AI Illustrator: Translating Raw Descriptions into Images by Prompt-based Cross-Modal Generation

AI illustrator aims to automatically design visually appealing images for books to provoke rich thoughts and emotions. To achieve this goal, we propose a framework for translating raw descriptions with complex semantics into semantically corresponding images. The main challenge lies in the complexity of the semantics of raw descriptions, which may be hard to be visualized (e.g., "gloomy" or "Asian"). It usually poses challenges for existing methods to handle such descriptions. To address this issue, we propose a Prompt-based Cross-Modal Generation Framework (PCM-Frame) to leverage two powerful pre-trained models, including CLIP and StyleGAN. Our framework consists of two components: a projection module from Text Embeddings to Image Embeddings based on prompts, and an adapted image generation module built on StyleGAN which takes Image Embeddings as inputs and is trained by combined semantic consistency losses. To bridge the gap between realistic images and illustration designs, we further adopt a stylization model as post-processing in our framework for better visual effects. Benefiting from the pre-trained models, our method can handle complex descriptions and does not require external paired data for training. Furthermore, we have built a benchmark that consists of 200 raw descriptions. We conduct a user study to demonstrate our superiority over the competing methods with complicated texts. We release our code at https://github.com/researchmm/AI_Illustrator

Role of inter-particle friction in granular materials under three dimensional conditions

The inter-particle friction is known to be an important contributor to the strength and deformation characteristics in granular materials. The mechanism of inter-particle friction to the macroscopic responses can be explained by microscopic investigations. Based on the discrete element method (DEM), a series of true triaxial tests for the cubic granular assembly are carried out and the effects of inter-particle friction coefficient (μ) on the evolutions of macro- and micromechanical parameters of granular materials are studied. The macroscopic stress, the distribution of coordination numbers and contact force with regard to strong and weak contact networks are concerned, as well as the corresponding fabric tensor and anisotropies. Findings indicate that increasing inter-particle friction sharpens the peak value of deviatoric stress and enhances the degree of dilatancy of the granular assembly at the macroscopic level. From the microscopic perspective, the distribution of the coordination number of the weak contact system varies dramatically, while the number of particles with smaller coordination number in the strong contact system changes little with different μ. Besides, the difference between strong and weak contact networks is enlarged, and anisotropy indicators are significantly enhanced, which strengthen the bearing ability of anisotropic stresses in granular materials

Solving Diffusion ODEs with Optimal Boundary Conditions for Better Image Super-Resolution

Diffusion models, as a kind of powerful generative model, have given impressive results on image super-resolution (SR) tasks. However, due to the randomness introduced in the reverse process of diffusion models, the performances of diffusion-based SR models are fluctuating at every time of sampling, especially for samplers with few resampled steps. This inherent randomness of diffusion models results in ineffectiveness and instability, making it challenging for users to guarantee the quality of SR results. However, our work takes this randomness as an opportunity: fully analyzing and leveraging it leads to the construction of an effective plug-and-play sampling method that owns the potential to benefit a series of diffusion-based SR methods. More in detail, we propose to steadily sample high-quality SR images from pretrained diffusion-based SR models by solving diffusion ordinary differential equations (diffusion ODEs) with optimal boundary conditions (BCs) and analyze the characteristics between the choices of BCs and their corresponding SR results. Our analysis shows the route to obtain an approximately optimal BC via an efficient exploration in the whole space. The quality of SR results sampled by the proposed method with fewer steps outperforms the quality of results sampled by current methods with randomness from the same pretrained diffusion-based SR model, which means that our sampling method "boosts" current diffusion-based SR models without any additional training

The global state of research in stem cells therapy for spinal cord injury (2003–2022): a visualized analysis

ObjectiveOur study aimed to visualize the global status and frontiers in stem cell therapy for spinal cord injury by using bibliometric methodology.MethodsPublication citation information related to stem cell therapy for spinal cord injury (SCI) studies between 2003 and 2022 was retrieved from the Web of Science Core Collection database. For the visualized study, VOS viewer software and Graph Pad Prism 9.5 were used to perform bibliometric analysis of included data and publication number statistics in stem cell therapy for the SCI domain.ResultsA total of 6,686 publications were retrieved. The USA and China made the highest contributions to global research with the highest number of citations and link strength. The journal Experimental Neurology ranks as the top journal, combining the publication amount and bibliometrics results. The University of Toronto, based in Canada, was the first-ranking institution. The directions of the current study could be divided into five clusters. The research of Transplantation and Regenerative Medicine and Neurosciences Mechanism Research may be the emerging frontiers in this domain.ConclusionIn summary, stem cell therapy for spinal cord injuries is poised for more valuable advances

Atomic Sn–enabled high-utilization, large-capacity, and long-life Na anode

Constructing robust nucleation sites with an ultrafine size in a confined environment is essential toward simultaneously achieving superior utilization, high capacity, and long-term durability in Na metal-based energy storage, yet remains largely unexplored. Here, we report a previously unexplored design of spatially confined atomic Sn in hollow carbon spheres for homogeneous nucleation and dendrite-free growth. The designed architecture maximizes Sn utilization, prevents agglomeration, mitigates volume variation, and allows complete alloying-dealloying with high-affinity Sn as persistent nucleation sites, contrary to conventional spatially exposed large-size ones without dealloying. Thus, conformal deposition is achieved, rendering an exceptional capacity of 16 mAh cm−2 in half-cells and long cycling over 7000 hours in symmetric cells. Moreover, the well-known paradox is surmounted, delivering record-high Na utilization (e.g., 85%) and large capacity (e.g., 8 mAh cm−2) while maintaining extraordinary durability over 5000 hours, representing an important breakthrough for stabilizing Na anode

Building osteogenic microenvironments with a double-network composite hydrogel for bone repair

The critical factor determining the in vivo effect of bone repair materials is the microenvironment, which greatly depends on their abilities to promote vascularization and bone formation. However, implant materials are far from ideal candidates for guiding bone regeneration due to their deficient angiogenic and osteogenic microenvironments. Herein, a double-network composite hydrogel combining vascular endothelial growth factor (VEGF)-mimetic peptide with hydroxyapatite (HA) precursor was developed to build an osteogenic microenvironment for bone repair. The hydrogel was prepared by mixing acrylated β-cyclodextrins and octacalcium phosphate (OCP), an HA precursor, with gelatin solution, followed by ultraviolet photo-crosslinking. To improve the angiogenic potential of the hydrogel, QK, a VEGF-mimicking peptide, was loaded in acrylated β-cyclodextrins. The QK-loaded hydrogel promoted tube formation of human umbilical vein endothelial cells and upregulated the expression of angiogenesis-related genes, such as Flt1 , Kdr , and VEGF , in bone marrow mesenchymal stem cells. Moreover, QK could recruit bone marrow mesenchymal stem cells. Furthermore, OCP in the composite hydrogel could be transformed into HA and release calcium ions facilitating bone regeneration. The double-network composite hydrogel integrated QK and OCP showed obvious osteoinductive activity. The results of animal experiments showed that the composite hydrogel enhanced bone regeneration in skull defects of rats, due to perfect synergistic effects of QK and OCP on vascularized bone regeneration. In summary, improving the angiogenic and osteogenic microenvironments by our double-network composite hydrogel shows promising prospects for bone repair

The Oncogene IARS2 Promotes Non-small Cell Lung Cancer Tumorigenesis by Activating the AKT/MTOR Pathway

A limited number of studies have indicated an association between isoleucyl-tRNA synthetase 2 (IARS2) and tumorigenesis. We evaluated IARS2 protein expression in lung tumor tissues and paired non-tumor tissues. We found higher IARS2 expression in the tumor tissues, which was associated with the late Tumor and Node stages of the Tumor, Node, Metastasis staging system. Silencing IARS2 inhibited the activity of A549 and H1299 cells, resulting in G0/G1 stasis of A549 cells and mitochondrial apoptosis. IARS2 silencing was also found to inhibit NSCLC tumor growth in nude mice. Complementary DNA microarray analysis revealed 742 differentially expressed genes (507 upregulated and 235 downregulated) in IARS2-silenced A549 cells compared to controls. Ingenuity Pathway Analysis of the differential expression data suggested that multiple pathways are associated with IARS2 silencing in NSCLC cells; upstream analysis predicted the activation or inhibition of transcriptional regulators. Correlation analysis revealed that AKT and MTOR activities were significantly inhibited in IARS2-silenced cells, but were partially restored by the AKT-stimulating agent SC79. IARS2 appears to regulate lung cancer cell proliferation via the AKT/MTOR pathway. Our results help clarify the complex roles of IARS2 in tumorigenesis and suggest that it may be a novel regulator of lung cancer development