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

Understanding the Biological and Environmental Implications of Nanomaterials

The last two decades have witnessed the discovery, development, and large-scale manufacturing of novel nanomaterials. While nanomaterials bring in exciting and extraordinary properties in all areas of materials, electronics, mechanics, and medicine, they also could generate potential adverse effects in biological systems and in the environment. The currently limited application of nanomaterials in biological and ecological systems results from the insufficient and often controversial data on describing the complex behaviors of nanomaterials in living systems. The purpose of this dissertation intends to fill such a knowledge void with methodologies from the disciplines of biophysics, biology, and materials science and engineering. Chapter 1 of this dissertation provides a comprehensive review on the structures and properties of carbon nanomaterials (CBNMs), metal oxides, and quantum dots (QDs). This chapter also details the state-of-the-art on the biological applications, ecological applications, and toxicity of nanomaterials. With Chapter 1 serving as a background, Chapters 2-5 present my Doctor of Philosophy (PhD) research, an inquiry on the fate of nanomaterials in biological and ecological systems, on the whole organism and cellular levels. Specifically, CBNMs are introduced to rice plant seedlings and the uptake, translocation and generational transfer of fullerene C70 in the plant compartments are imaged and characterized. The interactions between CBNMs and rice plants on the whole organism level are initiated by the binding between CBNMs and natural organic matter (NOM), driven by the transpiration of water from the roots to the leaves of the plants and mediated by both the physiochemical properties of the CBNMs and plant physiology. In Chapter 3, semiconducting nanocrystals quantum dots (QDs) are introduced to green algae Chlamydomonas to probe the interactions of nanomaterials with ecological systems on the cellular level. The adsorption of QDs onto the algal cell wall is quantified by UV-vis spectrophotometry and fitted with the Freundlich isothem. Effects of the adsorption of QDs on the photosynthetic activities of the algae are evaluated using O2 evolution and CO2 depletion assays, and the ecological impact of such adsorption is discussed. To understand the effects of nanomaterials on the cell membrane, nanoparticles (Au, TiO2, and QDs) of different surface charges and chemical compositions are introduced to HT-29 mammalian cells in Chapter 4. The polarization of the cell membrane is investigated using a FLIPR membrane potential kit. The phase of the cell membrane, in the presence of both positively and negatively charged nanoparticles, are examined using laurden, a lipophilic dye that serves as a molecular reporter on the fluidic or gel phase of the host membrane. To address the effects of nanomaterials on biological and ecological systems within the same context, Chapter 5 offers a first parallel comparison between mammalian and plant cell responses to nanomaterials. This study is conducted using a plant cell viability assay, complimented by bright field, fluorescence, and electron microscopy imaging. Discussions of this study are presented based on the hydrophobicity and solubility of C60(OH)20 and of supramolecular complex C70-NOM, hydrophobicity and porous structure of the plant Allium cepa cell wall, and the amphiphilic structure and endocytosis of the plasma cell membrane of both Allium cepa and HT-29 cells. Chapter 6 summarizes and rationalizes results obtained from the entire dissertation research. Future work inspired by this research is presented at the end of the chapter. Specifically, this dissertation is structured to embody the following essential and complementary chapters: * Chapter 1: Literature review * Chapter 2: Nano-Eco interactions at the whole organism level; * Chapter 3: Nano-Eco interactions at the cellular level; * Chapter 4: Nano-Bio interactions at the cellular level; * Chapter 5: Parallel comparison of Nano-Eco and Nano-Bio interactions at the cellular level. * Chapter 6: Conclusions and future work The overarching goal of this research is to advance our understanding on the fate of nanomaterials in biological and ecological systems. Knowledge obtained from this dissertation is expected to benefit future research on the implications and applications of engineered nanomaterials

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

Dynamics of human body skeletons convey significant information for human action recognition. Conventional approaches for modeling skeletons usually rely on hand-crafted parts or traversal rules, thus resulting in limited expressive power and difficulties of generalization. In this work, we propose a novel model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous methods by automatically learning both the spatial and temporal patterns from data. This formulation not only leads to greater expressive power but also stronger generalization capability. On two large datasets, Kinetics and NTU-RGBD, it achieves substantial improvements over mainstream methods.Comment: Accepted by AAAI 201

UrbanFM: Inferring Fine-Grained Urban Flows

Urban flow monitoring systems play important roles in smart city efforts around the world. However, the ubiquitous deployment of monitoring devices, such as CCTVs, induces a long-lasting and enormous cost for maintenance and operation. This suggests the need for a technology that can reduce the number of deployed devices, while preventing the degeneration of data accuracy and granularity. In this paper, we aim to infer the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations. This task is challenging due to two reasons: the spatial correlations between coarse- and fine-grained urban flows, and the complexities of external impacts. To tackle these issues, we develop a method entitled UrbanFM based on deep neural networks. Our model consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs by using a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influences of different external factors. Extensive experiments on two real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness and efficiency of our method compared to seven baselines, demonstrating the state-of-the-art performance of our approach on the fine-grained urban flow inference problem

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