"Pseudo-Beijing": evidence for convergent evolution in the direct repeat region of Mycobacterium tuberculosis

Background Mycobacterium tuberculosis has a global population structure consisting of six main phylogenetic lineages associated with specific geographic regions and human populations. One particular M. tuberculosis genotype known as “Beijing” has repeatedly been associated with drug resistance and has been emerging in some parts of the world. “Beijing” strains are traditionally defined based on a characteristic spoligotyping pattern. We used three alternative genotyping techniques to revisit the phylogenetic classification of M. tuberculosis complex (MTBC) strains exhibiting the typical “Beijing” spoligotyping pattern. Methods and Findings MTBC strains were obtained from an ongoing molecular epidemiological study in Switzerland and Nepal. MTBC genotyping was performed based on SNPs, genomic deletions, and 24-loci MIRU-VNTR. We identified three MTBC strains from patients originating from Tibet, Portugal and Nepal which exhibited a spoligotyping patterns identical to the classical Beijing signature. However, based on three alternative molecular markers, these strains were assigned to Lineage 3 (also known as Delhi/CAS) rather than to Lineage 2 (also known as East-Asian lineage). Sequencing of the RD207 in one of these strains showed that the deletion responsible for this “Pseudo-Beijing” spoligotype was about 1,000 base pairs smaller than the usual deletion of RD207 in classical “Beijing” strains, which is consistent with an evolutionarily independent deletion event in the direct repeat (DR) region of MTBC. Conclusions We provide an example of convergent evolution in the DR locus of MTBC, and highlight the limitation of using spoligotypes for strain classification. Our results indicate that a proportion of “Beijing” strains may have been misclassified in the past. Markers that are more phylogenetically robust should be used when exploring strain-specific differences in experimental or clinical phenotypes.

Generalized eMC implementation for Monte Carlo dose calculation of electron beams from different machine types

The electron Monte Carlo (eMC) dose calculation algorithm available in the Eclipse treatment planning system (Varian Medical Systems) is based on the macro MC method and uses a beam model applicable to Varian linear accelerators. This leads to limitations in accuracy if eMC is applied to non-Varian machines. In this work eMC is generalized to also allow accurate dose calculations for electron beams from Elekta and Siemens accelerators. First, changes made in the previous study to use eMC for low electron beam energies of Varian accelerators are applied. Then, a generalized beam model is developed using a main electron source and a main photon source representing electrons and photons from the scattering foil, respectively, an edge source of electrons, a transmission source of photons and a line source of electrons and photons representing the particles from the scrapers or inserts and head scatter radiation. Regarding the macro MC dose calculation algorithm, the transport code of the secondary particles is improved. The macro MC dose calculations are validated with corresponding dose calculations using EGSnrc in homogeneous and inhomogeneous phantoms. The validation of the generalized eMC is carried out by comparing calculated and measured dose distributions in water for Varian, Elekta and Siemens machines for a variety of beam energies, applicator sizes and SSDs. The comparisons are performed in units of cGy per MU. Overall, a general agreement between calculated and measured dose distributions for all machine types and all combinations of parameters investigated is found to be within 2% or 2 mm. The results of the dose comparisons suggest that the generalized eMC is now suitable to calculate dose distributions for Varian, Elekta and Siemens linear accelerators with sufficient accuracy in the range of the investigated combinations of beam energies, applicator sizes and SSDs.

Applied Graph Theory in Computer Vision and Pattern Recognition

This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.

Conformationally homogeneous heterocyclic pseudotetrapeptides as three-dimensional scaffolds for rational drug design: receptor-selective somatostatin analogues

A would-be amide: A 1,4-disubstituted 1,2,3-triazole was used as a surrogate for a trans amide bond to create a library of 16 diastereomeric pseudotetrapeptides as beta-turn mimetics. High-resolution structural analysis indicated that these scaffolds adopt distinct, rigid, conformationally homogeneous beta-turn-like structures (see example), some of which bind somatostatin receptor subtypes selectively, and some of which show broad-spectrum activity.