Notes

Arterial stiffness anticipates amputation along with dying in people with long-term limb-threatening ischemia.
Early recordings of nervous conduction revealed a notable thermal signature associated with the electrical signal. The observed production and subsequent absorption of heat arise from physicochemical processes that occur at the cell membrane level during the conduction of the action potential. In particular, the reversible release of electrostatic energy stored as a difference of potential across the cell membrane appears as a simple yet consistent explanation for the heat production, as proposed in the "Condenser Theory." However, the Condenser Theory has not been analyzed beyond the analogy between the cell membrane and a parallel-plate capacitor, i.e., a condenser, and cannot account for the magnitude of the heat signature. In this work, we use a detailed electrostatic model of the cell membrane to revisit the Condenser Theory. We derive expressions for free energy and entropy changes associated with the depolarization of the membrane by the action potential, which give a direct measure of the heat produced and absorbed by neurons. We show how the density of surface charges on both sides of the membrane impacts the energy changes. Finally, considering a typical action potential, we show that if the membrane holds a bias of surface charges, such that the internal side of the membrane is 0.05Cm^-2 more negative than the external side, the size of the heat predicted by the model reaches the range of experimental values. Based on our study, we identify the release of electrostatic energy by the membrane as the primary mechanism of heat production and absorption by neurons during nervous conduction.Spreading processes are conventionally monitored on a macroscopic level by counting the number of incidences over time. The spreading process can then be modeled either on the microscopic level, assuming an underlying interaction network, or directly on the macroscopic level, assuming that microscopic contributions are negligible. The macroscopic characteristics of both descriptions are commonly assumed to be identical. In this work we show that these characteristics of microscopic and macroscopic descriptions can be different due to coalescence, i.e., a node being activated at the same time by multiple sources. In particular, we consider a (microscopic) branching network (probabilistic cellular automaton) with annealed connectivity disorder, record the macroscopic activity, and then approximate this activity by a (macroscopic) branching process. In this framework we analytically calculate the effect of coalescence on the collective dynamics. We show that coalescence leads to a universal nonlinear scaling function for the conditional expectation value of successive network activity. This allows us to quantify the difference between the microscopic model parameter and established estimates of the macroscopic branching parameter. To overcome this difference, we propose a nonlinear estimator that correctly infers the microscopic model parameter for all system sizes.Organoids are prototypes of human organs derived from cultured human stem cells. They provide a reliable and accurate experimental model to study the physical mechanisms underlying the early developmental stages of human organs and, in particular, the early morphogenesis of the cortex. Here we propose a mathematical model to elucidate the role played by two mechanisms which have been experimentally proven to be crucial in shaping human brain organoids the contraction of the inner core of the organoid and the microstructural remodeling of its outer cortex. N-Acetyl-DL-methionine Our results show that both mechanisms are crucial for the final shape of the organoid and that perturbing those mechanisms can lead to pathological morphologies which are reminiscent of those associated with lissencephaly (smooth brain).The work presented in this paper shows with the help of two-dimensional hydrodynamic simulations that intense heavy-ion beams are a very efficient tool to induce high energy density (HED) states in solid matter. These simulations have been carried out using a computer code BIG2 that is based on a Godunov-type numerical algorithm. This code includes ion beam energy deposition using the cold stopping model, which is a valid approximation for the temperature range accessed in these simulations. Different phases of matter achieved due to the beam heating are treated using a semiempirical equation-of-state (EOS) model. To take care of the solid material properties, the Prandl-Reuss model is used. The high specific power deposited by the projectile particles in the target leads to phase transitions on a timescale of the order of tens of nanosecond, which means that the sample material achieves thermodynamic equilibrium during the heating process. In these calculations we use Pb as the sample material that is irradiated by an intense uranium beam. The beam parameters including particle energy, focal spot size, bunch length, and bunch intensity are considered to be the same as the design parameters of the ion beam to be generated by the SIS100 heavy-ion synchrotron at the Facility for Antiprotons and Ion Research (FAIR), at Darmstadt. The purpose of this work is to propose experiments to measure the EOS properties of HED matter including studies of the processes of phase transitions at the FAIR facility. Our simulations have shown that depending on the specific energy deposition, solid lead will undergo phase transitions leading to an expanded hot liquid state, two-phase liquid-gas state, or the critical parameter regime. In a similar manner, other materials can be studied in such experiments, which will be a very useful addition to the knowledge in this important field of research.Self-assembly and force generation are two central processes in biological systems that usually are considered in separation. However, the signals that activate nonmuscle myosin II molecular motors simultaneously lead to self-assembly into myosin II minifilaments as well as progression of the motor heads through the cross-bridge cycle. Here we investigate theoretically the possible effects of coupling these two processes. Our assembly model, which builds on a consensus architecture of the minifilament, predicts a critical aggregation concentration at which the assembly kinetics slows down dramatically. The combined model predicts that increasing actin filament concentration and force both lead to a decrease in the critical aggregation concentration. We suggest that due to these effects, myosin II minifilaments in a filamentous context might be in a critical state that reacts faster to varying conditions than in solution. We finally compare our model to experiments by simulating fluorescence recovery after photobleaching.
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