A New Characterization of Fine Scale Diffusion on the Cell Membrane

We use a large single particle tracking data set to analyze the short time and small spatial scale motion of quantum dots labeling proteins in cell membranes. Our analysis focuses on the jumps which are the changes in the position of the quantum dots between frames in a movie of their motion. Previously we have shown that the directions of the jumps are uniformly distributed and the jump lengths can be characterized by a double power law distribution. Here we show that the jumps over a small number of time steps can be described by scalings of a {\em single} double power law distribution. This provides additional strong evidence that the double power law provides an accurate description of the fine scale motion. This more extensive analysis provides strong evidence that the double power law is a novel stable distribution for the motion. This analysis provides strong evidence that an earlier result that the motion can be modeled as diffusion in a space of fractional dimension roughly 3/2 is correct. The form of the power law distribution quantifies the excess of short jumps in the data and provides an accurate characterization of the fine scale diffusion and, in fact, this distribution gives an accurate description of the jump lengths up to a few hundred nanometers. Our results complement of the usual mean squared displacement analysis used to study diffusion at larger scales where the proteins are more likely to strongly interact with larger membrane structures.Comment: 18 pages, 7 figure

Pulsar spin-down: the glitch-dominated rotation of PSR J0537-6910

The young, fast-spinning, X-ray pulsar J0537-6910 displays an extreme glitch activity, with large spin-ups interrupting its decelerating rotation every ~100 days. We present nearly 13 years of timing data from this pulsar, obtained with the {\it Rossi X-ray Timing Explorer}. We discovered 22 new glitches and performed a consistent analysis of all 45 glitches detected in the complete data span. Our results corroborate the previously reported strong correlation between glitch spin-up size and the time to the next glitch, a relation that has not been observed so far in any other pulsar. The spin evolution is dominated by the glitches, which occur at a rate ~3.5 per year, and the post-glitch recoveries, which prevail the entire inter-glitch intervals. This distinctive behaviour provides invaluable insights into the physics of glitches. The observations can be explained with a multi-component model which accounts for the dynamics of the neutron superfluid present in the crust and core of neutron stars. We place limits on the moment of inertia of the component responsible for the spin-up and, ignoring differential rotation, the velocity difference it can sustain with the crust. Contrary to its rapid decrease between glitches, the spin-down rate increased over the 13 years, and we find the long-term braking index $n_{\rm l}=-1.22(4)$, the only negative braking index seen in a young pulsar. We briefly discuss the plausible interpretations of this result, which is in stark contrast to the predictions of standard models of pulsar spin-down.Comment: Minor changes to match the MNRAS accepted versio

Phenolics, depsides and triterpenes from the chilean lichen pseudocyphellaria nudata (zahlbr.) D.J. Galloway

Indexación: ScieloThe lichen Pseudocyphellaria nudata is a species endemic to southern South América. From the lichen tallus, methyl orsellinate, 2-methoxy-3,6-dimethyl-4-hydroxybenzaldehyde, methyl-evernate, tenuiorin, hopan-6ß,22-diol and hopan-6α,76,22-triol were isolated and identified as the main lichen constituents. This is the first report of the occurrence of 2-methoxy-3,6-dimethyl-4-hydroxybenzaldehyde in lichens.http://www.scielo.cl/scielo.php?script=sci_arttext&pid=s0717-97072008000300017&nrm=is

Identification of novel post-transcriptional features in olfactory receptor family mRNAs.

Olfactory receptor (Olfr) genes comprise the largest gene family in mice. Despite their importance in olfaction, how most Olfr mRNAs are regulated remains unexplored. Using RNA-seq analysis coupled with analysis of pre-existing databases, we found that Olfr mRNAs have several atypical features suggesting that post-transcriptional regulation impacts their expression. First, Olfr mRNAs, as a group, have dramatically higher average AU-content and lower predicted secondary structure than do control mRNAs. Second, Olfr mRNAs have a higher density of AU-rich elements (AREs) in their 3'UTR and upstream open reading frames (uORFs) in their 5 UTR than do control mRNAs. Third, Olfr mRNAs have shorter 3' UTR regions and with fewer predicted miRNA-binding sites. All of these novel properties correlated with higher Olfr expression. We also identified striking differences in the post-transcriptional features of the mRNAs from the two major classes of Olfr genes, a finding consistent with their independent evolutionary origin. Together, our results suggest that the Olfr gene family has encountered unusual selective forces in neural cells that have driven them to acquire unique post-transcriptional regulatory features. In support of this possibility, we found that while Olfr mRNAs are degraded by a deadenylation-dependent mechanism, they are largely protected from this decay in neural lineage cells

Application of the Gaussian mixture model in pulsar astronomy -- pulsar classification and candidates ranking for {\it Fermi} 2FGL catalog

Machine learning, algorithms to extract empirical knowledge from data, can be used to classify data, which is one of the most common tasks in observational astronomy. In this paper, we focus on Bayesian data classification algorithms using the Gaussian mixture model and show two applications in pulsar astronomy. After reviewing the Gaussian mixture model and the related Expectation-Maximization algorithm, we present a data classification method using the Neyman-Pearson test. To demonstrate the method, we apply the algorithm to two classification problems. Firstly, it is applied to the well known period-period derivative diagram, where we find that the pulsar distribution can be modeled with six Gaussian clusters, with two clusters for millisecond pulsars (recycled pulsars) and the rest for normal pulsars. From this distribution, we derive an empirical definition for millisecond pulsars as $\frac{\dot{P}}{10^{-17}} \leq3.23(\frac{P}{100 \textrm{ms}})^{-2.34}$. The two millisecond pulsar clusters may have different evolutionary origins, since the companion stars to these pulsars in the two clusters show different chemical composition. Four clusters are found for normal pulsars. Possible implications for these clusters are also discussed. Our second example is to calculate the likelihood of unidentified \textit{Fermi} point sources being pulsars and rank them accordingly. In the ranked point source list, the top 5% sources contain 50% known pulsars, the top 50% contain 99% known pulsars, and no known active galaxy (the other major population) appears in the top 6%. Such a ranked list can be used to help the future follow-up observations for finding pulsars in unidentified \textit{Fermi} point sources.Comment: 9 pages, 4 figures, accepted by MNRA

Targeting MDM2 by the small molecule RITA: towards the development of new multi-target drugs against cancer

BACKGROUND: The use of low-molecular-weight, non-peptidic molecules that disrupt the interaction between the p53 tumor suppressor and its negative regulator MDM2 has provided a promising alternative for the treatment of different types of cancer. Among these compounds, RITA (reactivation of p53 and induction of tumor cell apoptosis) has been shown to be effective in the selective induction of apoptosis, and this effect is due to its binding to the p53 tumor suppressor. Since biological systems are highly dynamic and MDM2 may bind to different regions of p53, new alternatives should be explored. On this basis, the computational "blind docking" approach was employed in this study to see whether RITA would bind to MDM2. RESULTS: It was observed that RITA binds to the MDM2 p53 transactivation domain-binding cleft. Thus, RITA can be used as a lead compound for designing improved "multi-target" drugs. This novel strategy could provide enormous benefits to enable effective anti-cancer strategies. CONCLUSION: This study has demonstrated that a single molecule can target at least two different proteins related to the same disease