Pyramid Separable Channel Attention Network for Single Image Super-Resolution

Single Image Super-Resolution(SISR)technology aims to reconstruct a clear,high-resolution image with more information from an input low-resolution image that is blurry and contains less information.This technology has significant research value and is widely used in fields such as medical imaging,satellite image processing,and security surveillance.Despite significant progress in existing research,challenges remain in reconstructing clear and complex texture details,with issues such as edge blurring and artifacts still present.The visual perception effect still needs further enhancement.Therefore,this study proposes a Pyramid Separable Channel Attention Network(PSCAN)for the SISR task.Thismethod designs a convolutional backbone network composed of Pyramid Separable Channel Attention blocks to effectively extract and fuse multi-scale features.This expands the model’s receptive field,reduces resolution loss,and enhances the model’s ability to reconstruct texture details.Additionally,an innovative artifact loss function is designed to better distinguish between artifacts and real edge details,reducing artifacts in the reconstructed images.We conducted comprehensive ablation and comparative experiments on the Arabidopsis root image dataset and several public datasets.The experimental results show that the proposed PSCAN method achieves the best-known performance in both subjective visual effects and objective evaluation metrics,with improvements of 0.84 in Peak Signal-to-Noise Ratio(PSNR)and 0.017 in Structural Similarity Index(SSIM).This demonstrates that the method can effectively preserve high-frequency texture details,reduce artifacts,and have good generalization performance.

Cost-Sensitive Dual-Stream Residual Networks for Imbalanced Classification

Imbalanced data classification is the task of classifying datasets where there is a significant disparity in the number of samples between different classes.This task is prevalent in practical scenarios such as industrial fault diagnosis,network intrusion detection,cancer detection,etc.In imbalanced classification tasks,the focus is typically on achieving high recognition accuracy for the minority class.However,due to the challenges presented by imbalanced multi-class datasets,such as the scarcity of samples in minority classes and complex inter-class relationships with overlapping boundaries,existing methods often do not perform well in multi-class imbalanced data classification tasks,particularly in terms of recognizing minority classes with high accuracy.Therefore,this paper proposes a multi-class imbalanced data classification method called CSDSResNet,which is based on a cost-sensitive dualstream residual network.Firstly,to address the issue of limited samples in the minority class within imbalanced datasets,a dual-stream residual network backbone structure is designed to enhance the model’s feature extraction capability.Next,considering the complexities arising fromimbalanced inter-class sample quantities and imbalanced inter-class overlapping boundaries in multi-class imbalanced datasets,a unique cost-sensitive loss function is devised.This loss function places more emphasis on the minority class and the challenging classes with high interclass similarity,thereby improving the model’s classification ability.Finally,the effectiveness and generalization of the proposed method,CSDSResNet,are evaluated on two datasets:‘DryBeans’and‘Electric Motor Defects’.The experimental results demonstrate that CSDSResNet achieves the best performance on imbalanced datasets,with macro_F1-score values improving by 2.9%and 1.9%on the two datasets compared to current state-of-the-art classification methods,respectively.Furthermore,it achieves the highest precision in single-class recognition tasks for the minority class.

Trans-cinnamaldehyde inhibits Penicillium italicum by damaging mitochondria and inducing apoptosis mechanisms

Plant-derived essential oils have excellent antifungal effects and can be used for the preservation of fresh foods such as fruits and vegetables, but the detailed mechanism has not been fully elucidated. In this study, we investigated the inhibitory effects of trans-cinnamaldehyde on Penicillium italicum, a common pollution fungus in citrus, and explored the antifungal mechanism of trans-cinnamaldehyde by detecting fungal oxidative damage, mitochondrial metabolism, and cell apoptosis. These results showed that transcinnamaldehyde made the carboxylic acid cycle deregulated by altering the related enzyme activities(succinate dehydrogenase, malate dehydrogenase) and mid product. Moreover, the level of reactive oxygen species rose sharply while the redox level was out of regulation. The mitochondrial membrane potential collapsed, leading to the leakage of cytochrome c, and then triggering the activation of apoptotic protease, which was further confirmed by the significant increase in caspase-3 activity from(3.6 ± 0.6) U to(8.8 ± 1.1) U(P < 0.05). The cytochrome c in mitochondria was detected by confocal Raman microspectroscopy, the characteristic intensity index(I750/I2944) was decreased, indicating that the cytochrome c in mitochondria was reduced and leakage. Besides, the strong negative correlation between Raman intensity and the amount of cytochrome c leakage was established with the correlation coefficient of-0.981 7. This study revealed that destroying the integrity of the mitochondrial membrane, activating the mitochondrial-mediated apoptosis pathway was the in-depth antifungal mechanism of trans-cinnamaldehyde;and Raman spectroscopy technology provided new ideas to study this process with high sensitivity determination of cytochrome c.

Pharmacokinetics of H002, a novel S1PR_1 modulator, and its metabolites in rat blood using liquid chromatography–tandem mass spectrometry

A rapid and sensitive liquid chromatography–tandem mass spectrometry(LC–MS/MS) method was developed and validated for the simultaneous determination of H002 and its phosphorylated metabolite, H002-P and hydroxylated metabolite H002-M, in rat blood. H001, an analogue of H002, was used as the internal standard.Blood samples were prepared by simple protein precipitation. The analytes and internal standard were separated on a Zorbax SB-C18 column with a gradient mobile phase consisting of methanol and water containing 0.1% formic acid at a flow rate of 0.2 mL /min with an operating temperature of 20 1C. The detection was performed on a triple quadrupole tandem mass spectrometer with positive electrospray ionization in multiple-reaction monitoring mode.Linear detection responses were obtained from 0.2–100 ng/mL for H002 and H002-M, while 0.5–100 ng/mL for H002-P. The intra- and inter-day precision(RSD%) was within 11.76%, with the accuracy(RE%) ranging from –9.84% to 9.12%. The analytes were shown to be stable during sample storage, preparation and analytic procedures.The method was applied to determine the pharmacokinetics of H002 in rats, and a preliminary study showed that the pharmacokinetics of H002 correlated with its biological effect on peripheral blood lymphocytes.