Multi-class Image Segmentation in Fluorescence Microscopy Using Polytrees

Multi-class segmentation is a crucial step in cell image analysis. This process becomes challenging when little information is available for recognising cells from the background, due to their poor discriminative features. To alleviate this, directed acyclic graphs such as trees have been proposed to model top-down statistical dependencies as a prior for improved image segmentation. However, using trees, modelling the relations between labels of multiple classes becomes difficult. To overcome this limitation, we propose a polytree graphical model that captures label proximity relations more naturally compared to tree based approaches. A novel recursive mechanism based on two-pass message passing is developed to efficiently calculate closed form posteriors of graph nodes on the polytree. The algorithm is evaluated using simulated data, synthetic images and real fluorescence microscopy images. Our method achieves Dice scores of 94.5% and 98% on macrophage and seed classes, respectively, outperforming GMM based classifiers

Bayesian Polytrees With Learned Deep Features for Multi-Class Cell Segmentation.

The recognition of different cell compartments, the types of cells, and their interactions is a critical aspect of quantitative cell biology. However, automating this problem has proven to be non-trivial and requires solving multi-class image segmentation tasks that are challenging owing to the high similarity of objects from different classes and irregularly shaped structures. To alleviate this, graphical models are useful due to their ability to make use of prior knowledge and model inter-class dependences. Directed acyclic graphs, such as trees, have been widely used to model top-down statistical dependences as a prior for improved image segmentation. However, using trees, a few inter-class constraints can be captured. To overcome this limitation, we propose polytree graphical models that capture label proximity relations more naturally compared to tree-based approaches. A novel recursive mechanism based on two-pass message passing was developed to efficiently calculate closed-form posteriors of graph nodes on polytrees. The algorithm is evaluated on simulated data and on two publicly available fluorescence microscopy datasets, outperforming directed trees and three state-of-the-art convolutional neural networks, namely, SegNet, DeepLab, and PSPNet. Polytrees are shown to outperform directed trees in predicting segmentation error by highlighting areas in the segmented image that do not comply with prior knowledge. This paves the way to uncertainty measures on the resulting segmentation and guide subsequent segmentation refinement

Holographic dark energy in a non-flat universe with Granda-Oliveros cut-off

Motivated by Granda and Oliveros (GO) model, we generalize their work to the non-flat case. We obtain the evolution of the dark energy density, the deceleration and the equation of state parameters for the holographic dark energy model in a non-flat universe with GO cut-off. In the limiting case of a flat universe, i.e. $k = 0$, all results given in GO model are obtained.Comment: 11 pages, 5 figure

Comparative Genomics and Transcriptomics of Propionibacterium acnes

The anaerobic Gram-positive bacterium Propionibacterium acnes is a human skin commensal that is occasionally associated with inflammatory diseases. Recent work has indicated that evolutionary distinct lineages of P. acnes play etiologic roles in disease while others are associated with maintenance of skin homeostasis. To shed light on the molecular basis for differential strain properties, we carried out genomic and transcriptomic analysis of distinct P. acnes strains. We sequenced the genome of the P. acnes strain 266, a type I-1a strain. Comparative genome analysis of strain 266 and four other P. acnes strains revealed that overall genome plasticity is relatively low; however, a number of island-like genomic regions, encoding a variety of putative virulence-associated and fitness traits differ between phylotypes, as judged from PCR analysis of a collection of P. acnes strains. Comparative transcriptome analysis of strains KPA171202 (type I-2) and 266 during exponential growth revealed inter-strain differences in gene expression of transport systems and metabolic pathways. In addition, transcript levels of genes encoding possible virulence factors such as dermatan-sulphate adhesin, polyunsaturated fatty acid isomerase, iron acquisition protein HtaA and lipase GehA were upregulated in strain 266. We investigated differential gene expression during exponential and stationary growth phases. Genes encoding components of the energy-conserving respiratory chain as well as secreted and virulence-associated factors were transcribed during the exponential phase, while the stationary growth phase was characterized by upregulation of genes involved in stress responses and amino acid metabolism. Our data highlight the genomic basis for strain diversity and identify, for the first time, the actively transcribed part of the genome, underlining the important role growth status plays in the inflammation-inducing activity of P. acnes. We argue that the disease-causing potential of different P. acnes strains is not only determined by the phylotype-specific genome content but also by variable gene expression

The Opportunistic Pathogen Propionibacterium acnes: Insights into Typing, Human Disease, Clonal Diversification and CAMP Factor Evolution

We previously described a Multilocus Sequence Typing (MLST) scheme based on eight genes that facilitates population genetic and evolutionary analysis of P. acnes. While MLST is a portable method for unambiguous typing of bacteria, it is expensive and labour intensive. Against this background, we now describe a refined version of this scheme based on two housekeeping (aroE; guaA) and two putative virulence (tly; camp2) genes (MLST4) that correctly predicted the phylogroup (IA1, IA2, IB, IC, II, III), clonal complex (CC) and sequence type (ST) (novel or described) status for 91% isolates (n = 372) via cross-referencing of the four gene allelic profiles to the full eight gene versions available in the MLST database (http:// pubmlst.org/pacnes/). Even in the small number of cases where specific STs were not completely resolved, the MLST4 method still correctly determined phylogroup and CC membership. Examination of nucleotide changes within all the MLST loci provides evidence that point mutations generate new alleles approximately 1.5 times as frequently as recombination; although the latter still plays an important role in the bacterium’s evolution. The secreted/cell-associated ‘virulence’ factors tly and camp2 show no clear evidence of episodic or pervasive positive selection and have diversified at a rate similar to housekeeping loci. The co-evolution of these genes with the core genome might also indicate a role in commensal/normal existence constraining their diversity and preventing their loss from the P. acnes population. The possibility that members of the expanded CAMP factor protein family, including camp2, may have been lost from other propionibacteria, but not P. acnes, would further argue for a possible role in niche/host adaption leading to their retention within the genome. These evolutionary insights may prove important for discussions surrounding camp2 as an immunotherapy target for acne, and the effect such treatments may have on commensal lineages

The therapeutic potential of epigenetic manipulation during infectious diseases.

Epigenetic modifications are increasingly recognized as playing an important role in the pathogenesis of infectious diseases. They represent a critical mechanism regulating transcriptional profiles in the immune system that contributes to the cell-type and stimulus specificity of the transcriptional response. Recent data highlight how epigenetic changes impact macrophage functional responses and polarization, influencing the innate immune system through macrophage tolerance and training. In this review we will explore how post-translational modifications of histone tails influence immune function to specific infectious diseases. We will describe how these may influence outcome, highlighting examples derived from responses to acute bacterial pathogens, models of sepsis, maintenance of viral latency and HIV infection. We will discuss how emerging classes of pharmacological agents, developed for use in oncology and other settings, have been applied to models of infectious diseases and their potential to modulate key aspects of the immune response to bacterial infection and HIV therapy