Fatty acid 16:4(n-3) stimulates a GPR120-induced signaling cascade in splenic macrophages to promote chemotherapy resistance

Although chemotherapy is designed to eradicate tumor cells, it also has significant effects on normal tissues. The platinum-induced fatty acid 16:4(n-3) (hexadeca-4,7,10,13-tetraenoic acid) induces systemic resistance to a broad range of DNA-damaging chemotherapeutics. We show that 16:4(n-3) exerts its effect by activating splenic F4/80+/CD11blow macrophages, which results in production of chemoprotective lysophosphatidylcholines (LPCs). Pharmacologic studies, together with analysis of expression patterns, identified GPR120 on F4/80+/CD11blow macrophages as the relevant receptor for 16:4(n-3). Studies that used splenocytes from GPR120-deficient mice have confirmed this conclusion. Activation of the 16:4(n-3)-GPR120 axis led to enhanced cPLA2 activity in these splenic macrophages and secretion of the resistance-inducing lipid mediator, lysophosphatidylcholine(24:1). These studies identify a novel and unexpected function for GPR120 and suggest that antagonists of this receptor might be effective agents to limit development of chemotherapy resistance.—Houthuijzen, J. M., Oosterom, I., Hudson, B. D., Hirasawa, A., Daenen, L. G. M., McLean, C. M., Hansen, S. V. F., van Jaarsveld, M. T. M., Peeper, D. S., Jafari Sadatmand, S., Roodhart, J. M. L., van de Lest, C. H. A., Ulven, T., Ishihara, K., Milligan, G., Voest, E. E. Fatty acid 16:4(n-3) stimulates a GPR120-induced signaling cascade in splenic macrophages to promote chemotherapy resistance

Path-Integral bosonization of a non-local interaction and its application to the study of 1-d many-body systems

We extend the path-integral approach to bosonization to the case in which the fermionic interaction is non-local. In particular we obtain a completely bosonized version of a Thirring-like model with currents coupled by general (symmetric) bilocal potentials. The model contains the Tomonaga-Luttinger model as a special case; exploiting this fact we study the basic properties of the 1-d spinless fermionic gas: fermionic correlators, the spectrum of collective modes, etc. Finally we discuss the generalization of our procedure to the non-Abelian case, thus providing a new tool to be used in the study of 1-d many-body systems with spin-flipping interactions.Comment: 26 pages LATEX, La Plata 94-0

Adsorption-desorption kinetics in nanoscopically confined oligomer films under shear

The method of molecular dynamics computer simulations is employed to study oligomer melts confined in ultra-thin films and subjected to shear. The focus is on the self-diffusion of oligomers near attractive surfaces and on their desorption, together with the effects of increasing energy of adsorption and shear. It is found that the mobility of the oligomers near an attractive surface is strongly decreased. Moreover, although shearing the system forces the chains to stretch parallel to the surfaces and thus increase the energy of adsorption per chain, flow also promotes desorption. The study of chain desorption kinetics reveals the molecular processes responsible for the enhancement of desorption under shear. They involve sequences of conformations starting with a desorbed tail and proceeding in a very fast, correlated, segment-by-segment manner to the desorption of the oligomers from the surfaces.

APRIL: Active Preference-learning based Reinforcement Learning

This paper focuses on reinforcement learning (RL) with limited prior knowledge. In the domain of swarm robotics for instance, the expert can hardly design a reward function or demonstrate the target behavior, forbidding the use of both standard RL and inverse reinforcement learning. Although with a limited expertise, the human expert is still often able to emit preferences and rank the agent demonstrations. Earlier work has presented an iterative preference-based RL framework: expert preferences are exploited to learn an approximate policy return, thus enabling the agent to achieve direct policy search. Iteratively, the agent selects a new candidate policy and demonstrates it; the expert ranks the new demonstration comparatively to the previous best one; the expert's ranking feedback enables the agent to refine the approximate policy return, and the process is iterated. In this paper, preference-based reinforcement learning is combined with active ranking in order to decrease the number of ranking queries to the expert needed to yield a satisfactory policy. Experiments on the mountain car and the cancer treatment testbeds witness that a couple of dozen rankings enable to learn a competent policy

Scaling Analysis of Fluctuating Strength Function

We propose a new method to analyze fluctuations in the strength function phenomena in highly excited nuclei. Extending the method of multifractal analysis to the cases where the strength fluctuations do not obey power scaling laws, we introduce a new measure of fluctuation, called the local scaling dimension, which characterizes scaling behavior of the strength fluctuation as a function of energy bin width subdividing the strength function. We discuss properties of the new measure by applying it to a model system which simulates the doorway damping mechanism of giant resonances. It is found that the local scaling dimension characterizes well fluctuations and their energy scales of fine structures in the strength function associated with the damped collective motions.Comment: 22 pages with 9 figures; submitted to Phys. Rev.

Strange Meson Enhancement in PbPb Collisions

The NA44 Collaboration has measured yields and differential distributions of K+, K-, pi+, pi- in transverse kinetic energy and rapidity, around the center-of-mass rapidity in 158 A GeV/c Pb+Pb collisions at the CERN SPS. A considerable enhancement of K+ production per pi is observed, as compared to p+p collisions at this energy. To illustrate the importance of secondary hadron rescattering as an enhancement mechanism, we compare strangeness production at the SPS and AGS with predictions of the transport model RQMD.Comment: 11 pages, including 4 figures, LATE

Dynamic Computed Tomography Angiography for capturing vessel wall motion:A phantom study for optimal image reconstruction

Background Reliably capturing sub-millimeter vessel wall motion over time, using dynamic Computed Tomography Angiography (4D CTA), might provide insight in biomechanical properties of these vessels. This may improve diagnosis, prognosis, and treatment decision making in vascular pathologies. Purpose The aim of this study is to determine the most suitable image reconstruction method for 4D CTA to accurately assess harmonic diameter changes of vessels. Methods An elastic tube (inner diameter 6 mm, wall thickness 2 mm) was exposed to sinusoidal pressure waves with a frequency of 70 beats-per-minute. Five flow amplitudes were set, resulting in increasing sinusoidal diameter changes of the elastic tube, measured during three simulated pulsation cycles, using ECG-gated 4D CTA on a 320-detector row CT system. Tomographic images were reconstructed using one of the following three reconstruction methods: hybrid iterative (Hybrid-IR), model-based iterative (MBIR) and deep-learning based (DLR) reconstruction. The three reconstruction methods where based on 180 degrees (half reconstruction mode) and 360 degrees (full reconstruction mode) raw data. The diameter change, captured by 4D CTA, was computed based on image registration. As a reference metric for diameter change measurement, a 9 MHz linear ultrasound transducer was used. The sum of relative absolute differences (SRAD) between the ultrasound and 4D CTA measurements was calculated for each reconstruction method. The standard deviation was computed across the three pulsation cycles. Results MBIR and DLR resulted in a decreased SRAD and standard deviation compared to Hybrid-IR. Full reconstruction mode resulted in a decreased SRAD and standard deviations, compared to half reconstruction mode. Conclusions 4D CTA can capture a diameter change pattern comparable to the pattern captured by US. DLR and MBIR algorithms show more accurate results than Hybrid-IR. Reconstruction with DLR is &gt;3 times faster, compared to reconstruction with MBIR. Full reconstruction mode is more accurate than half reconstruction mode.</p

Evaluating pathway enumeration algorithms in metabolic engineering case studies

The design of cell factories for the production of compounds involves the search for suitable heterologous pathways. Different strategies have been proposed to infer such pathways, but most are optimization approaches with specific objective functions, not suited to enumerate multiple pathways. In this work, we analyze two pathway enumeration algorithms based on graph representations: the Solution Structure Generation and the Find Path algorithms. Both are capable of enumerating exhaustively multiple pathways using network topology. We study their capabilities and limitations when designing novel heterologous pathways, by applying these methods on two case studies of synthetic metabolic engineering related to the production of butanol and vanillin