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computational design of serine hydrolases

science (New York, N.Y.) 2025年第6744期388卷 eadu2454页

作者： Lauko, Anna Pellock, Samuel J. Sumida, Kiera H. Anishchenko, Ivan Juergens, David Ahern, Woody Jeung, Jihun Shida, Alexander F. Hunt, Andrew Kalvet, Indrek Norn, Christoffer Humphreys, Ian R. Jamieson, Cooper Krishna, Rohith Kipnis, Yakov Kang, Alex Brackenbrough, Evans Bera, Asim K. Sankaran, Banumathi Houk, K.N. Baker, David Department of Biochemistry University of Washington Seattle WA United States Institute for Protein Design University of Washington Seattle WA United States Graduate Program in Biological Physics Structure and Design University of Washington Seattle WA United States Department of Chemistry University of Washington Seattle WA United States Graduate Program in Molecular Engineering University of Washington Seattle WA United States Paul G. Allen School of Computer Science and Engineering University of Washington Seattle WA United States Medical Scientist Training Program University of Washington Seattle WA United States Howard Hughes Medical Institute University of Washington Seattle WA United States Department of Chemistry and Biochemistry University of California Los Angeles CA United States

The design of enzymes with complex active sites that mediate multistep reactions remains an outstanding challenge. With serine hydrolases as a model system, we combined the generative capabilities of RFdiffusion with an ensemble generation method for assessing active site preorganization at each step in the reaction to design enzymes starting from minimal active site descriptions. Experimental characterization revealed catalytic efficiencies (kcat/Km) up to 2.2 × 105 M-1 s-1 and crystal structures that closely match the design models (Cα root mean square deviations <1 angstrom). Selection for structural compatibility across the reaction coordinate enabled identification of new catalysts remove with five different folds distinct from those of natural serine hydrolases. Our de novo approach provides insight into the geometric basis of catalysis and a roadmap for designing enzymes that catalyze multistep transformations.

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Control of perpendicular magnetic anisotropy at the Fe/MgO interface by phthalocyanine insertion

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Physical Review B 2022年第18期105卷 184414-184414页

作者： Shoya Sakamoto Edward Jackson Takeshi Kawabe Takuya Tsukahara Yoshinori Kotani Kentaro Toyoki Emi Minamitani Yoshio Miura Tetsuya Nakamura Atsufumi Hirohata Shinji Miwa The Institute for Solid State Physics The University of Tokyo Chiba 277-8581 Japan Department of Electronic Engineering University of York York YO10 5DD United Kingdom Graduate School of Engineering Science Osaka University Osaka 560-8531 Japan Japan Synchrotron Radiation Research Institute (JASRI) Hyogo 679-5198 Japan Department of Theoretical and Computational Molecular Science Institute for Molecular Science Aichi 444-8585 Japan National Institute for Materials Science (NIMS) Ibaraki 305-0047 Japan Trans-scale Quantum Science Institute The University of Tokyo Bunkyo Tokyo 113-0033 Japan

Perpendicular magnetic anisotropy at an interface between Fe and MgO with Co-phthalocyanine (CoPc) adsorption has been investigated. We find that the CoPc adsorption at an Fe/MgO interface increases the perpendicular magnetic anisotropy energy (PMA) while maintaining the voltage-controlled magnetic anisotropy effect. By performing x-ray magnetic circular dichroism spectroscopy measurements, we reveal that the CoPc adsorption induces hole accumulation at the Fe surface. The origin of the PMA enhancement is attributed to the increased orbital magnetic moment anisotropy of the Fe atoms.

关键词： Magnetic anisotropy Spintronics X-ray magnetic circular dichroism

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Dynamics of active liquid interfaces

arXiv

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arXiv 2022年

作者： Adkins, Raymond Kolvin, Itamar You, Zhihong Witthaus, Sven Marchetti, M. Cristina Dogic, Zvonimir Department of Physics University of California at Santa Barbara Santa BarbaraCA93106 United States Graduate program in Biomolecular Science and Engineering University of California at Santa Barbara Santa BarbaraCA93106 United States

Controlling interfaces of phase separating fluid mixtures is key to creating diverse functional soft materials. Traditionally, this is accomplished with surface-modifying chemical agents. Using experiment and theory, we study how mechanical activity shapes soft interfaces that separate an active and a passive fluid. Chaotic flows in the active fluid give rise to giant interfacial fluctuations and non-inertial propagating active waves. At high activities, stresses disrupt interface continuity and drive droplet generation, producing an emulsion-like active state comprised of finite-sized droplets. When in contact with a solid boundary, active interfaces exhibit non-equilibrium wetting transitions, wherein the fluid climbs the wall against gravity. These results demonstrate the promise of mechanically driven interfaces for creating a new class of soft active matter. © 2022, CC BY.

关键词： Emulsification

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Churn Prediction in Financial Risk Management using Deep Learning Techniques

Churn Prediction in Financial Risk Management using Deep Lea...

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication engineering (ICATIECE), International Conference on

作者： V Sunil Kumar Sowmyashree S Piyush Kumar Pareek Ashish Khanna Victor Hugo Costa de Albuquerque Deepak Gupta Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru Computer Science & Engineering East West Institute of Technology Bengaluru Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Cearά Fortaleza Brazil CSE dept Maharaja Agrasen Institute of Technology GGSIPU Department of Teleinformatics Engineering Federal University of Cearά Brazil

ISBN: (纸本)9781665493970

It is essential to develop an efficient Client Relationship Model in order to make use of the estimations about customer turnover (CRM). Finding and breaking down the facts surrounding the business information understanding is necessary for client produce prediction, which should be made possible effectively by altering the strategies used for business insight. The tools that are provided by company insight allow for the historical, current, and prospective perspectives of business activity to be anticipated and dissected. It is possible to deal with it in an efficient way by providing the information mining approaches that choose the data from the created informative index that is the most helpful. Fundamental leadership should be possible to achieve in a fruitful way if one has this information. The actions that consumers have taken in the recent past are going to be analyzed, and an accurate forecast is going to be formed about those customers who are likely going to get unhappy in the near future based on the results of that prediction. The problem is that deciding on a certain period to investigate the client's activities and also establishing on a specific day and age for each of the clients won't be appropriate6. This is due to the fact that the problem is caused by the fact that deciding on a certain period to investigate the client's activities will take too long. The challenge lies just in this aspect. How to decide on a certain amount of time to spend researching the activities of the customer. For instance, when planning a model, 100 customers are used, 70 of those customers are considered “active,” which indicates that they are still conducting business with the organisation, and the remaining 30 customers are considered “agitate,” which indicates that they have severed their ties with the organisation. The primary purpose of the work that has been suggested is to carry out exploratory research and management as a multicriteria basic leadership issue, a

关键词： Leadership Roads Companies Predictive models Media Planning Data mining

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PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems

arXiv

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arXiv 2023年

作者： Mao, Shunyuan Dong, Ruobing Lu, Lu Yi, Kwang Moo Wang, Sifan Perdikaris, Paris Department of Physics and Astronomy University of Victoria VictoriaBCV8P 5C2 Canada Department of Chemical and Biomolecular Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Computer Science University of British Columbia VancouverBCV6T 1Z4 Canada Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania PhiladelphiaPA19104 United States Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania PhiladelphiaPA19104 United States

We develop a tool, which we name Protoplanetary Disk Operator Network (PPDONet), that can predict the solution of disk-planet interactions in protoplanetary disks in real-time. We base our tool on Deep Operator Networks (DeepONets), a class of neural networks capable of learning non-linear operators to represent deterministic and stochastic differential equations. With PPDONet we map three scalar parameters in a disk-planet system - the Shakura & Sunyaev viscosity α, the disk aspect ratio h0, and the planet-star mass ratio q - to steady-state solutions of the disk surface density, radial velocity, and azimuthal velocity. We demonstrate the accuracy of the PPDONet solutions using a comprehensive set of tests. Our tool is able to predict the outcome of disk-planet interaction for one system in less than a second on a laptop. A public implementation of PPDONet is available at https://***/smao-astro/PPDONet. Copyright © 2023, The Authors. All rights reserved.

关键词： Open source software

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Funneling of Lattice Vibrations Through Topological Phathways in Mid-Infrared Metasurfaces

Funneling of Lattice Vibrations Through Topological Phathway...

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CLEO: science and Innovations, S and I 2022

作者： Guddala, S. Komissarenko, F. Kiriushechkina, S. Vakulenko, A. Li, M. Menon, V.M. Alù, A. Khanikaev, A.B. Department of Electrical Engineering City College of the City University of New York New YorkNY10031 United States Department of Physics City College of New York New YorkNY10031 United States Physics Program Graduate Center of the City University of New York New YorkNY10016 United States Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States

We demonstrate novel hybrid states of strongly-coupled photon and lattice vibrations in hexagonal boron nitride (hBN) integrated with mid-infrared metasurface and show their unconventional helical nature and resilient... 详细信息

ISBN: (纸本)9781557528209

关键词： Infrared devices

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Discovering an aid policy to minimize student evasion using offline reinforcement learning

arXiv

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arXiv 2021年

作者： De Lima, Leandro M. Krohling, Renato A. Graduate Program in Computer Science PPGI Federal University of Espirito Santo UFES Vitória Brazil Graduate Program in Computer Science PPGI Production Engineering Department LABCIN Federal University of Espirito Santo UFES Vitória Brazil

High dropout rates in tertiary education expose a lack of efficiency that causes frustration of expectations and financial waste. Predicting students at risk is not enough to avoid student dropout. Usually, an appropriate aid action must be discovered and applied in the proper time for each student. To tackle this sequential decision-making problem, we propose a decision support method to the selection of aid actions for students using offline reinforcement learning to support decision-makers effectively avoid student dropout. Additionally, a discretization of student's state space applying two different clustering methods is evaluated. Our experiments using logged data of real students shows, through off-policy evaluation, that the method should achieve roughly 1.0 to 1.5 times as much cumulative reward as the logged policy. So, it is feasible to help decision-makers apply appropriate aid actions and, possibly, reduce student dropout. © 2021, CC BY.

关键词： Students

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Special issue on genome wide association study

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Quantitative Biology 2021年第2期9卷 105-106页

作者： Lin Hou Qiongshi Lu Can Yang Hongyu Zhao Center for Statistical Science Department of Industrial EngineeringMOE Key Laboratory of BioinformaticsSchool of Life SciencesTsinghua UniversityBeijing 100084China Department of Biostatistics and Medical Informatics Department of StatisticsCenter for Demography of Health and AgingUniversity of Wisconsin-MadisonWl 53726USA Department of Mathematics the Hong Kong University of Science and TechnologyHong KongChina Department of Biostatistics Yale School of Public HealthNew HavenCT 06510USA Department of Genetics Yale UniversityNew HavenCT 06510USA Program of Computational Biology and Bioinformatics Yale UniversityNew HavenCT 06510USA

Since the first success of genome wide association study(GWAS)was reported in 2005 by Hoh and colleagues on the identification of a major gene for age related macular degeneration,many thousands of published studies have reported reproducible association signals between hundreds of thousands of genetic variants and thousands of traits and diseases in the past 16 *** is a growing number of biobanks with up to one million participants developed across the world to facilitate genetic studies of complex traits through ***,many consortium projects have been initiated to collect diverse types of data that have proved very valuable for integrated analysis of GWAS *** rich data present both great opportunities and challenges to scientists on data management,computation,analysis,integration,and *** special issue of Quantitative Biology collects nine papers that cover major progresses,challenges,and future directions for GWAS analysis.

关键词： hundreds GWAS initiated

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Microscopic picture of paraelectric perovskites from structural prototypes

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Physical Review Research 2022年第1期4卷 L012042-L012042页

作者： Michele Kotiuga Samed Halilov Boris Kozinsky Marco Fornari Nicola Marzari Giovanni Pizzi Theory and Simulation of Materials (THEOS) National Centre for Computational Design and Discovery of Novel Materials (MARVEL) École Polytechnique Fédérale de Lausanne CH-1015 Lausanne Switzerland Designed Material Technologies LLC P.O. Box 14548 Richmond Virginia 23221-9998 USA John A. Paulson School of Engineering and Applied Sciences Harvard University 29 Oxford Street Cambridge Massachusetts 02138 USA Robert Bosch LLC Research and Technology Center Cambridge Massachusetts 02139 USA Department of Physics and Science of Advanced Materials Program Central Michigan University Mt. Pleasant Michigan 48859 USA

We highlight with first-principles molecular dynamics the persistence of intrinsic 〈111〉 Ti off-centerings for BaTiO3 in its cubic paraelectric phase. Intriguingly, these are inconsistent with the Pm3¯m space group often used to atomistically model this phase using density-functional theory or similar methods. Therefore, we deploy a systematic symmetry analysis to construct representative structural models in the form of supercells that satisfy a desired point symmetry but are built from the combination of lower-symmetry primitive cells. We define as structural prototypes the smallest of these that are both energetically and dynamically stable. Remarkably, two 40-atom prototypes can be identified for paraelectric BaTiO3; these are also common to many other ABO3 perovskites. These prototypes can offer structural models of paraelectric phases that can be used for the computational engineering of functional materials. Last, we show that the emergence of B-cation off-centerings and the primitive-cell phonon instabilities is controlled by the equilibrium volume, in turn, dictated by the filler A cation.

关键词： Crystal symmetry Ferroelectricity Structural order parameter Paraelectrics

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Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks

arXiv

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Shen, Xuemin Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Artificial Intelligence-Generated Content (AIGC) refers to the use of AI to automate the information creation process while fulfilling the personalized requirements of users. However, due to the instability of AIGC models, e.g., the stochastic nature of diffusion models, the quality and accuracy of the generated content can vary significantly. In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources. Thus, a dynamic AIGC service provider (ASP) selection scheme is required to enable users to connect to the most suited ASP, improving the users’ satisfaction and quality of generated content. In this article, we first review the AIGC techniques and their applications in wireless networks. We then present the AIGC-as-a-service (AaaS) concept and discuss the challenges in deploying AaaS at the edge networks. Yet, it is essential to have performance metrics to evaluate the accuracy of AIGC services. Thus, we introduce several image-based perceived quality evaluation metrics. Then, we propose a general and effective model to illustrate the relationship between computational resources and user-perceived quality evaluation metrics. To achieve efficient AaaS and maximize the quality of generated content in wireless edge networks, we propose a deep reinforcement learning-enabled algorithm for the optimal ASP selection. Simulation results show that the proposed algorithm can provide a higher quality of generated content to users and achieve fewer crashed tasks by comparing with four benchmarks, i.e., overloading-avoidance, random, round-robin policies, and the upper-bound schemes. © 2023, CC BY.

关键词： Reinforcement learning

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