Changing trends in the global burden of mental disorders from 1990 to 2019 and predicted levels in 25 years.

AIMS: The burden of mental disorders is increasing worldwide, thus, affecting society and healthcare systems. This study investigated the independent influences of age, period and cohort on the global prevalence of mental disorders from 1990 to 2019; compared them by sex; and predicted the future burden of mental disorders in the next 25 years. METHODS: The age-specific and sex-specific incidence of mental disorders worldwide was analysed according to the general analysis strategy used in the Global Burden of Disease Study in 2019. The incidence and mortality trends of mental disorders from 1990 to 2019 were evaluated through joinpoint regression analysis. The influences of age, period and cohort on the incidence of mental disorders were evaluated with an age-period-cohort model. RESULTS: From 1990 to 2019, the sex-specific age-standardized incidence and disability-adjusted life years (DALY) rate decreased slightly. Joinpoint regression analysis from 1990 to 2019 indicated four turning points in the male DALY rate and five turning points in the female DALY rate. In analysis of age effects, the relative risk (RR) of incidence and the DALY rate in mental disorders in men and women generally showed an inverted U-shaped pattern with increasing age. In analysis of period effects, the incidence of mental disorders increased gradually over time, and showed a sub-peak in 2004 (RR, 1.006 for males; 95% CI, 1.000-1.012; 1.002 for women, 0.997-1.008). Analysis of cohort effects showed that the incidence and DALY rate decreased in successive birth cohorts. The incidence of mental disorders is expected to decline slightly over the next 25 years, but the number of cases is expected to increase. CONCLUSIONS: Although the age-standardized burden of mental disorders has declined in the past 30 years, the number of new cases and deaths of mental disorders worldwide has increased, and will continue to increase in the near future. Therefore, relevant policies should be used to promote the prevention and management of known risk factors and strengthen the understanding of risk profiles and incidence modes of mental disorders, to help guide future research on control and prevention strategies.

Machine Learning Using Presentation CT Perfusion Imaging for Predicting Clinical Outcomes in Patients With Aneurysmal Subarachnoid Hemorrhage.

BACKGROUND. Prediction of outcomes in patients with aneurysmal subarachnoid hemorrhage (aSAH) is challenging using current clinical predictors. OBJECTIVE. The purpose of our study was to evaluate the utility of machine learning (ML) models incorporating presentation clinical and CT perfusion imaging (CTP) data in predicting delayed cerebral ischemia (DCI) and poor functional outcome in patients with aSAH. METHODS. This study entailed retrospective analysis of data from 242 patients (mean age, 60.9 ± 11.8 [SD] years; 165 women, 77 men) with aSAH who, as part of a prospective trial, underwent CTP followed by standardized evaluation for DCI during initial hospitalization and poor 3-month functional outcome (i.e., modified Rankin scale score ≥ 4). Patients were randomly divided into training (n = 194) and test (n = 48) sets. Five ML models (k-nearest neighbor [KNN], logistic regression [LR], support vector machine [SVM], random forest [RF], and category boosting [CatBoost]) were developed for predicting outcomes using presentation clinical and CTP data. The least absolute shrinkage and selection operator method was used for feature selection. Ten-fold cross-validation was performed in the training set. Traditional clinical models were developed using stepwise LR analysis of clinical, but not CTP, data. RESULTS. Qualitative CTP analysis was identified as the most impactful feature for both outcomes. In the test set, the traditional clinical model, KNN, LR, SVM, RF, and CatBoost showed AUC for predicting DCI of 0.771, 0.812, 0.824, 0.908, 0.930, and 0.949, respectively, and AUC for predicting poor 3-month functional outcome of 0.863, 0.858, 0.879, 0.908, 0.926, and 0.958. CatBoost was selected as the optimal model. In the test set, AUC was higher for CatBoost than for the traditional clinical model for predicting DCI (p = .004) and poor 3-month functional outcome (p = .04). In the test set, sensitivity and specificity for predicting DCI were 92.3% and 60.0% for the traditional clinical model versus 92.3% and 85.7% for CatBoost, and sensitivity and specificity for predicting poor 3-month functional outcome were 100.0% and 65.8% for the traditional clinical model versus 90.0% and 94.7% for CatBoost. A web-based prediction tool based on CatBoost was created. CONCLUSION. ML models incorporating presentation clinical and CTP data outperformed traditional clinical models in predicting DCI and poor 3-month functional outcome. CLINICAL IMPACT. ML models may help guide early management of patients with aSAH.

Corrigendum: Study on characteristic of epileptic multi-electroencephalograph base on Hilbert-Huang transform and brain network dynamics.

[This corrects the article DOI: 10.3389/fnins.2023.1117340.].

Study on characteristic of epileptic multi-electroencephalograph base on Hilbert-Huang transform and brain network dynamics.

Lots of studies have been carried out on characteristic of epileptic Electroencephalograph (EEG). However, traditional EEG characteristic research methods lack exploration of spatial information. To study the characteristics of epileptic EEG signals from the perspective of the whole brain，this paper proposed combination methods of multi-channel characteristics from time-frequency and spatial domains. This paper was from two aspects: Firstly, signals were converted into 2D Hilbert Spectrum (HS) images which reflected the time-frequency characteristics by Hilbert-Huang Transform (HHT). These images were identified by Convolutional Neural Network (CNN) model whose sensitivity was 99.8%, accuracy was 98.7%, specificity was 97.4%, F1-score was 98.7%, and AUC-ROC was 99.9%. Secondly, the multi-channel signals were converted into brain networks which reflected the spatial characteristics by Symbolic Transfer Entropy (STE) among different channels EEG. And the results show that there are different network properties between ictal and interictal phase and the signals during the ictal enter the synchronization state more quickly, which was verified by Kuramoto model. To summarize, our results show that there was different characteristics among channels for the ictal and interictal phase, which can provide effective physical non-invasive indicators for the identification and prediction of epileptic seizures.

Whole-brain CT Perfusion at Admission and During Delayed Time-window Detects the Delayed Cerebral Ischemia in Patients with Aneurysmal Subarachnoid Hemorrhage.

OBJECTIVE: To evaluate the utility of computed tomography perfusion (CTP) both at admission and during delayed cerebral ischemia time-window (DCITW) in the detection of delayed cerebral ischemia (DCI) and the change in CTP parameters from admission to DCITW following aneurysmal subarachnoid hemorrhage. METHODS: Eighty patients underwent CTP at admission and during DCITW. The mean and extreme values of all CTP parameters at admission and during DCITW were compared between the DCI group and non-DCI group, and comparisons were also made between admission and DCITW within each group. The qualitative color-coded perfusion maps were recorded. Finally, the relationship between CTP parameters and DCI was assessed by receiver operating characteristic (ROC) analyses. RESULTS: With the exception of cerebral blood volume (P=0.295, admission; P=0.682, DCITW), there were significant differences in the mean quantitative CTP parameters between DCI and non-DCI patients both at admission and during DCITW. In the DCI group, the extreme parameters were significantly different between admission and DCITW. The DCI group also showed a deteriorative trend in the qualitative color-coded perfusion maps. For the detection of DCI, mean transit time to the center of the impulse response function (Tmax) at admission and mean time to start (TTS) during DCITW had the largest area under curve (AUC), 0.698 and 0.789, respectively. CONCLUSION: Whole-brain CTP can predict the occurrence of DCI at admission and diagnose DCI during DCITW. The extreme quantitative parameters and qualitative color-coded perfusion maps can better reflect the perfusion changes of patients with DCI from admission to DCITW.

Mr.Vc v2: An updated version of database with increased data of transcriptome and experimental validated interactions.

Mr.Vc is a database of curated Vibrio cholerae transcriptome data and annotated information. The main objective is to facilitate the accessibility and reusability of the rapidly growing Vibrio cholerae omics data and relevant annotation. To achieve these goals, we performed manual curation on the transcriptome data and organized the datasets in an experiment-centric manner. We collected unknown operons annotated through text-mining analysis that would provide more clues about how Vibrio cholerae modulates gene regulation. Meanwhile, to understand the relationship between genes or experiments, we performed gene co-expression analysis and experiment-experiment correlation analysis. In additional, functional module named "Interactions" which dedicates to collecting experimentally validated interactions about Vibrio cholerae from public databases, MEDLINE documents and literature in life science journals. To date, Mr.Vc v2, which is significantly increased from the previous version, contains 107 microarray experiments, 106 RNA-seq experiments, and 3 Tn-seq projects, covering 56,839 entries of DEGs (Differentially Expressed Genes) from transcriptomes and 7,463 related genes from Tn-seq, respectively. and a total of 270,129 gene co-expression entries and 11,990 entries of experiment-experiment correlation was obtained, in total 1,316 entries of interactions were collected, including 496 protein-chemical signaling molecule interactions, 472 protein-protein interactions, 306 TF (Transcription Factor)-gene interactions and 42 Vibrio cholerae-virus interactions, most of which obtained from 402 literature through text-mining analysis. To make the information easier to access, Mr.Vc v2 is equipped with a search widget, enabling users to query what they are interested in. Mr.Vc v2 is freely available at http://mrvcv2.biownmc.info.

m6A RNA methylation regulator-based signature for prognostic prediction and its potential immunological role in uterine corpus endometrial carcinoma.

BACKGROUND: Uterine corpus endometrial carcinoma (UCEC) is the most common female pelvic malignancy worldwide. N6-methyladenosine (m6A) plays an important role in various cellular responses, especially in cancer progression. However, the correlation between prognostic UCEC and m6A RNA methylation regulators remains unclear. METHODS: We used The Cancer Genome Atlas (TCGA) to provide a gene signature that could improve the prognostic evaluation of UCEC patients according to the distinct genetic trait of m6A RNA methylation regulators from a bioinformatics perspective. After comparing UCEC subgroups with different genetic profiles of m6A regulators, we identified 71 differentially expressed genes associated with overall survival (OS) and generated a nine-gene signature through least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Finally, we used in vitro and in vivo tumor cell experiments as well as the immune correlation analysis to verify the function of each gene in the proposed gene signature. RESULTS: Time-dependent receiver operating characteristic (ROC) curves revealed that the proposed gene signature could predict the outcome of UCEC patients accurately. We found that CDKN2A mainly acted from the perspective of tumor cells, while COL4A4, PXDN, TIGIT, CHODL, LMO3, KCNJ12, L1CAM, and EPHB1 might play a role in UCEC from an immunological point of view. CONCLUSIONS: From an epigenetics perspective, the m6A RNA methylation regulator-based gene signature can predict the prognosis of UCEC patients and immune therapeutic efficacy.

A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms.

An IA is an abnormal swelling of cerebral vessels, and a subset of these IAs can rupture causing aneurysmal subarachnoid hemorrhage (aSAH), often resulting in death or severe disability. Few studies have used an appropriate method of feature selection combined with machine learning by analyzing transcriptomic sequencing data to identify new molecular biomarkers. Following gene ontology (GO) and enrichment analysis, we found that the distinct status of IAs could lead to differential innate immune responses using all 913 differentially expressed genes, and considering that there are numerous irrelevant and redundant genes, we propose a mixed filter- and wrapper-based feature selection. First, we used the Fast Correlation-Based Filter (FCBF) algorithm to filter a large number of irrelevant and redundant genes in the raw dataset, and then used the wrapper feature selection method based on the he Multi-layer Perceptron (MLP) neural network and the Particle Swarm Optimization (PSO), accuracy (ACC) and mean square error (MSE) were then used as the evaluation criteria. Finally, we constructed a novel 10-gene signature (YIPF1, RAB32, WDR62, ANPEP, LRRCC1, AADAC, GZMK, WBP2NL, PBX1, and TOR1B) by the proposed two-stage hybrid algorithm FCBF-MLP-PSO and used different machine learning models to predict the rupture status in IAs. The highest ACC value increased from 0.817 to 0.919 (12.5% increase), the highest area under ROC curve (AUC) value increased from 0.87 to 0.94 (8.0% increase), and all evaluation metrics improved by approximately 10% after being processed by our proposed gene selection algorithm. Therefore, these 10 informative genes used to predict rupture status of IAs can be used as complements to imaging examinations in the clinic, meanwhile, this selected gene signature also provides new targets and approaches for the treatment of ruptured IAs.

Finger Vein Verification on Different Datasets Based on Deep Learning with Triplet Loss.

In this study, deep learning and triplet loss function methods are used for finger vein verification research, and the model is trained and validated between different kinds of datasets including FV-USM, HKPU, and SDUMLA-HMT datasets. This work gives the accuracy and other evaluation indexes of finger vein verification calculated for different training-validation set combinations and gives the corresponding ROC curves and AUC values. The accuracy of the best result has reached 98%, and all the ROC AUC values are above 0.98, indicating that the obtained model can identify the finger veins well. Since the experiments are cross-validated between different kinds of datasets, the model has good adaptability and applicability. From the experimental results, it is also found that the model trained on the dataset that is more difficult to be distinguished will be a better and more robust model.

Generative Adversarial Network Combined with SE-ResNet and Dilated Inception Block for Segmenting Retinal Vessels.

This study develops an accurate method based on the generative adversarial network (GAN) that targets the issue of the current discontinuity of micro vessel segmentation in the retinal segmentation images. The processing of images has become increasingly efficient since the advent of deep learning method. We have proposed an improved GAN combined with SE-ResNet and dilated inception block for the segmenting retinal vessels (SAD-GAN). The GAN model has been improved with respect to the following points. (1) In the generator, the original convolution block is replaced with SE-ResNet module. Furthermore, SE-Net can extract the global channel information, while concomitantly strengthening and weakening the key features and invalid features, respectively. The residual structure can alleviate the issue of gradient disappearance. (2) The inception block and dilated convolution are introduced into the discriminator, which enhance the transmission of features and expand the acceptance domain for improved extraction of the deep network features. (3) We have included the attention mechanism in the discriminator for combining the local features with the corresponding global dependencies, and for highlighting the interdependent channel mapping. SAD-GAN performs satisfactorily on public retina datasets. On DRIVE dataset, ROC_AUC and PR_AUC reach 0.9813 and 0.8928, respectively. On CHASE_DB1 dataset, ROC_AUC and PR_AUC reach 0.9839 and 0.9002, respectively. Experimental results demonstrate that the generative adversarial model, combined with deep convolutional neural network, enhances the segmentation accuracy of the retinal vessels far above that of certain state-of-the-art methods.

MRF-IUNet: A Multiresolution Fusion Brain Tumor Segmentation Network Based on Improved Inception U-Net.

The automatic segmentation method of MRI brain tumors uses computer technology to segment and label tumor areas and normal tissues, which plays an important role in assisting doctors in the clinical diagnosis and treatment of brain tumors. This paper proposed a multiresolution fusion MRI brain tumor segmentation algorithm based on improved inception U-Net named MRF-IUNet (multiresolution fusion inception U-Net). By replacing the original convolution modules in U-Net with the inception modules, the width and depth of the network are increased. The inception module connects convolution kernels of different sizes in parallel to obtain receptive fields of different sizes, which can extract features of different scales. In order to reduce the loss of detailed information during the downsampling process, atrous convolutions are introduced in the inception module to expand the receptive field. The multiresolution feature fusion modules are connected between the encoder and decoder of the proposed network to fuse the semantic features learned by the deeper layers and the spatial detail features learned by the early layers, which improves the recognition and segmentation of local detail features by the network and effectively improves the segmentation accuracy. The experimental results on the BraTS (the Multimodal Brain Tumor Segmentation Challenge) dataset show that the Dice similarity coefficient (DSC) obtained by the method in this paper is 0.94 for the enhanced tumor area, 0.83 for the whole tumor area, and 0.93 for the tumor core area. The segmentation accuracy has been improved.

SRV-GAN: A generative adversarial network for segmenting retinal vessels.

In the field of ophthalmology, retinal diseases are often accompanied by complications, and effective segmentation of retinal blood vessels is an important condition for judging retinal diseases. Therefore, this paper proposes a segmentation model for retinal blood vessel segmentation. Generative adversarial networks (GANs) have been used for image semantic segmentation and show good performance. So, this paper proposes an improved GAN. Based on R2U-Net, the generator adds an attention mechanism, channel and spatial attention, which can reduce the loss of information and extract more effective features. We use dense connection modules in the discriminator. The dense connection module has the characteristics of alleviating gradient disappearance and realizing feature reuse. After a certain amount of iterative training, the generated prediction map and label map can be distinguished. Based on the loss function in the traditional GAN, we introduce the mean squared error. By using this loss, we ensure that the synthetic images contain more realistic blood vessel structures. The values of area under the curve (AUC) in the retinal blood vessel pixel segmentation of the three public data sets DRIVE, CHASE-DB1 and STARE of the proposed method are 0.9869, 0.9894 and 0.9885, respectively. The indicators of this experiment have improved compared to previous methods.

XGBoost-based and tumor-immune characterized gene signature for the prediction of metastatic status in breast cancer.

BACKGROUND: For a long time, breast cancer has been a leading cancer diagnosed in women worldwide, and approximately 90% of cancer-related deaths are caused by metastasis. For this reason, finding new biomarkers related to metastasis is an urgent task to predict the metastatic status of breast cancer and provide new therapeutic targets. METHODS: In this research, an efficient model of eXtreme Gradient Boosting (XGBoost) optimized by a grid search algorithm is established to realize auxiliary identification of metastatic breast tumors based on gene expression. Estimated by ten-fold cross-validation, the optimized XGBoost classifier can achieve an overall higher mean AUC of 0.82 compared to other classifiers such as DT, SVM, KNN, LR, and RF. RESULTS: A novel 6-gene signature (SQSTM1, GDF9, LINC01125, PTGS2, GVINP1, and TMEM64) was selected by feature importance ranking and a series of in vitro experiments were conducted to verify the potential role of each biomarker. In general, the effects of SQSTM in tumor cells are assigned as a risk factor, while the effects of the other 5 genes (GDF9, LINC01125, PTGS2, GVINP1, and TMEM64) in immune cells are assigned as protective factors. CONCLUSIONS: Our findings will allow for a more accurate prediction of the metastatic status of breast cancer and will benefit the mining of breast cancer metastasis-related biomarkers.

Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble.

Ensemble learning combines multiple learners to perform combinatorial learning, which has advantages of good flexibility and higher generalization performance. To achieve higher quality cancer classification, in this study, the fast correlation-based feature selection (FCBF) method was used to preprocess the data to eliminate irrelevant and redundant features. Then, the classification was carried out in the stacking ensemble learner. A library for support vector machine (LIBSVM), K-nearest neighbor (KNN), decision tree C4.5 (C4.5), and random forest (RF) were used as the primary learners of the stacking ensemble. Given the imbalanced characteristics of cancer gene expression data, the embedding cost-sensitive naive Bayes was used as the metalearner of the stacking ensemble, which was represented as CSNB stacking. The proposed CSNB stacking method was applied to nine cancer datasets to further verify the classification performance of the model. Compared with other classification methods, such as single classifier algorithms and ensemble algorithms, the experimental results showed the effectiveness and robustness of the proposed method in processing different types of cancer data. This method may therefore help guide cancer diagnosis and research.

Multimodal Glioma Image Segmentation Using Dual Encoder Structure and Channel Spatial Attention Block.

Multimodal medical images provide significant amounts of complementary semantic information. Therefore, multimodal medical imaging has been widely used in the segmentation of gliomas through computational neural networks. However, inputting images from different sources directly to the network does not achieve the best segmentation effect. This paper describes a convolutional neural network called F-S-Net that fuses the information from multimodal medical images and uses the semantic information contained within these images for glioma segmentation. The architecture of F-S-Net is formed by cascading two sub-networks. The first sub-network projects the multimodal medical images into the same semantic space, which ensures they have the same semantic metric. The second sub-network uses a dual encoder structure (DES) and a channel spatial attention block (CSAB) to extract more detailed information and focus on the lesion area. DES and CSAB are integrated into U-Net architectures. A multimodal glioma dataset collected by Yijishan Hospital of Wannan Medical College is used to train and evaluate the network. F-S-Net is found to achieve a dice coefficient of 0.9052 and Jaccard similarity of 0.8280, outperforming several previous segmentation methods.

Helicid Reverses Lipopolysaccharide-Induced Inflammation and Promotes GDNF Levels in C6 Glioma Cells through Modulation of Prepronociceptin.

Helicid suppresses inflammatory factors and protects nerve cells in the hippocampus of rats with depression, but the mechanisms underlying its protective effects are unclear at present. In this investigation, we conducted gene silencing, Helicid intervention and rescue experiments to explore the protective actions of PNOC, the prepronociceptin gene known to regulate inflammatory processes, and Helicid on a C6 cell model of inflammation induced by LPS. Collective data from Western blots, ELISA, immunofluorescence and flow cytometry experiments showed that PNOC silencing or administration of Helicid led to reduced inflammatory factor levels, oxidative stress and expression of glial fibrillary acidic protein (GFAP), along with increased glial cell lines-derived neurotrophic factor (GDNF) expression. Furthermore, expression of p-Akt in the Akt signaling pathway was increased. Interestingly, overexpression of PNOC in the Helicid treatment group partially reversed the Helicid-induced changes in the above biochemical indexes. Our collective results provide strong evidence of Helicid-mediated regulation of the Akt signaling pathway through PNOC to improve cell inflammation and oxidative stress.

Hydrothermal synthesis and characterization of novel yellow pigments based on V5+ doped BiPO4 with high near-infrared reflectance.

In this study, a series of novel pigments based on V5+ doped BiPO4 have been prepared for the first time via a facile hydrothermal method and characterized using several analytical techniques, such as X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), ultraviolet-visible-near-infrared (UV-vis-NIR) spectrometry, the Commission International de l'Eclairage (CIE) L*a*b* color scales and thermogravimetry and differential thermal analysis (TG-DTA). The investigation demonstrated that the synthesized pigments of BiP1-x V x O4 (x = 0.00, 0.01, 0.05, 0.08, 0.10, 0.15) had a monazite-type phase structure and were about 0.25-2 µm in size. Meanwhile, the substitution of V5+ for P5+ in BiPO4 resulted in the band gap of the pigments varying from 3.657 to 3.244 eV and its mechanism was explained by charge-transfer and energy band theory, while the color changed from white to yellow. More importantly, the V5+ doped pigments possessed high NIR reflectance (>72%) and NIR solar reflectance (≥75.64%) in the range 700-2500 nm. Moreover, coatings colored with synthetic pigments have higher NIR solar reflectance (≥78.59%) than conventional pigments. Additionally, the pigments showed good thermal/chemical stabilities in high-temperature/acid/alkaline tests. In conclusion, the pigments have the potential to be applied as "cool pigments" to reduce energy consumption.

Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classification.

It remains a great challenge to achieve sufficient cancer classification accuracy with the entire set of genes, due to the high dimensions, small sample size, and big noise of gene expression data. We thus proposed a hybrid gene selection method, Information Gain-Support Vector Machine (IG-SVM) in this study. IG was initially employed to filter irrelevant and redundant genes. Then, further removal of redundant genes was performed using SVM to eliminate the noise in the datasets more effectively. Finally, the informative genes selected by IG-SVM served as the input for the LIBSVM classifier. Compared to other related algorithms, IG-SVM showed the highest classification accuracy and superior performance as evaluated using five cancer gene expression datasets based on a few selected genes. As an example, IG-SVM achieved a classification accuracy of 90.32% for colon cancer, which is difficult to be accurately classified, only based on three genes including CSRP1, MYL9, and GUCA2B.

Cancer Classification Based on Support Vector Machine Optimized by Particle Swarm Optimization and Artificial Bee Colony.

Intelligent optimization algorithms have advantages in dealing with complex nonlinear problems accompanied by good flexibility and adaptability. In this paper, the FCBF (Fast Correlation-Based Feature selection) method is used to filter irrelevant and redundant features in order to improve the quality of cancer classification. Then, we perform classification based on SVM (Support Vector Machine) optimized by PSO (Particle Swarm Optimization) combined with ABC (Artificial Bee Colony) approaches, which is represented as PA-SVM. The proposed PA-SVM method is applied to nine cancer datasets, including five datasets of outcome prediction and a protein dataset of ovarian cancer. By comparison with other classification methods, the results demonstrate the effectiveness and the robustness of the proposed PA-SVM method in handling various types of data for cancer classification.

[Effect of high-temperature phase change material on the performance of infrared decoy].

The impact of the high-temperature phase change material on conventional infrared decoy's combustion performance and infrared radiation characteristics was studied. The selected high-temperature phase change materials did not reduce infrared radiation in the 3-5 microm or 8-14 microm band of infrared decoy, while extended the burning time, and reduced the burning rate of the grain, thus prolonged the effective interference time of IR decoy. The results show the phase change material is effective infrared decoy functional additives.