A novel concept for a reinforced glass beam carrying long term loads

This paper presents a novel concept for improving the long-term load-bearing performance of reinforced glass beams (hybrid beams). The concept of reinforcing glass beams using steel or other (ductile) materials have been investigated over the last couple of decades utilising the fracture pattern of annealed glass to ensure a ductile behaviour. However, it is well known that the long-term strength of annealed glass is rather low due to so-called static fatigue leading to a relatively poor performance for most hybrid-beams. As an example will a hybrid beam based on annealed glass exposed to 26 MPa permanent load fail in less than a day according to a European code. The novel concept suggested here utilises a combination of annealed and fully tempered glass in an arrangement where the tempered glass carries the long-term loading whereas short-term loading is carried by the reinforced annealed glass. The concept is based on the relaxation of shear stresses in the PVB (polyvinyl butyral) interlayer, material properties of PVB from different authors have been compared, and a set of average parameters have been suggested. The main purpose of the paper is to introduce the concepts and mechanisms of such beams and provide a basis for further optimisation

Building Performance Simulation and Characterisation of Adaptive Facades

The book “Performance Simulation and Characterisation of Adaptive Facades” responds to the need of providing a general framework, standardised and recognised methods and tools to evaluate the performance of adaptive facades in a quantitative way, by means of numerical and experimental methods, in different domains of interest. This book represents the main outcome of the activities of the Working Group 2 of the COST Action TU1403 Adaptive Façades Network, “Components performance and characterisation methods”, by integrating in one publication the main deliverables of WG2 described in the Memorandum of Understanding: D 2.1. Report on current adaptive facades modelling techniques; D 2.4. Report on the validation of developed simulation tools and models; D 2.5. Report on the developed experimental procedures. These are extended by additional sections regarding structural aspects and key performance indicators for adaptive façade systems. This book is a comprehensive review of different areas of research on adaptive façade systems and provides both general and specific knowledge about numerical and experimental research methods in this field. The fast pace at which building technologies and materials develop, is slowly but constantly followed by the development of numerical and experimental methods and tools to quantify their performance. Therefore this book focuses primarily on general methods and requirements, in an attempt to provide a coherent picture of current and near future possibilities to simulate and characterise the performance of adaptive facades in different domains, which could remain relevant in the coming years. In addition, specific know-how on selected cases is also presented, as a way to clarify and apply the more general approaches and methods described. The present book is published to support practitioners, researchers and students who are interested in designing, researching, and integrating adaptive façade systems in buildings. It targets both the academic and the not-academic sectors, and intends to contribute positively to an increased market penetration of adaptive façade systems, components and materials, aimed at rationalising energy and material resources while achieving a high standard of indoor environmental quality, health and safety in the built environment

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

Molecular Evolution of Regulatory Genes in Spruces from Different Species and Continents: Heterogeneous Patterns of Linkage Disequilibrium and Selection but Correlated Recent Demographic Changes

Genes involved in transcription regulation may represent valuable targets in association genetics studies because of their key roles in plant development and potential selection at the molecular level. Selection and demographic signatures at the sequence level were investigated for five regulatory genes belonging to the knox-I family (KN1, KN2, KN3, KN4) and the HD-Zip III family (HB-3) in three Picea species affected by post-glacial recolonization in North America and Europe. To disentangle neutral and selective forces and estimate linkage disequilibrium (LD) on a gene basis, complete or nearly complete gene sequences were analysed. Nucleotide variation within species, haplotype structure, LD, and neutrality tests, in addition to coalescent simulations based on Tajima’s D and Fay and Wu’s H, were estimated. Nucleotide diversity was generally low in all species (average π = 0.002–0.003) and much heterogeneity was seen in LD and selection signatures among genes and species. Most of the genes harboured an excess of both rare and frequent alleles in the three species. Simulations showed that this excess was significantly higher than that expected under neutrality and a bottleneck during the Last Glacial Maximum followed by population expansion at the Pleistocene/Holocene boundary or shortly after best explains the correlated sequence patterns. These results indicate that despite recent large demographic changes in the three boreal species from two continents, species-specific selection signatures could still be detected from the analysis of nearly complete regulatory gene sequences. Such different signatures indicate differential subfunctionalization of gene family members in the three congeneric species

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