Genome-wide association mapping and genomic prediction of stalk rot in two mid-altitude tropical maize populations

Maize stalk rot reduces grain yield and quality.Information about the genetics of resistance to maize stalk rot could help breeders design effective breeding strategies for the trait.Genomic prediction may be a more effective breeding strategy for stalk-rot resistance than marker-assisted selection.We performed a genome-wide association study(GWAS)and genomic prediction of resistance in testcross hybrids of 677 inbred lines from the Tuxpe?o and non-Tuxpe?o heterotic pools grown in three environments and genotyped with 200,681 single-nucleotide polymorphisms(SNPs).Eighteen SNPs associated with stalk rot shared genomic regions with gene families previously associated with plant biotic and abiotic responses.More favorable SNP haplotypes traced to tropical than to temperate progenitors of the inbred lines.Incorporating genotype-by-environment(G×E)interaction increased genomic prediction accuracy.

Prediction model for hepatitis B e antigen seroconversion in chronic hepatitis B with peginterferon-alfa treated based on a responseguided therapy strategy

BACKGROUND Models for predicting hepatitis B e antigen(HBeAg)seroconversion in patients with HBeAg-positive chronic hepatitis B(CHB)after nucleos(t)ide analog treatment are rare.AIM To establish a simple scoring model based on a response-guided therapy(RGT)strategy for predicting HBeAg seroconversion and hepatitis B surface antigen(HBsAg)clearance.METHODS In this study,75 previously treated patients with HBeAg-positive CHB underwent a 52-week peginterferon-alfa(PEG-IFNα)treatment and a 24-wk follow-up.Logistic regression analysis was used to assess parameters at baseline,week 12,and week 24 to predict HBeAg seroconversion at 24 wk post-treatment.The two best predictors at each time point were used to establish a prediction model for PEG-IFNαtherapy efficacy.Parameters at each time point that met the corresponding optimal cutoff thresholds were scored as 1 or 0.RESULTS The two most meaningful predictors were HBsAg≤1000 IU/mL and HBeAg≤3 S/CO at baseline,HBsAg≤600 IU/mL and HBeAg≤3 S/CO at week 12,and HBsAg≤300 IU/mL and HBeAg≤2 S/CO at week 24.With a total score of 0 vs 2 at baseline,week 12,and week 24,the response rates were 23.8%,15.2%,and 11.1%vs 81.8%,80.0%,and 82.4%,respectively,and the HBsAg clearance rates were 2.4%,3.0%,and 0.0%,vs 54.5%,40.0%,and 41.2%,respectively.CONCLUSION We successfully established a predictive model and diagnosis-treatment process using the RGT strategy to predict HBeAg and HBsAg seroconversion in patients with HBeAg-positive CHB undergoing PEG-IFNαtherapy.

Predicting Purchasing Behavior on E-Commerce Platforms: A Regression Model Approach for Understanding User Features that Lead to Purchasing

This research introduces a novel approach to improve and optimize the predictive capacity of consumer purchase behaviors on e-commerce platforms. This study presented an introduction to the fundamental concepts of the logistic regression algorithm. In addition, it analyzed user data obtained from an e-commerce platform. The original data were preprocessed, and a consumer purchase prediction model was developed for the e-commerce platform using the logistic regression method. The comparison study used the classic random forest approach, further enhanced by including the K-fold cross-validation method. Evaluation of the accuracy of the model’s classification was conducted using performance indicators that included the accuracy rate, the precision rate, the recall rate, and the F1 score. A visual examination determined the significance of the findings. The findings suggest that employing the logistic regression algorithm to forecast customer purchase behaviors on e-commerce platforms can improve the efficacy of the approach and yield more accurate predictions. This study serves as a valuable resource for improving the precision of forecasting customers’ purchase behaviors on e-commerce platforms. It has significant practical implications for optimizing the operational efficiency of e-commerce platforms.

载脂蛋白E联合临床相关指标预测阿尔兹海默病模型的建立与验证

目的 以载脂蛋白E(ApoE)为基础,结合阿尔茨海默病发病危险因素及临床常见指标,构建阿尔兹海默病发病风险的临床预测模型。方法 从南京市中医院大数据平台中,检索时间为2019年1月至2021年1月两年时间内,阿尔兹海默病患者61例,模糊匹配健康体检者111例。利用LASSO回归筛选危险因素,构建logistic回归预测模型,10折交叉进行区分度验证,bootstrap法进行校准度验证,临床决策曲线评判预测模型的临床指导性,最后以列线图可视化呈现临床预测模型。结果 筛选出12个变量,最终纳入4个危险因素:年龄、游离三碘甲状腺素(FT3)、性别、ApoE。全样本ROC曲线下面积为0.879,10折交叉9次验证后ROC曲线下平均面积为0.864;采用Bootstrap法抽样及Hosmer-Lemeshow校准度检验,结果χ^(2)=6.496,P=0.592>0.05。临床决策曲线阈值概率区间为1%~88.6%。结论 利用年龄、游离三碘甲状腺素、性别、 ApoE构建的临床预测模型对患者进行个体化评估,可以预警阿尔兹海默病,开展早期预防干预,减缓该病的发生发展。

Impact of observational MJO forcing on ENSO predictability in the Zebiak-Cane model: PartⅠ.Effect on the maximum prediction error

With the observational wind data and the Zebiak-Cane model, the impact of Madden-Iulian Oscillation （MJO） as external forcing on El Nino-Southern Oscillation （ENSO） predictability is studied. The observational data are analyzed with Continuous Wavelet Transform （CWT） and then used to extract MJO signals, which are added into the model to get a new model. After the Conditional Nonlinear Optimal Perturbation （CNOP） method has been used, the initial errors which can evolve into maximum prediction error, model errors and their join errors are gained and then the Nifio 3 indices and spatial structures of three kinds of errors are investigated. The results mainly show that the observational MJO has little impact on the maximum prediction error of ENSO events and the initial error affects much greater than model error caused by MJO forcing. These demonstrate that the initial error might be the main error source that produces uncertainty in ENSO prediction, which could provide a theoretical foundation for the adaptive data assimilation of the ENSO forecast and contribute to the ENSO target observation.

经阴道超声E-cervix技术对中孕期妇女自发性早产的预测价值

目的探讨应用经阴道超声宫颈弹性成像(E-Cervix)技术预测中孕期妇女自发性早产的价值。方法选取2022年1月至2023年3月在江西省妇幼保健院进行产检的225例孕产妇为研究对象。根据是否孕足月将其划分为早产组(n=105)和足月产组(n=120),回顾性分析2组临床资料和超声检查参数[宫颈长度(CL)、宫颈弹性成像]。运用多元逻辑回归分析影响自发性早产发生的相关因素,应用受试者工作特征曲线(ROC)评估妊娠中期CL及宫颈弹性值预测自发性早产的效能。结果双胎(Z=9.909,P=0.002)和宫颈机能不全(Z=14.189,P<0.001)是早产的危险因素。妊娠中期CL越短(t=2.312,P=0.022),硬度比值(t=6.189,P<0.001)越小,弹性对比指数(t=-3.002,P=0.003)、宫颈内口(t=-4.890,P<0.001)应变值、宫颈外口应变值(t=-5.787,P<0.001)越大,早产发生的风险越高。ROC分析结果显示,宫颈内口应变值(AUC=0.689)、宫颈外口应变值(AUC=0.612)、弹性对比指数(AUC=0.718),各指标联合诊断较单一指标具有更高的预测早产的价值(AUC=0.764)。结论应用E-Cervix技术能够有效地预测中孕期孕妇自发性早产的可能性。

阴道微生态失衡联合血清HLA-G、HLA-E水平评估HSIL患者CKC术后HPV持续感染价值

目的:探究阴道微生态失衡联合血清人类白细胞抗原-G(HLA-G)、人类白细胞抗原-E(HLA-E)水平对宫颈高级别上皮内瘤变(HSIL)患者冷刀锥切(CKC)术后人乳头瘤病毒(HPV)持续感染的预测价值。方法:回顾性收集2021年1月-2022年12月本院收治HSIL患者137例临床资料,均行CKC治疗并术后随访1年,观察术后HPV持续感染情况,测定阴道微生态及血清HLA-G、HLA-E,分析阴道微生态及血清HLA-G、HLA-E水平与HPV持续感染关系以及评估价值。结果:137例术后1年内HPV持续感染31例(阳性组),HPV持续感染率为22.6%,未持续感染106例(阴性组),两组阴道pH值,阴道菌群密集度、多样性及滴虫阴道炎发生等均有差异,阳性组阴道微生态失衡率高于阴性组,血清HLA-G(19.84±3.53 ng/ml)、HLA-E(75.39±15.83 pg/ml)水平均高于阴性组(16.93±2.96 ng/ml、68.42±12.51 pg/ml),阳性组中阴道微生态失衡者血清HLA-G、HLA-E水平高于阴道微生态正常者(均P<0.05);Spearman相关分析显示,阴道微生态失衡、血清HLA-G、HLA-E水平与术后HPV持续感染呈正相关(r=0.633、0.529、0.608,均P<0.05);受试者工作特征曲线分析显示,阴道微生态失衡、血清HLA-G、HLA-E水平及联合评估术后HPV持续感染的曲线下面积分别为0.601、0.773、0.805、0.885(P<0.05)。结论:阴道微生态失衡联合血清HLA-G、HLA-E水平有对HSIL患者CKC术后HPV持续感染有一定预测价值。

水平管内超临界R1234ze(E)冷却传热性能的神经网络预测

为探究神经网络在预测:超临界传热方面的有效性,建立了水平直管内超临界R1234ze(E)冷却传热的神经网络预测模型,并与修正的Dittus-Boelter(D-B)型传热关联式进行比较分析.研究表明,输入参数对于反向传播神经网络(BPNN)预测精度的影响很大,且并非所有BPNN输入参数组合都能带来比传热关联式更好的预测结果.输入参数组合Re_(b),Pr_(b),ρ_(b)/ρ_(w),C_(p)/C_(pw),λ_(b)/λ_(w),μ_(b)//μ_(w)的预测表现最好,对于试验集的预测结果的平均绝对偏差和最大偏差仅为2.02%和9.34%,远低于传热关联式预测偏差,且对于高温段h的趋势、h最大值以及h峰值位置的预测比关联式更加准确.此外,将遗传算法优化的BP(GA-BP)模型与BP模型在两种不同的适应度值计算方式下进行比较,揭示GA-BP在提高超临界传热预测精度方面的有效性.研究表明,当网络训练与适应度值计算采用相同数据时,将引起过拟合,并不能进一步提高预测精度;当网络训练与适应度值计算采用不同数据时,可使得网络泛化性能提高,预测结果的均方根偏差和最大偏差均有进一步的降低.

ECT2和E2F1蛋白在乳腺癌中的表达及临床意义

目的探讨ECT2和E2F1蛋白在乳腺癌中的表达并分析其临床意义。方法应用免疫组织化学En Vision法检测在山西医科大学第一医院病理科收集的133例乳腺癌和80例正常乳腺组织中ECT2和E2F1蛋白的表达情况。结果ECT2和E2F1蛋白在乳腺癌中的阳性表达率分别为55.6%和74.4%,二者分别与正常组织(8.8%和2.5%)比较,差异有统计学意义(P<0.05);ECT2和E2F1蛋白表达与患者年龄、肿瘤大小及肿瘤类型无关(P>0.05),与肿瘤分级、淋巴结转移及TNM分期有关(P<0.05)。相关性分析显示,ECT2和E2F1蛋白在乳腺癌中的表达呈显著正相关性(r=0.344,P<0.05)。结论 ECT2和E2F1蛋白表达在乳腺癌的发生发展过程中相互之间存在一定的作用,ECT2在乳腺癌中的作用可能受E2F/RB信号通路的调控,二者共同表达对评估患者预后有一定的价值。

Initial Error-induced Optimal Perturbations in ENSO Predictions, as Derived from an Intermediate Coupled Model

The initial errors constitute one of the main limiting factors in the ability to predict the E1 Nino-Southem Oscillation （ENSO） in ocean-atmosphere coupled models. The conditional nonlinear optimal perturbation （CNOP） approach was em- ployed to study the largest initial error growth in the E1 Nino predictions of an intermediate coupled model （ICM）. The optimal initial errors （as represented by CNOPs） in sea surface temperature anomalies （SSTAs） and sea level anomalies （SLAs） were obtained with seasonal variation. The CNOP-induced perturbations, which tend to evolve into the La Nifia mode, were found to have the same dynamics as ENSO itself. This indicates that, if CNOP-type errors are present in the initial conditions used to make a prediction of E1 Nino, the E1 Nino event tends to be under-predicted. In particular, compared with other seasonal CNOPs, the CNOPs in winter can induce the largest error growth, which gives rise to an ENSO amplitude that is hardly ever predicted accurately. Additionally, it was found that the CNOP-induced perturbations exhibit a strong spring predictability barrier （SPB） phenomenon for ENSO prediction. These results offer a way to enhance ICM prediction skill and, particularly, weaken the SPB phenomenon by filtering the CNOP-type errors in the initial state. The characteristic distributions of the CNOPs derived from the ICM also provide useful information for targeted observations through data assimilation. Given the fact that the derived CNOPs are season-dependent, it is suggested that seasonally varying targeted observations should be implemented to accurately predict ENSO events.

Distribution Prediction of Suitable Growth Area for Eucommia ulmoides in China under Climatic Change Background

[ Objective] The research aimed to study distribution prediction of suitable growth area for Eucommia ulmoides in China under climatic change background. [ Method] By using the maximum entropy model and many kinds of climate change scenarios, we predicted current and future distribution pattems of suitable growth area for Eucommia ulmoides in China and its change process. [ Result ] At present, highly suitable growth area of E. ulmoides mainly distributed in Sichuan, Shaanxi and Chongqing, Under climate change background, total suitable growth areas in future three decades all drastically reduced when compared with that at present. It was noteworthy that moderately and highly suitable growth areas of wild E. ulmoides all disappeared, and junction between Shaanxi and Gansu and Taibai Mountain would be stable suitable growth area of wild E. ulmoides. [ Condusioa] The research could provide useful reference data for investigation, protection and sustainable development of the wild E. ulmoides resources.

Traffic-prediction-assisted dynamic power saving mechanism for IEEE 802.16e wireless MANs

How to reduce the energy consumption powered mainly by battery to prolong the standby time is one of the crucial issues for IEEE 802.16e wireless MANs.By predicting the next downlink inter-packet arrival time,three traffic-prediction-assisted power saving mechanisms based on P-PSCI,i.e.,PSCI-PFD,PSCI-ED and PSCI-LD,were proposed.In addition,the corresponding adjustment strategies for P-PSCI were also presented when there were uplink packets to be transmitted during sleep mode.Simulation results reveal that compared with the sleep mode algorithm recommended by IEEE 802.16e,the proposed mechanism P-PSCI can improve both energy efficiency and packet delay for IEEE 802.16e due to the consideration of the traffic characteristics and rate changes.Moreover,the results also demonstrate that PSCI-PFD (a=-2) significantly outperforms PSCI-ED,PSCI-LD,and the standard sleep mode in IEEE 802.16e is in terms of energy efficiency and packet delay.

Using Neural Networks to Predict Secondary Structure for Protein Folding

Protein Secondary Structure Prediction (PSSP) is considered as one of the major challenging tasks in bioinformatics, so many solutions have been proposed to solve that problem via trying to achieve more accurate prediction results. The goal of this paper is to develop and implement an intelligent based system to predict secondary structure of a protein from its primary amino acid sequence by using five models of Neural Network (NN). These models are Feed Forward Neural Network (FNN), Learning Vector Quantization (LVQ), Probabilistic Neural Network (PNN), Convolutional Neural Network (CNN), and CNN Fine Tuning for PSSP. To evaluate our approaches two datasets have been used. The first one contains 114 protein samples, and the second one contains 1845 protein samples.

Electroviscous effect on electromagnetohydrodynamic flows of Maxwell fluids in parallel plate microchannels

Considering the influence of the streaming potential and electroviscous effects, the analytical solutions for electromagnetohydrodynamic (EMHD) flows in parallel plate microchannels are obtained. The electrolyte solutions in the microchannels are taken as generalized Maxwell fluids, and slip boundary conditions are adopted. To accurately analyze the EMHD flow characteristics, the variation trends of the electroviscous effects with the corresponding parameters must be understood. The results show that the electroviscous effects increase with the increase in the relaxation time De, the slip coefficient , and the wall zeta potential 0. However, the increase in the inverse of the electrical double-layer (EDL) thickness K, the electrical oscillating Reynolds number Re, and the ionic P'eclet number Pe can decrease the electroviscous effects. We also demonstrate that the electroviscous effect on the EMHD flows of generalized Maxwell fluids is larger than that of Newtonian fluids. This work will be useful in designing EMHD flows in parallel plate microchannels.

The Relationship between the El Nio/La Nia Cycle and the Transition Chains of Four Atmospheric Oscillations. Part Ⅱ:The Relationship and a New Approach to the Prediction of El Nio

ABSTRACT The authors explored the connection and transition chains of the Northern Oscillation （NO） and the North Pacific Oscilla tion （NPO）, the Southern Oscillation （SO）, and the Antarctic Oscillation （AAO） on the interannual timescale in a companion paper. In this study, the connection between the transition chains of the four oscillations （the NO and NPO, the SO and AAO） and the El Nifio/La Nifia cycle were examined. It was found that during the transitions of the four oscillations, alternate anticyclonic/cyclonic correlation centers propagated from the Western Pacific to the Eastern Pacific along both sides of the equator. Between the anticyclonic/cyclonic correlation centers, the zonal wind anomalies also moved eastwardly, favoring the advection of sea surface temperature anomalies from the tropical Western Pacific to the Eastern Pacific. When the anti cyclonic anomalies arrived in the Eastern Pacific, the positive phase of NO/SO and La Nifia were established and vice versa. Thus, in 4-6 years, with an entire transition chain of the four oscillations, an E1 Nifio/La Nifia cycle completed. The eastward propagation of the covarying anomalies of the sea level pressure, zonal wind, and sea surface temperature was critical to the transition chains of the four oscillations and the cycle of E1 Nifio/La Nifia. Based on their close link, a new empirical prediction method of the timing of E1 Nifio by the transition chains of the four oscillations was proposed. The assessment provided confidence in the ability of the new method to supply information regarding the long-term variations of the ocean and atmosphere in the tropical Pacific.

基于Morse势的可重建有限温度下太赫兹吸收谱的半经验模型

太赫兹吸收谱起源于对材料低频振动模或低频声子模的激发,强烈依赖于体系的化学组成和微观结构,它是材料的一种指纹特性.太赫兹振动激发能较低(0.414~414 meV),导致振动模对应的势能面较浅,且振动激发态可通过热涨落占据.较浅的势能面通常导致振动模非谐性较强,使得激发态吸收发生红移;结合较为明显的振动激发态占据,在温度升高时,太赫兹特征信号通常发生不同程度的红移.本文基于第一性原理振动分析,结合已有的低温太赫兹实验谱和Morse势函数,发展了一个半经验模型,用于描述低频振动模的非谐性效应.通过对嘌呤分子晶体的测试,模型可以重现不同温度下的太赫兹吸收谱;对实验结果的准确预测表明,该模型可用于分子晶体的太赫兹光谱表征.

基于双定时混合截尾数据Gompertz分布的统计分析

假设受试样品的寿命服从Gompertz分布,基于双定时混合截尾数据在2种损失函数下求出了形状参数的Bayes估计.取超参数的3个先验分布计算出形状参数的E-Bayes估计.利用经典方法得到了未来次序失效时刻的点预测和区间预测.当形状参数的共轭先验分布取为Gamma分布时,利用Bayes方法得到了未来次序失效时刻的预测值和等尾预测区间.通过随机模拟计算出形状参数的均值及绝对值相对偏差.通过一个数值例子对文中的结论进行了计算.

e^(N)方法用于高超声速圆锥边界层转捩预测的可靠性

e^(N)方法基于扰动在边界层中线性演化过程中的幅值增长程度来预测转捩。以来流Mach数为6、不同壁面温度条件下不同钝度圆锥为研究对象,结合直接数值模拟和抛物化稳定性方程,从e^(N)方法是否能够准确描述扰动在上述边界层中线性增长的角度,分析了该方法预测转捩的可靠性。研究结果表明,在小钝度或高壁面温度情况下,扰动在向下游的演化过程中从第1模态转变为第2模态,基于线性稳定性理论的e^(N)方法变得不再可靠。壁面温度相同,头部钝度越大,e^(N)方法越可靠;同等钝度下,壁面温度越低,e^(N)方法越可靠。由于存在模态转换时,线性稳定性理论总是低估扰动的增长,因而对于给定的转捩判据N_(T)(可由某一工况实验标定给出),若钝度减小或壁面温度增加到一定程度,e^(N)方法给出的转捩位置比实际情况更靠后。重新标定转捩判据时,钝度越小,壁面温度越高,N_(T)的修正程度就越大。

SARS病毒基因组所编码的E蛋白的二级结构和B细胞表位预测

目的 预测SARS病毒E蛋白的B细胞表位和二级结构。方法 以SARS病毒基因组序列为基础 ,采用Gar nier Robson方法、Chou Fasman方法和Karplus Schultz方法预测E蛋白质的二级结构 ;用Kyte Doolittle方案预测蛋白质的亲水性 ;用Emini方案预测蛋白质的表面可能性 ;用Jameson Wolf方案预测氨基酸的抗原性指数。综合评判 ,预测SARS病毒E蛋白的B细胞表位。结果 在SARS病毒E蛋白N 端的第 1～ 6、13～ 19、39～ 4 3、4 7～ 6 4区段和第 73～ 76区段有 β 折叠中心 ;第 6～ 12区段和第 6 7～ 6 9区段可能形成转角或无规则卷曲 ,是柔性区域。E蛋白N端第 2～ 13区段和第 6 1～ 74区段为B细胞优势表位区域。结论 用多参数预测SARS病毒E蛋白的二级结构和B细胞表位 。

SARS冠状病毒E蛋白的结构研究及功能预测

结合生物信息学方法及分子模拟手段,选择较高准确度的方法,预测了SARSE蛋白的分子结构并探讨其潜在的生物学活性和功能.研究结果表明,SARSE蛋白跨膜区25个疏水的氨基酸形成α-螺旋结构,包埋于病毒外壳磷脂双分子层中;N端10个氨基酸残基位于膜外;C端41个残基则附着于磷脂双分子膜内侧.同时发现,C端由9个氨基酸组成的劈裂是一个可能的活性部位.对分子进行进一步静电势分析证实,E蛋白C端可能的活性部位具有较大的静电势,可能的活性残基具有最大电荷密度,故有较强的结合受体或与其它蛋白相互作用的能力.