MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot.However,there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images.A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper.The main innovations are as follows:Firstly,the Multi-scale Residual Feature Extraction Module(MRFEM)is designed to effectively extract multi-scale features.The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively.Secondly,the Multi-scale Directional Feature Perception Module(MDFPM)is designed,which uses a three-branch structure of different sizes convolution to transmit direction feature layer by layer,and focuses on the target region to enhance the feature information.Thirdly,the Axial Compression Former Module(ACFM)is designed to perform global calculations to enhance the perception ability of global features in different directions.To verify the effectiveness of the MSD-Net,comparative experiments and ablation experiments are carried out.In the COVID-19 RADIOGRAPHY DATABASE,the Accuracy,Recall,Precision,F1 Score,and Specificity of MSD-Net are 97.76%,95.57%,95.52%,95.52%,and 98.51%,respectively.In the chest X-ray dataset,the Accuracy,Recall,Precision,F1 Score and Specificity of MSD-Net are 97.78%,95.22%,96.49%,95.58%,and 98.11%,respectively.This model improves the accuracy of lung image recognition effectively and provides an important clinical reference to pneumonia Computer-Aided Diagnosis.

线粒体代谢介导的表观遗传改变与衰老研究

线粒体是细胞物质代谢与能量代谢的中心,在多种生理和病理过程中扮演着重要角色。表观遗传修饰是一种独立于DNA序列并在建立与维持特定基因表达谱中发挥主要作用的遗传调控模式。近年来的研究表明,线粒体能量代谢通过中间产物,介导线粒体–核信号的传递,调节染色质的表观修饰状态,进而影响基因表达。线粒体代谢紊乱可以诱导表观遗传重编程,进而启动衰老表型及退行性疾病的发生。本文综述了线粒体代谢与染色质表观遗传修饰关系的研究进展,探讨了线粒体应激在染色质重组中发挥的作用,展望了其在认知功能障碍等衰老相关性疾病研究中的前景。

Compressive properties of a novel slurry-infiltrated fiber concrete reinforced with arc-shaped steel fibers

Slurry-infiltrated fiber concrete(SIFCON)is a sort of strain hardening cement-based composite material,typically made with 5%–20%steel fibers.This study focused on a novel type of SIFCON in which hooked-end steel fibers were replaced by arc-shaped steel fibers.The quasi-static compressive properties of the SIFCON were first measured.Test results suggested that using arc-shaped steel fibers in lieu of hooked-end steel fibers increased the quasi-static compressive strength by 47.1%and the strain at peak stress by 56.3%.We attribute these improvements to new crack-resisting mechanisms,namely“fiber crosslock”,“dual bridging”,and“confinement loops”,when the arc-shaped steel fibers are introduced into SIFCON.As high impact resistance is a special property of SIFCON that is of practical significance,the dynamic compressive properties of arc-shaped steel fiber SIFCON were studied by using an 80-mm-diameter split Hopkinson pressure bar(SHPB).The results showed that the dynamic compressive strength,dynamic increase factor(DIF),and dynamic toughness of SIFCON all increased with the strain rate.The SIFCON incorporating arc-shaped steel fibers proved to have significant advantages in structural applications requiring high impact resistance.

Guided-YNet: Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network

Multimodal lung tumor medical images can provide anatomical and functional information for the same lesion.Such as Positron Emission Computed Tomography(PET),Computed Tomography(CT),and PET-CT.How to utilize the lesion anatomical and functional information effectively and improve the network segmentation performance are key questions.To solve the problem,the Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network(Guide-YNet)is proposed in this paper.Firstly,a double-encoder single-decoder U-Net is used as the backbone in this model,a single-coder single-decoder U-Net is used to generate the saliency guided feature using PET image and transmit it into the skip connection of the backbone,and the high sensitivity of PET images to tumors is used to guide the network to accurately locate lesions.Secondly,a Cross Scale Feature Enhancement Module(CSFEM)is designed to extract multi-scale fusion features after downsampling.Thirdly,a Cross-Layer Interactive Feature Enhancement Module(CIFEM)is designed in the encoder to enhance the spatial position information and semantic information.Finally,a Cross-Dimension Cross-Layer Feature Enhancement Module(CCFEM)is proposed in the decoder,which effectively extractsmultimodal image features through global attention and multi-dimension local attention.The proposed method is verified on the lung multimodal medical image datasets,and the results showthat theMean Intersection overUnion(MIoU),Accuracy(Acc),Dice Similarity Coefficient(Dice),Volumetric overlap error(Voe),Relative volume difference(Rvd)of the proposed method on lung lesion segmentation are 87.27%,93.08%,97.77%,95.92%,89.28%,and 88.68%,respectively.It is of great significance for computer-aided diagnosis.