On the zero mass limit of tagged particle diffusion in the 1-d Rayleigh-gas

We consider the M -> 0 limit for tagged particle diffusion in a 1-dimensional Rayleigh-gas, studied originaly by Sinai and Soloveichik (1986), respectively by Szasz and Toth (1986). In this limit we derive a new type of model for tagged paricle diffusion, with Calogero-Moser-Sutherland (i.e. inverse quadratic) interaction potential between the two central particles. Computer simulations on this new model reproduce exactly the numerical value of the limiting variance obtained by Boldrighini, Frigio and Tognetti (2002).Comment: Dedicated to Domokos Szasz on his 65th birthda

Limit theorems in the stadium billiard

We prove that the Birkhoff sums for ``almost every'' relevant observable in the stadium billiard obey a non-standard limit law. More precisely, the usual central limit theorem holds for an observable if and only if its integral along a one-codimensional invariant set vanishes, otherwise a $\sqrt{n\log n}$ normalization is needed. As one of the two key steps in the argument, we obtain a limit theorem that holds in Young towers with exponential return time statistics in general, an abstract result that seems to be applicable to many other situations.Comment: 46 page

Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning

Antihydrogen is at the forefront of antimatter research at the CERN Antiproton Decelerator. Experiments aiming to test the fundamental CPT symmetry and antigravity effects require the efficient detection of antihydrogen annihilation events, which is performed using highly granular tracking detectors installed around an antimatter trap. Improving the efficiency of the antihydrogen annihilation detection plays a central role in the final sensitivity of the experiments. We propose deep learning as a novel technique to analyze antihydrogen annihilation data, and compare its performance with a traditional track and vertex reconstruction method. We report that the deep learning approach yields significant improvement, tripling event coverage while simultaneously improving performance by over 5% in terms of Area Under Curve (AUC)

A practical review on the measurement tools for cellular adhesion force

Cell cell and cell matrix adhesions are fundamental in all multicellular organisms. They play a key role in cellular growth, differentiation, pattern formation and migration. Cell-cell adhesion is substantial in the immune response, pathogen host interactions, and tumor development. The success of tissue engineering and stem cell implantations strongly depends on the fine control of live cell adhesion on the surface of natural or biomimetic scaffolds. Therefore, the quantitative and precise measurement of the adhesion strength of living cells is critical, not only in basic research but in modern technologies, too. Several techniques have been developed or are under development to quantify cell adhesion. All of them have their pros and cons, which has to be carefully considered before the experiments and interpretation of the recorded data. Current review provides a guide to choose the appropriate technique to answer a specific biological question or to complete a biomedical test by measuring cell adhesion

Building Protein Domain Based Composite Biobricks for Mammalian Expression Systems

The purpose of this RFC is to describe a method that allows the design of protein domain based parts, starting with gene centered information and translate these informations into BBF RFC 25 compatible part. The method is designed to be used in mammalian expression systems

Southern leaf blight disease severity is correlated with decreased maize leaf epiphytic bacterial species richness and the phyllosphere bacterial diversity decline is enhanced by nitrogen fertilization

Plant leaves are inhabited by a diverse group of microorganisms that are important contributors to optimal growth. Biotic and abiotic effects on plant growth are usually studied in controlled settings examining response to variation in single factors and in field settings with large numbers of variables. Multi-factor experiments with combinations of stresses bridge this gap, increasing our understanding of the genotype-environment-phenotype functional map for the host plant and the affiliated epiphytic community. The maize inbred B73 was exposed to single and combination abiotic and the biotic stress treatments: low nitrogen fertilizer and high levels of infection with southern leaf blight (causal agent Cochliobolus heterostrophus). Microbial epiphyte samples were collected at the vegetative early-season phase and species composition was determined using 16S ribosomal intergenic spacer analysis. Plant traits and level of southern leaf blight disease were measured late-season. Bacterial diversity was different among stress treatment groups (P < 0.001). Lower species richness—alpha diversity—was correlated with increased severity of southern leaf blight disease when disease pressure was high. Nitrogen fertilization intensified the decline in bacterial alpha diversity. While no single bacterial ribotype was consistently associated with disease severity, small sets of ribotypes were good predictors of disease levels. Difference in leaf bacterial-epiphyte diversity early in the season were correlated with plant disease severity, supporting further tests of microbial epiphyte-disease correlations for use in predicting disease progression

Colour changes upon cooling of Lepidoptera scales containing photonic nanoarchitectures

The effects produced by the condensation of water vapours from the ambient in the various intricate nanoarchitectures occurring in the wing scales of several Lepidoptera species were investigated by controlled cooling (from room temperature to -5 - -10 {\deg}C) combined with in situ measurement of changes in the reflectance spectra. It was determined that, due to this procedure, all photonic nanoarchitectures giving a reflectance maximum in the visible range and having an open nanostructure exhibited alteration of the position of the reflectance maximum associated with the photonic nanoarchitectures. The photonic nanoarchitectures with a closed structure exhibited little to no alteration in colour. Similarly, control specimens coloured by pigments did not exhibit a colour change under the same conditions. Hence, this effect can be used to identify species with open photonic nanoarchitectures in their scales. For certain species, an almost complete disappearance of the reflectance maximum was found. All specimens recovered their original colours following warming and drying. Cooling experiments using thin copper wires demonstrated that colour alterations could be limited to a millimetre, or below. Dried museum specimens do not exhibit colour changes when cooled in the absence of a heat sink due to the low heat capacity of the wings.Comment: 18 pages, 9 figures, including supplemen

Room temperature manipulation of long lifetime spins in metallic-like carbon nanospheres

The time-window for processing electron spin information (spintronics) in solid-state quantum electronic devices is determined by the spin–lattice and spin–spin relaxation times of electrons. Minimizing the effects of spin–orbit coupling and the local magnetic contributions of neighbouring atoms on spin–lattice and spin–spin relaxation times at room temperature remain substantial challenges to practical spintronics. Here we report conduction electron spin–lattice and spin–spin relaxation times of 175 ns at 300 K in 37±7 nm carbon spheres, which is remarkably long for any conducting solid- state material of comparable size. Following the observation of spin polarization by electron spin resonance, we control the quantum state of the electron spin by applying short bursts of an oscillating magnetic field and observe coherent oscillations of the spin state. These results demonstrate the feasibility of operating electron spins in conducting carbon nanospheres as quantum bits at room temperature

The resilience of EU Member States to the financial and economic crisis What are the characteristics of resilient behaviour?

This study presents an empirical analysis of the resilience of European countries to the financial and economic crisis that started in 2007. The analysis addresses the following questions: Which countries showed a resilient behaviour during and after the crisis? Is resilience related only to the economic dimension? Has any of the EU countries been able to use the crisis as an opportunity and 'bounce forward'? Is it possible to identify any particular country characteristics linked to resilience? The analysis is based on the JRC conceptual framework for resilience (Manca et al., 2017) which places at its core the wellbeing of individuals, thus going beyond the merely economic growth perspective. The study carefully selects a number of key economic and social variables that aim to capture the resilience capacities of our society. Resilience is measured by investigating the dynamic response of these variables to the crisis in the short and medium run. In particular, we define four resilience indicators: the impact of the crisis, the recovery, the medium-run, and the ‘bouncing forward’. Results from a narrow exercise focusing on macroeconomic and financial variables confirm the validity of the proposed measurement approach: Germany appears to be among the most resilient countries; Ireland, after having been severely hit, shows a good absorptive capacity; Italy seems to be still struggling with the recovery, while Greece remains the most affected. After measuring resilience, we identify underlying country characteristics that may be associated with resilient behaviour. As such, these could indicate entry points for policies to increase countries' resilience to economic and financial shocks.JRC.B.1-Finance and Econom