Carbon Nanomaterials in Biological Systems

ABSTRACT This thesis intends to present, from the biophysical viewpoint, my study on understanding carbon nanomaterials in biological and environmental systems. Carbon nanotubes and fullerenes represent a major family of carbon nanoparticles which possess distinct electrical, optical and mechanical properties. However, the major hurdle for making carbon nanomaterials bioavailable lies in their tendency toward bundling, driven by hydrophobic interaction, van der Waals force, and pi-stacking. To overcome this problem, we used non-covalent binding of zwitterionic lysophopholipids (LPL) onto the external surfaces of single-walled carbon nanotubes (SWNTs). This method affords superior SWNT solubility in aqueous solution. The stability of SWNT-LPL complex has been found to be dependent on the pH of the solvent, but independent of solvent temperature. Based on this method, the translocation of rhodamine-lysophosphoethanolamine-SWNT (Rd-LPE-SWNT) complex across cell membranes, as well as the dissociation of Rd-LPE from SWNTs in the cellular environment was detected using the technique of fluorescence resonance energy transfer (FRET). Towards understanding the environmental impact of carbon nanomaterials, we have studied the biomodification of SWNT-lyso-phosphatidylcholine (SWNT-LPC) by aquatic organism Daphnia magna. Through normal feeding behavior, Daphnia magna ingested SWNT-LPC and stripped out the lipids as food source. SWNTs rebundled inside the guts of daphnia and were excreted into the water column. Acute toxicity was observed only in the highest test concentrations of 0.5 mg/L under starvation conditions. Regarding fullerenes, C70, the shortest \u27SWNT\u27, was solubilized in water by gallic acid, a natural anti-oxidant and anticancer agent. The suparmolecular complex of C70-gallic acid, assembled through pi-stacking, emitted green fluorescence in aqueous solution. Utilizing this optical property, we succeeded in labeling biological systems at cellular, tissue and living organism levels. We have further discovered that the fluorescence of C70 is far more resistant to photobleaching than calcein AM, a conventional dye for bioimaging. Using confocal fluorescence microscopy we have obtained the first real-time observation of nanoparticle translocation across cell membranes. In summary, the objectives of this thesis are: Solubilizating SWNT in aqueous solution afforded by different solvating agents, including DOPA, sodium dodecyl sulfate (SDS) and LPC, and testing the stability of the solution at different temperatures, pH and ionic strengths; Using SWNT as a transporter for lipid delivery across cell membranes; Understanding the fate of SWNT in aquatic organism Daphnia magna; and Coating C70 with gallic acid and utilizing the optical properties of C70 for detecting its cell translocation. The studies documented in this thesis further our understanding of the interactions between carbon nanomaterials and biological and environmental systems. Water soluble carbon nanomaterials enable future field studies in imaging, sensing, drug delivery, nanotoxicity, nanomedicine, and environmental science and engineering

Fast Color-guided Depth Denoising for RGB-D Images by Graph Filtering

Depth images captured by off-the-shelf RGB-D cameras suffer from much stronger noise than color images. In this paper, we propose a method to denoise the depth images in RGB-D images by color-guided graph filtering. Our iterative method contains two components: color-guided similarity graph construction, and graph filtering on the depth signal. Implemented in graph vertex domain, filtering is accelerated as computation only occurs among neighboring vertices. Experimental results show that our method outperforms state-of-art depth image denoising methods significantly both on quality and efficiency.Comment: 5 pages, 4 figure

GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction

This paper aims to efficiently enable Large Language Models (LLMs) to use multimodal tools. Advanced proprietary LLMs, such as ChatGPT and GPT-4, have shown great potential for tool usage through sophisticated prompt engineering. Nevertheless, these models typically rely on prohibitive computational costs and publicly inaccessible data. To address these challenges, we propose the GPT4Tools based on self-instruct to enable open-source LLMs, such as LLaMA and OPT, to use tools. It generates an instruction-following dataset by prompting an advanced teacher with various multi-modal contexts. By using the Low-Rank Adaptation (LoRA) optimization, our approach facilitates the open-source LLMs to solve a range of visual problems, including visual comprehension and image generation. Moreover, we provide a benchmark to evaluate the ability of LLMs to use tools, which is performed in both zero-shot and fine-tuning ways. Extensive experiments demonstrate the effectiveness of our method on various language models, which not only significantly improves the accuracy of invoking seen tools, but also enables the zero-shot capacity for unseen tools. The code and demo are available at https://github.com/StevenGrove/GPT4Tools

Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy

As an emerging education strategy, learnersourcing offers the potential for personalized learning content creation, but also grapples with the challenge of predicting student performance due to inherent noise in student-generated data. While graph-based methods excel in capturing dense learner-question interactions, they falter in cold start scenarios, characterized by limited interactions, as seen when questions lack substantial learner responses. In response, we introduce an innovative strategy that synergizes the potential of integrating Signed Graph Neural Networks (SGNNs) and Large Language Model (LLM) embeddings. Our methodology employs a signed bipartite graph to comprehensively model student answers, complemented by a contrastive learning framework that enhances noise resilience. Furthermore, LLM's contribution lies in generating foundational question embeddings, proving especially advantageous in addressing cold start scenarios characterized by limited graph data interactions. Validation across five real-world datasets sourced from the PeerWise platform underscores our approach's effectiveness. Our method outperforms baselines, showcasing enhanced predictive accuracy and robustness