355 research outputs found
Comment on “Associations of Prenatal Exposure to Per- and Polyfluoroalkyl Substances with the Neonatal Birth Size and Hormones in the Growth Hormone/Insulin-Like Growth Factor Axis”: What Is the Origin of PFHxS Found in the Human Body?
No full textThis letter is a reaction to an article published by Dan Luo, Weixiang Wu, Yanan Pan, Bibai Du, Mingjie Shen, and Lixi Zeng in Environmental Science & Technology Vol. 55(17) August 2021, pp. 11859-11873
Analysis on the power and efficiency in wireless power transfer system via coupled magnetic resonances
No full textDefect Characterization of Cu2ZnSnSe4 Thin Film Solar Cells Using Advanced Microscopic Techniques
No full textThin film chalcogenide solar cells have been utilized in a broad range of application for their tunable direct bandgap and high efficiency. In this work, we performeda novel fabrication and multiple high-resolution characterizations of Cu2ZnSnSe4(CZTSe) solar cells, which is believed to be a better candidate compared to well-developed CuInxGa(1-x)Se2(CIGS)for its earth-abundant contents. The fabrication is based on nanoparticle precursor production by liquid-phase pulsed laser ablation, electrophoretic deposition of precursor thin film under ambient condition, and selenization. Such non-vacuum fabrication has the advantage of low cost and minimum impact on the environment. By studying the CZTSe and CIGS fabricated in the above methods using techniques including Raman integrated scanning probe microscope, electron holography, scanning transmission electron microscopy and in-situ transmission electron microscopy. We discoveredthe origin of the performance limit of the CZTSe compared to CIGS as well as the defect of our non-vacuum fabrication methods. The presented results, including the characterization methods, create a novel way to correlate the solar cell performance with the microstructure in a nanometer scale. It opens up the possibility for developing high performance solar cell devices from the prospective of nanostructure and defect engineering.PhDMaterials Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/140886/1/mjxu_1.pd
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Machine learning for analog/mixed-signal IC design : scaling from circuits to systems
No full textAnalog and mixed-signal integrated circuits are widely used in many emerging applications, and the increasing demand calls for a shorter design cycle and time-to-market. Traditionally the design process is dominated by manual efforts and is very time-consuming, which involves design experts iterating between the circuit sizing and layout implementation steps. Despite attempts to automate the design process for analog circuits, prior works fail to scale from design automation of simple component level circuits to systems of larger size. Furthermore, few works have considered post-layout parasitic effects during circuit sizing, limiting the practical application in designing real chips targeting tape-out fabrication. This dissertation will explore methods to apply machine learning to analog and mixed signal circuit design automation in terms of practicality and scalability. Specifically, this dissertation will study and explore: Graph spectral and machine learning methods to automatically assign layout symmetry constraints from unannotated netlists of large circuit systems such as analog to digital converters (ADCs); Machine learning methods and models (such as convolutional neural networks and graph neural networks) to quantify analog layout quality and layout parasitic estimation without invoking parasitic extraction and circuit simulations; Efficient circuit sizing with Bayesian Optimization and in-the-loop layout generation that guarantees post layout performance, allowing design automation techniques to scale to the system-level design of an ADC. Finally, this work will be backed up by both circuit simulation results and real chip tape-out measurements to demonstrate the effectiveness of applying machine learning to automatically design analog circuits, which scales to analog system designs such as ADCsElectrical and Computer Engineerin
Application as Commissioning Tool of Various HVAC Simulation Programs and Visual Tools
No full textABSTRACT Various simulation programs and visual tools of HVAC system ever released, which were not originally developed to be used as commissioning tools, are considered to be potentially powerful tools for commissioning, as use of these programs facilitates the confirmation/comparison of function and performance of HVAC systems and detailed analysis of parameters influencing HVAC system performance. In the present paper, application and convenience of several programs such as Micro HASP, Micro ACSS, FACES, LCEM, TRNSYS, DOE-2, EnergyPlus and DeST and their visual tools as tools to support commissioning were assessed by investigating their functions and performances
Structures of usher syndrome 1 proteins and their complexes
No full textUsher syndrome 1 (USH1) is the most common and severe form of hereditary loss of hearing and vision. Genetic, physiological, and cell biological studies, together with recent structural investigations, have not only uncovered the physiological functions of the five USH1 proteins but also provided mechanistic explanations for the hearing and visual deficiencies in humans caused by USH1 mutations. This review focuses on the structural basis of the USH1 protein complex organization.</p
Table_1_A Pan-Cancer Analysis of Predictive Methylation Signatures of Response to Cancer Immunotherapy.xlsx
No full textRecently, tumor immunotherapy based on immune checkpoint inhibitors (ICI) has been introduced and widely adopted for various tumor types. Nevertheless, tumor immunotherapy has a few drawbacks, including significant uncertainty of outcome, the possibility of severe immune-related adverse events for patients receiving such treatments, and the lack of effective biomarkers to determine the ICI treatments’ responsiveness. DNA methylation profiles were recently identified as an indicator of the tumor immune microenvironment. They serve as a potential hot spot for predicting responses to ICI treatment for their stability and convenience of measurement by liquid biopsy. We demonstrated the possibility of DNA methylation profiles as a predictor for responses to the ICI treatments at the pan-cancer level by analyzing DNA methylation profiles considered responsive and non-responsive to the treatments. An SVM model was built based on this differential analysis in the pan-cancer levels. The performance of the model was then assessed both at the pan-cancer level and in specific tumor types. It was also compared to the existing gene expression profile-based method. DNA methylation profiles were shown to be predictable for the responses to the ICI treatments in the TCGA cases in pan-cancer levels. The proposed SVM model was shown to have high performance in pan-cancer and specific cancer types. This performance was comparable to that of gene expression profile-based one. The combination of the two models had even higher performance, indicating the potential complementarity of the DNA methylation and gene expression profiles in the prediction of ICI treatment responses.</p
Table_3_A Pan-Cancer Analysis of Predictive Methylation Signatures of Response to Cancer Immunotherapy.xlsx
No full textRecently, tumor immunotherapy based on immune checkpoint inhibitors (ICI) has been introduced and widely adopted for various tumor types. Nevertheless, tumor immunotherapy has a few drawbacks, including significant uncertainty of outcome, the possibility of severe immune-related adverse events for patients receiving such treatments, and the lack of effective biomarkers to determine the ICI treatments’ responsiveness. DNA methylation profiles were recently identified as an indicator of the tumor immune microenvironment. They serve as a potential hot spot for predicting responses to ICI treatment for their stability and convenience of measurement by liquid biopsy. We demonstrated the possibility of DNA methylation profiles as a predictor for responses to the ICI treatments at the pan-cancer level by analyzing DNA methylation profiles considered responsive and non-responsive to the treatments. An SVM model was built based on this differential analysis in the pan-cancer levels. The performance of the model was then assessed both at the pan-cancer level and in specific tumor types. It was also compared to the existing gene expression profile-based method. DNA methylation profiles were shown to be predictable for the responses to the ICI treatments in the TCGA cases in pan-cancer levels. The proposed SVM model was shown to have high performance in pan-cancer and specific cancer types. This performance was comparable to that of gene expression profile-based one. The combination of the two models had even higher performance, indicating the potential complementarity of the DNA methylation and gene expression profiles in the prediction of ICI treatment responses.</p
Table_2_A Pan-Cancer Analysis of Predictive Methylation Signatures of Response to Cancer Immunotherapy.xlsx
No full textRecently, tumor immunotherapy based on immune checkpoint inhibitors (ICI) has been introduced and widely adopted for various tumor types. Nevertheless, tumor immunotherapy has a few drawbacks, including significant uncertainty of outcome, the possibility of severe immune-related adverse events for patients receiving such treatments, and the lack of effective biomarkers to determine the ICI treatments’ responsiveness. DNA methylation profiles were recently identified as an indicator of the tumor immune microenvironment. They serve as a potential hot spot for predicting responses to ICI treatment for their stability and convenience of measurement by liquid biopsy. We demonstrated the possibility of DNA methylation profiles as a predictor for responses to the ICI treatments at the pan-cancer level by analyzing DNA methylation profiles considered responsive and non-responsive to the treatments. An SVM model was built based on this differential analysis in the pan-cancer levels. The performance of the model was then assessed both at the pan-cancer level and in specific tumor types. It was also compared to the existing gene expression profile-based method. DNA methylation profiles were shown to be predictable for the responses to the ICI treatments in the TCGA cases in pan-cancer levels. The proposed SVM model was shown to have high performance in pan-cancer and specific cancer types. This performance was comparable to that of gene expression profile-based one. The combination of the two models had even higher performance, indicating the potential complementarity of the DNA methylation and gene expression profiles in the prediction of ICI treatment responses.</p
Image_1_A Pan-Cancer Analysis of Predictive Methylation Signatures of Response to Cancer Immunotherapy.tiff
No full textRecently, tumor immunotherapy based on immune checkpoint inhibitors (ICI) has been introduced and widely adopted for various tumor types. Nevertheless, tumor immunotherapy has a few drawbacks, including significant uncertainty of outcome, the possibility of severe immune-related adverse events for patients receiving such treatments, and the lack of effective biomarkers to determine the ICI treatments’ responsiveness. DNA methylation profiles were recently identified as an indicator of the tumor immune microenvironment. They serve as a potential hot spot for predicting responses to ICI treatment for their stability and convenience of measurement by liquid biopsy. We demonstrated the possibility of DNA methylation profiles as a predictor for responses to the ICI treatments at the pan-cancer level by analyzing DNA methylation profiles considered responsive and non-responsive to the treatments. An SVM model was built based on this differential analysis in the pan-cancer levels. The performance of the model was then assessed both at the pan-cancer level and in specific tumor types. It was also compared to the existing gene expression profile-based method. DNA methylation profiles were shown to be predictable for the responses to the ICI treatments in the TCGA cases in pan-cancer levels. The proposed SVM model was shown to have high performance in pan-cancer and specific cancer types. This performance was comparable to that of gene expression profile-based one. The combination of the two models had even higher performance, indicating the potential complementarity of the DNA methylation and gene expression profiles in the prediction of ICI treatment responses.</p
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