Transient absorption imaging of hemeprotein in fresh muscle fibers

2022 Summer.Includes bibliographical references.Mitochondrial diseases affect 1 in 4000 individuals in the U.S. among adults and children of all races and genders. Nevertheless, these diseases are hard to diagnose because they affect each person differently. Meanwhile the gold standard diagnosis methods are usually invasive and time- consuming. Therefore, a non-invasive and in-vivo diagnosis method is highly demanded in this area. Our goal is to develop a non-invasive diagnosis method based on the endogenous nonlinear optical effect of the live tissues. Mitochondrial disease is frequently the result of a defective electron transport chain (ETC). Our goal is to develop a non-invasive way to measure redox within the ETC, specifically, of cytochromes. Cytochromes are iron porphyrins that are essential to the ETC. Their redox states can indicate cellular oxygen consumption and mitochondrial ATP production. So being able to differentiate the redox states of cytochromes will offer us a method to characterize mitochondrial function. Meanwhile, Chergui's group found out that the two redox states of cytochrome c have different pump-probe spectroscopic responses, meaning that the transient absorption (TA) decay lifetime can be a potential molecular contrast for cytochrome redox state discrimination. Their research leads us to utilize the pump-probe spectroscopic idea to develop a time-resolved optical microscopic method to differentiate not only cytochromes from other chemical compounds but also reduced cytochromes from oxidized ones. This dissertation describes groundbreaking experiments where transient absorption is used to reveal excited-state lifetime differences between healthy controls and an animal model of mitochondrial disease, in addition to differences between reduced and oxidized ETC in isolated mitochondria and fresh preparations of muscle fibers. For our initial experiments, we built a pump-probe microscopic system with a fiber laser source, producing 530nm pump and 490nm probe using a 3.5kHz laser scanning rate. The pulse durations of pump and probe are both 800fs. For the preliminary results, we have successfully achieved TA decay contrast between reduced and oxidized cytochromes in solution form. Then we have achieved SNR enhanced pump-probe image of BGO crystal particles with the help of the software- based adaptive filter noise canceling method. We also have installed a FPGA-based adaptive filter to enhance the pump-probe signals of the electrophoresis gels that contain different mitochondrial respiratory chain supercomplexes. However, because the noise floor was still 30 dB higher than shot noise limit, cytochrome imaging in live tissues was still problematic. We then built another pump-probe microscope with a solid- state ultrafast laser source. In that way, we do not need to worry about laser relative intensity noise (RIN) anymore, since the noise floor of the solid-state laser source can reach the shot noise limit at MHz region. One other advantage of the new laser source is that it can provide one tunable laser output that can be directly converted to the probe pulse with tunable center wavelength. Its tunability can cover the entire visible spectrum. We realized a pump-probe microscopy with a 520nm pump pulse and a tunable probe pulse. The tunability on the probe arm allows us to explore better pump-probe contrast between two redox states. What's more, I will introduce my preliminary results of utilizing supercontinuum generation in a photonic crystal fiber (PCF) to realize tunability on pump wavelength. In that way, more possibilities will be unlocked. And the hyperspectral pump-probe microscope will be able to distinguish more molecules

Robustness, dissipations and coherence of the oscillation of circadian clock: potential landscape and flux perspectives

Finding the global probabilistic nature of a non-equilibrium circadian clock is essential for addressing important issues of robustness and function. We have uncovered the underlying potential energy landscape of a simple cyanobacteria biochemical network, and the corresponding flux which is the driving force for the oscillation. We found that the underlying potential landscape for the oscillation in the presence of small statistical fluctuations is like an explicit ring valley or doughnut shape in the three dimensional protein concentration space. We found that the barrier height separating the oscillation ring and other area is a quantitative measure of the oscillation robustness and decreases when the fluctuations increase. We also found that the entropy production rate characterizing the dissipation or heat loss decreases as the fluctuations decrease. In addition, we found that, as the fluctuations increase, the period and the amplitude of the oscillations is more dispersed, and the phase coherence decreases. We also found that the properties from exploring the effects of the inherent chemical rate parameters on the robustness. Our approach is quite general and can be applied to other oscillatory cellular network

Exploration of Multi-State Conformational Dynamics and Underlying Global Functional Landscape of Maltose Binding Protein

An increasing number of biological machines have been revealed to have more than two macroscopic states. Quantifying the underlying multiple-basin functional landscape is essential for understanding their functions. However, the present models seem to be insufficient to describe such multiple-state systems. To meet this challenge, we have developed a coarse grained triple-basin structure-based model with implicit ligand. Based on our model, the constructed functional landscape is sufficiently sampled by the brute-force molecular dynamics simulation. We explored maltose-binding protein (MBP) which undergoes large-scale domain motion between open, apo-closed (partially closed) and holo-closed (fully closed) states responding to ligand binding. We revealed an underlying mechanism whereby major induced fit and minor population shift pathways co-exist by quantitative flux analysis. We found that the hinge regions play an important role in the functional dynamics as well as that increases in its flexibility promote population shifts. This finding provides a theoretical explanation of the mechanistic discrepancies in PBP protein family. We also found a functional “backtracking” behavior that favors conformational change. We further explored the underlying folding landscape in response to ligand binding. Consistent with earlier experimental findings, the presence of ligand increases the cooperativity and stability of MBP. This work provides the first study to explore the folding dynamics and functional dynamics under the same theoretical framework using our triple-basin functional model

Relay Hindsight Experience Replay: Self-Guided Continual Reinforcement Learning for Sequential Object Manipulation Tasks with Sparse Rewards

Exploration with sparse rewards remains a challenging research problem in reinforcement learning (RL). Especially for sequential object manipulation tasks, the RL agent always receives negative rewards until completing all sub-tasks, which results in low exploration efficiency. To solve these tasks efficiently, we propose a novel self-guided continual RL framework, RelayHER (RHER). RHER first decomposes a sequential task into new sub-tasks with increasing complexity and ensures that the simplest sub-task can be learned quickly by utilizing Hindsight Experience Replay (HER). Secondly, we design a multi-goal & multi-task network to learn these sub-tasks simultaneously. Finally, we propose a Self-Guided Exploration Strategy (SGES). With SGES, the learned sub-task policy will guide the agent to the states that are helpful to learn more complex sub-task with HER. By this self-guided exploration and relay policy learning, RHER can solve these sequential tasks efficiently stage by stage. The experimental results show that RHER significantly outperforms vanilla-HER in sample-efficiency on five singleobject and five complex multi-object manipulation tasks (e.g., Push, Insert, ObstaclePush, Stack, TStack, etc.). The proposed RHER has also been applied to learn a contact-rich push task on a physical robot from scratch, and the success rate reached 10/10 with only 250 episodes.Comment: 10 pages, 15 figures. https://github.com/kaixindelele/RHE

An Empirical Study on Challenging Math Problem Solving with GPT-4

Employing Large Language Models (LLMs) to address mathematical problems is an intriguing research endeavor, considering the abundance of math problems expressed in natural language across numerous science and engineering fields. While several prior works have investigated solving elementary mathematics using LLMs, this work explores the frontier of using GPT-4 for solving more complex and challenging math problems. We evaluate various ways of using GPT-4. Some of them are adapted from existing work, and one is \MathChat, a conversational problem-solving framework newly proposed in this work. We perform the evaluation on difficult high school competition problems from the MATH dataset, which shows the advantage of the proposed conversational approach

AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework

This technical report presents AutoGen, a new framework that enables development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools. AutoGen's design offers multiple advantages: a) it gracefully navigates the strong but imperfect generation and reasoning abilities of these LLMs; b) it leverages human understanding and intelligence, while providing valuable automation through conversations between agents; c) it simplifies and unifies the implementation of complex LLM workflows as automated agent chats. We provide many diverse examples of how developers can easily use AutoGen to effectively solve tasks or build applications, ranging from coding, mathematics, operations research, entertainment, online decision-making, question answering, etc.Comment: 28 page

Automatic Data Transformation Using Large Language Model: An Experimental Study on Building Energy Data

Existing approaches to automatic data transformation are insufficient to meet the requirements in many real-world scenarios, such as the building sector. First, there is no convenient interface for domain experts to provide domain knowledge easily. Second, they require significant training data collection overheads. Third, the accuracy suffers from complicated schema changes. To bridge this gap, we present a novel approach that leverages the unique capabilities of large language models (LLMs) in coding, complex reasoning, and zero-shot learning to generate SQL code that transforms the source datasets into the target datasets. We demonstrate the viability of this approach by designing an LLM-based framework, termed SQLMorpher, which comprises a prompt generator that integrates the initial prompt with optional domain knowledge and historical patterns in external databases. It also implements an iterative prompt optimization mechanism that automatically improves the prompt based on flaw detection. The key contributions of this work include (1) pioneering an end-to-end LLM-based solution for data transformation, (2) developing a benchmark dataset of 105 real-world building energy data transformation problems, and (3) conducting an extensive empirical evaluation where our approach achieved 96% accuracy in all 105 problems. SQLMorpher demonstrates the effectiveness of utilizing LLMs in complex, domain-specific challenges, highlighting the potential of their potential to drive sustainable solutions.Comment: 10 pages, 7 figure