No abstract

Patients with head and neck squamous cell carcinoma harbor T-cell inflamed and non-T-cell inflamed tumors. Despite this, only 20% of patients respond to checkpoint inhibitor immunotherapy. Lack of induction of innate immunity through pattern-recognition receptors such as the stimulator of interferon (IFN) genes (STING) receptor may represent a significant barrier to the development of effective antitumor immunity. Here, we demonstrate robust control of a T-cell inflamed (MOC1), but not non-T-cell inflamed (MOC2), model of head and neck cancer by activation of the STING pathway with the synthetic cyclic dinucleotide RP,RP dithio-c-di-GMP. Rejection or durable tumor control of MOC1 tumors was dependent upon a functional STING receptor and CD8 T lymphocytes. STING activation resulted in increased tumor microenvironment type 1 and type 2 IFN and greater expression of PD-1–pathway components in vivo. Established MOC1 tumors were rejected and distant tumors abscopally controlled, after adaptive immune resistance had been reversed by the addition of PD-L1 mAb. These findings suggest that PD-1-pathway blockade may reverse adaptive immune resistance following cyclic dinucleotide treatment, enhancing both local and systemic antitumor immunity.

We present a novel algorithm to perform continuous collision detection for articulated models.

No abstract

We present a fast continuous collision detection (CCD) algorithm for articulated models using Taylor models and temporal culling. Our algorithm is a generalization of conservative advancement (CA) from convex models [Mirtich 1996] to articulated models with non-convex links. Given the initial and final configurations of a moving articulated model, our algorithm creates a continuous motion with constant translational and rotational velocities for each link, and checks for interferences between the articulated model under continuous motion and other models in the environment and for self-collisions. If collisions occur, our algorithm reports the first time of contact (TOC) as well as collision witness features. We have implemented our CCD algorithm and applied it to several challenging scenarios including locomotion generation, articulated-body dynamics and character motion planning. Our algorithm can perform CCDs including self-collision detection for articulated models consisting of many links and tens of thousands of triangles in 1.22 ms on average running on a 3.6 GHz Pentium 4 PC. This is an improvement on the performance of prior algorithms of more than an order of magnitude.

Purpose Since TLR agonists have been well characterized as DC activators, we hypothesized that the admixture of TLR4 agonist into a cellular vector could improve the anti-tumor response in vivo. Experimental Design GM-CSF secreting whole cell tumor cell vector (GVAX) was formulated with LPS, a TLR4 agonist, and its intratumoral therapeutic efficacy was tested in three different murine models. We utilized immunohistochemistry, FACS, ELISPOT, and in vivo CTL analysis to assess both local innate immune responses within the tumor tissue as well as the downstream generation of anti-tumor T-cell responses. Results Intratumoral treatment of LPS absorbed GVAX showed efficacy in improving an antitumor response in vivo in comparison to GVAX alone. Improved anti-tumor efficacy of this novel admixture was not present in TLR4 signaling impaired mice. In the CT26 model, 40-60% of the mice showed regression of the transplanted tumor. When rechallenged with CT26 tumor cells, these mice proved to be immunized against the tumor. Tumors treated with TLR4 agonist absorbed GVAX showed increased infiltrating CD4 and CD8 T-cells as well as increased numbers of CD86+ cells in the tumor tissue. Draining lymph nodes from the treated mice had enhanced number of activated CD86+, MHCII+, and CD80+ dendritic cells in comparison to GVAX alone and mock treated groups. ELISPOT assay and in vivo CTL assay showed increased numbers of CTLs specific for the AH1 tumor antigen in mice treated with LPS absorbed GVAX. Conclusions TLR4 on APCs in the tumor microenvironment may be targeted using cell-based vectors for improved anti-tumor response in vivo.

We present a highly interactive, continuous collision detection algorithm for rigid, general polyhedra. Given initial and final configurations of a moving polyhedral model, our algorithm creates a continuous motion with constant translational and angular velocities, thereby interpolating the initial and final configurations of the model. Then, our algorithm reports whether the model under the interpolated motion collides with other rigid polyhedral models in environments, and if it does, the algorithm reports its first time of contact (TOC) with the environment as well as its associated contact features at TOC.Our algorithm is a generalization of conservative advancement [20] to general polyhedra. In this approach, we calculate the motion bound of a moving polyhedral model and estimate the TOC based on this bound, and advance the model by the current TOC estimate. We iterate this process until the inter-distance between the moving model and the other objects in the environments becomes below a user-defined distance threshold.We pose the problem of calculating the motion bound as a linear programming problem and provide an efficient, novel solution based on the simplex method. Moreover, we also provide a hierarchical advancement technique based on bounding volume traversal tree to generalize the conservative advancement for non-convex models.Our algorithm is relatively simple to implement and has very small computational overhead of merely performing discrete collision detection multiple times. We extensively benchmarked our algorithm in different scenarios, and in comparison to other known continuous collision detection algorithm, the performance improvement ranges by a factor of 1.4 ∼ 45.5 depending on benchmarking scenarios. Moreover, our algorithm can perform CCD at 120 ∼ 15460 frames per second on a 3.6 GHz Pentium 4 PC for complex models consisting of 10K ∼ 70K triangles.

Penetration depth (PD) is a distance metric that is used to describe the extent of overlap between two intersecting objects. Most of the prior work in PD computation has been restricted to translational PD, which is defined as the minimal translational motion that one of the overlapping objects must undergo in order to make the two objects disjoint. In this paper, we extend the notion of PD to take into account both translational and rotational motion to separate the intersecting objects, namely generalized PD. When an object undergoes rigid transformation, some point on the object traces the longest trajectory. The generalized PD between two overlapping objects is defined as the minimum of the longest trajectories of one object under all possible rigid transformations to separate the overlapping objects.We present three new results to compute generalized PD between polyhedral models. First, we show that for two overlapping convex polytopes, the generalized PD is same as the translational PD. Second, when the complement of one of the objects is convex, we pose the generalized PD computation as a variant of the convex containment problem and compute an upper bound using optimization techniques. Finally, when both the objects are non-convex, we treat them as a combination of the above two cases, and present an algorithm that computes a lower and an upper bound on generalized PD. We highlight the performance of our algorithms on different models that undergo rigid motion in the 6-dimensional configuration space. Moreover, we utilize our algorithm for complete motion planning of polygonal robots undergoing translational and rotational motion in a plane. In particular, we use generalized PD computation for checking path non-existence.