MCIF-Transformer Mask RCNN:Multi-Branch Cross-Scale Interactive Feature Fusion Transformer Model for PET/CT Lung Tumor Instance Segmentation

The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis.However,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are complex,the edges are blurred,and the sample numbers are unbalanced.To solve these problems,this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model(MCIF-Transformer Mask RCNN)for PET/CT lung tumor instance segmentation,The main innovative works of this paper are as follows:Firstly,the ResNet-Transformer backbone network is used to extract global feature and local feature in lung images.The pixel dependence relationship is established in local and non-local fields to improve the model perception ability.Secondly,the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained features.Thirdly,the Cross-scale Interactive Feature fusion FPN network(CIF-FPN)is constructed to realize bidirectional interactive fusion between deep features and shallow features,and the low-level features are enhanced in deep semantic features.Finally,4 ablation experiments,3 comparison experiments of detection,3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image datasets.The results showed that APdet,APseg,ARdet and ARseg indexes are improved by 5.5%,5.15%,3.11%and 6.79%compared with Mask RCNN(resnet50).Based on the above research,the precise detection and segmentation of the lesion region are realized in this paper.This method has positive significance for the detection of lung tumors.

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.

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.

Perceptions of climate change in China:The research and policy connection

Global climate change has evolved from a scientific problem into an economic and political problem oI worlOwloe rater- est. National perspectives play a crucial role in addressing climate change. Mutual understanding of perspectives is nec- essary to result in rational policies and a consensus among stakeholders with divergent interests. Conceptual frameworks for understanding the problem of climate change in China, the largest developing country and the largest greenhouse gas emitter, are of great significance to national and international efforts to address the problems of climate change. Chinese perceptions of climate change as a sustainable development problem have recently been in tension with an emerging Western perspective that frames climate change as a security issue. This paper explores Chinese perceptions of climate change as expressed in recent governmental policy statements, public opinion surveys, and academic scholarship with a focus on publications in Chinese-language journals, often unfamiliar in the West. It looks at the relationship between Chinese research and policy and finds that the Chinese policy frame of climate change as a sustainable development problem draws from the body of domestic research and is reflective of the perspectives and multidisciplinary approach of Chinese researchers in areas of climate change.

Myocardial Infarction and Intracerebral Hemorrhage in a Chinese Population: Relationship with Lipoproteins and Adipokines

BACKGROUND: Adipokines and inflammatory factors play an important role in disease progression. Two cardiovascular diseases which have important contributions to mortality and morbidity in China are in-tracerebral hemorrhage (ICH) and myocardial infarction (MI). Acylation stimulating protein has been shown in North American populations to have strong associations with risk factors for MI. Complement C3 (C3) a component of the innate complement immune system is the precursor protein to ASP;C3 has been impli-cated in the pathogenesis of ICH. OBJECTIVE: In this case-control study we examined the association be-tween BMI, lipoproteins adiponectin, C3 and ASP) in a Chinese population. METHODS AND RESULTS: Three groups of subjects were studied: ICH group (N = 41), MI group (N = 60) and a control group (N = 44). There was no difference in BMI for either ICH or MI compared to controls (Control: 22.3 ± 0.3 kg/m2;ICH: 21.3 ± 0.4 vs MI: 22.5 ± 0.2, ICH and MI versus control pNS). The ICH group had lower LDL-C (Control: 3.21 ± 0.13 mmol/L;ICH: 2.54 ± 0.13;MI: 2.99 ± 0.13;ICH vs control p < 0.05), total cholesterol (Control: 5.06 ± 0.16 mmol/L;ICH: 4.40 ± 0.15;MI: 4.51 ± 0.14;ICH and MI vs control p < 0.05),, HDL-C (Control: 1.34 ± 0.05 mmol/L;ICH: 1.22 ± 0.06;MI: 0.95 ± 0.04;ICH and MI vs control p < 0.05), and C3 (Control: 2.58 ? 0.21 g/L;ICH: 1.85 ? 0.19;MI: 2.87 ? 0.16;ICH vs control p < 0.05), and higher TG (Control: 1.10 ± 0.07 mmol/L;ICH: 1.77 ± 0.17;MI: 1.61 ± 0.10, ICH and MI vs control p < 0.05), compared to the controls. The MI group had lower total cholesterol and HDL-C and higher TG and ASP (Control: 33.70 ? 2.07 nM;ICH: 35.10 ? 2.33;MI: 41.50 ? 1.81;MI vs control p < 0.05) compared to control. CONCLUSION: Chinese men and women who had an MI displayed elevated ASP unrelated to an increase in the precursor protein, C3. Chinese men and women with ICH had ASP levels similar to controls yet lower C3 suggesting that C3, and the regulation of C3 conversion to ASP may be important in ICH disease pathology.

COR-Like Gene Is Involved in Induced-Expression Response to Multiple Abiotic Stresses in Grape Vine (<i>Vitis amurensis</i>) Tissues

Cold stress, which causes dehydration damage to plants, is one of the most common abiotic stresses that limit plant distributions and affect crop growth and development. To improve their cold tolerance, plants often upregulate the expression of some cold-related genes. In this study, a cold-regulated (COR) gene was isolated from Vitis amurensis and designated as VaCOR. RT-PCR analysis demonstrated that VaCOR was expressed at high levels in the roots, stems, leaves, and petioles under low temperature, but it was not detected under normal temperatures. Further analysis revealed that salinity and the application of exogenous abscisic acid and salicylic acid significantly induced VaCOR transcription, with apparent differences in its expression in different organs. The data also showed that COR gene expression was higher in cold-resistant wild V. amurensis than in cold-sensitive Vitis vinifera “Manicure Finger” under low temperature. These results suggest that the VaCOR gene in V. amurensis grapes is involved in multiple stresses and plays a central role in stress-induced and stress-tolerance.

The mechanism underlying extrapulmonary complications of the coronavirus disease 2019 and its therapeutic implication

The coronavirus disease 2019(COVID-19)is a highly transmissible disease caused by the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)that poses a major threat to global public health.Although COVID-19 primarily affects the respiratory system,causing severe pneumonia and acute respiratory distress syndrome in severe cases,it can also result in multiple extrapulmonary complications.