基于重采样技术的肌电信号动作电位传导速度估计研究

目的：本文针对表面肌电（sEMG）信号探讨动作电位传导速度（APCV）估计问题。方法：以生理学仿真sEMG信号为基础,采用基于互相关分析的时延估计技术来获取相应的APCV估计值,并利用重采样技术来提高估计的精度。结果：实验表明。针对重采样后的仿真信号,其APCV的估计误差得到了明显降低。结论：所采用方法能够有效获取满意的APCV估计效果。     

表面肌电信号采集与降噪处理

目的:本文以设计的表面肌电(sEMG)信号采集系统为基础,探讨sEMG信号中的降噪处理问题。方法:结合sEMG信号的噪声影响情况,首先利用带通滤波器消除肌电信号频带外噪声,再通过频谱插值法来抑制工频干扰分量,最后使用小波分析方法来削弱肌电信号频带内噪声。结果:通过对检测sEMG信号的降噪处理,信号噪声得到明显抑制。结论:所设计采集系统能够获得满意的sEMG信号检测效果,所采用降噪方法能够有效提高sEMG信号的质量。     

基于小波特征分析的手指动作识别研究

目的:本文利用表面肌电(sEMG)信号来研究多种手指组合动作的识别问题。方法:在对采集的四个通道sEMG信号进行降噪预处理的基础上,采用移动加窗处理方法来提取关于手指运动状态的信号活动段,再分析各个信号活动段的小波系数统计特征,进而利用多类支持向量机(SVM)分类算法来实现手指组合动作的识别。结果:动作识别率最高达到100%。结论:所采用方法能够有效地识别多种手势动作,并为后续基于肌电信号的实时人机接口系统的研究奠定了理论基础。     

表面肌电信号采集与降噪处理

目的：本文以设计的表面~g（sEMG）信号采集系统为基础,探讨sEMG信号中的降噪处理问题。方法：结合sEMG信号的噪声影响情况,首先利用带通滤波器消除肌电信号频带外噪声,再通过频谱插值法来抑制工频干扰分量,最后使用小波分析方法来削弱肌电信号频带内噪声。结果：通过对检测sEMG信号的降噪处理,信号噪声得到明显抑制。结论：所设计采集系统能够获得满意的sEMG信号检测效果,所采用降噪方法能够有效提高sEMG信号的质量。     

自适应干扰对消技术提取胎儿心电的可视化仿真实现

目的：用可视化编程技术实现胎儿心电信号提取和分析。方法：获取受试者仰卧位的腹部平行放置的两对电极的二道心电信号;在Windows系统下,用Delphi嵌入汇编技术实现对模/数采样卡的低层I/O操作;用自适应干扰对消技术提取胎儿心电信号。结果和结论：计算机仿真结果表明,该方法的能获取较清晰的胎儿心电信号。讨论：①基于Windows开发的系统具有较好的交互性和移植性,②获得胎儿心电后可用我们巳开发的成熟技术实现对胎儿心动周期信号的混沌特征分析,以估计胎儿自主神经系统功能。     

基于小波特征分析的手指动作识别研究

目的：本文利用表面肌电（sEMG）信号来研究多种手指组合动作的识别问题。方法：在对采集的四个通道sEMG信号进行降噪预处理的基础上,采用移动加窗处理方法来提取关于手指运动状态的信号活动段,再分析各个信号活动段的小波系数统计特征,进而利用多类支持向量机（SVM分类算法来实现手指组合动作的识别。结果：动作识别率最高达到100％。结论：所采用方法能够有效地识别多种手势动作,并为后续基于肌电信号的实时人机接口系统的研究奠定了理论基础。     

不同采样密度下县域森林碳储量仿真估计

采用浙江省台州市仙居县森林资源二类调查样地实测地上部分碳储量数据,结合Landsat TM影像数据,利用序列高斯协同仿真(SGCS)算法、序列高斯块协同仿真(SGBCS)算法,以4种地面采样密度(SD)SD_1=0.012%、SD_2=0.010%、SD_3=0.007%、SD_4=0.005%,估计全县森林碳储量及其空间分布,分析不同地面采样密度对区域森林碳储量及其分布格局估计精度的影响。结果表明:1)不同采样密度下SGCS和SGBCS估计的森林碳储量分布趋势相似,SGCS估计在采样密度为SD_2时可以满足精度要求,且均值与实测最相符;SGBCS估计受采样密度影响较小,在四种采样密度下均可满足精度要求。2)SGCS、SGBCS估计的不确定性随着采样密度的降低均呈现出整体升高的趋势,增长速率在SD_2采样密度时最低,相对SD_1分别升高1.08%、-1.71%;当SGBCS算法的采样密度由SD_2变为SD_3时,样地数的减少对不确定性影响最大,但对区域空间变异格局估计没有实质性影响。3)将采样密度控制在SD_2(0.010%)水平,利用SGCS和SGBCS算法均能得到准确可靠的森林碳储量及其分布信息,同时能节省至少20%左右的森林调查工作量。     

基于估计准度和调查费用的渔业资源调查采样设计优化

合理的调查采样设计及优化可以降低调查费用,确保调查数据的准确性,实现调查采样效益的最大化。本研究量化了调查费用,在考虑估计准确度基础上,将调查费用作为评价指标加入到采样设计优化中,应用计算机模拟重抽样方法和模拟退火算法进行航线规划。基于2013—2015年春、秋季海州湾渔业资源底拖网调查数据,使用克里金插值法模拟了该海域星康吉鳗(Conger myriaster)、方氏云鳚(Enedrias fangi)和大泷六线鱼(Hexagrammos otakii)在春、秋季的相对资源量分布,作为其分布的“真值”。应用分层随机抽样模拟不同样本量下的调查采样,估计各目标鱼种的资源量指数,以相对偏差(RB)的绝对值来评价目标鱼种资源量指数估计的准确度,以调查费用、超标概率作为费用评价指标,以综合评价指标(IEI)作为衡量准确度和费用的综合指标。结果表明: 各鱼种资源量指数估计的RB值随样本量增加而降低,但各鱼种的RB值不同。不同评价指标确定的最优站位数不同,按RB值确定的最优站位数较高,调查费用超过预算;按调查费用确定的站位数较低,但无法有效保证资源量指数估计的准确性;按IEI确定的最优站位数处于中间水平,能兼顾调查费用和资源量指数估计准度。综合考虑调查费用和航线规划的采样设计不仅能确定最优站位数,还可以确定调查设计的站位空间位置、站位调查顺序和调查费用。调查者可以根据实际费用预算,从模拟方案中选择费用低、RB低的理想采样设计方案开展调查。     

局部肌肉疲劳的表面肌电信号复杂度和熵变化

目的 在于探讨静态和动态疲劳性运动过程中肱二头肌和腰部脊竖肌表面肌电(surface electromyography,sEMG)信号的Lempel-Ziv复杂度和Kolmogorov熵的变化规律。18名男性大学生志愿者被随机分为肱二头肌和腰部脊竖肌运动负荷组,分别完成静态和动态疲劳运动负荷试验。运动负荷期间连续记录sEMG信号,在对运动负荷时间和重复次数进行标准化处理后,截取相应时段的sEMG信号,计算Lempel-Ziv复杂度和Kolmogorov熵,观察它们随肌肉疲劳发展的变化规律。研究结果表明,无论是静态还是动态疲劳运动条件下,被检肌肉sEMG信号的复杂度和熵均随着运动负荷时间呈现明显的单调递减型变化。该变化可能与神经系统渐进性协调众多运动单位同步收缩的‘协同效应”有关。     

不同姿势久坐下女性腰部肌肉的sEMG信号变化

目的:观察女性受试者在不同坐姿久坐前后腰部肌肉表面肌电(sEMG)信号的变化,探讨不同姿势的久坐对竖脊肌功能状态的影响。方法:32名女性受试者随机分成4组,分别在4种不同的座椅(座椅A、B、C、D)上久坐2 h。记录受试者腰部竖脊肌在久坐前后2次最大随意收缩力量(MVC)测试中的sEMG信号,观察测试过程中的前3 s时频指标及全程频域指标的变化。结果:3 s的时频指标平均肌电振幅(AEMG)、平均功率谱频率(MPF)在不同坐姿久坐前后无显著性差异,其中AEMG在座椅B组中明显大于座椅A组;全程信号的频域指标MPF在久坐后显著减小,但在不同坐姿之间无显著性差异。结论:女性受试者在4种不同坐姿2 h久坐前后腰部竖脊肌的最大活动水平无明显改变;最大持续收缩能力在久坐后下降,但在不同坐姿间并无显著差异。     

Using Re-Sampling Methods in Mortality Studies

Traditional methods of computing standardized mortality ratios (SMR) in mortality studies rely upon a number of conventional statistical propositions to estimate confidence intervals for obtained values. Those propositions include a common but arbitrary choice of the confidence level and the assumption that observed number of deaths in the test sample is a purely random quantity. The latter assumption may not be fully justified for a series of periodic “overlapping” studies. We propose a new approach to evaluating the SMR, along with its confidence interval, based on a simple re-sampling technique. The proposed method is most straightforward and requires neither the use of above assumptions nor any rigorous technique, employed by modern re-sampling theory, for selection of a sample set. Instead, we include all possible samples that correspond to the specified time window of the study in the re-sampling analysis. As a result, directly obtained confidence intervals for repeated overlapping studies may be tighter than those yielded by conventional methods. The proposed method is illustrated by evaluating mortality due to a hypothetical risk factor in a life insurance cohort. With this method used, the SMR values can be forecast more precisely than when using the traditional approach. As a result, the appropriate risk assessment would have smaller uncertainties.     

In situ analysis of cross-hybridisation on microarrays and the inference of expression correlation

Background

When conducting multiple hypothesis tests, it is important to control the number of false positives, or the False Discovery Rate (FDR). However, there is a tradeoff between controlling FDR and maximizing power. Several methods have been proposed, such as the q-value method, to estimate the proportion of true null hypothesis among the tested hypotheses, and use this estimation in the control of FDR. These methods usually depend on the assumption that the test statistics are independent (or only weakly correlated). However, many types of data, for example microarray data, often contain large scale correlation structures. Our objective was to develop methods to control the FDR while maintaining a greater level of power in highly correlated datasets by improving the estimation of the proportion of null hypotheses.

Results

We showed that when strong correlation exists among the data, which is common in microarray datasets, the estimation of the proportion of null hypotheses could be highly variable resulting in a high level of variation in the FDR. Therefore, we developed a re-sampling strategy to reduce the variation by breaking the correlations between gene expression values, then using a conservative strategy of selecting the upper quartile of the re-sampling estimations to obtain a strong control of FDR.

Conclusion

With simulation studies and perturbations on actual microarray datasets, our method, compared to competing methods such as q-value, generated slightly biased estimates on the proportion of null hypotheses but with lower mean square errors. When selecting genes with controlling the same FDR level, our methods have on average a significantly lower false discovery rate in exchange for a minor reduction in the power.     

The impact of pedigree structure on heritability estimates for pulse pressure in three studies

OBJECTIVES: Pulse pressure (PP) is a measure of large artery stiffness and has been shown to be an important predictor of cardiovascular morbidity and mortality. The aims of the present study were to investigate the heritability of PP in three studies, the Diabetes Heart Study (DHS), the Insulin Resistance Atherosclerosis Family Study (IRAS FS), and the NHLBI Family Heart Study (FHS), to estimate the residual heritability after inclusion of a common set of covariates, and to investigate the impact of pedigree structure on estimating heritability. METHODS AND RESULTS: DHS is primarily a sibling pair nuclear family study design, while both IRAS FS and FHS have large pedigrees. Heritability estimates of log-transformed PP were obtained using variance component models. After adjusting for age, gender, ethnicity/center, height, diabetes status, and mean arterial pressure (MAP), heritability estimates of PP were 0.40 +/- 0.08 , 0.22 +/- 0.05, and 0.19 +/- 0.03 in DHS, IRAS FS, and FHS, respectively. The heritability estimate from DHS was significantly different from both IRAS FS and FHS (both p values <0.05). A random re-sampling technique (modified bootstrap) was used to explore the heritability in the IRAS FS and FHS data when these pedigrees were trimmed to mimic the DHS pedigree structure. The re-sampling method (mimicking a sibling pair nuclear family design in all studies) yielded PP heritability estimates of 0.37, 0.34, and 0.27 in DHS, IRAS FS, and FHS, respectively. There was no significant difference among the heritability estimates from the three studies based on the re-sampling method. CONCLUSION: We have shown that PP has a moderately heritable component in three different studies. These data illustrate the influence of pedigree structure can have on estimating heritability. Thoughtful comparisons of heritability estimates must consider study design factors such as pedigree structure.     

Antimicrobial biosurfactants from marine Bacillus circulans: extracellular synthesis and purification

Aims:  To purify the biosurfactant produced by a marine Bacillus circulans strain and evaluate the improvement in surface and antimicrobial activities.
Methods and Results:  The study of biosurfactant production by B. circulans was carried out in glucose mineral salts (GMS) medium using high performance thin layer chromatography (HPTLC) for quantitative estimation. The biosurfactant production by this strain was found to be growth-associated showing maximum biosurfactant accumulation at 26 h of fermentation. The crude biosurfactants were purified using gel filtration chromatography with Sephadex^® G-50 matrix. The purification attained by employing this technique was evident from UV–visible spectroscopy and TLC analysis of crude and purified biosurfactants. The purified biosurfactants showed an increase in surface activity and a decrease in critical micelle concentration values. The antimicrobial action of the biosurfactants was also enhanced after purification.
Conclusions:  The marine B. circulans used in this study produced biosurfactants in a growth-associated manner. High degree of purification could be obtained by using gel filtration chromatography. The purified biosurfactants showed enhanced surface and antimicrobial activities.
Significance and Impact of the Study:  The antimicrobial biosurfactant produced by B. circulans could be effectively purified using gel filtration and can serve as new potential drugs in antimicrobial chemotherapy.     

Uncertainty in allometric exponent estimation: a case study in scaling metabolic rate with body mass

Many factors could influence the allometric scaling exponent β estimation, but have not been explored systematically. We investigated the influences of three factors on the estimate of β based on a data set of 626 species of basal metabolic rate and mass in mammals. The influence of sampling error was tested by re-sampling with different sample sizes using a Monte Carlo method. Small random errors were introduced to measured data to examine their influence on parameter estimations. The influence of analysis method was also evaluated by applying nonlinear and linear regressions to the original data. Results showed that a relative large sample size was required to lower statistical inference errors. When sample size n was 10% of the base population size (n=63), 35% of the samples supported β=2/3, 39% supported β=3/4, and 15% rejected β=0.711, even though the base population had a β=0.711. The controversy surrounding the estimation of β in the literature could be partially attributable to such small sample sizes in many studies. Measurement errors in body mass and base metabolic rate, especially in body mass, could largely increase alpha and beta errors. Analysis methods also affected parameter estimations. Nonlinear regressions provided better estimates of the scaling exponent that were significantly higher than these commonly estimated by linear regressions. This study demonstrated the importance of the quantity and quality of data as well as analysis method in power law analysis, raising caution in interpreting power law results. Meta-data synthesis using data from independent studies seems to be a proper approach in the future, but caution should be taken to make sure that such measurements are made using similar protocols.     

Jackknife and bootstrap tests of the composition vector trees

Composition vector trees(CVTrees)are inferred from whole-genome data by an alignment-free and parameter-free method.The agreement of these trees with the corresponding taxonomy provides an objective justification of the inferred phylogeny.In this work,we show the stability and self-consistency of CVTrees by performing bootstrap and jackknife re-sampling tests adapted to this alignment-free approach.Our ultimate goal is to advocate the viewpoint that time-consuming statistical re-sampling tests can be avoided at all in using this alignment-free approach.Agreement with taxonomy should be taken as a major criterion to estimate prokaryotic phylogenetic trees.     

Neural network estimation of balance control during locomotion

Gait patterns of the elderly are often adjusted to accommodate for reduced function in the balance control system and a general reduction in skeletal muscle strength. Recent studies have demonstrated that measures related to motion of whole body center of mass (COM) can distinguish elderly individuals with balance impairment from healthy peers. Accurate COM estimation requires a multiple-segment anthropometric model, which may restrict its broad application in assessment of dynamic instability. Although temporal-distance measures and electromyography have been used in evaluation of overall gait function and determination of gait dysfunction, no studies have examined the use of gait measurements in predicting COM motion during gait. The purpose of this study was to demonstrate the effectiveness of an artificial neural network (ANN) model in mapping gait measurements onto COM motion in the frontal plane. Data from 40 subjects of varied age and balance impairment were entered into a 3-layer feed-forward model with back-propagated error correction. Bootstrap re-sampling was used to enhance the generalization accuracy of the model, using 20 re-sampling trials. The ANN model required minimal processing time (5 epochs, with 20 hidden units) and accurately mapped COM motion (R-values up to 0.89). As training proportion and number of hidden units increased, so did model accuracy. Overall, this model appears to be effective as a mapping tool for estimating balance control during locomotion. With easily obtained gait measures as input and a simple, computationally efficient architecture, the model may prove useful in clinical scenarios where electromyography equipment exists.     

Minicolumn chromatography for the detection of sterigmatocystin in agricultural commodities

A simple method for detecting sterigmatocystin in agricultural commodities by minicolumn chromatography was developed. Clean-up as well as semi-quantitative estimation could be done on the same column. The method is suitable for the analysis of samples of rice, wheat, maize and sorghum contaminated with sterigmatocystin and could be used as an effective preliminary screening technique in public health laboratories.     

Network measures for dyadic interactions: stability and reliability

Social network analysis (SNA) is a general heading for a collection of statistical tools that aim to describe social interactions and social structure by representing individuals and their interactions as graph objects. It was originally developed for the social sciences, but more recently it was also adopted by behavioral ecologists. However, although SNA offers a full range of exciting possibilities for the study of animal societies, some authors have raised concerns about the correct application and interpretation of network measures. In this article, we investigate how reliable and how stable network measures are (i.e. how much variation they show under re-sampling and how much they are influenced by erroneous observations). For this purpose, we took a data set of 44 nonhuman primate grooming networks and studied the effects of re-sampling at lower re-sampling rates than the originally observed ones and the inclusion of two types of errors, "mis-identification" and "mis-classification," on six different network metrics, i.e. density, degree variance, vertex strength variance, edge weight disparity, clustering coefficient, and closeness centrality. Although some measures were tolerant toward reduced sample sizes, others were sensitive and even slightly reduced samples could yield drastically different results. How strongly a metric is affected seems to depend on both the sample size and the structure of the specific network. The same general effects were found for the inclusion of sampling errors. We, therefore, emphasize the importance of calculating valid confidence intervals for network measures and, finally, we suggest a rough research plan for network studies.