet's parameters size and GFLOPs are only 0.78M and 0.21, respectively. Our findings suggest U-SEANNet is the state-of-the-art model for nasal diseases diagnosis in endoscopic images.Comment: This manuscript has been submitted to ICASSP 202

Effect of Metformin on Lactate Metabolism in Normal Hepatocytes under High Glucose Stress in Vitro

Objective: To study the effect of metformin on lactate metabolism in hepatocytes in vitro under high glucose stress. Method: LO2 hepatocytes was cultured in vitro, hepatocytes were randomly divided into blank control group, 25 mmol/L glucose solution, 27 mmol/L glucose solution, 29 mmol/L glucose solution, 31 mmol/L glucose solution, 33 mmol/L glucose solution, 35 mmol/L glucose solution treatment group, after determining the optimal concentration as 31 mmol/L, use 30 mmol/L metformin solution, and then divided into blank control group, normal hepatocytes + the optimal concentration of glucose solution, normal hepatocytes + metformin solution , normal hepatocytes+. The optimal concentration of glucose solution normal hepatocytes + metformin solution, calculate the number of hepatocytes on cell count plate respectively in the 12 h, 24 h, 48 h, and use the lactic acid kit to determine the lactic acid value of the cell culture medium of normal liver cells + optimal concentration glucose solution and normal liver cells + optimal concentration glucose solution + metformin solution at 12 h, 24 h, and 48 h, respectively. Results: There was no significant change in the lactic acid concentration but significant increase in the number of surviving hepatocytes in the high-glycemic control group compared with that in the high-glycemic control group without metformin. Conclusions: Metformin has no significant effect on lactic acid metabolism of hepatocytes under high glucose stress in vitro, and has a protective effect on hepatocytes under high glucose stress. Based on this, it is preliminarily believed that metformin is not the direct factor leading to diabetic lactic acidosis

Experimental study of cold inflow effect on a small natural draft dry cooling tower

The heat rejection rate of natural draft dry cooling tower, as well as the operating performance of a power plant, can be affected by numerous ambient factors. The cold inflow is an unfavourable air turbulence at the top of the cooling tower and has a significant negative effect on the performance of natural draft cooling towers. In the present research, results are given for a 20 m high natural draft dry cooling tower experimental system tested at different ambient conditions. Several events of cold air incursion into the top of the cooling tower are identified and the detailed experimental data are presented. The experimental data show that this effect could seriously impair the thermal performance of the cooling tower. The water outlet temperature of the cooling tower has increased by as much as to 3 °C in these tests because of the cold inflow effect. The mechanism and the solution are discussed based on the experimental data. The findings in this paper can lay an important foundation for future small natural draft cooling tower design and operation

Ultrafast-and-Ultralight ConvNet-Based Intelligent Monitoring System for Diagnosing Early-Stage Mpox Anytime and Anywhere

Due to the lack of more efficient diagnostic tools for monkeypox, its spread remains unchecked, presenting a formidable challenge to global health. While the high efficacy of deep learning models for monkeypox diagnosis has been demonstrated in related studies, the overlook of inference speed, the parameter size and diagnosis performance for early-stage monkeypox renders the models inapplicable in real-world settings. To address these challenges, we proposed an ultrafast and ultralight network named Fast-MpoxNet. Fast-MpoxNet possesses only 0.27M parameters and can process input images at 68 frames per second (FPS) on the CPU. To counteract the diagnostic performance limitation brought about by the small model capacity, it integrates the attention-based feature fusion module and the multiple auxiliary losses enhancement strategy for better detecting subtle image changes and optimizing weights. Using transfer learning and five-fold cross-validation, Fast-MpoxNet achieves 94.26% Accuracy on the Mpox dataset. Notably, its recall for early-stage monkeypox achieves 93.65%. By adopting data augmentation, our model's Accuracy rises to 98.40% and attains a Practicality Score (A new metric for measuring model practicality in real-time diagnosis application) of 0.80. We also developed an application system named Mpox-AISM V2 for both personal computers and mobile phones. Mpox-AISM V2 features ultrafast responses, offline functionality, and easy deployment, enabling accurate and real-time diagnosis for both the public and individuals in various real-world settings, especially in populous settings during the outbreak. Our work could potentially mitigate future monkeypox outbreak and illuminate a fresh paradigm for developing real-time diagnostic tools in the healthcare field

The Influence of Common Monovalent and Divalent Chlorides on Chalcopyrite Flotation

Much attention has been paid to the flotation of chalcopyrite using saline seawater. However, the influence of salt ions on mineral flotation is complex, and different salts may play various roles—either beneficial or detrimental. This study investigated the effects of common chlorides (Cl−) of Na+, K+, Mg2+, and Ca2+ in seawater on chalcopyrite floatability. The presence of Na+, K+, and Ca2+ resulted in greater chalcopyrite recovery, with this effect being more pronounced for the monovalent cations. In contrast, the addition of Mg2+ resulted in decreased chalcopyrite flotation efficiency. Contact angle measurements showed that the presence of monovalent cations increased the hydrophobicity of the chalcopyrite surface, while the presence of divalent cations reduced its hydrophobicity, depending on the concentration. Zeta potential, pulp species, and X-ray photoelectron spectroscopy (XPS) cross-confirmed the precipitation of Mg(OH)2 on the chalcopyrite surface when Mg concentration was 10−2 M and pulp pH was 10

Online Learning Management for Primary and Secondary Students during the COVID-19 Epidemic: An Evolutionary Game Theory Approach

The purpose of this study is to explore the management of primary and secondary school students’ online learning during the COVID-19 pandemic and to analyze the impact of stakeholders’ behavioral choices on students’ online learning management. Based on evolutionary game theory, this paper constructs two-game models of "schools-students" and “schools-students-parents”, analyzes the influence of the behavioral interaction of game subjects on the game equilibrium in the two scenarios, and uses MATLAB 2018 software to carry out the numerical simulation. The results show significant differences in different game players’ strategy choices on students’ online learning management. Among them, the benefits brought by learning are the main factors affecting students’ strategic choices. Although the participation of parents has a positive effect on promoting students’ game strategy selection towards cooperation, there is a participation boundary to the involvement of parents. The school’s choice of punishment or reward has no significant effect on students’ online learning management. Compared with schools, punishments and rewards from parents have a substantial impact on promoting students’ strategic choices towards cooperation

Online Learning Management for Primary and Secondary Students during the COVID-19 Epidemic: An Evolutionary Game Theory Approach

The purpose of this study is to explore the management of primary and secondary school students’ online learning during the COVID-19 pandemic and to analyze the impact of stakeholders’ behavioral choices on students’ online learning management. Based on evolutionary game theory, this paper constructs two-game models of "schools-students" and “schools-students-parents”, analyzes the influence of the behavioral interaction of game subjects on the game equilibrium in the two scenarios, and uses MATLAB 2018 software to carry out the numerical simulation. The results show significant differences in different game players’ strategy choices on students’ online learning management. Among them, the benefits brought by learning are the main factors affecting students’ strategic choices. Although the participation of parents has a positive effect on promoting students’ game strategy selection towards cooperation, there is a participation boundary to the involvement of parents. The school’s choice of punishment or reward has no significant effect on students’ online learning management. Compared with schools, punishments and rewards from parents have a substantial impact on promoting students’ strategic choices towards cooperation