Validity of the N\'{e}el-Arrhenius model for highly anisotropic Co_xFe_{3-x}O_4 nanoparticles

We report a systematic study on the structural and magnetic properties of Co_{x}Fe_{3-x}O_{4} magnetic nanoparticles with sizes between $5$ to $25$ nm, prepared by thermal decomposition of Fe(acac)_{3} and Co(acac)_{2}. The large magneto-crystalline anisotropy of the synthesized particles resulted in high blocking temperatures ($42$ K \leqq $T_B$ $\leqq 345$ K for $5 \leqq$ d $\leqq 13$ nm ) and large coercive fields ($H_C \approxeq 1600$ kA/m for $T = 5$ K). The smallest particles ($=5$ nm) revealed the existence of a magnetically hard, spin-disordered surface. The thermal dependence of static and dynamic magnetic properties of the whole series of samples could be explained within the N\'{e}el-Arrhenius relaxation framework without the need of ad-hoc corrections, by including the thermal dependence of the magnetocrystalline anisotropy constant $K_1(T)$ through the empirical Br\"{u}khatov-Kirensky relation. This approach provided $K_1(0)$ values very similar to the bulk material from either static or dynamic magnetic measurements, as well as realistic values for the response times ($\tau_0 \simeq 10^{-10}$ s). Deviations from the bulk anisotropy values found for the smallest particles could be qualitatively explained based on Zener\'{}s relation between $K_1(T)$ and M(T)

Dynamic Patterns of Transcript Abundance of Transposable Element Families in Maize.

Transposable Elements (TEs) are mobile elements that contribute the majority of DNA sequences in the maize genome. Due to their repetitive nature, genomic studies of TEs are complicated by the difficulty of properly attributing multi-mapped short reads to specific genomic loci. Here, we utilize a method to attribute RNA-seq reads to TE families rather than particular loci in order to characterize transcript abundance for TE families in the maize genome. We applied this method to assess per-family expression of transposable elements in >800 published RNA-seq libraries representing a range of maize development, genotypes, and hybrids. While a relatively small proportion of TE families are transcribed, expression is highly dynamic with most families exhibiting tissue-specific expression. A large number of TE families were specifically detected in pollen and endosperm, consistent with reproductive dynamics that maintain silencing of TEs in the germ line. We find that B73 transcript abundance is a poor predictor of TE expression in other genotypes and that transcript levels can differ even for shared TEs. Finally, by assessing recombinant inbred line and hybrid transcriptomes, complex patterns of TE transcript abundance across genotypes emerged. Taken together, this study reveals a dynamic contribution of TEs to maize transcriptomes

The Brightest Cluster Galaxy in Abell 85: The Largest Core Known so far

We have found that the brightest cluster galaxy (BCG) in Abell~85, Holm 15A, displays the largest core so far known. Its cusp radius, $r_{\gamma} = 4.57 \pm 0.06$ kpc ($4.26^{\prime\prime}\pm 0.06^{\prime\prime}$), is more than 18 times larger than the mean for BCGs, and $\geq1$ kpc larger than A2261-BCG, hitherto the largest-cored BCG (Postman, Lauer, Donahue, et al. 2012) Holm 15A hosts the luminous amorphous radio source 0039-095B and has the optical signature of a LINER. Scaling laws indicate that this core could host a supermassive black hole (SMBH) of mass $M_{\bullet}\thicksim (10^{9}-10^{11})\,M_{\odot}$. We suggest that cores this large represent a relatively short phase in the evolution of BCGs, whereas the masses of their associated SBMH might be set by initial conditions.Comment: 14 pages, 3 figure, 2 tables, accepted for publication in ApJ Letters on October 6th, 2014, replacement of previous manuscript submitted on May 30th, 2014 to astro-p

Nonlinear software sensor for monitoring genetic regulation processes with noise and modeling errors

Nonlinear control techniques by means of a software sensor that are commonly used in chemical engineering could be also applied to genetic regulation processes. We provide here a realistic formulation of this procedure by introducing an additive white Gaussian noise, which is usually found in experimental data. Besides, we include model errors, meaning that we assume we do not know the nonlinear regulation function of the process. In order to illustrate this procedure, we employ the Goodwin dynamics of the concentrations [B.C. Goodwin, Temporal Oscillations in Cells, (Academic Press, New York, 1963)] in the simple form recently applied to single gene systems and some operon cases [H. De Jong, J. Comp. Biol. 9, 67 (2002)], which involves the dynamics of the mRNA, given protein, and metabolite concentrations. Further, we present results for a three gene case in co-regulated sets of transcription units as they occur in prokaryotes. However, instead of considering their full dynamics, we use only the data of the metabolites and a designed software sensor. We also show, more generally, that it is possible to rebuild the complete set of nonmeasured concentrations despite the uncertainties in the regulation function or, even more, in the case of not knowing the mRNA dynamics. In addition, the rebuilding of concentrations is not affected by the perturbation due to the additive white Gaussian noise and also we managed to filter the noisy output of the biological systemComment: 21 pages, 7 figures; also selected in vjbio of August 2005; this version corrects a misorder in the last three references of the published versio

A Brief Review on Dark Matter Annihilation Explanation for e±e^\pm Excesses in Cosmic Ray

Recently data from PAMELA, ATIC, FERMI-LAT and HESS show that there are $e^{\pm}$ excesses in the cosmic ray energy spectrum. PAMELA observed excesses only in $e^+$, but not in anti-proton spectrum. ATIC, FERMI-LAT and HESS observed excesses in $e^++e^-$ spectrum, but the detailed shapes are different which requires future experimental observations to pin down the correct data set. Nevertheless a lot of efforts have been made to explain the observed $e^\pm$ excesses, and also why PAMELA only observed excesses in $e^+$ but not in anti-proton. In this brief review we discuss one of the most popular mechanisms to explain the data, the dark matter annihilation. It has long been known that about 23% of our universe is made of relic dark matter. If the relic dark matter was thermally produced, the annihilation rate is constrained resulting in the need of a large boost factor to explain the data. We will discuss in detail how a large boost factor can be obtained by the Sommerfeld and Briet-Wigner enhancement mechanisms. Some implications for particle physics model buildings will also be discussed.Comment: 22 pages, 6 figures. Several typoes corrected and some references added. Published in Mod. Phys. Lett. A, Vol. 24, No. 27 (2009) pp. 2139-216

SDSS-IV MANGA: Spatially Resolved Star Formation Main Sequence and LI(N)ER Sequence

We present our study on the spatially resolved H_alpha and M_star relation for 536 star-forming and 424 quiescent galaxies taken from the MaNGA survey. We show that the star formation rate surface density (Sigma_SFR), derived based on the H_alpha emissions, is strongly correlated with the M_star surface density (Sigma_star) on kpc scales for star- forming galaxies and can be directly connected to the global star-forming sequence. This suggests that the global main sequence may be a consequence of a more fundamental relation on small scales. On the other hand, our result suggests that about 20% of quiescent galaxies in our sample still have star formation activities in the outer region with lower SSFR than typical star-forming galaxies. Meanwhile, we also find a tight correlation between Sigma_H_alpha and Sigma_star for LI(N)ER regions, named the resolved "LI(N)ER" sequence, in quiescent galaxies, which is consistent with the scenario that LI(N)ER emissions are primarily powered by the hot, evolved stars as suggested in the literature.Comment: 6 pages, 4 figures. ApJ Letter accepte

Solving Medium-Density Subset Sum Problems in Expected Polynomial Time: An Enumeration Approach

The subset sum problem (SSP) can be briefly stated as: given a target integer $E$ and a set $A$ containing $n$ positive integer $a_j$, find a subset of $A$ summing to $E$. The \textit{density} $d$ of an SSP instance is defined by the ratio of $n$ to $m$, where $m$ is the logarithm of the largest integer within $A$. Based on the structural and statistical properties of subset sums, we present an improved enumeration scheme for SSP, and implement it as a complete and exact algorithm (EnumPlus). The algorithm always equivalently reduces an instance to be low-density, and then solve it by enumeration. Through this approach, we show the possibility to design a sole algorithm that can efficiently solve arbitrary density instance in a uniform way. Furthermore, our algorithm has considerable performance advantage over previous algorithms. Firstly, it extends the density scope, in which SSP can be solved in expected polynomial time. Specifically, It solves SSP in expected $O(n\log{n})$ time when density $d \geq c\cdot \sqrt{n}/\log{n}$, while the previously best density scope is $d \geq c\cdot n/(\log{n})^{2}$. In addition, the overall expected time and space requirement in the average case are proven to be $O(n^5\log n)$ and $O(n^5)$ respectively. Secondly, in the worst case, it slightly improves the previously best time complexity of exact algorithms for SSP. Specifically, the worst-case time complexity of our algorithm is proved to be $O((n-6)2^{n/2}+n)$, while the previously best result is $O(n2^{n/2})$.Comment: 11 pages, 1 figur