An Interdisciplinary Perspective on Carbon Capture and Storage Assessment Methods

Climate change is one of the most serious threats facing humankind. Mitigating climate change will require a suite of actions to reduce greenhouse gas emissions. Carbon capture and storage (CCS) is a new technology aimed at mitigating climate change by capturing and storing carbon dioxide, typically in deep geological reservoirs. CCS has risks characteristic of new technologies, as well as risks unique to this technology and its application. Large‐scale CCS decision making is complex, encompassing environmental, social, and economic considerations and requiring the risks to be taken into consideration. CCS projects have been cancelled as a result of inadequate assessments of risks. To date, studies assessing CCS have been limited mostly to environmental, social, and economic fields in isolation from each other, predominantly using life cycle assessments (LCAs), cost benefit analyses (CBAs), or surveys of public perception. LCAs, CBAs, and surveys of public perception all have limitations for assessing difficult multifaceted problems. Incompatibilities across CCS assessment methods have hindered the comparison of the results across these single‐discipline studies and limited the possibility of drawing broader conclusions about CCS development. More standardization across assessment methods, study assumptions, functional units, and assessment criteria for CCS could be beneficial to the integration of multiple study results. We propose a set of criteria, which decision analysts could use to develop CCS‐project–specific criteria lists in order to comprehensively assess a CCS project's viability. This list was created by determining the frequency of use of each criterion in recent studies, with a focus on their use across disciplines.     

Optimal experimental design for a nonlinear response in environmental toxicology

A start-stop experiment in environmental toxicology provides a backdrop for this design discussion. The basic problem is to decide when to sample a nonlinear response in order to minimize the generalized variance of the estimated parameters. An easily coded heuristic optimization strategy can be applied to this problem to obtain optimal or nearly optimal designs. The efficiency of the heuristic approach allows a straightforward exploration of the sensitivity of the suggested design with respect to such problem-specific concerns as variance heterogeneity, time-grid resolution, design criteria, and interval specification of planning values for parameters. A second illustration of design optimization is briefly presented in the context of concentration spacing for a reproductive toxicity study.     

蛋白质组研究中离子阱串联质谱数据搜库结果解释方法

基于离子阱串联质谱仪的鸟枪法是一种高通量的蛋白质鉴定方法。得到的数据一般使用软件SEQUEST搜索蛋白质序列数据库,得到肽段鉴定列表以及相应的打分。为了得到蛋白质鉴定列表,还需要进行肽段鉴定结果的过滤和假阳性率的计算,然后根据肽段鉴定结果组装蛋白质列表。这两个问题目前还没有很好地解决。对已有的方法进行总结和比较,可以给搜库结果解释方法的选择提供参考,对数据质量控制方法的改进也有所帮助。     

宫颈液基细胞学检测筛查宫颈病变2445例分析

目的：采用液基细胞学检测（TCT）方法,探讨其在妇科宫颈病变细胞学筛查中的临床应用价值。方法：选择我院2007年1月-2008年12月接受宫颈病变筛查的患者2445例,收集宫颈脱落细胞,采用TCT技术检测,细胞学分类采用TBS分类标准诊断,对细胞学检查阳性者行阴道镜指引下多点活检,将细胞学检查结果与活检结果作对比分析。结果：2445例受检者中细胞学阳性病例196例,阳性检出率8.02%。其中非典型鳞状细胞（ASCUS）占5.8%;低度鳞状上皮内病变（LSIL）占1.5%;高度鳞状上皮内病变（HSIL）占0.6%;鳞癌（SCC）占0.1%。阳性病例阴道镜下活检结果与细胞学检查结果符合。结论：TCT检查能准确反映宫颈病变情况,在妇科宫颈病变筛查中有较高的临床应用价值,可作为临床宫颈癌普查方法。     

IS YOUR PHYLOGENY INFORMATIVE? MEASURING THE POWER OF COMPARATIVE METHODS

Phylogenetic comparative methods may fail to produce meaningful results when either the underlying model is inappropriate or the data contain insufficient information to inform the inference. The ability to measure the statistical power of these methods has become crucial to ensure that data quantity keeps pace with growing model complexity. Through simulations, we show that commonly applied model choice methods based on information criteria can have remarkably high error rates; this can be a problem because methods to estimate the uncertainty or power are not widely known or applied. Furthermore, the power of comparative methods can depend significantly on the structure of the data. We describe a Monte Carlo-based method which addresses both of these challenges, and show how this approach both quantifies and substantially reduces errors relative to information criteria. The method also produces meaningful confidence intervals for model parameters. We illustrate how the power to distinguish different models, such as varying levels of selection, varies both with number of taxa and structure of the phylogeny. We provide an open-source implementation in the pmc ("Phylogenetic Monte Carlo") package for the R programming language. We hope such power analysis becomes a routine part of model comparison in comparative methods.     

Perspectives on setting success criteria for wetland restoration

The task of determining the success of wetland restoration has long been challenging and sometimes contentious because success is an imprecise term that means different things in different situations and to different people. Compliance success is determined by evaluating compliance with the terms of an agreement, e.g. a contract or permit, whereas functional success is determined by evaluating whether the ecological functions of the system have been restored. Compliance and functional success have historically focused on the individual project (the site being restored); we are only beginning to consider another important factor, the success of restoration at the landscape scale. Landscape success is a measure of how restoration (or management, in general) has contributed to the ecological integrity of the region or landscape and to achievement of goals such as the maintenance of biodiversity. The utility of all definitions of success is ultimately constrained by the current status of the science of restoration ecology and by our ability to use that information to make sound management decisions and to establish measurable success criteria. Measurements of vegetation are most commonly used in evaluations of restoration projects, with less frequent analysis of soils, fauna, and hydrologic characteristics. Although particular characteristics of projects, such as vegetative cover and production, can resemble those in similar naturally occurring wetlands, overall functional equivalency has not been demonstrated. However, ongoing research is providing information on what can and cannot be accomplished, valuable insights on how to correct mistakes, and new approaches to defining success. The challenge is how to recognize and deal with the uncertainty, given that projects are ecologically young and that our knowledge of the process of restoration is evolving. One way to deal with the uncertainty is to use scientific principles of hypothesis testing and model building in an adaptive management framework. In this way, options can be systematically evaluated and needs for corrective actions identified when a project is not progressing toward goals. By taking such an approach we can improve our ability to reliably restore wetlands while contributing to our understanding of the basic structure and function of ecosystems.     

Large‐scale,long‐term ecosystem monitoring: Interview with David Lindenmayer

David Lindenmayer leads six large‐scale, long‐term research programmes in south‐eastern Australia to help conserve biodiversity in restored areas on farmland as well as conservation in reserves, national parks, wood production forests and plantations. What makes this award‐winning researcher tick and how is the research influencing biodiversity management and policy?     

Dietary protein requirement of fingerling sterlet sturgeon (Acipenser ruthenus)

Although sterlet sturgeon is a valuable candidate for aquaculture development, information regarding the nutrient requirements of this species is limited. Therefore, in this study, we aimed to determine the optimum protein level required by sterlet sturgeon fingerlings. Groups of fingerlings (mean initial body weight ± SD: 7.02 ± 0.02 g; N = 3) were assigned to one of the six dietary protein levels at 29.1%, 39.1%, 46.8%, 49.2%, 53.7%, and 57.6% on a dry matter basis. Isocaloric (18.3 ± 0.2 kJ GE/g diet) semi-purified diets were prepared using white fish meal and casein as the major sources of protein. After the 8-week feeding trial, the fingerlings fed the 49.2% protein diet exhibited the best results in terms of weight gain, feed efficiency, and whole-body nutrient composition (crude protein, crude lipid, gross energy, and amino acid contents), whereas those fed the 53.7% and 57.6% protein diets showed poor performance. Application of the two-slope straight broken-line and quadratic models on the dataset of weight gain revealed the optimum protein level to be 45.9% and 44.1%, respectively. Based on the model selection criteria, including the adjusted coefficient of correlation and corrected Akaike information criterion, the protein level (44.1%) estimated using the quadratic model was more accurate than that obtained using another model (45.9%). Taken together, 44.1% protein level (18.3 kJ GE/g diet on dry matter basis) resulted in the optimal growth of the sterlet sturgeon fingerlings (initial body weight: approximately 7 g).     

Principles and applications of Multidimensional Protein Identification Technology

Multidimensional chromatography coupled to tandem mass spectrometry is an emerging technique for the analysis of proteomes and is rapidly being implemented by many researchers for proteomic analysis. In this technology profile, a particular proteomic approach known as multidimensional protein identification technology (MudPIT) is discussed. In MudPIT, a biphasic microcapillary column is packed with high-performance liquid chromatography grade reversed phase and strong cation exchange packing materials, loaded with a complex peptide mixture and placed in line with quaternary high-performance liquid chromatography and a tandem mass spectrometer. MudPIT has the capability to analyze highly complex proteomic mixtures such as whole proteomes, organelles and protein complexes.     

How effective are the sustainability criteria accompanying the European Union 2020 biofuel targets?

The expansion of biofuel production can lead to an array of negative environmental impacts. Therefore, the European Union (EU) has recently imposed sustainability criteria on biofuel production in the Renewable Energy Directive (RED). In this article, we analyse the effectiveness of the sustainability criteria for climate change mitigation and biodiversity conservation. We first use a global agriculture and forestry model to investigate environmental effects of the EU member states National Renewable Energy Action Plans (NREAPs) without sustainability criteria. We conclude that these targets would drive losses of 2.2 Mha of highly biodiverse areas and generate 95 Mt CO ₂ eq of additional greenhouse gas (GHG) emissions. However, in a second step, we demonstrate that the EU biofuel demand could be satisfied ‘sustainably’ according to RED despite its negative environmental effects. This is because the majority of global crop production is produced ‘sustainably’ in the sense of RED and can provide more than 10 times the total European biofuel demand in 2020 if reallocated from sectors without sustainability criteria. This finding points to a potential policy failure of applying sustainability regulation to a single sector in a single region. To be effective this policy needs to be more complete in targeting a wider scope of agricultural commodities and more comprehensive in its membership of countries.