A novel method for prokaryotic promoter prediction based on DNA stability

Background: In the post-genomic era, correct gene prediction has become one of the biggest challenges in genome annotation. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. This work presents a novel prokaryotic promoter prediction method based on DNA stability.Results: The promoter region is less stable and hence more prone to melting as compared to other genomic regions. Our analysis shows that a method of promoter prediction based on the differences in the stability of DNA sequences in the promoter and non-promoter region works much better compared to existing prokaryotic promoter prediction programs, which are based on sequence motif searches. At present the method works optimally for genomes such as that of Escherichia coli, which have near 50% G+C composition and also performs satisfactorily in case of other prokaryotic promoters.Conclusions: Our analysis clearly shows that the change in stability of DNA seems to provide a much better clue than usual sequence motifs, such as Pribnow box and -35 sequence, for differentiating promoter region from non-promoter regions. To a certain extent, it is more general and is likely to be applicable across organisms. Hence incorporation of such features in addition to the signature motifs can greatly improve the presently available promoter prediction programs

Dopant Induced Stabilization of Silicon Cluster at Finite Temperature

With the advances in miniaturization, understanding and controlling properties of significant technological systems like silicon in nano regime assumes considerable importance. It turns out that small silicon clusters in the size range of 15-20 atoms are unstable upon heating and in fact fragment in the temperature range of 1200 K to 1500 K. In the present work we demonstrate that it is possible to stabilize such clusters by introducing appropriate dopant (in this case Ti). Specifically, by using the first principle density functional simulations we show that Ti doped Si$_{16}$, having the Frank-Kasper geometry, remains stable till 2200 K and fragments only above 2600 K. The observed melting transition is a two step process. The first step is initiated by the surface melting around 600 K. The second step is the destruction of the cage which occurs around 2250 K giving rise to a peak in the heat capacity curve.Comment: 6 pages, 8 Figs. Submitted to PR

Finite Temperature Behavior of Small Silicon and Tin Clusters: An Ab Initio Molecular Dynamics Study

The finite temperature behavior of small Silicon (Si$_{10}$, Si$_{15}$, and Si$_{20}$) and Tin (Sn$_{10}$ and Sn$_{20}$) clusters is studied using isokinetic Born-Oppenheimer molecular dynamics. The lowest equilibrium structures of all the clusters are built upon a highly stable tricapped trigonal prism unit which is seen to play a crucial role in the finite temperature behavior of these clusters. Thermodynamics of small tin clusters (Sn$_{10}$ and Sn$_{20}$) is revisited in light of the recent experiments on tin clusters of sizes 18-21 [G. A. Breaux et. al. Phys. Rev. B {\bf 71} 073410 (2005)]. We have calculated heat capacities using multiple histogram technique for Si$_{10}$, Sn$_{10}$ and Si$_{15}$ clusters. Our calculated specific heat curves have a main peak around 2300 K and 2200 K for Si$_{10}$ and Sn$_{10}$ clusters respectively. However, various other melting indicators such as root mean square bond length fluctuations, mean square displacements show that diffusive motion of atoms within the cluster begins around 650 K. The finite temperature behavior of Si$_{10}$ and Sn$_{10}$ is dominated by isomerization and it is rather difficult to discern the temperature range for transition region. On the other hand, Si$_{15}$ does show a liquid like behavior over a short temperature range followed by the fragmentation observed around 1800 K. Finite temperature behavior of Si$_{20}$ and Sn$_{20}$ show that these clusters do not melt but fragment around 1200 K and 650 K respectively.Comment: 9 figure

Why do gallium clusters have a higher melting point than the bulk?

Density functional molecular dynamical simulations have been performed on Ga$_{17}$ and Ga$_{13}$ clusters to understand the recently observed higher-than-bulk melting temperatures in small gallium clusters [Breaux {\em et al.}, Phys. Rev. Lett. {\bf 91}, 215508 (2003)]. The specific-heat curve, calculated with the multiple-histogram technique, shows the melting temperature to be well above the bulk melting point of 303 K, viz. around 650 K and 1400 K for Ga$_{17}$ and Ga$_{13}$, respectively. The higher-than-bulk melting temperatures are attributed mainly to the covalent bonding in these clusters, in contrast with the covalent-metallic bonding in the bulk.Comment: 4 pages, including 6 figures. accepted for publication in Phys. Rev. Let

Size--sensitive melting characteristics of gallium clusters: Comparison of Experiment and Theory for Ga17+_{17}{}^{+} and Ga20+_{20}{}^{+}

Experiments and simulations have been performed to examine the finite-temperature behavior of Ga$_{17}{}^{+}$ and Ga$_{20}{}^{+}$ clusters. Specific heats and average collision cross sections have been measured as a function of temperature, and the results compared to simulations performed using first principles Density--Functional Molecular--Dynamics. The experimental results show that while Ga$_{17}{}^{+}$ apparently undergoes a solid--liquid transition without a significant peak in the specific--heat, Ga$_{20}{}^{+}$ melts with a relatively sharp peak. Our analysis of the computational results indicate a strong correlation between the ground--state geometry and the finite--temperature behavior of the cluster. If the ground--state geometry is symmetric and "ordered" the cluster is found to have a distinct peak in the specific--heat. However, if the ground--state geometry is amorphous or "disordered" the cluster melts without a peak in the specific--heat.Comment: 6 figure

First principles calculations of melting temperatures for free Na clusters

Density-functional simulations have been performed on Na55, Na92, and Na142 clusters in order to understand the experimentally observed melting properties [M. Schmidt et al., Nature (London) 393, 238 (1998)]. The calculated melting temperatures are in excellent agreement with the experimental ones. The calculations reveal a rather subtle interplay between geometric and electronic shell effects, and bring out the fact that the quantum mechanical description of the metallic bonding is crucial for understanding quantitatively the variation in melting temperatures observed experimentally

Emergence of noncollinear magnetic ordering in small magnetic clusters: Mnn_n and As@Mnn_n

Using first-principles density functional calculations, we have studied the magnetic ordering in pure Mn$_n$ ($n=2-$10, 13, 15, 19) and As@Mn$_n$ ($n=1-$10) clusters. Although, for both pure and doped manganese clusters, there exists many collinear and noncollinear isomers close in energy, the smaller clusters with $n\leqslant$5 have collinear magnetic ground state and the emergence of noncollinear ground states is seen for $n\geqslant$6 clusters. Due to strong $p-d$ hybridization in As@Mn$_n$ clusters, the binding energy is substantially enhanced and the magnetic moment is reduced compared to the corresponding pure Mn$_n$ clusters.Comment: 10 Pages and 5 Figure

mmWave V2V Localization in MU-MIMO Hybrid Beamforming

Recent trends for vehicular localization in millimetre-wave (mmWave) channels include employing a combination of parameters such as angle of arrival (AOA), angle of departure (AOD), and time of arrival (TOA) of the transmitted/received signals. These parameters are challenging to estimate, which along with the scattering and random nature of mmWave channels, and vehicle mobility lead to errors in localization. To circumvent these challenges, this paper proposes mmWave vehicular localization employing difference of arrival for time and frequency, with multiuser (MU) multiple-input-multiple-output (MIMO) hybrid beamforming; rather than relying on AOD/AOA/TOA estimates. The vehicular localization can exploit the number of vehicles present, as an increase in a number of vehicles reduces the Cramr-Rao bound (CRB) of error estimation. At 10 dB signal-to-noise ratio (SNR) both spatial multiplexing and beamforming result in comparable localization errors. At lower SNR values, spatial multiplexing leads to larger errors compared to beamforming due to formation of spurious peaks in the cross ambiguity function. Accuracy of the estimated parameters is improved by employing an extended Kalman filter leading to a root mean square (RMS) localization error of approximately 6.3 meters