Time-dependent ROC curves for censored survival data and a diagnostic marker

ROC curves are a popular method for displaying sensitivity and specificity of a continuous diagnostic marker, X, for a binary disease variable, D. However, many disease outcomes are time dependent, D(t), and ROC curves that vary as a function of time may be more appropriate. A common example of a time-dependent variable is vital status, where D(t) = 1 if a patient has died prior to time t and zero otherwise. We propose summarizing the discrimination potential of a marker X, measured at baseline (t = 0), by calculating ROC curves for cumulative disease or death incidence by time t, which we denote as ROC(t). A typical complexity with survival data is that observations may be censored. Two ROC curve estimators are proposed that can accommodate censored data. A simple estimator is based on using the Kaplan-Meier estimator for each possible subset X > c. However, this estimator does not guarantee the necessary condition that sensitivity and specificity are monotone in X. An alternative estimator that does guarantee monotonicity is based on a nearest neighbor estimator for the bivariate distribution function of (X, T), where T represents survival time (Akritas, M. J., 1994, Annals of Statistics 22, 1299-1327). We present an example where ROC(t) is used to compare a standard and a modified flow cytometry measurement for predicting survival after detection of breast cancer and an example where the ROC(t) curve displays the impact of modifying eligibility criteria for sample size and power in HIV prevention trials.     

γ辐照对小麦种子延迟发光的影响

应用延迟发光的理论与方法,研究了γ辐照对扬麦13、扬麦15两个品种小麦种子的影响。结果表明:被测样品延迟发光的弛豫过程可以用非线性动力学方程Y=b1+b2e-b3t/1+b4t来进行描述,该方程对测量结果拟合的相关系数达到了99%以上。当t=0时,Y0=b1+b2,即样品的初始发光强度,在光照条件一定时,辐照剂量越高,样品的初始发光强度就越低。F测验结果表明不同辐照剂量对小麦种子初始发光强度的影响达到了极显著的水平,即受照剂量越高,初始发光强度越低,而两种供试小麦间的初始发光强度并无显著差异。结合处理种子苗期的生物学观察,样品(即种子)的初始发光强度与幼苗株高具有一致的变化趋势,即种子的初始发光强度越高,幼苗株高也高。因此,对辐射种子延迟发光过程的分析,可能是了解γ辐照对作物种子影响的一种快速可靠的检测方法。     

奇异数据筛选法在玉米籽粒蛋白质近红外光谱检测中的应用

相对于传统生化测定方法,基于近红外光谱(Near infrared spectroscopy,NIRS)玉米籽粒蛋白质含量检测是一种快速、非破坏、且适用于多组分同时检测的新方法。但在建模过程中,由于奇异数据(异常值)的存在会影响近红外光谱模型的预测精度和稳定性,我们采用奇异数据筛选法剔除了玉米籽粒近红外光谱中的奇异数据并建立了玉米籽粒蛋白质含量的偏最小二乘支持向量机(Least squares support vector machine,LS-SVM)模型。本文分别采用杠杆值法(Leverage)、半数重采样法(Resampling by Half-Mean,RHM)和蒙特卡洛采样法(Monte-Carlo Sampling,MCS)剔除了玉米籽粒蛋白质光谱数据中的奇异数据并对模型结果进行比较。在剔除奇异数据的基础上,采用偏最小二乘回归法(Partial least squares regression,PLSR)提取主成分,并基于小生境蚁群算法(Niche ant colony algorithm,NACA)优化偏最小二乘支持向量机(LS-SVM)模型参数(γ和σ2),建立基于LS-SVM的玉米籽粒蛋白质定量分析模型。结果表明,采用3种奇异数据筛选法剔除奇异数据后所建LS-SVM模型的预测结果都优于采用原光谱数据所建模型,相比较而言,蒙特卡洛采样法为基于近红外光谱检测玉米籽粒蛋白质的最佳奇异数据筛选法。     

The effects of interpolation error and location quality on animal track reconstruction

The Global Positioning System (GPS) gives precise estimates of location. However, the investigation of animal movement and behavior often requires interpolation to examine events between such fixes. We obtained 6,288 GPS locations from an electronic tag deployed for 170 d on an adult male gray seal ( Halichoerus grypus ) that ranged freely off the east coast of Scotland, and interpolated between subsamples of these data to investigate the growth of uncertainty within the intervals between observations. Average uncertainty over the path increased linearly as the interval between interpolating locations increased, reaching 12 km in longitude and 6 km in latitude at 2-d separation. The decrease in precision caused by duty-cycling, only collecting data in part of the day, was demonstrated. Adding noise to the GPS locations to simulate data from the ARGOS satellite system had little effect on the total errors for observations separated by more than 12 h. While the rate of growth in interpolation error is likely to vary between species, these results suggest that frequent, and preferably evenly spaced, location fixes are required to take full advantage of the precision of GPS data in the reconstruction of animal tracks.     

Ultrastructure of Macadamia (Proteaceae) embryos: implications for their breakage properties

BACKGROUND AND AIMS: Macadamia integrifolia, M. tetraphylla and their hybrids are cultivated for their edible kernels. Whole kernels, i.e. intact mature embryos with cotyledons fused together, are highly valued and breakage of embryos into halves results in loss of value for the commercial macadamia industry. The morphology and ultrastructure of the mature macadamia embryo, with particular emphasis on the break zone between cotyledons, were investigated. Differences in breakage between different macadamia cultivars were also examined. METHODS: Manual cracking was used to compare breakage in five cultivars and the ultrastructure of the break zone between the cotyledons was examined using light and transmission electron microscopy. KEY RESULTS: Breakage of macadamia embryos was strongly dependent on genotype of the female parent, with cultivars 'HAES 344' and 'HAES 741' much more likely to break than 'HV A16' and 'HAES 835'. Cotyledons were surrounded by a layer of cuticle resulting in a double cuticle in the break zone between the cotyledons. Three major differences have been found in the ultrastructure of the double cuticle between cultivars: a thicker cuticle in the low-whole cultivar; convolutions in the cuticle of a low-whole cultivar, and the presence of more electron-dense objects in the high-whole cultivar. CONCLUSIONS: Breakage of macadamia embryos depends on the cultivar, with clear ultrastructural differences in the break zone between cultivars. To ensure commercial benefits, macadamia breeding programs should identify germplasm with structural characteristics that ensure high percentages of whole kernel.     

Identification of microRNA precursors with support vector machine and string kernel

MicroRNAs （miRNAs） are one family of short （21-23 nt） regulatory non-coding RNAs processed from long （70-110 nt） miRNA precursors （pre-miRNAs）. Identifying true and false precursors plays an important role in computational identification of miRNAs. Some numerical features have been extracted from precursor sequences and their secondary structures to suit some classification methods; however, they may lose some usefully discriminative information hidden in sequences and structures. In this study, pre-miRNA sequences and their secondary structures are directly used to construct an exponential kernel based on weighted Levenshtein distance between two sequences. This string kernel is then combined with support vector machine （SVM） for detecting true and false pre-miRNAs. Based on 331 training samples of true and false human pre-miRNAs, 2 key parameters in SVM are selected by 5-fold cross validation and grid search, and 5 realizations with different 5-fold partitions are executed. Among 16 independent test sets from 3 human, 8 animal, 2 plant, 1 virus, and 2 artificially false human pre-miRNAs, our method statistically outperforms the previous SVM-based technique on 11 sets, including 3 human, 7 animal, and 1 false human pre-miRNAs. In particular, premiRNAs with multiple loops that were usually excluded in the previous work are correctly identified in this study with an accuracy of 92.66%.     

A database of computational models of a half-center oscillator for analyzing how neuronal parameters influence network activity

A half-center oscillator (HCO) is a common circuit building block of central pattern generator networks that produce rhythmic motor patterns in animals. Here we constructed an efficient relational database table with the resulting characteristics of the Hill et al.’s (J Comput Neurosci 10:281–302, 2001) HCO simple conductance-based model. The model consists of two reciprocally inhibitory neurons and replicates the electrical activity of the oscillator interneurons of the leech heartbeat central pattern generator under a variety of experimental conditions. Our long-range goal is to understand how this basic circuit building block produces functional activity under a variety of parameter regimes and how different parameter regimes influence stability and modulatability. By using the latest developments in computer technology, we simulated and stored large amounts of data (on the order of terabytes). We systematically explored the parameter space of the HCO and corresponding isolated neuron models using a brute-force approach. We varied a set of selected parameters (maximal conductance of intrinsic and synaptic currents) in all combinations, resulting in about 10 million simulations. We classified these HCO and isolated neuron model simulations by their activity characteristics into identifiable groups and quantified their prevalence. By querying the database, we compared the activity characteristics of the identified groups of our simulated HCO models with those of our simulated isolated neuron models and found that regularly bursting neurons compose only a small minority of functional HCO models; the vast majority was composed of spiking neurons.     

Solution NMR views of dynamical ordering of biomacromolecules

Background

To understand the mechanisms related to the ‘dynamical ordering’ of macromolecules and biological systems, it is crucial to monitor, in detail, molecular interactions and their dynamics across multiple timescales. Solution nuclear magnetic resonance (NMR) spectroscopy is an ideal tool that can investigate biophysical events at the atomic level, in near-physiological buffer solutions, or even inside cells.

The influence of zinc and sulphur deficiency on oil-filling in peanut (Arachis hypogaea L.) kernels

In the developing peanut (Arachis hypogaea L.) kernels, the period between 15 and 35 days after podding (DAP) was identified as the active period of oil-filling. The period of active oil-filling was associated with a decrease in the starch, soluble sugars and proteins so as to make available the energy and carbon skeleton for the synthesis of oil. The oil content in the mature kernels decreased by 11, 12 and 25 per cent with Zn, S and Zn+S deficiency, respectively. In addition, proteins and starch content decreased significantly while that of soluble sugars increased slightly. The activity of malate dehydrogenase and glucose-6-phosphate dehydrogenase also decreased due to Zn as well as S deficiency. The deficiency treatments resulted in a decrease in phospholipids, free fatty acids and triacylglycerols in mature kernels. Further the proportion of 16∶0 and 18∶2 decreased while that of 18∶1 increased in developing kernels.     

Control of kernel weight and kernel water relations by post-flowering source-sink ratio in maize

The maize (Zea mays L.) kernel undergoes large changes in water content during its development. Whether such changes regulate the pattern of kernel development or are simply a consequence of it has not yet been established because other factors, such as assimilate supply, can also affect the rate and duration of kernel growth. This study was conducted to determine whether variation in kernel weight (KW) in response to source-sink treatments is mediated by a change in kernel water relations. Two hybrids were sown at three stand densities (one, eight and 18 plants m-2), and kernel numbers were restricted to control the post-flowering source-sink ratio within each stand density. Kernel development and water relations [water content, water potential (psiw), osmotic potential (psis) and turgor] were monitored throughout grain filling. Final KW varied from 253 to 372 mg per kernel in response to source-sink treatments. For both genotypes, variation in KW was a result of a change in kernel growth rate (r2 = 0.91; P < 0.001) and not in the duration of kernel filling. Final KW was closely correlated with maximum kernel water content (r2 = 0.94; P < 0.001) achieved during rapid dry matter accumulation. However, variation in KW was not reflected in kernel water status parameters (psiw, psis or turgor), which remained fairly stable across treatments. These results indicate that maximum water content provides an easily quantifiable measure of kernel sink capacity in maize. Kernel water status parameters may affect the duration of grain filling, but have no discernible impact on kernel growth rate.