Three dimensional photoacoustic tomography in Bayesian framework

The image reconstruction problem (or inverse problem) in photoacoustic tomography is to resolve the initial pressure distribution from detected ultrasound waves generated within an object due to an illumination by a short light pulse. Recently, a Bayesian approach to photoacoustic image reconstruction with uncertainty quantification was proposed and studied with two dimensional numerical simulations. In this paper, the approach is extended to three spatial dimensions and, in addition to numerical simulations, experimental data are considered. The solution of the inverse problem is obtained by computing point estimates, i.e., maximum a posteriori estimate and posterior covariance. These are computed iteratively in a matrix-free form using a biconjugate gradient stabilized method utilizing the adjoint of the acoustic forward operator. The results show that the Bayesian approach can produce accurate estimates of the initial pressure distribution in realistic measurement geometries and that the reliability of these estimates can be assessed

In silico modelling to differentiate the contribution of sugar frequency versus total amount in driving biofilm dysbiosis in dental caries

Dental caries is the most prevalent infection globally and a substantial economic burden in developed countries. Dietary sugars are the main risk factor, and drive increased proportions of acid-producing and acid-tolerating (aciduric) bacterial species within dental bio lms. Recent longitudinal studies have suggested that caries is most strongly correlated with total sugar intake, contrasting with the prevailing view that intake frequency is the primary determinant. To explore this possibility, we employed a computational model for supragingival plaque to systematically sample combinations of sugar frequency and total amount, allowing their independent contributions on the ratio of aciduric (i.e. cariogenic) to non-aciduric bacteria to be unambiguously determined. Sugar frequency was found to be irrelevant for either very high or very low daily total amounts as the simulated bio lm was predicted to be always or never cariogenic, respectively. Frequency was a determining factor for intermediate total amounts of sugar, including the estimated average human consumption. An increased risk of caries (i.e. high prevalence of aciduric/non-aciduric species) was predicted for high intake frequencies. Thus, both total amount and frequency of sugar intake may combine to in uence plaque cariogenicity. These ndings could be employed to support public guidance for dietary change, leading to improved oral healthcare

CodingQuarry: Highly accurate hidden Markov model gene prediction in fungal genomes using RNA-seq transcripts

Background: The impact of gene annotation quality on functional and comparative genomics makes gene prediction an important process, particularly in non-model species, including many fungi. Sets of homologous protein sequences are rarely complete with respect to the fungal species of interest and are often small or unreliable, especially when closely related species have not been sequenced or annotated in detail. In these cases, protein homology-based evidence fails to correctly annotate many genes, or significantly improve ab initio predictions. Generalised hidden Markov models (GHMM) have proven to be invaluable tools in gene annotation and, recently, RNA-seq has emerged as a cost-effective means to significantly improve the quality of automated gene annotation. As these methods do not require sets of homologous proteins, improving gene prediction from these resources is of benefit to fungal researchers. While many pipelines now incorporate RNA-seq data in training GHMMs, there has been relatively little investigation into additionally combining RNA-seq data at the point of prediction, and room for improvement in this area motivates this study. Results: CodingQuarry is a highly accurate, self-training GHMM fungal gene predictor designed to work with assembled, aligned RNA-seq transcripts. RNA-seq data informs annotations both during gene-model training and in prediction. Our approach capitalises on the high quality of fungal transcript assemblies by incorporating predictions made directly from transcript sequences. Correct predictions are made despite transcript assembly problems, including those caused by overlap between the transcripts of adjacent gene loci. Stringent benchmarking against high-confidence annotation subsets showed CodingQuarry predicted 91.3% of Schizosaccharomyces pombe genes and 90.4% of Saccharomyces cerevisiae genes perfectly. These results are 4-5% better than those of AUGUSTUS, the next best performing RNA-seq driven gene predictor tested. Comparisons against whole genome Sc. pombe and S. cerevisiae annotations further substantiate a 4-5% improvement in the number of correctly predicted genes. Conclusions: We demonstrate the success of a novel method of incorporating RNA-seq data into GHMM fungal gene prediction. This shows that a high quality annotation can be achieved without relying on protein homology or a training set of genes. CodingQuarry is freely available (https://sourceforge.net/projects/codingquarry/), and suitable for incorporation into genome annotation pipelines

Pleosporales

One hundred and five generic types of Pleosporales are described and illustrated. A brief introduction and detailed history with short notes on morphology, molecular phylogeny as well as a general conclusion of each genus are provided. For those genera where the type or a representative specimen is unavailable, a brief note is given. Altogether 174 genera of Pleosporales are treated. Phaeotrichaceae as well as Kriegeriella, Zeuctomorpha and Muroia are excluded from Pleosporales. Based on the multigene phylogenetic analysis, the suborder Massarineae is emended to accommodate five families, viz. Lentitheciaceae, Massarinaceae, Montagnulaceae, Morosphaeriaceae and Trematosphaeriaceae

Combined Analysis of Murine and Human Microarrays and ChIP Analysis Reveals Genes Associated with the Ability of MYC To Maintain Tumorigenesis

The MYC oncogene has been implicated in the regulation of up to thousands of genes involved in many cellular programs including proliferation, growth, differentiation, self-renewal, and apoptosis. MYC is thought to induce cancer through an exaggerated effect on these physiologic programs. Which of these genes are responsible for the ability of MYC to initiate and/or maintain tumorigenesis is not clear. Previously, we have shown that upon brief MYC inactivation, some tumors undergo sustained regression. Here we demonstrate that upon MYC inactivation there are global permanent changes in gene expression detected by microarray analysis. By applying StepMiner analysis, we identified genes whose expression most strongly correlated with the ability of MYC to induce a neoplastic state. Notably, genes were identified that exhibited permanent changes in mRNA expression upon MYC inactivation. Importantly, permanent changes in gene expression could be shown by chromatin immunoprecipitation (ChIP) to be associated with permanent changes in the ability of MYC to bind to the promoter regions. Our list of candidate genes associated with tumor maintenance was further refined by comparing our analysis with other published results to generate a gene signature associated with MYC-induced tumorigenesis in mice. To validate the role of gene signatures associated with MYC in human tumorigenesis, we examined the expression of human homologs in 273 published human lymphoma microarray datasets in Affymetrix U133A format. One large functional group of these genes included the ribosomal structural proteins. In addition, we identified a group of genes involved in a diverse array of cellular functions including: BZW2, H2AFY, SFRS3, NAP1L1, NOLA2, UBE2D2, CCNG1, LIFR, FABP3, and EDG1. Hence, through our analysis of gene expression in murine tumor models and human lymphomas, we have identified a novel gene signature correlated with the ability of MYC to maintain tumorigenesis

Diet and asthma: looking back, moving forward

Asthma is an increasing global health burden, especially in the western world. Public health interventions are sought to lessen its prevalence or severity, and diet and nutrition have been identified as potential factors. With rapid changes in diet being one of the hallmarks of westernization, nutrition may play a key role in affecting the complex genetics and developmental pathophysiology of asthma. The present review investigates hypotheses about hygiene, antioxidants, lipids and other nutrients, food types and dietary patterns, breastfeeding, probiotics and intestinal microbiota, vitamin D, maternal diet, and genetics. Early hypotheses analyzed population level trends and focused on major dietary factors such as antioxidants and lipids. More recently, larger dietary patterns beyond individual nutrients have been investigated such as obesity, fast foods, and the Mediterranean diet. Despite some promising hypotheses and findings, there has been no conclusive evidence about the role of specific nutrients, food types, or dietary patterns past early childhood on asthma prevalence. However, diet has been linked to the development of the fetus and child. Breastfeeding provides immunological protection when the infant's immune system is immature and a modest protective effect against wheeze in early childhood. Moreover, maternal diet may be a significant factor in the development of the fetal airway and immune system. As asthma is a complex disease of gene-environment interactions, maternal diet may play an epigenetic role in sensitizing fetal airways to respond abnormally to environmental insults. Recent hypotheses show promise in a biological approach in which the effects of dietary factors on individual physiology and immunology are analyzed before expansion into larger population studies. Thus, collaboration is required by various groups in studying this enigma from epidemiologists to geneticists to immunologists. It is now apparent that this multidisciplinary approach is required to move forward and understand the complexity of the interaction of dietary factors and asthma

A Coarse-Grained Biophysical Model of E. coli and Its Application to Perturbation of the rRNA Operon Copy Number

We propose a biophysical model of Escherichia coli that predicts growth rate and an effective cellular composition from an effective, coarse-grained representation of its genome. We assume that E. coli is in a state of balanced exponential steadystate growth, growing in a temporally and spatially constant environment, rich in resources. We apply this model to a series of past measurements, where the growth rate and rRNA-to-protein ratio have been measured for seven E. coli strains with an rRNA operon copy number ranging from one to seven (the wild-type copy number). These experiments show that growth rate markedly decreases for strains with fewer than six copies. Using the model, we were able to reproduce these measurements. We show that the model that best fits these data suggests that the volume fraction of macromolecules inside E. coli is not fixed when the rRNA operon copy number is varied. Moreover, the model predicts that increasing the copy number beyond seven results in a cytoplasm densely packed with ribosomes and proteins. Assuming that under such overcrowded conditions prolonged diffusion times tend to weaken binding affinities, the model predicts that growth rate will not increase substantially beyond the wild-type growth rate, as indicated by other experiments. Our model therefore suggests that changing the rRNA operon copy number of wild-type E. coli cells growing in a constant rich environment does not substantially increase their growth rate. Other observations regarding strains with an altered rRNA operon copy number, such as nucleoid compaction and the rRNA operon feedback response, appear to be qualitatively consistent with this model. In addition, we discuss possible design principles suggested by the model and propose further experiments to test its validity

The genome of the emerging barley pathogen Ramularia collo-cygni

Background Ramularia collo-cygni is a newly important, foliar fungal pathogen of barley that causes the disease Ramularia leaf spot. The fungus exhibits a prolonged endophytic growth stage before switching life habit to become an aggressive, necrotrophic pathogen that causes significant losses to green leaf area and hence grain yield and quality. Results The R. collo-cygni genome was sequenced using a combination of Illumina and Roche 454 technologies. The draft assembly of 30.3 Mb contained 11,617 predicted gene models. Our phylogenomic analysis confirmed the classification of this ascomycete fungus within the family Mycosphaerellaceae, order Capnodiales of the class Dothideomycetes. A predicted secretome comprising 1053 proteins included redox-related enzymes and carbohydrate-modifying enzymes and proteases. The relative paucity of plant cell wall degrading enzyme genes may be associated with the stealth pathogenesis characteristic of plant pathogens from the Mycosphaerellaceae. A large number of genes associated with secondary metabolite production, including homologs of toxin biosynthesis genes found in other Dothideomycete plant pathogens, were identified. Conclusions The genome sequence of R. collo-cygni provides a framework for understanding the genetic basis of pathogenesis in this important emerging pathogen. The reduced complement of carbohydrate-degrading enzyme genes is likely to reflect a strategy to avoid detection by host defences during its prolonged asymptomatic growth. Of particular interest will be the analysis of R. collo-cygni gene expression during interactions with the host barley, to understand what triggers this fungus to switch from being a benign endophyte to an aggressive necrotroph

Ευρετικές προσεγγίσεις του μοναδιάστατου προβλήματος πακετοποίησης

Article 59.1, of the International Code of Nomenclature for Algae, Fungi, and Plants (ICN; Melbourne Code), which addresses the nomenclature of pleomorphic fungi, became effective from 30 July 2011. Since that date, each fungal species can have one nomenclaturally correct name in a particular classification. All other previously used names for this species will be considered as synonyms. The older generic epithet takes priority over the younger name. Any widely used younger names proposed for use, must comply with Art. 57.2 and their usage should be approved by the Nomenclature Committee for Fungi (NCF). In this paper, we list all genera currently accepted by us in Dothideomycetes (belonging to 23 orders and 110 families), including pleomorphic and non-pleomorphic genera. In the case of pleomorphic genera, we follow the rulings of the current ICN and propose single generic names for future usage. The taxonomic placements of 1261 genera are listed as an outline. Protected names and suppressed names for 34 pleomorphic genera are listed separately. Notes and justifications are provided for possible proposed names after the list of genera. Notes are also provided on recent advances in our understanding of asexual and sexual morph linkages in Dothideomycetes. A phylogenetic tree based on four gene analyses supported 23 orders and 75 families, while 35 families still lack molecular data