Invasive breast carcinoma(BRCA)is associated with poor prognosis and high risk of mortality.Therefore,it is critical to identify novel biomarkers for the prognostic assessment of BRCA.Methods:The expression data of po...Invasive breast carcinoma(BRCA)is associated with poor prognosis and high risk of mortality.Therefore,it is critical to identify novel biomarkers for the prognostic assessment of BRCA.Methods:The expression data of polo-like kinase 1(PLK1)in BRCA and the corresponding clinical information were extracted from TCGA and GEO databases.PLK1 expression was validated in diverse breast cancer cell lines by quantitative real-time polymerase chain reaction(qRT-PCR)and western blotting.Single sample gene set enrichment analysis(ssGSEA)was performed to evaluate immune infiltration in the BRCA microenvironment,and the random forest(RF)and support vector machine(SVM)algorithms were used to screen for the hub infiltrating cells and calculate the immunophenoscore(IPS).The RF algorithm and COX regression model were applied to calculate survival risk scores based on the PLK1 expression and immune cell infiltration.Finally,a prognostic nomogram was constructed with the risk score and pathological stage,and its clinical potential was evaluated by plotting calibration charts and DCA curves.The application of the nomogram was further validated in an immunotherapy cohort.Results:PLK1 expression was significantly higher in the tumor samples in TCGA-BRCA cohort.Furthermore,PLK1 expression level,age and stage were identified as independent prognostic factors of BRCA.While the IPS was unaffected by PLK1 expression,the TMB and MATH scores were higher in the PLK1-high group,and the TIDE scores were higher for the PLK1-low patients.We also identified 6 immune cell types with high infiltration,along with 11 immune cell types with low infiltration in the PLK1-high tumors.A risk score was devised using PLK1 expression and hub immune cells,which predicted the prognosis of BRCA patients.In addition,a nomogram was constructed based on the risk score and pathological staging,and showed good predictive performance.Conclusions:PLK1 expression and immune cell infiltration can predict post-immunotherapy prognosis of BRCA patients.展开更多
针对现有网络入侵检测方法的不足,提出了一种新的网络入侵检测方法——GATS-LSVM算法。该方法采用遗传算法(GA)与禁忌搜索(TS)相混合的搜索策略对特征子集空间进行随机搜索,利用提供的数据在无约束优化线性支持向量机(LSVM)上的分类错...针对现有网络入侵检测方法的不足,提出了一种新的网络入侵检测方法——GATS-LSVM算法。该方法采用遗传算法(GA)与禁忌搜索(TS)相混合的搜索策略对特征子集空间进行随机搜索,利用提供的数据在无约束优化线性支持向量机(LSVM)上的分类错误率作为特征子集的评估标准获取最优特征子集,从而有效地对入侵进行检测。大量基于著名的KDD Cup 1999数据集的实验表明,该新方法相对于其它一些传统的网络入侵检测方法,能在保证较高检测率的前提下,有效地降低误报率、入侵检测的计算复杂度和提高检测速度,能更适用于现实高速网络应用环境。展开更多
归一化植被指数(normalized difference vegetation index,NDVI)时间序列已广泛应用于植被信息提取研究,然而目前NDVI时间序列的研究主要集中于中低分辨率遥感影像,从而影响了植被信息提取的精度。随着中国高分专项首颗卫星高分一号(GF...归一化植被指数(normalized difference vegetation index,NDVI)时间序列已广泛应用于植被信息提取研究,然而目前NDVI时间序列的研究主要集中于中低分辨率遥感影像,从而影响了植被信息提取的精度。随着中国高分专项首颗卫星高分一号(GF-1)的发射,为高分辨率NDVI时间序列的构建提供了可能。该文尝试利用GF-1卫星16 m宽覆盖(wide field of view,WFV)影像,构建16 m分辨率NDVI时间序列,以河北省唐山市南部区域为研究区,开展作物分类研究。该文采用覆盖作物完整生长期的GF-1数据构建NDVI时间序列,避免了利用自然年(1-12月)数据构建NDVI时间序列的不足,有助于作物信息的提取。通过分析样地的NDVI时序曲线,发现GF-1/WFV NDVI时间序列能够清晰地区分不同作物的物候差异,捕捉作物特有的生长特性,而且能够识别研究区当年的作物种植模式。该文分别采用最大似然法、马氏距离、最小距离、神经网络分类、支持向量机(support vector machine,SVM)等分类方法,基于GF-1/WFV NDVI时间序列对研究区作物进行分类,研究结果表明SVM分类方法总体精度最高,达到96.33%。同时该文还采用时间序列谐波分析法(harmonic analysis of time series,HANTS)对NDVI时间序列进行了平滑处理,结果表明处理后的NDVI时间序列能更好地描述作物的物候特性,作物分类精度得到进一步提高。展开更多
基金funded by the Natural Science Foundation of Higher Education Institutions of Auhui Province(Grant No.KJ2021A0352)the Research Fund Project of Anhui Medical University(Grant No.2020xkj236)Applied Medicine Research Project of Hefei Health Commission(Grant No.HWKJ2019-172-14).
文摘Invasive breast carcinoma(BRCA)is associated with poor prognosis and high risk of mortality.Therefore,it is critical to identify novel biomarkers for the prognostic assessment of BRCA.Methods:The expression data of polo-like kinase 1(PLK1)in BRCA and the corresponding clinical information were extracted from TCGA and GEO databases.PLK1 expression was validated in diverse breast cancer cell lines by quantitative real-time polymerase chain reaction(qRT-PCR)and western blotting.Single sample gene set enrichment analysis(ssGSEA)was performed to evaluate immune infiltration in the BRCA microenvironment,and the random forest(RF)and support vector machine(SVM)algorithms were used to screen for the hub infiltrating cells and calculate the immunophenoscore(IPS).The RF algorithm and COX regression model were applied to calculate survival risk scores based on the PLK1 expression and immune cell infiltration.Finally,a prognostic nomogram was constructed with the risk score and pathological stage,and its clinical potential was evaluated by plotting calibration charts and DCA curves.The application of the nomogram was further validated in an immunotherapy cohort.Results:PLK1 expression was significantly higher in the tumor samples in TCGA-BRCA cohort.Furthermore,PLK1 expression level,age and stage were identified as independent prognostic factors of BRCA.While the IPS was unaffected by PLK1 expression,the TMB and MATH scores were higher in the PLK1-high group,and the TIDE scores were higher for the PLK1-low patients.We also identified 6 immune cell types with high infiltration,along with 11 immune cell types with low infiltration in the PLK1-high tumors.A risk score was devised using PLK1 expression and hub immune cells,which predicted the prognosis of BRCA patients.In addition,a nomogram was constructed based on the risk score and pathological staging,and showed good predictive performance.Conclusions:PLK1 expression and immune cell infiltration can predict post-immunotherapy prognosis of BRCA patients.
文摘针对现有网络入侵检测方法的不足,提出了一种新的网络入侵检测方法——GATS-LSVM算法。该方法采用遗传算法(GA)与禁忌搜索(TS)相混合的搜索策略对特征子集空间进行随机搜索,利用提供的数据在无约束优化线性支持向量机(LSVM)上的分类错误率作为特征子集的评估标准获取最优特征子集,从而有效地对入侵进行检测。大量基于著名的KDD Cup 1999数据集的实验表明,该新方法相对于其它一些传统的网络入侵检测方法,能在保证较高检测率的前提下,有效地降低误报率、入侵检测的计算复杂度和提高检测速度,能更适用于现实高速网络应用环境。
文摘归一化植被指数(normalized difference vegetation index,NDVI)时间序列已广泛应用于植被信息提取研究,然而目前NDVI时间序列的研究主要集中于中低分辨率遥感影像,从而影响了植被信息提取的精度。随着中国高分专项首颗卫星高分一号(GF-1)的发射,为高分辨率NDVI时间序列的构建提供了可能。该文尝试利用GF-1卫星16 m宽覆盖(wide field of view,WFV)影像,构建16 m分辨率NDVI时间序列,以河北省唐山市南部区域为研究区,开展作物分类研究。该文采用覆盖作物完整生长期的GF-1数据构建NDVI时间序列,避免了利用自然年(1-12月)数据构建NDVI时间序列的不足,有助于作物信息的提取。通过分析样地的NDVI时序曲线,发现GF-1/WFV NDVI时间序列能够清晰地区分不同作物的物候差异,捕捉作物特有的生长特性,而且能够识别研究区当年的作物种植模式。该文分别采用最大似然法、马氏距离、最小距离、神经网络分类、支持向量机(support vector machine,SVM)等分类方法,基于GF-1/WFV NDVI时间序列对研究区作物进行分类,研究结果表明SVM分类方法总体精度最高,达到96.33%。同时该文还采用时间序列谐波分析法(harmonic analysis of time series,HANTS)对NDVI时间序列进行了平滑处理,结果表明处理后的NDVI时间序列能更好地描述作物的物候特性,作物分类精度得到进一步提高。
