p53 directly regulates the glycosidase FUCA1 to promote chemotherapy-induced cell death

p53 is a central factor in tumor suppression as exemplified by its frequent loss in human cancer. p53 exerts its tumor suppressive effects in multiple ways, but the ability to invoke the eradication of damaged cells by programmed cell death is considered a key factor. The ways in which p53 promotes cell death can involve direct activation or engagement of the cell death machinery, or can be via indirect mechanisms, for example though regulation of ER stress and autophagy. We present here another level of control in p53-mediated tumor suppression by showing that p53 activates the glycosidase, FUCA1, a modulator of N-linked glycosylation. We show that p53 transcriptionally activates FUCA1 and that p53 modulates fucosidase activity via FUCA1 up-regulation. Importantly, we also report that chemotherapeutic drugs induce FUCA1 and fucosidase activity in a p53-dependent manner. In this context, while we found that over-expression of FUCA1 does not induce cell death, RNAi-mediated knockdown of endogenous FUCA1 significantly attenuates p53-dependent, chemotherapy-induced apoptotic death. In summary, these findings add an additional component to p53s tumor suppressive response and highlight another mechanism by which the tumor suppressor controls programmed cell death that could potentially be exploited for cancer therapy

The CGM and IGM at z∼\sim5: metal budget and physical connection

We present further results of a survey for absorption line systems in the spectra of four high redshift quasars (5.79 $\le$ z$_{\textrm{em}}$ $\le$ 6.13) obtained with the ESO Very Large Telescope X-Shooter. We identify 36 $\textrm{CIV}$ and 7 $\textrm{SiIV}$ systems with a $\ge$ 5$\sigma$ significance. The highest redshift $\textrm{CIV}$ and $\textrm{SiIV}$ absorbers identified in this work are at z = 5.80738 $\pm$ 0.00017 and z = 5.77495 $\pm$ 0.00038, respectively. We compute the comoving mass density of $\textrm{SiIV}$ ($\Omega_{\textrm{SiIV}}$) and find that it evolves from $\Omega_{\textrm{SiIV}}$ = 4.3$^{+2.1}_{-2.1}$ $\times$10$^{-9}$ at = 5.05 to $\Omega_{\textrm{SiIV}}$ = 1.4$^{+0.6}_{-0.4}$ $\times$10$^{-9}$ at = 5.66. We also measure $\Omega_{\textrm{CIV}}$ = 1.6$^{+0.4}_{-0.1}$ $\times$10$^{-8}$ at = 4.77 and $\Omega_{\textrm{CIV}}$ = 3.4$^{+1.6}_{-1.1}$ $\times$10$^{-9}$ at = 5.66. We classify our $\textrm{CIV}$ absorber population by the presence of associated $\textit{low}$ and/or $\textit{high ionisation}$ systems and compute their velocity width ($\Delta$v$_{90}$). We find that all $\textrm{CIV}$ systems with $\Delta$v$_{90}$ > 200 kms$^{-1}$ have associated $\textit{low ionisation}$ systems. We investigate two such systems, separated by 550 physical kpc along a line of sight, and find it likely that they are both tracing a multi-phase medium where hot and cold gas is mixing at the interface between the CGM and IGM. We further discuss the \textrm{MgII} systems presented in a previous work and we identify 5 $\textrm{SiII}$, 10 $\textrm{AlII}$, 12 $\textrm{FeII}$, 1 $\textrm{CII}$, 7 $\textrm{MgI}$ and 1 $\textrm{CaII}$ associated transitions. We compute the respective comoving mass densities in the redshift range 2 to 6, as allowed by the wavelength coverage.Comment: Accepted for publication in MNRAS 22 pages, 19 figures, 6 table

Is there a clinically significant seasonal component to hospital admissions for atrial fibrillation?

BACKGROUND: Atrial fibrillation is a common cardiac dysrhythmia, particularly in the elderly. Recent studies have indicated a statistically significant seasonal component to atrial fibrillation hospitalizations. METHODS: We conducted a retrospective population cohort study using time series analysis to evaluate seasonal patterns of atrial fibrillation hospitalizations for the province of Ontario for the years 1988 to 2001. Five different series methods were used to analyze the data, including spectral analysis, X11, R-Squared, autocorrelation function and monthly aggregation. RESULTS: This study found evidence of weak seasonality, most apparent at aggregate levels including both ages and sexes. There was dramatic increase in hospitalizations for atrial fibrillation over the years studied and an age dependent increase in rates per 100,000. Overall, the magnitude of seasonal difference between peak and trough months is in the order of 1.4 admissions per 100,000 population. The peaks for hospitalizations were predominantly in April, and the troughs in August. CONCLUSIONS: Our study confirms statistical evidence of seasonality for atrial fibrillation hospitalizations. This effect is small in absolute terms and likely not significant for policy or etiological research purposes

Solar Oscillations and Convection: II. Excitation of Radial Oscillations

Solar p-mode oscillations are excited by the work of stochastic, non-adiabatic, pressure fluctuations on the compressive modes. We evaluate the expression for the radial mode excitation rate derived by Nordlund and Stein (Paper I) using numerical simulations of near surface solar convection. We first apply this expression to the three radial modes of the simulation and obtain good agreement between the predicted excitation rate and the actual mode damping rates as determined from their energies and the widths of their resolved spectral profiles. We then apply this expression for the mode excitation rate to the solar modes and obtain excellent agreement with the low l damping rates determined from GOLF data. Excitation occurs close to the surface, mainly in the intergranular lanes and near the boundaries of granules (where turbulence and radiative cooling are large). The non-adiabatic pressure fluctuations near the surface are produced by small instantaneous local imbalances between the divergence of the radiative and convective fluxes near the solar surface. Below the surface, the non-adiabatic pressure fluctuations are produced primarily by turbulent pressure fluctuations (Reynolds stresses). The frequency dependence of the mode excitation is due to effects of the mode structure and the pressure fluctuation spectrum. Excitation is small at low frequencies due to mode properties -- the mode compression decreases and the mode mass increases at low frequency. Excitation is small at high frequencies due to the pressure fluctuation spectrum -- pressure fluctuations become small at high frequencies because they are due to convection which is a long time scale phenomena compared to the dominant p-mode periods.Comment: Accepted for publication in ApJ (scheduled for Dec 10, 2000 issue). 17 pages, 27 figures, some with reduced resolution -- high resolution versions available at http://www.astro.ku.dk/~aake/astro-ph/0008048