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OPRLM: A Web Tool and a Database for Positioning and Simulations of Proteins in Realistic Lipid Membranes

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Journal of Molecular Biology 2025年第15期437卷 168966页

作者： Park, Sang-Jun Schnitzer, Kyle A. Kovalenko, Alexey Cherepanov, Stanislav Patro, L. Ponoop Prasad Song, Zigang Pogozheva, Irina D. Lomize, Andrei L. Im, Wonpil Department of Computer Science and Engineering Lehigh University 111 Research Drive Bethlehem 18015 PA United States Department of Electrical Engineering and Computer Science College of Engineering University of Michigan 1221 Beal Ave Ann Arbor 48109-2102 MI United States Department of Computer Science College of Literature Science and the Arts University of Michigan 2260 Hayward Street Ann Arbor 48109-2121 MI United States Biophysics Program University of Michigan 930 N. University Avenue Ann Arbor 48109-1065 MI United States Department of Biological Sciences Lehigh University 111 Research Drive Bethlehem 18015 PA United States Department of Medicinal Chemistry College of Pharmacy University of Michigan 428 Church St. Ann Arbor 48109-1065 MI United States

Molecular dynamics (MD) simulations in explicit lipid bilayers enable modeling of protein-lipid interactions essential for membrane protein functions and regulation. The newly developed computational web tool, OPRLM (Orientations of Proteins in Realistic Lipid Membranes), automates the assembly of membrane protein structures with explicit lipids corresponding to 18 biological membrane types with symmetric or asymmetric lipid distributions, as well as 5 types of two-component lipid bilayers with varying cholesterol content. Built upon the CHARMM-GUI toolset and the PPM method, OPRLM simplifies the setup of complex simulation system involving integral and/or peripheral membrane proteins with explicit lipid mixtures and generates all necessary files for subsequent all-atom MD simulations. OPRLM has successfully generated protein-membrane systems for 286 tested protein structures in various biomembranes, including 138 structures containing ligands. The OPRLM database, an advanced successor of the OPM database, includes explicit protein-lipid systems for tested proteins in their native biomembranes. It provides coordinates of integral and peripheral membrane proteins from the Protein Data Bank embedded in planar or curved implicit lipid bilayers. Additionally, it includes the classification of proteins into types, superfamilies, and families, along with the information on intracellular localizations and membrane topology and visualization options. The OPRLM web tool and the database are publicly accessible at https://***. © 2025 The Authors

关键词： biomembrane explicit lipid lipid bilayer peripheral membrane protein transmembrane

Bifunctional Noble Metal-free Ternary Chalcogenide Electrocatalysts for Overall Water Splitting

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arXiv 2024年

作者： Singh, Shantanu Irshad, Ahamed De la Cruz, Germany Diaz Zhao, Boyang Zayat, Billal Chang, Qiaowan Narayan, Sri Ravichandran, Jayakanth Mork Family Department of Chemical Engineering and Materials Science University of Southern California Los AngelesCA90089 United States Department of Chemistry University of Southern California Los AngelesCA90089 United States The Gene and Linda Voiland School of Chemical Engineering and Bioengineering Washington State University PullmanWA99164 United States Ming Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Core Center of Excellence in Nano Imaging University of Southern California Los AngelesCA90089 United States

Hydrogen has been identified as a clean, zero carbon, sustainable, and promising energy source for the future, and electrochemical water splitting for hydrogen production is an emission-free, efficient energy conversion technology. A major limitation of this approach is the unavailability of efficient, abundant, inexpensive catalysts, which prompts the need for new catalytic materials. Here, we report the synthesis and electrocatalytic properties of a novel transition metal-based ternary chalcogenide family, LaMS3 (M = Mn, Fe, Co, Ni). Powder X-ray diffraction confirms the phase purity of these materials, while composition analysis using energy dispersive spectroscopy (EDS) confirms the presence of the stoichiometric ratio of elements in these compounds. X-ray photoelectron spectroscopy (XPS) and X-ray absorption spectroscopy (XAS) were used to study the chemical states on the surface and in bulk, respectively. These materials exhibit bifunctional catalytic activity towards the two half-reactions of the water-splitting process, with LaNiS3 being the most active material for both hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The LaMS3 compounds show long-term stability with negligible change in the overpotential at a constant current density of 10 mA cm-2 over 18 hours of measurements. As compared to the corresponding ternary oxides, the LaMS3 materials exhibit higher activity and significantly lower Tafel slopes. The ability to catalyze both half-reactions of water electrolysis makes these materials promising candidates for bifunctional catalysts and presents a new avenue to search for high-efficiency electrocatalysts for water splitting. © 2024, CC BY.

关键词： Electrocatalysts

Room-temperature multiple ligands-tailored SnO_(2) quantum dots endow in situ dual-interface binding for upscaling efficient perovskite photovoltaics with high V_(OC)

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Light(science & Applications) 2021年第12期10卷 2467-2481页

作者： Zhiwei Ren Kuan Liu Hanlin Hu Xuyun Guo Yajun Gao Patrick W.K.Fong Qiong Liang Hua Tang Jiaming Huang Hengkai Zhang Minchao Qin Li Cui Hrisheekesh Thachoth Chandran Dong Shen Ming-Fai Lo Annie Ng Charles Surya Minhua Shao Chun-Sing Lee Xinhui Lu Frédéric Laquai Ye Zhu Gang Li Department of Electronic and Information Engineering Research Institute for Smart Energy(RISE)Guangdong-Hong Kong-Macao(GHM)Joint Laboratory for Photonic-Thermal-Electrical Energy Materials and DevicesThe Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina Department of Electrical and Computer Engineering Nazarbayev UniversityNur-SultanKazakhstan The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen 518057China Hoffmann Institute of Advanced Materials Shenzhen Polytechnic7098 Liuxian BoulevardShenzhen 518055China Department of Applied Physics The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina Department of Chemical and Biological Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina King Abdullah University of Science and Technology(KAUST) KAUST Solar Center(KSC)Physical Sciences and Engineering Division(PSE)Material Science and Engineering Program(MSE)Thuwal 23955-6900Kingdom of Saudi Arabia Department of Physics The Chinese University of Hong KongShatin 999077 Hong KongChina Center of Super-Diamond and Advanced Films(COSDAF) Department of ChemistryCity University of Hong KongHong KongChina

The benchmark tin oxide(SnO_(2))electron transporting layers(ETLs)have enabled remarkable progress in planar perovskite solar cell(PSCs).However,the energy loss is still a challenge due to the lack of“hidden interface”*** report a novel ligand-tailored ultrafine SnO_(2) quantum dots(QDs)via a facile rapid room temperature ***,the ligand-tailored SnO_(2) QDs ETL with multi-functional terminal groups in situ refines the buried interfaces with both the perovskite and transparent electrode via enhanced interface binding and perovskite *** novel ETLs induce synergistic effects of physical and chemical interfacial modulation and preferred perovskite crystallization-directing,delivering reduced interface defects,suppressed non-radiative recombination and elongated charge carrier *** conversion efficiency(PCE)of 23.02%(0.04 cm^(2))and 21.6%(0.98 cm^(2),V_(OC) loss:0.336 V)have been achieved for the blade-coated PSCs(1.54 eV E_(g))with our new ETLs,representing a record for SnO_(2) based blade-coated ***,a substantially enhanced PCE(V_(OC))from 20.4%(1.15 V)to 22.8%(1.24 V,90 mV higher V_(OC),0.04 cm^(2) device)in the blade-coated 1.61 eV PSCs system,via replacing the benchmark commercial colloidal SnO_(2) with our new ETLs.

关键词： perovskite interface quantum

Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects

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arXiv 2022年

作者： Ghassami, AmirEmad Yang, Alan Richardson, David Shpitser, Ilya Tchetgen, Eric Tchetgen Department of Computer Science Johns Hopkins University United States Department of Electrical Engineering Stanford University United States Department of Environmental and Occupational Health Program in Public Health University of California Irvine United States Department of Statistics and Data Science The Wharton School University of Pennsylvania United States

We consider the task of identifying and estimating the causal effect of a treatment variable on a long-term outcome variable using data from an observational domain and an experimental domain. The observational domain is subject to unobserved confounding. Furthermore, subjects in the experiment are only followed for a short period of time;hence, long-term effects of treatment are unobserved but short-term effects will be observed. Therefore, data from neither domain alone suffices for causal inference about the effect of the treatment on the long-term outcome, and must be pooled in a principled way, instead. Athey et al. (2020) proposed a method for systematically combining such data for identifying the downstream causal effect in view. Their approach is based on the assumptions of internal and external validity of the experimental data, and an extra novel assumption called latent unconfoundedness. In this paper, we first review their proposed approach, and then we propose three alternative approaches for data fusion for the purpose of identifying and estimating average treatment effect as well as the effect of treatment on the treated. Our first approach is based on assuming equi-confounding bias for the short-term and long-term outcomes. Our second approach is based on a relaxed version of the equi-confounding bias assumption, where we assume the existence of an observed confounder such that the short-term and long-term potential outcome variables have the same partial additive association with that confounder. Our third approach is based on the proximal causal inference framework, in which we assume the existence of an extra variable in the system which is a proxy of the latent confounder of the treatment-outcome relation. We propose influence function-based estimation strategies for each of our data fusion frameworks and study the robustness properties of the proposed estimators. © 2022, CC BY.

关键词： Data fusion

Layer-dependent optically induced spin polarization in InSe

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Physical Review B 2023年第11期107卷 115304-115304页

作者： Jovan Nelson Teodor K. Stanev Dmitry Lebedev Trevor LaMountain J. Tyler Gish Hongfei Zeng Hyeondeok Shin Olle Heinonen Kenji Watanabe Takashi Taniguchi Mark C. Hersam Nathaniel P. Stern Applied Physics Program Northwestern University Evanston Illinois 60208 USA Department of Physics and Astronomy Northwestern University Evanston Illinois 60208 USA Department of Material Science and Engineering Northwestern University Evanston Illinois 60208 USA Computational Science Division Argonne National Laboratory Lemont Illinois 60439 USA Material Science Division Argonne National Laboratory Lemont Illinois 60439 USA Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan Department of Chemistry Northwestern University Evanston Illinois 60208 USA Department of Electrical Engineering and Computer Science Northwestern University Evanston Illinois 60208 USA

Optical control of spin in semiconductors has been pioneered using nanostructures of III-V and II-VI semiconductors, but the emergence of two-dimensional van der Waals materials offers an alternative low-dimensional platform for spintronic phenomena. Indium selenide (InSe), a group-III monochalcogenide van der Waals material, has shown promise for optoelectronics due to its high electron mobility, tunable direct band gap, and quantum transport. In addition to these confirmed properties, there are predictions of spin-dependent optical selection rules, suggesting the potential for all-optical excitation and control of spin in a two-dimensional layered material. Despite these predictions, layer-dependent optical spin phenomena in InSe have yet to be explored. Here, we present measurements of layer-dependent optical spin dynamics in few-layer and bulk InSe. Polarized photoluminescence reveals layer-dependent optical orientation of spin, thereby demonstrating the optical selection rules in few-layer InSe. Spin dynamics are also studied in many-layer InSe using time-resolved Kerr rotation spectroscopy. By applying out-of-plane and in-plane static magnetic fields for polarized emission measurements and Kerr measurements, respectively, the g factor for InSe was extracted. Further investigations are done by calculating precession values using a k·p model, which is supported by ab initio density functional theory. Comparison of predicted precession rates with experimental measurements highlights the importance of excitonic effects in InSe for understanding spin dynamics. Optical orientation of spin is an important prerequisite for optospintronic phenomena and devices, and these first demonstrations of layer-dependent optical excitation of spins in InSe lay the foundation for combining layer-dependent spin properties with advantageous electronic properties found in this material.

关键词： Magneto-optics Spin dynamics Chalcogenides Layered semiconductors Van der Waals systems Density functional theory Kerr effect Photoluminescence k dot p method

Quantitative particle agglutination assay using mobile holographic imaging and neural networks

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Quantitative particle agglutination assay using mobile holog...

Conference on Lasers and Electro-Optics (CLEO)

作者： Yi Luo Hyou-Arm Joung Sarah Esparza Jingyou Rao Omai Garner Aydogan Ozcan Electrical and Computer Engineering Department University of California Los Angeles CA USA Bioengineering Department University of California Los Angeles CA USA California NanoSystems Institute (CNSI) Los Angeles CA USA Computer Science Department University of California Los Angeles CA USA Department of Pathology and Laboratory Medicine University of California Los Angeles CA USA

A quantitative particle agglutination assay using mobile holography and neural networks is demonstrated to rapidly and accurately measure C-reactive protein (CRP) concentration in human serum samples.

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Proteins Atmospheric measurements Neural networks Measurement by laser beam Lasers and electrooptics Holography Particle measurements

A Conceptual Exploration of Generative AI-Induced Cognitive Dissonance and its Emergence in University-Level Academic Writing

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arXiv 2025年

作者： Seran, Carl Errol Tan, Myles Joshua Toledo Karim, Hezerul Abdul AlDahoul, Nouar Biology Program College of Arts and Sciences University of St. La Salle Bacolod Philippines Department of Natural Sciences College of Arts and Sciences University of St. La Salle Bacolod Philippines Faculty of Education University of the Philippines Open University Los Baños Philippines Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Department of Epidemiology College of Public Health and Health Professions College of Medicine University of Florida GainesvilleFL United States Department of Chemical Engineering College of Engineering and Technology University of St. La Salle Bacolod Philippines Department of Electronics Engineering College of Engineering and Technology University of St. La Salle Bacolod Philippines Yo-Vivo Corporation Bacolod Philippines Faculty of Engineering Multimedia University Persiaran Multimedia Cyberjaya Malaysia Department of Computer Science Division of Science New York University Abu Dhabi Abu Dhabi United Arab Emirates

The integration of Generative Artificial Intelligence (GenAI) intouniversity-level academic writing presents both opportunities and challenges,particularly in relation to cognitive dissonance (CD). This work explores howGenAI serves as both a trigger and amplifier of CD, as students navigateethical concerns, academic integrity, and self-efficacy in their writingpractices. By synthesizing empirical evidence and theoretical insights, weintroduce a hypothetical construct of GenAI-induced CD, illustrating thepsychological tension between AI-driven efficiency and the principles oforiginality, effort, and intellectual ownership. We further discuss strategiesto mitigate this dissonance, including reflective pedagogy, AI literacyprograms, transparency in GenAI use, and discipline-specific task *** approaches reinforce critical engagement with AI, fostering a balancedperspective that integrates technological advancements while safeguarding humancreativity and learning. Our findings contribute to ongoing discussions on AIin education, self-regulated learning, and ethical AI use, offering aconceptual framework for institutions to develop guidelines that align AIadoption with academic values. © 2025, CC BY.

关键词： Generative adversarial networks

Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

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science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BASED ON ARTIFICIAL NEURAL NETWORKS AND TREE DECISION MODELS

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arXiv 2022年

作者： Yazdanpanah, Hamed Silva, Augusto C.M. Guedes, Murilo Morales, Hugo M.P. Coelho, Leandro dos S. Moro, Fernando G. Department of Research Robô Laura PR Curitiba Brazil Department of Computer Science University of São Paulo SP São Paulo Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana PR Curitiba Brazil Electrical Engineering Graduate Program Federal University of Parana PR Curitiba Brazil

Early recognition of clinical deterioration (CD) has vital importance in patients' survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network's performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models' performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data. © 2022, CC BY-NC-SA.

关键词： Machine learning