Identification and validation of a pyroptosis-related prognostic model for colorectal cancer based on bulk and single-cell RNA sequencing data

BACKGROUND Pyroptosis impacts the development of malignant tumors,yet its role in colorectal cancer(CRC)prognosis remains uncertain.AIM To assess the prognostic significance of pyroptosis-related genes and their association with CRC immune infiltration.METHODS Gene expression data were obtained from The Cancer Genome Atlas(TCGA)and single-cell RNA sequencing dataset GSE178341 from the Gene Expression Omnibus(GEO).Pyroptosis-related gene expression in cell clusters was analyzed,and enrichment analysis was conducted.A pyroptosis-related risk model was developed using the LASSO regression algorithm,with prediction accuracy assessed through K-M and receiver operating characteristic analyses.A nomo-gram predicting survival was created,and the correlation between the risk model and immune infiltration was analyzed using CIBERSORTx calculations.Finally,the differential expression of the 8 prognostic genes between CRC and normal samples was verified by analyzing TCGA-COADREAD data from the UCSC database.RESULTS An effective pyroptosis-related risk model was constructed using 8 genes-CHMP2B,SDHB,BST2,UBE2D2,GJA1,AIM2,PDCD6IP,and SEZ6L2(P<0.05).Seven of these genes exhibited differential expression between CRC and normal samples based on TCGA database analysis(P<0.05).Patients with higher risk scores demonstrated increased death risk and reduced overall survival(P<0.05).Significant differences in immune infiltration were observed between low-and high-risk groups,correlating with pyroptosis-related gene expression.CONCLUSION We developed a pyroptosis-related prognostic model for CRC,affirming its correlation with immune infiltration.This model may prove useful for CRC prognostic evaluation.

早期补充维生素C对脓毒症患者预后的影响

目的探讨早期给予静脉补充维生素C能否改善脓毒症患者的预后。方法选取2017年6月1日-2018年5月31日河南省人民医院重症监护病房(ICU)收治的脓毒症患者234例,通过前瞻性随机对照研究方法,将患者分为维生素C组(122例)及对照组(112例)。对照组采用基础治疗方案,并应用5%葡萄糖注射液100 ml静脉滴注;维生素C组在基础治疗方案上,用3 g维生素C溶解于5%葡萄糖注射液100 ml,静脉滴注,1次/d。比较两组28 d后的病死率、疗效及影响因素。结果维生素C组28 d病死率(27.9%)较对照组(42.9%)降低(P<0.05),72 h△SOFA评分较对照组升高[4.0(1.0~6.0)VS 2.0(1.0~4.0)](P<0.05),血管活性药物应用时间较对照组缩短[25.0(18.0~40.0)VS 43.0(24.0~66.0)](P<0.05),降钙素原清除率较对照组增加[79.0%(66.0%~85.0%)VS 60.0%(50.0%~66.0%)](P<0.05)。两组ICU住院时间差异无统计学意义[4.00(3.00~8.00)d VS 4.00(3.00~7.25)d](P>0.05)。结论早期静脉补充维生素C可以降低脓毒症患者的28 d病死率,降低SOFA评分,缩短血管活性药物应用的时间,提高降钙素原清除率,从而改善脓毒症患者的预后。

Synchronization mechanism of clapping rhythms in mutual interacting individuals

In recent years, clapping synchronization between individuals has been widely studied as one of the typical synchronization phenomena. In this paper, we aim to reveal the synchronization mechanism of clapping interactions by observing two individuals’ clapping rhythms in a series of experiments. We find that the two synchronizing clapping rhythm series exhibit long-range cross-correlations(LRCCs);that is, the interaction of clapping rhythms can be seen as a strong-anticipation process. Previous studies have demonstrated that the interactions in local timescales or global matching in statistical structures of fluctuation in long timescales can be sources of the strong-anticipation process. However, the origin of the strong anticipation process often appears elusive in many complex systems. Here, we find that the clapping synchronization process may result from the local interaction between two clapping individuals and may result from the more global coordination between two clapping individuals. We introduce two stochastic models for mutually interacting clapping individuals that generate the LRCCs and prove theoretically that the generation of clapping synchronization process needs to consider both local interaction and global matching. This study provides a statistical framework for studying the internal synchronization mechanism of other complex systems. Our theoretical model can also be applied to study the dynamics of other complex systems with the LRCCs, including finance, transportation, and climate.

The Chromosome-Based Rubber Tree Genome Provides New Insights into Spurge Genome Evolution and Rubber Biosynthesis

The rubber tree,Hevea brasiliensis,produces natural rubber that serves as an essential industrial raw material.Here,we present a high-quality reference genome for a rubber tree cultivar GT1 using single-molecule real-time sequencing(SMRT)and Hi-C technologies to anchor the～1.47-Gb genome assembly into 18 pseudochromosomes.The chromosome-based genome analysis enabled us to establish a model of spurge chromosome evolution,since the common paleopolyploid event occurred before the split of Hevea and Manihot.We show recent and rapid bursts of the three Hevea-specific LTR-retrotransposon families during the last 10 million years,leading to the massive expansion by～65.88%(～970 Mbp)of the whole rubber tree genome since the divergence from Manihot.We identify large-scale expansion of genes associated with whole rubber biosynthesis processes,such as basal metabolic processes,ethylene biosynthesis,and the activation of polysaccharide and glycoprotein lectin,which are important properties for latex production.A map of genomic variation between the cultivated and wild rubber trees was obtained,which contains～15.7 million high-quality single-nucleotide polymorphisms.We identified hundreds of candidate domestication genes with drastically lowered genomic diversity in the cultivated but not wild rubber trees despite a relatively short domestication history of rubber tree,some of which are involved in rubber biosynthesis.This genome assembly represents key resources for future rubber tree research and breeding,providing novel targets for improving plant biotic and abiotic tolerance and rubber production.

Microstructure and mechanical properties of magnesium matrix composite reinforced with carbon nanotubes by ultrasonic vibration

A novel approach was successfully developed to fabricate bulk carbon nanotube-reinforced Mg matrix com-posites with uniform carbon nanotubes(CNTs).The approach consists of pre-dispersion and ultrasonic vibration.Homoge-neous and single CNTs on flake Zn powder can be achieved simply by slurry blending.The pre-dispersed CNTs were added to Mg melt,and then,the melt was ultrasonically pro-cessed.After ultrasonic vibration,the CNTs/Mg-6Zn melt was cast into a metal mold.Most CNTs distribute homoge-neously and singly in the bulk composites.Moreover,good interfacial bonding is achieved,and Raman spectroscopy analysis shows that the damage to CNTs is insignificant.Meanwhile,CNTs evidently improve the ultimate tensile strength,yield strength and elongation.The Kelly-Tyson formula agrees well with the experimental tensile value,and the load-transfer efficiency is nearly equal to 1.

An Approach to Automatic Performance Prediction forCloud-Enhanced Mobile Applications with Sparse Data

In mobile cloud computing （MCC）, offloading compute-intensive parts of a mobile application onto the cloud is an attractive method to enhance application performance. To make good offloading decisions, history-based machine-learning techniques are proposed to predict application performance under various offloading schemes. However, the data sparsity problem is common in a realistic MCC scenario but is rarely the concern of existing work. In this paper, we employ a two-phase hybrid framework to predict performance for cloud-enhanced mobile applications, which is designed to be robust to the data sparsity. By training several multi-layer neural networks with historical execution records, the first phase automatically predicts some intermediate parameters for each execution of an application. The models learned by these neural networks can be shared among different applications, thus alleviating the data sparsity. Based on these predicted intermediate parameters and the application topology, the second phase deterministically calculates the estimated values of the performance metrics. The deterministic algorithm can partially guarantee the prediction accuracy of newly published applications even with no execution records. We evaluate our approach with a cloud-enhanced object recognition application and show that our approach can precisely predict the application performance and is robust to data sparsity.