ACID-Style: An adaptive condition injection diffusion model for arbitrary style transfer

Arbitrary style transfer (AST), a popular AI-powered photo editing function, aims to strike an optimal balance between content and style injection from two images in order to generate a novel high-fidelity stylised image. Recently, diffusion models have been applied to AST due to their high generation quality as well as flexibility to embed conditions. However, these models are still not satisfactory and may exhibit inferior performance compared to non-diffusion based methods. This is due to the diffusion process not being purposely designed for AST, leading to suboptimal solutions to trade off content preservation and style embedding. In this paper, we propose ACID-Style, a novel adaptive condition injection diffusion-based AST framework for improved content/style feature injection to address this research challenge. Using two lightweight adapters, a content and a style injection module, and an adaptive injection mechanism, our approach is able to fully exploit a pre-trained stable diffusion model for AST specific adaptation and our diffusion model thus learns the most effective timing for content and style injection in the diffusion sampling process. Comprehensive evaluations demonstrate that our method achieves superior style transfer performance, both quantitatively and qualitatively, compared to other state-of-the-art style transfer methods.</p

Towards compact reversible image representations for neural style transfer

Arbitrary neural style transfer aims to stylise a content image by referencing a provided style image. Despite various efforts to achieve both content preservation and style transferability, learning effective representations for this task remains challenging since the redundancy of content and style features leads to unpleasant image artefacts. In this paper, we learn compact neural representations for style transfer motivated from an information theoretical perspective. In particular, we enforce compressive representations across sequential modules of a reversible flow network in order to reduce feature redundancy without losing content preservation capability. We use a Barlow twins loss to reduce channel dependency and thus to provide better content expressiveness, and optimise the Jensen-Shannon divergence of style representations between reference and target images to avoid under- and over-stylisation. We demonstrate the effectiveness of our proposed method in comparison to other state-of-the-art style transfer methods.</p

UniStyleDiff: A unified diffusion-driven framework for image and video style transfer

Arbitrary style transfer aims to stylize images/videos using provided style references. While significant advancements have been achieved in individual modalities, the inherent conflict between stylization quality and temporal consistency makes it challenging to seamlessly switch between image and video stylization without compromising stylization fidelity. To this end, we propose UniStyleDiff, a dual-modal style transfer framework based on Stable Diffusion that allows smooth modality switching in a plug-and-play manner. Specifically, we first introduce a dual-branch adaptive feature injection architecture for efficient and consistent image stylization without per-style fine-tuning or inversion. Then, we extend our framework to videos through a pluggable Inter-frame Consistency Module (ICM), capturing long-range inter-frame dependencies. Building on ICM, we introduce a Motion-Dynamics Preserved (MDP) sampling strategy to better retain intrinsic relationships between frames. Extensive experiments demonstrate that compared with state-of-the-art methods, our approach achieves superior stylization quality and temporal consistency, offering a unified solution for image and video stylization.</p

Additional file 3 of Post-translational modification of CDK1–STAT3 signaling by fisetin suppresses pancreatic cancer stem cell properties

Additional file 3: Table S2. Protein kinase like domain associated genes

Additional file 2 of Post-translational modification of CDK1–STAT3 signaling by fisetin suppresses pancreatic cancer stem cell properties

Additional file 2: Table S1. Proteins identified by proteomic

Additional file 1 of Post-translational modification of CDK1–STAT3 signaling by fisetin suppresses pancreatic cancer stem cell properties

Additional file 1: Figure S1. a Representative flow cytometry plots for CD44 and CD24 expression in human pancreatic cancer HPC-Y5 cells with DMSO or fisetin treatment. Cells were treated with fisetin (100 µM) for 48 h. b Statistical plot of ratio of CD44 + /CD24 + positive and CD44-/CD24- negative cells in control or fisetin treatment HPC-Y5 cells. Data are presented as mean ± SD (n = 3); *P  1.5). d Heat map of Biological Process in GO enrichment analysis of differentially expressed proteins in each Q subset according to P value of Fisher's exact test. Figure S2. Heat map of Cellular Component in GO enrichment analysis of differentially expressed proteins in each Q subset according to P value of Fisher's exact test. Figure S3. Heat map of Molecular Function in GO enrichment analysis of differentially expressed proteins in each Q subset according to P value of Fisher's exact test. Figure S4. a Heat map of KEGG pathway enrichment analysis of differentially expressed proteins in each Q subset according to P value of Fisher's exact test. Enrichment pathways of Q1 and Q2 indicated proteins in important pathways including PI3K–Akt signaling, pathways in cancer, metabolism pathways and ECM-receptor interaction were declined in PANC-1 cells with fisetin treatment. b Heat map of protein domain enrichment analysis of differentially expressed proteins in each Q subset. Enrichment protein domain of Q1 and Q2 indicated proteins with EGF-like domain and Laminin EGF domain were reduced by fisetin treatment. c Protein domain enrichment analysis of whole differentially expressed proteins quantified by proteomics analysis. Figure S5. a Summary of acetylated sites and proteins quantified by acetyl-proteomics analysis. b Summary of differentially expressed acetylated sites and proteins quantified by acetyl-proteomics analysis. 368 sites were changed over 1.2-folds (P < 0.05) including 307 up-regulated and 61 down-regulated in 264 proteins. c Go enrichment analysis of differentially expressed acetylated proteins. d Immunoprecipitation and western blot determined that EP300 was acetyl-transferase of CDK1. Figure S6. Protein motif analysis was performed by statistical analysis of the patterns of amino acid sequences before and after all acetylated sites in samples. 19 types of conserved motifs were identified by motif analysis. Figure S7. a Western blot analysis was used to determine expression of CDK1, STAT3, CD44 and Sox2 in adherent PANC-1 cells or spheres generated from PANC-1 cells. Adhe, adherent cells. b Inhibition of CD44 and Sox2 by CDK1 silencing can be rescued by over-expressing STAT3. Expression of CD44, Sox2, CDK1, STAT3, p-CDK1 and p-STAT3 were examined by western blot analysis. c Inhibition of CDK1-STAT3 signaling by fisetin in purified pancreatic cancer stem cell spheres. Expression of CD44, Sox2, CDK1, STAT3, p-CDK1 and p-STAT3 were performed by western blot analysis. d-e Direct suppression of purified pancreatic oncospheres by fisetin. Second generation of PANC-1 and HPC-Y5 cells from oncospheres were subjected to the tumor sphere-formation assay in ultra-low cluster plates. After the cultivation process to initial point, these tumor spheres were treated with or without fisetin (100 μM) for 48 h. Scale bars, 100 μm. Data are presented as mean ± SD (n = 3),*P < 0.05; ns, no significance. Figure S8. a HDAC3 silencing reduced levels of p-CDK1, p-STAT3, CD44 and Sox2. Western blot analysis was used to determine expression of CDK1, STAT3, CD44 and Sox2 in HDAC3 silencing PANC-1 cells. b-c Lack of HDAC3 weakened tumor sphere formatting capacity in PDAC cells. Scale bars, 100 μm. Data are presented as mean ± SD (n = 4); *P < 0.05. d HDAC3 over-expression increased levels of p-CDK1, p-STAT3, CD44 and Sox2. Western blot analysis was used to determine expression of CDK1, STAT3, CD44 and Sox2 in HDAC3 over-expression PANC-1 cells. e–f Over-expressing HDAC3 enhanced the sphere formatting capacity both in PANC-1 and HPC-Y5 cells. Scale bars, 100 μm. Data are presented as mean ± SD (n = 4); *P < 0.05, **P < 0.01. g Fisetin reduced expression of HDAC3 both in PANC-1 and HPC-Y5 cells. Figure S9. a Fraction-affected (Fa) and CI are explored after 48-h incubation with fisetin and gemcitabine combination, CI < 1 represents synergy. b Representative section of magnetic resonance imaging (MRI) for spontaneous pancreatic ductal adenocarcinoma in KPC mice. Figure S10. a Western blot analysis was used to determine expression of CDK1 in stable CDK1 knockout PANC-1 cells. CDK1-KO: CDK1-knockout. b Quantification of relative phosphorylation of CDK1 was performed by analyzing western blot results with image J software. The relative phosphorylation of CDK1 was significant reduced in PANC-1 and HPC-Y5 cells with fisetin (100 μM) treatment. Fis, fisetin; *P < 0.05; ns, no significance. c qRT-PCR showed that mRNA expression of CDK1 was not influenced by fisetin treatment in pancreatic cancer cells. Data are presented as mean ± SD (n = 3). ns, no significance. d Proteasome inhibitor MG132 restore the expression of CDK1 in PDAC cells with fisetin treatment. PANC-1 and HPC-Y5 ells were cultured with or without fisetin (100 μM) treatment for 48 h. Before the collection of cells, MG132 (10 μM) were added to culture medium for 0, 6 and 12 h. h, hours. e Immunoprecipitation and western blot determined that fisetin induced prominent ubiquitination of CDK1 in pancreatic cancer cells. IP, immunoprecipitation. Ub, ubiquitin. Figure S11. a Western blot analysis was used to determine phosphorylation of CDK1 at Thr14 and Thr161 residues in pancreatic cancer PANC-1 and HPC-Y5 cells. p-CDK1(T161): phosphorylation of CDK1 at Thr161; p-CDK1(T14): phosphorylation of CDK1 at Thr14

Additional file 4 of Post-translational modification of CDK1–STAT3 signaling by fisetin suppresses pancreatic cancer stem cell properties

Additional file 4: Table S3. Levels of transcriptome and protein phosphorylation of genes in HIF-1 signaling

Additional file 5 of Post-translational modification of CDK1–STAT3 signaling by fisetin suppresses pancreatic cancer stem cell properties

Additional file 5: Table S4. Levels of transcriptome and protein phosphorylation of genes in JAK–STAT signaling