Hierarchical Bayesian LSTM for Head Trajectory Prediction on Omnidirectional Images

DNA Tetrahedron Delivering miR-21-5p Promotes Senescent Bone Defects Repair through Synergistic Regulation of Osteogenesis and Angiogenesis

Compromised osteogenesis and angiogenesis is the character of stem cell senescence, which brought difficulties for bone defects repairing in senescent microenvironment. As the most abundant bone-related miRNA, miRNA-21-5p plays a crucial role in inducing osteogenic and angiogenic differentiation. However, highly efficient miR-21-5p delivery still confronts challenges including poor cellular uptake and easy degradation. Herein, TDN-miR-21-5p nanocomplex is constructed based on DNA tetrahedral (TDN) and has great potential in promoting osteogenesis and alleviating senescence of senescent bone marrow stem cells (O-BMSCs), simultaneously enhancing angiogenic capacity of senescent endothelial progenitor cells (O-EPCs). Of note, the activation of AKT and Erk signaling pathway may direct regulatory mechanism of TDN-miR-21-5p mediated osteogenesis and senescence of O-BMSCs. Also, TDN-miR-21-5p can indirectly mediate osteogenesis and senescence of O-BMSCs through pro-angiogenic growth factors secreted from O-EPCs. In addition, gelatin methacryloyl (GelMA) hydrogels are mixed with TDN and TDN-miR-21-5p to fabricate delivery scaffolds. TDN-miR-21-5p@GelMA scaffold exhibits greater bone repair with increased expression of osteogenic- and angiogenic-related markers in senescent critical-size cranial defects in vivo. Collectively, TDN-miR-21-5p can alleviate senescence and induce osteogenesis and angiogenesis in senescent microenvironment, which provides a novel candidate strategy for senescent bone repair and widen clinical application of TDNs-based gene therapy.

Blind VQA on 360° Video via Progressively Learning from Pixels, Frames and Video

Blind visual quality assessment (BVQA) on 360° video plays a key role in optimizing immersive multimedia systems. When assessing the quality of 360° video, human tends to perceive its quality degradation from the viewport-based spatial distortion of each spherical frame to motion artifact across adjacent frames, ending with the video-level quality score, i.e., a progressive quality assessment paradigm. However, the existing BVQA approaches for 360° video neglect this paradigm. In this paper, we take into account the progressive paradigm of human perception towards spherical video quality, and thus propose a novel BVQA approach (namely ProVQA) for 360° video via progressively learning from pixels, frames and video. Corresponding to the progressive learning of pixels, frames and video, three sub-nets are designed in our ProVQA approach, i.e., the spherical perception aware quality prediction (SPAQ), motion perception aware quality prediction (MPAQ) and multi-frame temporal non-local (MFTN) sub-nets. The SPAQ sub-net first models the spatial quality degradation based on spherical perception mechanism of human. Then, by exploiting motion cues across adjacent frames, the MPAQ sub-net properly incorporates motion contextual information for quality assessment on 360° video. Finally, the MFTN sub-net aggregates multi-frame quality degradation to yield the final quality score, via exploring long-term quality correlation from multiple frames. The experiments validate that our approach significantly advances the state-of-the-art BVQA performance on 360° video over two datasets, the code of which has been public in https://github.com/yanglixiaoshen/ProVQA.

A Review of Performance Prediction Based on Machine Learning in Materials Science

With increasing demand in many areas, materials are constantly evolving. However, they still have numerous practical constraints. The rational design and discovery of new materials can create a huge technological and social impact. However, such rational design and discovery require a holistic, multi-stage design process, including the design of the material composition, material structure, material properties as well as process design and engineering. Such a complex exploration using traditional scientific methods is not only blind but also a huge waste of time and resources. Machine learning (ML), which is used across data to find correlations in material properties and understand the chemical properties of materials, is being considered a new way to explore the materials field. This paper reviews some of the major recent advances and applications of ML in the field of properties prediction of materials and discusses the key challenges and opportunities in this cross-cutting area.