VPMCD: variable interaction modeling approach for class discrimination in biological systems

Data classification algorithms applied for class prediction in computational biology literature are data specific and have shown varying degrees of performance. Different classes cannot be distinguished solely based on interclass distances or decision boundaries. We propose that inter-relations among the features be exploited for separating observations into specific classes. A new variable predictive model based class discrimination (VPMCD) method is described here. Three well established and proven data sets of varying statistical and biological significance are utilized as benchmark. The performance of the new method is compared with advanced classification algorithms. The new method performs better during different tests and shows higher stability and robustness. The VPMCD is observed to be a potentially strong classification approach and can be effectively extended to other data mining applications involving biological systems.     

桂西北石灰土土壤有机碳矿化对外源有机物质和碳酸钙的响应

为明确外源有机物质和无机碳酸盐对桂西北石灰土土壤有机碳矿化的影响,加深对土壤有机碳周转特征的认识,本文以广西环江县喀斯特地区的棕色石灰土、黑色石灰土和地带性红壤(对照)为研究对象,进行为期100 d的室内培养试验[对照(无外源物添加,CK)、添加14C-稻草(S)、添加Ca14CO3(C)],并对土壤呼吸释放的CO2及14C-CO2含量进行测定.结果表明:培养100 d后,外源物的添加均明显促进了红壤、棕色和黑色石灰土有机碳的矿化,外源14C-稻草和Ca14 CO3对上述土壤有机碳矿化的激发效应分别为28.7％、46.2％ 、15.5％和127.0％ 、175.3％、100.1％;土壤表观累积矿化量中外源Ca14CO3的贡献率分别为40.4％、48.4％、19.6％;土壤类型和添加物及两者间的交互作用均对土壤有机碳矿化的激发效应、土壤表观累积矿化量中外源物的贡献、土壤有机碳的矿化速率、土壤有机碳累积矿化量/率有显著影响.因此,外源有机物质和碳酸钙的添加改变了土壤有机碳的矿化特征,对于含碳酸盐的石灰土,研究土壤有机碳矿化、周转规律,评估其对大气CO2的影响必须考虑无机碳酸盐的贡献.     

Glucagon-like peptide-1 of brainstem origin activates dorsomedial hypothalamic neurons in satiated rats

A high number of neurons express c-fos in response to unlimited food intake in fasted rats in the ventral subdivision of the hypothalamic dorsomedial nucleus (DMHv). We report here, that in same conditions, limited food consumption failed to induce Fos expression in DMHv neurons suggesting that satiation should be one of the important signals that activate these neurons. The possible origin of fibers conducting satiation signals to the DMHv could be in the lower brainstem, especially glucagon-like peptide-1 (GLP-1)-containing neurons in the nucleus of the solitary tract (NTS). We demonstrate that GLP-1-immunoreactive fibers and fiber terminals topographically overlap with activated Fos-positive neurons in the DMHv in refed rats. Using immunocytochemistry and in situ hybridization histochemistry, we demonstrated GLP-1 receptors in Fos-expressing neurons of the DMH. Unilateral transections of ascending GLP-1-containing fibers from the NTS inside the pons in refed rats (unlimited food consumption) resulted in a dramatic decrease in the density of GLP-1 fibers and in the number of Fos-immunoreactive neurons in the DMHv, but only on the side of the transection. Contralateral to the transection, neither the GLP-1 fiber density nor the number of Fos-positive cells changed significantly. Meanwhile, the density of GLP-1 immunoreactivity was markedly accumulated in transected nerve fibers caudal to the cuts, as a consequence of the interruption of the ascending GLP-1 transport route. These findings suggest that the solitary-hypothalamic projections may represent the neuronal route through GLP-1 neurons of the NTS activate DMHv neurons via GLP-1 receptors by conveying information on satiety.     

Modeling gross primary productivity of alpine meadow in the northern Tibet Plateau by using MODIS images and climate data

The Vegetation Photosynthesis Model (VPM) was used to simulate the gross primary productivities (GPP) of the alpine meadow ecosystem in the northern Tibet Plateau at three different spatial resolutions of 0.5 km, 1.5 km and 2.5 km, respectively. The linear relationships between enhanced vegetation indices (EVI) and GPP, with higher correlative coefficients, were better than those between normalized difference vegetation indices (NDVI) and GPP at the three resolutions. VPM could well simulate the seasonal changes and inter-annual variations of GPP, with similar trends at the three resolutions. There were significant differences (P < 0.0001) among the three modeled GPP with the three resolutions. Therefore, the modeled GPP at high resolution could not be directly extrapolated to low resolution, and vice versa. The contribution levels of different model parameters, including photosynthetically active radiation (PAR), air temperature (Ta), NDVI, EVI and land surface water indices (LSWI), to modeled GPP could vary with spatial resolution based on multiple stepwise linear regression analysis. This indicated that it was important to choose parameters properly and consider their effects on modeled GPP.     

Modeling gross primary productivity of alpine meadow in the northern Tibet Plateau by using MODIS images and climate data

The Vegetation Photosynthesis Model (VPM) was used to simulate the gross primary productivities (GPP) of the alpine meadow ecosystem in the northern Tibet Plateau at three different spatial resolutions of 0.5 km, 1.5 km and 2.5 km, respectively. The linear relationships between enhanced vegetation indices (EVI) and GPP, with higher correlative coefficients, were better than those between normalized difference vegetation indices (NDVI) and GPP at the three resolutions. VPM could well simulate the seasonal changes and inter-annual variations of GPP, with similar trends at the three resolutions. There were significant differences (P < 0.0001) among the three modeled GPP with the three resolutions. Therefore, the modeled GPP at high resolution could not be directly extrapolated to low resolution, and vice versa. The contribution levels of different model parameters, including photosynthetically active radiation (PAR), air temperature (Ta), NDVI, EVI and land surface water indices (LSWI), to modeled GPP could vary with spatial resolution based on multiple stepwise linear regression analysis. This indicated that it was important to choose parameters properly and consider their effects on modeled GPP.     

基于FLUXNET观测数据与VPM模型的森林生态系统光合作用关键参数优化及验证

生态系统总初级生产力(GPP)是全球生态系统碳循环研究的重要组成部分.植被最大光能利用率(ε_max)是陆地生态系统GPP模拟的关键参数.本文基于植被光合模型(VPM)和全球通量网(FLUXNET)40个站点(179条站点年数据)的涡度相关通量观测数据,采用单因素轮换法对VPM模型进行参数敏感性分析,并利用交叉验证法对全球森林生态系统的光合作用关键参数进行优化和验证.结果表明:森林生态系统GPP模型受ε_max、光合最高温度(T_max)以及光合最适温度(T_opt)的影响最大;优化后的ε_max在不同植被类型之间存在明显差异,介于0.05～0.08 μmol CO₂·μmol^-1 PAR,常绿阔叶林>常绿针叶林>混交林>落叶阔叶林;优化后的森林生态系统T_max为38～48 ℃,T_opt为18～22 ℃;利用分植被类型优化后的模型关键参数,VPM模型可较好地模拟全球主要森林生态系统GPP的季节和年际变化.     

Application of two remote sensing GPP algorithms at a semiarid grassland site of North China

Aims Estimation of gross primary production (GPP) from remote sensing data is an important approach to study regional or global carbon cycle. However, for a given algorithm, it usually has its limitation on applications to a wide range of vegetation types and/or under diverse environmental conditions. This study was conducted to compare the performance of two remote sensing GPP algorithms, the MODIS GPP and the vegetation photosynthesis model (VPM), in a semiarid temperate grassland ecosystem.Methods The study was conducted at a typical grassland site in Ujimuqin of Inner Mongolia, North China, over 2 years in 2006 and 2007. Environmental controls on GPP measured by the eddy covariance (EC) technique at the study site were first investigated with path analysis of meteorological and soil moisture data at a daily and 8-day time steps. The estimates of GPP derived from the MODIS GPP and the VPM with site-specific inputs were then compared with the values of EC measurements as ground truthing at the site. Site-specific ? max (α) was estimated by using rectangular hyperbola function based on the 7-day flux data at 30-min intervals over the peak period of the growing season (May to September).Important findings Between the two remote sensing GPP algorithms and various estimates of the fraction of absorbed photosynthetic active radiation (FPAR), the VPM based on FPAR derived from the enhanced vegetation index (EVI) works the best in predicting GPP against the ground truthing of EC GPP. A path analysis indicates that the EC GPP in this semiarid temperate grassland ecosystem is controlled predominantly by both soil water and temperature. The site water condition is slightly better simulated by the moisture multiplier in the VPM than in the MODIS GPP algorithm, which is a most probable explanation for a better performance of the VPM than MODIS GPP algorithm in this semiarid grassland ecosystem.