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computers in Biology and Medicine 2025年 186卷 109681-109681页

作者： Torok, Rebeka Meszaros, Brigitta Gombas, Veronika Vathy-Fogarassy, Agnes Szabo, Miklos Csanky, Eszter Jarvas, Gabor Guttman, Andras Research Institute of Biomolecular and Chemical Engineering University of Pannonia Veszprem Hungary Horváth Csaba Memorial Laboratory of Bioseparation Sciences Research Center for Molecular Medicine Doctoral School of Molecular Medicine Faculty of Medicine University of Debrecen Debrecen Hungary Department of Computer Science and Systems Technology University of Pannonia Veszprem Hungary Department of Pulmonology Borsod Academic County Hospital Miskolc Hungary

An efficient novel approach is introduced to predict the effectiveness of chemotherapy treatment in lung cancer by monitoring the serum N-glycome of patients combined with artificial intelligence-based data analysis. The study involved thirty-three lung cancer patients undergoing chemotherapy treatments. Serum samples were taken before and after the treatment. The N-linked oligosaccharides were enzymatically released, fluorophore-labeled, and analyzed by capillary electrophoresis with laser-induced fluorescence detection. The resulting electropherograms were thoroughly processed and evaluated by artificial intelligence-based classifiers, i.e., utilizing a machine learning algorithm to categorize the data into two (binary) classes. The classifier analysis method revealed a strong association between the structural changes in the N-glycans and the outcomes of the chemotherapy treatments (ROC >0.9). This novel combination of bioanalytical and AI methods provided a precise and rapid tool for predicting the effectiveness of chemotherapy. © 2025 The Authors

关键词： Lung cancer

Locating saddle points using gradient extremals on manifolds adaptively revealed as point clouds

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arXiv 2023年

作者： Georgiou, A. Vandecasteele, H. Bello-Rivas, J.M. Kevrekidis, I. Department of Chemical and Biomolecular Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Computer Science KU Leuven Leuven3001 Belgium Department of Applied Mathematics & Statistics Johns Hopkins University BaltimoreMD21218 United States

Steady states are invaluable in the study of dynamical systems. High-dimensional dynamical systems, due to a separation of time-scales, often evolve towards a lower dimensional manifold M. We introduce an approach to locate saddle points (and other fixed points) that utilizes gradient extremals on such a priori unknown (Riemannian) manifolds, defined by adaptively sampled point clouds, with local coordinates discovered on-the-fly through manifold learning. The technique, which efficiently biases the dynamical system along a curve (as opposed to exhaustively exploring the state space), requires knowledge of a single minimum and the ability to sample around an arbitrary point. We demonstrate the effectiveness of the technique on the Müller-Brown potential mapped onto an unknown surface (namely, a sphere). Previous work employed a similar algorithmic framework to find saddle points using Newton trajectories and gentlest ascent dynamics;we therefore also offer a brief comparison with these methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamical systems

Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

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MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National Laboratory, Lemont, USA." Argonne National Laboratory was left off the original publication. © The Author(s) 2025.

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Mechanism for plasmon-generated solvated electrons

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Proceedings of the National Academy of sciences of the United States of America 2023年第3期120卷 e2217035120-e2217035120页

作者： Al-Zubeidi, Alexander Ostovar, Behnaz Carlin, Claire C. Li, Boxi Cam Lee, Stephen A. Chiang, Wei-Yi Gross, Niklas Dutta, Sukanya Misiura, Anastasiia Searles, Emily K. Chakraborty, Amrita Roberts, Sean T. Dionne, Jennifer A. Rossky, Peter J. Landes, Christy F. Link, Stephan Department of Chemistry Rice University Houston 77005 TX United States Center for Adapting Flaws into Features Rice University Houston 77005 TX United States Department of Electrical and Computer Engineering Rice University Houston 77005 TX United States Department of Applied Physics Stanford University Stanford 94305 CA United States Department of Chemistry University of Texas at Austin Austin 78712 TX United States Department of Materials Science and Engineering Stanford University Stanford 94305 CA United States Department of Radiology Stanford University Stanford 94305 CA United States Department of Chemical and Biomolecular Engineering Rice University Houston 77005 TX United States

Solvated electrons are powerful reducing agents capable of driving some of the most energetically expensive reduction reactions. Their generation under mild and sustainable conditions remains challenging though. Using near-ultraviolet irradiation under low-intensity one-photon conditions coupled with electrochemical and optical detection, we show that the yield of solvated electrons in water is increased more than 10 times for nanoparticle-decorated electrodes compared to smooth silver electrodes. Based on the simulations of electric fields and hot carrier distributions, we determine that hot electrons generated by plasmons are injected into water to form solvated electrons. Both yield enhancement and hot carrier production spectrally follow the plasmonic near-field. The ability to enhance solvated electron yields in a controlled manner by tailoring nanoparticle plasmons opens up a promising strategy for exploiting solvated electrons in chemical reactions. Copyright © 2023 the Author(s).

关键词： hot carriers hydrated electrons nanoparticles photoemission

chemical Property-Guided Neural Networks for Naphtha Composition Prediction

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arXiv 2023年

作者： Joo, Chonghyo Kim, Jeongdong Cho, Hyungtae Lee, Jaewon Suh, Sungho Kim, Junghwan Department of Chemical and Biomolecular Engineering Yonsei University Seoul Korea Republic of Green Materials & Processes R&D Group Korea Institute of Industrial Technology Ulsan Korea Republic of Department of Computer Science RPTU Kaiserslautern-Landau Kaiserslautern Germany Kaiserslautern Germany

The naphtha cracking process heavily relies on the composition of naphtha, which is a complex blend of different hydrocarbons. Predicting the naphtha composition accurately is crucial for efficiently controlling the cracking process and achieving maximum performance. Traditional methods, such as gas chromatography and true boiling curve, are not feasible due to the need for pilot-plant-scale experiments or cost constraints. In this paper, we propose a neural network framework that utilizes chemical property information to improve the performance of naphtha composition prediction. Our proposed framework comprises two parts: a Watson K factor estimation network and a naphtha composition prediction network. Both networks share a feature extraction network based on Convolutional Neural Network (CNN) architecture, while the output layers use Multi-Layer Perceptron (MLP) based networks to generate two different outputs - Watson K factor and naphtha composition. The naphtha composition is expressed in percentages, and its sum should be 100%. To enhance the naphtha composition prediction, we utilize a distillation simulator to obtain the distillation curve from the naphtha composition, which is dependent on its chemical properties. By designing a loss function between the estimated and simulated Watson K factors, we improve the performance of both Watson K estimation and naphtha composition prediction. The experimental results show that our proposed framework can predict the naphtha composition accurately while reflecting real naphtha chemical properties. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

Large Fluorescence Enhancement via Lossless All-Dielectric Spherical Mesocavities

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arXiv 2023年

作者： Zakomirnyi, Vadim I. Moroz, Alexander Bhargava, Rohit Rasskazov, Ilia L. Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States *** Department of Bioengineering Electrical & Computer Engineering Mechanical Science & Engineering Chemical and Biomolecular Engineering and Chemistry Cancer Center at Illinois Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States SunDensity Inc. RochesterNY14604 United States

Nano- and microparticles are popular media to enhance optical signals, including fluorescence from a dye proximal to the particle. Here we show that homogeneous, lossless, all-dielectric spheres with diameters in the mesoscale range, between nano- ( 100 nm) and micro- ( 1 µm) scales, can offer surprisingly large fluorescence enhancements, up to F ∼ 104. With the absence of nonradiative Ohmic losses inherent to plasmonic particles, we show that F can increase, decrease or even stay the same with increasing intrinsic quantum yield q0, for suppressed, enhanced or intact radiative decay rates of a fluorophore, respectively. Further, the fluorophore may be located inside or outside the particle, providing additional flexibility and opportunities to design fit for purpose particles. The presented analysis with simple dielectric spheres should spur further interest in this less-explored scale of particles and experimental investigations to realize their potential for applications in imaging, molecular sensing, light coupling, and quantum information processing. © 2023, CC BY.

关键词： Spheres

Establishing best practices for modeling long duration energy storage in deeply decarbonized energy systems

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arXiv 2024年

作者： Mantegna, Gabriel Ricks, Wilson Manocha, Aneesha Patankar, Neha Mallapragada, Dharik Jenkins, Jesse Department of Mechanical and Aerospace Engineering Princeton University PrincetonNJ United States Andlinger Center for Energy and the Environment Princeton University PrincetonNJ United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ United States Department of Systems Science and Industrial Engineering Binghamton University BinghamtonNY United States Department of Chemical and Biomolecular Engineering New York University BrooklynNY United States

Long duration energy storage (LDES) may become a critical technology for the decarbonization of the power sector, as current commercially available Li-ion battery storage technologies cannot cost-effectively shift energy to address multi-day or seasonal variability in demand and renewable energy availability. LDES is difficult to model in existing energy system planning models (such as electricity system capacity expansion models), as it is much more dependent on an accurate representation of chronology than other resources. Techniques exist for modeling LDES in these planning models;however, it is not known how spatial and temporal resolution affect the performance of these techniques, creating a research gap. In this study we examine what spatial and temporal resolution is necessarily to accurately capture the full value of LDES, in the context of a continent-scale capacity expansion model. We use the results to draw conclusions and present best practices for modelers seeking to accurately model LDES in a macro-energy systems planning context. Our key findings are: 1) modeling LDES with linked representative periods is crucial to capturing its full value, 2) LDES value is highly sensitive to the cost and availability of other resources, and 3) temporal resolution is more important than spatial resolution for capturing the full value of LDES, although how much temporal resolution is needed will depend on the specific model context. © 2024, CC BY.

关键词： Decarbonization

Porous silicon on paper sensors: Factors affecting biosensing performance 17

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Porous silicon on paper sensors: Factors affecting biosensin...

Frontiers in Biological Detection: From Nanosensors to Systems XVII 2025

作者： An, Huijin Laibinis, Paul E. Locke, Andrea K. Weiss, Sharon M. Interdisciplinary Material Science Program Vanderbilt University NashvilleTN37235 United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States Department of Chemical and Biomolecular Engineering Vanderbilt University NashvilleTN37235 United States Department of Chemistry Vanderbilt University NashvilleTN37235 United States Department of Biomedical Engineering Vanderbilt University NashvilleTN37235 United States

ISBN: (纸本)9781510684249

A paper-based biosensor integrating a functionalized porous silicon (PSi) membrane as the active sensing element for quantifiable protein detection has been developed. For similar short-time exposures to an analyte, improved molecular transport in PSi membranes when on paper leads to larger signal changes compared to traditional PSi films that remain on a silicon substrate. In this work, we discuss controlling the incubation time of the analyte and the overall testing time of the sensor by incorporating different combinations of wicking and absorbent paper beneath the PSi membrane. With this control, the PSi-on-paper sensor platform has the potential to serve as an effective low-cost rapid diagnostic test with highly sensitive, quantitative readout for a wide range of analytes. © 2025 SPIE.

关键词： Porous silicon

Machine Learning Models of Intermittent Operation of Ro Wellhead Water Treatment for Salinity Reduction and Nitrate Removal

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SSRN

SSRN 2024年

作者： Zhou, Yang Khan, Bilal Marki, Nora Jarma, Yakubu Aguilar, Christian Cohen, Yoram Department of Automation College of Mechatronics Engineering and Automation Shanghai University Shanghai200072 China Water Technology Research Center Department of Chemical and Biomolecular Engineering Henry Samueli School of Engineering Applied Science University of California Los Angeles Los AngelesCA90095-1592 United States School of Computer Science and Engineering California State University San BernardinoCA United States Institute of the Environment and Sustainability University of California Los Angeles Los AngelesCA United States

Machine learning models were developed for the intermittent multi-mode operation of a wellhead reverse osmosis water purification and desalination system to predict salt passage, nitrate passage, and permeate flux. The models, based on long short-term memory (LSTM) recurrent neural network (RNN) architecture, included an attention mechanism to increase model performance in proximity of the regulatory limit for nitrate. Training and testing of the models for the Startup, Production, Shutdown and Flushing operational modes were based on operational data (consisting of 22 process variables per data sample) acquired every 2-5 seconds over a six-month period. The significant sets of model input attributes for the different operational modes were assessed via Spearman ranking correlation, Self-Organizing Map (SOM) analysis and feed forward feature selection (FFFS). Although the variability of nitrate passage, salt passage and permeate flux was significant over the four operational modes, prediction performance for the three outcomes were with R2and Average Absolute Relative Error (AARE) of 0.78-0.95 and 2.96-6.16%, respectively. Model updates post membrane elements replacement demonstrated similar levels of prediction accuracy. The study results suggest that there is merit in exploring the utility of multi-mode models for sensor fault detection, data imputation, and for potential use in model-predictive control. © 2024, The Authors. All rights reserved.

关键词： Nitrates

On Learning what to Learn: heterogeneous observations of dynamics and establishing (possibly causal) relations among them

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arXiv 2024年

作者： Sroczynski, David W. Dietrich, Felix Koronaki, Eleni D. Talmon, Ronen Coifman, Ronald R. Bollt, Erik Kevrekidis, Ioannis G. Department of Chemical and Biological Engineering Princeton University PrincetonNJ United States School of Computation Information and Technology Technical University of Munich Munich Germany Faculty of Science Technology and Medicine University of Luxembourg Esch-sur-Alzette Luxembourg Viterbi Faculty of Electrical Engineering Technion Israel Institute of Technology Haifa Israel School of Engineering & Applied Science Yale University New HavenCT United States Electrical & Computer Engineering Clarkson University PotsdamNY United States Department of Chemical and Biomolecular Engineering Department of Applied Mathematics and Statistics Department of Urology Johns Hopkins University BaltimoreMD United States

Before we attempt to (approximately) learn a function between two (sets of) observables of a physical process, we must first decide what the inputs and what the outputs of the desired function are going to be. Here we demonstrate two distinct, data-driven ways of first deciding "the right quantities" to relate through such a function, and then proceeding to learn it. This is accomplished by first processing multiple simultaneous heterogeneous data streams (ensembles of time series) from observations of a physical system: records of multiple observation processes of the system. We thus determine (a) what subsets of observables are common between the observation processes (and therefore observable from each other, relatable through a function);and (b) what information is unrelated to these common observables, and therefore particular to each observation process, and not contributing to the desired function. Any data-driven function approximation technique can subsequently be used to learn the input-output relation—from k-nearest neighbors and Geometric Harmonics to Gaussian Processes and Neural Networks. Two particular "twists" of the approach are discussed. The first has to do with the identifiability of particular quantities of interest from the measurements. We now construct mappings from a single set of observations from one process to entire level sets of measurements of the second process, consistent with this single set. The second attempts to relate our framework to a form of causality: if one of the observation processes measures "now", while the second observation process measures "in the future", the function to be learned among what is common across observation processes constitutes a dynamical model for the system evolution. Copyright © 2024, The Authors. All rights reserved.

关键词： Nearest neighbor search