Exhaled breath condensate: Determination of non-volatile compounds and their potential for clinical diagnosis and monitoring. A review

Exhaled breath condensate is a promising, non-invasive, diagnostic sample obtained by condensation of exhaled breath. Starting from a historical perspective of early attempts of breath testing towards the contemporary state-of-the-art breath analysis, this review article focuses mainly on the progress in determination of non-volatile compounds in exhaled breath condensate. The mechanisms by which the aerosols/droplets of non-volatile compounds are formed in the airways are discussed with methodological consequences for sampling. Dilution of respiratory droplets is a major problem for correct clinical interpretation of the measured data and there is an urgent need for standardization of EBC. This applies also for collection instrumentation and therefore various commercial and in-house built devices are described and compared with regard to their design, function and collection parameters. The analytical techniques and methods for determination of non-volatile compounds as potential markers of oxidative stress and lung inflammation are scrutinized with an emphasis on method suitability, sensitivity and appropriateness. The relevance of clinical findings for each group of possible non-volatile markers of selected pulmonary diseases and methodological recommendations with emphasis on interdisciplinary collaboration that is essential for future development into a fully validated clinical diagnostic tool are given.     

Gauss–Seidel Estimation of Generalized Linear Mixed Models With Application to Poisson Modeling of Spatially Varying Disease Rates

Generalized linear mixed models (GLMMs) are often fit by computational procedures such as penalized quasi-likelihood (PQL). Special cases of GLMMs are generalized linear models (GLMs), which are often fit using algorithms like iterative weighted least squares (IWLS). High computational costs and memory space constraints make it difficult to apply these iterative procedures to datasets having a very large number of records.

We propose a computationally efficient strategy based on the Gauss–Seidel algorithm that iteratively fits submodels of the GLMM to collapsed versions of the data. The strategy is applied to investigate the relationship between ischemic heart disease, socioeconomic status, and age/gender category in New South Wales, Australia, based on outcome data consisting of approximately 33 million records. For Poisson and binomial regression models, the Gauss–Seidel approach is found to substantially outperform existing methods in terms of maximum analyzable sample size. Remarkably, for both models, the average time per iteration and the total time until convergence of the Gauss–Seidel procedure are less than 0.3% of the corresponding times for the IWLS algorithm. Platform-independent pseudo-code for fitting GLMS, as well as the source code used to generate and analyze the datasets in the simulation studies, are available online as supplemental materials.     

Split least‐squares finite element methods for non‐Fickian flow in porous media

In this article, we introduce two least‐squares finite element procedures for parabolic integro‐differential equations arising in the modeling of non‐Fickian flow in porous media. By selecting the least‐squares functional properly the presented procedure can be split into two independent subprocedures, one subprocedure is for the primitive unknown and the other is for the flux. The optimal order convergence analysis is established. Numerical examples are given to show the efficiency of the introduced schemes. © 2012 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2013     

基于LIBS技术的水泥粉末在线成分分析

在工业现场生产水泥的过程中，各种成分的含量直接影响着水泥的质量，因此如何快速准确地监测水泥中各个成分的含量意义重大。采用的实验方式为，将不经过任何预处理的水泥粉末直接放入位于二维移动平台上的物料盒中，通过激光诱导击穿光谱(LIBS)直接对水泥粉末表面的不同位置进行激发检测，对得到的光谱数据首先进行归一化和主成分分析等预处理操作，然后针对水泥中Ca, Si, Al, Fe, Mg五种元素，分别建立偏最小二乘(PLS)和支持向量回归(SVR)两种定量分析模型进行方法比较。此外，对比了粉末状水泥与压片式水泥两种测量方式的结果。实验结果表明，采用粉末状水泥直接测量的方式下，针对水泥样品元素浓度与所得到的光谱中特征线强度的关系，SVR方法比PLS方法更具优势，粉末状水泥直接测量的精度接近压片式测量的精度，说明LIBS技术对水泥粉末状样品直接在线测量具有可行性。     

A Robust Near-Infrared Calibration Model for the Determination of Chlorophyll Concentration in Tree Leaves with a Calibration Transfer Method

A method based on piecewise direct standardization was developed to directly predict leaf chlorophyll concentrations by correction of near-infrared spectra to construct a robust calibration model. Chinar, camphor, and gingko leaves collected from two growth intervals were evaluated. Spectral pretreatment methods and wavelength selection were investigated. The first derivative combined with stability competitive adaptive reweighted sampling before piecewise direct standardization provided the best performance. Under the optimized parameters, the root mean square error of prediction was significantly reduced by using piecewise direct standardization. This study demonstrates that the calibration model may be used to rapidly characterize chlorophyll concentrations across species and growth intervals.     

2型糖尿病患者尿液的代谢组学研究

目的 从代谢组学角度分析并寻找2型糖尿病（T2DM）患者可能的代谢标记物。方法 选取30例初诊、或有糖尿病史经药物治疗控制不理想且无并发症的T2DM 患者,另选取30例性别、年龄匹配的健康者为正常对照。收集清晨空腹中段尿,以气相色谱- 质谱联用（GC-MS）技术对尿液样本进行代谢图谱分析,正交偏最小二乘法判别研究尿液内源性化合物在两组间的差异。结果 T2DM 组和正常对照组尿液代谢谱明显分离。与正常对照组比较,T2DM 组尿液2,3,4- 三羟基丁酸、肌醇、D- 葡萄糖、D- 葡萄糖酸及尿素含量升高（P＜0.05 或0.01）,马尿酸含量减少（P＜0.01）。结论 代谢组学检查提示T2DM 患者尿液中代谢标志物为2,3,4- 三羟基丁酸、肌醇、马尿酸、D-葡萄糖、D- 葡萄糖酸及尿素,观察这些标志物含量的变化有助于T2DM 的临床诊断及发病机制研究。     

An improved ensemble learning machine for biological activity prediction of tyrosine kinase inhibitors

Boosting is one of the most important strategies in ensemble learning because of its ability to improve the stability and performance of weak learners. It is nonparametric, multivariate, fast and interpretable but is not robust against outliers. To enhance its prediction accuracy as well as immunize it against outliers, a modified version of a boosting algorithm (AdaBoost R2) was developed and called AdaBoost R3. In the sampling step, extremum samples were added to the boosting set. In the robustness step, a modified Huber loss function was applied to overcome the outlier problem. In the output step, a deterministic threshold was used to guarantee that bad predictions do not participate in the final output. The performance of the modified algorithm was investigated with two anticancer data sets of tyrosine kinase inhibitors, and the mechanism of inhibition was studied using the relative weighted variable importance procedure. Investigating the effect of base learner's strength reveals that boosting is only successful using the classification and regression tree method (a weak to moderate learner) and does not have a significant effect using the radial basis functions partial least square method (a strong base learners). Copyright © 2015 John Wiley & Sons, Ltd.