The chemical evolution of r-process elements from neutron star mergers: the role of a 2-phase interstellar medium

Neutron star mergers (NM) are a plausible source of heavy r-process elements such as Europium, but previous chemical evolution models have either failed to reproduce the observed Europium trends for Milky Way thick disc stars (with [Fe/H] ≈ −1) or have done so only by adopting unrealistically short merger time-scales. Using analytic arguments and numerical simulations, we demonstrate that models with a single-phase interstellar medium (ISM) and metallicity-independent yields cannot reproduce observations showing [Eu/α] > 0 or [Eu/Fe] > [α/Fe] for α-elements such as Mg and Si. However, this problem is easily resolved if we allow for a 2-phase ISM, with hot-phase cooling times τcool of the order of 1Gyr and a larger fraction of NM yields injected directly into the cold star-forming phase relative to α-element yields from core-collapse supernovae (ccSNe). We find good agreement with observations in models with a cold phase injection ratio fc,NM/fc,ccSN of the order of 2, and a characteristic merger time-scale τNM=150Myr⁠. We show that the observed supersolar [Eu/α] at intermediate metallicities implies that a significant fraction of Eu originates from NM or another source besides ccSNe, and that these non-ccSN yields are preferentially deposited in the star-forming phase of the ISM at early times

Systems approaches and algorithms for discovery of combinatorial therapies

Effective therapy of complex diseases requires control of highly non-linear complex networks that remain incompletely characterized. In particular, drug intervention can be seen as control of signaling in cellular networks. Identification of control parameters presents an extreme challenge due to the combinatorial explosion of control possibilities in combination therapy and to the incomplete knowledge of the systems biology of cells. In this review paper we describe the main current and proposed approaches to the design of combinatorial therapies, including the empirical methods used now by clinicians and alternative approaches suggested recently by several authors. New approaches for designing combinations arising from systems biology are described. We discuss in special detail the design of algorithms that identify optimal control parameters in cellular networks based on a quantitative characterization of control landscapes, maximizing utilization of incomplete knowledge of the state and structure of intracellular networks. The use of new technology for high-throughput measurements is key to these new approaches to combination therapy and essential for the characterization of control landscapes and implementation of the algorithms. Combinatorial optimization in medical therapy is also compared with the combinatorial optimization of engineering and materials science and similarities and differences are delineated.Comment: 25 page

A network approach for managing and processing big cancer data in clouds

Translational cancer research requires integrative analysis of multiple levels of big cancer data to identify and treat cancer. In order to address the issues that data is decentralised, growing and continually being updated, and the content living or archiving on different information sources partially overlaps creating redundancies as well as contradictions and inconsistencies, we develop a data network model and technology for constructing and managing big cancer data. To support our data network approach for data process and analysis, we employ a semantic content network approach and adopt the CELAR cloud platform. The prototype implementation shows that the CELAR cloud can satisfy the on-demanding needs of various data resources for management and process of big cancer data

Origin of bistability underlying mammalian cell cycle entry

Mammalian cell cycle entry is controlled at the restriction point by a bistable and resettable switch, which is shown to emerge from a minimal gene circuit containing a mutual-inhibition feedback loop between Rb and E2F modules, coupled with a feed-forward loop between Myc and E2F modules

Homeostatic competition drives tumor growth and metastasis nucleation

We propose a mechanism for tumor growth emphasizing the role of homeostatic regulation and tissue stability. We show that competition between surface and bulk effects leads to the existence of a critical size that must be overcome by metastases to reach macroscopic sizes. This property can qualitatively explain the observed size distributions of metastases, while size-independent growth rates cannot account for clinical and experimental data. In addition, it potentially explains the observed preferential growth of metastases on tissue surfaces and membranes such as the pleural and peritoneal layers, suggests a mechanism underlying the seed and soil hypothesis introduced by Stephen Paget in 1889 and yields realistic values for metastatic inefficiency. We propose a number of key experiments to test these concepts. The homeostatic pressure as introduced in this work could constitute a quantitative, experimentally accessible measure for the metastatic potential of early malignant growths.Comment: 13 pages, 11 figures, to be published in the HFSP Journa

Baryon-Baryon Interactions

After a short survey of some topics of interest in the study of baryon-baryon scattering, the recent Nijmegen energy dependent partial wave analysis (PWA) of the nucleon-nucleon data is reviewed. In this PWA the energy range for both pp and np is now 0 < Tlab < 350 MeV and a chi^2_{d.o.f.}=1.08 was reached. The implications for the pion-nucleon coupling constants are discussed. Comments are made with respect to recent discussions around this coupling constant in the literature. In the second part, we briefly sketch the picture of the baryon in several, more or less QCD-based, quark-models that have been rather prominent in the literature. Inspired by these pictures we constructed a new soft-core model for the nucleon-nucleon interaction and present the first results of this model in a chi^2 -fit to the new multi-energy Nijmegen PWA. With this new model we succeeded in narrowing the gap between theory and experiment at low energies. For the energies Tlab = 25-320 MeV we reached a record low chi^2_{p.d.p.} = 1.16. We finish the paper with some conclusions and an outlook describing the extension of the new model to baryon-baryon scattering.Comment: 12 pages LaTeX and one postscript figure included. Invited talk presented at the XIVth European Conference of Few-Body Problems in Physics, Amsterdam, August 23-28, 199

Classical Effective Field Theory for Weak Ultra Relativistic Scattering

Inspired by the problem of Planckian scattering we describe a classical effective field theory for weak ultra relativistic scattering in which field propagation is instantaneous and transverse and the particles' equations of motion localize to the instant of passing. An analogy with the non-relativistic (post-Newtonian) approximation is stressed. The small parameter is identified and power counting rules are established. The theory is applied to reproduce the leading scattering angle for either a scalar interaction field or electro-magnetic or gravitational; to compute some subleading corrections, including the interaction duration; and to allow for non-zero masses. For the gravitational case we present an appropriate decomposition of the gravitational field onto the transverse plane together with its whole non-linear action. On the way we touch upon the relation with the eikonal approximation, some evidence for censorship of quantum gravity, and an algebraic ring structure on 2d Minkowski spacetime.Comment: 29 pages, 2 figures. v4: Duration of interaction is determined in Sec 4 and detailed in App C. Version accepted for publication in JHE

Search algorithms as a framework for the optimization of drug combinations

Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms, originally developed for digital communication, modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs with only one third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6-9 interventions in 80-90% of tests, compared with 15-30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution.Comment: 36 pages, 10 figures, revised versio

Monopoles and Holography

We present a holographic theory in AdS_4 whose zero temperature ground state develops a crystal structure, spontaneously breaking translational symmetry. The crystal is induced by a background magnetic field, but requires no chemical potential. This lattice arises from the existence of 't Hooft-Polyakov monopole solitons in the bulk which condense to form a classical object known as a monopole wall. In the infra-red, the magnetic field is screened and there is an emergent SU(2) global symmetry.Comment: 33 pages, 16 figures; v2: ref adde