Lattice Kinetics of Diffusion-Limited Coalescence and Annihilation with Sources

We study the 1D kinetics of diffusion-limited coalescence and annihilation with back reactions and different kinds of particle input. By considering the changes in occupation and parity of a given interval, we derive sets of hierarchical equations from which exact expressions for the lattice coverage and the particle concentration can be obtained. We compare the mean-field approximation and the continuum approximation to the exact solutions and we discuss their regime of validity.Comment: 24 pages and 3 eps figures, Revtex, accepted for publication in J. Phys.

Defining forgiveness: Christian clergy and general population perspectives.

The lack of any consensual definition of forgiveness is a serious weakness in the research literature (McCullough, Pargament &amp; Thoresen, 2000). As forgiveness is at the core of Christianity, this study returns to the Christian source of the concept to explore the meaning of forgiveness for practicing Christian clergy. Comparisons are made with a general population sample and social science definitions of forgiveness to ensure that a shared meaning of forgiveness is articulated. Anglican and Roman Catholic clergy (N = 209) and a general population sample (N = 159) completed a postal questionnaire about forgiveness. There is agreement on the existence of individual differences in forgiveness. Clergy and the general population perceive reconciliation as necessary for forgiveness while there is no consensus within psychology. The clergy suggests that forgiveness is limitless and that repentance is unnecessary while the general population suggests that there are limits and that repentance is necessary. Psychological definitions do not conceptualize repentance as necessary for forgiveness and the question of limits has not been addressed although within therapy the implicit assumption is that forgiveness is limitless.</p

Large-scale analysis of microRNA expression, epi-transcriptomic features and biogenesis.

MicroRNAs are important genetic regulators in both animals and plants. They have a range of functions spanning development, differentiation, growth, metabolism and disease. The advent of next-generation sequencing technologies has made it a relatively straightforward task to detect these molecules and their relative expression via sequencing. There are a large number of published studies with deposited datasets. However, there are currently few resources that capitalize on these data to better understand the features, distribution and biogenesis of miRNAs. Herein, we focus on Human and Mouse for which the majority of data are available. We reanalyse sequencing data from 461 samples into a coordinated catalog of microRNA expression. We use this to perform large-scale analyses of miRNA function and biogenesis. These analyses include global expression comparison, co-expression of miRNA clusters and the prediction of miRNA strand-specificity and underlying constraints. Additionally, we report for the first time a global analysis of miRNA epi-transcriptomic modifications and assess their prevalence across tissues, samples and families. Finally, we report a list of potentially mis-annotated miRNAs in miRBase based on their aggregated modification profiles. The results have been collated into a comprehensive online repository of miRNA expression and features such as modifications and RNA editing events, which is available at: http://wwwdev.ebi.ac.uk/enright-dev/miratlas. We believe these findings will further contribute to our understanding of miRNA function in animals and benefit the miRNA community in general

Intracellular targets in heme protein-induced renal injury

Intracellular targets in heme protein-induced renal injury. We examined two potential intracellular targets in the glycerol model of acute renal failure, namely, the mitochondrion and the nucleus. Within three hours, alterations in mitochondrial function are already apparent. With either glutamate/malate or succinate/rotenone, state 3 and uncoupled respirations were decreased at three hours, and at 24 hours, such decrements were quite pronounced; in the presence of glutamate/malate, state 2 respiration was also depressed at 24 hours, while with succinate/rotenone state 2 was increased. Marked ultrastructural changes were observed in mitochondria studied at three hours, including the novel finding of degenerate mitochondria in autophagic vacuoles. Since the heme content in mitochondria was increased some tenfold within three hours, mitochondrial function was studied after exposure to concentrations of heme that reproduced such contents of heme: mitochondria initially displayed increased respiration, and subsequently, a persistent decline in oxygen consumption until oxygen consumption was virtually undetectable. With higher concentrations of heme, the early increase in oxygen consumption was blunted and the progressive decline in oxygen consumption was hastened. The antioxidant iron chelator, deferoxamine, prevented the early rise in oxygen consumption but did not prevent or delay the subsequent decline. We also assessed nuclear damage as a potential lesion in the glycerol model. DNA laddering was not observed at any time point. At 3 and 24 hours there was DNA injury by the TUNEL technique in the distal nephron but not in the proximal nephron. The 8-hydroxydeoxyguanosine/deoxyguanosine content was increased in the glycerol kidneys at 24 hours but not at three hours. At neither time point was evidence of apoptosis observed by light or electron microscopy. In studies undertaken in cell culture models, heme, at concentrations of 10 μM, failed to evince any such changes in LLC-PK1 cells, a cell line from the proximal tubule, or in MDCK cells, a cell line derived from the distal tubule. At concentrations of 50 μM, heme induced approximately 20% positivity in MDCK cells but none in LLC-PK1 cells by the TUNEL technique. We conclude that mitochondria and nuclei are prominent targets for injury in the glycerol model of acute renal failure. The presence of TUNEL-positive cells in the distal nephron but not at proximal sites in vivo underscores the increasing appreciation of the distinct responses of these nephron sites to nephrotoxic insults

Performance of nonparametric species richness estimators in a high diversity plant community

The efficiency of four nonparametric species richness estimators - first-order Jackknife, second-order Jackknife, Chao2 and Bootstrap - was tested using simulated quadrat sampling of two field data sets (a sandy 'Dune' and adjacent 'Swale') in high diversity shrublands (kwongan) in south-western Australia. The data sets each comprised > 100 perennial plant species and < 10 000 individuals, and the explicit (x-y coordinate) location of every individual. We applied two simulated sampling strategies to these data sets based on sampling quadrats of unit sizes 1/400th and 1/100th of total plot area. For each site and sampling strategy we obtained 250 independent sample curves, of 250 quadrats each, and compared the estimators' performances by using three indices of bias and precision: MRE (mean relative error), MSRE (mean squared relative error) and OVER (percentage overestimation). The analysis presented here is unique in providing sample estimates derived from a complete, field-based population census for a high diversity plant community. In general the true reference value was approached faster for a comparable area sampled for the smaller quadrat size and for the swale field data set, which was characterized by smaller plant size and higher plant density. Nevertheless, at least 15-30% of the total area needed to be sampled before reasonable estimates of St (total species richness) were obtained. In most field surveys, typically less than 1% of the total study domain is likely to be sampled, and at this sampling intensity underestimation is a problem. Results showed that the second-order Jackknife approached the actual value of St more quickly than the other estimators. All four estimators were better than Sobs (observed number of species). However, the behaviour of the tested estimators was not as good as expected, and even with large sample size (number of quadrats sampled) all of them failed to provide reliable estimates. First- and second-order Jackknives were positively biased whereas Chao2 and Bootstrap were negatively biased. The observed limitations in the estimators' performance suggests that there is still scope for new tools to be developed by statisticians to assist in the estimation of species richness from sample data, especially in communities with high species richness

Dirac cohomology, elliptic representations and endoscopy

The first part (Sections 1-6) of this paper is a survey of some of the recent developments in the theory of Dirac cohomology, especially the relationship of Dirac cohomology with (g,K)-cohomology and nilpotent Lie algebra cohomology; the second part (Sections 7-12) is devoted to understanding the unitary elliptic representations and endoscopic transfer by using the techniques in Dirac cohomology. A few problems and conjectures are proposed for further investigations.Comment: This paper will appear in `Representations of Reductive Groups, in Honor of 60th Birthday of David Vogan', edited by M. Nervins and P. Trapa, published by Springe

Multi-species temporal network of livestock movements for disease spread

Introduction: The objective of this study is to show the importance of interspecies links and temporal network dynamics of a multi-species livestock movement network. Although both cattle and sheep networks have been previously studied, cattle-sheep multi-species networks have not generally been studied in-depth. The central question of this study is how the combination of cattle and sheep movements affects the potential for disease spread on the combined network. Materials and methods: Our analysis considers static and temporal representations of networks based on recorded animal movements. We computed network-based node importance measures of two single-species networks, and compared the top-ranked premises with the ones in the multi-species network. We propose the use of a measure based on contact chains calculated in a network weighted with transmission probabilities to assess the importance of premises in an outbreak. To ground our investigation in infectious disease epidemiology, we compared this suggested measure with the results of disease simulation models with asymmetric probabilities of transmission between species. Results: Our analysis of the temporal networks shows that the premises which are likely to drive the epidemic in this multi-species network differ from the ones in both the cattle and the sheep networks. Although sheep movements are highly seasonal, the estimated size of an epidemic is significantly larger in the multi-species network than in the cattle network, independently of the period of the year. Finally, we demonstrate that a measure based on contact chains allow us to identify around 30% of the key farms in a simulated epidemic, ignoring markets, whilst static network measures identify less than 10% of these farms. Conclusion: Our results ascertain the importance of combining species networks, as well as considering layers of temporal livestock movements in detail for the study of disease spread

Application of regulatory sequence analysis and metabolic network analysis to the interpretation of gene expression data

We present two complementary approaches for the interpretation of clusters of co-regulated genes, such as those obtained from DNA chips and related methods. Starting from a cluster of genes with similar expression profiles, two basic questions can be asked: 1. Which mechanism is responsible for the coordinated transcriptional response of the genes? This question is approached by extracting motifs that are shared between the upstream sequences of these genes. The motifs extracted are putative cis-acting regulatory elements. 2. What is the physiological meaning for the cell to express together these genes? One way to answer the question is to search for potential metabolic pathways that could be catalyzed by the products of the genes. This can be done by selecting the genes from the cluster that code for enzymes, and trying to assemble the catalyzed reactions to form metabolic pathways. We present tools to answer these two questions, and we illustrate their use with selected examples in the yeast Saccharomyces cerevisiae. The tools are available on the web (http://ucmb.ulb.ac.be/bioinformatics/rsa-tools/; http://www.ebi.ac.uk/research/pfbp/; http://www.soi.city.ac.uk/~msch/)

Mirnovo: genome-free prediction of microRNAs from small RNA sequencing data and single-cells using decision forests.

The discovery of microRNAs (miRNAs) remains an important problem, particularly given the growth of high-throughput sequencing, cell sorting and single cell biology. While a large number of miRNAs have already been annotated, there may well be large numbers of miRNAs that are expressed in very particular cell types and remain elusive. Sequencing allows us to quickly and accurately identify the expression of known miRNAs from small RNA-Seq data. The biogenesis of miRNAs leads to very specific characteristics observed in their sequences. In brief, miRNAs usually have a well-defined 5' end and a more flexible 3' end with the possibility of 3' tailing events, such as uridylation. Previous approaches to the prediction of novel miRNAs usually involve the analysis of structural features of miRNA precursor hairpin sequences obtained from genome sequence. We surmised that it may be possible to identify miRNAs by using these biogenesis features observed directly from sequenced reads, solely or in addition to structural analysis from genome data. To this end, we have developed mirnovo, a machine learning based algorithm, which is able to identify known and novel miRNAs in animals and plants directly from small RNA-Seq data, with or without a reference genome. This method performs comparably to existing tools, however is simpler to use with reduced run time. Its performance and accuracy has been tested on multiple datasets, including species with poorly assembled genomes, RNaseIII (Drosha and/or Dicer) deficient samples and single cells (at both embryonic and adult stage)

Infinite-Order Percolation and Giant Fluctuations in a Protein Interaction Network

We investigate a model protein interaction network whose links represent interactions between individual proteins. This network evolves by the functional duplication of proteins, supplemented by random link addition to account for mutations. When link addition is dominant, an infinite-order percolation transition arises as a function of the addition rate. In the opposite limit of high duplication rate, the network exhibits giant structural fluctuations in different realizations. For biologically-relevant growth rates, the node degree distribution has an algebraic tail with a peculiar rate dependence for the associated exponent.Comment: 4 pages, 2 figures, 2 column revtex format, to be submitted to PRL 1; reference added and minor rewording of the first paragraph; Title change and major reorganization (but no result changes) in response to referee comments; to be published in PR