Branching Ratios of the Charged Kaon Decays and Radiative Corrections for the Ke3 Decay Mode

The CMD--2 experiment at the VEPP-2M accelerator at the Budker Institute ofNuclear Physics has collected $approx$ 1 million charged kaon decays, from which we extract a clean sample of $approx 74,000 K^+$ decays, with $approx$ 50,000 $kmunu$, 18,000 $kpipi$, 4000 ($kmu3 + ke3$), and 2000 ($k3pc + 3pi0$) events. Based on these samples we present measurement of$R_{2body} equiv Br(kpipi)/Br(kmunu) = 0.3292 pm 0.0048 mbox{stat} pm 0.011 mbox{sys}$, $R_{semilep} equiv (Br(kmu3) + Br(ke3))/Br(kpipi) = 0.477 pm 0.016 mbox{stat} pm 0.10 mbox{sys}$, and$R_{3pion} equiv (Br(k3pc)+Br(3pi0))/Br(kpipi) = 0.315 pm 0.014 mbox{stat} pm 0.054 mbox{sys}$. The ratio of the two semileptonic decays is extracted from the $K^-$ decays only and yields $R_{emu} equiv Br(K o pi^0 e u)/Br(K o pi^0 mu u) = 1.97 pm 0.09 mbox{stat} pm 0.81 mbox{sys}$. The strength of these measurements is the presence of all the major decay modes and systematics different from some other experiments.In this dissertation I also consider the radiative correctionsfor the $K_{e3}$ decay. This decay is of particular importance since it provides the best way to extract the value of the $V_{us}$ element of the CKM matrix. In turn, precise knowledge of $V_{us}$ is needed to resolve a long standing problem with a unitarity test of the CKM matrix

Protein co-expression network analysis (ProCoNA)

Abstract Background Biological networks are important for elucidating disease etiology due to their ability to model complex high dimensional data and biological systems. Proteomics provides a critical data source for such models, but currently lacks robust de novo methods for network construction, which could bring important insights in systems biology. Results We have evaluated the construction of network models using methods derived from weighted gene co-expression network analysis (WGCNA). We show that approximately scale-free peptide networks, composed of statistically significant modules, are feasible and biologically meaningful using two mouse lung experiments and one human plasma experiment. Within each network, peptides derived from the same protein are shown to have a statistically higher topological overlap and concordance in abundance, which is potentially important for inferring protein abundance. The module representatives, called eigenpeptides, correlate significantly with biological phenotypes. Furthermore, within modules, we find significant enrichment for biological function and known interactions (gene ontology and protein-protein interactions). Conclusions Biological networks are important tools in the analysis of complex systems. In this paper we evaluate the application of weighted co-expression network analysis to quantitative proteomics data. Protein co-expression networks allow novel approaches for biological interpretation, quality control, inference of protein abundance, a framework for potentially resolving degenerate peptide-protein mappings, and a biomarker signature discovery

An Experimental and Computational Study of Effects of Microtubule Stabilization on T-Cell Polarity

T-killer cells eliminate infected and cancerous cells with precision by positioning their centrosome near the interface (immunological synapse) with the target cell. The mechanism of centrosome positioning has remained controversial, in particular the role of microtubule dynamics in it. We re-examined the issue in the experimental model of Jurkat cells presented with a T cell receptor-binding artificial substrate, which permits controlled stimulation and reproducible measurements. Neither 1-µM taxol nor 100-nM nocodazole inhibited the centrosome positioning at the “synapse” with the biomimetic substrate. At the same time, in micromolar taxol but not in nanomolar nocodazole the centrosome adopted a distinct peripheral rather than the normally central position within the synapse. This effect was reproduced in a computational energy-minimization model that assumed no microtubule dynamics, but only a taxol-induced increase in the length of the microtubules. Together, the experimental and computational results indicate that microtubule dynamics are not essential for the centrosome positioning, but that the fit of the microtubule array in the deformed body of the conjugated T cell is a major factor. The possibility of modulating the T-cell centrosome position with well-studied drugs and of predicting their effects in silico appears attractive for designing anti-cancer and antiviral therapies

Microtubule cytoskeleton orientation in the experiment.

<p>Indirect immunostaining of tubulin in Jurkat cells attached to the stimulatory substrate. Representative fields of view are shown (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003861#pone-0003861-t001" target="_blank">Table 1</a> for the statistics). For each experimental condition, a horizontal confocal section is shown in the panel above the panel containing the side view (a projection of the three-dimensional image). The level of the horizontal optical section is indicated by bars in the corresponding side-view image. The planar substrate under the cells is nonfluorescent. The centrosome is at the point of convergence of the fluorescently labeled microtubules, and therefore can alternatively be recognized as the brighter area in the image of each cell. The scale bar in <i>A</i> is 10 µm long. <i>A</i>, control cells, horizontal section. <i>B</i>, cells treated with 1-µM taxol, horizontal section. <i>C</i>, control cells, side view. <i>D</i>, cells treated with 1-µM taxol, side view. <i>E</i>, cells treated with 100-nM nocodazole, horizontal section. <i>F</i>, cells treated with 1-µM nocodazole, horizontal section. <i>G</i>, cells treated with 100-nM nocodazole, side view. <i>H</i>, cells treated with 1-µM nocodazole, side view.</p

Position of the centrosome in the experiment (the percentage is the fraction of the total number of cells).

<p>Position of the centrosome in the experiment (the percentage is the fraction of the total number of cells).</p

Distributions of predicted centrosome orientations for the indicated microtubule number (rows) and length (columns).

<p>α = 0 means the ideal functional orientation centrosome-down, and α = 180° means the opposite, non-functional orientation with the centrosome away from the stimulatory substrate. Numbers with arrows indicate histogram bins, sample microtubule cytoskeletons from which are shown in the correspondingly labeled parts of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003861#pone-0003861-g004" target="_blank">Fig. 4</a>.</p

Sample predicted microtubule cytoskeleton structures and orientations.

<p>Only 24 microtubules are drawn in each computer-generated image, according to the three-dimensional shapes specified by the numerical algorithm. Top and side views of each sample structure are shown next to each other. Orientation centrosome-down is the ideal functional orientation in our plotting convention, to match our experimental setup. The number between the top and side view of the same structure corresponds to the number of the peak as labeled in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003861#pone-0003861-g003" target="_blank">Fig. 3</a>. <i>A</i> and <i>B</i>, the major type of structure predicted when there are 88 microtubules 12 µm long. <i>C</i> and <i>D</i>, the minor type of structure predicted when there are 88 microtubules 12 µm long. <i>E</i> and <i>F</i>, the dominant type of structure, when there are 300 microtubules 12 µm long. <i>G</i> and <i>H</i>, the dominant type of structure, when there are 300 microtubules 15 µm long. <i>I</i> and <i>J</i>, the minor type of structure predicted when there are 300 microtubules 18 µm long. <i>K</i> and <i>L</i>, the major type of structure predicted when there are 300 microtubules 18 µm long. <i>M</i> and <i>N</i>, the major type of structure predicted when there are 300 microtubules 9 µm long.</p

Dependence of the cell conformation energy that is achieved by the minimization algorithm on the number of segmented chains representing the microtubule cytoskeleton.

<p>The physical idealization of the cytoskeleton here has <i>N</i> = 88 microtubules, each having length <i>L</i> = 12 µm and flexural rigidity <i>κ</i> = 24 aJ µm. In the numerical algorithm, the rigidity <i>κ_eff</i> of each of the <i>N</i>_sim chains is set to satisfy <i>N</i>_sim<i>κ_eff</i> = <i>N κ</i> as <i>N</i>_sim is varied. Dashed lines, the 95% confidence interval. Based on 300 minimization runs for each point that are independent in the sense of the pseudorandom starting conformation.</p

Protein co-expression network analysis (ProCoNA)

BACKGROUND: Biological networks are important for elucidating disease etiology due to their ability to model complex high dimensional data and biological systems. Proteomics provides a critical data source for such models, but currently lacks robust de novo methods for network construction, which could bring important insights in systems biology. RESULTS: We have evaluated the construction of network models using methods derived from weighted gene co-expression network analysis (WGCNA). We show that approximately scale-free peptide networks, composed of statistically significant modules, are feasible and biologically meaningful using two mouse lung experiments and one human plasma experiment. Within each network, peptides derived from the same protein are shown to have a statistically higher topological overlap and concordance in abundance, which is potentially important for inferring protein abundance. The module representatives, called eigenpeptides, correlate significantly with biological phenotypes. Furthermore, within modules, we find significant enrichment for biological function and known interactions (gene ontology and protein-protein interactions). CONCLUSIONS: Biological networks are important tools in the analysis of complex systems. In this paper we evaluate the application of weighted co-expression network analysis to quantitative proteomics data. Protein co-expression networks allow novel approaches for biological interpretation, quality control, inference of protein abundance, a framework for potentially resolving degenerate peptide-protein mappings, and a biomarker signature discovery