Skip to main content
Banner [Small]

Test out our new Bento Search

test area
x
# results
shortcut
Sections
HTML elements
Section Tiles
expand
Tile Cover
Mouse
Math Lab
Space
Tile Short Summary
Math Lab Rooms located in the Main Library in rooms 300X and 300Y
expand
Tile Cover
coffee
CC's Coffee House
Space
Tile Short Summary
Located at the first floor of the LSU Main Library.
expand
Tile Cover
People troubleshooting on a computer
Ask Us
Service
Tile Short Summary
Check our FAQs, submit a question using our form, or launch the chat widget to find help.

Website

207

Gear

44

FAQ

169

Database Listing

375

Staff

101

Discovery

2058653
Orthogonally Functionalizable Redox-Responsive Polymer Brushes: Catch and Release Platform for Proteins and Cells
Polymer brushes engineered to “specifically capture” and “release on demand” analytes such as dyes, proteins, and cells find biomedical applications ranging from protein immobilization to cell death. Utilizing a disulfide-linker-containing monomer as a building block enables the fabrication of a redox-responsive polymer brush platform with the “catch and release” attribute. Herein, thiol-reactive redox-responsive polymer brushes are fabricated using a pyridyl disulfide-based monomer, and their postpolymerization functionalization is demonstrated via thiol–disulfide exchange reaction with thiol-containing dyes, (bio)­molecules, and cell adhesive ligands. After establishing reversible conjugation using a fluorescent dye and other model compounds, copolymer brushes postmodified with thiol-containing mannose demonstrated selective immobilization of concanavalin A in the presence of peanut agglutinin. In addition, a thiolated RGD peptide was conjugated to the side chain of polymer brushes to facilitate cell adhesion, followed by on-demand harvesting. To enable localized drug delivery to surface-adhered cells, orthogonal chain end and side chain functionalization using the thiol-Michael addition and thiol–disulfide exchange reaction, respectively, was used to conjugate the cell adhesive RGD peptide and the anticancer drug doxorubicin (DOX). On-demand DOX release and internalization by surface-bound cancer cells were demonstrated via cleavage of disulfide linkages in the presence of a reducing agent. This approach may provide an attractive methodology to deliver therapeutic agents precisely to specific cells.
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning
Vision-language models (VLMs) have shown remarkable advancements in multimodal reasoning tasks. However, they still often generate inaccurate or irrelevant responses due to issues like hallucinated image understandings or unrefined reasoning paths. To address these challenges, we introduce Critic-V, a novel framework inspired by the Actor-Critic paradigm to boost the reasoning capability of VLMs. This framework decouples the reasoning process and critic process by integrating two independent components: the Reasoner, which generates reasoning paths based on visual and textual inputs, and the Critic, which provides constructive critique to refine these paths. In this approach, the Reasoner generates reasoning responses according to text prompts, which can evolve iteratively as a policy based on feedback from the Critic. This interaction process was theoretically driven by a reinforcement learning framework where the Critic offers natural language critiques instead of scalar rewards, enabling more nuanced feedback to boost the Reasoner’s capability on complex reasoning tasks. The Critic model is trained using Direct Preference Optimization (DPO), leveraging a preference dataset of critiques ranked by Rule-based Reward (RBR) to enhance its critic capabilities. Evaluation results show that the Critic-V framework significantly outperforms existing methods, including GPT-4V, on 5 out of 8 benchmarks, especially regarding reasoning accuracy and efficiency. Combining a dynamic text-based policy for the Reasoner and constructive feedback from the preference-optimized Critic enables a more reliable and context-sensitive multimodal reasoning process. Our approach provides a promising solution to enhance the reliability of VLMs, improving their performance in real-world reasoning-heavy multimodal applications such as autonomous driving and embodied intelligence. Our data and code are released at https://github.com/kyrieLei/Critic-V.