The rate of death caused by shooting in an one-against-one attack, as a function of the gun control policy, , where corresponds to a ban of private firearm possession, and to the “gun availability to all” policy.

<p>(a) The fraction of people who possess the gun and have it with them when attacked is relatively low, with . The different lines correspond to different values of . For all values of , the shooting death rate is minimal for . (b) The fraction of people who possess the gun and have it with them when attacked is relatively high, with . As long as condition (5) holds, the shooting death rate is minimal for (ban of private firearm possession, solid lines). If condition (5) is violated, then the shooting death rate is minimized for (“gun availability to all”, dashed lines).</p

In order for the memory model (2) to reproduce the radioadaptive response, the probability for a cell to become permanently altered by radiation exposure, <i>p</i>, must increase sufficiently at the higher radiation dose, <i>α</i>.

<p>In other words, the radioadaptive response is not observed in the model if the increase in the parameter <i>p</i> at the higher value of <i>α</i> lies below a threshold. The graph plots the number of permanently altered cells in the presence of priming divided by the number of altered cells generated in the absence of priming, as a function of <i>n</i>-fold increase in the value of <i>p</i> at high <i>α</i>. If the value of <i>p</i> for high α is not increased sufficiently, more permanently altered cells are generated in the presence of priming. In contrast, if the value of <i>p</i> is increased by a threshold amount at high <i>α</i>, then priming lowers the total number of altered cells relative to the scenario where no low-dose priming is given. The horizontal line represents the ratio of one, where priming makes no difference. Base parameters are given as follows: <i>α = 0.1</i> for priming low dose radiation, and <i>α = 100</i> for higher dose radiation, <i>c = 1</i>, <i>η = 0.01</i>, <i>x₀ = 100</i>, <i>y₀ = 0</i>, <i>z₀ = 0</i>, <i>w₀ = 0</i>. For low-dose priming, <i>p = 0.05</i>. For high dose challenge, the value of <i>p</i> is increased <i>n</i>-fold, the horizontal axis of the graph.</p

Variability of the parameter estimates across the different experimental setups and cell lines.

<p>Parameters have been estimated by fitting model (2) independently to eight experimental data sets, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003513#pcbi-1003513-g002" target="_blank">Figure 2</a> of Supporting <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003513#pcbi.1003513.s001" target="_blank">Text S1</a> and references therein. Box-and-whiskers diagrams for parameters are presented. The dimensionless quantities <i>p₀</i> and <i>p₁</i> parameterize the saturating function , as defined in the caption for <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003513#pcbi-1003513-g004" target="_blank">figure 4(ii)</a>. A box-and-whiskers plot consists of a box that spans the distance between two quantiles surrounding the median, with lines (“whiskers”) that extend to span the full range.</p

Hybrid Highway Landscape: Integrating highway into urban context

Urban highway, as a product of modern city, is one of the most important influencing factors that shape the contemporary urban landscape. It was built to ease the urban traffic pressure. However, the negative impacts on the urban space and the surrounding environment cannot be ignored. In the most cases, highway is widely regarded and treated as an only functional infrastructure. In my point of view, it also has potentials to be a public facility instead of strictly utilitarian by being contextualized, integrated and interacted with urban environment and urban life. Therefore, I have developed a concept of ‘hybrid highway landscape’ as a new phase of urban highway. In order to explore the potential of hybrid highway landscape, highway A40 in the centre of Duisburg, a highly urban context, and its right-of-way is regarded as an interesting experimental field for the research project

Dynamics of Cellular Responses to Radiation

<div><p>Understanding the consequences of exposure to low dose ionizing radiation is an important public health concern. While the risk of low dose radiation has been estimated by extrapolation from data at higher doses according to the linear non-threshold model, it has become clear that cellular responses can be very different at low compared to high radiation doses. Important phenomena in this respect include radioadaptive responses as well as low-dose hyper-radiosensitivity (HRS) and increased radioresistance (IRR). With radioadaptive responses, low dose exposure can protect against subsequent challenges, and two mechanisms have been suggested: an intracellular mechanism, inducing cellular changes as a result of the priming radiation, and induction of a protected state by inter-cellular communication. We use mathematical models to examine the effect of these mechanisms on cellular responses to low dose radiation. We find that the intracellular mechanism can account for the occurrence of radioadaptive responses. Interestingly, the same mechanism can also explain the existence of the HRS and IRR phenomena, and successfully describe experimentally observed dose-response relationships for a variety of cell types. This indicates that different, seemingly unrelated, low dose phenomena might be connected and driven by common core processes. With respect to the inter-cellular communication mechanism, we find that it can also account for the occurrence of radioadaptive responses, indicating redundancy in this respect. The model, however, also suggests that the communication mechanism can be vital for the long term survival of cell populations that are continuously exposed to relatively low levels of radiation, which cannot be achieved with the intracellular mechanism in our model. Experimental tests to address our model predictions are proposed.</p></div

Effect of Synaptic Transmission on Viral Fitness in HIV Infection

<div><p>HIV can spread through its target cell population either via cell-free transmission, or by cell-to-cell transmission, presumably through virological synapses. Synaptic transmission entails the transfer of tens to hundreds of viruses per synapse, a fraction of which successfully integrate into the target cell genome. It is currently not understood how synaptic transmission affects viral fitness. Using a mathematical model, we investigate how different synaptic transmission strategies, defined by the number of viruses passed per synapse, influence the basic reproductive ratio of the virus, R₀, and virus load. In the most basic scenario, the model suggests that R₀ is maximized if a single virus particle is transferred per synapse. R₀ decreases and the infection eventually cannot be maintained for larger numbers of transferred viruses, because multiple infection of the same cell wastes viruses that could otherwise enter uninfected cells. To explain the relatively large number of HIV copies transferred per synapse, we consider additional biological assumptions under which an intermediate number of viruses transferred per synapse could maximize R₀. These include an increased burst size in multiply infected cells, the saturation of anti-viral factors upon infection of cells, and rate limiting steps during the process of synapse formation.</p> </div

Virus-mediated induction of intracellular defense factors can lead to peaks in <i>R₀</i> for two different viral strategies, <i>s</i>: a “stealth strategy” and a “saturation strategy”.

<p>Plotted is the basic reproductive ratio, <i>R₀</i>, as a function of strategy, <i>s</i>, for two different values of the infectivity parameter, <i>r₂</i>. The rest of the parameters are as follows: , <i>d = 0.1</i>, <i>a = 4</i>, <i>n₁ = 16</i>. The infectivity parameter <i>r</i> depends on the strategy. It is given by <i>r = r₂-s(r₂-r₂/10)/10</i> if <i>s<10</i>, and <i>r = r₂/10</i> if . These functions are shown in the inset for the two values of <i>r₂</i>.</p

Additional modeling details from Passenger mutations can accelerate tumour suppressor gene inactivation in cancer evolution

Further details about the mathematical models in the text are provided

The evolutionary simulations.

<p>The time-dependent solution of the evolutionary virus dynamics simulation is presented (please note the log axes). The uninfected cell population is <i>x₀₀</i>, and the infected populations are presented by two lines, one showing the sum of all cells containing the <i>s₁</i> virus, , and the other containing the <i>s₂</i> virus,. The inset shows the infectivity of the two strains. We used the base-line model for this simulation. The parameters are <i>s₁ = 1, s₂ = 3, z = 0, Q = 0.1,, a = d = 1, N = 5.</i></p

Effect of continuous radiation on the cell population (a) in the memory model (equation 2) and (b) the communication model (equation 3).

<p>Note that the long time-spans considered are important to demonstrate the quasi-equilibrium behavior in the communication model, and the absence of this behavior in the memory model. Parameters were chosen as follows (a) <i>α = 0.1</i>, <i>p = 0.05</i>, <i>c = 1</i>, <i>η = 0.01</i>, <i>x₀ = 100</i>, <i>y₀ = 0</i>, <i>z₀ = 0</i>, <i>w₀ = 0</i>. (b) <i>α = 0.1</i>, <i>p = 0.05</i>, <i>c = 1</i>, <i>η = 0.01</i>, β<i>₀ = 10</i>, β<i>₁ = 0</i>, <i>x₀ = 100</i>, <i>y₀ = 0</i>, <i>z₀ = 0</i>, <i>w₀ = 0</i>.</p