Mechanotransduction through cytoskeleton and junctions in cardiomyopathies

Cardiomyopathies represent a heterogeneous group of diseases of the heart muscle that often lead to progressive heart failure with high morbidity and mortality. In a significant and increasing percentage of the patient population, cardiomyopathies have been associated with hereditary mutations in genes encoding critical cellular components that make up the cytoarchitecture of cardiac muscle cells, or cardiomyocytes. While specific mutations have been linked to different classes of cardiomyopathies, it is however not well understood how these mutations cause cytostructural abnormalities that ultimately lead to dysfunction of cardiomyocytes. To gain insights into the pathogenesis of inherited cardiomyopathies, we focus in this thesis on a particular set of mutations in the cardiac cytoskeleton and desmosomes that are associated with dilated and arrhythmogenic cardiomyopathies, and probe their pathogenic mechanisms using cardiomyocytes derived from human induced pluripotent stem cells and bioengineered culture platforms. In part one, we describe the mechanical and molecular basis for the assembly of sarcomeres, the fundamental contractile units within cardiomyocytes, and reveal how mutations in titin (TTN) abolish this process by disrupting cell-matrix interaction and impairing diastolic force generation, a hallmark of dilated cardiomyopathy. In the second part of this thesis, we reveal that plakophilin-2 (PKP2) mutations that are associated with arrhythmogenic cardiomyopathy lead to impaired systolic function by destabilizing cell-cell junctions and in turn disrupting sarcomere stability and organization. Together, our studies establish a deeper understanding of how cell-matrix and cell-cell interactions contribute to the organization and function of cardiomyocytes and how disruption of these interactions by pathogenic mutations lead to cardiac dysfunction.2022-05-18T00:00:00

Tracing the Uncertain Chinese Mercury Footprint within the Global Supply Chain Using a Stochastic, Nested Input-Output Model

A detailed understanding of the mercury footprint at subnational entity levels can facilitate the implementation of the "Minamata Convention on Mercury", especially for China, the largest mercury emitter worldwide. Some provinces of China have more than 100 million people, with economic activities and energy consumption levels comparable to those of smaller G7 countries. We constructed a stochastic, nested multiregion input-output (MRIO) model, which regionalized the China block in the EXIOBASE global-scale MRIO table, to model the mercury footprint associated with global supply chains spanning China's regions and other countries. The results show that Tianjin, Shanghai, and Ningxia had the highest per capita mercury footprint in China, which was comparable to the footprint of Australia and Norway and exceeded the footprint of most other countries. Some developed regions in China (e.g., Guangdong, Jiangsu) had higher mercury final product-based inventories (FBI) and consumption-based inventories (CBI) than production-based inventories (PBI), emphasizing the role of these regions as centers of both consumption and economic control. Uncertainties of Chinese provincial mercury footprint varied from 8% to 34%. Our research also revealed that international and inter-regional final product and intermediate product trades reshape the mercury emissions of Chinese provinces and other countries to a certain extent

The Polarizing Trend of Regional CO2 Emissions in China and Its Implications

CO2 emissions are unevenly distributed both globally and regionally within nation-states. Given China's entrance into the new stage of economic development, an updated study on the largest CO2 emitter's domestic emission distribution is needed for effective and coordinated global CO2 mitigation planning. We discovered that domestic CO2 emissions in China are increasingly polarized for the 2007-2017 period. Specifically, the domestically exported CO2 emissions from the less developed and more polluting northwest region to the rest of China has drastically increased from 165 Mt in 2007 to 230 Mt in 2017. We attribute the polarizing trend to the simultaneous industrial upgrading of all regions and the persistent disparity in the development and emission decoupling of China's regions. We also noted that CO2 emissions exported from China to the rest of the world has decreased by 41% from 2007 to 2017, with other developing countries filling up the vacancy. As this trend is set to intensify, we intend to send an alarm message to policy makers to devise and initiate actions and avoid the continuation of pollution migration

Sarc-Graph: automated segmentation, tracking, and analysis of sarcomeres in hiPSC-derived cardiomyocytes

A better fundamental understanding of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) has the potential to advance applications ranging from drug discovery to cardiac repair. Automated quantitative analysis of beating hiPSC-CMs is an important and fast developing component of the hiPSC-CM research pipeline. Here we introduce "Sarc-Graph," a computational framework to segment, track, and analyze sarcomeres in fluorescently tagged hiPSC-CMs. Our framework includes functions to segment z-discs and sarcomeres, track z-discs and sarcomeres in beating cells, and perform automated spatiotemporal analysis and data visualization. In addition to reporting good performance for sarcomere segmentation and tracking with little to no parameter tuning and a short runtime, we introduce two novel analysis approaches. First, we construct spatial graphs where z-discs correspond to nodes and sarcomeres correspond to edges. This makes measuring the network distance between each sarcomere (i.e., the number of connecting sarcomeres separating each sarcomere pair) straightforward. Second, we treat tracked and segmented components as fiducial markers and use them to compute the approximate deformation gradient of the entire tracked population. This represents a new quantitative descriptor of hiPSC-CM function. We showcase and validate our approach with both synthetic and experimental movies of beating hiPSC-CMs. By publishing Sarc-Graph, we aim to make automated quantitative analysis of hiPSC-CM behavior more accessible to the broader research community.https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009443Published versio

What indeed can GPT models do in chemistry? A comprehensive benchmark on eight tasks

Large Language Models (LLMs) with strong abilities in natural language processing tasks have emerged and have been rapidly applied in various kinds of areas such as science, finance and software engineering. However, the capability of LLMs to advance the field of chemistry remains unclear. In this paper,we establish a comprehensive benchmark containing 8 practical chemistry tasks, including 1) name prediction, 2) property prediction, 3) yield prediction, 4) reaction prediction, 5) retrosynthesis (prediction of reactants from products), 6)text-based molecule design, 7) molecule captioning, and 8) reagent selection. Our analysis draws on widely recognized datasets including BBBP, Tox21, PubChem, USPTO, and ChEBI, facilitating a broad exploration of the capacities of LLMs within the context of practical chemistry. Three GPT models (GPT-4, GPT-3.5,and Davinci-003) are evaluated for each chemistry task in zero-shot and few-shot in-context learning settings with carefully selected demonstration examples and specially crafted prompts. The key results of our investigation are 1) GPT-4 outperforms the other two models among the three evaluated; 2) GPT models exhibit less competitive performance in tasks demanding precise understanding of molecular SMILES representation, such as reaction prediction and retrosynthesis;3) GPT models demonstrate strong capabilities in text-related explanation tasks such as molecule captioning; and 4) GPT models exhibit comparable or better performance to classical machine learning models when applied to chemical problems that can be transformed into classification or ranking tasks, such as property prediction, and yield prediction

Graph-based Molecular Representation Learning

Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science. In particular, it encodes molecules as numerical vectors preserving the molecular structures and features, on top of which the downstream tasks (e.g., property prediction) can be performed. Recently, MRL has achieved considerable progress, especially in methods based on deep molecular graph learning. In this survey, we systematically review these graph-based molecular representation techniques, especially the methods incorporating chemical domain knowledge. Specifically, we first introduce the features of 2D and 3D molecular graphs. Then we summarize and categorize MRL methods into three groups based on their input. Furthermore, we discuss some typical chemical applications supported by MRL. To facilitate studies in this fast-developing area, we also list the benchmarks and commonly used datasets in the paper. Finally, we share our thoughts on future research directions

Benzosiloles with Crystallization-induced Emission Enhancement of Electrochemiluminescence: Synthesis, Electrochemistry, and Crystallography

Crystallization-induced emission enhancement (CIEE) was demonstrated for the first time for electrochemilunimescence (ECL) with two new benzosiloles. Compared with their solution, the films of the two benzosiloles gave CIEE of 24 times and 16 times. The mechanism of the CIEE-ECL was examined by spooling ECL spectroscopy, X-ray crystal structure analysis, photoluminescence, and DFT calculation. This CIEE-ECL system is a complement to the well-established aggregation-induced emission enhancement (AIEE) systems. Unique intermolecular interactions are noted in the crystalline chromophore. The first heterogeneous ECL system is established for organic compounds with highly hydrophobic properties

Modulated model predictive current control of an indirect matrix converter with active damping

A modulated model predictive control (M²PC) scheme for an indirect matrix converter is proposed in this paper, including an active damping method to mitigate the input filter resonance. The control strategy allows the instantaneous power control and the output current control at the same time, operating at a fixed frequency. An optimal switching pattern is used to emulate the desired waveform quality features of space vector modulation and achieve zero-current switching operations. The active damping technique emulates a virtual resistor which damps the filter resonance. Simulation results present a good tracking to the output-current references, unity input displacement power factor, the low input-current distortions and a reduced common-mode voltage (CMV)