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arXiv 2019年

作者： Comer, Benjamin M. Lenk, Max H. Rajanala, Aradhya P. Flynn, Emma L. Medford, Andrew J. School of Chemical and Biomolecular Engineering Georgia Institute of Technology School of Materials Science and Engineering Georgia Institute of Technology School of Physics Georgia Institute of Technology School of Computer Science Georgia Institute of Technology 311 Ferst Drive NW AtlantaGA30318 United States

Synthesis of ammonia through photo- and electrocatalysis is a rapidly growing field. Titania-based catalysts are widely reported for photocatalytic ammonia synthesis and have also been suggested as electro-catalysts. The addition of transition-metal dopants is one strategy for improving the performance of titania-based catalysts. In this work, we screen d-block transition metal dopants for surface site stability and evaluate trends in their performance as the active site for the reduction of nitrogen to ammonia on TiO2. We find a linear relationship between the d-band center and formation energy of the dopant site, while the binding energies of N2, N2H, and NH2 all are strongly correlated with the cohesive energies of the dopant metals. The activity of the metal-doped systems shows a volcano type relationship with the NH2 and N2H energies as descriptors. Some metals such as Co, Mo, and V are predicted to slightly improve photo- and electrocatalytic performance, but most metals inhibit the ammonia synthesis reaction. The results provide insight into the role of transition-metal dopants for promoting ammonia synthesis, and the trends are based on unexpected electronic structure factors that may have broader implications for single-atom catalysis and doped oxides. Copyright © 2019, The Authors. All rights reserved.

关键词： Electronic structure

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Large graphene-induced shift of surface-plasmon resonances of gold films: Effective-medium theory for atomically thin materials

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Physical Review Research 2020年第1期2卷 013008-013008页

作者： Md Kamrul Alam Chao Niu Yanan Wang Wei Wang Yang Li Chong Dai Tian Tong Xiaonan Shan Earl Charlson Steven Pei Xiang-Tian Kong Yandi Hu Alexey Belyanin Gila Stein Zhaoping Liu Jonathan Hu Zhiming Wang Jiming Bao Materials Science and Engineering University of Houston Houston Texas 77204 USA Department of Electrical & Computer Engineering Baylor University Waco Texas 76798 USA Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu Sichuan 610054 China Department of Electrical and Computer Engineering University of Houston Houston Texas 77204 USA Ningbo Institute of Materials Technology & Engineering Chinese Academy of Sciences Ningbo Zhejiang 315201 China Key Laboratory of Graphene Technologies and Applications of Zhejiang Province Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo Zhejiang 315201 China Department of Civil and Environmental Engineering University of Houston Houston Texas 77204 USA Department of Physics & Astronomy Texas A&M University College Station Texas 77843 USA Chemical and Biomolecular Engineering The University of Tennessee Knoxville Tennessee 37996 USA State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu Sichuan 610054 China

Despite successful modeling of graphene as a 0.34-nm-thick optical film synthesized by exfoliation or chemical vapor deposition (CVD), graphene-induced shift of surface-plasmon resonance (SPR) of gold films has remained controversial. Here we report the resolution of this controversy by developing a clean CVD graphene transfer method and extending Maxwell-Garnett effective-medium theory (EMT) to two-dimensional (2D) materials. A SPR shift of 0.24° is obtained and it agrees well with 2D EMT in which wrinkled graphene is treated as a 3-nm graphene/air layered composite, in agreement with the average roughness measured by atomic force microscopy. Because the anisotropic built-in boundary condition of 2D EMT is compatible with graphene's optical anisotropy, graphene can be modeled as a film thicker than 0.34 nm without changing its optical property; however, its actual roughness, i.e., effective thickness, will significantly alter its response to strong out-of-plane fields, leading to a larger SPR shift.

关键词： Dielectric properties Optical conductivity Plasmonics Plasmons Refraction

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Three-dimensional printing of complex graphite structures

arXiv

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arXiv 2020年

作者： Sajadi, Seyed Mohammad Enayat, Shayan Vásárhelyi, Lívia Alabastri, Alessandro Lou, Minghe Sassi, Lucas M. Kutana, Alex Bhowmick, Sanjit Durante, Christian Kukovecz, Ákos Puthirath, Anand B. Kónya, Zoltán Vajtai, Robert Boul, Peter Tiwary, Chandra Shekhar Rahman, Muhammad M. Ajayan, Pulickel M. Department of Materials Science and NanoEngineering Rice University Texas United States Department of Chemical and Biomolecular Engineering Rice University Texas United States University of Szeged Interdisciplinary Excellence Centre Department of Applied and Environmental Chemistry Szeged Hungary Department of Electrical and Computer Engineering Rice University Texas United States Department of Chemistry Rice University Texas United States Bruker Nano Surfaces MinneapolisMN United States Aramco Americas Texas United States Metallurgical and Materials Engineering Indian Institute of Technology Kharagpur India

Graphite, with many industrial applications, is one of the widely sought-after allotropes of carbon. The sp2 hybridized and thermodynamically stable form of carbon forms a layered structure with strong in-plane carbon bonds and weak inter-layer van der Waals bonding. Graphite is also a high-temperature ceramic, and shaping them into complex geometries is challenging, given its limited sintering behavior even at high temperatures. Although the geometric design of the graphite structure in many of the applications could dictate its precision performance, conventional synthesis methods for formulating complex geometric graphite shapes are limited due to the intrinsic brittleness and difficulties of high-temperature processing. Here, we report the development of colloidal graphite ink from commercial graphite powders with reproducible rheological behavior that allows the fabrication of any complex architectures with tunable geometry and directionality via 3D printing at room temperature. The method is enabled via using small amounts of clay, another layered material, as an additive, allowing the proper design of the graphene ink and subsequent binding of graphite platelets during printing. Sheared layers of clay are easily able to flow, adapt, and interface with graphite layers forming strong binding between the layers and between particles that make the larger structures. The direct ink printing of complex 3D architectures of graphite without further heat treatments could lead to easy shape engineering and related applications of graphite at various length scales, including complex graphite molds or crucibles. The 3D printed complex graphitic structures exhibit excellent thermal, electrical, and mechanical properties, and the clay additive does not seem to alter these properties due to the excellent inter-layer dispersion and mixing within the graphite material. © 2020, CC BY.

关键词： Geometry

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Influence of Training Times and Sample Size on Results in Visual Try-On with GAN

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Journal of Physics: Conference Series 2021年第1期1903卷

作者： Wenqing Jiang Ruiyu Qi Juntao Zheng Computer Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Zhuhai Guangdong 519087 China Chemical and Biomolecular Engineering Lehigh University Bethlehem PA 18015 U.S. Internet of things Guangdong Ocean University Zhanjiang Guangdong 524088 China

With online shopping becoming more and more trendy these days, Virtual Try-On Approach is proposed. It is a method of Generative Adversarial Network (GAN) composes of two models: Geometry Matching Module (GMM) and Try-On Module (TOM). It is proposed to solve the inconvenience of customers for not being able to see the fitting effect before receiving the goods. This paper is about finding the relationship between dataset and the effect of the experiment in the GAN experiment. After implementing the models and gaining the result images, we do some observations on the GMM one and try to analyze it and improve the effect. Comparison is made on different aspects: training times of the model and sample size of the training input. Similar trends are shown on the decrease of loss value when the training times or sample size rises. However, the visual effect is not necessarily improved all the time. Compared with the times it has been trained, the improvement from training times is far less than that from the sample size. Finally, we draw the conclusion that the effect is gradually improved as the training times or sample size increases at the beginning. Nevertheless, the improvement cannot be recognized evidently when the training times and sample size have grown large enough. Additionally, the sample size seems to be a more significant factor in the improvement of the visual effect than the training times.

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Automated prostate cancer diagnosis based on gleason grading using convolutional neural network

arXiv

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arXiv 2020年

作者： Xie, Haotian Zhang, Yong Wang, Jun Zhang, Jingjing Ma, Yifan Yang, Zhaogang Department of Mathematics Ohio State University ColumbusOH43210 United States Department of Computer Science and Engineering Ohio State University ColumbusOH43210 United States Department of Informatics King’s College London LondonWC2R 2LS United Kingdom Department of Chemical and Biomolecular Engineering Ohio State University ColumbusOH United States Department of Radiation Oncology University of Texas Southwestern Medical Center DallasTX75390 United States

Prostate cancer (PCa) is the most common deadly cancer in the United States. The Gleason grading system using histological images is the most powerful diagnostic and prognostic predictor of PCa and is essential in treatment planning for patients. The current standard inspection is evaluating Gleason H&E-stained histopathology images by pathologists. However, it is complicated, time-consuming, and subject to observers. Hence, an automatic classification system is necessary to reduce variations and improve clinical outcomes accuracy. Deep learning (DL) based-methods that automatically learn image features and achieve higher generalization ability have attracted significant attention. However, challenges remain especially using DL to train the whole slide image (WSI), a predominant clinical source in the current diagnostic setting, containing billions of pixels, morphological heterogeneity, and artifacts. To make DL become a reality in the clinical practice, the above difficulties need to be addressed. Hence, we proposed a convolutional neural network (CNN)-based automatic classification method for accurate grading of PCa using whole slide histopathology images. In this paper, a data augmentation method named Patch-Based Image Reconstruction (PBIR) was proposed to reduce the high resolution and increase the diversity of WSIs. In addition, a distribution correction (DC) module was developed to enhance the adaption of pretrained model to the target dataset by adjusting the data distribution. Besides, a Quadratic Weighted Mean Square Error (QWMSE) function was presented to reduce the misdiagnosis caused by equal Euclidean distances. These strategies enabled our method to take advantage of the tremendous amount of clinical information in large and small patches in the labeled dataset. We studied their effects on the classification performance empirically. Our experiments indicated the combination of PBIR, DC, and QWMSE function was necessary for achieving superior expert-l

关键词： Diseases

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Tuning extracellular fluid viscosity to enhance transfection efficiency

Nature Chemical Engineering

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Nature chemical engineering 2024年 576-587页

作者： Jingyao Ma Yining Zhu Jiayuan Kong Di Yu Wu Han Toh Milun Jain Qin Ni Zhuoxu Ge Jinghan Lin Joseph Choy Leonardo Cheng Konstantinos Konstantopoulos Maximilian F. Konig Sean X. Sun Hai-Quan Mao Institute for NanoBioTechnology Johns Hopkins University Baltimore MD USA Department of Materials Science and Engineering Johns Hopkins University Baltimore MD USA Translational Tissue Engineering Center Johns Hopkins University School of Medicine Baltimore MD USA Department of Biomedical Engineering Johns Hopkins University School of Medicine Baltimore MD USA Department of Computer Science Johns Hopkins University Baltimore MD USA Department of Biology Johns Hopkins University Baltimore MD USA Department of Mechanical Engineering Johns Hopkins University Baltimore MD USA Department of Chemical and Biomolecular Engineering Johns Hopkins University Baltimore MD USA Department of Oncology The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University School of Medicine Baltimore MD USA Division of Rheumatology Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA Ludwig Center for Cancer Genetics and Therapeutics Johns Hopkins University School of Medicine Baltimore MD USA Center for Cell Dynamics School of Medicine Johns Hopkins University Baltimore MD USA

Gene therapies and cellular programming rely on effective cell transfection. Despite continuous advancements in carrier development and transfection techniques to enhance efficiency, the biophysical parameter of extracellular fluid viscosity has been largely overlooked. Here we report a substantial impact of culture media viscosity on transfection efficiency of several delivery vehicles, including lipid nanoparticles, polyplexes, adeno-associated vectors and lentiviral vectors across a range of cell types. We observed substantially increased transfection efficiencies for lipid nanoparticles and polyplexes when the media viscosity matched that of biological fluids (2.0–4.0 centipoise (cP)). This enhancement correlates with higher levels of cellular uptake and improved endosomal escape. Moreover, cells cultured in optimized viscosity conditions exhibit a different profile of uptake pathways compared with those cultured at the standard viscosity of 0.8 cP. This discovery highlights the critical role of media viscosity in the transfection process and provides an additional method to optimize gene delivery and cell programming processes, potentially reducing production costs and increasing the accessibility of gene and cell therapies.

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Molecular modelling and de novo fragment-based design of potential inhibitors of beta-tubulin gene of Necator americanus from natural products

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Informatics in Medicine Unlocked 2021年 26卷 100734页

作者： Agyapong, Odame Asiedu, Seth O. Kwofie, Samuel K. Miller, Whelton A. Parry, Christian S. Sowah, Robert A. Wilson, Michael D. Department of Biomedical Engineering School of Engineering Sciences College of Basic & Applied Sciences University of Ghana PMB LG 77 Legon Accra Ghana Department of Parasitology Noguchi Memorial Institute for Medical Research (NMIMR) College of Health Sciences (CHS) University of Ghana P.O. Box LG 581 Legon Accra Ghana West African Centre for Cell Biology of Infectious Pathogens Department of Biochemistry Cell and Molecular Biology College of Basic and Applied Sciences University of Ghana Accra Ghana Department of Medicine Loyola University Medical Center Maywood 60153 IL United States University of Pennsylvania School of Engineering and Applied Science Department of Chemical and Biomolecular Engineering Philadelphia 19104 PA United States Center for Sickle Cell Disease And Department of Microbiology Howard University Washington DC 20059 United States Department of Computer Engineering School of Engineering Sciences College of Basic & Applied Sciences University of Ghana Legon Accra Ghana

The emergence of drug resistance against the known hookworm drugs namely albendazole and mebendazole and their reduced efficacies necessitate the need for new drugs. chemically diverse natural products present plausible templates to augment hookworm drug discovery. The present work utilized pharmacoinformatics techniques to predict African natural compounds ZINC95486082, ZINC95486052 and euphohelionon as potential inhibitory molecules of the hookworm Necator americanus β tubulin gene. A library of 3390 compounds was screened against a homology-modelled structure of β tubulin. The docking results obtained from AutoDock Vina was validated with an acceptable area under the curve (AUC) of 0.714 computed from the receiver operating characteristic (ROC) curve. The three selected compounds had favourable binding affinities and were predicted to form no interactions with the resistance-associated mutations Phe167, Glu198 and Phe200. The compounds were predicted as anthelmintics using a Bayesian-based technique and were pharmacologically profiled to be druglike. Further molecular dynamics simulations and MM-PBSA calculations showed the compounds as promising anthelmintic drug leads. Novel critical residues comprising Leu246, Asn247 and Asn256 were also predicted for binding. Euphohelionon was selected as a template for the de novo fragment-based design of five compounds labelled A1, A2, A3, A4 and A5;with four of them having SAscore values below 6, denoting easy synthesis. All the five de novo molecules docked firmly in the binding pocket of the β tubulin with no binding interactions with the three known resistance mutation residues. Binding energies of −8.2, −7.6, −7.3, −7.2 and −6.8 kcal/mol were obtained for A1, A2, A3, A4 and A5, respectively. The identified compounds can serve as treasure troves from which future potent anthelmintics can be designed. The current study strives to assuage the hookworm disease burden, especially making available molecules with the potentia

关键词： Anthelmintics Drug discovery Molecular modelling Natural products Necator americanus Pharmacoinformatics

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A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections

arXiv

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arXiv 2021年

作者： Falahkheirkhah, Kianoush Guo, Tao Hwang, Michael Tamboli, Pheroze Wood, Christopher G. Karam, Jose A. Sircar, Kanishka Bhargava, Rohit Department of Chemical and Biomolecular Engineering University of Illinois at Urbana- Champaign UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois at Urbana- Champaign UrbanaIL61801 United States Department of Bioengineering University of Illinois at Urbana- Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana- Champaign UrbanaIL61801 United States Mechanical Science and Engineering University of Illinois at Urbana- Champaign UrbanaIL61801 United States Cancer Center at Illinois University of Illinois at Urbana- Champaign UrbanaIL61801 United States Department of Pathology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Translational Molecular Pathology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Urology University of Texas MD Anderson Cancer Center HoustonTX United States

In clinical diagnostics and research involving histopathology, formalin fixed paraffin embedded (FFPE) tissue is almost universally favored for its superb image quality. However, tissue processing time (>24 hours) ... 详细信息

关键词： Generative adversarial networks

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Breast cancer histopathology using infrared spectroscopic imaging: The impact of instrumental configurations

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Clinical Spectroscopy 2021年 3卷

作者： Shachi Mittal Tomasz P. Wrobel Michael Walsh Andre Kajdacsy-Balla Rohit Bhargava Department of Bioengineering and Beckman Institute for Advanced Science and Technology University of Illinois at Urbana−Champaign Urbana IL United States Department of Materials Science and Biomedical Engineering University of Wisconsin – Eau Claire WI United States Department of Pathology University of Illinois at Chicago IL United States Departments of Mechanical Science and Engineering Electrical and Computer Engineering Chemical and Biomolecular Engineering and Chemistry University of Illinois at Urbana−Champaign Urbana IL United States Cancer Center at Illinois University of Illinois at Urbana−Champaign Urbana IL United States

Digital analysis of cancer specimens using spectroscopic imaging coupled to machine learning is an emerging area that links spatially localized spectral signatures to tissue structure and disease. In this study, we examine the role of spatial-spectral tradeoffs in infrared spectroscopic imaging configurations for probing tumors and the associated microenvironment profiles at different levels of model complexity. We image breast tissue using standard and high-definition Fourier Transform Infrared (FT-IR) imaging and systematically examine the localization, spectral origins, and utility of data for classification. Results demonstrate that higher spatial detail provides high sensitivity and specificity of tissue segmentation, despite the increased subcellular variability. High definition imaging also allows accurate analysis of complex, multiclass models of breast tissue without compromising accuracy. A comparison of results also highlights the key differences in the data distributions and classification performance across modalities to better guide decision making for acquiring and analyzing IR imaging data for specific histopathological models.

关键词： High definition imaging Standard definition Breast pathology Machine learning Tissue segmentation

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Author Correction: GRAFENE: Graphlet-based alignment-free network approach integrates 3D structural and sequence (residue order) data to improve protein structural comparison

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Scientific reports 2020年第1期10卷 13455页

作者： Fazle E Faisal Khalique Newaz Julie L Chaney Jun Li Scott J Emrich Patricia L Clark Tijana Milenković Department of Computer Science and Engineering University of Notre Dame Notre Dame IN 46556 USA. Interdisciplinary Center for Network Science and Applications University of Notre Dame Notre Dame IN 46556 USA. Eck Institute for Global Health University of Notre Dame Notre Dame IN 46556 USA. Department of Chemistry and Biochemistry University of Notre Dame Notre Dame IN 46556 USA. Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre Dame IN 46556 USA. Department of Chemical and Biomolecular Engineering University of Notre Dame Notre Dame IN 46556 USA. Department of Computer Science and Engineering University of Notre Dame Notre Dame IN 46556 USA. tmilenko@nd.edu. Interdisciplinary Center for Network Science and Applications University of Notre Dame Notre Dame IN 46556 USA. tmilenko@nd.edu. Eck Institute for Global Health University of Notre Dame Notre Dame IN 46556 USA. tmilenko@nd.edu.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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