气象因子对江苏省水稻单产的影响

全球气候变暖作为一个不争的客观事实,不可避免地对农业生产产生影响。针对传统多元线性回归分析方法,不能直接分析气象因子与水稻气象单产时序关系,根据1978—2010年间江苏省水稻单产数据和同期气象时序数据,研究了水稻单产的影响因素,提取了水稻气象单产;利用连续小波变换方法研究了水稻气象单产、水稻营养生长与生殖生长期间的日照时数、降水和气温等气象因子的时序变化特征;利用交叉小波和相干小波变换方法,研究了水稻营养生长与生殖生长期间气象因子与水稻气象单产间的相互影响关系。结果显示:(1)近33 a间,江苏省水稻气象单产占实际单产的比重逐渐减小,水稻生产抵御气象灾害能力逐渐增强。(2)水稻气象单产与日照时数、降水量和气温等气象因子有几乎一致的特征周期。(3)在水稻分蘖期、孕穗期与开花结实期,气象单产与日照时数、降水量和气温间的相位差关系较为复杂。水稻分蘖期日照时数的增多有利于水稻单产的增加,降水的增多导致水稻单产的下降。水稻开花结实期日照时数的增强、昼夜温差的变大有利于水稻单产的增加,夜间最低气温的上升会导致水稻单产的下降。为了应对全球气候变暖,需要进一步改变水稻种植方式,加强土地利用监管,积极开展农村土地综合整治,加强高标准基本农田建设,加大农田水利设施建设,调整作物播种期,加强气象灾害应对防范体系建设,更好地发挥生物技术在适应气候变化中的作用。     

Wavelet transforms in the analysis of mechanical heart valve cavitation

Cavitation is known to cause blood element damage and may introduce gaseous emboli into the cerebral circulation, increasing the patient's risk of stroke. Discovering methods to reduce the intensity of cavitation induced by mechanical heart valves (MHVs) has long been an area of interest. A novel approach for analyzing MHV cavitation is presented. A wavelet denoising method is explored because currently used analytical techniques fail to suitably unmask the cavitation signal from other valve closing sounds and noise detected with a hydrophone. Wavelet functions are used to denoise the cavitation signal during MHV closure and rebound. The wavelet technique is applied to the signal produced by closure of a 29-mm Medtronic-Hall MHV in degassed water with a gas content of 5 ppm. Valve closing dynamics are investigated under loading conditions of 500, 2500, and 4500 mm Hg/s. The results display a marked improvement in the quantity and quality of information that can be extracted from acoustic cavitation signals using the wavelet technique compared to conventional analytical techniques. Time and frequency data indicate the likelihood and characteristics of cavitation formation under specified conditions. Using this wavelet technique we observe an improved signal-to-noise ratio, an enhanced time-dependent aspect, and the potential to minimize valve closing sounds, which disguise individual cavitation events. The overall goal of this work is to eventually link specific valves with characteristic waveforms or distinct types of cavitation, thus promoting improved valve designs.     

Robust Classification of Functional and Quantitative Image Data Using Functional Mixed Models

Summary This article introduces new methods for performing classification of complex, high‐dimensional functional data using the functional mixed model (FMM) framework. The FMM relates a functional response to a set of predictors through functional fixed and random effects, which allows it to account for various factors and between‐function correlations. The methods include training and prediction steps. In the training steps we train the FMM model by treating class designation as one of the fixed effects, and in the prediction steps we classify the new objects using posterior predictive probabilities of class. Through a Bayesian scheme, we are able to adjust for factors affecting both the functions and the class designations. While the methods can be used in any FMM framework, we provide details for two specific Bayesian approaches: the Gaussian, wavelet‐based FMM (G‐WFMM) and the robust, wavelet‐based FMM (R‐WFMM). Both methods perform modeling in the wavelet space, which yields parsimonious representations for the functions, and can naturally adapt to local features and complex nonstationarities in the functions. The R‐WFMM allows potentially heavier tails for features of the functions indexed by particular wavelet coefficients, leading to a down‐weighting of outliers that makes the method robust to outlying functions or regions of functions. The models are applied to a pancreatic cancer mass spectroscopy data set and compared with other recently developed functional classification methods.     

Gas dispersion in volume-cycled tube flow. II. Tracer bolus experiments.

We present a new method for rapid measurement of local gas dispersion in volume-cycled tube flow. After a small bolus of tracer gas (argon) was injected into the oscillating flow, the time-averaged effective diffusion coefficient (mean value of Deff/D) for axial transport of a tracer gas is evaluated from local argon concentration measurements taken by a mass spectrometer. Two methods are presented for the evaluation of mean value of Deff/D from the concentration measurements: one uses all the sampled data, and the other uses only the local peaks of the concentration. Experiments were conducted in two tubes (radius = 0.85 or 1.0 cm) over a range of frequencies (0.42 less than or equal to f less than or equal to 8.5 Hz) and tidal volumes (7 less than or equal to VT less than or equal to 48 ml). The experimental results show very good agreement with the theoretical predictions of Elad et al. (J. Appl. Physiol. 72: 312-320, 1992). In the absence of oscillations (static fluid), the resulting mean value of Deff/D converges to that of molecular diffusion. We also show that concentration data may be acquired at any radial or axial position, not necessarily at the tracer gas injection point, and the resulting mean value of Deff/D is independent of the spatial position of the sampling catheter. This method is of similar accuracy and is substantially faster than previous methods for measuring gas dispersion in oscillatory flows. The rapidity of these measurements may permit this method to be used for the in vivo assessment of gas transport properties within the pulmonary system.     

Approaches to measuring fluxes of methane and nitrous oxide between landscapes and the atmosphere

The theory, applications, strengths and weaknesses of approaches commonly used for measuring trace gas fluxes are reviewed. Chambers, representing the smallest scale (～1 m²), are the most common tools. Their operating principle is simple, they can be highly sensitive, the cost can be low and field requirements small. Problems include leaks, stickiness of some gases, inhibition of fluxes through concentration build-up, pressure effects and spatial and temporal variability in gas fluxes. Mass balance techniques are suitable for small, defined source areas, typically tens to thousands of square metres in extent. Emissions are calculated from the difference in the rates at which the gas is carried into a control volume above the source area by the wind and carried out. The required primary data are profiles of gas concentration on the downwind boundaries as well as the wind speed profile, the wind direction and the upwind background gas concentration. They have been used to measure gas emissions from landfills, treated fields and small animal herds. Circular test areas make the method independent of wind direction. A newly developed technique based on a backward Lagrangian stochastic dispersion model is also applicable to small, well-defined source areas of any shape. The surface flux is calculated form measurements of atmospheric turbulence and stability and the gas concentration at any height downwind. Implementation of the method is aided greatly by a software package WindTrax. The combination provides a powerful new tool for measuring gas emissions from treated areas and intensive animal production systems. Finally, techniques suitable for measuring gas emissions on large landscape scales (ha) are discussed. Eddy covariance is the micrometeorologist’s preferred technique for this scale. The method uses fast response anemometers and gas sensors to make direct measurements of the vertical gas flux at a point, several times a second. However, it is not feasible for many trace gases for a variety of reasons. These are discussed. Relaxed eddy accumulation is an alternative technique that retains the attraction of eddy covariance by providing a direct point measurement. It removes the need for a fast response gas sensor by substituting for it a fast solenoid valve sampling system. Flux–gradient methods are in more common use. Fluxes are calculated as the product of an eddy diffusivity and the vertical concentration gradient of the gas or the product of a transfer coefficient and the difference in gas concentration between two heights. Assumptions of the method and precautions in its application are discussed.     

Quantitative ultrasound can assess the regeneration process of tissue-engineered cartilage using a complex between adherent bone marrow cells and a three-dimensional scaffold

Articular cartilage (hyaline cartilage) defects resulting from traumatic injury or degenerative joint disease do not repair themselves spontaneously. Therefore, such defects may require novel regenerative strategies to restore biologically and biomechanically functional tissue. Recently, tissue engineering using a complex of cells and scaffold has emerged as a new approach for repairing cartilage defects and restoring cartilage function. With the advent of this new technology, accurate methods for evaluating articular cartilage have become important. In particular, in vivo evaluation is essential for determining the best treatment. However, without a biopsy, which causes damage, articular cartilage cannot be accurately evaluated in a clinical context. We have developed a novel system for evaluating articular cartilage, in which the acoustic properties of the cartilage are measured by introducing an ultrasonic probe during arthroscopy of the knee joint. The purpose of the current study was to determine the efficacy of this ultrasound system for evaluating tissue-engineered cartilage in an experimental model involving implantation of a cell/scaffold complex into rabbit knee joint defects. Ultrasonic echoes from the articular cartilage were converted into a wavelet map by wavelet transformation. On the wavelet map, the percentage maximum magnitude (the maximum magnitude of the measurement area of the operated knee divided by that of the intact cartilage of the opposite, nonoperated knee; %MM) was used as a quantitative index of cartilage regeneration. Using this index, the tissue-engineered cartilage was examined to elucidate the relations between ultrasonic analysis and biochemical and histological analyses. The %MM increased over the time course of the implant and all the hyaline-like cartilage samples from the histological findings had a high %MM. Correlations were observed between the %MM and the semiquantitative histologic grading scale scores from the histological findings. In the biochemical findings, the chondroitin sulfate content increased over the time course of the implant, whereas the hydroxyproline content remained constant. The chondroitin sulfate content showed a similarity to the results of the %MM values. Ultrasonic measurements were found to predict the regeneration process of the tissue-engineered cartilage as a minimally invasive method. Therefore, ultrasonic evaluation using a wavelet map can support the evaluation of tissue-engineered cartilage using cell/scaffold complexes.     

An analysis of estimation of pulmonary blood flow by the single-breath method

The single-breath method of determining pulmonary blood flow is a simple technique involving no inert gas or special maneuvers such as rebreathing or breath holding. The use of this elegant technique has been limited, however, largely because of questions regarding its accuracy. Previous analyses of the method have indicated that large errors in the estimated blood flow could result if data reduction is not handled carefully. In addition, an uncertain amount of error is introduced, if the CO2 retained by the lung tissue while measurements are being made is not taken into account in the calculations. This paper presents a rigorous approach for estimating the pulmonary blood flow by the single-breath method, which would minimize considerably the effects of measurement errors and would also allow for possible CO2 absorption by the lung tissue. It is based on the exact solution of the underlying equations that describe the dynamics of gas exchange in the lung. The analytic solution provides insight into the difficulties involved in extracting the desired information from the experimental data.     

The use of wavelets for spatial pattern analysis in ecology

Abstract. We investigate the characteristics of the wavelet transform as an approach to analyzing spatial pattern. Compared to the familiar methods of paired quadrat or blocked quadrat variance calculations, the wavelet method seems to offer several advantages. First, when wavelet variance is plotted as a function of scale, the peak variance height is determined by pattern intensity and does not increase with scale and, depending on the wavelet chosen, the position of the variance peak matches the scale exactly. Second, the method produces only faint resonance peaks, if any, and third, by using several different wavelet forms, different characteristics of the pattern can be investigated. Fourth, the method is able to portray very clearly trends in the data, when the pattern is non-stationary. Lastly, the wavelet position variance can be used to identify patches and gaps in data with random error. We demonstrate these characteristics using artificial data and data from previously published studies for comparison. We show that two versions of the familiar blocked quadrat variance technique are forms of wavelet analysis.     

Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data

Background  

Mass spectrometry for biological data analysis is an active field of research, providing an efficient way of high-throughput proteome screening. A popular variant of mass spectrometry is SELDI, which is often used to measure sample populations with the goal of developing (clinical) classifiers. Unfortunately, not only is the data resulting from such measurements quite noisy, variance between replicate measurements of the same sample can be high as well. Normalisation of spectra can greatly reduce the effect of this technical variance and further improve the quality and interpretability of the data. However, it is unclear which normalisation method yields the most informative result.     

Denoising Single-Molecule FRET Trajectories with Wavelets and Bayesian Inference

A method to denoise single-molecule fluorescence resonance energy (smFRET) trajectories using wavelet detail thresholding and Bayesian inference is presented. Bayesian methods are developed to identify fluorophore photoblinks in the time trajectories. Simulated data are used to quantify the improvement in static and dynamic data analysis. Application of the method to experimental smFRET data shows that it distinguishes photoblinks from large shifts in smFRET efficiency while maintaining the important advantage of an unbiased approach. Known sources of experimental noise are examined and quantified as a means to remove their contributions via soft thresholding of wavelet coefficients. A wavelet decomposition algorithm is described, and thresholds are produced through the knowledge of noise parameters in the discrete-time photon signals. Reconstruction of the signals from thresholded coefficients produces signals that contain noise arising only from unquantifiable parameters. The method is applied to simulated and observed smFRET data, and it is found that the denoised data retain their underlying dynamic properties, but with increased resolution.     

Wavelet transform to quantify heart rate variability and to assess its instantaneous changes.

Heart rate variability is a recognized parameter for assessing autonomous nervous system activity. Fourier transform, the most commonly used method to analyze variability, does not offer an easy assessment of its dynamics because of limitations inherent in its stationary hypothesis. Conversely, wavelet transform allows analysis of nonstationary signals. We compared the respective yields of Fourier and wavelet transforms in analyzing heart rate variability during dynamic changes in autonomous nervous system balance induced by atropine and propranolol. Fourier and wavelet transforms were applied to sequences of heart rate intervals in six subjects receiving increasing doses of atropine and propranolol. At the lowest doses of atropine administered, heart rate variability increased, followed by a progressive decrease with higher doses. With the first dose of propranolol, there was a significant increase in heart rate variability, which progressively disappeared after the last dose. Wavelet transform gave significantly better quantitative analysis of heart rate variability than did Fourier transform during autonomous nervous system adaptations induced by both agents and provided novel temporally localized information.     

Bayesian Wavelet Shrinkage of the Haar-Fisz Transformed Wavelet Periodogram

It is increasingly being realised that many real world time series are not stationary and exhibit evolving second-order autocovariance or spectral structure. This article introduces a Bayesian approach for modelling the evolving wavelet spectrum of a locally stationary wavelet time series. Our new method works by combining the advantages of a Haar-Fisz transformed spectrum with a simple, but powerful, Bayesian wavelet shrinkage method. Our new method produces excellent and stable spectral estimates and this is demonstrated via simulated data and on differenced infant electrocardiogram data. A major additional benefit of the Bayesian paradigm is that we obtain rigorous and useful credible intervals of the evolving spectral structure. We show how the Bayesian credible intervals provide extra insight into the infant electrocardiogram data.     

Pattern detection in plant populations through the analysis of plant-to-all-plants distances

A method of sampling and analysis is proposed to detect pattern parameters in plant populations from two-dimensional data. the use of aerial photographs to find the coordinates of trees and the measurements of plant-to-all-plants distances yields conditioned probability spectra which can be interpreted in terms of pattern parameters. Two artificial populations and a set of real data have been analysed to test the accuracy of the method.     

Wavelet transform as a potential tool for ECG analysis and compression

The recently introduced wavelet transform is a member of the class of time-frequency representations which include the Gabor short-time Fourier transform and Wigner-Ville distribution. Such techniques are of significance because of their ability to display the spectral content of a signal as time elapses. The value of the wavelet transform as a signal analysis tool has been demonstrated by its successful application to the study of turbulence and processing of speech and music. Since, in common with these subjects, both the time and frequency content of physiological signals are often of interest (the ECG being an obvious example), the wavelet transform represents a particularly relevant means of analysis. Following a brief introduction to the wavelet transform and its implementation, this paper describes a preliminary investigation into its application to the study of both ECG and heart rate variability data. In addition, the wavelet transform can be used to perform multiresolution signal decomposition. Since this process can be considered as a sub-band coding technique, it offers the opportunity for data compression, which can be implemented using efficient pyramidal algorithms. Results of the compression and reconstruction of ECG data are given which suggest that the wavelet transform is well suited to this task.     

Modeling aerobic carbon oxidation and storage by integrating respirometric, titrimetric, and off-gas CO2 measurements

A method for detailed investigation of aerobic carbon degradation processes by microorganisms is presented. The method relies on an integrated use of the respirometric, titrimetric, and off-gas CO(2) measurements. The oxygen uptake rate (OUR), hydrogen ion production rate (HPR), and the carbon dioxide transfer rate (CTR) resulting from the biological as well as physicochemical processes, coupled with a metabolic model characterizing both the growth and carbon storage processes, enables the comprehensive study of the carbon degradation processes. The method allows the formation of carbon storage products and the biomass growth rates to be estimated without requiring any off-line biomass or liquid-phase measurements, although the practical identifiability of the system could be improved with additional measurements. Furthermore, the combined yield for biomass growth and carbon storage is identifiable, along with the affinity constant with respect to the carbon substrate. However, the individual yields for growth and carbon storage are not identifiable without further knowledge about the metabolic pathways employed by the microorganisms in the carbon conversion. This is true even when more process variables are measured. The method is applied to the aerobic carbon substrate degradation by a full-scale sludge using acetate as an example carbon source. The sludge was able to quickly take up the substrate and store it as poly-beta-hydroxybutyrate (PHB). The PHB formation rate was a few times faster than the biomass growth rate, which was confirmed by off-line liquid- and solid-phase analysis. The estimated combined yield for biomass growth and carbon storage compared closely to that determined from the theoretical yields reported in literature based on thermodynamics. This suggests that the theoretical yields may be used as default parameters for modeling purposes.     

Kinetics and Yields of Pesticide Biodegradation at Low Substrate Concentrations and under Conditions Restricting Assimilable Organic Carbon

The fundamentals of growth-linked biodegradation occurring at low substrate concentrations are poorly understood. Substrate utilization kinetics and microbial growth yields are two critically important process parameters that can be influenced by low substrate concentrations. Standard biodegradation tests aimed at measuring these parameters generally ignore the ubiquitous occurrence of assimilable organic carbon (AOC) in experimental systems which can be present at concentrations exceeding the concentration of the target substrate. The occurrence of AOC effectively makes biodegradation assays conducted at low substrate concentrations mixed-substrate assays, which can have profound effects on observed substrate utilization kinetics and microbial growth yields. In this work, we introduce a novel methodology for investigating biodegradation at low concentrations by restricting AOC in our experiments. We modified an existing method designed to measure trace concentrations of AOC in water samples and applied it to systems in which pure bacterial strains were growing on pesticide substrates between 0.01 and 50 mg liter⁻¹. We simultaneously measured substrate concentrations by means of high-performance liquid chromatography with UV detection (HPLC-UV) or mass spectrometry (MS) and cell densities by means of flow cytometry. Our data demonstrate that substrate utilization kinetic parameters estimated from high-concentration experiments can be used to predict substrate utilization at low concentrations under AOC-restricted conditions. Further, restricting AOC in our experiments enabled accurate and direct measurement of microbial growth yields at environmentally relevant concentrations for the first time. These are critical measurements for evaluating the degradation potential of natural or engineered remediation systems. Our work provides novel insights into the kinetics of biodegradation processes and growth yields at low substrate concentrations.     

Ecological application of wavelet analysis in the scaling of spatial distribution patterns of Ceratoides ewersmanniana

Ecological experiments are usually conducted on small scales, but the ecological and environmental issues are usually on large scales. Hence, there is a clear need of scaling. Namely, when we deal with patterns and processes on larger scales, a special connection needs to be established on the small scales that we are familiar with. Here we presented a wavelet analysis method that could build relationships between spatial distribution patterns on different scales. With this method, we also studied how spatial heterogeneity and distribution patterns changed with the scale. We investigated the distribution and the habitat of C. ewersmanniana in two plots (200 m × 200 m; the distance between these two plots is 15 km) at Mosuowan desert. The results demonstrated that spatial heterogeneity and distribution patterns were incorporated into larger scales when the wavelet scale varied from one (5 m) to four (20 m). However, if the wavelet scale was above five (25 m), the spatial distribution patterns varied placidly, the oscillation frequency of landforms stabilized at 110 m, and the dynamic quantity period of C. ewersmanniana stabilized at 115–125 m. We also identified signal mutation points with wavelet analysis and verified the heterogeneity degree of local space with position variance. We found that position variance decomposed the distribution patterns on large scales into small sampling plots, and the position with the largest variance also had the strongest heterogeneity. In a word, the wavelet analysis method could scale-up spatial distribution patterns and habitat heterogeneity. With this method and other methods derived from this one, such as wavelet scale, wavelet variance, position variance and extremely direct-viewing graphs, wavelet analysis could be widely applied in solving the scaling problem in ecological and environmental studies.     

小波分析方法在心叶驼绒藜(Ceratoides ewersmanniana)空间格局尺度推绎研究中的应用

以古尔班通古特沙漠南缘莫索湾沙地选取相隔15km的两个200m×200m样地,以建群种心叶驼绒藜(Ceratoides ewersmanniana)及其生境地形为研究对象,应用小波分析定量研究了多尺度上空间格局的推绎以及空间异质性、空间格局依赖于尺度的变化关系。研究发现：小波分析尺度由1(5m)变化到4(20m)时,两个样地小尺度上的异质性和格局被合并到更大的尺度上,当小波分析的尺度大于等于5(25m)时,两个样地的格局变化平稳,对应地形（丘顶、丘坡、丘底）的基频稳定在110m左右,心叶驼绒藜的数量动态变化周期稳定在115～125m之间。结果表明：小波分析对信号整体特征的提取作用实现了小尺度上的信息到大尺度上的聚合。结合小波分析对信号突变点的检测,利用位置方差检验局部空间异质性程度,发现位置方差将大尺度上的格局分解到每个取样小样方,位置方差最大的地点对应的异质性也最强,实现了大尺度上的信息到小尺度上的分解。总结认为应用小波分析可以实现对空间格局的尺度推绎,具有对植被、环境的分布格局以及异质性有双重度量作用,由小波系数以及由其衍生的小波方差、位置方差来实现这种度量,图形表现直观,优越性明显。     

Wavelet thresholding with bayesian false discovery rate control

The false discovery rate (FDR) procedure has become a popular method for handling multiplicity in high-dimensional data. The definition of FDR has a natural Bayesian interpretation; it is the expected proportion of null hypotheses mistakenly rejected given a measure of evidence for their truth. In this article, we propose controlling the positive FDR using a Bayesian approach where the rejection rule is based on the posterior probabilities of the null hypotheses. Correspondence between Bayesian and frequentist measures of evidence in hypothesis testing has been studied in several contexts. Here we extend the comparison to multiple testing with control of the FDR and illustrate the procedure with an application to wavelet thresholding. The problem consists of recovering signal from noisy measurements. This involves extracting wavelet coefficients that result from true signal and can be formulated as a multiple hypotheses-testing problem. We use simulated examples to compare the performance of our approach to the Benjamini and Hochberg (1995, Journal of the Royal Statistical Society, Series B57, 289-300) procedure. We also illustrate the method with nuclear magnetic resonance spectral data from human brain.     

基因表达数据小波降噪与肿瘤识别

建立了基于小波降噪和支持向量机的结肠癌基因表达数据肿瘤识别模型.对试验数据进行小波分解,并利用交叉验证的方法计算试验样本的平均分类准确率,确定小波函数与小波分解层数;引入能量阈值方法对小波分解系数进行阈值处理,达到降噪的目的;提出了基因分类贡献率与主成分分析结合的方法,提取结肠癌样本数据特征;利用支持向量机强大的非线性映射能力,实现对结肠癌样本数据的非线性分类.为了减弱样本集的划分对分类准确率的影响,本文采取Jackknife检验方法对支持向量分类器的分类器检验,其分类准确率为96.77%.试验结果证明了该方法的有效性,该方法对结肠癌的识别具有一定的参考价值.