Selection of causal gene sets for lymphoma prognostication from expression profiling and construction of prognostic fuzzy neural network models

To assess the response of lymphomas to chemotherapy, gene expression profiling data from DNA microarrays were analyzed using the fuzzy neural network (FNN) modeling method. We used the FNN modeling method to produce 10 noninferior models. Using these models, we were able to predict diffuse large B-cell lymphoma (DLBCL) patient outcome with 93% accuracy. Of the 37 genes in the 10 models, 13 genes were repeatedly selected, indicating that these genes are important for prognostication. On Kaplan-Meier plots of overall survival, patients predicted by the FNN model to be cured survived significantly longer than those predicted to be refractory (P<0.0001), indicating that the FNN could successfully identify patients with a relatively poor prognosis among low-clinical-risk patients. The FNN modeling method presented here is able to precisely extract significant biological markers affecting prognosis.     

Prediction of peptide binding to major histocompatibility complex class II molecules through use of boosted fuzzy classifier with SWEEP operator method

To treat autoimmune diseases, it is important to identify which peptides bind to major histocompatibility complex (MHC) class II molecules (HLA-DRs). Predicting the peptides that bind to MHC class II molecules can effectively reduce the number of experiments required for identifying helper T cell epitopes. In our previous study, we applied fuzzy neural networks (FNNs) to solve this problem. However, an FNN requires a long calculation time and a large number of peptides; this means performing several experiments. In this study, we applied a boosted fuzzy classifier with the SWEEP operator method (BFCS) to solve this problem. For comparison, two other conventional modeling methods, namely, support vector machine and FNN combined with the SWEEP operator method (FNN-SWEEP) instead of using solely an FNN, were employed. Compared with FNN, FNN-SWEEP is extremely fast and has an almost identical prediction accuracy. The model constructed by BFCS showed an accuracy approximately 5%-10% higher than that constructed by FNN-SWEEP. In addition, BFCS was 30,000-120,000 times faster than FNN-SWEEP. This result suggests that BFCS has the potential to function as a new method of predicting peptides that bind to various protein receptors.     

Construction of COD simulation model for activated sludge process by fuzzy neural network

Fuzzy neural network (FNN) was applied to construct a simulation model for estimating the effluent chemical oxygen demand (COD) value of an activated sludge process in a "U" plant, in which most of process variables were measured once an hour. The constructed FNN model could simulate periodic changes in COD with high accuracy. Comparing the simulation result obtained using the FNN model with that obtained using the multiple regression analysis (MRA) model, it was found that the FNN model had 3.7 times higher accuracy than the MRA model. The FNN models corresponding to each of the four seasons were also constructed. Analyzing the fuzzy rules acquired from the FNN models after learning, the operational characteristic of this plant could be elucidated. Construction of the simulation model for another plant "A", in which process variables were measured once a day, was also carried out. This FNN model also had a relatively high accuracy.     

Sensory Modeling of Coffee with a Fuzzy Neural Network

ABSTRACT: Models were constructed to predict sensory evaluation scores from the blending ratio of coffee beans. Twenty-two blended coffees were prepared from 3 representative beans and were evaluated with respect to 10 sensory attributes by 5 coffee cup-tasters and by models constructed using the response surface method (RSM), multiple regression analysis (MRA), and a fuzzy neural network (FNN). The RSM and MRA models showed good correlations for some sensory attributes, but lacked sufficient overall accuracy. The FNN model exhibited high correlations for all attributes, clearly demonstrated the relationships between blending ratio and flavor characteristics, and was accurate enough for practical use. FNN, thus, constitutes a powerful tool for accelerating product development.     

Fuzzy neural network-based prediction of the motif for MHC class II binding peptides

Characterizing the interaction between major histocompatibility complex (MHC) molecules and antigenic peptides is critical for understanding immunity and developing immunotherapies for autoimmune diseases and cancer. To identify the peptide binding motif and predict peptides that bind to the human MHC classII molecule HLA-DR4(*0401), we applied a fuzzy neural network (FNN) capable of extracting the relationship between input and output. Analysis of the peptide binding motif revealed that the hydrophilicity of the position 1 residue located on the N-terminal side of the nonamer (9mer) was the most important variable and that the van der Waals volume and hydrophilicity of the position 6 residue and the hydrophilicity of the position 7 residue were also important variables. The estimation accuracy (A(ROC) value) was high and the binding motif extracted from the FNN agreed with that derived experimentally. This study demonstrates that FNN modeling allows candidate antigenic peptides to be selected without the need for further experiments.     

朝鲜族大酱中异黄酮的提取工艺优化及其抗肿瘤活性研究

为优化朝鲜族大酱异黄酮的提取工艺,并研究朝鲜族大酱中异黄酮的抗肿瘤活性,本实验用超声波法提取朝鲜族大酱中的异黄酮,通过单因素实验和正交实验优化其提取工艺,并通过CCK-8法对正常乳腺细胞、MDA-MB-231和MCF-7两种乳腺癌细胞的增殖抑制情况进行了测定,用Western Blot法检测了朝鲜族大酱中异黄酮处理后的两种乳腺癌细胞凋亡相关蛋白的表达情况。通过单因素和正交试验确定其最佳提取条件为提取时间80 min、料液比1:24、温度50 ℃,此时得率最高,为0.802%。CCK-8结果显示,不同浓度的朝鲜族大酱异黄酮对正常细胞无毒副作用,对MDA-MB-231和MCF-7两种乳腺癌细胞的增殖抑制情况均呈剂量-时间依赖性,且均有显著性差异(p<0.05)。对不同浓度朝鲜族大酱异黄酮处理过的两种细胞凋亡相关蛋白的检测结果显示,两种细胞随着异黄酮浓度的增加,其促凋亡蛋白Bax,caspase-3的表达量与空白对照组相比均显著上升(p<0.05),而抗凋亡蛋白Bcl-2的表达量显著下降(p<0.05),且呈现剂量依赖性。表明朝鲜族大酱异黄酮对乳腺癌细胞的生长能够起到抑制作用。     

基于三维点云的烤烟三维模型构建及株型分析

  目的  为了构建烟株的三维模型、提取烤烟株型信息。  方法  采用结构光扫描仪获取烤烟的三维点云数据,经过点云配准、点云去噪以及孔洞修补等步骤后构建了烤烟的三维模型,并对烤烟株型参数进行了误差估计。  结果  利用该方法构建的烤烟三维模型能够精确还原烤烟的形态结构,叶长、叶宽和株高的机器测量值和人工测量值拟合方程的R2均达0.8以上。  结论  基于三维点云构建的烤烟三维模型精度高,为烟草科研和烟叶生产提供了一种高精度的烤烟株型判别方法。      

In vitro and in vivo evaluation of anticancer actions of natural and synthetic vitamin E forms

The goal of these studies was to investigate the potential anticancer properties of two naturally occurring plant sources and two manufactured synthetic forms of vitamin E, i. e., RRR-alpha-tocopherol (alphaT), RRR-gamma-tocopherol (gammaT), all-rac-alpha-tocopherol (all-rac-alphaT), and all-rac-alpha-tocopheryl acetate (all-rac-alphaTAc) in breast cancer models. Vitamin E compounds were evaluated in vitro for inhibition of colony formation and induction of apoptosis in human MDA-MB-435 and MCF-7 breast cancer cells and murine 66cl-4 mammary cancer cells and in vivo for ability to reduce tumor growth and lung and lymph node metastases using the transplantable syngeneic BALB/c mouse 66cl-4-GFP mammary cancer model. gammaT inhibited colony formation and induced apoptosis in all three cancer cell lines. alphaT and all-rac-alphaT were less effective and all-rac-alphaTAc was ineffective. gammaT-induced apoptosis was correlated with activation of caspases-8 and -9 and down-regulation of protein expression of c-FLIP and survivin. In vivo study 1 analyses showed that all-rac-alphaT and all-rac-alphaTAc significantly inhibited tumor growth and inhibited both visible and microscopic size lung metastases. In vivo study 2 analyses showed that alphaT and gammaT reduced tumor growth, but only gammaT reduced tumor growth significantly in comparison to control. In conclusion, synthetic, but not natural, vitamin E exhibits promising anti-cancer properties in vivo.     

基于PCA-1DCNN的近红外光谱粮食作物主要成分检测方法

针对传统的近红外光谱定量技术难以选择合适的光谱预处理方法且模型预测精度低的问题,以3个谷物数据集的近红外光谱数据集为研究对象,构建了基于主成分分析光谱筛选算法的一维卷积神经网络模型。与传统的偏最小二乘回归和支持向量机模型的性能做了对比后,一维卷积神经网络构建的模型性能均为最优。其中在对玉米数据集的水分、油脂、蛋白质、淀粉的定量建模中,模型的决定系数分别为99.09%、98.15%、98.89%、99.60%;在对grain数据集的定量建模中,四种成分模型的决定系数分别为100%、100%、100%、99.99%;在对小麦数据集的定量建模中,小麦蛋白质模型的决定系数为99.80%。为了验证主成分分析光谱筛选算法对粮食作物主要成分定量回归模型的有效性,在3个光谱数据集上去除了主成分分析算法进行消融实验。研究结果表明：基于主成分分析算法与一维卷积神经网络的回归建模方法为粮食作物成分含量的检测提供一种快速无损精确的判定方式,研究结果对于粮食作物成分的含量检测具有促进作用。     

高效液相色谱法测定母乳及牛乳中总核苷酸含量

建立高效液相色谱测定母乳和牛乳中总核苷酸含量和组成的分析方法。总核苷酸包括游离核苷酸、游离核苷、核苷聚合物及加合物等不同来源的可利用核苷总量。利用酶解法释放出母乳中的核苷,以硼化聚丙烯酰胺凝胶（Affi-Gel 601）作为固相萃取介质进行样品净化,用反相高效液相色谱仪检测,内标法定量,检测结果以核苷酸计。且针对母乳样品的特性,增加高温灭活步骤进行样品前处理,以检测母乳中核苷酸的天然含量及组成。对所建方法进行全面验证,结果表明其具有良好的线性,相关系数（r2）均在0.999以上;母乳中各核苷酸的检出限和定量限分别在0.18～0.45 mg/L和0.60～1.51 mg/L之间;母乳基质中3 种不同加标水平下总核苷酸的回收率均在99%～108%之间,连续3 d 6 次检测结果的相对标准偏差在0.8%～7.8%之间,显示了良好的准确性及精密度。然后进一步利用所建检测方法,评估母乳及5 种市面常见的牛乳中的核苷酸总量及组成用以验证该方法的适用性。该方法的灵敏度、准确性及精密度均可满足对母乳和牛乳样品中天然的核苷酸含量及组成的测定要求。本研究为今后大样本量调查母乳及牛乳样品中核苷酸的含量和组成提供了更加科学准确的方法。     

基于特征参数的青年女性乳房形态分析

为探究青年女性乳房形态区别,提出了乳房边界定义方法以保证乳房形态参数测量的一致性。使用[TC]²三维扫描仪对140名18~25岁在校未婚孕青年女性进行扫描,获取了包括高度、宽度、角度、弧线等28项乳房形态相关参数;通过变异系数和相关性分析筛选出6个影响乳房形态的主要参数作为聚类指标,从乳房立体形态和聚拢程度两方面对乳房形态进行细分;基于乳房形态分类结果,利用Fisher判别函数对样本进行回判验证。结果表明,青年女性乳房形态可分为9类,基于形态判别规则对初始样本数据整体回判的准确率高达97.1%,说明此判别方法具有较高的准确性,为现有的乳房形态研究提供了新思路。     

Methodology to model the energy and greenhouse gas emissions of electronic software distributions

A new electronic software distribution (ESD) life cycle analysis (LCA) methodology and model structure were constructed to calculate energy consumption and greenhouse gas (GHG) emissions. In order to counteract the use of high level, top-down modeling efforts, and to increase result accuracy, a focus upon device details and data routes was taken. In order to compare ESD to a relevant physical distribution alternative, physical model boundaries and variables were described. The methodology was compiled from the analysis and operational data of a major online store which provides ESD and physical distribution options. The ESD method included the calculation of power consumption of data center server and networking devices. An in-depth method to calculate server efficiency and utilization was also included to account for virtualization and server efficiency features. Internet transfer power consumption was analyzed taking into account the number of data hops and networking devices used. The power consumed by online browsing and downloading was also factored into the model. The embedded CO(2)e of server and networking devices was proportioned to each ESD process. Three U.K.-based ESD scenarios were analyzed using the model which revealed potential CO(2)e savings of 83% when ESD was used over physical distribution. Results also highlighted the importance of server efficiency and utilization methods.     

马铃薯α-淀粉酶基因的克隆及生物信息学分析

利用RT-PCR 方法从马铃薯(Solanum tuberosum)茎段总RNA 中扩增、克隆amyA1 基因(NCBI 登录号GQ406048.1)。采用半定量RT-PCR 方法检测amyA1 基因在马铃薯茎、叶等不同组织中的表达强度,表明在茎组织中的表达丰度略高。利用生物信息学软件分析amyA1 密码子的偏好性;同时对AmyA1 氨基酸的理化性质、细胞内定位、保守结构及高级结构进行预测。基于NCBI 数据库中有物种代表性的29 种α - 淀粉酶基因序列构建了基因进化树。amyA1 基因全长1224bp,可编码一条理论分子质量为46.40kD、407 个氨基酸残基组成的、可能为亲水性的胞外酶。与NCBI已登记的马铃薯α - 淀粉酶基因(登录号M79328.1)核苷酸及氨基酸序列同源性达98%。第20～348 位点范围内的氨基酸残基含有与淀粉酶13 家族及亚家族相似的催化活性域(PF00128、SM00624),第349～407位点范围内的氨基酸残基含有α - 淀粉酶C- 末端β折叠区域(PF07821)。蛋白质结构预测表明氨基酸残基序列有维持淀粉酶活性的(β /α) 8 桶状结构以及其他几个功能域结构。所构建的基因进化树表明,两个马铃薯α - 淀粉酶基因与木薯、苹果的序列同源性较高,与菜豆的次之,与水稻、大麦、玉米等单子叶植物的序列同源性较低。     

基于IGWO-SVR短期储藏小麦品质预测模型研究

储藏小麦品质具有复杂性、易变性、多耦合特性，导致难以准确预测其品质状况。为此，本研究从小麦多生理生化指标关联性研究角度提出了一种新的品质预测方法。利用柯西核函数和改进的线性核函数来构造支持向量回归机（SVR）混合核函数，并用改进的灰狼算法（IGWO）对混合核函数SVR参数寻优，由此建立IGWO-SVR模型用于短期储藏小麦的品质预测。选用周麦22对模型进行验证，结果显示：混合核函数IGWO-SVR模型的平均相对误差相比于线性核、多项式核和径向基核的模型分别下降了4.24%、2.56%和1.74%；IGWO-SVR各预测效果评价指标均优于GS-SVR、CS-SVR和GWO-SVR模型，模型整体预测精度和拟合效果显著提高。最后通过周麦22的发芽率作为品质评估指标和郑麦9023多指标数据分别对IGWO-SVR模型的有效性和适用性进行检验，得到平均绝对百分比误差MAPE分别为1.85%和3.87%，表明模型性能良好。试验结果表明了新建立模型在短期储藏小麦品质预测方面的可行性。     

酿酒酵母表达侧耳源单基因生物合成麦角硫因

运用生物信息学方法对侧耳属蘑菇（平菇、杏鲍菇和白灵菇）的麦角硫因合成酶基因Egt 1进行挖掘,对其蛋白结构和功能进行预测,并构建酿酒酵母表达载体,在酿酒酵母EC 1118中进行表达研究。结果表明：克隆得到侧耳属平菇、杏鲍菇和白灵菇麦角硫因合成基因PoEgt 1、PeEgt 1和PtEgt 1,三者核苷酸序列同源性为97.03%,氨基酸序列同源性为97.93%,联合菌体破碎法并通过体外酶促反应后检测到麦角硫因,产量为（2.5±0.08）mg/L,证明了该基因具有单基因合成麦角硫因的活性。     

Adequate design of customized cDNA macroarray for convenient multiple gene expression analysis

To establish a convenient, cost-effective, and reasonably reliable method for monitoring multiple gene expression using customized membrane-based macroarray, we constructed a cDNA macroarray with multiple probes for 13 human vascular endothelial genes and assessed the accuracy of the macroarray measurements. For each gene, two cDNA probes (450-550 bp) were designed from different regions (coding region and 3'-untranslated region [3'-UTR], respectively) on the basis of simple criteria concerning length and sequence specificity and spotted on the macroarray. In addition, unmodified oligonucleotide probes (80 mer) targeted to a unique sequence from the coding region of each gene were spotted on the same macroarray. Using this macroarray, shear stress-induced mRNA expression changes were analyzed in human coronary artery endothelial cells. Comparison of the expression ratios obtained with those measured using quantitative real-time polymerase chain reaction (PCR) as a reference method revealed that cDNA probes designed from a sequence within the coding region provided a highly accurate expression profile, whereas results obtained from oligonucleotide probes showed no correlation with real-time PCR data, which might be caused by inadequate immobilization of oligonucletotide probes on the nylon membrane. In addition, we observed that cDNA probes targeting different regions of a gene yielded different signal intensities. Most cDNA probes designed from a sequence within the coding region showed detectable signals, whereas few cDNA probes designed from 3'-UTR did.     

高光谱图像感兴趣区域对苹果糖度模型的影响

高光谱图像技术作为一种强有力的新兴技术,已应用于食品农产品品质与安全检测研究,然而高光谱图像中感兴趣区域形状大小的选择直接影响着检测的精度和稳定性。首先采集苹果330~1100 nm的高光谱图像,分别提取不同大小的圆形感兴趣区域和方形感兴趣区域的平均光谱,经光谱预处理以消除噪声及无关信息的影响,然后采用偏最小二乘法分别建立苹果的糖度定量分析模型,并以独立样本的预测集进行验证,分析感兴趣区域形状大小对高光谱图像建模精度的影响。结果表明,提取直径为150像素的圆形感兴趣区域建立的苹果糖度模型精度最高,预测能力最强,校正集相关系数Rc为0.9305,校正均方根误差RMSEC为0.4331,预测集相关系数Rp为0.9232,预测均方根误差RMSEP为0.4568。研究表明,针对研究对象选择合适形状和大小的感兴趣区域,对提高模型精度、发挥高光谱图像的技术优势具有重要意义。     

基因组De novo组装结合比较RNA-Seq策略在VBNC状态乳杆菌转录组学研究中的应用

本文应用有参考基因组组装策略对一株提取自啤酒中的乳杆菌的两种生理状态进行转录组学分析。利用基因组测序技术得到此株乳杆菌的全部基因组信息之后,把此株乳杆菌用低温诱导至VBNC状态,对正常状态和VBNC状态乳杆菌提取总RNA。对RNA质量进行验证,将符合要求的RNA进行逆转录形成c DNA,之后进行文库构建和Solexa测序。通过相关指标对测序结果进行评估,之后以此前获得的全基因组序列为对照,进行转录组与基因组的序列比对和基因表的达差异分析。结果显示比对率大于95%,表达上调的基因有21个,表达下降的基因有7个。本文所应用的基因组De novo组装结合比较RNA-Seq策略与常规De novo RNA-Seq策略相比,省却De novo拼接与组装的繁琐步骤,具有更高的准确性,为今后食品微生物在不同生理状态中分子调控机制的研究提供重要基础。     

3步混合变量选择策略在鸡肉近红外水分检测中的应用

基于光谱多元校正中有效变量选择的3步混合策略(初筛、精挑、细选),提出了间隔偏最小二乘(iPLS)、区间变量迭代空间收缩法(iVISSA)和迭代保留信息变量(IRIV)联用的特征变量选择方法,对生鲜鸡胸肉的近红外光谱进行特征波长选择,建立了鸡肉水分检测模型。结果表明,建模波长数量经iPLS-iVISSA-IRIV 3步选择后减少为全光谱建模的0.76%,但模型精确度和稳定性逐步提高。选定8个特征波长建模,其校正相关系数RC=0.907 7,校正均方根误差RMSEC=0.516 1;预测相关系数RP=0.943 5,预测均方根误差RMSEP=0.612 3。表明基于3步混合策略提出的iPLS-iVISSA-IRIV方法能有效选择鸡肉水分检测的特征波长。     

inlA和inlB基因缺失对单核细胞增生性李斯特菌侵袭HT29结肠癌细胞的影响

单核细胞增生性李斯特菌（Listeria monocytogenes,LM）是人畜共患的食源性致病菌,其可以穿透多个宿主屏障,而内化素蛋白家族被认为是LM穿透宿主屏障过程中起重要作用的毒力因子。本研究利用同源重组的方法构建了LM野生菌株EGDe的inlA和inlB基因双缺失菌株,利用实时荧光定量聚合酶链式反应检测其主要毒力基因表达的变化,并以HT29结肠癌细胞为对象,研究inlA和inlB基因缺失对LM侵袭宿主细胞能力的影响。结果表明基因的缺失对其生长能力没有影响,但多个毒力基因的表达发生了不同程度的变化,同时发现inlA和inlB基因的缺失使LM侵袭HT29结肠癌细胞的能力显著下降（P＜0.05）。本研究成功构建LM的inlA和inlB基因双缺失菌株,并初步研究了基因缺失对LM侵袭宿主细胞能力的影响,为深入研究内化素InlA和InlB在LM入侵宿主细胞过程中的具体作用提供了支持。