Indices of lipid peroxidation in vivo: strengths and limitations

Oxidant stress has been widely implicated as a mechanism of disease, yet clinical trials of antioxidants have not included a biochemical basis for dose selection or patient inclusion. Many of the indices traditionally employed to assess lipid peroxidation have relied on measurements performed in ex vivo systems of questionable relevance to events in vivo. Commonly employed in vivo indices of lipid peroxidation are constrained by such issues as the nonspecificity or instability of the target anylate, contamination of the anylate by events ex vivo, and nonspecificity of analytical methodology. More recently, specific methodology based on mass spectrometry has been applied to both 4-hydroxynonenal and a variety of isoprostanes in human biological fluids. Measurement of these compounds in urine reflects lipid peroxidation in vivo and offers a noninvasive approach that may be readily applied to clinical trials.     

Loop analysis and qualitative modeling: limitations and merits

Richard Levins has advocated the scientific merits of qualitative modeling throughout his career. He believed an excessive and uncritical focus on emulating the models used by physicists and maximizing quantitative precision was hindering biological theorizing in particular. Greater emphasis on qualitative properties of modeled systems would help counteract this tendency, and Levins subsequently developed one method of qualitative modeling, loop analysis, to study a wide variety of biological phenomena. Qualitative modeling has been criticized for being conceptually and methodologically problematic. As a clear example of a qualitative modeling method, loop analysis shows this criticism is indefensible. The method has, however, some serious limitations. This paper describes loop analysis, its limitations, and attempts to clarify the differences between quantitative and qualitative modeling, in content and objective. Loop analysis is but one of numerous types of qualitative analysis, so its limitations do not detract from the currently underappreciated and underdeveloped role qualitative modeling could have within science.

Evaluating dynamics of national economies through cluster analysis within the input-state-output sustainability framework

In this paper a cluster analysis is applied to an input-state-output indicator framework that represents the interconnection of the three aspects of sustainability, namely environmental, social and economic. This framework is a useful and comprehensive tool for assessing country performances over time and improving guidelines for the classification of countries under a sustainability perspective. The method enables identification of trends and traps that characterize the evolution of countries over time. The analysis is performed for 83 countries in 2000 and 2008 in order to observe system behaviour and development patterns.     

Evaluating the performance of species richness estimators: sensitivity to sample grain size

1. Fifteen species richness estimators (three asymptotic based on species accumulation curves, 11 nonparametric, and one based in the species-area relationship) were compared by examining their performance in estimating the total species richness of epigean arthropods in the Azorean Laurisilva forests. Data obtained with standardized sampling of 78 transects in natural forest remnants of five islands were aggregated in seven different grains (i.e. ways of defining a single sample): islands, natural areas, transects, pairs of traps, traps, database records and individuals to assess the effect of using different sampling units on species richness estimations. 2. Estimated species richness scores depended both on the estimator considered and on the grain size used to aggregate data. However, several estimators (ACE, Chao 1, Jackknifel and 2 and Bootstrap) were precise in spite of grain variations. Weibull and several recent estimators [proposed by Rosenzweig et al. (Conservation Biology, 2003, 17, 864-874), and Ugland et al. (Journal of Animal Ecology, 2003, 72, 888-897)] performed poorly. 3. Estimations developed using the smaller grain sizes (pair of traps, traps, records and individuals) presented similar scores in a number of estimators (the above-mentioned plus ICE, Chao2, Michaelis-Menten, Negative Exponential and Clench). The estimations from those four sample sizes were also highly correlated. 4. Contrary to other studies, we conclude that most species richness estimators may be useful in biodiversity studies. Owing to their inherent formulas, several nonparametric and asymptotic estimators present insensitivity to differences in the way the samples are aggregated. Thus, they could be used to compare species richness scores obtained from different sampling strategies. Our results also point out that species richness estimations coming from small grain sizes can be directly compared and other estimators could give more precise results in those cases. We propose a decision framework based on our results and on the literature to assess which estimator should be used to compare species richness scores of different sites, depending on the grain size of the original data, and of the kind of data available (species occurrence or abundance data).     

Reconstructing paleoecology and paleoenvironmental variables using factor analysis and regression: some limitations

We test the success of Principal Components, Factor and Regression Analysis at recovering environmental signals using numerical experiments in which we control species environmental responses, the environmental conditions and the sampling scheme used for calibration. We use two general conditions, one in which sampling of a continental margin for benthic foraminiferal assemblages is done in a standard grid and the driving environmental variables are correlated to one another, and the other where sampling is done so that the environmental variables are uncorrelated. The first condition mimics many studies in the literature. We find that where the controlling environmental variables are correlated, Principal Components/Factor Analysis yield factors that reflect the common variance (correlation) of those variables. Since this common variance is largely a product of the sampling scheme, the factors extracted do not reliably present true species ecologic behavior. This behavior cannot be accurately diagnosed and faulty interpretations may lead to substantial error when using factor coefficients to reconstruct conditions in the past. When the sampling scheme is constructed so that the controlling environmental variables for the calibration data set are uncorrelated the factor patterns will reflect these variables more accurately. Species responses can be more successfully interpreted from the Principal Components/Factor Analysis structure matrices. Additionally, regression analysis can successfully extract the independent environmental signals from the biotic data set. However, matrix closure is a confounding effect in all our numerical results as it distorts species' abundances and spatial distribution in the calibration data set. Our results show clearly that a knowledge of the controlling environmental variables, and the correlations among these variables over a study area, is essential for the successful application of multivariate techniques for paleoenvironmental reconstruction.     

Statistical Series: Opportunities and challenges of sperm motility subpopulation analysis

Computer-assisted sperm analysis (CASA) allows assessing the motility of individual spermatozoa, generating huge datasets. These datasets can be analyzed using data mining techniques such as cluster analysis, to group the spermatozoa in subpopulations with biological meaning. This review considers the use of statistical techniques for clustering CASA data, their challenges and possibilities. There are many clustering approaches potentially useful for grouping sperm motility data, but some options may be more appropriate than others. Future development should focus not only in improvements of subpopulation analysis, but also in finding consistent biological meanings for these subpopulations.     

Use of information on disease diagnoses from databases for animal health economic,welfare and food safety purposes: strengths and limitations of recordings

Many animal health, welfare and food safety databases include data on clinical and test-based disease diagnoses. However, the circumstances and constraints for establishing the diagnoses vary considerably among databases. Therefore results based on different databases are difficult to compare and compilation of data in order to perform meta-analysis is almost impossible. Nevertheless, diagnostic information collected either routinely or in research projects is valuable in cross comparisons between databases, but there is a need for improved transparency and documentation of the data and the performance characteristics of tests used to establish diagnoses. The objective of this paper is to outline the circumstances and constraints for recording of disease diagnoses in different types of databases, and to discuss these in the context of disease diagnoses when using them for additional purposes, including research. Finally some limitations and recommendations for use of data and for recording of diagnostic information in the future are given. It is concluded that many research questions have such a specific objective that investigators need to collect their own data. However, there are also examples, where a minimal amount of extra information or continued validation could make sufficient improvement of secondary data to be used for other purposes. Regardless, researchers should always carefully evaluate the opportunities and constraints when they decide to use secondary data. If the data in the existing databases are not sufficiently valid, researchers may have to collect their own data, but improved recording of diagnostic data may improve the usefulness of secondary diagnostic data in the future.     

Protein-protein interactions between two-component system transmitter and receiver domains of Myxococcus xanthus

We present a novel dataset assessing the specificity of protein-protein interactions between 69 transmitter and receiver domains from two-component system (TCS)-signalling pathways. TCS require a conserved protein-protein interaction between partner transmitter and receiver domains for signal transduction. The complex prokaryote Myxococcus xanthus possesses an unusually large number of TCS genes, many of which have no obvious interaction partners. Interactions between TCS domains of M. xanthus were assessed using a yeast two-hybrid assay, in which domains were expressed as both bait and prey translational fusions. LacZ production was monitored as an indicator of protein-protein interaction, and the strength of interactions classified as weak, medium or strong. Two-hundred and fifty-five transmitter-receiver domain interactions were observed (46 strong), allowing identification of potential signalling partners for individual M. xanthus TCS proteins. In addition, the dataset provides interesting 'meta' information. For instance, many strong interactions were identified between different transmitter domain pairs (34) and receiver domain pairs (23), suggesting a surprisingly large degree of heterodimerisation of these domains. Proteins in our dataset that exhibited similar 'profiles' of interactions, often shared a similar biological function, suggesting that interaction profiles can provide information on biological function, even considering sets of homologous domains.     

Human disease clinical treatment network for the elderly: analysis of the medicare inpatient length of stay and readmission data

Clinical treatment outcomes are the quality and cost targets that health-care providers aim to improve. Most existing outcome analysis focuses on a single disease or all diseases combined. Motivated by the success of molecular and phenotypic human disease networks (HDNs), this article develops a clinical treatment network that describes the interconnections among diseases in terms of inpatient length of stay (LOS) and readmission. Here one node represents one disease, and two nodes are linked with an edge if their LOS and number of readmissions are conditionally dependent. This is the very first HDN that jointly analyzes multiple clinical treatment outcomes at the pan-disease level. To accommodate the unique data characteristics, we propose a modeling approach based on two-part generalized linear models and estimation based on penalized integrative analysis. Analysis is conducted on the Medicare inpatient data of 100,000 randomly selected subjects for the period of January 2010 to December 2018. The resulted network has 1008 edges for 106 nodes. We analyze key network properties including connectivity, module/hub, and temporal variation. The findings are biomedically sensible. For example, high connectivity and hub conditions, such as disorders of lipid metabolism and essential hypertension, are identified. There are also findings that are less/not investigated in the literature. Overall, this study can provide additional insight into diseases' properties and their interconnections and assist more efficient disease management and health-care resources allocation.     

A morphometric analysis of extant and early miocene fossil hominoid maxillo-dental specimens

It is demonstrated in this paper that before we can hope to formulate phylogenetic relationships between and amongst fossil hominoid material it is first necessary to sex the material accurately. In order to determine whether the morphological and morphometrical variability seen in fossil specimens is due to sexual or inter species dimorphism, it is necessary to calibrate fossil specimens against extant hominoid species' morphologies. Only after fossil specimens have been sexed is it possible to differentiate between morphologies that are related to sex and those that are species specific. This will help reduce fossil misallocation. A morphometric analysis of extant and fossilProconsul hominoid material is presented. Each fossil specimen has been sexed according to symplesiomorphic sex morphologies as defined in this paper. After the fossil specimens have been sexed they are analyzed using multivariate statistics. The identification of differing sex patterns within the specimens examined here suggests that a new species ofProconsul may have to be considered.     

Microbial biomass and community structure of a phase-separated methanogenic reactor determined by lipid analysis

Summary An anaerobic phase-separation biomass reactor was established on cellulose with the hydrolysis and fermentation steps occurring in the first stage, and acetogenesis and methanogenesis in the second stage. Based upon lipid biomarker analysis, eubacterial and eukaryotic cells accounted for approximately 6% of the volatile solids of the first stage and 17% of the second, while methanogens were approximately 1% of the volatile solids in the first stage and 9% of the second. Clustering the polar lipid fatty acids into groups based upon their distributions between the two stages of the reactor clarified the differences in community structure caused by phase-separated operation. Although inoculated from the same source, the two stages maintained very different microbial communities. Signature fatty acids known as indicators of unbalanced growth in eubacteria were significantly higher in the first stage of the reactor.     

流感病毒基因的密码子偏好性及聚类分析

流行性感冒病毒是一种造成人类及动物患流行性感冒的RNA病毒,它造成急性上呼吸道感染,并由空气迅速传播,在世界各地常有周期性的大流行。根据该病毒的基因组CDS序列,探讨了基因组序列密码子的使用模式和特性,并进行了病毒间的聚类分析。结果表明:流感病毒的G+C含量均低于A+U含量,偏向使用以A、U结尾的密码子的程度比使用以G、C结尾的较高,CUG、UCA、AGU、AGC、AGA、AGG、GUG、CCA、ACA、GGA、GCA、AUU、UGA、CAU、CAA、AAU、AAA、GAA等18个密码子为流感病毒共有的偏好性密码子,且以A结尾的居多,尤其偏爱AGA、GGA。聚类结果表明首先亚洲流感病毒H2N2和香港流感病毒H2N2聚为一类,亚洲流感病毒H1N1和俄罗斯流感病毒H1N1聚为一类,1997年和2003年~2004年发生的人禽流感聚为一类,说明它们的密码子使用的偏好性相似;而2009年爆发的甲型H1N1流感和任何一个流感的距离都比较远,说明甲型H1N1流感病毒是一种新型的病毒,不同于以往任何一种流感病毒。     

Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses

Microbes exist in a range of metabolic states (for example, dormant, active and growing) and analysis of ribosomal RNA (rRNA) is frequently employed to identify the ‘active'' fraction of microbes in environmental samples. While rRNA analyses are no longer commonly used to quantify a population''s growth rate in mixed communities, due to rRNA concentration not scaling linearly with growth rate uniformly across taxa, rRNA analyses are still frequently used toward the more conservative goal of identifying populations that are currently active in a mixed community. Yet, evidence indicates that the general use of rRNA as a reliable indicator of metabolic state in microbial assemblages has serious limitations. This report highlights the complex and often contradictory relationships between rRNA, growth and activity. Potential mechanisms for confounding rRNA patterns are discussed, including differences in life histories, life strategies and non-growth activities. Ways in which rRNA data can be used for useful characterization of microbial assemblages are presented, along with questions to be addressed in future studies.     

The implications and limitations of the findings of the Viking organic analysis experiment

Summary The gas chromatograph mass spectrometer instrument of the Viking mission has demonstrated the absence of organic compounds in the immediate surface layer of the two landing sites. The demonstration of the successful operation of the instrument (comparison of ground-based test data with those obtained during interplanetary flight and the data from the surface of the planet) and its limitations (e.g., the detection of highly cross-linked polymers or polymeric carbon suboxide) are reviewed. The measurements for bound water are based on indirect data, the detectability of evolved carbon dioxide and ammonia is poor, and oxygen, liberated from the soil samples, can not be detected.     

Differentiation along a gradient of environmental productivity and predictability in populations of Hordeum spontaneum Koch: multilevel selection analysis

A contextual analysis combined with path analysis was applied to detect ecotype-specific past selection in hierarchically structured populations of wild barley, Hordeum spontaneum . In our analysis a multiple regression model incorporated several individual and ecotype-level unmeasured (derived) traits obtained by factor analysis from 20 measured morphological and phenological traits. Under favourable conditions (high water and nutrients) both individual and ecotype plant size (RF1) were significant predictors of individual plant fitness, estimated by either reproductive biomass or yield. Both individual and ecotype size of reproductive structures (RF2) were significantly related to individual reproductive biomass. Individual yield, however, significantly correlated with ecotype RF2 only. Transition to reproduction (RF3) correlated with neither reproductive biomass nor yield at individual level, but correlated with two estimates of fitness at ecotype level. In all cases, selection at the individual and ecotype levels was in opposition. We interpret the observed effect of ecotype identity on individual fitness not as a current group selection, but as a constraining effect of ecotype-specific past selection. The four ecotypes went through an environmentally specific selection process in their own environments with the optimal strategy evolved. Consequently, this strategy may have a constraining effect on plant performance in other environments. Under conditions of either low water or low nutrients the ecotype level did not contribute to individual fitness. The latter may suggest that a mechanism for plant responses to stress is largely independent of plant origin, with a difference between ecotypes under stressful conditions due entirely to the difference in amount, not architecture, of plasticity. © 2002 The Linnean Society of London, Biological Journal of the Linnean Society , 2002, 75 , 313–318.     

Genetic diversity analysis of <Emphasis Type="Italic">Monascus</Emphasis> strains using SRAP and ISSR markers

In this study, sequence-related amplification polymorphism (SRAP) and inter-simple sequence repeat (ISSR) were analyzed for accessing the genetic diversity of 37 Monascus isolates and 14 control strains. According to the dendrogram produced by SRAP data, all the tested strains were grouped into four clusters at a 78% similarity level. Comparatively, 51 tested strains were divided into four major groups at a similarity level of 74% based on the dendrogram generated via ISSR marker analysis. Based on the two sets of dendrograms, Monascus aurantiacus, M. purpureus, M. serorubescens, M. anka, and M. ruber were clustered in the same clade; M. albidus, M. fuliginosus, and M. barkeri were clustered with M. pilosus in a second clade; and M. lunisporas and M. argentinensis occurred together in a third cluster distinct from the other Monascus species. The cluster result produced by SRAP data shared great similarity with that by ISSR data with minor differences in the subgroups, which is basically in agreement with morphological observations. In general, SRAP and ISSR are more simple, rapid, and efficient, which may provide alternative molecular approaches to studying genetic diversity, classification, and identification of Monascus strains.