Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning

In this work, we tackle the challenging problem of arbitrary image style transfer using a novel style feature representation learning method. A suitable style representation, as a key component in image stylization tasks, is essential to achieve satisfactory results. Existing deep neural network based approaches achieve reasonable results with the guidance from second-order statistics such as Gram matrix of content features. However, they do not leverage sufficient style information, which results in artifacts such as local distortions and style inconsistency. To address these issues, we propose to learn style representation directly from image features instead of their second-order statistics, by analyzing the similarities and differences between multiple styles and considering the style distribution. Specifically, we present Contrastive Arbitrary Style Transfer (CAST), which is a new style representation learning and style transfer method via contrastive learning. Our framework consists of three key components, i.e., a multi-layer style projector for style code encoding, a domain enhancement module for effective learning of style distribution, and a generative network for image style transfer. We conduct qualitative and quantitative evaluations comprehensively to demonstrate that our approach achieves significantly better results compared to those obtained via state-of-the-art methods. Code and models are available at https://github.com/zyxElsa/CAST_pytorchComment: Accepted by SIGGRAPH 202

ProSpect: Expanded Conditioning for the Personalization of Attribute-aware Image Generation

Personalizing generative models offers a way to guide image generation with user-provided references. Current personalization methods can invert an object or concept into the textual conditioning space and compose new natural sentences for text-to-image diffusion models. However, representing and editing specific visual attributes like material, style, layout, etc. remains a challenge, leading to a lack of disentanglement and editability. To address this, we propose a novel approach that leverages the step-by-step generation process of diffusion models, which generate images from low- to high-frequency information, providing a new perspective on representing, generating, and editing images. We develop Prompt Spectrum Space P*, an expanded textual conditioning space, and a new image representation method called ProSpect. ProSpect represents an image as a collection of inverted textual token embeddings encoded from per-stage prompts, where each prompt corresponds to a specific generation stage (i.e., a group of consecutive steps) of the diffusion model. Experimental results demonstrate that P* and ProSpect offer stronger disentanglement and controllability compared to existing methods. We apply ProSpect in various personalized attribute-aware image generation applications, such as image/text-guided material/style/layout transfer/editing, achieving previously unattainable results with a single image input without fine-tuning the diffusion models

Knowledge mapping concerning applications of nanocomposite hydrogels for drug delivery: A bibliometric and visualized study (2003–2022)

Background: Nanocomposite Hydrogels (NHs) are 3D molecular networks formed by physically or covalently crosslinking polymer with nanoparticles or nanostructures, which are particularly suitable for serving as carriers for drug delivery systems. Many articles pertaining to the applications of Nanocomposite Hydrogels for drug delivery have been published, however, the use of bibliometric and visualized analysis in this area remains unstudied. The purpose of this bibliometric study intended to comprehensively analyze the knowledge domain, research hotspots and frontiers associated with the applications of Nanocomposite Hydrogels for drug delivery.Methods: We identified and retrieved the publications concerning the applications of NHs for drug delivery between 2003 and 2022 from Web of Science Core Collection Bibliometric and visualized analysis was utilized in this investigative study.Results: 631 articles meeting the inclusion criteria were identified and retrieved from WoSCC. Among those, 2,233 authors worldwide contributed in the studies, accompanied by an average annual article increase of 24.67%. The articles were co-authored by 764 institutions from 52 countries/regions, and China published the most, followed by Iran and the United States. Five institutions published more than 40 papers, namely Univ Tabriz (n = 79), Tabriz Univ Med Sci (n = 70), Islamic Azad Univ (n = 49), Payame Noor Univ (n = 42) and Texas A&M Univ (n = 41). The articles were published in 198 journals, among which the International Journal of Biological Macromolecules (n = 53) published the most articles, followed by Carbohydrate Polymers (n = 24) and ACS Applied Materials and Interfaces (n = 22). The top three journals most locally cited were Carbohydrate Polymers, Biomaterials and Advanced materials. The most productive author was Namazi H (29 articles), followed by Bardajee G (15 articles) and Zhang J (11 articles) and the researchers who worked closely with other ones usually published more papers. “Doxorubicin,” “antibacterial” and “responsive hydrogels” represent the current research hotspots in this field and “cancer therapy” was a rising research topic in recent years. “(cancer) therapeutics” and “bioadhesive” represent the current research frontiers.Conclusion: This bibliometric and visualized analysis offered an investigative study and comprehensive understanding of publications regarding the applications of Nanocomposite Hydrogels for drug delivery from 2003 to 2022. The outcome of this study would provide insights for researchers in the field of Nanocomposite Hydrogels applications for drug delivery

Thymosin alpha 1 in the prevention of infected pancreatic necrosis following acute necrotising pancreatitis (TRACE trial): protocol of a multicentre, randomised, double-blind, placebo-controlled, parallel-group trial

Introduction Infected pancreatic necrosis (IPN) and its related septic complications are the major causes of death in patients with acute necrotising pancreatitis (ANP). Therefore, the prevention of IPN is of great clinical value, and immunomodulatory therapy with thymosin alpha 1 may be beneficial. This study was designed to test the hypothesis that the administration of thymosin alpha 1 during the acute phase of ANP will result in a reduced incidence of IPN. Methods and analysis This is a randomised, multicentre, double-blind, placebo-controlled study. 520 eligible patients with ANP will be randomised in a 1:1 ratio to receive either the thymosin alpha 1 or the placebo using the same mode of administration. The primary endpoint is the incidence of IPN during the index admission. Most of the secondary endpoints will be registered within the index admission including in-hospital mortality, the incidence of new-onset organ failure and new-onset persistent organ failure (respiration, cardiovascular and renal), receipt of new organ support therapy, requirement for drainage or necrosectomy, bleeding requiring intervention, human leucocyte antigens-DR(HLA-DR) on day 0, day 7, day 14, and so on and adverse events. Considering the possibility of readmission, an additional follow-up will be arranged 90 days after enrolment, and IPN and death at day 90 will also be served as secondary outcomes. Ethics and dissemination This study was approved by the ethics committee of Jinling Hospital, Nanjing University (Number 2015NZKY-004-02). The thymosin alpha 1 in the prevention of infected pancreatic necrosis following acute necrotising pancreatitis(TRACE) trial was designed to test the effect of a new therapy focusing on the immune system in preventing secondary infection following ANP. The results of this trial will be disseminated in peer-reviewed journals and at scientific conferences. Trial registration number ClinicalTrials.gov Registry (NCT02473406)