Creating a suite of macros for meta-analysis in SAS: A case study in collaboration

A series of macros that have been created to perform fixed and random effects meta-analysis in SAS^® are described as is the motivation for their creation. These macros are being made freely available on the internet for others to use. The application of the macros is illustrated using an example of trials in pre-eclampsia.     

土壤重金属沿山地森林海拔梯度的分布特征

采用电感耦合等离子体发射光谱法与石墨炉原子吸收光谱法,研究了14种重金属(Fe,Al,Ti,Cu,Cr,Mn,V,Zn,Ni,Co,Pb,Se,Cd,As)沿山地森林海拔梯度的分布特征.结果表明海拔梯度对14种重金属中的6种重金属浓度(Fe,A1,Ti,V,Pb与As)有显著的影响,对其他八种重金属浓度(Cu,Cr,M...     

Multifractal behavior of wild-land and forest fire time series in Brazil

We examine statistical properties of a daily hot pixel time series recorded in Brazil during the period 1998–2006, using Multifractal Detrended Fluctuation Analysis (MF-DFA). We find that generalized scaling exponent h(q)$h\left(q\right)$ is a decreasing function of q$q$, indicating multifractal behavior of hot pixel dynamics. We also calculate multifractal spectra f(α)$f\left(\alpha \right)$ and use fourth-degree polynomial regression to estimate complexity parameters that describe the degree of multifractality of the underlying process. After July 2002, when a significant increase of the number of hot pixel observations is recorded, the complexity of the series is reduced (manifested by the reduction of width of the f(α)$f\left(\alpha \right)$ spectrum), while small fluctuations increase their dominance over large scale fluctuations (manifested by the increase of skew parameter r$r$). These results should be taken into account when devising ecological and climatic models for Brazil, that contemplate the phenomena of wild-land and forest fires.     

Chemoinformatic Approaches To Predict the Viscosities of Ionic Liquids and Ionic Liquid-Containing Systems

Modelling, predicting, and understanding the factors influencing the viscosities of ionic liquids and related mixtures are sequentially checked in this work. The molecular maps of atom-level properties (MOLMAP codification system) is adapted for a straightforward inclusion of ionic liquids and mixtures containing ionic liquids. Random Forest models have been tested in this context and an optimal model was selected. The interpretability of the selected Random Forest model is highlighted with selected structural features that might contribute to identify low viscosities. The constructed model is able to recognize the influence of different structural variables, temperature, and pressure for a correct classification of the different systems. The codification and interpretation systems are highlighted in this work.     

Transboundary Pathogenic microRNA Analysis Framework for Crop Fungi Driven by Biological Big Data and Artificial Intelligence Model

Plant fungal diseases have been affecting the world's agricultural production and economic levels for a long time, such as rice blast, gray tomato mold, potato late blight etc. Recent studies have shown that fungal pathogens transmit microRNA as an effector to host plants for infection. However, bioassay-based verification analysis is time-consuming and challenging, and it is difficult to analyze from a global perspective. With the accumulation of fungal and plant-related data, data analysis methods can be used to analyze pathogenic fungal microRNA further. Based on the microRNA expression data of fungal pathogens infecting plants before and after, this paper discusses the selection strategy of sample data, the extraction strategy of pathogenic fungal microRNA, the prediction strategy of a fungal pathogenic microRNA target gene, the bicluster-based fungal pathogenic microRNA functional analysis strategy and experimental verification methods. A general analysis pipeline based on machine learning and bicluster-based function module was proposed for plant-fungal pathogenic microRNA.The pipeline proposed in this paper is applied to the infection process of Magnaporthe oryzae and the infection process of potato late blight. It has been verified to prove the feasibility of the pipeline. It can be extended to other relevant crop pathogen research, providing a new idea for fungal research on plant diseases. It can be used as a reference for understanding the interaction between fungi and plants.     

基于混合型遗传算法的森林可燃物热解动力学参数优化方法

森林可燃物热解动力学参数的优化计算是构建综合热解模型的关键步骤。传统的基于梯度的优化方法收敛速度快但全局寻优能力不足,基于“生物进化理论”的遗传算法具有全局寻优能力但收敛速度慢。本研究首先探讨了单纯的遗传算法对初始值设置的依赖,发现设定合适的初始值能够稳定计算结果,加快算法的收敛速度。针对初始值未知的情况,本文提出了将单纯的遗传算法与迭代算法相结合构建混合型遗传算法的流程。然后以樟子松松枝为例,采用热重分析仪开展了森林可燃物热解实验。假设可燃物热解失重过程遵循三步一级平行反应模型,通过对比单纯遗传算法和混合型遗传算法的收敛过程,发现混合型遗传算法能够快速地获取全局最优的动力学参数,显著地提高遗传算法的优化性能。     

基于混沌理论的室内轰燃判据

根据混沌理论,在研究大量小规模室内火灾实验结果的基础上,得到了一种新的、通过温度变化率 表征的轰燃判据;利用大规模室内火灾的实验结果,对该判据的实用性进行了初步验证。结果表明:这一判据 可以应用于预测大规模实际室内火灾中轰燃的发生。     

基于五种大气校正的多时相森林碳储量遥感反演研究

大气校正已广泛应用于区域生态植被的动态监测,但是不同校正方法和模型对遥感影像光谱和森林碳储量估算结果的影响不得而知,同时这种差异在多时相遥感监测与应用时经常被忽略。以多期Landsat影像为数据源,借助植被指数MNDVI和野外实地调查的马尾松林样方数据,进行马尾松林碳储量反演。然后采用几种常用的大气校正算法：6S,FLAASH(fast line-sight atmospheric),IACM(illumination and atmospheric)和QUAC(quick atmospheric correction),并结合地面同步实测的光谱数据,以评估其对马尾松冠层光谱曲线、植被指数以及林分碳储量估算的影响;同时从遥感动态监测角度出发,分析了相对大气校正(pseudo-invariant feature, PIF)对多时相影像植被指数与碳储量反演结果的校正效果。结果表明,经大气校正后的影像波段反射率与实测光谱结果较为接近,其中近红外和短波红外波段光谱反射率明显上升,同时可见光波段减弱,NDVI(normalized difference vegetation index)增加明显。不同大气校正模型对研究区马尾松林碳储量的遥感反演结果影响较大,其中IACM与6S模型分别具有较高的精度和较低的误差。此外,经过PIF校正后不同时相影像的NDVI相对偏差降低了85.16%,同时马尾松林碳储量反演模型精度得到明显提升,表明辐射归一化处理对于多时相遥感影像的应用十分必要。研究发现ICAM与PIF的大气校正模型组合可较好纠正大气效应,适用于多时相遥感数据的森林碳储量反演与监测研究。