AvatarStudio: High-fidelity and Animatable 3D Avatar Creation from Text

We study the problem of creating high-fidelity and animatable 3D avatars from only textual descriptions. Existing text-to-avatar methods are either limited to static avatars which cannot be animated or struggle to generate animatable avatars with promising quality and precise pose control. To address these limitations, we propose AvatarStudio, a coarse-to-fine generative model that generates explicit textured 3D meshes for animatable human avatars. Specifically, AvatarStudio begins with a low-resolution NeRF-based representation for coarse generation, followed by incorporating SMPL-guided articulation into the explicit mesh representation to support avatar animation and high resolution rendering. To ensure view consistency and pose controllability of the resulting avatars, we introduce a 2D diffusion model conditioned on DensePose for Score Distillation Sampling supervision. By effectively leveraging the synergy between the articulated mesh representation and the DensePose-conditional diffusion model, AvatarStudio can create high-quality avatars from text that are ready for animation, significantly outperforming previous methods. Moreover, it is competent for many applications, e.g., multimodal avatar animations and style-guided avatar creation. For more results, please refer to our project page: http://jeff95.me/projects/avatarstudio.htmlComment: Project page at http://jeff95.me/projects/avatarstudio.htm

Single-shot deterministic complex amplitude imaging with a single-layer metalens

Conventional imaging systems can only capture light intensity. Meanwhile, the lost phase information may be critical for a variety of applications such as label-free microscopy and optical metrology. Existing phase retrieval techniques typically require a bulky setup, multi-frame measurements, or prior information of the target scene. Here, we proposed an extremely compact system for complex amplitude imaging, leveraging the extreme versatility of a single-layer metalens to generate spatially-multiplexed and polarization-phase-shifted point spread functions. Combining the metalens with a polarization camera, the system can simultaneously record four polarization shearing interference patterns along both in-plane directions, thus allowing the deterministic reconstruction of the complex amplitude light field in a single shot. Using an incoherent light-emitting diode as the illumination, we experimentally demonstrated speckle-noise-free complex amplitude imaging for both static and moving objects with tailored magnification ratio and field-of-view. The miniaturized and robust system may open the door for complex amplitude imaging in portable devices for point-of-care applications

Observation of superconductivity in 3D Dirac semimetal Cd3As2 crystal

Lately, the three-dimensional (3D) Dirac semimetal, which possesses 3D linear dispersion in electronic structure as a bulk analogue of graphene, has generated widespread interests in both material science and condensed matter physics. Very recently, crystalline Cd3As2 has been proposed and proved to be one of 3D Dirac semimetals which can survive in atmosphere. Here, by controlled point contact (PC) measurement, we observe the exotic superconductivity around point contact region on the surface of Cd3As2 crystal. The observation of zero bias conductance peak (ZBCP) and double conductance peaks (DCPs) symmetric to zero bias further reveal p-wave like unconventional superconductivity in Cd3As2 quantum matter. Considering the topological property of the 3D Dirac semimetal, our findings may indicate that the Cd3As2 crystal under certain conditions is a candidate of the topological superconductor, which is predicted to support Majorana zero modes or gapless Majorana edge/surface modes in the boundary depending on the dimensionality of the material

Identifying the Risky SNP of Osteoporosis with ID3-PEP Decision Tree Algorithm

In the past 20 years, much progress has been made on the genetic analysis of osteoporosis. A number of genes and SNPs associated with osteoporosis have been found through GWAS method. In this paper, we intend to identify the suspected risky SNPs of osteoporosis with computational methods based on the known osteoporosis GWAS-associated SNPs. The process includes two steps. Firstly, we decided whether the genes associated with the suspected risky SNPs are associated with osteoporosis by using random walk algorithm on the PPI network of osteoporosis GWAS-associated genes and the genes associated with the suspected risky SNPs. In order to solve the overfitting problem in ID3 decision tree algorithm, we then classified the SNPs with positive results based on their features of position and function through a simplified classification decision tree which was constructed by ID3 decision tree algorithm with PEP (Pessimistic-Error Pruning). We verified the accuracy of the identification framework with the data set of GWAS-associated SNPs, and the result shows that this method is feasible. It provides a more convenient way to identify the suspected risky SNPs associated with osteoporosis

Optimal dispatch based on prediction of distributed electric heating storages in combined electricity and heat networks

The volatility of wind power generations could significantly challenge the economic and secure operation of combined electricity and heat networks. To tackle this challenge, this paper proposes a framework of optimal dispatch with distributed electric heating storage based on a correlation-based long short-term memory prediction model. The prediction model of distributed electric heating storage is developed to model its behavior characteristics which are obtained by the autocorrelation and correlation analysis with external factors including weather and time-of-use price. An optimal dispatch model of combined electricity and heat networks is then formulated and resolved by a constraint reduction technique with clustering and classification. Our method is verified through numerous simulations. The results show that, compared with the state-of-the-art techniques of support vector machine and recurrent neural networks, the mean absolute percentage error with the proposed correlation-based long short-term memory can be reduced by 1.009 and 0.481 respectively. Compared with conventional method, the peak wind power curtailment with dispatching distributed electric heating storage is reduced by nearly 30% and 50% in two cases respectively

Tumor‐derived exosomal PD-L1: a new perspective in PD-1/PD-L1 therapy for lung cancer

Exosomes play a crucial role in facilitating intercellular communication within organisms. Emerging evidence indicates that a distinct variant of programmed cell death ligand-1 (PD-L1), found on the surface of exosomes, may be responsible for orchestrating systemic immunosuppression that counteracts the efficacy of anti-programmed death-1 (PD-1) checkpoint therapy. Specifically, the presence of PD-L1 on exosomes enables them to selectively target PD-1 on the surface of CD8+ T cells, leading to T cell apoptosis and impeding T cell activation or proliferation. This mechanism allows tumor cells to evade immune pressure during the effector stage. Furthermore, the quantification of exosomal PD-L1 has the potential to serve as an indicator of the dynamic interplay between tumors and immune cells, thereby suggesting the promising utility of exosomes as biomarkers for both cancer diagnosis and PD-1/PD-L1 inhibitor therapy. The emergence of exosomal PD-L1 inhibitors as a viable approach for anti-tumor treatment has garnered significant attention. Depleting exosomal PD-L1 may serve as an effective adjunct therapy to mitigate systemic immunosuppression. This review aims to elucidate recent insights into the role of exosomal PD-L1 in the field of immune oncology, emphasizing its potential as a diagnostic, prognostic, and therapeutic tool in lung cancer

Efficacy and safety of triazavirin therapy for coronavirus disease 2019 : A pilot randomized controlled trial

Acknowledgements: We are deeply grateful to the front-line clinicians who participated in the study while directly fighting the epidemic. This study was supported by the Chinese Academy of Engineering Projects for COVID-19 (2020-KYGG-01-04) and Heilongjiang Province Urgent Project-6 for COVID-19. Data and safety monitoring board members of this trial included Kang Li, Yong Zhang, Songjiang Liu, and Yaohui Shi.Peer reviewedPublisher PD

A Protective Role by Interleukin-17F in Colon Tumorigenesis

Interleukin-17F (IL-17F), produced by Th17 cells and other immune cells, is a member of IL-17 cytokine family with highest homology to IL-17A. IL-17F has been shown to have multiple functions in inflammatory responses. While IL-17A plays important roles in cancer development, the function of IL-17F in tumorigenesis has not yet been elucidated. In the current study, we found that IL-17F is expressed in normal human colonic epithelial cells, but this expression is greatly decreased in colon cancer tissues. To examine the roles of IL-17F in colon cancer, we have used IL-17F over-expressing colon cancer cell lines and IL-17F-deficient mice. Our data showed decreased tumor growth of IL-17F-transfected HCT116 cells comparing to mock transfectants when transplanted in nude mice. Conversely, there were increased colonic tumor numbers and tumor areas in Il-17f−/− mice than those from wild-type controls after colon cancer induction. These results indicate that IL-17F plays an inhibitory role in colon tumorigenesis in vivo. In IL-17F over-expressing tumors, there was no significant change in leukocyte infiltration; instead, we found decreased VEGF levels and CD31+ cells. While the VEGF levels were increased in the colon tissues of Il-17f−/− mice with colon cancer. Together, our findings demonstrate a protective role for IL-17F in colon cancer development, possibly via inhibiting tumor angiogenesis