Mechanisms of HDAC2 Function in the Regulation of Adult Cardiac Hypertrophy and Embryonic Myocyte Proliferation

HDACs can modify the structure and function of chromatin to regulate gene expression and can also modify many non-histone proteins that regulate cell function and signaling. HDACs have attracted increasing interest because they are involved in a variety of physiologic and pathophysiologic processes including stem cell maintenance, differentiation, cancer, inflammation and cardiac diseases. In the absence of Hdac2, mice display myocyte hyper-proliferation during embryonic development and resistance to cardiac hypertrophy in adulthood. This doctoral dissertation examines the function of two Hdac2 downstream targets, Inpp5f and Gata4, in regulating adult cardiac hypertrophy and embryonic myocyte proliferation, respectively. My results suggest that Inpp5f is an important endogenous modulator of the cardiac response to stress. I also performed studies to show that, in the absence of Hdac2 in the embryonic heart, hyper-acetylated Gata4 is responsible for myocyte hyper-proliferation. These findings provide a framework for understanding the role of Hdac2 in embryonic heart development and adult cardiac diseases

The Relationship Between ESG Disclosure and Corporate Financial Performance (CFP): Evidence from FTSE350.

In recent years, companies worldwide have transformed their short-term goals into profit maximization to achieve long-term sustainable environmental, social, and governance (ESG). Stakeholders have gradually realized that ESG has become an essential source of enterprise risk, which may influence the enterprises financial performance and profitability of the company. This research aims to understand the relationship between ESG disclosure and corporate financial performance (CFP). Many studies have shown that ESG could increase the financial performance of companies. However, the impact of ESG disclosure, E disclosure, S disclosure, and G disclosure on financial performance has not been further explored. As people pay more attention to ESG information, ESG information is also important to investors, which requires better disclosure practice. This study will investigate the relationship between ESG disclosure and CFP. This dissertation takes the UK FTSE350 from 2016-2018 as a sample to explore the relationship between the two. Two various test methods, OLS and GLS, are used to study the relationship between ESG disclosure and CFP. The results show that ROA and Tobin'Q positively correlate with ESG disclosure. Satisfactory ESG disclosure does improve financial performance. Nevertheless, in this study, Tobin'Q has no significant positive correlation with ESG disclosure, E, S, and G disclosure. There is a significant and positive correlation between ROA and them

GlycoPep MassList: Software to Generate Massive Inclusion Lists for Glycopeptide Analyses

Protein glycosylation drives many biological processes and serves as markers for disease; therefore, the development of tools to study glycosylation is an essential and growing area of research. Mass spectrometry can be used to identify both the glycans of interest and the glycosylation sites to which those glycans are attached, when proteins are proteolytically digested and their glycopeptides are analyzed by a combination of high-resolution mass spectrometry (MS) and tandem mass spectrometry (MS/MS) methods. One major challenge in these experiments is collecting the requisite MS/MS data. The digested glycopeptides are often present in complex mixtures and in low abundance, and the most commonly used approach to collect MS/MS data on these species is data-dependent acquisition (DDA), where only the most intense precursor ions trigger MS/MS. DDA results in limited glycopeptide coverage. Semi-targeted data acquisition is an alternative experimental approach that can alleviate this difficulty. However, due to the massive heterogeneity of glycopeptides, it is not obvious how to expediently generate inclusion lists for these types of analyses. To solve this problem, we developed the software tool GlycoPep MassList, which can be used to generate inclusion lists for liquid chromatography tandem-mass spectrometry (LC-MS/MS) experiments. The utility of the software was tested by conducting comparisons between semi-targeted and untargeted data-dependent analysis experiments on a variety of proteins, including IgG, a protein whose glycosylation must be characterized during its production as a biotherapeutic. When the GlycoPep MassList software was used to generate inclusion lists for LC-MS/MS experiments, more unique glycopeptides were selected for fragmentation. Generally, ∼30 % more unique glycopeptides can be analyzed per protein, in the simplest cases, with low background. In cases where background ions from proteins or other interferents are high, usage of an inclusion list is even more advantageous. The software is freely publically accessible

Background-Free Microwave Signal Generation Based on Unbalanced Temporal Pulse Shaping

We propose a new method to generate background-free high-frequency pulsed microwave signal based on an unbalanced temporal optical pulse shaping (TPS) system and balanced photodetection. The proposed system consists of a polarization modulator and an unbalanced TPS system realized by two conjugate dispersion elements. The carrier frequency of the pulsed microwave signal could be tuned by changing the residual dispersion of the TPS. The proposed method is theoretically analyzed and experimentally demonstrated. The experimental results show that the carrier frequency of the generated microwave pulse could be tuned over a broad frequency range. Moreover, the generated microwave pulse signal is background-free by suppressing the baseband frequency components using balanced photodetection

A national dataset of 30 m annual urban extent dynamics (1985–2015) in the conterminous United States

Dynamics of the urban extent at fine spatial and temporal resolutions over large areas are crucial for developing urban growth models and achieving sustainable development goals. However, there are limited practices of mapping urban dynamics with these two merits combined. In this study, we proposed a new method to map urban dynamics from Landsat time series data using the Google Earth Engine (GEE) platform and developed a national dataset of annual urban extent (1985–2015) at a fine spatial resolution (30 m) in the conterminous United States (US). First, we derived the change information of urbanized years in four periods that were determined from the National Land Cover Database (NLCD), using a temporal segmentation approach. Then, we classified urban extents in the beginning (1985) and ending (2015) years at the cluster level through the implementation of a change vector analysis (CVA)-based approach. We also developed a hierarchical strategy to apply the CVA-based approach due to the spatially explicit urban sprawl over large areas. The overall accuracy of mapped urbanized years is around 90 % with the 1-year tolerance strategy. The mapped urbanized areas in the beginning and ending years are reliable, with overall accuracies of 96 % and 88 %, respectively. Our results reveal that the total urban area increased by about 20 % during the period of 1985–2015 in the US, and the annual urban area growth is not linear over the years. Overall, the growth pattern of urban extent in most coastal states is plateaued over the past three decades while the states in the Midwestern US show an accelerated growth pattern. The derived annual urban extents are of great use for relevant urban studies such as urban area projection and urban sprawl modeling over large areas. Moreover, the proposed mapping framework is transferable for developing annual dynamics of urban extent in other regions and even globally. The data are available at https://doi.org/10.6084/m9.figshare.8190920.v2 (Li et al., 2019c)

Metabonomic Profiles Delineate the Effect of Traditional Chinese Medicine Sini Decoction on Myocardial Infarction in Rats

Background: In spite of great advances in target-oriented Western medicine for treating myocardial infarction (MI), it is still a leading cause of death in a worldwide epidemic. In contrast to Western medicine, Traditional Chinese medicine (TCM) uses a holistic and synergistic approach to restore the balance of Yin-Yang of body energy so the body’s normal function can be restored. Sini decoction (SND) is a well-known formula of TCM which has been used to treat MI for many years. However, its holistic activity evaluation and mechanistic understanding are still lacking due to its complex components. Methodology/Principal Findings: A urinary metabonomic method based on nuclear magnetic resonance and ultra highperformance liquid chromatography coupled to mass spectrometry was developed to characterize MI-related metabolic profiles and delineate the effect of SND on MI. With Elastic Net for classification and selection of biomarkers, nineteen potential biomarkers in rat urine were screened out, primarily related to myocardial energy metabolism, including the glycolysis, citrate cycle, amino acid metabolism, purine metabolism and pyrimidine metabolism. With the altered metabolism pathways as possible drug targets, we systematically analyze the therapeutic effect of SND, which demonstrated that SND administration could provide satisfactory effect on MI through partially regulating the perturbed myocardial energy metabolism. Conclusions/Significance: Our results showed that metabonomic approach offers a useful tool to identify MI-relate

Trichoderma reesei FS10-C enhances phytoremediation of Cd-contaminated soil by Sedum plumbizincicola and associated soil microbial activities

This study aimed to explore the effects of Trichoderma reesei FS10-C on the phytoremediation of Cd-contaminated soil by Sedum plumbizincicola as well as soil fertility. After characterizing the Cd tolerance of T. reesei FS10-C, a pot experiment was carried out to investigate the plant growth and Cd uptake of S. plumbizincicola with the addition of inoculation agents with/without T. reesei FS10-C. The soil samples in pots were analyzed for pH, available phosphorus (P), microbial biomass C, enzyme activities, microbial community functional diversity and Trichoderma colonization ability. The results indicated that FS10-C possessed high Cd resistance up to 300 mg L-1. All inoculation agents enhanced the biomass of plant shoots by 6-53% fresh weight and 16-61% dry weight as well as Cd uptake in plant shoots by 10-53% compared with the control. In addition, soil biomass C, enzyme activities and microbial community evenness were all increased to varying degrees by all inoculation agents, indicating that soil microbial biomass and activities were both enhanced. It was also found that the two inoculation agents accompanied by FS10-C were better compared with the inoculation agents without FS10-C on all accounts, from which it could be concluded that T. reesei FS10-C was effective in improving Cd phytoremediation of S. plumbizincicola and soil fertility. Furthermore, among all the inoculation agents, solid fermentation powder of FS10-C demonstrated the greatest capacity to enhance plant growth, Cd uptake, nutrient release, and microbial biomass and activities, as indicated by its superior ability to colonize Trichoderma. Thus, we could also conclude that solid fermentation powder of FS10-C was a good candidate for use as an inoculation agent for T. reesei FS10-C to improve the phytoremediation of Cd-contaminated soil and soil fertility

BOLT: Privacy-Preserving, Accurate and Efficient Inference for Transformers

The advent of transformers has brought about significant advancements in traditional machine learning tasks. However, their pervasive deployment has raised concerns about the potential leakage of sensitive information during inference. Existing approaches using secure multiparty computation (MPC) face limitations when applied to transformers due to the extensive model size and resource-intensive matrix-matrix multiplications. In this paper, we present BOLT, a privacy-preserving inference framework for transformer models that supports efficient matrix multiplications and nonlinear computations. Combined with our novel machine learning optimizations, BOLT reduces the communication cost by 10.91x. Our evaluation on diverse datasets demonstrates that BOLT maintains comparable accuracy to floating-point models and achieves 4.8-9.5x faster inference across various network settings compared to the state-of-the-art system

Potential Biomarkers in Mouse Myocardium of Doxorubicin-Induced Cardiomyopathy: A Metabonomic Method and Its Application

BACKGROUND: Doxorubicin (DOX) is one of the most potent antitumor agents available; however, its clinical use is limited because of the risk of severe cardiotoxicity. Though numerous studies have ascribed DOX cardiomyopathy to specific cellular pathways, the precise mechanism remains obscure. Sini decoction (SND) is a well-known formula of Traditional Chinese Medicine (TCM) and is considered as efficient agents against DOX-induced cardiomyopathy. However, its action mechanisms are not well known due to its complex components. METHODOLOGY/PRINCIPAL FINDINGS: A tissue-targeted metabonomic method using gas chromatography-mass spectrometry was developed to characterize the metabolic profile of DOX-induced cardiomyopathy in mice. With Elastic Net for classification and selection of biomarkers, twenty-four metabolites corresponding to DOX-induced cardiomyopathy were screened out, primarily involving glycolysis, lipid metabolism, citrate cycle, and some amino acids metabolism. With these altered metabolic pathways as possible drug targets, we systematically analyzed the protective effect of TCM SND, which showed that SND administration could provide satisfactory effect on DOX-induced cardiomyopathy through partially regulating the perturbed metabolic pathways. CONCLUSIONS/SIGNIFICANCE: The results of the present study not only gave rise to a systematic view of the development of DOX-induced cardiomyopathy but also provided the theoretical basis to prevent or modify expected damage