电针分型预测结合面神经肌电图治疗周围性面神经麻痹80例

目的:观察电针穴位分型预测与面神经肌电图检查相结合,治疗周围性面神经麻痹的临床疗效。方法:选择2002-09/2005-02济南第四人民医院门诊及住院80例周围性面神经麻痹患者,按电针穴位分型法划分为一般型和顽固型,一般型为电针1组,顽固型分为电针2组、综合治疗组。电针1组、电针2组均都采用电针治疗:取穴阳白、太阳、地仓、下关、风池、翳风、迎香、攒竹、承浆。均取患侧。同时取健侧的合谷。针刺得气后采用电针治疗仪治疗,频率120～250次/min,每次留针30min,治疗1次/d,10次为1个疗程,休息两天,再行第2个疗程。综合治疗组:在常规电针治疗的同时,针刺时均采用中等手法刺激,同时加取双侧足三里穴,均采用补法,每10min行针1次,面部患侧采用特定电磁波照射,以下关、翳风穴为主进行局部照射,距离30～40cm,温度舒适为宜,每次电针加照射40min,1次/d,10次为1个疗程,休息两天再进行第2个疗程。所有患者均行面部面神经肌电图检查。结果:80例患者进入结果分析,无脱落者。①各组疗效的比较:电针1组43例中42例痊愈,占95%,顽固型37例仅痊愈15例,电针2组和综合治疗组分别占30%,53%,差异明显(P<0.05)。②各分型患者有效病例疗程长短比较:电针1组基本上在两个疗程内痊愈,电针2组,综合治疗组多在两三个疗程或3个疗程后好转,治疗时间较长。③不同分型患者面神经潜伏期M波比较:80例患侧面神经运动潜伏期有不同程度的延长,M波波幅降低,与健侧比较差异有极显著性(P<0.01)。其中患健侧M波波幅比值>50%35例、50%～30%32例,<30%13例,3个月后随访,M波波幅比值<30%中的5例仍有明显面瘫体征,>30%的67例中面瘫体征基本恢复。④按肌电图分型标准各型所占比例数比较:按肌电图分类的一般型43例中治愈41例,占95%,而顽固型中,按肌电图分类的轻中型31例中痊愈15例,占48%,无效的5例均提示为重度失神经性损害。虽然面神经肌电图,从面神经运动潜伏期是否延长及根据其失神经性损害进行分类,判断患者预后有一定的准确性,但同电针穴位分型两法合用,分型预测更直接明确。结论:电针分型可以预测该病的预后,对比面神经肌电图情况,判断其预后基本一致。同时根据分型,及时调整治疗方案,可减少后遗症的发生。

基于影像组学的肺癌分型预测

目的基于影像组学特征对肺癌中的两大亚型分类(小细胞肺癌与非小细胞肺癌)进行分型预测。方法在131名小细胞肺癌与非小细胞肺癌患者中(其中训练集包含119人,测试集中包含12人),从手动分割的病灶区域提取107维组学特征,使用R统计学软件中的FSelector包对影像组学特征进行关键特征筛选,构建支持向量机模型和k折交叉验证模型对肺癌患者的病理进行表型分类和验证,通过绘制受试者工作特征曲线(ROC曲线)图和计算曲线下面积(AUC)数值来对训练集和测试集中的肺癌分型预测效果进行评估。结果挑选出20个主要的影像组学特征用于小细胞肺癌与非小细胞肺癌的分型鉴别,这些特征对于训练集和测试集中的小细胞肺癌与非小细胞肺癌均有较好的区分能力。在测试集中,预肺癌亚型分类的准确率为75%,组学特征的AUC结果为0. 69。结论通过构建独特的影像组学特征,以用作区分小细胞肺癌与非小细胞肺癌的诊断因素。这对实现非侵入性的肺癌病理有效分型预测,指导肺癌患者后续治疗方案的选择具有重要指导意义。

基于logistic回归分析构建肝功能不全临床分型预测模型

目的探讨肝功能不全临床分型的影响因素并建立预测模型。方法选取75例肝功能不全患者作为研究对象,采用国际医学科学组织委员会的分型标准将患者分为肝细胞损伤型与胆汁淤积型,比较两组一般资料与生化指标的差异。应用logistic回归方法建立合适的模型,并绘制相应的列线图。最后,绘制受试者工作特征曲线(ROC)与临床决策曲线进一步验证预测模型的准确性。结果75例患者中肝细胞损伤型41例,胆汁淤积型34例。多因素回归筛选出谷草转氨酶与血红蛋白为临床分型的影响因素(OR分别=0.98、0.92,P均<0.05)。基于这2个因素构建预测模型列线图。验证模型准确性的ROC曲线下面积为0.90,临床决策曲线分析表明,当阈值概率在0.1~0.9范围内时,该模型在临床上的净获益最高。结论谷草转氨酶、与血红蛋白影响肝功能不全患者的临床分型,基于这2个因素构建预测模型列线图的预测模型准确性较高。

强震类型划分和后续强震预测方法探索

地震能量、振幅与周期的关系 ,由震源性质决定 ,因而其相关程度可作为强震类型划分的指标 .初步将强震划分为主余震、双主震和强震群型 3类 .同类型强震在震源性质定量指标上有相似性 ,且有一定重复性 .它们可为后续强震预测提供依据 .从 1 988～ 1 997年 50多例 CDSN宽频带 ( BPZ波 )强震记录中 ,分析得出的后续强震预测参考指标 。

考虑不同背景场低频降水的延伸期预测

基于中国国家气象观测站的逐日降水资料、NCEP逐日全球再分析资料和NOAA逐日向外长波辐射资料,选取与10—30 d低频降水相关显著的热带、中高纬环流作为影响因子,针对1979—2013年江南4—6月延伸期低频降水,依照不同背景场下低频降水与影响因子之间的相关性,进行了预测试验。结果表明:江南4—6月降水以10—30 d的低频周期最为显著。印度洋、印尼附近的热带对流和欧亚高纬度地区的大气环流共同影响着我国江南4—6月低频降水,可作为延伸期降水的预测因子。当欧洲及西西伯利亚地区位势高度出现负距平、北美及贝湖以西附近位势高度正距平,且热带对流异常偏弱时,对应江南低频降水异常偏少,异常中心主要位于长江中下游地区; 30 d以上的大尺度500 h Pa低频位势高度场主要表现为3种空间分布型,根据这3种分布型可将逐日降水个例的大尺度背景场划分为3类,每种背景场下低频降水与热带、中高纬度环流因子在前期30 d内的相关特征均不同; 30 d以上时间尺度的500 h Pa低频环流可为10—30 d延伸期变化提供相对稳定的大尺度背景场,不同背景场下区域低频降水与相应低频环流之间的关系演变不同。考虑不同背景,其相关性增强,且显著相关超前的时间更长。

扩散磁共振成像振荡梯度自旋回波原理及其在脑胶质瘤中的应用进展

脑胶质瘤作为成人最常见的恶性中枢神经系统肿瘤,具有高病死率及侵袭性强的特点。脑胶质瘤的肿瘤分级及遗传表型异质性将影响治疗方案制订及预后,治疗后疗效评估及肿瘤复发早期诊断有助于提高患者生存期。基于振荡梯度自旋回波(oscillating gradient spin echo,OGSE)的扩散MRI是一种新型扩散MRI技术,它通过探测细胞直径、细胞密度、细胞内外体积分数等微结构特征,在脑胶质瘤影像诊断上具有广阔的应用前景。本文就OGSE成像的技术原理、OGSE成像在脑胶质瘤分类分级、分子分型预测、疗效评估、鉴别诊断中的研究进展及OGSE成像的应用现状进行综述,为脑胶质瘤的术前诊断及术后评估提供新思路。

化学交换饱和转移成像在胶质瘤的应用研究进展

胶质瘤是最常见的原发性中枢神经系统肿瘤,侵袭性强,预后极差。其生长过程中伴随着肿瘤组织内蛋白质、谷氨酸、胺等物质含量的改变。化学交换饱和转移成像(Chemical exchange saturation transfer,CEST)是一种依赖于酰胺(-NH)、胺(-NH2)和羟基(-OH)基团中可移动质子和自由水进行化学交换的新型无创性MRI技术,从分子水平反映组织内蛋白质或多肽中可移动质子含量及pH值的变化。该技术可发现并量化胶质瘤内部分子水平的改变。本文就CEST技术原理及在胶质瘤术前诊断及鉴别诊断、分级、分子分型预测、疗效评估的研究进展及CEST技术存在的不足进行综述。

Relationship between Rice Planthopper Occurrence Area in China and Atmospheric Circulation Indices

[Objective] This study aimed to establish models based on atmospheric cir- culation indices for forecasting the area attacked by rice planthopper every year, and to provide guide for preventing and controlling planthopper damage. [Method] The data related to rice planthopper occurrence and atmospheric circulation were collected and analyzed with the method of stepwise regression to establish the prediction models. [Result] The factors significantly related to the area attacked by rice plan-thopper were selected. Two types of prediction models were established. One was for Sogatella furcifera （Horvath）, based on Atlantic-Europe circulation pattern W in October in that year, Pacific polar vortex area index in October in that year, North America subtropical high index in August in that year, Atlantic-Europe circulation pattern W in June in that year, northern boundary of North America subtropical high in February in that year, Atlantic-Europe polar vortex intensity index in October in that year and Asia polar vortex intensity index in November in the last year; the other type of prediction models were for Nilaparvata lugens （Stal）, based on the Eastern Pacific subtropical high intensity index in July in that year, northern hemi- sphere polar vortex area index in October in the last year, Asia polar vortex strength index in November in the last year, north boundary of North America-At- lantic subtropical high in September in that year, north boundary of North Africa-At- lantic-North America subtropical high in January in that year, sunspot in September of the last year and eastern Pacific subtropical high area index in September in that year. [Conclusion] With the stepwise regression, the forecasting equations of the rice planthopper occurrence established based on the atmospheric circulation indices could be used for actual forecast.

PREDICTION-BASED BANDWIDTH ALLOCATION FOR VBR TRAFFIC

A prediction based bandwidth allocation scheme for transporting MPEG VBR traffic is proposed by using the FARIMA (p,d,q) (fractional autoregressive integrated moving average) model.FARIMA (p,d,q) model is capable of capturing both the long range and short range dependence in the video traffic.A method is suggested to simplify the FARIMA model fitting procedure and hence to reduce the time of traffic modeling and prediction.The simulation experiments show that this scheme can significantly reduce the requirement of buffer size and the frame loss rate.

Studies on stand dynamic growth model for larch in Jilin in China

The stand growth and yield dynamic models for Larch in Jilin Province were developed based on the forest growth theories with the forest continuous inventory data. The results indicated that the developed models had high precision, and they could be used for the updating data of inventory of planning and designing and optimal decision of forest management.

Application of discrete choice model in trip mode structure forecast:a case study of Bengbu

In order to find the main factors that influence the urban traffic structure,a relational model between the travelers＇ characteristics and the trip mode choice is built.The data of urban residents＇ characteristics are obtained from statistical data,while the trip mode split data is collected through a trip survey in Bengbu.In addition,the discrete choice model is adopted to build the functional relationship between the mode choice and the travelers＇ personal characteristics,as well as family characteristics and trip characteristics.The model shows that the relationship between the mode split and the personal,as well as family and trip characteristics is stable and changes little as the time changes.Deduced by the discrete model,the mode split result is relatively accurate and can be feasibly used for trip mode structure forecasts.Furthermore,the proposed model can also contribute to find the key influencing factors on trip mode choice,and restructure or optimize the urban trip mode structure.

Connection Between Liquid Distribution and Gas-Liquid Mass Transfer in Monolithic Bed

With a particular focus on the connection between liquid flow distribution and gas-liquid mass transfer in monolithic beds in the Taylor flow regime, hydrodynamic and gas-liquid mass transfer experiments were carriedout in a column with a monolithic bed of cell density of 50 cpsi with trio different distributors （nozzle and packed bed distributors）. Liquid saturation in individual channels was measured by using self-made micro-conductivity probes. A mal-distribution factor was used to evaluate uniform degree of phase distribution in monoliths. Overall bed pressure drop and mass transfer coefficients were measured. For liquid flow distribution and gas-liquid masstransfer, it is found that the superficial liquid velocity is a crucial factor and the packed bed distributor is better than the nozzle distributor. A semi-theoretical analysis using single channel models shows that the packed bed distributor always yields shorter and uniformly distributed liquid slugs compared to the nozzle distributor, which in turn ensures a better mass transfer performance. A bed scale mass transfer model is proposed by employing the single channel models in individual channels and incorporating effects of non-uniform liquid distribution along the bedcross-section. The model predicts the overall gas-liquid mass transfer coefficient wig a relative error within ＋30%.

Predict typhoon-induced storm surge deviation in a principal component back-propagation neural network model

To reduce typhoon-caused damages, numerical and empirical methods are often used to forecast typhoon storm surge. However, typhoon surge is a complex nonlinear process that is difficult to forecast accurately. We applied a principal component back-propagation neural network (PCBPNN) to predict the deviation in typhoon storm surge, in which data of the typhoon, upstream flood, and historical case studies were involved. With principal component analysis, 15 input factors were reduced to five principal components, and the application of the model was improved. Observation data from Huangpu Park in Shanghai, China were used to test the feasibility of the model. The results indicate that the model is capable of predicting a 12-hour warning before a typhoon surge.

Analysis of external noise spectrum of high-speed railway

A schematic to make the spectra of the exterior noise of high speed railway was put forward. The exterior noise spectrum was defined based on the characteristics of the high-speed train exterior noise. Its characteristics considered here include identifying the exterior main sources and their locations, their frequency components including the Doppler effect due to the noise sources moving at high speed, the sound field intensity around the train in high-speed operation, the sound radiation path out of the train, and the pressure level and frequency components of the noise at the measuring points specified by the International Organization for Standardization(ISO). The characteristics of the high-speed train exterior noise of the high speed railways in operation were introduced. The advanced measuring systems and their principles for clearly indentifying the exterior noise sources were discussed in detail. Based on the concerned noise results measured at sites, a prediction model was developed to calculate the sound level and the characteristics of the exterior noise at any point where it is difficult to measure and to help to make the exterior noise spectrums. This model was also verified with the test results. The verification shows that there is a good agreement between the theoretical and experimental results.

Analyses and predictions of rock cuttabilities under different confining stresses and rock properties based on rock indentation tests by conical pick

The rock indentation tests by a conical pick were conducted to investigate the rock cuttability correlated to confining stress conditions and rock strength.Based on the test results,the regression analyses,support vector machine(SVM)and generalized regression neural network(GRNN)were used to find the relationship among rock cuttability,uniaxial confining stress applied to rock,uniaxial compressive strength(UCS)and tensile strength of rock material.It was found that the regression and SVM-based models can accurately reflect the variation law of rock cuttability,which presented decreases followed by increases with the increase in uniaxial confining stress and the negative correlation to UCS and tensile strength of rock material.Based on prediction models for revealing the optimal stress condition and determining the cutting parameters,the axial boom roadheader with many conical picks mounted was satisfactorily utilized to perform rock cutting in hard phosphate rock around pillar.

Theoretical and experimental study of initial cracking mechanism of an expansive soil due to moisture-change

Swelling and shrinkage due to moisture-change is one of the characteristics of the expansive soil,which is similar to the behavior of most materials under thermal effect,If the deformation is restricted,stress in expansive soil is caused by the swell-shrinking.The stress is defined as "moisture-change stress" and is adopted to analyze swell-shrinkage deformation based on the elasticity mechanics theory.The state when the total stress becomes equal to the soil tensile strength is considered as the cracking criterion as moisture-change increases.Then,the initial cracking mechanism due to evaporation is revealed as follows:Different rates of moisture loss at different depths result in greater shrinkage deformation on the surface while there is smaller shrinkage deformation at the underlayer in expansive soil;cracks will grow when the nonuniform shrinkage deformation increases to a certain degree.A theoretical model is established,which may be used to calculate the stress caused by moisture-change.The depth of initial cracks growing is predicted by the proposed model in expansive soil,A series of laboratory tests are carried out by exposing expansive soil samples with different moisture-changes.The process of crack propagation is investigated by resistivity method.The test results show good consistency with the predicted results by the proposed theoretical model.

Prediction of rock mass rating using fuzzy logic and multi-variable RMR regression model

Rock mass rating system （RMR） is based on the six parameters which was defined by Bieniawski （1989） [1]. Experts frequently relate joint and discontinuities and ground water conditions in linguistic terms with rough calculation. As a result, there is a sharp transition between two modules which create doubts. So, in this paper the proposed weights technique was applied for linguistic criteria. Then by using the fuzzy inference system and the multi-variable regression analysis, the accurate RMR is predicted. Before the performing of regression analysis, sensitivity analysis was applied for each of Bieniawski parameters. In this process, the best function was selected among linear, logarithmic, exponential and inverse func- tions and finally it was applied in the regression analysis for construction of a predictive equation. From the constructed regression equation the relative importance of the input parameters can also be observed. It should be noted that joint condition was identified as the most important effective parameter upon RMR. Finally, fuzzy and regression models were validated with the test datasets and it was found that the fuzzy model predicts more accurately RMR than reression models.

Evaluation of High-Resolution WRF Model Simulations of Surface Wind over the West Coast of India

This paper presents results from a statistical validation of the hindcasts of surface wind by a high-reso-ution-mesoscale atmospheric numerical model Advanced Research WRF （ARW3.3）, which is set up to force the operational coastal ocean forecast system at Indian Na- tional Centre for Ocean Information Services （INCOIS）. Evaluation is carried out based on comparisons of day-3 forecasts of surface wind with in situ and remote-sensing data. The results show that the model predicts the surface wind fields fairly accurately over the west coast of India, with high skill in predicting the surface wind during the pre-monsoon season. The model predicts the diurnal variability of the surface wind with reasonable accuracy. The model simulates the land-sea breeze cycle in the coastal region realistically, which is very clearly observed during the northeast monsoon and pre-monsoon season and is less prominent during the southwest monsoon season.

A risk prediction score model for predicting occurrence of post-PCI vasovagal reflex syndrome： a single center study in Chinese population

Background The vasovagal reflex syndrome （VVRS） is common in the patiems undergoing percutaneous coronary intervemion （PCI） However, prediction and prevention of the risk for the VVRS have not been completely fulfilled. This study was conducted to develop a Risk Prediction Score Model to identify the determinants of VVRS in a large Chinese population cohort receiving PCI. Methods From the hos- pital electronic medical database, we idemified 3550 patients who received PCI （78.0% males, mean age 60 years） in Chinese PLA General Hospital from January 1, 2000 to August 30, 2016. The multivariate analysis and receiver operating characteristic 01OC） analysis were performed. Results The adverse events of VVRS in the patients were significantly increased after PCI procedure than before the operation （all P 〈 0.001）. The rate of VVRS [95% confidence interval （CI）] in patients receiving PCI was 4.5% （4.1%-5.6%）. Compared to the patients suffering no VVRS, incidence of VVRS involved the following factors, namely female gender, primary PCI, hypertension, over two stems im- plantation in the left anterior descending （LAD）, and the femoral puncture site. The multivariate analysis suggested that they were independ- ent risk factors for predicting the incidence of VVRS （all P 〈 0.001）. We developed a risk prediction score model for VVRS. ROC analysis showed that the risk prediction score model was effectively predictive of the incidence of VVRS in patients receiving PCI （c-statistic 0.76, 95% CI： 0.72-0.79, P 〈 0.001）. There were decreased evems of VVRS in the patients receiving PCI whose diastolic blood pressure dropped by more than 30 mmHg and heart rate reduced by 10 times per minute （AUC： 0.84, 95% CI： 0.81-0.87, P 〈 0.001）. Conclusion The risk prediction score is quite efficient in predicting the incidence of VVRS in patients receiving PCI. In which, the following factors may be in- volved, the femoral puncture site, female gender, hypertension, primary PCI, and over 2 stents implanted in LAD.

Flood Frequency Analysis at Oshun River in Asejire Dam Site, Nigeria

Flood frequency analysis procedure was performed on annual maximum discharge data of River Oshun at Iwo in Osun State, Nigeria for the period 1985 to 2002 utilizing three probability distribution models namely： Extreme EVI （value Type-l）, LN （Log normal） and LPIII （Log Pearson Type III）. The models were used to predict and compare corresponding flood discharge estimates at 2, 5, 10, 25, 50, 100 and 200 years return periods. The results indicated that Extreme Value Type 1 distribution predicted discharge values ranging from 26.6 m3/s for two years to 431.8 m3/s for 200 years return periods; the Log Pearson Type III distribution predicted discharge values ranging from 127.2 m3/s for two years to 399.54 m3/s for 200 years return periods and the Log normal distribution predicted discharge values ranging from 116.2 m3/s for two years to 643.9 m3/s for 200 years return periods. From the results~ it was concluded that for lower return periods （T_〈 50 yrs） Extreme Value Type 1 and Log Pearson Type III could be used to estimate flood quantile values at the station while for higher return periods （T 〉 50 yrs） Log Normal probability distribution model which gives higher estimates could be utilized for safe design in view of the short length of discharge records used for the analysis.