2001—2003年杭州地区婴幼儿腹泻轮状病毒VP7型别分析

目的研究杭州地区秋冬季婴幼儿腹泻轮状病毒(RV)的感染特点及RV分子流行病学特点。方法收集2001—2003年秋冬季来本院就诊拟诊为病毒性肠炎患儿的粪便共683份,采用乳胶凝集试验(LAT)检测A组RV,阳性者再用酶联免疫吸附试验(ELISA)分型检测RVVP7(G)血清型;将ELISA确定VP7(G)血清型的标本进一步做逆转录聚合酶链反应(RTPCR)扩增及RV基因测序,证实其VP7(G)血清型。结果683份标本中,RV阳性297份,检出率为43.5%(297/683);ELISA分型:VP7(G)血清型以G1、G3型为主,其中G1型占36.7%(109/297)、G3型占30.9%(92/297),其余G2、G4、混合型和未分型分别占4.0%(12/297)、7.1%(21/297)、11.8%(35/297)和9.4%(28/297);每年流行的VP7(G)血清型的主流不同,2001年以G1型为主,占54.9%(45/82)、G3型占14.6%(12/82),2003年以G3型为主,占43.2%(63/146)、G1型占29.5%(43/146)。RTPCR扩增、基因序列测定和同源性分析证实了ELISA分型的可信性。结论2001—2003年杭州地区秋冬季婴幼儿腹泻RVVP7(G)血清型以G1、G3型为主,且每年流行的主流血清型不同。

2016年至2017年广州市手足口病流行病学及非肠道病毒A组71型非柯萨奇病毒A组16型肠道病毒型别分析

目的了解2016年至2017年广州市手足口病流行病学及非肠道病毒A组71型(enterovirus 71, EV-A71)病毒非柯萨奇病毒A组16型(coxsaekievirus group A 16,CV-A16)病毒的分子流行病学特征,为其治疗和防控提供依据。方法运用荧光反转录-PCR对2016年至2017年疑似手足口病患者标本同时进行EV通用型、EV-A71、CV-A16检测,并选取EV-A71和CV-A16是阴性而EV通用型是阳性的标本进行型别鉴定,设计5′非编码区(UTR)引物,RT-PCR扩增后进行序列测定,用BLAST程序进行序列的EV型别确定。结果2016年至2017年同时进行EV通用型、EV-A71型、CV-A16型3类病毒的疑似手足口病例标本共25 779份,总阳性16 300份,阳性率为63.23%;其中EV-A71阳性1 178份,占4.57%;CV-A16阳性3 274份,占12.70%;非EV-A71非CV-A16EV阳性标本11 848份,占45.96%。2017年非EV-A71非CV-A16EV全年平均检出率(55.68%)比2016年检出率(35.14%)更高。95份非EV-A71非CV-Al6阳性标本的序列分析结果有16种型别,分别为CV-A6、CV-A10、CV-A4、CV-A2、CV-A8、CV-A12、CV-A9、CV-B5、CV-B2、CV-B4、CV-B3、E1、E16、E30、E2和E18,其中CV-A6占主,(26.32%),其次是CV-A10(15.79%),这两种病毒占到非EV-A71非CV-A16EV的42.11%。结论2016年至2017年EV-A71感染较低,2016年4月至7月CV-A16存在一个感染小高峰。2016年至2017年手足口病主要以非EV-A71非CV-A16为主,序列分析表明非EV-A71非CV-A16中以CV-A6和CV-A10为主,应加强对以CV-A6、CV-A10为主的非EV-A71非CV-A16的研究及监测。

新型冠状病毒肺炎疫情发生前及疫情期间广州市手足口病肠道病毒的流行病学及型别分析

目的对比了解新型冠状病毒肺炎(COVID-19)发生前及疫情期间广州市手足口病肠道病毒的分子流行病学特征。方法 2019年1月至2021年12月, 采集到于广州市妇女儿童医疗中心就诊的手足口病住院或门诊疑似患儿的标本共73 740份, 其中2019年10 243份, 2020年24 568份, 2021年38 929份。采用实时荧光定量聚合酶链反应进行肠道病毒通用型、肠道病毒71型(enterovirus 71, EV71)、柯萨奇病毒A组(Coxsackie virus A group, CA)16检测, 并选取EV71和CA16双阴性而肠道病毒通用型为阳性的标本进行型别鉴定。结果 2019年10 243份同时进行肠道病毒通用型、EV71、CA16检测的手足口病标本中, 肠道病毒通用型阳性6 590份(64.34%), EV71阳性37份(0.36%), CA16阳性1 107份(10.81%), 非EV71非CA16肠道病毒阳性5 446份(53.17%)。2020年1 450份同时进行肠道病毒通用型、EV71、CA16检测的手足口病标本中, 肠道病毒通用型阳性476份(32.83%), EV71阳性1份(0.07%, 10月检测到), CA16阳性22份(1.52%), 非EV71非CA16肠道病毒阳性453份(31.24%);仅进行肠道病毒通用型检测的23 118份标本中, 阳性1 450份(6.27%)。2021年2 572份同时进行肠道病毒通用型、EV71、CA16检测的手足口病标本中, 肠道病毒通用型阳性1 155份(44.91%), EV71全年未检出, CA16阳性219份(8.51%), 非EV71非CA16肠道病毒阳性936份(36.39%);仅进行肠道病毒通用型检测的36 357份标本中, 阳性5 938份(16.33%)。非EV71非CA16肠道病毒的序列分析确定有14种型别, 其中CA6和CA10占44.14%(49/111)。结论 COVID-19疫情期间的2020年和2021年的肠道病毒阳性率低于疫情前的2019年。2019年至2021年手足口病的病原体以非EV71非CA16肠道病毒中的CA6和CA10为主。

杭州地区新生儿至1岁患儿肠道病毒分型分析

目的:分析2021年杭州地区新生儿至1岁婴儿患者肠道病毒的感染情况,了解其分子特征及流行趋势,从而为疾病的预防提供科学依据。方法:采集患者的肛拭子或咽拭子,采用实时荧光定量PCR技术检测肠道病毒(EV)通用型和柯萨奇病毒A组16型(CA16型);然后通过PCR扩增和测序验证CA16阳性样本;对于EV阳性而非CA16样本,则针对部分VP1区设计引物,进行PCR扩增和测序分析以鉴定型别;最后,在NCBI网站利用BioEdit、MEGA4生信分析软件对PCR产物序列进行比对并构建进化树以确定肠道病毒基因分型。结果:在2021年杭州地区临床初步诊断疑似肠道病毒感染的60例儿童患者中,共检出EV阳性标本29份,总阳性检出率为48%,其中CA16阳性检出率为20%。在随机抽取的10份EV阳性标本中,CA16阳性5份,EV其他分型(CA6)阳性样本1份。结论:2021年杭州地区EV CA16为主要感染型别,同时也存在EV其他型别如CA6等。因此,加强肠道病毒的监测,对解释EV的感染趋势及疾病的防控至关重要。

福建恙虫病东方体型别的调查

: [目的 ]调查福建省恙虫病东方体流行毒株的型别。 [方法 ]分别用不同抗原型、型特异性抗体的间接免疫荧光技术及补体结合试验进行型别调查。 [结果 ]闽东南沿海地区人群恙虫病感染率最高。在 192份抗体阳性的血清标本中 ,Gilliam型最多 ,占 42 .7% ,其次为 Karp型及 Karp- Gilliam的混合感染 ,分别占 2 8.1%和 2 9.1%。从病人血液、鼠类宿主及恙螨媒介共分离出 12 6株恙虫病东方体 ,其中 Gilliam占 2 4.6 % ,Karp占 2 3.0 % ,Karp- Gilliam占 49.2 % ,并分离出 1株 TA716型。血清抗体分型及病原体特异性分析结果基本一致。 [结论 ]Gilliam和

四川省彝族地区珠蛋白生成障碍性贫血基因型的分析

目的初步了解四川省彝族地区珠蛋白生成障碍性贫血(以下简称地贫)基因缺失突变类型及民族分布特点。方法收集2021年4月-2022年11月研究单位门诊部及住院部红细胞相关参数的分析结果,满足MCV<80 fL;MCH<27 pg;MCV<80 fL和MCH<27 pg标准的标本采用PCR+导流杂交法进行α、β地贫基因检测。结果1128例检出地贫基因携带者546例,阳性率48.40%。检出a-地贫256例(46.89%);β-地贫275例(50.37%);aβ-复合地贫15例(2.75%)。包含8种民族,以汉、彝族为主。其中汉族人群携带率60.76%(429/706);彝族人群携带率24.15%(85/352)。汉族人群携带地贫基因主要为:--SEA/αα、Codon 17(A>T)、CD41-42(-TTCT)、IVS-II-654(C>T)、-α3.7/αα;彝族人群携带率24.15%(85/352),彝族人群携带地贫基因主要为:--SEA/αα、-α3.7/αα、Codon 17(A>T)、CD41-42(-TTCT)、CD26(G>T)、IVS-II-654(C>T)。男性地贫基因携带率49.47%(233/471),女性47.64%(313/657)。结论研究初步发现四川省彝族地区a地贫基因缺失突变型别与其他地方研究情况一致,β-地贫突变型与国内其他区域的缺失突变类型有异同,不同性别研究对象的地贫基因突变情况相同。为四川省彝族地区地贫预防提供了初步依据,也为四川省彝族地区制定地贫预防控制策略提供了参考。

Facial expression recognition based on fuzzy-LDA/CCA

A novel fuzzy linear discriminant analysis method by the canonical correlation analysis （fuzzy-LDA/CCA）is presented and applied to the facial expression recognition. The fuzzy method is used to evaluate the degree of the class membership to which each training sample belongs. CCA is then used to establish the relationship between each facial image and the corresponding class membership vector, and the class membership vector of a test image is estimated using this relationship. Moreover, the fuzzy-LDA/CCA method is also generalized to deal with nonlinear discriminant analysis problems via kernel method. The performance of the proposed method is demonstrated using real data.

Judgment of Aroma Types of Middle and Upper Flue-cured Tobacco Leaves Based on Proportions of Aroma Components

[Objective] This study aimed to establish mathematical models for judging the aroma types of flue-cured tobacco leaves from the upper and middle parts of plants. [Method] A total of 128 samples （63 C3F and 65 B2F） from 11 main tobac- co production provinces of China were selected as materials. Stepwise discriminant analysis was applied to samples with different aroma types and discriminant function was expressed with the proportions of 67 aroma components in total aroma con- stituents as the index. [Result] The ratio of most aroma components in clear and full aroma tobacco leaves was higher than that in middle aroma leaves. The ratios of 51, 43 and 40 aroma components of clear, middle and full aroma tobaccos were higher in upper leaves than that in middle leaves. Aroma components dominated certain aroma types differed between middle and upper leaves. The proportions of 18 and 11 aroma components in upper and middle leaves were led in the stepwise discriminant function respectively. Self-validation and cross-validation methods were applied to evaluate the original samples, and the accuracy rates reached 100% and 98.6% on middle leaves, 96.37% and 94.4% on upper leaves. The accuracy rates on some other samples reached 100% on middle leaves and 91.7% on upper leaves predicted with the model. [Conclusion] The ratio of aroma components as discriminant index could improve discriminant accuracy significantly in the middle and upper leaves. It could be used to analyze aroma types objectively, accurately and quickly.

Fault Diagnosis for Batch Processes by Improved Multi-model Fisher Discriminant Analysis

Since there are not enough fault data in historical data sets, it is very difficult to diagnose faults for batch processes. In addition, a complete batch trajectory can be obtained till the end of its operation. In order to overcome the need for estimated or filled up future unmeasured values in the online fault diagnosis, sufficiently utilize the finite information of faults, and enhance the diagnostic performance, an improved multi-model Fisher discriminant analysis is represented. The trait of the proposed method is that the training data sets are made of the current measured information and the past major discriminant information, and not only the current information or the whole batch data. An industrial typical multi-stage streptomycin fermentation process is used to test the performance of fault diagnosis of the proposed method.

Fisher discriminant analysis model and its application for prediction of classification of rockburst in deep-buried long tunnel

A Fisher discriminant analysis （FDA） model for the prediction of classification of rockburst in deep-buried long tunnel was established based on the Fisher discriminant theory and the actual characteristics of the project. First, the major factors of rockburst, such as the maximum tangential stress of the cavern wall σθ, uniaxial compressive strength σc, uniaxial tensile strength or, and the elastic energy index of rock Wet, were taken into account in the analysis. Three factors, Stress coefficient σθ/σc, rock brittleness coefficient σc/σt, and elastic energy index Wet, were defined as the criterion indices for rockburst prediction in the proposed model. After training and testing of 12 sets of measured data, the discriminant functions of FDA were solved, and the ratio of misdiscrimina- tion is zero. Moreover, the proposed model was used to predict rockbursts of Qinling tunnel along Xi＇an-Ankang railway. The results show that three forecast results are identical with the actual situation. Therefore, the prediction accuracy of the FDA model is acceptable.

Hazard and population vulnerability analysis: a step towards landslide risk assessment

In this paper, an attempt to analyse landslide hazard and vulnerability in the municipality of Pahuatlfin, Puebla, Mexico, is presented. In order to estimate landslide hazard, the susceptibility, magnitude （area-velocity ratio） and landslide frequency of the area of interest were produced based on information derived from a geomorphological landslide inventory; the latter was generated by using very high resolution satellite stereo pairs along with information derived from other sources （Google Earth, aerial photographs and historical information）. Estimations of landslide susceptibility were determined by combining four statistical techniques： （i） logistic regression, （ii） quadratic discriminant analysis, （iii） linear discriminant analysis, and （iv） neuronal networks. A Digital Elevation Model （DEM） of lo m spatial resolution was used to extract the slope angle, aspect, curvature, elevation and relief. These factors, in addition to land cover, lithology anddistance to faults, were used as explanatory variables for the susceptibility models. Additionally, a Poisson model was used to estimate landslide temporal frequency, at the same time as landslide magnitude was obtained by using the relationship between landslide area and the velocity of movements. Then, due to the complexity of evaluating it, vulnerability of population was analysed by applying the Spatial Approach to Vulnerability Assessment （SAVE） model which considered levels of exposure, sensitivity and lack of resilience. Results were expressed on maps on which different spatial patterns of levels of landslide hazard and vulnerability were found for the inhabited areas. It is noteworthy that the lack of optimal methodologies to estimate and quantify vulnerability is more notorious than that of hazard assessments. Consequently, levels of uncertainty linked to landslide risk assessment remain a challenge to be addressed.

Two-stage approach to full Chinese parsing

Natural language parsing is a task of great importance and extreme difficulty. In this paper, we present a full Chinese parsing system based on a two-stage approach. Rather than identifying all phrases by a uniform model, we utilize a divide and conquer strategy. We propose an effective and fast method based on Markov model to identify the base phrases. Then we make the first attempt to extend one of the best English parsing models i.e. the head-driven model to recognize Chinese complex phrases. Our two-stage approach is superior to the uniform approach in two aspects. First, it creates synergy between the Markov model and the head-driven model. Second, it reduces the complexity of full Chinese parsing and makes the parsing system space and time efficient. We evaluate our approach in PARSEVAL measures on the open test set, the parsing system performances at 87.53% precision, 87.95% recall.

Bayesian discriminant analysis for prediction of coal and gas outbursts and application

Based on the principle of Bayesian discriminant analysis, we established a model of Bayesian discriminant analysis for predicting coal and gas outbursts. We selected five major indices which affect outbursts, i.e., initial speed of methane diffusion, a consistent coal coefficient, gas pressure, destructive style of coal and mining depth, as discriminating factors of the model. In our model, we divided the type of coal and gas outbursts into four grades regarded as four normal populations. We then obtained the corresponding discriminant functions through training a set of data from engineering examples as learning samples and evaluated their criteria by a back substitution method to verify the optimal properties of the model. Finally, we applied the model to the prediction of coal and gas outbursts in the Yunnan Enhong Mine. Our results coincided completely with the actual situation. These results show that a model of Bayesian discriminant analysis has excellent recognition performance, high prediction accuracy and a low error rate and is an effective method to predict coal and gas outbursts.

Kernel Model Applied in Kernel Direct Discriminant Analysis for the Recognition of Face with Nonlinear Variations

A kernel-based discriminant analysis method called kernel direct discriminant analysis is employed, which combines the merit of direct linear discriminant analysis with that of kernel trick. In order to demonstrate its better robustness to the complex and nonlinear variations of real face images, such as illumination, facial expression, scale and pose variations, experiments are carried out on the Olivetti Research Laboratory, Yale and self-built face databases. The results indicate that in contrast to kernel principal component analysis and kernel linear discriminant analysis, the method can achieve lower （7%） error rate using only a very small set of features. Furthermore, a new corrected kernel model is proposed to improve the recognition performance. Experimental results confirm its superiority （1% in terms of recognition rate） to other polynomial kernel models.

Phase Analysis and Identification Method for Multiphase Batch Processes with Partitioning Multi-way Principal Component Analysis (MPCA) Model

Multi-way principal component analysis (MPCA) is the most widely utilized multivariate statistical process control method for batch processes. Previous research on MPCA has commonly agreed that it is not a suitable method for multiphase batch process analysis. In this paper, abundant phase information is revealed by way of partitioning MPCA model, and a new phase identification method based on global dynamic information is proposed. The application to injection molding shows that it is a feasible and effective method for multiphase batch process knowledge understanding, phase division and process monitoring.

Comprehensive evaluation weight-variable identification model of top coal caving effect and its application

Based on the theory of attribute identification, a weight-variable identification model was put forward on top coal caving effect in fully mechanized top coal caving face. Contribution value of all kinds of evaluation factor of the caving coal and waste were used to determine weight coefficient. And then comprehensively estimated it by the given credible degree value. This kind of method can not only classify for attribute identification, but also can classify it into sub-classification according to comprehensive score compositor that of the same attribute. The comprehensive estimate result of plane and solid caving experiments shows that the result is true, credible, simple and that is not only one of the effective method of theory study, but also can be regarded as a quantitative examine method of the top coal caving effect in scene.

Study on Acoustic Modeling in a Mandarin Continuous Speech Recognition

The design of acoustic models is of vital importance to build a reliable connection between acoustic wave-form and linguistic messages in terms of individual speech units. According to the characteristic of Chinese phonemes, the base acoustic phoneme units set is decided and refined and a decision tree based state tying approach is explored. Since one of the advantages of top-down tying method is flexibility in maintaining a balance between model accuracy and complexity, relevant adjustments are conducted, such as the stopping criterion of decision tree node splitting, during which optimal thresholds are captured. Better results are achieved in improving acoustic modeling accuracy as well as minimizing the scale of the model to a trainable extent.

Comparison of wrist motion classification methods using surface electromyogram

The Gaussian mixture model (GMM), k-nearest neighbor (k-NN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) were compared to classify wrist motions using surface electromyogram (EMG). Effect of feature selection in EMG signal processing was also verified by comparing classification accuracy of each feature, and the enhancement of classification accuracy by normalization was confirmed. EMG signals were acquired from two electrodes placed on the forearm of twenty eight healthy subjects and used for recognition of wrist motion. Features were extracted from the obtained EMG signals in the time domain and were applied to classification methods. The difference absolute mean value (DAMV), difference absolute standard deviation value (DASDV), mean absolute value (MAV), root mean square (RMS) were used for composing 16 double features which were combined of two channels. In the classification methods, the highest accuracy of classification showed in the GMM. The most effective combination of classification method and double feature was (MAV, DAMV) of GMM and its classification accuracy was 96.85%. The results of normalization were better than those of non-normalization in GMM, k-NN, and LDA.