∂ˉ\bar\partial Poincar\'e inequality and an improved L2L^2-estimate of ∂ˉ\bar\partial on bounded strictly pseudoconvex domains

We prove several inequalities related to the $\bar\partial$-operator on bounded domains in $\mathbb{C}^n$, which can be viewed as a $\bar\partial$-version of the classical Poincar\'e inequality and its various generalizations, and apply them to derive a generalization of Sobolev Inequality with Trace in $\mathbb{R}^n$. As applications to complex analysis, we get an integral form of Maximum Modulus Principle for holomorphic functions, and an improvement of H\"ormander's $L^2$-estimate for $\bar\partial$ on bounded strictly pseudoconvex domains

Recombinant Listeria promotes tumor rejection by CD8+ T cell-dependent remodeling of the tumor microenvironment.

Agents that remodel the tumor microenvironment (TME), prime functional tumor-specific T cells, and block inhibitory signaling pathways are essential components of effective immunotherapy. We are evaluating live-attenuated, double-deleted Listeria monocytogenes expressing tumor antigens (LADD-Ag) in the clinic. Here we show in numerous mouse models that while treatment with nonrecombinant LADD induced some changes in the TME, no antitumor efficacy was observed, even when combined with immune checkpoint blockade. In contrast, LADD-Ag promoted tumor rejection by priming tumor-specific KLRG1+PD1loCD62L- CD8+ T cells. These IFNγ-producing effector CD8+ T cells infiltrated the tumor and converted the tumor from an immunosuppressive to an inflamed microenvironment that was characterized by a decrease in regulatory T cells (Treg) levels, a proinflammatory cytokine milieu, and the shift of M2 macrophages to an inducible nitric oxide synthase (iNOS)+CD206- M1 phenotype. Remarkably, these LADD-Ag-induced tumor-specific T cells persisted for more than 2 months after primary tumor challenge and rapidly controlled secondary tumor challenge. Our results indicate that the striking antitumor efficacy observed in mice with LADD-based immunotherapy stems from TME remodeling which is a direct consequence of eliciting potent, systemic tumor-specific CD8+ T cells

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

An Adaptive Energy Management System for Electric Vehicles Based on Driving Cycle Identification and Wavelet Transform

Since driving cycle greatly affects load power demand, driving cycle identification (DCI) is proposed to predict power demand that can be expected to prepare for the power distribution between battery and supercapacitor. The DCI is developed based on a learning vector quantization (LVQ) neural network method, which is assessed in both training and validation based on the statistical data obtained from six standard driving cycles. In order to ensure network accuracy, characteristic parameter and slide time window, which are two important factors ensuring the network accuracy for onboard hybrid energy storage system (HESS) applications in electric vehicles, are discussed and designed. Based on the identification results, Multi-level Haar wavelet transform (Haar-WT) is proposed for allocating the high frequency components of power demand into the supercapacitor which could damage battery lifetime and the corresponding low frequency components into the battery system. The proposed energy management system can better increase system efficiency and battery lifetime compared with the conventional sole frequency control. The advantages are demonstrated based on a randomly generated driving cycle from the standard driving cycle library via simulation

A simulation study of vehicle curve speed control system,”

ABSTRACT In this paper, a speed control system for vehicle approaching, traveling, and coming out of a curve is investigated using simulation. In order to assist driver in ahcieving safer and more comfortable curve following performance, vehicle speed is controlled using the preview data of the road ahead, which is obtained from the GPS and Map or vision system. The control system is comprised of a method for determining a desired curve speed profile to help achieve the best cornering performance for an up-coming curve and regulating vehicle speed toward the desired speed. The lateral dynamics of vehicle is used for computing the desired speed for a curve. From the current vehicle speed and the desired speed profile, the acceleration profile for a curve ahead of the vehicle is computed. Then, the acceleration command is generated to slow the vehicle so the driver can achieve the optimal curve following. From the simulation studies, it is concluded that the system can potentially improve driver cornering performance

Anchor Generation Optimization and Region of Interest Assignment for Vehicle Detection

Region proposal network (RPN) based object detection, such as Faster Regions with CNN (Faster R-CNN), has gained considerable attention due to its high accuracy and fast speed. However, it has room for improvements when used in special application situations, such as the on-board vehicle detection. Original RPN locates multiscale anchors uniformly on each pixel of the last feature map and classifies whether an anchor is part of the foreground or background with one pixel in the last feature map. The receptive field of each pixel in the last feature map is fixed in the original faster R-CNN and does not coincide with the anchor size. Hence, only a certain part can be seen for large vehicles and too much useless information is contained in the feature for small vehicles. This reduces detection accuracy. Furthermore, the perspective projection results in the vehicle bounding box size becoming related to the bounding box position, thereby reducing the effectiveness and accuracy of the uniform anchor generation method. This reduces both detection accuracy and computing speed. After the region proposal stage, many regions of interest (ROI) are generated. The ROI pooling layer projects an ROI to the last feature map and forms a new feature map with a fixed size for final classification and box regression. The number of feature map pixels in the projected region can also influence the detection performance but this is not accurately controlled in former works. In this paper, the original faster R-CNN is optimized, especially for the on-board vehicle detection. This paper tries to solve these above-mentioned problems. The proposed method is tested on the KITTI dataset and the result shows a significant improvement without too many tricky parameter adjustments and training skills. The proposed method can also be used on other objects with obvious foreshortening effects, such as on-board pedestrian detection. The basic idea of the proposed method does not rely on concrete implementation and thus, most deep learning based object detectors with multiscale feature maps can be optimized with it