Epigeal fauna of urban food production sites show no obvious relationships with soil characteristics or site area

Urban food production is a growing area of interest as a way of increasing food security, social capital and biodiversity. As food production relies upon ecosystem services provided by invertebrates (e.g. decomposition), it is important to understand the underlying factors affecting their distribution. Here we investigated the influence of soil characteristics and patch area on the abundance and diversity of epigeal invertebrates. Seventeen sites of different size from in and around Leeds, UK, were selected from an open source database on urban food production. Pitfall traps were placed along transects to collect beetles, springtails, and spiders. These invertebrates were identified and counted, adjusting total counts for the number of traps used at each location. Soil samples from the trap locations were homogenized, dried, and analysed to measure organic carbon content, moisture content, and pH, while productivity was assessed by growing radish Raphanus sativus on the soils under uniform conditions. This study found no evidence of correlation of epigeal abundance and diversity with site area or soil characteristics. These findings suggest that there is no evidence as yet of urban food production sites that are too small to be able to draw upon ecosystem services delivered by epigeal invertebrates

PHASE BEHAVIOR OF STAR-SHAPED DNA NANO-STRUCTURES

In this thesis we studied the collective behavior of limited valence DNA particles. By exploiting the selectivity of Watson-Crick pairing, we synthesized star-shaped DNA particles having either three or four arms, each arm terminating in a sticky overhang sequence that provides interactions between individual particles. Each nano-star can thus be viewed as limited valence particle whose valence number f is dictated by the number of star arms. Solutions of such structures are found to exhibit liquid-vapour-like phase separation. Our results show that by reducing the valence of the structures, the coexistence region is greatly shrunk both in temperature and in concentration, thereby confirming for the first time recent theoretical predictions. As the temperature of the system is reduced, and the critical point approached from above, the dynamic behavior slows down and becomes characterized by a two-step relaxation process. The two characteristic times behave differently: the faster one (\u3c4f) changes only very mildly while the slower one (\u3c4s) slows down by more than three orders of magnitude in an Arrhenius fashion, without any noticeable divergence as Tc is approached. Quite remarkably, \u3c4s does not show the power-law divergence expected for critical slowing down. The colloidal system here proposed makes use of DNA not only to introduce mutual interactions between individual particles, but to model their geometry controlling internal interactions at the nanoscale level. This work proves that DNA is a powerful tool to produce particles with directional interactions, and can be used to design complex structures as colloidal molecules at the nanoscale

Enrichment of the hot intracluster medium: observations

Four decades ago, the firm detection of an Fe-K emission feature in the X-ray spectrum of the Perseus cluster revealed the presence of iron in its hot intracluster medium (ICM). With more advanced missions successfully launched over the last 20 years, this discovery has been extended to many other metals and to the hot atmospheres of many other galaxy clusters, groups, and giant elliptical galaxies, as evidence that the elemental bricks of life - synthesized by stars and supernovae - are also found at the largest scales of the Universe. Because the ICM, emitting in X-rays, is in collisional ionisation equilibrium, its elemental abundances can in principle be accurately measured. These abundance measurements, in turn, are valuable to constrain the physics and environmental conditions of the Type Ia and core-collapse supernovae that exploded and enriched the ICM over the entire cluster volume. On the other hand, the spatial distribution of metals across the ICM constitutes a remarkable signature of the chemical history and evolution of clusters, groups, and ellipticals. Here, we summarise the most significant achievements in measuring elemental abundances in the ICM, from the very first attempts up to the era of XMM-Newton, Chandra, and Suzaku and the unprecedented results obtained by Hitomi. We also discuss the current systematic limitations of these measurements and how the future missions XRISM and Athena will further improve our current knowledge of the ICM enrichment.Comment: 49 pages. Review paper. Accepted for publication on Space Science Reviews. This is the companion review of "Enrichment of the hot intracluster medium: numerical simulations

Cool Core Clusters from Cosmological Simulations

We present results obtained from a set of cosmological hydrodynamic simulations of galaxy clusters, aimed at comparing predictions with observational data on the diversity between cool-core (CC) and non-cool-core (NCC) clusters. Our simulations include the effects of stellar and AGN feedback and are based on an improved version of the smoothed particle hydrodynamics code GADGET-3, which ameliorates gas mixing and better captures gas-dynamical instabilities by including a suitable artificial thermal diffusion. In this Letter, we focus our analysis on the entropy profiles, the primary diagnostic we used to classify the degree of cool-coreness of clusters, and on the iron profiles. In keeping with observations, our simulated clusters display a variety of behaviors in entropy profiles: they range from steadily decreasing profiles at small radii, characteristic of cool-core systems, to nearly flat core isentropic profiles, characteristic of non-cool-core systems. Using observational criteria to distinguish between the two classes of objects, we find that they occur in similar proportions in both simulations and in observations. Furthermore, we also find that simulated cool-core clusters have profiles of iron abundance that are steeper than those of NCC clusters, which is also in agreement with observational results. We show that the capability of our simulations to generate a realistic cool-core structure in the cluster population is due to AGN feedback and artificial thermal diffusion: their combined action allows us to naturally distribute the energy extracted from super-massive black holes and to compensate for the radiative losses of low-entropy gas with short cooling time residing in the cluster core.Comment: 6 pages, 4 figures, accepted in ApJL, v2 contains some modifications on the text (results unchanged

The Overlooked Potential of Generalized Linear Models in Astronomy - I: Binomial Regression

Revealing hidden patterns in astronomical data is often the path to fundamental scientific breakthroughs; meanwhile the complexity of scientific inquiry increases as more subtle relationships are sought. Contemporary data analysis problems often elude the capabilities of classical statistical techniques, suggesting the use of cutting edge statistical methods. In this light, astronomers have overlooked a whole family of statistical techniques for exploratory data analysis and robust regression, the so-called Generalized Linear Models (GLMs). In this paper -- the first in a series aimed at illustrating the power of these methods in astronomical applications -- we elucidate the potential of a particular class of GLMs for handling binary/binomial data, the so-called logit and probit regression techniques, from both a maximum likelihood and a Bayesian perspective. As a case in point, we present the use of these GLMs to explore the conditions of star formation activity and metal enrichment in primordial minihaloes from cosmological hydro-simulations including detailed chemistry, gas physics, and stellar feedback. We predict that for a dark mini-halo with metallicity $\approx 1.3 \times 10^{-4} Z_{\bigodot}$, an increase of $1.2 \times 10^{-2}$ in the gas molecular fraction, increases the probability of star formation occurrence by a factor of 75%. Finally, we highlight the use of receiver operating characteristic curves as a diagnostic for binary classifiers, and ultimately we use these to demonstrate the competitive predictive performance of GLMs against the popular technique of artificial neural networks.Comment: 20 pages, 10 figures, 3 tables, accepted for publication in Astronomy and Computin

The developmental pathways of preschool children with acute lymphoblastic leukemia: communicative and social sequelae one year after treatment

Early childhood is considered to be a period of rapid development, with the acquisition of abilities predicting future positive school competences. Motor, cognitive, and social diculties related to cancer therapies heavily impact the development of children with cancer. This study focused on two main aims: To assess the developmental pathways of preschool children with acute lymphoblastic leukemia one year post-treatment and to compare these abilities both with those of a control group of healthy peers and with Italian norms. Forty-four children and their families, recruited through the Hematology-Oncologic Clinic of the Department of Child andWoman Health (University of Padua), agreed to participate in this study. The children\u2019s mean age was 4.52 years (SD = 0.94, range = 2.5\u20136 years), equally distributed by gender, all diagnosed with acute lymphoblastic leukemia. Matched healthy peers were recruited through pediatricians\u2019 ambulatories. Each family was interviewed adopting the Vineland adaptive behavior scales. Paired sampleWilcoxon tests revealed that children were reported to have significantly more developmental diculties than their healthy peers. When compared with Italian norms, they scored particularly low in verbal competence, social, and coping skills. No significant association was found between treatment variables and developmental abilities. These findings suggest that the creation of specialized interventions, both for parents and children, may fill the possible delays in children\u2019s development probably due to stress, lack of adequate stimulation, or dicult adaptation

Robust PCA and MIC statistics of baryons in early minihaloes

We present a novel approach, based on robust principal components analysis (RPCA) and maximal information coefficient (MIC), to study the redshift dependence of halo baryonic properties. Our data are composed of a set of different physical quantities for primordial minihaloes: dark matter mass (M-dm), gas mass (M-gas), stellar mass (M-star), molecular fraction (x(mol)), metallicity (Z), star formation rate (SFR) and temperature. We find that M-dm and M-gas are dominant factors for variance, particularly at high redshift. Nonetheless, with the emergence of the first stars and subsequent feedback mechanisms, x(mol), SFR and Z start to have a more dominant role. Standard PCA gives three principal components (PCs) capable to explain more than 97 per cent of the data variance at any redshift (two PCs usually accounting for no less than 92 per cent), whilst the first PC from the RPCA analysis explains no less than 84 per cent of the total variance in the entire redshift range (with two PCs explaining greater than or similar to 95 per cent anytime). Our analysis also suggests that all the gaseous properties have a stronger correlation with M-gas than with M-dm, while M-gas has a deeper correlation with x(mol) than with Z or SFR. This indicates the crucial role of gas molecular content to initiate star formation and consequent metal pollution from Population III and Population II/I regimes in primordial galaxies. Finally, a comparison between MIC and Spearman correlation coefficient shows that the former is a more reliable indicator when halo properties are weakly correlated

Investigating Cardiac Motion Patters Using Synthetic High-Resolution 3D Cardiovascular Magnetic Resonance Images and Statistical Shape Analysis

Diagnosis of ventricular dysfunction in congenital heart disease is more and more based on medical imaging, which allows investigation of abnormal cardiac morphology and correlated abnormal function. Although analysis of 2D images represents the clinical standard, novel tools performing automatic processing of 3D images are becoming available, providing more detailed and comprehensive information than simple 2D morphometry. Among these, statistical shape analysis (SSA) allows a consistent and quantitative description of a population of complex shapes, as a way to detect novel biomarkers, ultimately improving diagnosis and pathology understanding. The aim of this study is to describe the implementation of a SSA method for the investigation of 3D left ventricular shape and motion patterns and to test it on a small sample of 4 congenital repaired aortic stenosis patients and 4 age-matched healthy volunteers to demonstrate its potential. The advantage of this method is the capability of analyzing subject-specific motion patterns separately from the individual morphology, visually and quantitatively, as a way to identify functional abnormalities related to both dynamics and shape. Specifically, we combined 3D, high-resolution whole heart data with 2D, temporal information provided by cine cardiovascular magnetic resonance images, and we used an SSA approach to analyze 3D motion per se. Preliminary results of this pilot study showed that using this method, some differences in end-diastolic and end-systolic ventricular shapes could be captured, but it was not possible to clearly separate the two cohorts based on shape information alone. However, further analyses on ventricular motion allowed to qualitatively identify differences between the two populations. Moreover, by describing shape and motion with a small number of principal components, this method offers a fully automated process to obtain visually intuitive and numerical information on cardiac shape and motion, which could be, once validated on a larger sample size, easily integrated into the clinical workflow. To conclude, in this preliminary work, we have implemented state-of-the-art automatic segmentation and SSA methods, and we have shown how they could improve our understanding of ventricular kinetics by visually and potentially quantitatively highlighting aspects that are usually not picked up by traditional approaches