An Exploratory Framework for the Empirical Measurement of Resilience

Deliberate progress towards the goal of long-term sustainability depends on understanding the dynamics of linked social and ecological systems. The concept of social-ecological resilience holds promise for interdisciplinary syntheses. Resilience is a multifaceted concept that as yet has not been directly operationalized, particularly in systems for which our ignorance is such that detailed, parameter-rich simulation models are difficult to develop. We present an exploratory framework as a step towards the operationalization of resilience for empirical studies. We equate resilience with the ability of a system to maintain its identity, where system identity is defined as a property of key components and relationships (networks) and their continuity through space and time. Innovation and memory are also fundamental to understanding identity and resilience. By parsing our systems into the elements that we subjectively consider essential to identity, we obtain a small set of specific focal variables that reflect changes in identity. By assessing the potential for changes in identity under specified drivers and perturbations, in combination with a scenario-based approach to considering alternative futures, we obtain a surrogate measure of the current resilience of our study system as the likelihood of a change in system identity under clearly specified conditions, assumptions, drivers and perturbations. Although the details of individual case studies differ, the concept of identity provides a level of generality that can be used to compare measure of resilience across cases. Our approach will also yield insights into the mechanisms of change and the potential consequences of different policy and management decisions, providing a level of decision support for each case study area.     

An application programming interface for CellNetAnalyzer

CellNetAnalyzer (CNA) is a MATLAB toolbox providing computational methods for studying structure and function of metabolic and cellular signaling networks. In order to allow non-experts to use these methods easily, CNA provides GUI-based interactive network maps as a means of parameter input and result visualization. However, with the availability of high-throughput data, there is a need to make CNA's functionality also accessible in batch mode for automatic data processing. Furthermore, as some algorithms of CNA are of general relevance for network analysis it would be desirable if they could be called as sub-routines by other applications. For this purpose, we developed an API (application programming interface) for CNA allowing users (i) to access the content of network models in CNA, (ii) to use CNA's network analysis capabilities independent of the GUI, and (iii) to interact with the GUI to facilitate the development of graphical plugins.Here we describe the organization of network projects in CNA and the application of the new API functions to these projects. This includes the creation of network projects from scratch, loading and saving of projects and scenarios, and the application of the actual analysis methods. Furthermore, API functions for the import/export of metabolic models in SBML format and for accessing the GUI are described. Lastly, two example applications demonstrate the use and versatile applicability of CNA's API. CNA is freely available for academic use and can be downloaded from http://www.mpi-magdeburg.mpg.de/projects/cna/cna.html.     

An Analytic Framework for Aligning Observational and Randomized Trial Data: Application to Postmenopausal Hormone Therapy and Coronary Heart Disease

We describe a conceptual analytic framework for aligning observational and randomized controlled trial (RCT) data. The framework allows one to (1) use observational data to estimate treatment effects comparable to their RCT counterparts, (2) properly include early events that occur soon after treatment initiation in the analysis of observational data, (3) estimate various treatment effects that are of clinical and scientific relevance while appropriately adjusting for time-varying confounders in both the RCT and observational analyses, (4) assess the generalizability of RCT findings in the more diverse populations generally found in the observational data, and (5) combine both types of data to study associations that cannot be addressed by one study or a single data set. We describe the theoretical application of this framework to the Women’s Health Initiative data to examine the relation between postmenopausal hormone therapy and coronary heart disease. The analytic framework can be tailored to specific exposure-outcome associations and data sources, and may be refined as more is learned about its strengths and limitations.     

An Overview of Precedence-Type Tests for Censored Data

Precedence tests are simple yet robust nonparametric procedures useful for comparing two or more distributions. In this paper precedence-type tests are considered when the data contain some censored observations. Generalizing the precedence statistic for uncensored data, the precedence tests for censored data are based on the Kaplan-Meier estimators of the respective distribution functions and the corresponding quantile functions. The literature is reviewed for some two-sample as well as some multi-sample problems.     

Maximum Likelihood Methods for Fitting the Burr Type XII Distribution to Life Test Data

This paper describes mathematical and computational methodology for estimating the parameters of the Burr Type XII distribution by the method of maximum likelihood. Expressions for the asymptotic variances and covariances of the parameter estimates are given, and the modality of the log-likelihood and conditional log-likelihood functions is analyzed. As a result of this analysis for various a priori known and unknown parameter combinations, conditions are given which guarantee that the parameter estimates obtained will, indeed, be maximum likelihood estimates. An efficient numerical method for maximizing the conditional log-likelihood function is described, and mathematical expressions are given for the various numerical approximations needed to evaluate the expressions given for the asymptotic variances and covariances of the parameter estimates. The methodology discussed is applied in a numerical example to life test data arising in a clinical setting.     

Application of neural networks for the prediction of cartilage stress in a musculoskeletal system

Traditional finite element (FE) analysis is computationally demanding. The computational time becomes prohibitively long when multiple loading and boundary conditions need to be considered such as in musculoskeletal movement simulations involving multiple joints and muscles. Presented in this study is an innovative approach that takes advantage of the computational efficiency of both the dynamic multibody (MB) method and neural network (NN) analysis. A NN model that captures the behavior of musculoskeletal tissue subjected to known loading situations is built, trained, and validated based on both MB and FE simulation data. It is found that nonlinear, dynamic NNs yield better predictions over their linear, static counterparts. The developed NN model is then capable of predicting stress values at regions of interest within the musculoskeletal system in only a fraction of the time required by FE simulation.     

A Framework for Service Robots in Smart Home: An Efficient Solution for Domestic Healthcare

Purpose

As the elder population grows, the need for domestic healthcare is on the rise. Both robotics and smart environments, including smart homes, provide a promising solution to monitor, interact and keep company to users. However, in real case scenarios, sensors data are not perfect and the environment changes over time, leading to erroneous understanding of the context and inappropriate responses. The purpose of this work is to tackle those challenges in order to improve the autonomy and efficiency of robots in smart environments.

Automata networks as preprocessing technique of artificial neural network in estimating primary production and dominating phytoplankton levels in a reservoir: An experimental work

Artificial Neural Networks (ANN) is computational architectures that can be used for estimating primary production levels and dominating phytoplankton species in reservoirs. Automata Networks (AN) were applied as a pre-processing method with subsequent ANN model development for Demirdöven Dam Reservoir. The primary purpose of using pre-processing technique was to distinguish the suitable and appropriate constituents of the input parameters' matrix, to eliminate redundancy, to enhance prediction power and calculation efficiency. The data were collected monthly over two years. The applications have yielded following results: The correlation coefficients (r values) between predicted and observed counts were as high as 0.83, 0.87, 0.83 and 0.88 for Cyclotella ocellata, Sphaerocystis schroeteri, Staurastrum longiradiatum counts, and Chlorophyll-a (Chl-a) concentrations respectively with AN. The performance of AN based pre-processing technique was compared with the performance of a well-known pre-processing technique, namely Principle Component Analysis(PCA), experimentally. r values between the predicted and observed C. ocellata, S. schroeteri and S. longiradiatum counts, and (Chl-a) were as high as 0.80, 0.86, 0.81 and 0.86 respectively with PCA.     

MeRIP-PF: An Easy-to-use Pipeline for High-resolution Peak-finding in MeRIP-Seq Data

RNA modifications, especially methylation of the N⁶ position of adenosine (A)—m⁶A, represent an emerging research frontier in RNA biology. With the rapid development of high-throughput sequencing technology, in-depth study of m⁶A distribution and function relevance becomes feasible. However, a robust method to effectively identify m⁶A-modified regions has not been available yet. Here, we present a novel high-efficiency and user-friendly analysis pipeline called MeRIP-PF for the signal identification of MeRIP-Seq data in reference to controls. MeRIP-PF provides a statistical P-value for each identified m⁶A region based on the difference of read distribution when compared to the controls and also calculates false discovery rate (FDR) as a cut off to differentiate reliable m⁶A regions from the background. Furthermore, MeRIP-PF also achieves gene annotation of m⁶A signals or peaks and produce outputs in both XLS and graphical format, which are useful for further study. MeRIP-PF is implemented in Perl and is freely available at http://software.big.ac.cn/MeRIP-PF.html.     

Framework for modelling data uncertainty in life cycle inventories

Modelling data uncertainty is not common practice in life cycle inventories (LCI), although different techniques are available for estimating and expressing uncertainties, and for propagating the uncertainties to the final model results. To clarify and stimulate the use of data uncertainty assessments in common LCI practice, the SETAC working group ‘Data Availability and Quality’ presents a framework for data uncertainty assessment in LCI. Data uncertainty is divided in two categories: (1) lack of data, further specified as complete lack of data (data gaps) and a lack of representative data, and (2) data inaccuracy. Filling data gaps can be done by input-output modelling, using information for similar products or the main ingredients of a product, and applying the law of mass conservation. Lack of temporal, geographical and further technological correlation between the data used and needed may be accounted for by applying uncertainty factors to the non-representative data. Stochastic modelling, which can be performed by Monte Carlo simulation, is a promising technique to deal with data inaccuracy in LCIs.     

Measuring membrane penetration with depth-dependent fluorescence quenching: Distribution analysis is coming of age

Depth-dependent fluorescence quenching by lipid-attached quenchers (e.g., bromine atoms and doxyl groups) is an important tool for determining the penetration of proteins and peptides into lipid bilayers. Extracting quantitative information and accurate calculations of the depth of the fluorophore are complicated by thermal disorder, resulting in broad distributions of the transverse positions of both quenchers and fluorophores. Twenty-one years ago a methodology called distribution analysis (DA) was introduced, based on the emerging view of the complexity of the transverse organization of lipid bilayer structure. The method is aimed at extracting quantitative information on membrane penetration, such as position and width of fluorophore's distribution along the depth coordinate and its exposure to the lipid phase. Here we review recent progress in refining the DA method and illustrate its applications to protein–membrane interactions. We demonstrate how basic assumptions of the DA approach can be validated using molecular dynamics simulations and how the precision of depth determination is improved by applying a new protocol based on a combination of steady-state and time-resolved fluorescence quenching. Using the example of the MPER fragment of the membrane-spanning domain of the HIV-1 gp41 fusion protein, we illustrate how DA applications and computer simulations can be used together to reveal the molecular organization of a protein–membrane complex. This article is part of a Special Issue entitled: Interfacially Active Peptides and Proteins. Guest Editors: William C. Wimley and Kalina Hristova.     

TELS: A Novel Computational Framework for Identifying Motif Signatures of Transcribed Enhancers

In mammalian cells, transcribed enhancers (TrEns) play important roles in the initiation of gene expression and maintenance of gene expression levels in a spatiotemporal manner. One of the most challenging questions is how the genomic characteristics of enhancers relate to enhancer activities. To date, only a limited number of enhancer sequence characteristics have been investigated, leaving space for exploring the enhancers’ DNA code in a more systematic way. To address this problem, we developed a novel computational framework, Transcribed Enhancer Landscape Search (TELS), aimed at identifying predictive cell type/tissue-specific motif signatures of TrEns. As a case study, we used TELS to compile a comprehensive catalog of motif signatures for all known TrEns identified by the FANTOM5 consortium across 112 human primary cells and tissues. Our results confirm that combinations of different short motifs characterize in an optimized manner cell type/tissue-specific TrEns. Our study is the first to report combinations of motifs that maximize classification performance of TrEns exclusively transcribed in one cell type/tissue from TrEns exclusively transcribed in different cell types/tissues. Moreover, we also report 31 motif signatures predictive of enhancers’ broad activity. TELS codes and material are publicly available at http://www.cbrc.kaust.edu.sa/TELS.     

Sustainable Consumption of Food: A Framework for Analyzing Scenarios about Changes in Diets

This article describes the integration of life-cycle assessment methods with a new input-output model of the world economy to analyze the environmental and economic implications of alternative future diets. The article reviews findings by industrial ecologists about the energy and land required for the production and consumption of alternative foods and diets in several European countries. It also reviews attributes of foods and diets identified by nutritionists as reducing the risks of obesity and major chronic diseases related to the diets of the affluent. The predominantly plant-based Mediterranean-type diet emerges as a dietary scenario that could satisfy both sets of concerns. The likely implications for agriculture and for farm policies of a shift toward this diet from the current average diet in the United States are discussed and shown to be substantial. The one-country studies reviewed in the article provide substantial insights into the potential ramifications of dietary change. Many of the limitations of these studies could be overcome by conducting the analysis in a global framework that represented the relationships among consumption, production, and trade and the physical constraints within which they operate. Analysis of the environmental and economic implications of alternative scenarios describing healthy diets can help stimulate more intensive dialogue, debate, and action among the interested parties; such analysis can both benefit from and contribute to initiatives such as the World Health Organization's global strategy on diet and health, which intends to enlist the support of governments, corporations, and civil society.     

Uncertainty for Data with Non-Detects: Air Toxic Emissions from Combustion

Air toxic emission factor datasets often contain one or more points below a single or multiple detection limits and such datasets are referred to as “censored.” Conventional methods used to deal with censored datasets include removing non-detects, replacing the censored points with zero, half of the detection limit, or the detection limit. However, the estimated means of the censored dataset by conventional methods are usually biased. Maximum likelihood estimation (MLE) and bootstrap simulation have been demonstrated as a statistically robust method to quantify variability and uncertainty of censored datasets and can provide asymptotically unbiased mean estimates. The MLE/bootstrap method is applied to 16 cases of censored air toxic emission factors, including benzene, formaldehyde, benzo(a)pyrene, mercury, arsenic, cadmium, total chromium, chromium VI and lead from coal, fuel oil, and/or wood waste external combustion sources. The proportion of censored values in the emission factor data ranges from 4 to 80%. Key factors that influence the estimated uncertainty in the mean of censored data are sample size and inter-unit variability. The largest range of uncertainty in the mean was obtained for the external coal combustion benzene emission factor, with 95 confidence interval of the mean equal to minus 93 to plus 411%.     

PFA: Program for the Quantitative Assessment of Cell Metabolism by Spectral Data Analysis

Assessment of mitochondrial oxidative metabolism has wide-ranging importance, from pharmacokinetic analysis to studies in cell viability and apoptosis. Here we present the Perfusion File Analyzer (PFA) application for the real-time analysis of spectral data to measure cytochrome c reduction, cytochrome a3 reduction, and other parameters important to cellular metabolism, which are collected during tissue perfusion experiments. Our current efforts are focused on quantitating changes in mitochondrial function by normalizing baseline drift of spectral data while addressing two major challenges: (1) a lack of real-time feedback from the system when aiming is compromised, and (2) an inability to adjust calculated data in the event of spectral shift. PFA has been developed to address these issues, and is currently used for quality assessment of human islets prior to clinical transplantation.     

GITAR: An Open Source Tool for Analysis and Visualization of Hi-C Data

Interactions between chromatin segments play a large role in functional genomic assays and developments in genomic interaction detection methods have shown interacting topological domains within the genome. Among these methods, Hi-C plays a key role. Here, we present the Genome Interaction Tools and Resources (GITAR), a software to perform a comprehensive Hi-C data analysis, including data preprocessing, normalization, and visualization, as well as analysis of topologically-associated domains (TADs). GITAR is composed of two main modules: (1) HiCtool, a Python library to process and visualize Hi-C data, including TAD analysis; and (2) processed data library, a large collection of human and mouse datasets processed using HiCtool. HiCtool leads the user step-by-step through a pipeline, which goes from the raw Hi-C data to the computation, visualization, and optimized storage of intra-chromosomal contact matrices and TAD coordinates. A large collection of standardized processed data allows the users to compare different datasets in a consistent way, while saving time to obtain data for visualization or additional analyses. More importantly, GITAR enables users without any programming or bioinformatic expertise to work with Hi-C data. GITAR is publicly available at http://genomegitar.org as an open-source software.     

生态网络分析与模拟的软件系统ASSEN及其应用

生态网络分析是研究生态网络的理论和方法,ＡＳＳＥＮ（ｔｈｅＡｎａｌｙｓｉｓａｎｄｓｉｍｕｌａｔｉｏｎＳｙｓｔｅｍｆｏＥｃｏｎｅｔｗｏｒｋｓ）是生态网络分析和模拟的软件系统,本文在简述生态网络分析基本方法的基础上,介绍了ＡＳＳＥＮ的结构和功能,并利用ＡＳＳＥＮ对一个简单水流生态网络进行了分析和模拟。