Cooling of Dark-Matter Admixed Neutron Stars with density-dependent Equation of State

We propose a dark-matter (DM) admixed density-dependent equation of state where the fermionic DM interacts with the nucleons via Higgs portal. Presence of DM can hardly influence the particle distribution inside neutron star (NS) but can significantly affect the structure as well as equation of state (EOS) of NS. Introduction of DM inside NS softens the equation of state. We explored the effect of variation of DM mass and DM Fermi momentum on the NS EOS. Moreover, DM-Higgs coupling is constrained using dark matter direct detection experiments. Then, we studied cooling of normal NSs using APR and DD2 EOSs and DM admixed NSs using dark-matter modified DD2 with varying DM mass and Fermi momentum. We have done our analysis by considering different NS masses. Also DM mass and DM Fermi momentum are varied for fixed NS mass and DM-Higgs coupling. We calculated the variations of luminosity and temperature of NS with time for all EOSs considered in our work and then compared our calculations with the observed astronomical cooling data of pulsars namely Cas A, RX J0822-43, 1E 1207-52, RX J0002+62, XMMU J17328, PSR B1706-44, Vela, PSR B2334+61, PSR B0656+14, Geminga, PSR B1055-52 and RX J0720.4-3125. It is found that APR EOS agrees well with the pulsar data for lighter and medium mass NSs but cooling is very fast for heavier NS. For DM admixed DD2 EOS, it is found that for all considered NS masses, all chosen DM masses and Fermi momenta agree well with the observational data of PSR B0656+14, Geminga, Vela, PSR B1706-44 and PSR B2334+61. Cooling becomes faster as compared to normal NSs in case of increasing DM mass and Fermi momenta. It is infered from the calculations that if low mass super cold NSs are observed in future that may support the fact that heavier WIMP can be present inside neutron stars.Comment: 24 Pages, 15 Figures and 2 Tables. Version accepted in The European Physical Journal

Primordial Black Holes: sirens of the early Universe

Primordial Black Holes (PBHs) are, typically light, black holes which can form in the early Universe. There are a number of formation mechanisms, including the collapse of large density perturbations, cosmic string loops and bubble collisions. The number of PBHs formed is tightly constrained by the consequences of their evaporation and their lensing and dynamical effects. Therefore PBHs are a powerful probe of the physics of the early Universe, in particular models of inflation. They are also a potential cold dark matter candidate.Comment: 21 pages. To be published in "Quantum Aspects of Black Holes", ed. X. Calmet (Springer, 2014

The Arabidopsis leucine-rich repeat receptor kinase MIK2/LRR-KISS connects cell wall integrity sensing, root growth and response to abiotic and biotic stresses

Plants actively perceive and respond to perturbations in their cell walls which arise during growth, biotic and abiotic stresses. However, few components involved in plant cell wall integrity sensing have been described to date. Using a reverse-genetic approach, we identified the Arabidopsis thaliana leucine-rich repeat receptor kinase MIK2 as an important regulator of cell wall damage responses triggered upon cellulose biosynthesis inhibition. Indeed, loss-of-function mik2 alleles are strongly affected in immune marker gene expression, jasmonic acid production and lignin deposition. MIK2 has both overlapping and distinct functions with THE1, a malectin-like receptor kinase previously proposed as cell wall integrity sensor. In addition, mik2 mutant plants exhibit enhanced leftward root skewing when grown on vertical plates. Notably, natural variation in MIK2 (also named LRR-KISS) has been correlated recently to mild salt stress tolerance, which we could confirm using our insertional alleles. Strikingly, both the increased root skewing and salt stress sensitivity phenotypes observed in the mik2 mutant are dependent on THE1. Finally, we found that MIK2 is required for resistance to the fungal root pathogen Fusarium oxysporum. Together, our data identify MIK2 as a novel component in cell wall integrity sensing and suggest that MIK2 is a nexus linking cell wall integrity sensing to growth and environmental cues

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules

Fingerprint resampling: A generic method for efficient resampling

In resampling methods, such as bootstrapping or cross validation, a very similar computational problem (usually an optimization procedure) is solved over and over again for a set of very similar data sets. If it is computationally burdensome to solve this computational problem once, the whole resampling method can become unfeasible. However, because the computational problems and data sets are so similar, the speed of the resampling method may be increased by taking advantage of these similarities in method and data. As a generic solution, we propose to learn the relation between the resampled data sets and their corresponding optima. Using this learned knowledge, we are then able to predict the optima associated with new resampled data sets. First, these predicted optima are used as starting values for the optimization process. Once the predictions become accurate enough, the optimization process may even be omitted completely, thereby greatly decreasing the computational burden. The suggested method is validated using two simple problems (where the results can be verified analytically) and two real-life problems (i.e., the bootstrap of a mixed model and a generalized extreme value distribution). The proposed method led on average to a tenfold increase in speed of the resampling method