The relationship between galaxy and dark matter halo size from z ∼ 3 to the present

We explore empirical constraints on the statistical relationship between the radial size of galaxies and the radius of their host dark matter haloes from z similar to 0.1-3 using the Galaxy And Mass Assembly (GAMA) and Cosmic Assembly Near Infrared Deep Extragalactic Legacy Survey (CANDELS) surveys. We map dark matter halo mass to galaxy stellar mass using relationships from abundance matching, applied to the Bolshoi-Planck dissipationless N-body simulation. We define SRHR equivalent to r(e)/R-h as the ratio of galaxy radius to halo virial radius, and SRHR lambda equivalent to r(e)/(lambda R-h) as the ratio of galaxy radius to halo spin parameter times halo radius. At z similar to 0.1, we find an average value of SRHR similar or equal to 0.018 and SRHR. similar or equal to 0.5 with very little dependence on stellar mass. Stellar radius-halo radius (SRHR) and SRHR lambda have a weak dependence on cosmic time since z similar to 3. SRHR shows a mild decrease over cosmic time for low-mass galaxies, but increases slightly or does not evolve formoremassive galaxies. We find hints that at high redshift (z similar to 2-3), SRHR. is lower for more massive galaxies, while it shows no significant dependence on stellar mass at z less than or similar to 0.5. We find that for both the GAMA and CANDELS samples, at all redshifts from z similar to 0.1-3, the observed conditional size distribution in stellar mass bins is remarkably similar to the conditional distribution of lambda R-h. We discuss the physical interpretation and implications of these results

MIR21-induced loss of junctional adhesion molecule A promotes activation of oncogenic pathways, progression and metastasis in colorectal cancer.

Junctional adhesion molecules (JAMs) play a critical role in cell permeability, polarity and migration. JAM-A, a key protein of the JAM family, is altered in a number of conditions including cancer; however, consequences of JAM-A dysregulation on carcinogenesis appear to be tissue dependent and organ dependent with significant implications for the use of JAM-A as a biomarker or therapeutic target. Here, we test the expression and prognostic role of JAM-A downregulation in primary and metastatic colorectal cancer (CRC) (n = 947). We show that JAM-A downregulation is observed in ~60% of CRC and correlates with poor outcome in four cohorts of stages II and III CRC (n = 1098). Using JAM-A knockdown, re-expression and rescue experiments in cell line monolayers, 3D spheroids, patient-derived organoids and xenotransplants, we demonstrate that JAM-A silencing promotes proliferation and migration in 2D and 3D cell models and increases tumour volume and metastases in vivo. Using gene-expression and proteomic analyses, we show that JAM-A downregulation results in the activation of ERK, AKT and ROCK pathways and leads to decreased bone morphogenetic protein 7 expression. We identify MIR21 upregulation as the cause of JAM-A downregulation and show that JAM-A rescue mitigates the effects of MIR21 overexpression on cancer phenotype. Our results identify a novel molecular loop involving MIR21 dysregulation, JAM-A silencing and activation of multiple oncogenic pathways in promoting invasiveness and metastasis in CRC

Signatures of arithmetic simplicity in metabolic network architecture

Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that several of the properties predicted by the artificial chemistry model hold for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity

Markedly Divergent Tree Assemblage Responses to Tropical Forest Loss and Fragmentation across a Strong Seasonality Gradient

We examine the effects of forest fragmentation on the structure and composition of tree assemblages within three seasonal and aseasonal forest types of southern Brazil, including evergreen, Araucaria, and deciduous forests. We sampled three southernmost Atlantic Forest landscapes, including the largest continuous forest protected areas within each forest type. Tree assemblages in each forest type were sampled within 10 plots of 0.1 ha in both continuous forests and 10 adjacent forest fragments. All trees within each plot were assigned to trait categories describing their regeneration strategy, vertical stratification, seed-dispersal mode, seed size, and wood density. We detected differences among both forest types and landscape contexts in terms of overall tree species richness, and the density and species richness of different functional groups in terms of regeneration strategy, seed dispersal mode and woody density. Overall, evergreen forest fragments exhibited the largest deviations from continuous forest plots in assemblage structure. Evergreen, Araucaria and deciduous forests diverge in the functional composition of tree floras, particularly in relation to regeneration strategy and stress tolerance. By supporting a more diversified light-demanding and stress-tolerant flora with reduced richness and abundance of shade-tolerant, old-growth species, both deciduous and Araucaria forest tree assemblages are more intrinsically resilient to contemporary human-disturbances, including fragmentation-induced edge effects, in terms of species erosion and functional shifts. We suggest that these intrinsic differences in the direction and magnitude of responses to changes in landscape structure between forest types should guide a wide range of conservation strategies in restoring fragmented tropical forest landscapes worldwide

The Ascent of the Abundant: How Mutational Networks Constrain Evolution

Evolution by natural selection is fundamentally shaped by the fitness landscapes in which it occurs. Yet fitness landscapes are vast and complex, and thus we know relatively little about the long-range constraints they impose on evolutionary dynamics. Here, we exhaustively survey the structural landscapes of RNA molecules of lengths 12 to 18 nucleotides, and develop a network model to describe the relationship between sequence and structure. We find that phenotype abundance—the number of genotypes producing a particular phenotype—varies in a predictable manner and critically influences evolutionary dynamics. A study of naturally occurring functional RNA molecules using a new structural statistic suggests that these molecules are biased toward abundant phenotypes. This supports an “ascent of the abundant” hypothesis, in which evolution yields abundant phenotypes even when they are not the most fit

A multifactorial approach including tumoural epidermal growth factor receptor, p53, thymidylate synthase and dihydropyrimidine dehydrogenase to predict treatment outcome in head and neck cancer patients receiving 5-fluorouracil

The prognostic value of tumoural epidermal growth factor receptor (EGFR), p53, thymidylate synthase (TS) and dihydropyrimidine dehydrogenase (DPD) was analysed on 82 advanced head and neck cancer patients (71 men, 11 women; mean age 59). Induction treatment was cisplatin–5-FU ± folinic acid (61 patients, Chem group) or concomitant cisplatin–5-FU–radiotherapy (21 patients, RChem group). EGFR (binding assay), p53 protein (Sangtec immunoluminometric assay), TS and DPD activities (radioenzymatic assays) were measured on biopsies obtained at time of diagnosis. Significant positive correlation was demonstrated between p53 and EGFR. In the RChem group, p53 was higher in non-complete responders (median 1.03 ng mg−1) than in complete responders (median 0.08 ng mg−1) (P = 0.057). Univariate Cox analyses stratified on treatment group showed that specific survival (33 events) was significantly related to T staging, p53 taken as continuous or categorial (below vs over 0.80 ng mg−1) variable, and EGFR (below vs over 220 fmol mg−1); survival increased when EGFR and p53 were below thresholds. Multivariate stepwise analysis including T staging, EGFR and p53 revealed that T staging and EGFR were independent predictors of survival; relative risks were 3.68 for T staging and 2.65 for EGFR. Overall, EGFR remained an independent prognostic factor when response to treatment and T staging were considered in the multivariate analysis. © 1999 Cancer Research Campaig

(Reinforcing) factors influencing a physical education teachers use of the direct instruction model teaching games

he purpose of this study was to explore how a physical education (PE) teacher employed the direct instruction model (DIM) teaching games in a United Kingdom secondary school. The research sought to identify how the teacher utilised the DIM and those factors that influenced his use of the model. Occupational socialization was used to identify the factors that encouraged his use of the DIM. Data were collected from interviews and lesson observations. Inductive data analysis showed that while the teacher presented a ‘full version’ of the DIM, his limited content knowledge impacted on the use of the model in teaching cricket. Factors influencing his use of the model were a sporting perspective, a Post Graduate Certificate in Education mentor and the ability and behaviour of the students. These factors reinforced his undergraduate learning and subsequent use of the DIM. It is suggested that the comparable backgrounds of many PE student teachers may make the DIM an apt model to learn in undergraduate and postgraduate PE courses. However, effective use of the model requires students to be taught and to possess in-depth content knowledge of the game(s)/activities being taught and learned

Topological Structure of the Space of Phenotypes: The Case of RNA Neutral Networks

The evolution and adaptation of molecular populations is constrained by the diversity accessible through mutational processes. RNA is a paradigmatic example of biopolymer where genotype (sequence) and phenotype (approximated by the secondary structure fold) are identified in a single molecule. The extreme redundancy of the genotype-phenotype map leads to large ensembles of RNA sequences that fold into the same secondary structure and can be connected through single-point mutations. These ensembles define neutral networks of phenotypes in sequence space. Here we analyze the topological properties of neutral networks formed by 12-nucleotides RNA sequences, obtained through the exhaustive folding of sequence space. A total of 412 sequences fragments into 645 subnetworks that correspond to 57 different secondary structures. The topological analysis reveals that each subnetwork is far from being random: it has a degree distribution with a well-defined average and a small dispersion, a high clustering coefficient, and an average shortest path between nodes close to its minimum possible value, i.e. the Hamming distance between sequences. RNA neutral networks are assortative due to the correlation in the composition of neighboring sequences, a feature that together with the symmetries inherent to the folding process explains the existence of communities. Several topological relationships can be analytically derived attending to structural restrictions and generic properties of the folding process. The average degree of these phenotypic networks grows logarithmically with their size, such that abundant phenotypes have the additional advantage of being more robust to mutations. This property prevents fragmentation of neutral networks and thus enhances the navigability of sequence space. In summary, RNA neutral networks show unique topological properties, unknown to other networks previously described

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Individual Assessment of Arteriosclerosis by Empiric Clinical Profiling

BACKGROUND: Arteriosclerosis is a common cause of chronic morbidity and mortality. Myocardial infarction, stroke or other cardiovascular events identify vulnerable patients who suffer from symptomatic arteriosclerosis. Biomarkers to identify vulnerable patients before cardiovascular events occur are warranted to improve care for affected individuals. We tested how accurately basic clinical data can describe and assess the activity of arteriosclerosis in the individual patient. METHODOLOGY/PRINCIPAL FINDINGS: 269 in-patients who were treated for various conditions at the department of general medicine of an academic tertiary care center were included in a cross-sectional study. Personal history and clinical examination were obtained. When paraclinical tests were performed, the results were added to the dataset. The numerical variables in the clinical examination were statistically compared between patients with proven symptomatic arteriosclerosis (n = 100) and patients who had never experienced cardiovascular events in the past (n = 110). 25 variables were different between these two patient groups and contributed to the disease activity score. The percentile distribution of these variables defined the empiric clinical profile. Anthropometric data, signs of arterial, cardiac and renal disease, systemic inflammation and health economics formed the major categories of the empiric clinical profile that described an individual patient's disease activity. The area under the curve of the receiver operating curve for symptomatic arteriosclerosis was 0.891 (95% CI 0.799-0.983) for the novel disease activity score compared to 0.684 (95% CI 0.600-0.769) for the 10-year risk calculated according to the Framingham score. In patients suffering from symptomatic arteriosclerosis, the disease activity score deteriorated more rapidly after two years of follow-up (from 1.25 to 1.48, P = 0.005) compared to age- and sex-matched individuals free of cardiovascular events (from 1.09 to 1.19, P = 0.125). CONCLUSIONS/SIGNIFICANCE: Empiric clinical profiling and the disease activity score that are based on accessible, available and affordable clinical data are valid markers for symptomatic arteriosclerosis