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Objective To establish a fungal image-assisted classification model using deep learning technology. Methods The microscope images of people infected with Aspergillus, Saccharomyces and Cryptococcus neoformans were retrospectively collected from the Eighth Medical Center of PLA General Hospital from September 2020 to April 2021. The images were randomly divided into training set, validation set and test set according to the ratio of 7∶1.5∶1.5. The improved MobileNetV2 network structure was trained using the training set, a convolutional neural network (CNN) fungal image 11 classification model based on multi-scale attention mechanism was constructed and the parameters were debugged based on the validation set. Machine identification results were taken as the gold standard, the performance of the model on 11 fungal image classification tasks was evaluated, and the results were shown by precision, recall and F1 value. In addition, the performance of the proposed model with 5 classic CNN models were compared, and the results were measured in terms of model parameters, memory usage, frames per second (FPS), accuracy, and area under the curve (AUC) of receiver operating characteristic curve. Results A total of 7666 fungal microscope images were collected, including 2781, 4115, and 770 images of Aspergillus, Saccharomyces, and Cryptococcus neoformans, respectively. Among them, there were 5366 training images, 1150 validation images, and 1150 test images. The improved MobileNetV2 model had high performance for the classification of 11 fungal images in the test set. The precision rate was distributed between 96.36% and 100%, the recall rate was distributed between 96.53% and 100%, and the F1 value was distributed between 97.01% and 100%. The parameters, memory usage, FPS, accuracy, and AUC of the improved MobileNetV2 model were 4.22 M, 356.89 M, 573, (99.09±0.18)%, and 0.9944±0.0018, respectively, and the comprehensive performance was better than 5 kinds of classical networks. Conclusion The proposed fungal image classification model based on the improved MobileNetV2 can obtain higher fungal image recognition ability while maintaining low computational cost, with an overall performance better than classical CNN model.

Using molecular dynamics simulation with reactive force-field (ReaxFF), we study the reactive oxygen species (ROS)-mediated damage to the core domain of human immunodeficiency virus (HIV) integrase. Our results demonstrate that with the increase in the ROS concentration, the structure of the core domain of HIV is more severely damaged, including dehydrogenation, increasing oxygen-containing groups, shorting or damaging spirals, and breaking peptide bonds. We particularly note that ROS has a significantly higher capability to abstract H atoms from N atoms than C atoms. The effects of different species of ROS on dehydrogenation and shorting or damaging spirals are not prominent. In contrast, for increasing oxygen-containing groups and breaking peptide bonds, the effect of O is more remarkable than O3 and O3 is greater than ·OH. Moreover, carboxyl dehydrogenation and nitrogen dehydrogenation are more easily dehydrogenated, resulting in higher dehydrogenation ratios for polar amino acid residues. These meticulous findings deepen our understanding of the role of ROS in regulating the structure and function of the core domain of HIV integrase and provide valuable insights into the plasma treatment of acquired immune deficiency syndrome. [ABSTRACT FROM AUTHOR]

Pansharpening refers to the process of fusing multispectral (MS) images with panchromatic (PAN) images to obtain high-resolution multispectral (HRMS) images. However, due to the low correlation and similarity between MS and PAN images, as well as inaccuracies in spatial information injection, HRMS images often suffer from significant spectral and spatial distortions. To address these issues, a pansharpening method based on multimodal texture correction and adaptive edge detail fusion is proposed in this paper. To obtain a texture-corrected ( T C ) image that is highly correlated and similar to the MS image, the target-adaptive CNN-based pansharpening (A-PNN) method is introduced. By constructing a multimodal texture correction model, intensity, gradient, and A-PNN-based deep plug-and-play correction constraints are established between the T C and source images. Additionally, an adaptive degradation filter algorithm is proposed to ensure the accuracy of these constraints. Since the T C image obtained can effectively replace the PAN image and considering that the MS image contains valuable spatial information, an adaptive edge detail fusion algorithm is also proposed. This algorithm adaptively extracts detailed information from the T C and MS images to apply edge protection. Given the limited spatial information in the MS image, its spatial information is proportionally enhanced before the adaptive fusion. The fused spatial information is then injected into the upsampled multispectral (UPMS) image to produce the final HRMS image. Extensive experimental results demonstrated that compared with other methods, the proposed algorithm achieved superior results in terms of both subjective visual effects and objective evaluation metrics. [ABSTRACT FROM AUTHOR]

Diabetic kidney disease (DKD) patients with anemia face an elevated risk of glomerular filtration rate decline. However, the association between hemoglobin and estimated Glomerular Filtration Rate (eGFR) progression remains to be elucidated. Methods: A retrospective cohort of 815 subjects with DKD was followed from January 2010 to January 2023. A Cox proportional hazard regression model was utilized to explore the predictive role of hemoglobin in renal outcomes. Renal outcomes were defined as a composite endpoint, including a 50% decline in eGFR from baseline or progression to End-Stage Renal Disease (ESRD). To unveil any nonlinear relationship between hemoglobin and renal outcomes, Cox proportional hazard regression with cubic spline functions and smooth curve fitting was conducted. Additionally, subgroup analyses were performed to identify specific patient populations that might derive greater benefits from higher hemoglobin. Results: Among the 815 DKD subjects, the mean age was 56.482 ± 9.924 years old, and 533 (65.4%) were male. The mean hemoglobin was 121.521± 22.960 g/L. The median follow-up time was 21.103± 18.335 months. A total of 182 (22.33%) individuals reached the renal composite endpoint during the study period. After adjusting for covariates, hemoglobin was found to exert a negative impact on the renal composite endpoint in patients with DKD (HR 0.975, 95% CI [0.966, 0.984]). A nonlinear relationship between hemoglobin and the renal composite endpoint was identified with an inflection point at 109 g/L. Subgroup analysis unveiled a more pronounced association between hemoglobin and renal prognosis in males. Conclusion: Hemoglobin emerges as a predictive indicator for the renal prognosis of diabetic kidney disease in China. This study reveals a negative and non-linear relationship between hemoglobin levels and the renal composite endpoint. A substantial association is noted when hemoglobin surpasses 109 g/L in relation to the renal composite endpoint. [ABSTRACT FROM AUTHOR]

Spectral unmixing (SU) is a significant preprocessing task for handling hyperspectral images (HSI), but its process is affected by nonlinearity and spectral variability (SV). Currently, SV is considered within the framework of linear mixing models (LMM), which ignores the nonlinear effects in the scene. To address that issue, we consider the effects of SV on SU while investigating the nonlinear effects of hyperspectral images. Furthermore, an augmented generalized bilinear model is proposed to address spectral variability (abbreviated AGBM-SV). First, AGBM-SV adopts a generalized bilinear model (GBM) as the basic framework to address the nonlinear effects caused by second-order scattering. Secondly, scaling factors and spectral variability dictionaries are introduced to model the variability issues caused by the illumination conditions, material intrinsic variability, and other environmental factors. Then, a data-driven learning strategy is employed to set sparse and orthogonal bases for the abundance and spectral variability dictionaries according to the distribution characteristics of real materials. Finally, the alternating direction method of multipliers (ADMM) optimization method is used to split and solve the objective function, enabling the AGBM-SV algorithm to estimate the abundance and learn the spectral variability dictionary more effectively. The experimental results demonstrate the comparative superiority of the AGBM-SV method in both qualitative and quantitative perspectives, which can effectively solve the problem of spectral variability in nonlinear mixing scenes and to improve unmixing accuracy. [ABSTRACT FROM AUTHOR]