Measuring ecological total-factor energy efficiency incorporating regional heterogeneities in China

This paper proposes a metafrontier slack-based efficiency measure (MSBM) approach to model ecological total-factor energy efficiency. This new advanced approach incorporates group heterogeneities of regions, total slacks, and undesirable outputs simultaneously into energy efficiency analysis. We incorporate global environmental technology into the proposed model so as to improve its discriminating power. We conduct an empirical analysis of regional ecological energy efficiency by incorporating carbon dioxide (CO₂) and sulfur dioxide (SO₂) emissions and the chemical oxygen demand (COD) of China during 2001-2010. The results indicate that most of the provinces are not performing at high ecological energy efficiency. Significant regional technology gaps in ecological energy efficiency exist in three areas. We present some policy suggestions based on the empirical results.     

A direct optimization approach to hidden Markov modeling for single channel kinetics

Hidden Markov modeling (HMM) provides an effective approach for modeling single channel kinetics. Standard HMM is based on Baum's reestimation. As applied to single channel currents, the algorithm has the inability to optimize the rate constants directly. We present here an alternative approach by considering the problem as a general optimization problem. The quasi-Newton method is used for searching the likelihood surface. The analytical derivatives of the likelihood function are derived, thereby maximizing the efficiency of the optimization. Because the rate constants are optimized directly, the approach has advantages such as the allowance for model constraints and the ability to simultaneously fit multiple data sets obtained at different experimental conditions. Numerical examples are presented to illustrate the performance of the algorithm. Comparisons with Baum's reestimation suggest that the approach has a superior convergence speed when the likelihood surface is poorly defined due to, for example, a low signal-to-noise ratio or the aggregation of multiple states having identical conductances.     

An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines

Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age.     

中国农业生态效率评价方法与实证——基于非期望产出的SBM模型分析

生态效率是衡量经济与资源环境协调发展的重要指标.基于将农业面源污染作为非期望产出的考量,在对传统DEA模型进行修正的基础上,采用非径向、非角度的SBM模型对中国30个省份的农业生态效率进行了测算,并给出了农业生态效率的改善途径.研究结果表明:1998-2009年中国农业生态效率虽然呈现缓慢上升态势,但整体仍处于较低水平,除北京、上海、海南、重庆外,其余省市都需要改变投入和产出来优化农业生态效率;资源的过度消耗和环境污染物的过量排放是农业生态效率损失的主要原因.提高资源利用效率、降低资源消耗量和环境污染物的排放量是农业生态效率改善的重要途径.     

Evaluation of environmental efficiency in China using data envelopment analysis

As a main method for achieving environmental protection, improvement of environmental efficiency is vitally important for reducing environmental risk and level of ecological scarcity. Thus, quantitative analysis of environmental efficiency is not only an important premise for understanding the situation of regional environmental protection, but also a prerequisite for designing and adjusting relevant policies. In this paper, we treat the undesirable output in an economic activity by a linear monotonic decreasing transformation approach. We employ data envelopment analysis (DEA) technique to evaluate environmental efficiency in 30 provinces of China during the period 2001–2010, and conduct hypothesis tests on these environmental efficiencies. Furthermore, we investigate the horizontal differences of environmental efficiency in six regions of China and the vertical differences among different years. The results show that our empirical tests have a high statistical reliability that provides support for prompting environmental policies in different parts of China.     

Bayesian association mapping of multiple quantitative trait loci and its application to the analysis of genetic variation among <Emphasis Type="Italic">Oryza sativa</Emphasis> L. germplasms

One way to use a crop germplasm collection directly to map QTLs without using line-crossing experiments is the whole genome association mapping. A major problem with association mapping is the presence of population structure, which can lead to both false positives and failure to detect genuine associations (i.e., false negatives). Particularly in highly selfing species such as Asian cultivated rice, high levels of population structure are expected and therefore the efficiency of association mapping remains almost unknown. Here, we propose an approach that combines a Bayesian method for mapping multiple QTLs with a regression method that directly incorporates estimates of population structure. That is, the effects due to both multiple QTLs and population structure were included in our statistical model. We evaluated the efficiency of our approach in simulated- and real-trait analyses of a rice germplasm collection. Simulation analyses based on real marker data showed that our model could suppress both false-positive and false-negative rates and the error of estimation of genetic effects over single QTL models, indicating that our model has statistically desirable attributes over single QTL models. As real traits, we analyzed the size and shape of milled rice grains and found significant markers that may be linked to QTLs reported previously. Association mapping should have good prospects in highly selfing species such as rice if proper methods are adopted. Our approach will be useful for the whole genome association mapping of various selfing crop species.     

A web based tool to merge geometric morphometric data from multiple characters

Geometric morphometrics (GM) is an important method of shape analysis and increasingly used in a wide range of scientific disciplines.Presently,a single character comparison system of geometric morphometric data is used in almost all empirical studies,and this approach is sufficient for many scientific problems.However,the estimation of overall similarity among taxa or objects based on multiple characters is crucial in a variety of contexts (e.g.(semi-)automated identification,phenetic relationships,tracing of character evolution,phylogenetic reconstruction).Here we propose a new web-based tool for merging several geometric morphometrics data files from multiple characters into a single data file.Using this approach information from multiple characters can be compared in combination and an overall similarity estimate can be obtained in a convenient and geometrically rigorous manner.To illustrate our method,we provide an example analysis of 25 dung beetle species with seven Procrustes superimposed landmark data files representing the morphological variation of body features:the epipharynx,right mandible,pronotum,elytra,hindwing,and the metendosternite in dorsal and lateral view.All seven files were merged into a single one containing information on 649 landmark locations.The possible applications of such merged data files in different fields of science are discussed.     

A multistep protein lysate array quantification method and its statistical properties

The protein lysate array is an emerging technology for quantifying the protein concentration ratios in multiple biological samples. Statistical inference for a parametric quantification procedure has been inadequately addressed in the literature, mainly because the appropriate asymptotic theory involves a problem with the number of parameters increasing with the number of observations. In this article, we develop a multistep procedure for the Sigmoidal models, ensuring consistent estimation of the concentration levels with full asymptotic efficiency. The results obtained in the article justify inferential procedures based on large sample approximations. Simulation studies and real data analysis are used in the article to illustrate the performance of the proposed method in finite samples. The multistep procedure is convenient to work with asymptotically, and is recommended for its statistical efficiency in protein concentration estimation and improved numerical stability by focusing on optimization of lower-dimensional objective functions.     

A self-organizing state-space-model approach for parameter estimation in hodgkin-huxley-type models of single neurons

Traditional approaches to the problem of parameter estimation in biophysical models of neurons and neural networks usually adopt a global search algorithm (for example, an evolutionary algorithm), often in combination with a local search method (such as gradient descent) in order to minimize the value of a cost function, which measures the discrepancy between various features of the available experimental data and model output. In this study, we approach the problem of parameter estimation in conductance-based models of single neurons from a different perspective. By adopting a hidden-dynamical-systems formalism, we expressed parameter estimation as an inference problem in these systems, which can then be tackled using a range of well-established statistical inference methods. The particular method we used was Kitagawa's self-organizing state-space model, which was applied on a number of Hodgkin-Huxley-type models using simulated or actual electrophysiological data. We showed that the algorithm can be used to estimate a large number of parameters, including maximal conductances, reversal potentials, kinetics of ionic currents, measurement and intrinsic noise, based on low-dimensional experimental data and sufficiently informative priors in the form of pre-defined constraints imposed on model parameters. The algorithm remained operational even when very noisy experimental data were used. Importantly, by combining the self-organizing state-space model with an adaptive sampling algorithm akin to the Covariance Matrix Adaptation Evolution Strategy, we achieved a significant reduction in the variance of parameter estimates. The algorithm did not require the explicit formulation of a cost function and it was straightforward to apply on compartmental models and multiple data sets. Overall, the proposed methodology is particularly suitable for resolving high-dimensional inference problems based on noisy electrophysiological data and, therefore, a potentially useful tool in the construction of biophysical neuron models.     

A self-consistent approach for determining pairwise interactions that underlie channel activation

Signaling proteins such as ion channels largely exist in two functional forms, corresponding to the active and resting states, connected by multiple intermediates. Multiparametric kinetic models based on sophisticated electrophysiological experiments have been devised to identify molecular interactions of these conformational transitions. However, this approach is arduous and is not suitable for large-scale perturbation analysis of interaction pathways. Recently, we described a model-free method to obtain the net free energy of activation in voltage- and ligand-activated ion channels. Here we extend this approach to estimate pairwise interaction energies of side chains that contribute to gating transitions. Our approach, which we call generalized interaction-energy analysis (GIA), combines median voltage estimates obtained from charge-voltage curves with mutant cycle analysis to ascertain the strengths of pairwise interactions. We show that, for a system with an arbitrary gating scheme, the nonadditive contributions of amino acid pairs to the net free energy of activation can be computed in a self-consistent manner. Numerical analyses of sequential and allosteric models of channel activation also show that this approach can measure energetic nonadditivities even when perturbations affect multiple transitions. To demonstrate the experimental application of this method, we reevaluated the interaction energies of six previously described long-range interactors in the Shaker potassium channel. Our approach offers the ability to generate detailed interaction energy maps in voltage- and ligand-activated ion channels and can be extended to any force-driven system as long as associated “displacement” can be measured.     

Efficient Inference of Parsimonious Phenomenological Models of Cellular Dynamics Using S-Systems and Alternating Regression

The nonlinearity of dynamics in systems biology makes it hard to infer them from experimental data. Simple linear models are computationally efficient, but cannot incorporate these important nonlinearities. An adaptive method based on the S-system formalism, which is a sensible representation of nonlinear mass-action kinetics typically found in cellular dynamics, maintains the efficiency of linear regression. We combine this approach with adaptive model selection to obtain efficient and parsimonious representations of cellular dynamics. The approach is tested by inferring the dynamics of yeast glycolysis from simulated data. With little computing time, it produces dynamical models with high predictive power and with structural complexity adapted to the difficulty of the inference problem.     

基于能值分析的中国生态经济系统可持续发展评估

本文基于能值理论全面评估了2006—2016年中国的可持续发展水平,并根据一次能源投入比例的不同将各省份分为4组进行比较分析,讨论一次能源投入水平对省域间可持续发展水平的影响。结果表明: 2006—2016年,随着经济快速增长,一次能源投入比例不断上涨,使得我国整体生态经济系统的环境负荷由2.78升至3.13。同时,受进口增加的影响,能值产出率相对下降,可持续性指标下降至5.40。能值密度变化趋势与一次能源投入比例的变化基本一致,目前大多数省份依旧通过能源消费带动区域经济发展。其中,北京、上海等虽现代化经济高度发达,但环境压力同样高于其他省份,可持续发展水平不容乐观。以江苏为代表的南方省份能源投入较低而能值产出较高,表现出较高的可持续发展活力。因此,提升内陆地区能源利用效率、调整经济发展模式,并适当减缓发展强度,以缓解经济发展与环境保护之间的矛盾,可以令我国稳步实现生态经济的可持续发展。     

Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking

One approach to incorporate protein flexibility in molecular docking is the use of an ensemble consisting of multiple protein structures. Sequentially docking each ligand into a large number of protein structures is computationally too expensive to allow large-scale database screening. It is challenging to achieve a good balance between docking accuracy and computational efficiency. In this work, we have developed a fast, novel docking algorithm utilizing multiple protein structures, referred to as ensemble docking, to account for protein structural variations. The algorithm can simultaneously dock a ligand into an ensemble of protein structures and automatically select an optimal protein structure that best fits the ligand by optimizing both ligand coordinates and the conformational variable m, where m represents the m-th structure in the protein ensemble. The docking algorithm was validated on 10 protein ensembles containing 105 crystal structures and 87 ligands in terms of binding mode and energy score predictions. A success rate of 93% was obtained with the criterion of root-mean-square deviation <2.5 A if the top five orientations for each ligand were considered, comparable to that of sequential docking in which scores for individual docking are merged into one list by re-ranking, and significantly better than that of single rigid-receptor docking (75% on average). Similar trends were also observed in binding score predictions and enrichment tests of virtual database screening. The ensemble docking algorithm is computationally efficient, with a computational time comparable to that for docking a ligand into a single protein structure. In contrast, the computational time for the sequential docking method increases linearly with the number of protein structures in the ensemble. The algorithm was further evaluated using a more realistic ensemble in which the corresponding bound protein structures of inhibitors were excluded. The results show that ensemble docking successfully predicts the binding modes of the inhibitors, and discriminates the inhibitors from a set of noninhibitors with similar chemical properties. Although multiple experimental structures were used in the present work, our algorithm can be easily applied to multiple protein conformations generated by computational methods, and helps improve the efficiency of other existing multiple protein structure(MPS)-based methods to accommodate protein flexibility.     

中国资源再生利用的生态效率演变特征与空间分异格局

解析资源生态效率演变特征和分异格局,可为我国实现资源再生利用区域均衡发展和提高生态利用效率水平提供参考。本研究以26个省(市、区)的资源再生利用面板为对象,采用超效率DEA模型测算了2004—2017年其产业生态效率水平,并基于地理区划,分析该产业生态效率演变特征和空间分异格局。结果表明: 2004—2017年,研究区的资源再生利用产业生态效率逐年提高,约有年均1.5%的增长势能,整体表现为由内陆逐渐向沿海递增的演变态势。该产业生态效率全局空间相关性显著,且省(市、区)之间存在明显的空间集聚特征;随着年份推移,区域差异性减弱,高水平省(市、区)辐射带动力较强。研究期间,我国资源再生利用的生态效率空间分异趋于缓和,主要因为华北、东北、华东、华南、华中和西北地区的空间分异减弱,而西南地区的分异不断增强,从而弱化了整体降低趋势。     

Linking city‐level input–output table to urban energy footprint: Construction framework and application

Multiregion input–output (MRIO) models have become increasingly important in economic and environmental analysis. However, the current resolution of most MRIO models fails to capture the heterogeneity between subregions, especially in cities. The lack of city‐level MRIO tables has impeded the accomplishment of city‐level studies and hampered the understanding of the relationship between urban growth and consumption, and teleconnections to other regions. In this paper, we propose a partial survey‐based multiple‐layer framework for MRIO table compilation of a Chinese province that distinguishes city‐based regions. This framework can effectively address a large number of data processes and retain consistency between layers. Using the framework, we first compile a nested Hebei‐China city‐level MRIO table and then apply city‐level energy footprint accounting of the North China urban agglomeration. Our results present the critical role of Hebei cities in energy supply in 2012 and quantify energy use embodied in goods for the domestic trade. Tangshan, Shijiazhuang, and Handan are distinctive cities in the energy supply chain of other regions, for both less developed and developed regions. This multiple‐layer framework represents a feasible approach for developing subregional‐level MRIO models and offers the possibility to analyze global trade at the subregional level with limited data. The data and results from the analysis in this article are available for download from China Emission Accounts and Datasets.     

Mathematical and kinetic modeling of biofilm reactor based on ant colony optimization

Inverse estimation of model parameters via mathematical modeling route, known as inverse modeling (IM), is an attractive alternative approach to the experimental methods. This approach makes use of efficient optimization techniques in the course of solution of an inverse problem with the aid of measured data. In this study, a novel optimization method based on ant colony optimization (ACO), denoted by ACO-IM, is presented for inverse estimation of kinetic and film thickness parameters of biofilm models that describe an experimental fixed bed anaerobic reactor. The proposed optimization method for parameter estimation emulates the fact that ants are capable of finding the shortest path from a food source to their nest by depositing a trial of pheromone during their walk. The efficacy of the ACO-IM for numerical estimation of bio-kinetic parameters is demonstrated through its application for the anaerobic treatment of industry wastewater in a fixed bed biofilm process. The results explain the rigorousness of mathematical models, the form of kinetic and film thickness models and the type of packing to be used with the biofilm process for accurate determination of kinetic and film thickness parameters so as to ensure reliable predictive performance of the biofilm reactor models.     

中国旅游业碳排放效率的空间格局及其影响因素

旅游业碳排放效率作为旅游业绿色全要素生产率指标,是衡量旅游业碳排放与旅游经济增长之间关系的重要工具。借助SBM模型测算了中国省际旅游业碳排放效率,并利用ESDA和GWR方法分析了旅游业碳排放效率的空间格局及其影响因素的时空异质性。结果表明:中国旅游业碳排放效率呈现缓慢提升态势,但总体水平仍较低。旅游业碳排放效率的空间集聚特征明显,形成了以上海为中心的高值集聚区和以西北省份为中心的低值集聚区。旅游业碳排放效率及其空间格局演化是多因素共同作用的结果,旅游经济规模对中西部地区旅游业碳排放效率的提升作用较强;城镇化的促进作用逐步减弱,且在多数省份开始产生抑制作用;技术效应的提升作用高值区从中东部转移至华北和东北地区;旅游业产权结构对南部地区的推动作用也逐步凸显;结构效应主要对西南地区起促进作用。这为优化和提升旅游业碳排放效率提供了理论依据。     

中国林地绿色利用效率时空差异分析

林地资源利用是一个多投入、多产出的系统,其产出不仅有直接的经济价值,而且具有宝贵的生态价值。权衡林地利用的经济价值和生态价值是可持续发展的问题。林地绿色利用效率是评价林地利用是否可持续的重要方法。采用价值当量法估算中国31省市林地的生态功能价值当量,在此基础上,运用SBM模型评价了1998年、2003年、2008年和2013年的林地绿色利用效率,分析了中国31省市林地绿色利用效率损失的主要原因和改善方向。得出结论:在1998—2013年期间,中国31省市的林地绿色利用效率呈现先下降后回升的态势;东部沿海以及拥有传统林区的省市的林地绿色利用效率表现最佳,而青海等地的林地绿色利用效率最低;对于缺乏效率的省市来说,应分清林地绿色利用效率损失的本质原因,由劳动力投入以及固定资产投资冗余导致的效率损失应加大重视程度,有的放矢,减少浪费;针对区域生态功能薄弱环节,应合理规划森林类型和林木种类,同时注意对区域森林生态系统的构建和维护。     

Multiple peak alignment in sequential data analysis: a scale-space-based approach

In this paper, we address the multiple peak alignment problem in sequential data analysis with an approach based on the Gaussian scale-space theory. We assume that multiple sets of detected peaks are the observed samples of a set of common peaks. We also assume that the locations of the observed peaks follow unimodal distributions (e.g., normal distribution) with their means equal to the corresponding locations of the common peaks and variances reflecting the extension of their variations. Under these assumptions, we convert the problem of estimating locations of the unknown number of common peaks from multiple sets of detected peaks into a much simpler problem of searching for local maxima in the scale-space representation. The optimization of the scale parameter is achieved using an energy minimization approach. We compare our approach with a hierarchical clustering method using both simulated data and real mass spectrometry data. We also demonstrate the merit of extending the binary peak detection method (i.e., a candidate is considered either as a peak or as a nonpeak) with a quantitative scoring measure-based approach (i.e., we assign to each candidate a possibility of being a peak).