CpG Oligodeoxynucleotides Enhance the Efficacy of Adoptive Cell Transfer Using Tumor Infiltrating Lymphocytes by Modifying the Th1 Polarization and Local Infiltration of Th17 Cells

Adoptive cell transfer immunotherapy using tumor infiltrating lymphocytes (TILs) was an important therapeutic strategy against tumors. But the efficacy remains limited and development of new strategies is urgent. Recent evidence suggested that CpG-ODNs might be a potent candidate for tumor immunotherapy. Here we firstly reported that CpG-ODNs could significantly enhance the antitumor efficacy of adoptively transferred TILs in vivo accompanied by enhanced activity capacity and proliferation of CD8+ T cells and CD8+ T cells, as well as a Th1 polarization immune response. Most importantly, we found that CpG-ODNs could significantly elevate the infiltration of Th17 cells in tumor mass, which contributed to anti-tumor efficacy of TILs in vivo. Our findings suggested that CpG ODNs could enhance the anti-tumor efficacy of adoptively transferred TILs through modifying Th1 polarization and local infiltration of Th17 cells, which might provide a clue for developing a new strategy for ACT based on TILs

Entropy Estimate for Degenerate SDEs with Applications to Nonlinear Kinetic Fokker-Planck Equations

The relative entropy for two different degenerate diffusion processes is estimated by using the Wasserstein distance of initial distributions and the difference between coefficients. As applications, the entropy cost inequality and exponential ergodicity in entropy are derived for distribution dependent stochastic Hamiltonian systems associated with nonlinear kinetic Fokker Planck equations.Comment: 22 page

Energy loss enhancement of very intense proton beams in dense matter due to the beam-density effect

Thoroughly understanding the transport and energy loss of intense ion beams in dense matter is essential for high-energy-density physics and inertial confinement fusion. Here, we report a stopping power experiment with a high-intensity laser-driven proton beam in cold, dense matter. The measured energy loss is one order of magnitude higher than the expectation of individual particle stopping models. We attribute this finding to the proximity of beam ions to each other, which is usually insignificant for relatively-low-current beams from classical accelerators. The ionization of the cold target by the intense ion beam is important for the stopping power calculation and has been considered using proper ionization cross section data. Final theoretical values agree well with the experimental results. Additionally, we extend the stopping power calculation for intense ion beams to plasma scenario based on Ohm's law. Both the proximity- and the Ohmic effect can enhance the energy loss of intense beams in dense matter, which are also summarized as the beam-density effect. This finding is useful for the stopping power estimation of intense beams and significant to fast ignition fusion driven by intense ion beams

Target density effects on charge tansfer of laser-accelerated carbon ions in dense plasma

We report on charge state measurements of laser-accelerated carbon ions in the energy range of several MeV penetrating a dense partially ionized plasma. The plasma was generated by irradiation of a foam target with laser-induced hohlraum radiation in the soft X-ray regime. We used the tri-cellulose acetate (C$_{9}$H$_{16}$O$_{8}$) foam of 2 mg/cm$^{-3}$ density, and $1$-mm interaction length as target material. This kind of plasma is advantageous for high-precision measurements, due to good uniformity and long lifetime compared to the ion pulse length and the interaction duration. The plasma parameters were diagnosed to be T$_{e}$=17 eV and n$_{e}$=4 $\times$ 10$^{20}$ cm$^{-3}$. The average charge states passing through the plasma were observed to be higher than those predicted by the commonly-used semiempirical formula. Through solving the rate equations, we attribute the enhancement to the target density effects which will increase the ionization rates on one hand and reduce the electron capture rates on the other hand. In previsous measurement with partially ionized plasma from gas discharge and z-pinch to laser direct irradiation, no target density effects were ever demonstrated. For the first time, we were able to experimentally prove that target density effects start to play a significant role in plasma near the critical density of Nd-Glass laser radiation. The finding is important for heavy ion beam driven high energy density physics and fast ignitions.Comment: 7 pages, 4 figures, 35 conference

Gait modification and optimization using neural network-genetic algorithm approach: Application to knee rehabilitation

Gait modification strategies play an important role in the overall success of total knee arthroplasty. There are a number of studies based on multi-body dynamic (MBD) analysis that have minimized knee adduction moment to offload knee joint. Reducing the knee adduction moment, without consideration of the actual contact pressure, has its own limitations. Moreover, MBD-based framework that mainly relies on iterative trial-and-error analysis, is fairly time consuming. This study embedded a time-delay neural network (TDNN) in a genetic algorithm (GA) as a cost effective computational framework to minimize contact pressure. Multi-body dynamic and finite element analyses were performed to calculate gait kinematics/kinetics and the resultant contact pressure for a number of experimental gait trials. A TDNN was trained to learn the nonlinear relation between gait parameters (inputs) and contact pressures (output). The trained network was then served as a real-time cost function in a GA-based global optimization to calculate contact pressure associated with each potential gait pattern. Two optimization problems were solved: first, knee flexion angle was bounded within the normal patterns and second, knee flexion angle was allowed to be increased beyond the normal walking. Designed gait patterns were evaluated through multi-body dynamic and finite element analyses. The TDNN-GA resulted in realistic gait patterns, compared to literature, which could effectively reduce contact pressure at the medial tibiofemoral knee joint. The first optimized gait pattern reduced the knee contact pressure by up to 21% through modifying the adjacent joint kinematics whilst knee flexion was preserved within normal walking. The second optimized gait pattern achieved a more effective pressure reduction (25%) through a slight increase in the knee flexion at the cost of considerable increase in the ankle joint forces. The proposed approach is a cost-effective computational technique that can be used to design a variety of rehabilitation strategies for different joint replacement with multiple objectives

Human lower extremity joint moment prediction: A wavelet neural network approach

Joint moment is one of the most important factors in human gait analysis. It can be calculated using multi body dynamics but might not be straight forward. This study had two main purposes; firstly, to develop a generic multi-dimensional wavelet neural network (WNN) as a real-time surrogate model to calculate lower extremity joint moments and compare with those determined by multi body dynamics approach, secondly, to compare the calculation accuracy of WNN with feed forward artificial neural network (FFANN) as a traditional intelligent predictive structure in biomechanics. To aim these purposes, data of four patients walked with three different conditions were obtained from the literature. A total of 10 inputs including eight electromyography (EMG) signals and two ground reaction force (GRF) components were determined as the most informative inputs for the WNN based on the mutual information technique. Prediction ability of the network was tested at two different levels of inter-subject generalization. The WNN predictions were validated against outputs from multi body dynamics method in terms of normalized root mean square error (NRMSE (%)) and cross correlation coefficient (ρ). Results showed that WNN can predict joint moments to a high level of accuracy (NRMSE 0.94) compared to FFANN (NRMSE 0.89). A generic WNN could also calculate joint moments much faster and easier than multi body dynamics approach based on GRFs and EMG signals which released the necessity of motion capture. It is therefore indicated that the WNN can be a surrogate model for real-time gait biomechanics evaluation

Entropy estimate for degenerate SDEs with applications to nonlinear kinetic Fokker–Planck equations

The relative entropy for two different degenerate diffusion processes is estimated by using the Wasserstein distance of initial distributions and the difference between coefficients. As applications, the entropy-cost inequality and exponential ergodicity in entropy are derived for distribution dependent stochastic Hamiltonian systems associated with nonlinear kinetic Fokker–Planck equations