Evaluation of two simplified Life Cycle assessment methods

Goal, Scope and Background  Two methods of simplified LCA were evaluated and compared to the results of a quantitative LCA. These are the Environmentally responsible product assessment matrix developed by Graedel and Allenby and the MECO-method developed in Denmark. Methods  We used these in a case study and compared the results with the results from a quantitative LCA. The evaluation also included other criteria, such as the field of application and the level of arbitrariness. Results and Discussion  The MECO-method has some positive qualities compared to the Environmentally responsible product assessment matrix. Examples of this are that it generates information complementary to the quantitative LCA and provides the possibility to consider quantitative information when such is available. Some of the drawbacks with the Environmentally responsible product assessment matrix are that it does not include the whole lifecycle and that it allows some arbitrariness. Conclusions  Our study shows that a simplified and semi-quantitative LCA (such as the MECO-method) can provide information that is complementary to a quantitative LCA. In this case the method generates more information on toxic substances and other impacts, than the quantitative LCA. We suggest that a simplified LCA can be used both as a pre-study to a quantitative LCA and as a parallel assessment, which is used together with the quantitative LCA in the interpretation. Recommendations and Outlook  A general problem with qualitative analyses is how to compare different aspects. Life cycle assessments are comparative. The lack of a quantitative dimension hinders the comparison and can thereby hinder the usefulness of the qualitative method. There are different approaches suggested to semiquantify simplified methods in order to make quantitative comparisons possible. We think that the use of fabricated scoring systems should be avoided. If quantitative information is needed, one should consider performing a simplified quantitative LCA instead.     

Haplotype frequencies at the DRD2 locus in populations of the East European Plain

Background

A key to increasing the power of multilocus association tests is to reduce the number of degrees of freedom by suppressing noise from data. One of the difficulties is to decide how much noise to suppress. An often overlooked problem is that commonly used association tests based on genotype data cannot utilize the genetic information contained in spatial ordering of SNPs (see proof in the Appendix), which may prevent them from achieving higher power.

Results

We develop a score test based on wavelet transform with empirical Bayesian thresholding. Extensive simulation studies are carried out under various LD structures as well as using HapMap data from many different chromosomes for both qualitative and quantitative traits. Simulation results show that the proposed test automatically adjusts the level of noise suppression according to LD structures, and it is able to consistently achieve higher or similar powers than many commonly used association tests including the principle component regression method (PCReg).

Conclusion

The wavelet-based score test automatically suppresses the right amount of noise and uses the information contained in spatial ordering of SNPs to achieve higher power.     

Information-Driven Active Audio-Visual Source Localization

We present a system for sensorimotor audio-visual source localization on a mobile robot. We utilize a particle filter for the combination of audio-visual information and for the temporal integration of consecutive measurements. Although the system only measures the current direction of the source, the position of the source can be estimated because the robot is able to move and can therefore obtain measurements from different directions. These actions by the robot successively reduce uncertainty about the source’s position. An information gain mechanism is used for selecting the most informative actions in order to minimize the number of actions required to achieve accurate and precise position estimates in azimuth and distance. We show that this mechanism is an efficient solution to the action selection problem for source localization, and that it is able to produce precise position estimates despite simplified unisensory preprocessing. Because of the robot’s mobility, this approach is suitable for use in complex and cluttered environments. We present qualitative and quantitative results of the system’s performance and discuss possible areas of application.     

Sensitive Melting Analysis after Real Time- Methylation Specific PCR (SMART-MSP): high-throughput and probe-free quantitative DNA methylation detection

DNA methylation changes that are recurrent in cancer have generated great interest as potential biomarkers for the early detection and monitoring of cancer. In such situations, essential information is missed if the methylation detection is purely qualitative. We describe a new probe-free quantitative methylation-specific PCR (MSP) assay that incorporates evaluation of the amplicon by high-resolution melting (HRM) analysis. Depending on amplicon design, different types of information can be obtained from the HRM analysis. Much of this information cannot be obtained by electrophoretic analysis. In particular, identification of false positives due to incomplete bisulphite conversion or false priming is possible. Heterogeneous methylation can also be distinguished from homogeneous methylation. As proof of principle, we have developed assays for the promoter regions of the CDH1, DAPK1, CDKN2A (p16^INK4a) and RARB genes. We show that highly accurate quantification is possible in the range from 100% to 0.1% methylated template when 25 ng of bisulphite-modified DNA is used as a template for PCR. We have named this new approach to quantitative methylation detection, Sensitive Melting Analysis after Real Time (SMART)-MSP.     

Parental phenotypes in family-based association analysis

Family-based association designs are popular, because they offer inherent control of population stratification based on age, sex, ethnicity, and environmental exposure. However, the efficiency of these designs is hampered by current analytic strategies that consider only offspring phenotypes. Here, we describe the incorporation of parental phenotypes and, specifically, the inclusion of parental genotype-phenotype correlation terms in association tests, providing a series of tests that effectively span an efficiency-robustness spectrum. The model is based on the between-within-sibship association model presented in 1999 by Fulker and colleagues for quantitative traits and extended here to nuclear families. By use of a liability-threshold-model approach, standard dichotomous and/or qualitative disease phenotypes can be analyzed (and can include appropriate corrections for phenotypically ascertained samples), which allows for the application of this model to analysis of the commonly used affected-proband trio design. We show that the incorporation of parental phenotypes can considerably increase power, as compared with the standard transmission/disequilibrium test and equivalent quantitative tests, while providing both significant protection against stratification and a means of evaluating the contribution of stratification to positive results. This methodology enables the extraction of more information from existing family-based collections that are currently being genotyped and analyzed by use of standard approaches.     

A powerful and flexible multilocus association test for quantitative traits

Association mapping of complex traits typically employs tagSNP genotype data to identify a trait locus within a region of interest. However, considerable debate exists regarding the most powerful strategy for utilizing such tagSNP data for inference. A popular approach tests each tagSNP within the region individually, but such tests could lose power as a result of incomplete linkage disequilibrium between the genotyped tagSNP and the trait locus. Alternatively, one can jointly test all tagSNPs simultaneously within the region (by using genotypes or haplotypes), but such multivariate tests have large degrees of freedom that can also compromise power. Here, we consider a semiparametric model for quantitative-trait mapping that uses genetic information from multiple tagSNPs simultaneously in analysis but produces a test statistic with reduced degrees of freedom compared to existing multivariate approaches. We fit this model by using a dimension-reducing technique called least-squares kernel machines, which we show is identical to analysis using a specific linear mixed model (which we can fit by using standard software packages like SAS and R). Using simulated SNP data based on real data from the International HapMap Project, we demonstrate that our approach often has superior performance for association mapping of quantitative traits compared to the popular approach of single-tagSNP testing. Our approach is also flexible, because it allows easy modeling of covariates and, if interest exists, high-dimensional interactions among tagSNPs and environmental predictors.     

Functional Evaluation of the Perivascular Innervation of the Skin of the Extremities Using Laser Doppler Flowmetry

Examination of 30 healthy subjects and 132 patients with hand denervation syndrome who were aged 16 to 30 years demonstrated that laser Doppler flowmetry is a highly effective approach to both qualitative and quantitative characterization of the nervous regulation of the microvasculature and the perivascular innervation of the skin. To study the sympathetic adrenergic perivascular innervation, it is expedient to evaluate the ratio between the neurogenic tone of afferent microvessels at rest and the decrease in microcirculation during the vasoconstrictive breath-holding test. The sensory peptidergic component of control is evaluated by electric stimulation of nociceptive afferent nerves, which causes antidromic vasodilation. The results of the study demonstrate the possibility of both concordant and discordant changes in sympathetic vascular regulation and isolated predominance of functional activity of the sympathetic or sensory peptidergic components of perivascular innervation.     

Constructing quantitative models from qualitative mutant phenotypes: preferences in selecting sensory organ precursors

MOTIVATION: To study biology from the systems level, mathematical models that describe the time-evolution of the system offer useful insights. Quantitative information is required for constructing such models, but such information is rarely provided. RESULTS: We propose a scheme-based on random searches over a parameter space, according to criteria set by qualitative experimental observations-for inferring quantitative parameters from qualitative experimental results. We used five mutant constraints to construct genetic network models for sensory organ precursor formation in Drosophila development. Most of the models were capable of generating expression patterns for the gene Enhancer of split that were compatible with experimental observations for wild type and two Notch mutants. We further examined factors differentiating the neural fate among cells in a proneural cluster, and found two opposite driving forces that bias the choice between middle cells and the peripheral cells. Therefore, it is possible to build numerical models from mutant screening and to study mechanisms behind the complicated network.     

Efficient Multiple-Trait Association and Estimation of Genetic Correlation Using the Matrix-Variate Linear Mixed Model

Multiple-trait association mapping, in which multiple traits are used simultaneously in the identification of genetic variants affecting those traits, has recently attracted interest. One class of approaches for this problem builds on classical variance component methodology, utilizing a multitrait version of a linear mixed model. These approaches both increase power and provide insights into the genetic architecture of multiple traits. In particular, it is possible to estimate the genetic correlation, which is a measure of the portion of the total correlation between traits that is due to additive genetic effects. Unfortunately, the practical utility of these methods is limited since they are computationally intractable for large sample sizes. In this article, we introduce a reformulation of the multiple-trait association mapping approach by defining the matrix-variate linear mixed model. Our approach reduces the computational time necessary to perform maximum-likelihood inference in a multiple-trait model by utilizing a data transformation. By utilizing a well-studied human cohort, we show that our approach provides more than a 10-fold speedup, making multiple-trait association feasible in a large population cohort on the genome-wide scale. We take advantage of the efficiency of our approach to analyze gene expression data. By decomposing gene coexpression into a genetic and environmental component, we show that our method provides fundamental insights into the nature of coexpressed genes. An implementation of this method is available at http://genetics.cs.ucla.edu/mvLMM.     

Alignments of mitochondrial genome arrangements: applications to metazoan phylogeny

Mitochondrial genomes provide a valuable dataset for phylogenetic studies, in particular of metazoan phylogeny because of the extensive taxon sample that is available. Beyond the traditional sequence-based analysis it is possible to extract phylogenetic information from the gene order. Here we present a novel approach utilizing these data based on cyclic list alignments of the gene orders. A progressive alignment approach is used to combine pairwise list alignments into a multiple alignment of gene orders. Parsimony methods are used to reconstruct phylogenetic trees, ancestral gene orders, and consensus patterns in a straightforward approach. We apply this method to study the phylogeny of protostomes based exclusively on mitochondrial genome arrangements. We, furthermore, demonstrate that our approach is also applicable to the much larger genomes of chloroplasts.     

The estimation of the amount of diphtheria toxin and its activity by various tests in the dynamic cultivation of Corynebacterium diphtheriae PW8]

Direct correlation between the results of tests for the biological activity of diphtheria toxin, carried out in vivo guinea pigs and in vitro in the microcytotoxicity test in CHO cell culture, has been established, which makes it possible to use the latter as one of the methods for the rapid, reproducible and economic evaluation of diphtheria toxin. The qualitative and quantitative evaluation of diphtheria toxin in the enzyme immunoassay with the use of monoclonal antibodies and in the microcytotoxicity test demonstrates that these two tests, when used for controlling cultivation processes, have essential advantages over the flocculation test as regards their specificity and information content.     

A Hierarchical Semiparametric Model for Incorporating Intergene Information for Analysis of Genomic Data

Summary For analysis of genomic data, e.g., microarray data from gene expression profiling experiments, the two‐component mixture model has been widely used in practice to detect differentially expressed genes. However, it naïvely imposes strong exchangeability assumptions across genes and does not make active use of a priori information about intergene relationships that is currently available, e.g., gene annotations through the Gene Ontology (GO) project. We propose a general strategy that first generates a set of covariates that summarizes the intergene information and then extends the two‐component mixture model into a hierarchical semiparametric model utilizing the generated covariates through latent nonparametric regression. Simulations and analysis of real microarray data show that our method can outperform the naïve two‐component mixture model.     

Identification of a genetic element that controls the organ-specific expression of adh1 in maize

Allozyme balances serve as markers of quantitative behavior of electrophoretically distinguishable alleles. By the use of ADH Set I allozyme balances, it is demonstrated that all Adh1-S/Adh1-F individuals from more than 20 diverse S/F families exhibit a reciprocal correlation between Adh1 quantitative behavior in two maize organs: the scutellum and primary root. Within an electrophoretic mobility class, the Adh1 allele that is relatively underexpressed in the scutellum is relatively overexpressed in the primary root, and vice versa. Segregation tests prove that this "reciprocal effect" is the property of a cis-acting site that is closely linked to or within the Adh1 structural gene, and it is not affected by diverse genetic backgrounds. Immunological and [(3)H]-leucine incorporation experiments establish that Adh1 quantitative variants differ in ADH1.ADH1 synthetic rates in the anaerobic primary root. The reciprocal-effect phenomenon suggests that the cis-acting loci controlling Adh1 quantitative expression in each respective organ are at least in close proximity, or may share common DNA sequences. We discuss the possibility that the reciprocal-effect locus is a regulatory component of the Adh1 cistron.     

A Combined Molecular Modelling and CIDNP Study of Similarities in the Pattern of Ligand Binding in Mammalian and Avian Galectins

Galectins (Galactose binding lectins) from bacteria, plants and animals have been shown to possess tyrosine or tryptophan residues that form hydrophobic contacts with their ligands in the binding sites. At the present time, the X-ray structures of only two galectins from human and bovine tissues are known. In the present study we applied X-ray data of bovine heart galectin-1 as a template for homology modelling of a number of galectins from mammalian and avian tissues. The conservation of one tryptophan and at least one histidine in binding pocket can be observed from the comparison of the model structures. We also show that it is possible to obtain information of the architecture of the binding pocket of several galectins in solution using CIDNP (Chemically Induced Dynamic Nuclear Polarisation) techniques. The CIDNP approach offers a possibility to analyse these lectins in solution thereby providing supplementary information to the available X-ray data. All studied galectins show comparable alterations when they are recorded by CIDNP-technique in the absence and in the presence of their specific carbohydrate ligands.     

Structural modeling extends QSAR analysis of antibody-lysozyme interactions to 3D-QSAR

This work shows that quantitative multivariate modeling is an emerging possibility for unraveling protein-protein interactions using a combination of designed mutations with sequence and structure information. Using this approach, it is possible to stereochemically determine which residue properties contribute most to the interaction. This is illustrated by results from modeling of the interaction of the wild-type and 17 single and double mutants of a camel antibody specific for lysozyme. Linear multivariate models describing association and dissociation rates as well as affinity were developed. Sequence information in the form of amino acid property scales was combined with 3D structure information (obtained using molecular mechanics calculations) in the form of coordinates of the alpha-carbons and the center of the side chains. The results show that in addition to the amino acid properties of the mutated residues 101 and 105, the dissociation rate is controlled by the side-chain coordinate of residue 105, whereas the association is determined by the coordinates of residues 99, 100, 105 (side chain), 111, and 112. The great difference between the models for association and dissociation rates illustrates that the event of molecular recognition and the property of binding stability rely on different physical processes.     

Fitness landscapes: an alternative theory for the dominance of mutation

Deleterious mutations tend to be recessive. Several theories, notably those of Fisher (based on selection) and Wright (based on metabolism), have been put forward to explain this pattern. Despite a long-lasting debate, the matter remains unresolved. This debate has focused on the average dominance of mutations. However, we also know very little about the distribution of dominance coefficients among mutations, and about its variation across environments. In this article we present a new approach to predicting this distribution. Our approach is based on a phenotypic fitness landscape model. First, we show that under a very broad range of conditions (and environments), the average dominance of mutation of small effects should be approximately one-quarter as long as adaptation of organisms to their environment can be well described by stabilizing selection on an arbitrary set of phenotypic traits. Second, the theory allows predicting the whole distribution of dominance coefficients among mutants. Because it provides quantitative rather than qualitative predictions, this theory can be directly compared to data. We found that its prediction on mean dominance (average dominance close to 0.25) agreed well with the data, based on a meta-analysis of dominance data for mildly deleterious mutations. However, a simple landscape model does not account for the dominance of mutations of large effects and we provide possible extension of the theory for this class of mutations. Because dominance is a central parameter for evolutionary theory, and because these predictions are quantitative, they set the stage for a wide range of applications and further empirical tests.     

A probabilistic methodology for integrating knowledge and experiments on biological networks.

Biological systems are traditionally studied by focusing on a specific subsystem, building an intuitive model for it, and refining the model using results from carefully designed experiments. Modern experimental techniques provide massive data on the global behavior of biological systems, and systematically using these large datasets for refining existing knowledge is a major challenge. Here we introduce an extended computational framework that combines formalization of existing qualitative models, probabilistic modeling, and integration of high-throughput experimental data. Using our methods, it is possible to interpret genomewide measurements in the context of prior knowledge on the system, to assign statistical meaning to the accuracy of such knowledge, and to learn refined models with improved fit to the experiments. Our model is represented as a probabilistic factor graph, and the framework accommodates partial measurements of diverse biological elements. We study the performance of several probabilistic inference algorithms and show that hidden model variables can be reliably inferred even in the presence of feedback loops and complex logic. We show how to refine prior knowledge on combinatorial regulatory relations using hypothesis testing and derive p-values for learned model features. We test our methodology and algorithms on a simulated model and on two real yeast models. In particular, we use our method to explore uncharacterized relations among regulators in the yeast response to hyper-osmotic shock and in the yeast lysine biosynthesis system. Our integrative approach to the analysis of biological regulation is demonstrated to synergistically combine qualitative and quantitative evidence into concrete biological predictions.     

A statistical framework for the interpretation of mtDNA mixtures: forensic and medical applications

Background

Mitochondrial DNA (mtDNA) variation is commonly analyzed in a wide range of different biomedical applications. Cases where more than one individual contribute to a stain genotyped from some biological material give rise to a mixture. Most forensic mixture cases are analyzed using autosomal markers. In rape cases, Y-chromosome markers typically add useful information. However, there are important cases where autosomal and Y-chromosome markers fail to provide useful profiles. In some instances, usually involving small amounts or degraded DNA, mtDNA may be the only useful genetic evidence available. Mitochondrial DNA mixtures also arise in studies dealing with the role of mtDNA variation in tumorigenesis. Such mixtures may be generated by the tumor, but they could also originate in vitro due to inadvertent contamination or a sample mix-up.

Methods/Principal Findings

We present the statistical methods needed for mixture interpretation and emphasize the modifications required for the more well-known methods based on conventional markers to generalize to mtDNA mixtures. Two scenarios are considered. Firstly, only categorical mtDNA data is assumed available, that is, the variants contributing to the mixture. Secondly, quantitative data (peak heights or areas) on the allelic variants are also accessible. In cases where quantitative information is available in addition to allele designation, it is possible to extract more precise information by using regression models. More precisely, using quantitative information may lead to a unique solution in cases where the qualitative approach points to several possibilities. Importantly, these methods also apply to clinical cases where contamination is a potential alternative explanation for the data.

Conclusions/Significance

We argue that clinical and forensic scientists should give greater consideration to mtDNA for mixture interpretation. The results and examples show that the analysis of mtDNA mixtures contributes substantially to forensic casework and may also clarify erroneous claims made in clinical genetics regarding tumorigenesis.