A systematic study of Erzhu Erchen decoction against damp-heat internalized type 2 diabetes based on data mining and experimental verification

Background:Erzhu Erchen decoction(EZECD),which is based on Erchen decoction and enhanced with Atractylodes lancea and Atractylodes macrocephala,is widely used for the treatment of dampness and heat(The clinical manifestations of Western medicine include thirst,inability to drink more,diarrhea,yellow urine,red tongue,et al.)internalized disease.Nevertheless,the mechanism of EZECD on damp-heat internalized Type 2 diabetes(T2D)remains unknown.We employed data mining,pharmacology databases and experimental verification to study how EZECD treats damp-heat internalized T2D.Methods:The main compounds or genes of EZECD and damp-heat internalized T2D were obtained from the pharmacology databases.Succeeding,the overlapped targets of EZECD and damp-heat internalized T2D were performed by the Gene Ontology,kyoto encyclopedia of genes and genomes analysis.And the compound-disease targets-pathway network were constructed to obtain the hub compound.Moreover,the hub genes and core related pathways were mined with weighted gene co-expression network analysis based on Gene Expression Omnibus database,the capability of hub compound and genes was valid in AutoDock 1.5.7.Furthermore,and violin plot and gene set enrichment analysis were performed to explore the role of hub genes in damp-heat internalized T2D.Finally,the interactions of hub compound and genes were explored using Comparative Toxicogenomics Database and quantitative polymerase chain reaction.Results:First,herb-compounds-genes-disease network illustrated that the hub compound of EZECD for damp-heat internalized T2D could be quercetin.Consistently,the hub genes were CASP8,CCL2,and AHR according to weighted gene co-expression network analysis.Molecular docking showed that quercetin could bind with the hub genes.Further,gene set enrichment analysis and Gene Ontology represented that CASP8,or CCL2,is negatively involved in insulin secretion response to the TNF or lipopolysaccharide process,and AHR or CCL2 positively regulated lipid and atherosclerosis,and/or including NOD-like receptor signaling pathway,and TNF signaling pathway.Ultimately,the quantitative polymerase chain reaction and western blotting analysis showed that quercetin could down-regulated the mRNA and protein experssion of CASP8,CCL2,and AHR.It was consistent with the results in Comparative Toxicogenomics Database databases.Conclusion:These results demonstrated quercetin could inhibit the expression of CASP8,CCL2,AHR in damp-heat internalized T2D,which improves insulin secretion and inhibits lipid and atherosclerosis,as well as/or including NOD-like receptor signaling pathway,and TNF signaling pathway,suggesting that EZECD may be more effective to treat damp-heat internalized T2D.

Engineering DNA Materials for Sustainable Data Storage Using a DNA Movable-Type System

DNA molecules are green materials with great potential for high-density and long-term data storage.However,the current data-writing process of DNA data storage via DNA synthesis suffers from high costs and the production of hazards,limiting its practical applications.Here,we developed a DNA movable-type storage system that can utilize DNA fragments pre-produced by cell factories for data writing.In this system,these pre-generated DNA fragments,referred to herein as“DNA movable types,”are used as basic writing units in a repetitive way.The process of data writing is achieved by the rapid assembly of these DNA movable types,thereby avoiding the costly and environmentally hazardous process of de novo DNA synthesis.With this system,we successfully encoded 24 bytes of digital information in DNA and read it back accurately by means of high-throughput sequencing and decoding,thereby demonstrating the feasibility of this system.Through its repetitive usage and biological assembly of DNA movable-type fragments,this system exhibits excellent potential for writing cost reduction,opening up a novel route toward an economical and sustainable digital data-storage technology.

Classification of vegetative types in Changbai Mountain based on optical and microwave remote sensing data

Highly accurate vegetative type distribution information is of great significance for forestry resource monitoring and management.In order to improve the classification accuracy of forest types,Sentinel-1 and 2 data of Changbai Mountain protection development zone were selected,and combined with DEM to construct a multi-featured random forest type classification model incorporating fusing intensity,texture,spectral,vegetation index and topography information and using random forest Gini index(GI)for optimization.The overall accuracy of classification was 94.60%and the Kappa coefficient was 0.933.Comparing the classification results before and after feature optimization,it shows that feature optimization has a greater impact on the classification accuracy.Comparing the classification results of random forest,maximum likelihood method and CART decision tree under the same conditions,it shows that the random forest has a higher performance and can be applied to forestry research work such as forest resource survey and monitoring.

Exploring medication rules in Chinese randomized controlled trials of type 2 diabetes based on data mining technology

Background:To systematically summarize and categorize the Chinese herbal medicine in the domestic traditional Chinese medicine(TCM)literature on type 2 diabetes mellitus(T2DM),in this paper,we mine traditional Chinese medicine data for relationships and provide for future practitioners and researchers.Methods:Taking randomized controlled trials on the treatment of T2DM in TCM as the research theme,we searched for full-text literature in three major clinical databases,including CNKI,Wan Fang,and VIP,published between 1990 and 2020.We then conducted frequency statistics,cluster analysis,association rules extraction,and principal component analysis based on a corpus of medical academic words extracted from 1116 research articles.Results:The most frequently used is Astragali Radix,and the most commonly used two-herb combination in T2DM treatment consisted of Coptidis Rhizoma and Moutan Cortex.Moutan Cortex,Alismatis Rhizoma,and Dioscoreae Rhizoma were the most frequently used three-herb combination.We found a“lung”and“liver”and“kidney”model and confirmed the value of classical meridian tropism theory and pattern identification.The treatment is mainly to fill deficiency and clear heat and consider water infiltration,dampness,blood circulation,and silt.Conclusion:This study provides an in-depth perspective on the TCM medication rules for T2DM and offers practitioners and researchers valuable information about the current status and frontier trends of TCM research on T2DM in terms of diagnosis and treatment.

Maximum Likelihood Estimation of the Parameters of Exponentiated Generalized Weibull Based on Progressive Type II Censored Data

Exponentiated Generalized Weibull distribution is a probability distribution which generalizes the Weibull distribution introducing two more shapes parameters to best adjust the non-monotonic shape. The parameters of the new probability distribution function are estimated by the maximum likelihood method under progressive type II censored data via expectation maximization algorithm.

Determination of Surface Precipitation Type Based on the Data Fusion Approach

Hazardous events related to atmospheric precipitation depend not only on the intensity of surface precipitation,but also on its type.Uncertainty related to determination of the precipitation type(PT)leads to financial losses in many areas of human activity,such as the power industry,agriculture,transportation,and many more.In this study,we use machine learning(ML)algorithms with the data fusion approach to more accurately determine surface PT.Based on surface synoptic observations,ERA5 reanalysis,and radar data,we distinguish between liquid,mixed,and solid precipitation types.The study domain considers the entire area of Poland and a period from 2015 to 2017.The purpose of this work is to address the question:“How can ML techniques applied in observational and NWP data help to improve the recognition of the surface PT?”Despite testing 33 parameters,it was found that a combination of the near-surface air temperature and the depth of the warm layer in the 0-1000 m above ground level(AGL)layer contains most of the signal needed to determine surface PT.The accrued probability of detection for liquid,solid,and mixed PTs according to the developed Random Forest model is 98.0%,98.8%,and 67.3%,respectively.The application of the ML technique and data fusion approach allows to significantly improve the robustness of PT prediction compared to commonly used baseline models and provides promising results for operational forecasters.

Test of the Cosmic Transparency with the Baryon Acoustic Oscillation and Type Ia Supernova Data

We use the latest baryon acoustic oscillation and Union 2.1 type Ia supernova data to test the cosmic opacity between different redshift regions without assuming any cosmological models. It is found that the universe may be opaque between the redshift regions 0.35 0.44, 0.44 0.57 and 0.6-0.73 since the best fit values of cosmic opacity in these regions are positive, while a transparent universe is favored in the redshift region 0.57-0.63. However, in general, a transparent universe is still consistent with observations at the lo confidence level.

Analysis of Evaluation Data Collected on Likert Type Items: Humanities-Courses

To improve high quality and/or retain achieved high quality of an academic program, time to time evaluation for quality of each covered course is often an integrated aspect considered in reputed institutions, however, there has been little effort regarding humanities courses. This research article deals with analysis of evaluation data collected regarding humanities course from a College of Commerce & Economics, Mumbai, Maharashtra, India, on Likert type items. Appropriateness of one parametric measure and three non-parametric measures are discussed and used in this regard which could provide useful clues for educational policy planners. Keeping in view of the analytical results using these four measures, regardless of the threshold regarding satisfaction among students, overall performance of almost every subject has been un-satisfactory. There is a need to make a focused approach to take every course at the level of high performance. The inconsistency noticed under every threshold further revealed that under such poorly performing subjects globally, one needs to analyze merely at the global level item. Once the global level analysis reveals high performance of a course, then only item specific analysis may need to be focused to find out the items requiring further improvements.

SOME PROBLEMS IN PARTIAL ABSTRACT DATA TYPE

In this paper, we discuss some characteristic properties of partial abstract data type (PADT) and show the diffrence between PADT and abstract data type (ADT) in specification of programming language. Finally, we clarify that PADT is necessary in programming language description.

Based on data mining to explore the medication rule of Professor Shao Zheng-bin in the treatment of coronary heart disease combined with type2 diabetes mellitus after PCI

Objective:To explore the clinical medication rule of Shao Zhengbin in the treatment of coronary heart disease with type 2 diabetes mellitus after PCI by using data mining technology.Methods:Shao Zhengbin was collected from January 2016 to may 2019 in the outpatient department of the First Affiliated Hospital of Anhui University of traditional Chinese medicine to treat patients with coronary heart disease combined with type 2 diabetes mellitus after PCI.The data base was established with Microsoft Excel 2016,SPSS statistic 24.0,SPSS modeler 18.0 computer software,and drug frequency analysis,high-frequency drug association rule analysis and clustering were carried out Analysis and factor analysis.Results:of the 133 prescriptions included in the study,86 Chinese herbs were involved,and the top 10 drugs were dangshen,Huangjing,Danshen,Gualou,chuanxiong,fried Atractylodes rhizome,Poria cocos,Fushen and Chenpi respectively;12 drug associations were generated by association analysis,including Huangjing,Huangjing,chuanxiong,dandelion,Dangshen and banpi;12 drug associations were obtained by cluster analysis,including Huangjing,huangxiong,Gualou and Huangqi There are 7 clustering formulas,such as Xia,Danshen,Fuling,Gualou,etc.Conclusion:Shao Zhengbin is good at the treatment of coronary heart disease combined with type 2 diabetes mellitus after PCI.

Bayesian Study Using MCMC of Three-Parameter Frechet Distribution Based on Type-I Censored Data

Type-I censoring mechanism arises when the number of units experiencing the event is random but the total duration of the study is fixed. There are a number of mathematical approaches developed to handle this type of data. The purpose of the research was to estimate the three parameters of the Frechet distribution via the frequentist Maximum Likelihood and the Bayesian Estimators. In this paper, the maximum likelihood method (MLE) is not available of the three parameters in the closed forms;therefore, it was solved by the numerical methods. Similarly, the Bayesian estimators are implemented using Jeffreys and gamma priors with two loss functions, which are: squared error loss function and Linear Exponential Loss Function (LINEX). The parameters of the Frechet distribution via Bayesian cannot be obtained analytically and therefore Markov Chain Monte Carlo is used, where the full conditional distribution for the three parameters is obtained via Metropolis-Hastings algorithm. Comparisons of the estimators are obtained using Mean Square Errors (MSE) to determine the best estimator of the three parameters of the Frechet distribution. The results show that the Bayesian estimation under Linear Exponential Loss Function based on Type-I censored data is a better estimator for all the parameter estimates when the value of the loss parameter is positive.

基于文献挖掘和网络药理学探析阴虚型免疫性血小板减少症用药规律与作用机制

目的利用数据挖掘技术及网络药理学探讨中药治疗阴虚型免疫性血小板减少症用药规律与潜在作用机制。方法运用中国知网、万方数据库检索中药治疗阴虚型免疫性血小板减少症相关文献,通过中医传承辅助平台V2.5软件分析组方规律,确定常用药对;运用中药系统药理学数据库与分析平台(TCMSP)、本草组鉴数据库、GeneCards数据库获取关键基因和主要靶点信息,利用STRING数据库构建交集靶点蛋白相互作用网络,采用Cytoscape 3.7.1软件中的CytoHubba和MCODE插件筛选出关键靶点信息,采用METASCAPE数据库基因本体(GO)功能富集分析和京都基因与基因组百科全书(KEGG)通路富集分析。结果共筛选得到129篇有效文献,共计130个中药处方,通过关联规则筛选出“生地黄—牡丹皮—旱莲草”为最佳中药复方,HSP90AA1、STAT3、VEGFA、MAPK3等为关键靶点,槲皮素、山柰酚、金合欢素与木犀草素等为主要有效成分,其主要作用机制可能是调控癌症通路、Th17细胞分化及核转录因子κB(NF-κB)信号通路等。结论治疗阴虚型免疫性血小板减少症应抓住止血、凉血、行血三要素,初步揭示常用复方“生地黄—牡丹皮—旱莲草”通过多成分—多靶点—多通路发挥治疗作用,为今后研究提供理论依据。

面向不同类型概念漂移的两阶段自适应集成学习方法

大数据时代,流数据大量涌现.概念漂移作为流数据挖掘中最典型且困难的问题,受到了越来越广泛的关注.集成学习是处理流数据中概念漂移的常用方法,然而在漂移发生后,学习模型往往无法对流数据的分布变化做出及时响应,且不能有效处理不同类型概念漂移,导致模型泛化性能下降.针对这个问题,提出一种面向不同类型概念漂移的两阶段自适应集成学习方法(two-stage adaptive ensemble learning method for different types of concept drift,TAEL).该方法首先通过检测漂移跨度来判断概念漂移类型,然后根据不同漂移类型,提出“过滤-扩充”两阶段样本处理机制动态选择合适的样本处理策略.具体地,在过滤阶段,针对不同漂移类型,创建不同的非关键样本过滤器,提取历史样本块中的关键样本,使历史数据分布更接近最新数据分布,提高基学习器有效性;在扩充阶段,提出一种分块优先抽样方法,针对不同漂移类型设置合适的抽取规模,并根据历史关键样本所属类别在当前样本块上的规模占比设置抽样优先级,再由抽样优先级确定抽样概率,依据抽样概率从历史关键样本块中抽取关键样本子集扩充当前样本块,缓解样本扩充后的类别不平衡现象,解决当前基学习器欠拟合问题的同时增强其稳定性.实验结果表明,所提方法能够对不同类型的概念漂移做出及时响应,加快漂移发生后在线集成模型的收敛速度,提高模型的整体泛化性能.

为形成新型生产关系提供数据法治保障

随着大数据、人工智能等数字技术的飞速发展与广泛应用,数据已成为推动经济社会发展的重要驱动力。数据不仅成为一种新型生产要素,而且还引发传统生产模式和生产主体关系发生变革,因此,必须形成与之相适应的新型生产关系。在此背景下,形成新型生产关系需要遵循数据法治逻辑,建立明晰的数据产权制度、合理的数据流通制度和公正的数据权益分配制度。为此,有必要制定综合性的数据法律规范、建立跨部门协同的数据治理机制、推动数据技术的负责任创新和构建数字化的权利救济机制。

基于SKNet注意力机制的飞机类型识别算法

飞机类型识别是细粒度图像分类的一种,重点在于设计神经网络模型使其能够分辨各飞机种类中细微而具有区分性的特征.针对当前飞机识别任务中飞机种类众多、类间差异细微、类内差异显著等问题,提出了一种基于改进SKNet注意力与数据增广的飞机类型识别算法.以ResNeXt101网络作为基础网络,改进CBAM注意力提出并行的通道-空间注意力PCSA并嵌入可选择卷积模块的不同分支,得到PCSA-SK注意力,将其嵌入基础网络以进一步融合基础网络提取的深层特征并为其分配权重.根据目标激活图中具有判别性信息的区域,在原图像上对判别性区域裁剪并加入训练集,实现数据增广.实验结果表明:该算法在FGVC-Aircraft数据集上取得了93.57%的识别准确率,优于其他飞机识别算法.

基于数据挖掘的大型邮轮船型特征及船型参数分析

以确定大型邮轮的最优船型参数,提升邮轮的航行性能为目标,提出基于数据挖掘的大型邮轮船型特征及船型参数分析方法。利用模糊C均值聚类算法,聚类大型邮轮船型数据,挖掘大型邮轮船型特征。基于粗糙集理论,对大型邮轮船型特征挖掘结果约简处理,约简后船型特征对应的参数,作为船型性能优化的船型参数。利用自由变形方法,构建大型邮轮船型变换模型。依据船型参数变化,调整模型控制体的控制顶点,变化大型邮轮形状。利用回归分析方法,拟合船型变换模型中的船型参数,确定大型邮轮的最佳船型参数。实验结果表明,该方法可以精准挖掘大型邮轮船型特征,确定最佳船型参数。最佳船型参数下的邮轮阻力下降了1.48%,提升了大型邮轮的航行性能。

基于大数据关联规则算法的卡林型金矿床元素富集规律及找矿方法研究

大数据时代的到来,为卡林型金矿床开拓了新的找矿思路。本研究应用关联规则算法,挖掘滇黔桂“金三角”卡林型金矿床内微量元素与金矿化海量数据之间的关联性,提取元素异常组合,分析控制因素,定量构建找矿标志。结果显示矿床内元素异常组合分为4组:(1)强正关联显著富集元素(As、Sb、Hg、Tl、Ag、W和Rb),显示硫化和黏土化作用;(2)较强正关联略富集元素I(Zr、Th、Ta、Nb和Hf)和强负关联强迁出元素(Li和Sr),显示去碳酸盐化作用;(3)较强正关联略富集元素II(Sn、Zn、Ni、V、Co和Cu),显示硫化作用;(4)弱关联基本无富集元素(Cd、Pb、Ba、Bi、U和Mo),与成矿无显著关联。从大数据角度获取的元素异常组合,与学界关于Au主要在去碳酸盐化、硫化和黏土化条件下形成的认识一致。通过关联规则算法分别对与硫化和去碳酸盐化相关的元素建立定量找矿标志。硫化找矿标志:样品中As、Hg、Sb、Tl、W、Ag和Rb等元素内中高含量项数≥1、≥2、≥3、≥4和≥5时,对应的Au矿化分别为≥4.5×10^(-9)、≥47.0×10^(-9)、≥150×10^(-9)、≥500×10^(-9)和≥1000×10^(-9);样品内高含量项数≥1、≥2和≥3时,对应的Au矿化分别为≥150×10^(-9)、≥500×10^(-9)和≥1000×10^(-9);找矿过程中两组指标配合使用,确保不漏矿,高效圈矿。去碳酸盐化找矿标志:样品中Zr、Th、Ta、Nb和Hf含量任意两项出现正异常,认为样品经历过去碳酸盐化作用。定量识别的硫化和去碳酸盐化找矿标志可望在卡林型金矿找矿预测中发挥重要作用。本研究基于关联规则算法分析矿床元素富集规律、控制因素和定量构建找矿标志的方法,也可为其他类型矿床开展类似研究提供新思路。

设施作物蒸腾蒸发模型的研究现状及存在问题

确定设施作物蒸腾蒸发量(ET_(c))对于准确制定设施作物灌溉制度、提高设施农业用水效率及设施蔬菜的产量和品质具有重要的意义.对确定设施作物ET_(c)的直接和间接模型进行优缺点及适用性分析,比较了不同类型参考界面蒸腾蒸发(ET_(0))模型的优势与局限性,对比了直接估算温室作物ET_(c)机理模型的准确性,并对不同机理模型中关键参数(冠层阻力参数)的确定方法及主要影响因素进行综述与分析.已有研究主要针对特定气候区域、温室通风类型、作物种类及种植季节,对冠层阻力参数进行修正,并验证模型的准确性,对温室作物ET_(c)模型的普适性研究不够充分,确定的模型参数往往不适用于其他类型温室或作物种植季节.推荐今后重点研究方向:研究温室作物ET_(c)变化过程对气候因子分布特征及变化规律的响应机理;研究不同ET_(c)机理模型对气候类型、作物种类及种植季节的适应性,构建更具普适性的温室作物ET_(c)模型.

支持压缩数据传输的MIPI接收端电路设计

随着移动设备摄像模块的像素越来越高,所需传输的数据量也越来越大,导致数据传输时间越来越长。文中提出了一种应用于MIPI CSI-2协议的接收端口电路,在传统的MIPI接口电路支持数据类型的基础上,增加了支持RLE和MJPEG两种压缩格式的数据类型,可有效减少所需传输的数据量,提高传输效率。这两种压缩格式数据可应用于不同的工作场景,采用无损压缩数据传输模式在不影响图像质量的情况下可将传输效率提升95%,而采用有损压缩数据传输模式则可在保证较高图像质量的前提下将提升效率约154倍,与传统MIPI接口相比具有显著优势。

COVID-19诊疗信息、中医证型分布及组方用药规律的文献研究

目的利用数据挖掘技术深入探究新型冠状病毒感染(COVID-19,简称新冠感染)患者的诊断、中医证型分布以及药物使用规律,以期为中医药治疗新冠感染提供有效的参考依据。方法通过搜索,可以获取来自国家卫生健康委员会和全国各地的《新型冠状病毒肺炎诊疗方案》内涉及的中医证型和中医药防治方案,以及中国生物医学文献服务系统、知网、维普、万方数据库收录的治疗新冠感染相关文献共249份。对文献通过筛选、整理和去重并建立中药复方数据库、诊疗信息数据库、证型数据库,运用频数分析、频率分析进行探究。结果新冠感染患者常见的症状频数较高的为咳嗽、咽干咽痛、发热、纳差、乏力;大多数患者呈淡红舌、红舌,脉象为滑脉、滑数脉;中医证型频数较高的有湿毒郁肺证、湿热蕴肺证、肺脾气虚证、寒湿郁肺证、气阴两伤证、兼夹瘀血证。此外,共纳入491首治疗新冠感染中药复方,涉及中药227味,得到高频中药共64个,药物类别以清热药、补益药、解表药、化痰止咳平喘药、化湿药为主;药性以温、平、寒为主,药味以甘、辛、苦为主,归经中归肺、脾、胃经中药居多;聚类分析结果根据中药性能将治疗新冠感染的高频药物聚为8类较好。结论中医药治疗新型冠状病毒感染用药具有以下特点:补益药用药次数较多体现攻邪不忘扶正;解表、清热、攻下、化湿、利湿、渗湿药物俱全体现多种逐邪之法;药类以清热药、补益药、化湿药、化痰止咳平喘药、解表药为主,彰显新冠感染基本治法为清热化湿、止咳平喘、补养气阴。可为指导临床用药及研发新药提供一定的参考与借鉴。