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Background: Osteoporosis is characterized by an imbalance in bone homeostasis, resulting in the excessive dissolution of bone minerals due to the acidified microenvironment mediated by overactive osteoclasts. Oroxylin A (ORO), a natural flavonoid, has shown potential in reversing osteoporosis by inhibiting osteoclast-mediated bone resorption. The limited water solubility and lack of targeting specificity hinder the effective accumulation of Oroxylin A within the pathological environment of osteoporosis. Results: Osteoclasts' microenvironment-responsive nanoparticles are prepared by incorporating Oroxylin A with amorphous calcium carbonate (ACC) and coated with glutamic acid hexapeptide-modified phospholipids, aiming at reinforcing the drug delivery efficiency as well as therapeutic effect. The obtained smart nanoparticles, coined as OAPLG, could instantly neutralize acid and release Oroxylin A in the extracellular microenvironment of osteoclasts. The combination of Oroxylin A and ACC synergistically inhibits osteoclast formation and activity, leading to a significant reversal of systemic bone loss in the ovariectomized mice model. Conclusion: The work highlights an intelligent nanoplatform based on ACC for spatiotemporally controlled release of lipophilic drugs, and illustrates prominent therapeutic promise against osteoporosis. [ABSTRACT FROM AUTHOR]

In the realm of electronic circuits, materials with high thermal conductivity are sought after as ideal packaging components. It is crucial that such materials not only possess excellent thermal conductivity, but also maintain desirable dielectric properties. To this end, boron nitride nanosheets (BNNS) and BNNS/nanodiamond (ND) aerogels with a three‐dimensional (3D) structure using the ice‐template method were prepared. Subsequently, these aerogels were utilized as a framework to produce 3D‐BNNS/epoxy and 3D‐BNNS/ND/epoxy composites via the vacuum‐assisted impregnation method, which exhibit enhanced anisotropic thermal conductivity. Furthermore, the thermal conductivity exhibited by the 3D‐BNNS/epoxy composites outperforms those containing randomly distributed BNNS. The experiment also reveals sintering the aerogel at elevated temperatures to remove the PVA can significantly improve the thermal conductivity exhibited by the 3D‐BNNS/epoxy and 3D‐BNNS/ND/epoxy composites. Importantly, the 3D‐BNNS/ND/epoxy composites illustrate exceptional thermal conductivity values of 1.326 W/(m·K), indicating a remarkable synergistic effect of BNNS and ND in improving thermal conductivity. It is noteworthy to state that the relative thermal conductivity ratio of the 3D‐BNNS/epoxy and 3D‐BNNS/ND/epoxy composites to pure epoxy resin was markedly higher at lower temperatures than the values measured at room temperature, signifying their superior heat transfer performance at lower temperatures. In addition, the composites depict excellent dielectric properties and lower coefficients of thermal expansion values. Finally, the 3D‐BNNS/ND/epoxy composites also have significantly improved Tg (132.5 and 152.6°C). The high‐performance polymer composites described in this study are envisaged for implementation within the realm of electronic packaging materials. [ABSTRACT FROM AUTHOR]

Loop tiling and fusion are two essential transformations in optimizing compilers to enhance the data locality of programs. Existing heuristics either perform loop tiling and fusion in a particular order, missing some of their profitable compositions, or execute ad-hoc implementations for domain-specific applications, calling for a generalized and systematic solution in optimizing compilers. In this article, we present a so-called basteln (an abbreviation for backward slicing of tiled loop nests) strategy in polyhedral compilation to better model the interplay between loop tiling and fusion. The basteln strategy first groups loop nests by preserving their parallelism/tilability and next performs rectangular/parallelogram tiling to the output groups that produce data consumed outside the considered program fragment. The memory footprints required by each tile are then computed, from which the upward exposed data are extracted to determine the tile shapes of the remaining fusion groups. Such a tiling mechanism can construct complex tile shapes imposed by the dependences between these groups, which are further merged by a post-tiling fusion algorithm for enhancing data locality without losing the parallelism/tilability of the output groups. The basteln strategy also takes into account the amount of redundant computations and the fusion of independent groups, exhibiting a general applicability. We integrate the basteln strategy into two optimizing compilers, with one a general-purpose optimizer and the other a domain-specific compiler for deploying deep learning models. The experiments are conducted on CPU, GPU, and a deep learning accelerator to demonstrate the effectiveness of the approach for a wide class of application domains, including deep learning, image processing, sparse matrix computation, and linear algebra. In particular, the basteln strategy achieves a mean speedup of 1.8× over cuBLAS/cuDNN and 1.1× over TVM on GPU when used to optimize deep learning models; it also outperforms PPCG and TVM by 11% and 20%, respectively, when generating code for the deep learning accelerator. [ABSTRACT FROM AUTHOR]