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Improved analytical workflow towards machine learning supported N-glycomics-based biomarker discovery

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Talanta 2025年 295卷 128389页

作者： Agnes Vathy-Fogarassy Veronika Gombas Rebeka Torok Gabor Jarvas Andras Guttman Department of Computer Science and Systems Technology University of Pannonia Egyetem u 10. Veszprem H-8200 Hungary. Electronic address: vathy.agnes@mik.uni-pannon.hu. Department of Computer Science and Systems Technology University of Pannonia Egyetem u 10. Veszprem H-8200 Hungary. Research Institute of Biomolecular and Chemical Engineering University of Pannonia Veszprem H-8200 Hungary. Research Institute of Biomolecular and Chemical Engineering University of Pannonia Veszprem H-8200 Hungary Horvath Csaba Memorial Laboratory of Bioseparation Sciences Research Center for Molecular Medicine Doctoral School of Molecular Medicine Faculty of Medicine University of Debrecen Debrecen H-4032 Hungary.

The composition and function of glycans are very complex thus manual data interpretation of their structural elucidation is difficult. Capillary electrophoresis is one of the liquid phase separation techniques, which is most frequently used to address these challenging tasks. Combining high-resolution capillary electrophoresis with machine learning-supported data interpretation holds the promise to gain as much chemical and clinical information from the analyzed samples as possible. However, this combination requires significant technological improvements both in the analytical and the data processing aspects. In this study we report on the development of an automated, liquid-handling robot-based sample preparation method to obtain reproducible and N-glycome profiles by capillary electrophoresis for the subsequent machine learning-supported data interpretation, which was optimized for the special needs of the analysis. The resulting new glycoanalytical workflow was then tested for a demanding problem to predict the effectiveness of chemotherapy treatments of lung cancer patients ensuring the effective management of the disease. Our findings revealed that the achieved N-glycan data contained important clinical information to accurately predict patient response to chemotherapy with AUC values ranged from 0.8290 to 0.8410.

关键词： Capillary electrophoresis Feature selection Lung cancer Machine learning N-glycome

DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

Charge and Energy Transfer Dynamics of Hybridized Exciton-Polaritons in 2D Halide Perovskites

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arXiv 2023年

作者： Anantharaman, Surendra B. Lynch, Jason Stevens, Christopher E. Munley, Christopher Li, Chentao Hou, Jin Zhang, Hao Torma, Andrew Darlington, Thomas Coen, Francis Li, Kevin Majumdar, Arka Schuck, P. James Mohite, Aditya Harutyunyan, Hayk Hendrickson, Joshua R. Jariwala, Deep Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States KBR Inc. BeavercreekOH45431 United States Air Force Research Laboratory Sensors Directorate Wright-Patterson Air Force Base OH45433 United States Department of Physics University of Washington SeattleWA98195 United States Department of Physics Emory University AtlantaGA30322 United States Department of Chemical and Biomolecular Engineering Rice University HoustonTX77005 United States Department of Materials Science and Nanoengineering Rice University HoustonTX77005 United States Applied Physics Program Smalley‐Curl Institute Rice University HoustonTX77005 United States Department of Mechanical Engineering Columbia University New YorkNY10027 United States Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States

Excitons, bound electron-hole pairs, in Two-Dimensional Hybrid Organic Inorganic Perovskites (2D HOIPs) are capable of forming hybrid light-matter states known as exciton-polaritons (E-Ps) when the excitonic medium is confined in an optical cavity. In the case of 2D HOIPs, they can self-hybridize into E-Ps at specific thicknesses of the HOIP crystals that form a resonant optical cavity with the excitons. However, the fundamental properties of these self-hybridized E-Ps in 2D HOIPs, including their role in ultrafast energy and/or charge transfer at interfaces, remain unclear. Here, we demonstrate that > 0.5 µm thick 2D HOIP crystals on Au substrates are capable of supporting multiple-orders of self-hybridized E-P modes. These E-Ps have high Q factors (> 100) and modulate the optical dispersion for the crystal to enhance sub-gap absorption and emission. Through varying excitation energy and ultrafast measurements, we also confirm energy transfer from higher energy upper E-Ps to lower energy, lower E-Ps. Finally, we also demonstrate that E-Ps are capable of charge transport and transfer at interfaces. Our findings provide new insights into charge and energy transfer in E-Ps opening new opportunities towards their manipulation for polaritonic devices. © 2023, CC BY.

关键词： Charge transfer

Controlled synthetic chirality in macroscopic assemblies of carbon nanotubes

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arXiv 2023年

作者： Doumani, Jacques Lou, Minhan Dewey, Oliver Hong, Nina Fan, Jichao Baydin, Andrey Yomogida, Yohei Yanagi, Kazuhiro Pasquali, Matteo Saito, Riichiro Kono, Junichiro Gao, Weilu Department of Electrical and Computer Engineering Rice University HoustonTX United States Applied Physics Graduate Program Smalley–Curl Institute Rice University HoustonTX United States Department of Electrical and Computer Engineering The University of Utah Salt Lake CityUT United States Carbon Hub Rice University HoustonTX United States Department of Chemical and Biomolecular Engineering Rice University HoustonTX United States J.A. Woollam Co. Inc. LincolnNE United States Smalley–Curl Institute Rice University HoustonTX United States Department of Physics Tokyo Metropolitan University Tokyo Japan Department of Chemistry Rice University HoustonTX United States Department of Materials Science and NanoEngineering Rice University HoustonTX United States Department of Physics Tohoku University Sendai Japan Department Physics and Astronomy Rice University HoustonTX United States

There is an emerging recognition that successful utilization of chiral degrees of freedom can bring new scientific and technological opportunities to diverse research areas. Hence, methods are being sought for creating artificial matter with controllable chirality in an uncomplicated and reproducible manner. Here, we report the development of two straightforward methods for fabricating wafer-scale chiral architectures of ordered carbon nanotubes (CNTs) with tunable and giant circular dichroism (CD). Both methods employ simple approaches, (i) mechanical rotation and (ii) twist-stacking, based on controlled vacuum filtration and do not involve any sophisticated nanofabrication processes. We used a racemic mixture of CNTs as the starting material, so the intrinsic chirality of chiral CNTs is not responsible for the observed chirality. In particular, by controlling the stacking angle and handedness in (ii), we were able to maximize the CD response and achieve a record-high deep-ultraviolet ellipticity of 40 ± 1 mdeg/nm. Our theoretical simulations using the transfer matrix method reproduce the salient features of the experimentally observed CD spectra and further predict that a film of twist-stacked CNTs with an optimized thickness will exhibit an ellipticity as high as 150 mdeg/nm. The created wafer-scale objects represent a new class of synthetic chiral matter consisting of ordered quantum wires whose macroscopic properties are governed by nanoscopic electronic signatures such as van Hove singularities. These artificial structures with engineered chirality will not only provide playgrounds for uncovering new chiral phenomena but also open up new opportunities for developing high-performance chiral photonic and optoelectronic devices. © 2023, CC BY.

关键词： Carbon nanotubes

Impact of surface and pore characteristics on fatigue life of laser powder bed fusion Ti-6Al-4V alloy described by neural network models

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arXiv 2021年

作者： Moon, Seunghyun Ma, Ruimin Attardo, Ross Tomonto, Charles Nordin, Mark Wheelock, Paul Glavicic, Michael Layman, Maxwell Billo, Richard Luo, Tengfei Department of Aerospace and Mechanical Engineering University of Notre Dame IN46556 United States 3D Printing Center Johnson & Johnson MiamiFL33126 United States Rolls-Royce Corporation 450 S. Meridian St. IndianapolisIN46225 United States Department of Computer Science and Engineering University of Notre Dame IN46556 United States Department of Chemical and Biomolecular Engineering University of Notre Dame IN46556 United States

In this study, the effects of surface roughness and pore characteristics on fatigue lives of laser powder bed fusion (LPBF) Ti-6Al-4V parts were investigated. The 197 fatigue bars were printed using the same laser power but with varied scanning speeds. These actions led to variations in the geometries of microscale pores, and such variations were characterized using micro-computed tomography. To generate differences in surface roughness in fatigue bars, half of the samples were grit-blasted and the other half machined. Fatigue behaviors were analyzed with respect to surface roughness and statistics of the pores. For the grit-blasted samples, the contour laser scan in the LPBF strategy led to a pore-depletion zone isolating surface and internal pores with different features. For the machined samples, where surface pores resemble internal pores, the fatigue life was highly correlated with the average pore size and projected pore area in the plane perpendicular to the stress direction. Finally, a machine learning model using a drop-out neural network (DONN) was employed to establish a link between surface and pore features to the fatigue data (logN), and good prediction accuracy was demonstrated. Besides predicting fatigue lives, the DONN can also estimate the prediction uncertainty. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

Electron Tomographic Reconstruction of Soft Nanomaterials for Morphometry Studies

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Microscopy and Microanalysis 2024年第SUPPLEMENT_1期30卷

作者： Kalutantirige, Falon C Bodgan, Paul Chen, Qian Department of Chemistry University of Illinois Urbana IL United States Department of Electrical and Computer Engineering University of Southern California Los Angeles CA United States Department of Materials Science and Engineering University of Illinois Urbana IL United States Department of Chemical and Biomolecular Engineering University of Illinois Urbana IL United States Materials Research Laboratory University of Illinois Urbana IL United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana IL United States

Deepfake histological images for enhancing digital pathology

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arXiv 2022年

作者： Falahkheirkhah, Kianoush Tiwari, Saumya Yeh, Kevin Gupta, Sounak Herrera-Hernandez, Loren McCarthy, Michael R. Jimenez, Rafael E. Cheville, John C. 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 Medicine University of California San Diego San DiegoCA92122 United States College of Medicine and Science Mayo Clinic RochesterMN55905 United States

A pathologist’s optical microscopic examination of thinly cut stained tissue on glass slides prepared from a formalin-fixed paraffin-embedded (FFPE) tissue blocks is the gold standard for tissue diagnostics. In addition, the diagnostic abilities and expertise of any pathologist is dependent on their direct experience with common as well as rarer variant morphologies. Recently, deep learning approaches have been used to successfully show a high level of accuracy for such tasks. However, obtaining expert-level annotated images is an expensive and time-consuming task and artificially synthesized histological images can prove greatly beneficial. Here, we present an approach to not only generate histological images that reproduce the diagnostic morphologic features of common disease but also provide a user ability to generate new and rare morphologies. Our approach involves developing a generative adversarial network (GAN) model that synthesizes pathology images constrained by class labels. We investigated the ability of this framework in synthesizing realistic prostate and colon tissue images and assessed the utility of these images in augmenting diagnostic ability of machine learning methods as well as their usability by a panel of experienced anatomic pathologists. Synthetic data generated by our framework performed similar to real data in training a deep learning model for diagnosis. Pathologists were not able to distinguish between real and synthetic images and showed a similar level of inter-observer agreement for prostate cancer grading. We extended the approach to significantly more complex images from colon biopsies and showed that the complex microenvironment in such tissues can also be reproduced. Finally, we present the ability for a user to generate deepfake histological images via a simple markup of sematic labels. These advances allow for building comprehensive diagnostic pipelines that can also handle complex or rare cases, a systematic exploration into t

关键词： Tissue

Label-free correlative morpho-chemical tomography of 3D kidney mesangial cells

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arXiv 2024年

作者： Butola, Ankit Ghosh, Biswajoy Park, Jaena Kwon, Minsung De la Cadena, Alejandro Mukherjee, Sudipta S. Bhargava, Rohit Boppart, Stephen A. Agarwal, Krishna Department of Physics and Technology UiT The Arctic University of Norway Tromsø9037 Norway University of Illinois Urbana-Champaign 405 N. Mathews Ave. UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign 405 N Mathews Avenue UrbanaIL61801 United States Department of Bioengineering University of Illinois Urbana-Champaign 1406 W Green Street UrbanaIL61801 United States Department of Chemical and Biomolecular Engineering University of Illinois Urbana-Champaign UrbanaIL United States Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign UrbanaIL United States Cancer Center at Illinois University of Illinois Urbana-Champaign UrbanaIL United States Carle Illinois College of Medicine University of Illinois Urbana-Champaign 506 S Mathews Avenue UrbanaIL61801 United States

Label-free characterization of biological specimens seeks to supplement existing imaging techniques and avoid the need for contrast agents that can disturb the native state of living samples. Conventional label-free optical imaging techniques are compatible with living samples but face challenges such as poor sectioning capability, fragmentary morphology, and lack chemical specific information. Here, we combined simultaneous label-free autofluorescence multi-harmonic (SLAM) microscopy and gradient light interference microscopy (GLIM) to extract both chemical specific and morphological tomography of 3D cultured kidney mesangial cells. Imaging 3D in vitro kidney models is essential to understand kidney function and pathology. Our correlative approach enables imaging and quantification of these cells to extract both morphology and chemical-specific signals that is crucial for understanding kidney function. In our approach, SLAM offers a nonlinear imaging platform with a single-excitation source to simultaneously acquire autofluorescence (FAD and NAD(P)H), second, and third harmonic signal from the 3D cultured cells. Complementarily, GLIM acquires high-contrast quantitative phase information to quantify structural changes in samples with thickness of up to 250 µm. Our correlative imaging results demonstrate a versatile and hassle-free platform for morpho-chemical cellular tomography to investigate functions such as metabolism and matrix deposition of kidney mesangial cells in 3D under controlled physiological conditions. Copyright © 2024, The Authors. All rights reserved.

关键词： Cell culture

Identification of novel phytotherapeutic agents for understanding hypertrophic cardiomyopathy via genetic mapping and advanced computational analysis

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Genome Instability & Disease 2024年第6期5卷 262-286页

作者： Aborode, Abdullahi Tunde Ayobami, Onifade Isreal Danazumi, Ammar Usman Olowosoke, Christopher Busayo Umar, Haruna Isiyaku Osinuga, Abraham Awaji, Aeshah A. Awaji, Fatmah Ali Omojowolo, Ebenezer Ayomide Kuthi, Najwa Ahmad Shaikh, Tanveer Anidu, Babatunde Shuaib Alexiou, Athanasios Adesola, Ridwan Olamilekan Olapade, Zainab Matthew, Awah Favour Ameh, Blessing Oni, Toluwalope Yinka Bakre, Adetolase Azizat Scott, Godfred Yawson Department of Chemistry Mississippi State University Starkville USA Department of Biology University at Albany Albany USA Faculty of Chemistry Warsaw University of Technology Warsaw Poland Groningen Research Institute of Pharmacy University of Groningen Groningen The Netherlands Department of Biotechnology Federal University of Technology Akure Nigeria Computer-Aided Therapeutic Discovery and Design Group FUTA Akure Nigeria Department of Chemical and Biomolecular Engineering University of Nebraska-Lincoln Lincoln USA Department of Biology Faculty of Science University College of Taymaa University of Tabuk Tabuk Saudi Arabia King Fahad Specialist Hospital Laboratory and Blood Bank Health Cluster Tabuk Saudi Arabia Department of Chemistry University of Albany State University of New York Albany USA Department of Chemistry Faculty of Science Universiti Teknologi Malaysia (UTM) Johor Bahru Malaysia Department of Integrative Biology and Physiology University of Minnesota Medical School Minneapolis USA Department of Science and Engineering Novel Global Community Educational Foundation Hebersham Australia AFNP Med Wien Austria Department of Veterinary Medicine Faculty of Veterinary Medicine University of Ibadan Ibadan Nigeria Department of Biology Lamar University Texas USA Department of Biochemistry Center for Discovery and Innovation City University of New York New York USA University of West Georgia Carrolton USA Department of Biochemistry Federal University of Technology Akure Nigeria Department of Medical Diagnostics Faculty of Allied Health Sciences Kwame Nkrumah University of Science and Technology Kumasi Ghana

Hypertrophic cardiomyopathy (HCM) is a monogenic cardiovascular disorder that has been poorly studied at the molecular, genetic, and computational levels. Here, we examined the genetic map of HCM polymorphic targets using a computational approach to identify new phytochemicals with potential therapeutic properties. We demonstrate the range of mutations associated with cardiomyopathies, and identify new associations between genes and phenotypes in this disease category. Specifically, our findings suggest that several genes associated with channelopathies might serve as genetic modifiers, altering the clinical features and severity of cardiomyopathic phenotypes, thus likely impacting disease manifestation.

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