Structure-based engineering of papillomavirus major capsid L1: controlling particle assembly

The outer shell of the papillomavirus particle is comprised of 72 pentamers of the major capsid L1 protein arranged on a T = 7 icosahedral lattice. The recombinant L1 can form T = 7 virus-like particles in vitro. The crystal structure of a T = 7 papilloma virion has not yet been determined; however, the crystal structure of a T = 1 particle containing 12 pentamers is known. The T = 1 structure reveals that helix-helix interactions, through three helices–h2, h3, and h4–near the C-terminus of L1, mediate the inter-pentameric bonding that is responsible for T = 1 assembly. Based on the T = 1 crystal structure, we have generated a set of internal deletions to test the role of the three C-terminal helices in T = 7 assembly. We have demonstrated that the h2, h3, and h4 near the C-terminal end of L1 are important for the L1 structure and particle assembly. In particular, we found that h2 and h3 are essential for L1 folding and pentamer formation, whereas h4 is indispensable for the assembly of not only T1, but also of the T7 virus-like particle

AU-Supervised Convolutional Vision Transformers for Synthetic Facial Expression Recognition

The paper describes our proposed methodology for the six basic expression classification track of Affective Behavior Analysis in-the-wild (ABAW) Competition 2022. In Learing from Synthetic Data(LSD) task, facial expression recognition (FER) methods aim to learn the representation of expression from the artificially generated data and generalise to real data. Because of the ambiguous of the synthetic data and the objectivity of the facial Action Unit (AU), we resort to the AU information for performance boosting, and make contributions as follows. First, to adapt the model to synthetic scenarios, we use the knowledge from pre-trained large-scale face recognition data. Second, we propose a conceptually-new framework, termed as AU-Supervised Convolutional Vision Transformers (AU-CVT), which clearly improves the performance of FER by jointly training auxiliary datasets with AU or pseudo AU labels. Our AU-CVT achieved F1 score as $0.6863$, accuracy as $0.7433$ on the validation set. The source code of our work is publicly available online: https://github.com/msy1412/ABAW

Role of the porous structure of the bioceramic scaffolds in bone tissue engineering

The porous structure of biomaterials plays a critical role in improving the efficiency of biomaterials in tissue engineering. Here we fabricate successfully porous bioceramics with accurately controlled pore parameters, and investigate the effect of pore parameters on the mechanical property, the cell seeding proliferation and the vascularization of the scaffolds. This study shows that the porosity play an important role on the mechanical property of the scaffolds, which is affected not only by the macropores size, but also by the interconnections of the scaffolds. Larger pores are beneficial for cell growth in scaffolds. In contrast, the interconnections do not affect cell growth much. The interconnections appear to limit the number of blood vessels penatrating through adjacent pores, and both the pores size and interconnections can determine the size of blood vessels. The results may be referenced on the selective design of porous structure of biomaterials to meet the specificity of biological application

Pushing the Physical Limits of IoT Devices with Programmable Metasurfaces

Small, low-cost IoT devices are typically equipped with only a single, low-quality antenna, significantly limiting communication range and link quality. In particular, these antennas are typically linearly polarized and therefore susceptible to polarization mismatch, which can easily cause 10-15 dBm of link loss on communication to and from such devices. In this work, we highlight this under-appreciated issue and propose the augmentation of IoT deployment environments with programmable, RF-sensitive surfaces made of metamaterials. Our smart meta-surface mitigates polarization mismatch by rotating the polarization of signals that pass through or reflect off the surface. We integrate our metasurface into an IoT network as LAMA, a Low-power Lattice of Actuated Metasurface Antennas, designed for the pervasively used 2.4 GHz ISM band. We optimize LAMA's metasurface design for both low transmission loss and low cost, to facilitate deployment at scale. We then build an end-to-end system that actuates the metasurface structure to optimize for link performance in real time. Our experimental prototype-based evaluation demonstrates gains in link power of up to 15 dBm, and wireless capacity improvements of 100 and 180 Kbit/s/Hz in through-surface and surface-reflective scenarios, respectively, attributable to the polarization rotation properties of LAMA'S metasurface