Plastic shrinkage cracking of concrete - Roles of osmotic suction

Plastic shrinkage cracking of concrete occurs when the stresses arising in the concrete, due to a combination of suction and restraints of deformation such as reinforcement or formwork, equal its strength. However, three different types of suctions should be distinguished, namely total, matric and osmotic suctions. Although the total suction comprises matric and osmotic suctions, it is often used interchangeably with matric suction, with the underlying unconfirmed assumption that either the osmotic suction or its effect is negligible. In this paper, after a discussion of the pore moisture suctions and strength of unsaturated early-age concrete, experimental investigations of the suctions arising in, and the tensile strength and shear strength of, fly ash mixed with solutions of different osmotic suctions are described. It was found that osmotic suction has negligible effect on the shear and tensile strength, and hence, by inference, the inter-particle stresses in the fly ash mixture and early-age concrete. This strongly suggests that the role played by osmotic suction in the plastic shrinkage cracking of concrete is minimal and, accordingly, justifies the focus of earlier researchers on matric suction only

Cytoreductive Nephrectomy in the Tyrosine Kinase Inhibitor Era: A Question That May Never Be Answered.

Despite great interest, two randomised controlled trials (RCTs) of cytoreductive nephrectomy in the tyrosine kinase inhibitor setting in metastatic renal cell carcinoma have either closed early (SURTIME) or are recruiting very slowly (CARMENA) after 7 yr. Challenges in RCT delivery in uro-oncologic surgery are many. Multiple steps are needed to ensure strong recruitment to trials addressing important urologic cancer questions. Feasibility/pilot studies are key stepping stones towards successful delivery of surgical RCTs

A Profile Likelihood Analysis of the Constrained MSSM with Genetic Algorithms

The Constrained Minimal Supersymmetric Standard Model (CMSSM) is one of the simplest and most widely-studied supersymmetric extensions to the standard model of particle physics. Nevertheless, current data do not sufficiently constrain the model parameters in a way completely independent of priors, statistical measures and scanning techniques. We present a new technique for scanning supersymmetric parameter spaces, optimised for frequentist profile likelihood analyses and based on Genetic Algorithms. We apply this technique to the CMSSM, taking into account existing collider and cosmological data in our global fit. We compare our method to the MultiNest algorithm, an efficient Bayesian technique, paying particular attention to the best-fit points and implications for particle masses at the LHC and dark matter searches. Our global best-fit point lies in the focus point region. We find many high-likelihood points in both the stau co-annihilation and focus point regions, including a previously neglected section of the co-annihilation region at large m_0. We show that there are many high-likelihood points in the CMSSM parameter space commonly missed by existing scanning techniques, especially at high masses. This has a significant influence on the derived confidence regions for parameters and observables, and can dramatically change the entire statistical inference of such scans.Comment: 47 pages, 8 figures; Fig. 8, Table 7 and more discussions added to Sec. 3.4.2 in response to referee's comments; accepted for publication in JHE

Microbial Co-occurrence Relationships in the Human Microbiome

The healthy microbiota show remarkable variability within and among individuals. In addition to external exposures, ecological relationships (both oppositional and symbiotic) between microbial inhabitants are important contributors to this variation. It is thus of interest to assess what relationships might exist among microbes and determine their underlying reasons. The initial Human Microbiome Project (HMP) cohort, comprising 239 individuals and 18 different microbial habitats, provides an unprecedented resource to detect, catalog, and analyze such relationships. Here, we applied an ensemble method based on multiple similarity measures in combination with generalized boosted linear models (GBLMs) to taxonomic marker (16S rRNA gene) profiles of this cohort, resulting in a global network of 3,005 significant co-occurrence and co-exclusion relationships between 197 clades occurring throughout the human microbiome. This network revealed strong niche specialization, with most microbial associations occurring within body sites and a number of accompanying inter-body site relationships. Microbial communities within the oropharynx grouped into three distinct habitats, which themselves showed no direct influence on the composition of the gut microbiota. Conversely, niches such as the vagina demonstrated little to no decomposition into region-specific interactions. Diverse mechanisms underlay individual interactions, with some such as the co-exclusion of Porphyromonaceae family members and Streptococcus in the subgingival plaque supported by known biochemical dependencies. These differences varied among broad phylogenetic groups as well, with the Bacilli and Fusobacteria, for example, both enriched for exclusion of taxa from other clades. Comparing phylogenetic versus functional similarities among bacteria, we show that dominant commensal taxa (such as Prevotellaceae and Bacteroides in the gut) often compete, while potential pathogens (e.g. Treponema and Prevotella in the dental plaque) are more likely to co-occur in complementary niches. This approach thus serves to open new opportunities for future targeted mechanistic studies of the microbial ecology of the human microbiome.National Institutes of Health (U.S.) (grant CA139193)Fonds Wetenschappelijk Onderzoek – VlaanderenJuvenile Diabetes Research Foundation InternationalNational Institutes of Health (U.S.) (grant NIH U54HG004969)Crohn's and Colitis Foundation of AmericaNational Science Foundation (U.S.) (NSF DBI-1053486)United States. Army Research Office (ARO W911NF-11-1-0473)National Institutes of Health (U.S.) (grant NIH 1R01HG005969

Systematic quantification of gene interactions by phenotypic array analysis

A phenotypic array method, developed for quantifying cell growth, was applied to the haploid and homozygous diploid yeast deletion strain sets. A growth index was developed to screen for non-additive interacting effects between gene deletion and induced perturbations. From a genome screen for hydroxyurea (HU) chemical-genetic interactions, 298 haploid deletion strains were selected for further analysis. The strength of interactions was quantified using a wide range of HU concentrations affecting reference strain growth. The selectivity of interaction was determined by comparison with drugs targeting other cellular processes. Bio-modules were defined as gene clusters with shared strength and selectivity of interaction profiles. The functions and connectivity of modules involved in processes such as DNA repair, protein secretion and metabolic control were inferred from their respective gene composition. The work provides an example of, and a general experimental framework for, quantitative analysis of gene interaction networks that buffer cell growth

The topographic evolution of the Tibetan Region as revealed by palaeontology

The Tibetan Plateau was built through a succession of Gondwanan terranes colliding with Asia during the Mesozoic. These accretions produced a complex Paleogene topography of several predominantly east–west trending mountain ranges separated by deep valleys. Despite this piecemeal assembly and resultant complex relief, Tibet has traditionally been thought of as a coherent entity rising as one unit. This has led to the widely used phrase ‘the uplift of the Tibetan Plateau’, which is a false concept borne of simplistic modelling and confounds understanding the complex interactions between topography climate and biodiversity. Here, using the rich palaeontological record of the Tibetan region, we review what is known about the past topography of the Tibetan region using a combination of quantitative isotope and fossil palaeoaltimetric proxies, and present a new synthesis of the orography of Tibet throughout the Paleogene. We show why ‘the uplift of the Tibetan Plateau’ never occurred, and quantify a new pattern of topographic and landscape evolution that contributed to the development of today’s extraordinary Asian biodiversity

Sunitinib Treatment Exacerbates intratumoral Heterogeneity in Metastatic Renal Cancer

This work was supported by the Chief Scientist Office, Scotland (ETM37; to G.D. Stewart, A.C.P. Riddick, M. Aitchison, and D.J. Harrison), Cancer Research UK (Experimental Cancer Medicine Centre; to T. Powles, London and D.J. Harrison, Edinburgh), Medical Research Council (to A. Laird and D.J. Harrison), Royal College of Surgeons of Edinburgh (to A. Laird), Melville Trust (to A. Laird), Medical Research Council (MC_UU_12018/25; to I.M. Overton), Royal Society of Edinburgh Scottish Government Fellowship cofunded by Marie Curie Actions (to I.M. Overton), Renal Cancer Research Fund (to G.D. Stewart), Kidney Cancer Scotland (to G.D. Stewart) and an educational grant from Pfizer (to T. Powles).Purpose: The aim of this study was to investigate the effect of VEGF targeted therapy (sunitinib) on molecular intratumoral heterogeneity (ITH) in metastatic clear cell renal cancer (mccRCC). Experimental design: Multiple tumor samples (n=187 samples) were taken from the primary renal tumors of mccRCC patients who were sunitinib treated (n=23, SuMR clinical trial) or untreated (n=23, SCOTRRCC study). ITH of pathological grade, DNA (aCGH), mRNA (Illumina Beadarray) and candidate proteins (reverse phase protein array) were evaluated using unsupervised and supervised analyses (driver mutations, hypoxia and stromal related genes). ITH was analysed using intratumoral protein variance distributions and distribution of individual patient aCGH and gene expression clustering. Results: Tumor grade heterogeneity was greater in treated compared to untreated tumors (P=0.002). In unsupervised analysis, sunitinib therapy was not associated with increased ITH in DNA or mRNA. However, there was an increase in ITH for the driver mutation gene signature (DNA and mRNA) as well as increasing variability of protein expression with treatment (p<0.05). Despite this variability, significant chromosomal and transcript changes to key targets of sunitinib, such as VHL, PBRM1 and CAIX, occurred in the treated samples. Conclusions: These findings suggest that sunitinib treatment has significant effects on the expression and ITH of key tumor and treatment specific genes/proteins in mccRCC. The results, based on primary tumor analysis, do not support the hypothesis that resistant clones are selected and predominate following targeted therapy.PostprintPeer reviewe