No abstract

Complex dermal wounds represent major medical and financial burdens, especially in the context of comorbidities such as diabetes, infection and advanced age. New approaches to accelerate and improve, or “fine tune” the healing process, so as to improve the quality of cutaneous wound healing and management, are the focus of intense investigation. Here, we investigate the topical application of a recombinant immune modulating protein which inhibits the interactions of chemokines with glycosaminoglycans, reducing damaging or excess inflammation responses in a splinted full-thickness excisional wound model in mice. M-T7 is a 37 kDa-secreted, virus-derived glycoprotein that has demonstrated therapeutic efficacy in numerous animal models of inflammatory immunopathology. Topical treatment with recombinant M-T7 significantly accelerated wound healing when compared to saline treatment alone. Healed wounds exhibited properties of improved tissue remodeling, as determined by collagen maturation. M-T7 treatment accelerated the rate of peri-wound angiogenesis in the healing wounds with increased levels of TNF, VEGF and CD31. The immune cell response after M-T7 treatment was associated with a retention of CCL2 levels, and increased abundances of arginase-1-expressing M2 macrophages and CD4 T cells. Thus, topical treatment with recombinant M-T7 promotes a pro-resolution environment in healing wounds, and has potential as a novel treatment approach for cutaneous tissue repair.

We describe three baseline beating systems for the high-resource English-only sub-task of the SIGMORPHON 2021 Shared Task 1: a small ensemble that Dialpad's 1 speech recognition team uses internally, a well-known off-the-shelf model, and a larger ensemble model comprising these and others. We additionally discuss the challenges related to the provided data, along with the processing steps we took.

Stencil computations are not well optimized by general-purpose production compilers and the increased use of multicore, manycore, and accelerator-based systems makes the optimization problem even more challenging. In this paper we present Snowflake, a Domain Specific Language (DSL) for stencils that uses a "micro-compiler" approach, i.e., small, focused, domain-specific code generators. The approach is similar to that used in image processing stencils, but Snowflake handles the much more complex stencils that arise in scientific computing, including complex boundary conditions, higherorder operators (larger stencils), higher dimensions, variable coefficients, non-unit-stride iteration spaces, and multiple input or output meshes. Snowflake is embedded in the Python language, allowing it to interoperate with popular scientific tools like SciPy and iPython; it also takes advantage of built-in Python libraries for powerful dependence analysis as part of a just-in-time compiler. We demonstrate the power of the Snowflake language and the micro-compiler approach with a complex scientific benchmark, HPGMG, that exercises the generality of stencil support in Snowflake. By generating OpenMP comparable to, and OpenCL within a factor of 2× of hand-optimized HPGMG, Snowflake demonstrates that a micro-compiler can support diverse processor architectures and is performance-competitive whilst preserving a high-level Python implementation.

We present fast implementations of linear interpolation operators for piecewise linear functions and multi-dimensional look-up tables. These operators are common for efficient transformations in image processing and are the core operations needed for lattice models like deep lattice networks, a popular machine learning function class for interpretable, shape-constrained machine learning. We present new strategies for an efficient compiler-based solution using MLIR to accelerate linear interpolation. For real-world machine-learned multi-layer lattice models that use multidimensional linear interpolation, we show these strategies run 5-10× faster on a standard CPU compared to an optimized C++ interpreter implementation.