HIV-1 drug resistance transmission networks in southwest Switzerland.

To determine viral subtypes and resistance mutations to antiretroviral treatment (ART) in untreated HIV-1 acutely infected subjects from Southwest Switzerland. Clinical samples were obtained from the HIV primary infection cohort from Lausanne. Briefly, pol gene was amplified by nested PCR and sequenced to generate a 1?kb sequence spanning protease and reverse transcriptase key protein regions. Nucleotide sequences were used to assess viral genotype and ART resistance mutations. Blood specimens and medical information were obtained from 30 patients. Main viral subtypes corresponded to clade B, CRF02_AG, and F1. Resistant mutations to PIs consisted of L10V and accessory mutations 16E and 60E present in all F1 clades. The NNRTI major resistant mutation 103N was detected in all F1 viruses and in other 2 clades. Additionally, we identified F1 sequences from other 6 HIV infected and untreated individuals from Southwest Switzerland, harboring nucleotide motifs and resistance mutations to ART as observed in the F1 strains from the cohort. These data reveal a high transmission rate (16.6%) for NNRTI resistant mutation 103N in a cohort of HIV acute infection. Three of the 5 resistant strains were F1 clades closely related to other F1 isolates from HIV-1 infection untreated patients also coming from Southwest Switzerland. Overall, we provide strong evidence towards an HIV-1 resistant transmission network in Southwest Switzerland. These findings have relevant implications for the local molecular mapping of HIV-1 and future ART surveillance studies in the region.

Understanding Interactions in Social Networks and Committees

While much of the literature on cross section dependence has focused mainly on estimation of the regression coefficients in the underlying model, estimation and inferences on the magnitude and strength of spill-overs and interactions has been largely ignored. At the same time, such inferences are important in many applications, not least because they have structural interpretations and provide useful interpretation and structural explanation for the strength of any interactions. In this paper we propose GMM methods designed to uncover underlying (hidden) interactions in social networks and committees. Special attention is paid to the interval censored regression model. Our methods are applied to a study of committee decision making within the Bank of England’s monetary policy committee.

Evidence for transcript networks composed of chimeric RNAs in human cells.

The classic organization of a gene structure has followed the Jacob and Monod bacterial gene model proposed more than 50 years ago. Since then, empirical determinations of the complexity of the transcriptomes found in yeast to human has blurred the definition and physical boundaries of genes. Using multiple analysis approaches we have characterized individual gene boundaries mapping on human chromosomes 21 and 22. Analyses of the locations of the 5' and 3' transcriptional termini of 492 protein coding genes revealed that for 85% of these genes the boundaries extend beyond the current annotated termini, most often connecting with exons of transcripts from other well annotated genes. The biological and evolutionary importance of these chimeric transcripts is underscored by (1) the non-random interconnections of genes involved, (2) the greater phylogenetic depth of the genes involved in many chimeric interactions, (3) the coordination of the expression of connected genes and (4) the close in vivo and three dimensional proximity of the genomic regions being transcribed and contributing to parts of the chimeric RNAs. The non-random nature of the connection of the genes involved suggest that chimeric transcripts should not be studied in isolation, but together, as an RNA network.

EEG-based functional brain networks: does the network size matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.

The Impact of Information Provision on Agglomeration Bonus Performance: An Experimental Study on Local Networks

The Agglomeration Bonus (AB) is a mechanism to induce adjacent landowners to spatially coordinate their land use for the delivery of ecosystem services from farmland. This paper uses laboratory experiments to explore the performance of the AB in achieving the socially optimal land management configuration in a local network environment where the information available to subjects varies. The AB poses a coordination problem between two Nash equilibria: a Pareto dominant and a risk dominant equilibrium. The experiments indicate that if subjects are informed about both their direct and indirect neighbors’ actions, they are more likely to coordinate on the Pareto dominant equilibrium relative to the case where subjects have information about their direct neighbors’ action only. However, the extra information can only delay – and not prevent – the transition to the socially inferior risk dominant Nash equilibrium. In the long run, the AB mechanism may only be partially effective in enhancing delivery of ecosystem services on farming landscapes featuring local networks.

Particulate formulations for the delivery of poly(I:C) as vaccine adjuvant.

Current research and development of antigens for vaccination often center on purified recombinant proteins, viral subunits, synthetic oligopeptides or oligosaccharides, most of them suffering from being poorly immunogenic and subject to degradation. Hence, they call for efficient delivery systems and potent immunostimulants, jointly denoted as adjuvants. Particulate delivery systems like emulsions, liposomes, nanoparticles and microspheres may provide protection from degradation and facilitate the co-formulation of both the antigen and the immunostimulant. Synthetic double-stranded (ds) RNA, such as polyriboinosinic acid-polyribocytidylic acid, poly(I:C), is a mimic of viral dsRNA and, as such, a promising immunostimulant candidate for vaccines directed against intracellular pathogens. Poly(I:C) signaling is primarily dependent on Toll-like receptor 3 (TLR3), and on melanoma differentiation-associated gene-5 (MDA-5), and strongly drives cell-mediated immunity and a potent type I interferon response. However, stability and toxicity issues so far prevented the clinical application of dsRNAs as they undergo rapid enzymatic degradation and bear the potential to trigger undue immune stimulation as well as autoimmune disorders. This review addresses these concerns and suggests strategies to improve the safety and efficacy of immunostimulatory dsRNA formulations. The focus is on technological means required to lower the necessary dosage of poly(I:C), to target surface-modified microspheres passively or actively to antigen-presenting cells (APCs), to control their interaction with non-professional phagocytes and to modulate the resulting cytokine secretion profile.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. Includes self-contained introductions to probability and decision theory. Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. Features implementation of the methodology with reference to commercial and academically available software. Presents standard networks and their extensions that can be easily implemented and that can assist in the reader's own analysis of real cases. Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.

Disease-causing mutations improving the branch site and polypyrimidine tract: pseudoexon activation of LINE-2 and antisense Alu lacking the poly(T)-tail.

Cryptic exons or pseudoexons are typically activated by point mutations that create GT or AG dinucleotides of new 5' or 3' splice sites in introns, often in repetitive elements. Here we describe two cases of tetrahydrobiopterin deficiency caused by mutations improving the branch point sequence and polypyrimidine tracts of repeat-containing pseudoexons in the PTS gene. In the first case, we demonstrate a novel pathway of antisense Alu exonization, resulting from an intronic deletion that removed the poly(T)-tail of antisense AluSq. The deletion brought a favorable branch point sequence within proximity of the pseudoexon 3' splice site and removed an upstream AG dinucleotide required for the 3' splice site repression on normal alleles. New Alu exons can thus arise in the absence of poly(T)-tails that facilitated inclusion of most transposed elements in mRNAs by serving as polypyrimidine tracts, highlighting extraordinary flexibility of Alu repeats in shaping intron-exon structure. In the other case, a PTS pseudoexon was activated by an A>T substitution 9 nt upstream of its 3' splice site in a LINE-2 sequence, providing the first example of a disease-causing exonization of the most ancient interspersed repeat. These observations expand the spectrum of mutational mechanisms that introduce repetitive sequences in mature transcripts and illustrate the importance of intronic mutations in alternative splicing and phenotypic variability of hereditary disorders.

Involvement of the RNA-binding protein ARE/poly(U)-binding factor 1 (AUF1) in the cytotoxic effects of proinflammatory cytokines on pancreatic beta cells.

AIMS/HYPOTHESIS: Chronic exposure of pancreatic beta cells to proinflammatory cytokines leads to impaired insulin secretion and apoptosis. ARE/poly(U)-binding factor 1 (AUF1) belongs to a protein family that controls mRNA stability and translation by associating with adenosine- and uridine-rich regions of target messengers. We investigated the involvement of AUF1 in cytokine-induced beta cell dysfunction. METHODS: Production and subcellular distribution of AUF1 isoforms were analysed by western blotting. To test for their role in the control of beta cell functions, each isoform was overproduced individually in insulin-secreting cells. The contribution to cytokine-mediated beta cell dysfunction was evaluated by preventing the production of AUF1 isoforms by RNA interference. The effect of AUF1 on the production of potential targets was assessed by western blotting. RESULTS: MIN6 cells and human pancreatic islets were found to produce four AUF1 isoforms (p42>p45>p37>p40). AUF1 isoforms were mainly localised in the nucleus but were partially translocated to the cytoplasm upon exposure of beta cells to cytokines and activation of the ERK pathway. Overproduction of AUF1 did not affect glucose-induced insulin secretion but promoted apoptosis. This effect was associated with a decrease in the production of the anti-apoptotic proteins, B cell leukaemia/lymphoma 2 (BCL2) and myeloid cell leukaemia sequence 1 (MCL1). Silencing of AUF1 isoforms restored the levels of the anti-apoptotic proteins, attenuated the activation of the nuclear factor-κB (NFκB) pathway, and protected the beta cells from cytokine-induced apoptosis. CONCLUSIONS/INTERPRETATION: Our findings point to a contribution of AUF1 to the deleterious effects of cytokines on beta cell functions and suggest a role for this RNA-binding protein in the early phases of type 1 diabetes.

Dynamical modeling and analysis of large cellular regulatory networks.

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

Decision-theoretic analysis of forensic sampling criteria using Bayesian decision networks

Sampling issues represent a topic of ongoing interest to the forensic science community essentially because of their crucial role in laboratory planning and working protocols. For this purpose, forensic literature described thorough (Bayesian) probabilistic sampling approaches. These are now widely implemented in practice. They allow, for instance, to obtain probability statements that parameters of interest (e.g., the proportion of a seizure of items that present particular features, such as an illegal substance) satisfy particular criteria (e.g., a threshold or an otherwise limiting value). Currently, there are many approaches that allow one to derive probability statements relating to a population proportion, but questions on how a forensic decision maker - typically a client of a forensic examination or a scientist acting on behalf of a client - ought actually to decide about a proportion or a sample size, remained largely unexplored to date. The research presented here intends to address methodology from decision theory that may help to cope usefully with the wide range of sampling issues typically encountered in forensic science applications. The procedures explored in this paper enable scientists to address a variety of concepts such as the (net) value of sample information, the (expected) value of sample information or the (expected) decision loss. All of these aspects directly relate to questions that are regularly encountered in casework. Besides probability theory and Bayesian inference, the proposed approach requires some additional elements from decision theory that may increase the efforts needed for practical implementation. In view of this challenge, the present paper will emphasise the merits of graphical modelling concepts, such as decision trees and Bayesian decision networks. These can support forensic scientists in applying the methodology in practice. How this may be achieved is illustrated with several examples. The graphical devices invoked here also serve the purpose of supporting the discussion of the similarities, differences and complementary aspects of existing Bayesian probabilistic sampling criteria and the decision-theoretic approach proposed throughout this paper.