Validation of 3D Model-Based Maximum-Likelihood Estimation of Normalisation Factors for Partial Ring Positron Emission Tomography

The next generation of organ specific Positron Emission Tomography (PET) scanners, e.g. for breast imaging, will use partial ring geometries. We propose a component-based Maximum-Likelihood (ML) estimation of normalisation factors for 3D PET data reconstruction applicable to partial ring geometries. This method is based on the Software for Tomographic Image Reconstruction (STIR) for full ring PET and is validated for a stationary partial ring scanner. The model includes the estimation for crystal efficiencies and geometric factors. The algorithm is validated using Maximum Likelihood Estimation Method (MLEM) based 3D reconstruction in STIR using Geant4 Application for Tomographic Emission (GATE) simulation data for full and partial ring scanners and experimental data from a demonstrator with partial ring geometry. The uniformity of the reconstructed images of simulated cylindrical and NEMAIQ phantoms in both scanner geometries and the image of a line source in the partial ring demonstrator is assessed. The results have shown that uniform images in both axial and transaxial directions are obtained after applying the estimated normalisation factors. The accuracy of the algorithm is validated by comparing the normalisation factors between the full and partial ring systems in simulation. We have shown that the estimated normalisation factors are almost identical, even though the separate components are not. This proves that the ML estimation of the 3D normalisation factors is valid and can be applied to the partial ring scanner

Updates in Rhea-a manually curated resource of biochemical reactions.

Rhea (http://www.ebi.ac.uk/rhea) is a comprehensive and non-redundant resource of expert-curated biochemical reactions described using species from the ChEBI (Chemical Entities of Biological Interest) ontology of small molecules. Rhea has been designed for the functional annotation of enzymes and the description of genome-scale metabolic networks, providing stoichiometrically balanced enzyme-catalyzed reactions (covering the IUBMB Enzyme Nomenclature list and additional reactions), transport reactions and spontaneously occurring reactions. Rhea reactions are extensively curated with links to source literature and are mapped to other publicly available enzyme and pathway databases such as Reactome, BioCyc, KEGG and UniPathway, through manual curation and computational methods. Here we describe developments in Rhea since our last report in the 2012 database issue of Nucleic Acids Research. These include significant growth in the number of Rhea reactions and the inclusion of reactions involving complex macromolecules such as proteins, nucleic acids and other polymers that lie outside the scope of ChEBI. Together these developments will significantly increase the utility of Rhea as a tool for the description, analysis and reconciliation of genome-scale metabolic models

Analytical, experimental and numerical study of a graded honeycomb structure under in-plane impact load with low velocity

Given the significance of energy absorption in various industries, light shock absorbers such as honeycomb structure under in-plane and out-of-plane loads have been in the core of attention. The purpose of this research is the analyses of graded honeycomb structure (GHS) behaviour under in-plane impact loading and its optimisation. Primarily, analytical equations for plateau stress and specific energy are represented, taking power hardening model (PHM) and elastic–perfectly plastic model (EPPM) into consideration. For the validation and comparison of acquired analytical equations, the energy absorption of a GHS made of five different aluminium grades is simulated in ABAQUS/CAE. In order to validate the numerical simulation method in ABAQUS, an experimental test has been conducted as the falling a weight with low velocity on a GHS. Numerical results retain an acceptable accordance with experimental ones with a 5.4% occurred error of reaction force. For a structure with a specific kinetic energy, the stress–strain diagram is achieved and compared with the analytical equations obtained. The maximum difference between the numerical and analytical plateau stresses for PHM is 10.58%. However, this value has been measured to be 38.78% for EPPM. In addition, the numerical value of absorbed energy is compared to that of analytical method for two material models. The maximum difference between the numerical and analytical absorbed energies for PHM model is 6.4%, while it retains the value of 48.08% for EPPM. Based on the conducted comparisons, the numerical and analytical results based on PHM are more congruent than EPPM results. Applying sequential quadratic programming method and genetic algorithm, the ratio of structure mass to the absorbed energy is minimised. According to the optimisation results, the structure capacity of absorbing energy increases by 18% compared to the primary model

Updates in Rhea - an expert curated resource of biochemical reactions.

Rhea (http://www.rhea-db.org) is a comprehensive and non-redundant resource of expert-curated biochemical reactions designed for the functional annotation of enzymes and the description of metabolic networks. Rhea describes enzyme-catalyzed reactions covering the IUBMB Enzyme Nomenclature list as well as additional reactions, including spontaneously occurring reactions, using entities from the ChEBI (Chemical Entities of Biological Interest) ontology of small molecules. Here we describe developments in Rhea since our last report in the database issue of Nucleic Acids Research. These include the first implementation of a simple hierarchical classification of reactions, improved coverage of the IUBMB Enzyme Nomenclature list and additional reactions through continuing expert curation, and the development of a new website to serve this improved dataset

Identification of a RAI1-associated disease network through integration of exome sequencing, transcriptomics, and 3D genomics.

Smith-Magenis syndrome (SMS) is a developmental disability/multiple congenital anomaly disorder resulting from haploinsufficiency of RAI1. It is characterized by distinctive facial features, brachydactyly, sleep disturbances, and stereotypic behaviors. We investigated a cohort of 15 individuals with a clinical suspicion of SMS who showed neither deletion in the SMS critical region nor damaging variants in RAI1 using whole exome sequencing. A combination of network analysis (co-expression and biomedical text mining), transcriptomics, and circularized chromatin conformation capture (4C-seq) was applied to verify whether modified genes are part of the same disease network as known SMS-causing genes. Potentially deleterious variants were identified in nine of these individuals using whole-exome sequencing. Eight of these changes affect KMT2D, ZEB2, MAP2K2, GLDC, CASK, MECP2, KDM5C, and POGZ, known to be associated with Kabuki syndrome 1, Mowat-Wilson syndrome, cardiofaciocutaneous syndrome, glycine encephalopathy, mental retardation and microcephaly with pontine and cerebellar hypoplasia, X-linked mental retardation 13, X-linked mental retardation Claes-Jensen type, and White-Sutton syndrome, respectively. The ninth individual carries a de novo variant in JAKMIP1, a regulator of neuronal translation that was recently found deleted in a patient with autism spectrum disorder. Analyses of co-expression and biomedical text mining suggest that these pathologies and SMS are part of the same disease network. Further support for this hypothesis was obtained from transcriptome profiling that showed that the expression levels of both Zeb2 and Map2k2 are perturbed in Rai1 (-/-) mice. As an orthogonal approach to potentially contributory disease gene variants, we used chromatin conformation capture to reveal chromatin contacts between RAI1 and the loci flanking ZEB2 and GLDC, as well as between RAI1 and human orthologs of the genes that show perturbed expression in our Rai1 (-/-) mouse model. These holistic studies of RAI1 and its interactions allow insights into SMS and other disorders associated with intellectual disability and behavioral abnormalities. Our findings support a pan-genomic approach to the molecular diagnosis of a distinctive disorder

Updates in Rhea: SPARQLing biochemical reaction data.

Rhea (http://www.rhea-db.org) is a comprehensive and non-redundant resource of over 11 000 expert-curated biochemical reactions that uses chemical entities from the ChEBI ontology to represent reaction participants. Originally designed as an annotation vocabulary for the UniProt Knowledgebase (UniProtKB), Rhea also provides reaction data for a range of other core knowledgebases and data repositories including ChEBI and MetaboLights. Here we describe recent developments in Rhea, focusing on a new resource description framework representation of Rhea reaction data and an SPARQL endpoint (https://sparql.rhea-db.org/sparql) that provides access to it. We demonstrate how federated queries that combine the Rhea SPARQL endpoint and other SPARQL endpoints such as that of UniProt can provide improved metabolite annotation and support integrative analyses that link the metabolome through the proteome to the transcriptome and genome. These developments will significantly boost the utility of Rhea as a means to link chemistry and biology for a more holistic understanding of biological systems and their function in health and disease