Energy spread of ultracold electron bunches extracted from a laser cooled gas

Ultrashort and ultracold electron bunches created by near-threshold femtosecond photoionization of a laser-cooled gas hold great promise for single-shot ultrafast diffraction experiments. In previous publications the transverse beam quality and the bunch length have been determined. Here the longitudinal energy spread of the generated bunches is measured for the first time, using a specially developed Wien filter. The Wien filter has been calibrated by determining the average deflection of the electron bunch as a function of magnetic field. The measured relative energy spread $\frac{\sigma_{U}}{U} = 0.64 \pm 0.09\%$ agrees well with the theoretical model which states that it is governed by the width of the ionization laser and the acceleration length

Study 2: Comprehension of E1’s Points in Three Conditions (18 Trials per Condition).

<p>No effect of condition was found on either frequency or accuracy of responses. While only Pini was above chance in responding accurately to E1’s points (<i>p</i> = 0.014), the group’s overall performance approached significance (<i>p</i> = 0.058).</p

Study 1: Individuals’ Responsiveness to and Comprehension of Conspecific Points.

<p>Study 1: Individuals’ Responsiveness to and Comprehension of Conspecific Points.</p

Accuracy of Responses to Bimbo’s Points by Females over 18 Trials.

<p>Where Bimbo’s points could be seen by conspecifics, they received one of three possible responses: no response, an accurate response, or an inaccurate response. No female responded inaccurately numerically more often than accurately. However, due to the small sample-size, no statistical analysis is made.</p

An Illustration of the Equipment Used in the Communication Game.

<p>The communicator on the left could see but not obtain the banana pellet hidden in one of the black boxes. The donor on the right could release the food to the communicator, but could not see its location for herself. However, the communicator could potentially indicate the location of the food by pointing through either the top or bottom row of holes on the opposite panel. A locking mechanism prevented the donor from releasing more than one side per trial.</p

Study 2: Individuals’ Responsiveness to and Comprehension of E1’s Points.

<p>Study 2: Individuals’ Responsiveness to and Comprehension of E1’s Points.</p

Diagram of the p53-MDM2 oscillator under the regulation of positive feedbacks via microRNA-192, -34a and -29a.

<p>p53 is translated from p53 mRNA and remains inactive. Phosphorylated by ATM*, p53 becomes active (p53*), and able to transcribe mdm2 mRNA. MDM2 protein, translated from mdm2 mRNA, promotes a fast degradation of p53 and a slow degradation of p53*. In addition to a basal self-degradation, MDM2 is degraded by a mechanism stimulated by ATM*. The three microRNAs, miR-192, miR-34a and miR-29a, are induced by p53*, and inhibit the mRNAs of mdm2, cdc42, wip1, sirt1 and yy1, whose protein products further regulate p53* and MDM2. Specifically, the microRNA binds with its target mRNA molecule with high affinity, forming a microRNA-mRNA complex, and subsequently dispose the complex into a degradation machinery. In other words, the microRNAs in our model are assumed to enhance the degradation of their mRNA target by complexation and subsequent disposal. In particular, CDC42, Wip1 and SIRT1 proteins deactivate p53 directly, while YY1 enhances the MDM2-dependent degradation of p53 and p53* proteins. In addition, Wip1 protein inhibits the degradation of MDM2 protein. The wip1 mRNA is also induced by p53*, whose protein product inhibits active ATM, forming a second negative feedback loop.</p

MiR-192-Mediated Positive Feedback Loop Controls the Robustness of Stress-Induced p53 Oscillations in Breast Cancer Cells

<div><p>The p53 tumor suppressor protein plays a critical role in cellular stress and cancer prevention. A number of post-transcriptional regulators, termed microRNAs, are closely connected with the p53-mediated cellular networks. While the molecular interactions among p53 and microRNAs have emerged, a systems-level understanding of the regulatory mechanism and the role of microRNAs-forming feedback loops with the p53 core remains elusive. Here we have identified from literature that there exist three classes of microRNA-mediated feedback loops revolving around p53, all with the nature of positive feedback coincidentally. To explore the relationship between the cellular performance of p53 with the microRNA feedback pathways, we developed a mathematical model of the core p53-MDM2 module coupled with three microRNA-mediated positive feedback loops involving miR-192, miR-34a, and miR-29a. Simulations and bifurcation analysis in relationship to extrinsic noise reproduce the oscillatory behavior of p53 under DNA damage in single cells, and notably show that specific microRNA abrogation can disrupt the wild-type cellular phenotype when the ubiquitous cell-to-cell variability is taken into account. To assess these <i>in silico</i> results we conducted microRNA-perturbation experiments in MCF7 breast cancer cells. Time-lapse microscopy of cell-population behavior in response to DNA double-strand breaks, together with image classification of single-cell phenotypes across a population, confirmed that the cellular p53 oscillations are compromised after miR-192 perturbations, matching well with the model predictions. Our study via modeling in combination with quantitative experiments provides new evidence on the role of microRNA-mediated positive feedback loops in conferring robustness to the system performance of stress-induced response of p53.</p></div

Additional file 1: of An application of restricted mean survival time in a competing risks setting: comparing time to ART initiation by injection drug use

Contains the R code used to set up the analysis, as well as an outline of how the various tables in results section were generated. (DOCX 94 kb

Bifurcation diagrams of the DNA double-strand-break triggered steady-state p53 response versus the association rates between the three microRNAs and their five target mRNAs (<i>k</i>_on1, <i>k</i>_on2, <i>k</i>_on3, <i>k</i>_on4, <i>k</i>_on5) embedded in the positive feedback loops, under the wild-type (black), miR-192 repressed (purple), miR-34a repressed (green), and miR-29a repressed (blue) conditions.

<p>Paired dots represent the bounds of p53 oscillation amplitude and the solid line represents stationary steady state. The red vertical line indicates the nominal parameter value. The inhibition of miR-192 leads to shrunk oscillating region or entirely non-oscillating region over varying parameter ranges. The inhibition of miR-34a and miR-29a only mildly affects the system behavior compared to the wild-type condition.</p