Signal peptide and helical bundle domains of virulent canine distemper virus fusion protein restrict fusogenicity.

Persistence in canine distemper virus (CDV) infection is correlated with very limited cell-cell fusion and lack of cytolysis induced by the neurovirulent A75/17-CDV compared to that of the cytolytic Onderstepoort vaccine strain. We have previously shown that this difference was at least in part due to the amino acid sequence of the fusion (F) protein (P. Plattet, J. P. Rivals, B. Zuber, J. M. Brunner, A. Zurbriggen, and R. Wittek, Virology 337:312-326, 2005). Here, we investigated the molecular mechanisms of the neurovirulent CDV F protein underlying limited membrane fusion activity. By exchanging the signal peptide between both F CDV strains or replacing it with an exogenous signal peptide, we demonstrated that this domain controlled intracellular and consequently cell surface protein expression, thus indirectly modulating fusogenicity. In addition, by serially passaging a poorly fusogenic virus and selecting a syncytium-forming variant, we identified the mutation L372W as being responsible for this change of phenotype. Intriguingly, residue L372 potentially is located in the helical bundle domain of the F(1) subunit. We showed that this mutation drastically increased fusion activity of F proteins of both CDV strains in a signal peptide-independent manner. Due to its unique structure even among morbilliviruses, our findings with respect to the signal peptide are likely to be specifically relevant to CDV, whereas the results related to the helical bundle add new insights to our growing understanding of this class of F proteins. We conclude that different mechanisms involving multiple domains of the neurovirulent A75/17-CDV F protein act in concert to limit fusion activity, preventing lysis of infected cells, which ultimately may favor viral persistence.

Maximum likelihood Linear Programming Data Fusion for Speaker Recognition

Biometric system performance can be improved by means of data fusion. Several kinds of information can be fused in order to obtain a more accurate classification (identification or verification) of an input sample. In this paper we present a method for computing the weights in a weighted sum fusion for score combinations, by means of a likelihood model. The maximum likelihood estimation is set as a linear programming problem. The scores are derived from a GMM classifier working on a different feature extractor. Our experimental results assesed the robustness of the system in front a changes on time (different sessions) and robustness in front a change of microphone. The improvements obtained were significantly better (error bars of two standard deviations) than a uniform weighted sum or a uniform weighted product or the best single classifier. The proposed method scales computationaly with the number of scores to be fussioned as the simplex method for linear programming.

Application of Multi-SNP Approaches Bayesian LASSO and AUC-RF to Detect Main Effects of Inflammatory-Gene Variants Associated with Bladder Cancer Risk

The relationship between inflammation and cancer is well established in several tumor types, including bladder cancer. We performed an association study between 886 inflammatory-gene variants and bladder cancer risk in 1,047 cases and 988 controls from the Spanish Bladder Cancer (SBC)/EPICURO Study. A preliminary exploration with the widely used univariate logistic regression approach did not identify any significant SNP after correcting for multiple testing. We further applied two more comprehensive methods to capture the complexity of bladder cancer genetic susceptibility: Bayesian Threshold LASSO (BTL), a regularized regression method, and AUC-Random Forest, a machine-learning algorithm. Both approaches explore the joint effect of markers. BTL analysis identified a signature of 37 SNPs in 34 genes showing an association with bladder cancer. AUC-RF detected an optimal predictive subset of 56 SNPs. 13 SNPs were identified by both methods in the total population. Using resources from the Texas Bladder Cancer study we were able to replicate 30% of the SNPs assessed. The associations between inflammatory SNPs and bladder cancer were reexamined among non-smokers to eliminate the effect of tobacco, one of the strongest and most prevalent environmental risk factor for this tumor. A 9 SNP-signature was detected by BTL. Here we report, for the first time, a set of SNP in inflammatory genes jointly associated with bladder cancer risk. These results highlight the importance of the complex structure of genetic susceptibility associated with cancer risk.

Learning about Bayesian networks for forensic interpretation : An example based on the "the problem of multiple propositions"

Both, Bayesian networks and probabilistic evaluation are gaining more and more widespread use within many professional branches, including forensic science. Notwithstanding, they constitute subtle topics with definitional details that require careful study. While many sophisticated developments of probabilistic approaches to evaluation of forensic findings may readily be found in published literature, there remains a gap with respect to writings that focus on foundational aspects and on how these may be acquired by interested scientists new to these topics. This paper takes this as a starting point to report on the learning about Bayesian networks for likelihood ratio based, probabilistic inference procedures in a class of master students in forensic science. The presentation uses an example that relies on a casework scenario drawn from published literature, involving a questioned signature. A complicating aspect of that case study - proposed to students in a teaching scenario - is due to the need of considering multiple competing propositions, which is an outset that may not readily be approached within a likelihood ratio based framework without drawing attention to some additional technical details. Using generic Bayesian networks fragments from existing literature on the topic, course participants were able to track the probabilistic underpinnings of the proposed scenario correctly both in terms of likelihood ratios and of posterior probabilities. In addition, further study of the example by students allowed them to derive an alternative Bayesian network structure with a computational output that is equivalent to existing probabilistic solutions. This practical experience underlines the potential of Bayesian networks to support and clarify foundational principles of probabilistic procedures for forensic evaluation.

JARID2 is a direct target of the PAX3-FOXO1 fusion protein and inhibits myogenic differentiation of rhabdomyosarcoma cells.

Rhabdomyosarcomas (RMS) are the most frequent soft-tissue sarcoma in children and characteristically show features of developing skeletal muscle. The alveolar subtype is frequently associated with a PAX3-FOXO1 fusion protein that is known to contribute to the undifferentiated myogenic phenotype of RMS cells. Histone methylation of lysine residues controls developmental processes in both normal and malignant cell contexts. Here we show that JARID2, which encodes a protein known to recruit various complexes with histone-methylating activity to their target genes, is significantly overexpressed in RMS with PAX3-FOXO1 compared with the fusion gene-negative RMS (t-test; P < 0.0001). Multivariate analyses showed that higher JARID2 levels are also associated with metastases at diagnosis, independent of fusion gene status and RMS subtype (n = 120; P = 0.039). JARID2 levels were altered by silencing or overexpressing PAX3-FOXO1 in RMS cell lines with and without the fusion gene, respectively. Consistent with this, we demonstrated that JARID2 is a direct transcriptional target of the PAX3-FOXO1 fusion protein. Silencing JARID2 resulted in reduced cell proliferation coupled with myogenic differentiation, including increased expression of Myogenin (MYOG) and Myosin Light Chain (MYL1) in RMS cell lines representative of both the alveolar and embryonal subtypes. Induced myogenic differentiation was associated with a decrease in JARID2 levels and this phenotype could be rescued by overexpressing JARID2. Furthermore, we that showed JARID2 binds to and alters the methylation status of histone H3 lysine 27 in the promoter regions of MYOG and MYL1 and that the interaction of JARID2 at these promoters is dependent on EED, a core component of the polycomb repressive complex 2 (PRC2). Therefore, JARID2 is a downstream effector of PAX3-FOXO1 that maintains an undifferentiated myogenic phenotype that is characteristic of RMS. JARID2 and other components of PRC2 may represent novel therapeutic targets for treating RMS patients.

The V-ATPase proteolipid cylinder promotes the lipid-mixing stage of SNARE-dependent fusion of yeast vacuoles.

The V-ATPase V(0) sector associates with the peripheral V(1) sector to form a proton pump. V(0) alone has an additional function, facilitating membrane fusion in the endocytic and late exocytic pathways. V(0) contains a hexameric proteolipid cylinder, which might support fusion as proposed in proteinaceous pore models. To test this, we randomly mutagenized proteolipids. We recovered alleles that preserve proton translocation, normal SNARE activation and trans-SNARE pairing but that impair lipid and content mixing. Critical residues were found in all subunits of the proteolipid ring. They concentrate within the bilayer, close to the ring subunit interfaces. The fusion-impairing proteolipid substitutions stabilize the interaction of V(0) with V(1). Deletion of the vacuolar v-SNARE Nyv1 has the same effect, suggesting that both types of mutations similarly alter the conformation of V(0). Also covalent linkage of subunits in the proteolipid cylinder blocks vacuole fusion. We propose that a SNARE-dependent conformational change in V(0) proteolipids might stimulate fusion by creating a hydrophobic crevice that promotes lipid reorientation and formation of a lipidic fusion pore.

A non-linear Bayesian approach for upscaling local-scale hydraulic conductivity measurements based on local- and regional-scale geophysical data

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the regional scale represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed an upscaling procedure based on a Bayesian sequential simulation approach. This method is then applied to the stochastic integration of low-resolution, regional-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities. Finally, the overall viability of this upscaling approach is tested and verified by performing and comparing flow and transport simulation through the original and the upscaled hydraulic conductivity fields. Our results indicate that the proposed procedure does indeed allow for obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

Web Services for Bayesian Learning

In this demonstration we present our web services to perform Bayesian learning for classification tasks.

Landmark detection for fusion of fundus and MRI toward a patient-specific multimodal eye model.

Ophthalmologists typically acquire different image modalities to diagnose eye pathologies. They comprise, e.g., Fundus photography, optical coherence tomography, computed tomography, and magnetic resonance imaging (MRI). Yet, these images are often complementary and do express the same pathologies in a different way. Some pathologies are only visible in a particular modality. Thus, it is beneficial for the ophthalmologist to have these modalities fused into a single patient-specific model. The goal of this paper is a fusion of Fundus photography with segmented MRI volumes. This adds information to MRI that was not visible before like vessels and the macula. This paper contributions include automatic detection of the optic disc, the fovea, the optic axis, and an automatic segmentation of the vitreous humor of the eye.

Mitochondrial fusion is increased by the nuclear coactivator PGC-1beta

Background There is no evidence to date on whether transcriptional regulators are able to shift the balance between mitochondrial fusion and fission events through selective control of gene expression. Methodology/Principal Findings Here, we demonstrate that reduced mitochondrial size observed in knock-out mice for the transcriptional regulator PGC-1β is associated with a selective reduction in Mitofusin 2 (Mfn2) expression, a mitochondrial fusion protein. This decrease in Mfn2 is specific since expression of the remaining components of mitochondrial fusion and fission machinery were not affected. Furthermore, PGC-1β increases mitochondrial fusion and elongates mitochondrial tubules. This PGC-1β-induced elongation specifically requires Mfn2 as this process is absent in Mfn2-ablated cells. Finally, we show that PGC-1β increases Mfn2 promoter activity and transcription by coactivating the nuclear receptor Estrogen Related Receptor α (ERRα). Conclusions/Significance Taken together, our data reveal a novel mechanism by which mammalian cells control mitochondrial fusion. In addition, we describe a novel role of PGC-1β in mitochondrial physiology, namely the control of mitochondrial fusion mainly through Mfn2.

Stochastic inversion of tracer test and electrical geophysical data to estimate hydraulic conductivities.

Quantifying the spatial configuration of hydraulic conductivity (K) in heterogeneous geological environments is essential for accurate predictions of contaminant transport, but is difficult because of the inherent limitations in resolution and coverage associated with traditional hydrological measurements. To address this issue, we consider crosshole and surface-based electrical resistivity geophysical measurements, collected in time during a saline tracer experiment. We use a Bayesian Markov-chain-Monte-Carlo (McMC) methodology to jointly invert the dynamic resistivity data, together with borehole tracer concentration data, to generate multiple posterior realizations of K that are consistent with all available information. We do this within a coupled inversion framework, whereby the geophysical and hydrological forward models are linked through an uncertain relationship between electrical resistivity and concentration. To minimize computational expense, a facies-based subsurface parameterization is developed. The Bayesian-McMC methodology allows us to explore the potential benefits of including the geophysical data into the inverse problem by examining their effect on our ability to identify fast flowpaths in the subsurface, and their impact on hydrological prediction uncertainty. Using a complex, geostatistically generated, two-dimensional numerical example representative of a fluvial environment, we demonstrate that flow model calibration is improved and prediction error is decreased when the electrical resistivity data are included. The worth of the geophysical data is found to be greatest for long spatial correlation lengths of subsurface heterogeneity with respect to wellbore separation, where flow and transport are largely controlled by highly connected flowpaths.

Fusion of the extended modified liquid drop model for nucleation and dynamical nucleation theory

We present a new phenomenological approach to nucleation, based on the combination of the extended modified liquid drop model and dynamical nucleation theory. The new model proposes a new cluster definition, which properly includes the effect of fluctuations, and it is consistent both thermodynamically and kinetically. The model is able to predict successfully the free energy of formation of the critical nucleus, using only macroscopic thermodynamic properties. It also accounts for the spinodal and provides excellent agreement with the result of recent simulations.

Citrus asymmetric somatic hybrids produced via fusion of gamma-irradiated and iodoacetamide-treated protoplasts

The objective of this study was to produce citrus somatic asymmetric hybrids by fusing gamma-irradiated protoplasts with iodoacetamide-treated protoplasts. Protoplasts were isolated from embryogenic suspension cells of grapefruit (Citrus paradisi Macfad.) cultivars Ruby Red and Flame, sweet oranges (C. sinensis Osbeck) 'Itaboraí', 'Natal', Valencia', and 'Succari', from 'Satsuma' (C. unshiu Marcow.) and 'Changsha' mandarin (C. reticulata Blanco) and 'Murcott' tangor (C. reticulata x C. sinensis). Donor protoplasts were exposed to gamma rays and receptor protoplasts were treated with 3 mmol L-1 iodoacetamide (IOA), and then they were fused for asymmetric hybridization. Asymmetric embryos were germinated, and the resulting shoots were either grafted onto sour orange, rough lemon or 'Swingle' (C. paradisi x Poncirus trifoliata) x 'Sunki' mandarin rootstock seedlings, or rooted after dipping their bases in indol-butyric acid (IBA) solution. The products were later acclimatized to greenhouse conditions. Ploidy was analyzed by flow cytometry, and hybridity was confirmed by amplified fragment length polymorphism (AFLP) analysis of plantlet DNAsamples. The best treatment was the donor-recipient fusion combination of 80 Gy-irradiated 'Ruby Red' protoplasts with 20 min IOA-treated 'Succari' protoplasts. Tetraploid and aneuploid plants were produced. Rooting recalcitrance was solved by dipping shoots' stems in 3,000 mg L-1 IBA solution for 10 min.