CosAvatar: Consistent and Animatable Portrait Video Tuning with Text Prompt

Recently, text-guided digital portrait editing has attracted more and more attentions. However, existing methods still struggle to maintain consistency across time, expression, and view or require specific data prerequisites. To solve these challenging problems, we propose CosAvatar, a high-quality and user-friendly framework for portrait tuning. With only monocular video and text instructions as input, we can produce animatable portraits with both temporal and 3D consistency. Different from methods that directly edit in the 2D domain, we employ a dynamic NeRF-based 3D portrait representation to model both the head and torso. We alternate between editing the video frames' dataset and updating the underlying 3D portrait until the edited frames reach 3D consistency. Additionally, we integrate the semantic portrait priors to enhance the edited results, allowing precise modifications in specified semantic areas. Extensive results demonstrate that our proposed method can not only accurately edit portrait styles or local attributes based on text instructions but also support expressive animation driven by a source video.Comment: Project page: https://ustc3dv.github.io/CosAvatar

An atlas of DNA methylomes in porcine adipose and muscle tissues

It is evident that epigenetic factors, especially DNA methylation, have essential roles in obesity development. Here, using pig as a model, we investigate the systematic association between DNA methylation and obesity. We sample eight variant adipose and two distinct skeletal muscle tissues from three pig breeds living within comparable environments but displaying distinct fat level. We generate 1,381 Gb of sequence data from 180 methylated DNA immunoprecipitation libraries, and provide a genome-wide DNA methylation map as well as a gene expression map for adipose and muscle studies. The analysis shows global similarity and difference among breeds, sexes and anatomic locations, and identifies the differentially methylated regions. The differentially methylated regions in promoters are highly associated with obesity development via expression repression of both known obesity-related genes and novel genes. This comprehensive map provides a solid basis for exploring epigenetic mechanisms of adipose deposition and muscle growth

Biomechanical study of two-level oblique lumbar interbody fusion with different types of lateral instrumentation: a finite element analysis

ObjectiveThe aim of this study was to verify the biomechanical properties of a newly designed angulated lateral plate (mini-LP) suited for two-level oblique lumbar interbody fusion (OLIF). The mini-LP is placed through the lateral ante-psoas surgical corridor, which reduces the operative time and complications associated with prolonged anesthesia and placement in the prone position.MethodsA three-dimensional nonlinear finite element (FE) model of an intact L1–L5 lumbar spine was constructed and validated. The intact model was modified to generate a two-level OLIF surgery model augmented with three types of lateral fixation (stand-alone, SA; lateral rod screw, LRS; miniature lateral plate, mini-LP); the operative segments were L2–L3 and L3–L4. By applying a 500 N follower load and 7.5 Nm directional moment (flexion-extension, lateral bending, and axial rotation), all models were used to simulate human spine movement. Then, we extracted the range of motion (ROM), peak contact force of the bony endplate (PCFBE), peak equivalent stress of the cage (PESC), peak equivalent stress of fixation (PESF), and stress contour plots.ResultsWhen compared with the intact model, the SA model achieved the least reduction in ROM to surgical segments in all motions. The ROM of the mini-LP model was slightly smaller than that of the LRS model. There were no significant differences in surgical segments (L1–L2, L4–L5) between all surgical models and the intact model. The PCFBE and PESC of the LRS and the mini-LP fixation models were lower than those of the SA model. However, the differences in PCFBE or PESC between the LRS- and mini-LP-based models were not significant. The fixation stress of the LRS- and mini-LP-based models was significantly lower than the yield strength under all loading conditions. In addition, the variances in the PESF in the LRS- and mini-LP-based models were not obvious.ConclusionOur biomechanical FE analysis indicated that LRS or mini-LP fixation can both provide adequate biomechanical stability for two-level OLIF through a single incision. The newly designed mini-LP model seemed to be superior in installation convenience, and equally good outcomes were achieved with both LRS and mini-LP for two-level OLIF

Coronavirus Papain-like Proteases Negatively Regulate Antiviral Innate Immune Response through Disruption of STING-Mediated Signaling

Viruses have evolved elaborate mechanisms to evade or inactivate the complex system of sensors and signaling molecules that make up the host innate immune response. Here we show that human coronavirus (HCoV) NL63 and severe acute respiratory syndrome (SARS) CoV papain-like proteases (PLP) antagonize innate immune signaling mediated by STING (stimulator of interferon genes, also known as MITA/ERIS/MYPS). STING resides in the endoplasmic reticulum and upon activation, forms dimers which assemble with MAVS, TBK-1 and IKKε, leading to IRF-3 activation and subsequent induction of interferon (IFN). We found that expression of the membrane anchored PLP domain from human HCoV-NL63 (PLP2-TM) or SARS-CoV (PLpro-TM) inhibits STING-mediated activation of IRF-3 nuclear translocation and induction of IRF-3 dependent promoters. Both catalytically active and inactive forms of CoV PLPs co-immunoprecipitated with STING, and viral replicase proteins co-localize with STING in HCoV-NL63-infected cells. Ectopic expression of catalytically active PLP2-TM blocks STING dimer formation and negatively regulates assembly of STING-MAVS-TBK1/IKKε complexes required for activation of IRF-3. STING dimerization was also substantially reduced in cells infected with SARS-CoV. Furthermore, the level of ubiquitinated forms of STING, RIG-I, TBK1 and IRF-3 are reduced in cells expressing wild type or catalytic mutants of PLP2-TM, likely contributing to disruption of signaling required for IFN induction. These results describe a new mechanism used by CoVs in which CoV PLPs negatively regulate antiviral defenses by disrupting the STING-mediated IFN induction

Synthesis and characterization of Zn₁₋ₓMnₓO nanowires

©2008 American Institute of Physics. The electronic version of this article is the complete one and can be found online at: http://link.aip.org/link/?APPLAB/92/162102/1DOI:10.1063/1.2905274Mn doped ZnO nanowires (NWs) were fabricated by a one-step vapor-solid process at 500°C. The doped Mn exists in the wurtzite lattice as substitutional atom without forming secondary phases. X-ray absorption near-edge structure reveals that the doped Mn atoms occupy the Zn sites, and they lead to an expansion in lattice constants. The I-V characteristic of a single Zn₁₋ₓMnₓO NW shows a typical Ohmic contact with gold electrodes. The as-received NWs could be suitable for studying spintronics in one-dimensional diluted magnetic semiconductors

Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey

Diabetic retinopathy (DR) is a microvascular disease that is associated with diabetes mellitus. DR can cause irreversible vision loss and low vision. DR classification, that is, early DR diagnosis and accurate DR grading, is critical for vision protection and immediate treatment. Deep learning-based automated systems led to significant expectations for DR classification based on fundus images with several advantages. In the past several years, many outstanding studies in this area have been conducted and several review articles have been published. However, the new trends and the future directions are need to furtherly analyzed. Thus, we carefully included and read 94 related articles published from 2018 to 2023 through Web of Science, PubMed, Scopus, and IEEE Xplore. From this review, we found that transfer learning has been used as an outstanding strategy for overcoming the issue of the limited data resources to support DR analysis. CNN models of ResNet and VGGNet with layers of tens or even hundreds are the most popular frameworks used for DR classification. The APTOS 2019 and EyePACS are the most widely used datasets for DR classification. In addition, some lightweight DL architectures like SqueezeNet and MobileNet have been proposed for DR classification tasks, especially for limited data resources and computational capabilities. Although deep learning has achieved or surpassed human-level accuracy in DR classification, there is still a long way to go in real clinical workflows. Further improvements in model interpretability, trustworthiness from ophthalmologists, cost-effective and reliable DR screening systems are needed