Protective antigen-mediated delivery of biomolecules

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemistry, 2018.Cataloged from PDF version of thesis.Includes bibliographical references.The intracellular delivery of therapeutic biomolecules such as oligonucleotides and proteins remains a key challenge today. Protective antigen, a naturally evolved protein translocase derived from Bacillus anthracis, has shown promise as a platform of protein delivery due to its ability to form a transmembrane pore that allows the cargo to have cytosolic access. We and others have used the LFN/PA system to deliver a wide variety of natural and non-natural peptides and proteins. Despite the significant progress made with the LFN/PA delivery platform, some aspects including cargo selection and targeting still remain limited. In the first part of the thesis, we greatly expand the application of the platform by demonstration of efficient delivery of peptide nucleic acids (PNAs), an oligonucleotide analog. Using this technology, we successfully exploited a cancer- specific gene dependency by the intracellular delivery of an anti-sense PNA in a receptor-dependent manner. In addition to exploiting new types of cargo for delivery, we developed a new strategy to target the LFN/PA system to specific cell types. In the second part of the thesis, we chemically conjugated a full-length immunoglobulin G (IgG) to a mutant PA (mPA). Significantly, we took advantage of the fact that PA activation is protease-dependent and created highly specific delivery vehicles that can only be activated by the concurrent presence of two entities on the cell surface. We showed a protein toxin delivered by these IgG-mPA variants effectively inhibited cell growth in different cancer cell lines and exhibited a significantly increased therapeutic window over previously reported PA variants both in vitro and in vivo. In the last part of the thesis, we explored the possibility of simplifying the LFN/PA system by directly ligating protein cargos to PA. In the absence of LFN, the chemically created single-component system significantly increased the amount of delivered cargo. Moreover, the single-component system combined with a short N-terminal polylysine tag further improved the delivery efficiency by more than 100-fold. Our findings raise the prospect of a simpler PA-mediated delivery platform..by Zeyu (Mike) Lu.Ph. D

Tac2Structure: Object Surface Reconstruction Only through Multi Times Touch

Inspired by humans' ability to perceive the surface texture of unfamiliar objects without relying on vision, the sense of touch can play a crucial role in robots exploring the environment, particularly in scenes where vision is difficult to apply, or occlusion is inevitable. Existing tactile surface reconstruction methods rely on external sensors or have strong prior assumptions, making the operation complex and limiting their application scenarios. This paper presents a framework for low-drift surface reconstruction through multiple tactile measurements, Tac2Structure. Compared with existing algorithms, the proposed method uses only a new vision-based tactile sensor without relying on external devices. Aiming at the difficulty that reconstruction accuracy is easily affected by the pressure at contact, we propose a correction algorithm to adapt it. The proposed method also reduces the accumulative errors that occur easily during global object surface reconstruction. Multi-frame tactile measurements can accurately reconstruct object surfaces by jointly using the point cloud registration algorithm, loop-closure detection algorithm based on deep learning, and pose graph optimization algorithm. Experiments verify that Tac2Structure can achieve millimeter-level accuracy in reconstructing the surface of objects, providing accurate tactile information for the robot to perceive the surrounding environment.Comment: Accepted for publication in IEEE Robotics And Automation Letter

A Dive into SAM Prior in Image Restoration

The goal of image restoration (IR), a fundamental issue in computer vision, is to restore a high-quality (HQ) image from its degraded low-quality (LQ) observation. Multiple HQ solutions may correspond to an LQ input in this poorly posed problem, creating an ambiguous solution space. This motivates the investigation and incorporation of prior knowledge in order to effectively constrain the solution space and enhance the quality of the restored images. In spite of the pervasive use of hand-crafted and learned priors in IR, limited attention has been paid to the incorporation of knowledge from large-scale foundation models. In this paper, we for the first time leverage the prior knowledge of the state-of-the-art segment anything model (SAM) to boost the performance of existing IR networks in an parameter-efficient tuning manner. In particular, the choice of SAM is based on its robustness to image degradations, such that HQ semantic masks can be extracted from it. In order to leverage semantic priors and enhance restoration quality, we propose a lightweight SAM prior tuning (SPT) unit. This plug-and-play component allows us to effectively integrate semantic priors into existing IR networks, resulting in significant improvements in restoration quality. As the only trainable module in our method, the SPT unit has the potential to improve both efficiency and scalability. We demonstrate the effectiveness of the proposed method in enhancing a variety of methods across multiple tasks, such as image super-resolution and color image denoising.Comment: Technical Repor

Beyond Sole Strength: Customized Ensembles for Generalized Vision-Language Models

Fine-tuning pre-trained vision-language models (VLMs), e.g., CLIP, for the open-world generalization has gained increasing popularity due to its practical value. However, performance advancements are limited when relying solely on intricate algorithmic designs for a single model, even one exhibiting strong performance, e.g., CLIP-ViT-B/16. This paper, for the first time, explores the collaborative potential of leveraging much weaker VLMs to enhance the generalization of a robust single model. The affirmative findings motivate us to address the generalization problem from a novel perspective, i.e., ensemble of pre-trained VLMs. We introduce three customized ensemble strategies, each tailored to one specific scenario. Firstly, we introduce the zero-shot ensemble, automatically adjusting the logits of different models based on their confidence when only pre-trained VLMs are available. Furthermore, for scenarios with extra few-shot samples, we propose the training-free and tuning ensemble, offering flexibility based on the availability of computing resources. The proposed ensemble strategies are evaluated on zero-shot, base-to-new, and cross-dataset generalization, achieving new state-of-the-art performance. Notably, this work represents an initial stride toward enhancing the generalization performance of VLMs via ensemble. The code is available at https://github.com/zhiheLu/Ensemble_VLM.git.Comment: Technical repor

Design of Magnesium Phosphate Cement Based Composite for High Performance Bipolar Plate of Fuel Cells

In this work, we report a comprehensive study on a magnesium phosphate cement (MPC) based composite as the construction material for high performance bipolar plates of fuel cells. MPC with partial replacement of fly ash was employed as the binding matrix. Some carbon-based materials, such as graphite, carbon black, carbon fiber, and multi-walled carbon nanotubes were used to construct the conductive phase. A simple hot-press process was applied to produce the composite. The formula and the structure of the composite was modified and adjusted to optimize the properties of the composite to meet the US DOE 2015 technical targets, including the introducing of a reinforcement support. Finally, all the technical targets such as electrical conductivity (\u3e100 S cm-1), the flexural strength (\u3e25 MPa), the corrosion resistance ( \u3c 1 μA cm-2), and gas permeability ( \u3c 10-5 cm3 (s cm2)-1) were achieved as well as low cost ( \u3c 5 $ per kW). The optimized formula and the detailed procedures to fabricate the MPC based composite were concluded

CupCleaner: A Data Cleaning Approach for Comment Updating

Recently, deep learning-based techniques have shown promising performance on various tasks related to software engineering. For these learning-based approaches to perform well, obtaining high-quality data is one fundamental and crucial issue. The comment updating task is an emerging software engineering task aiming at automatically updating the corresponding comments based on changes in source code. However, datasets for the comment updating tasks are usually crawled from committed versions in open source software repositories such as GitHub, where there is lack of quality control of comments. In this paper, we focus on cleaning existing comment updating datasets with considering some properties of the comment updating process in software development. We propose a semantic and overlapping-aware approach named CupCleaner (Comment UPdating's CLEANER) to achieve this purpose. Specifically, we calculate a score based on semantics and overlapping information of the code and comments. Based on the distribution of the scores, we filter out the data with low scores in the tail of the distribution to get rid of possible unclean data. We first conducted a human evaluation on the noise data and high-quality data identified by CupCleaner. The results show that the human ratings of the noise data identified by CupCleaner are significantly lower. Then, we applied our data cleaning approach to the training and validation sets of three existing comment updating datasets while keeping the test set unchanged. Our experimental results show that even after filtering out over 30\% of the data using CupCleaner, there is still an improvement in all performance metrics. The experimental results on the cleaned test set also suggest that CupCleaner may provide help for constructing datasets for updating-related tasks

TreeGen: A Tree-Based Transformer Architecture for Code Generation

A code generation system generates programming language code based on an input natural language description. State-of-the-art approaches rely on neural networks for code generation. However, these code generators suffer from two problems. One is the long dependency problem, where a code element often depends on another far-away code element. A variable reference, for example, depends on its definition, which may appear quite a few lines before. The other problem is structure modeling, as programs contain rich structural information. In this paper, we propose a novel tree-based neural architecture, TreeGen, for code generation. TreeGen uses the attention mechanism of Transformers to alleviate the long-dependency problem, and introduces a novel AST reader (encoder) to incorporate grammar rules and AST structures into the network. We evaluated TreeGen on a Python benchmark, HearthStone, and two semantic parsing benchmarks, ATIS and GEO. TreeGen outperformed the previous state-of-the-art approach by 4.5 percentage points on HearthStone, and achieved the best accuracy among neural network-based approaches on ATIS (89.1%) and GEO (89.6%). We also conducted an ablation test to better understand each component of our model

Generalized Equivariance and Preferential Labeling for GNN Node Classification

Existing graph neural networks (GNNs) largely rely on node embeddings, which represent a node as a vector by its identity, type, or content. However, graphs with unattributed nodes widely exist in real-world applications (e.g., anonymized social networks). Previous GNNs either assign random labels to nodes (which introduces artefacts to the GNN) or assign one embedding to all nodes (which fails to explicitly distinguish one node from another). Further, when these GNNs are applied to unattributed node classification problems, they have an undesired equivariance property, which are fundamentally unable to address the data with multiple possible outputs. In this paper, we analyze the limitation of existing approaches to node classification problems. Inspired by our analysis, we propose a generalized equivariance property and a Preferential Labeling technique that satisfies the desired property asymptotically. Experimental results show that we achieve high performance in several unattributed node classification tasks