Advancements in Design, Analysis, and Retrofitting of Structures Exposed to Blast

The objective of this special issue is to provide an overview on the current trends and recent advancements in terms of design, analysis, experimental testing, and retrofitting of structural systems and assemblies exposed to exceptional loads such as explosions

Article a new epigenetic model to stratify glioma patients according to their immunosuppressive state

Gliomas are the most common primary neoplasm of the central nervous system. A promising frontier in the definition of glioma prognosis and treatment is represented by epigenetics. Further-more, in this study, we developed a machine learning classification model based on epigenetic data (CpG probes) to separate patients according to their state of immunosuppression. We considered 573 cases of low-grade glioma (LGG) and glioblastoma (GBM) from The Cancer Genome Atlas (TCGA). First, from gene expression data, we derived a novel binary indicator to flag patients with a favorable immune state. Then, based on previous studies, we selected the genes related to the immune state of tumor microenvironment. After, we improved the selection with a data-driven procedure, based on Boruta. Finally, we tuned, trained, and evaluated both random forest and neural network classifiers on the resulting dataset. We found that a multi-layer perceptron network fed by the 338 probes selected by applying both expert choice and Boruta results in the best performance, achieving an out-of-sample accuracy of 82.8%, a Matthews correlation coefficient of 0.657, and an area under the ROC curve of 0.9. Based on the proposed model, we provided a method to stratify glioma patients according to their epigenomic state

Age dating of an early Milky Way merger via asteroseismology of the naked-eye star ν Indi

Over the course of its history, the Milky Way has ingested multiple smaller satellite galaxies1. Although these accreted stellar populations can be forensically identified as kinematically distinct structures within the Galaxy, it is difficult in general to date precisely the age at which any one merger occurred. Recent results have revealed a population of stars that were accreted via the collision of a dwarf galaxy, called Gaia–Enceladus1, leading to substantial pollution of the chemical and dynamical properties of the Milky Way. Here we identify the very bright, naked-eye star ν Indi as an indicator of the age of the early in situ population of the Galaxy. We combine asteroseismic, spectroscopic, astrometric and kinematic observations to show that this metal-poor, alpha-element-rich star was an indigenous member of the halo, and we measure its age to be 11.0 ± 0.7 (stat) ± 0.8 (sys) billion years. The star bears hallmarks consistent with having been kinematically heated by the Gaia–Enceladus collision. Its age implies that the earliest the merger could have begun was 11.6 and 13.2 billion years ago, at 68% and 95% confidence, respectively. Computations based on hierarchical cosmological models slightly reduce the above limits

Comprehensive genetic dissection of wood properties in a widely-grown tropical tree: Eucalyptus

Background: Eucalyptus is an important genus in industrial plantations throughout the world and is grown for use as timber, pulp, paper and charcoal. Several breeding programmes have been launched worldwide to concomitantly improve growth performance and wood properties (WPs). In this study, an interspecific cross between Eucalyptus urophylla and E. grandis was used to identify major genomic regions (Quantitative Trait Loci, QTL) controlling the variability of WPs. Results: Linkage maps were generated for both parent species. A total of 117 QTLs were detected for a series of wood and end-use related traits, including chemical, technological, physical, mechanical and anatomical properties. The QTLs were mainly clustered into five linkage groups. In terms of distribution of QTL effects, our result agrees with the typical L-shape reported in most QTL studies, i.e. most WP QTLs had limited effects and only a few (13) had major effects (phenotypic variance explained > 15%). The co-locations of QTLs for different WPs as well as QTLs and candidate genes are discussed in terms of phenotypic correlations between traits, and of the function of the candidate genes. The major wood property QTL harbours a gene encoding a Cinnamoyl CoA reductase (CCR), a structural enzyme of the monolignol-specific biosynthesis pathway. Conclusions: Given the number of traits analysed, this study provides a comprehensive understanding of the genetic architecture of wood properties in this Eucalyptus full-sib pedigree. At the dawn of Eucalyptus genome sequence, it will provide a framework to identify the nature of genes underlying these important quantitative traits. (Résumé d'auteur

Salmonella Strains Isolated from Galápagos Iguanas Show Spatial Structuring of Serovar and Genomic Diversity

It is thought that dispersal limitation primarily structures host-associated bacterial populations because host distributions inherently limit transmission opportunities. However, enteric bacteria may disperse great distances during food-borne outbreaks. It is unclear if such rapid long-distance dispersal events happen regularly in natural systems or if these events represent an anthropogenic exception. We characterized Salmonella enterica isolates from the feces of free-living Galápagos land and marine iguanas from five sites on four islands using serotyping and genomic fingerprinting. Each site hosted unique and nearly exclusive serovar assemblages. Genomic fingerprint analysis offered a more complex model of S. enterica biogeography, with evidence of both unique strain pools and of spatial population structuring along a geographic gradient. These findings suggest that even relatively generalist enteric bacteria may be strongly dispersal limited in a natural system with strong barriers, such as oceanic divides. Yet, these differing results seen on two typing methods also suggests that genomic variation is less dispersal limited, allowing for different ecological processes to shape biogeographical patterns of the core and flexible portions of this bacterial species' genome

A novel epigenetic machine learning model to define risk of progression for hepatocellular carcinoma patients

Although extensive advancements have been made in treatment against hepatocellular carcinoma (HCC), the prognosis of HCC patients remains unsatisfied. It is now clearly established that extensive epigenetic changes act as a driver in human tumors. This study exploits HCC epigenetic deregulation to define a novel prognostic model for monitoring the progression of HCC. We analyzed the genome-wide DNA methylation profile of 374 primary tumor specimens using the Illumina 450 K array data from The Cancer Genome Atlas. We initially used a novel combination of Machine Learning algorithms (Recursive Features Selection, Boruta) to capture early tumor progression features. The subsets of probes obtained were used to train and validate Random Forest models to predict a Progression Free Survival greater or less than 6 months. The model based on 34 epigenetic probes showed the best performance, scoring 0.80 accuracy and 0.51 Matthews Correlation Coefficient on testset. Then, we generated and validated a progression signature based on 4 methylation probes capable of stratifying HCC patients at high and low risk of progression. Survival analysis showed that high risk patients are characterized by a poorer progression free survival compared to low risk patients. Moreover, decision curve analysis confirmed the strength of this predictive tool over conventional clinical parameters. Functional enrichment analysis highlighted that high risk patients differentiated themselves by the upregulation of proliferative pathways. Ultimately, we propose the oncogenic MCM2 gene as a methylation-driven gene of which the representative epigenetic markers could serve both as predictive and prognostic markers. Briefly, our work provides several potential HCC progression epigenetic biomarkers as well as a new signature that may enhance patients surveillance and advances in personalized treatment

Environmentally Friendly La0.6Sr0.4Ga0.3Fe0.7O3 (LSGF)-Functionalized Fly-Ash Geopolymers for Pollutants Abatement in Industrial Processes

Abstract: A ready-to use, highly sustainable solution for large scale exhausts catalytic abatement was developed: the active bricks. An environmentally-friendly composite was synthesized by depositing La0.6Sr0.4Ga0.3Fe0.7O3, by combustion synthesis, on a fly ash-based geopolymer improved to bear the high temperatures (> 900 \ub0C) required for synthesis. The geopolymer was obtained using by-products and was synthesized at RT: its production is sustainable and cost efficient. Prepared composites have been tested for methane oxidation and show good, durable activity above 400 \ub0C. Thermal stability was also proved. Composites are a good solution for oxidation of fuel residues in industrial processes. Graphic Abstract: [Figure not available: see fulltext.]