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Thermalization of fluorescent protein exciton-polaritons at room temperature

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arXiv 2022年

作者： Satapathy, Sitakanta Liu, Bin Deshmukh, Prathmesh Molinaro, Paul M. Dirnberger, Florian Khatoniar, Mandeep Koder, Ronald L. Menon, Vinod M. Department of Physics Center for Discovery and Innovation The City College of New York 85 St. Nicholas Terrace New YorkNY10031 United States The PhD Program in Physics The Graduate Center of the City University of New York 365 5th Ave New YorkNY10016 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

Fluorescent proteins (FPs) have recently emerged as a serious contender for realizing ultra-low threshold room temperature exciton-polariton condensation and lasing. Our contribution investigates the thermalization of FP microcavity exciton-polaritons upon optical pumping under ambient conditions. We realize polariton cooling using a new FP molecule, called mScarlet, coupled strongly to the optical modes in a Fabry-Pérot cavity. Interestingly, at the threshold excitation energy (fluence) of ~9 nJ/pulse (15.6 mJ/cm2), we observe an effective temperature, Teff ~350 ± 35 K close to the lattice temperature indicative of strongly thermalized exciton-polaritons at equilibrium. This efficient thermalization results from the interplay of radiative pumping facilitated by the energetics of the lower polariton branch and the cavity Qfactor. Direct evidence for dramatic switching from an equilibrium state into a metastable state is observed for the organic cavity polariton device at room temperature via deviation from the Maxwell-Boltzmann statistics at k1 = 0 above the threshold. Thermalized polariton gases in organic systems at equilibrium hold substantial promise for designing room temperature polaritonic circuits, switches, and lattices for analog simulation. © 2022, CC BY-NC-SA.

关键词： Polariton

AN ALGEBRAIC CHARACTERIZATION OF k–COLORABILITY

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arXiv 2020年

作者： Flores, Ramón Kahrobaei, Delaram Koberda, Thomas Department of Geometry and Topology University of Seville Spain Department of Computer Science University of York UK New York University Tandon School of Engineering PhD Program in Computer Science CUNY Graduate Center Mathematics Department University of Virginia CharlottesvilleVA22904

We characterize k–colorability of a simplicial graph via the intrinsic algebraic structure of the associated right-angled Artin group. As a consequence, we show that a certain problem about the existence of homomorphisms from right-angled Artin groups to products of free groups is NP–complete. Copyright © 2020, The Authors. All rights reserved.

关键词： Algebra

Expanders and right-angled Artin groups

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arXiv 2020年

作者： Flores, Ramón Kahrobaei, Delaram Koberda, Thomas Department of Geometry and Topology University of Seville Spain Department of Computer Science University of York United Kingdom New York University Tandon School of Engineering PhD Program in Computer Science CUNY Graduate Center Department of Mathematics University of Virginia CharlottesvilleVA22904 United States

The purpose of this article is to give a characterization of families of expander graphs via right-angled Artin groups. We prove that a sequence of simplicial graphs {Γi}i∈ forms a family of expander graphs if and only if a certain natural mini-max invariant arising from the cup product in the cohomology rings of the groups {A(Γi)}i∈ agrees with the Cheeger constant of the sequence of graphs, thus allowing us to characterize expander graphs via cohomology. This result is proved in the more general framework of vector space expanders, a novel structure consisting of sequences of vector spaces equipped with vector-space-valued bilinear pairings which satisfy a certain mini-max condition. These objects can be considered to be analogues of expander graphs in the realm of linear algebra, with a dictionary being given by the cup product in cohomology, and in this context represent a different approach to expanders that those developed by Lubotzky-Zelmanov and Bourgain-Yehudayoff. Copyright © 2020, The Authors. All rights reserved.

关键词： Vector spaces

MiR34 contributes to spinal muscular atrophy and AAV9-mediated delivery of MiR34a ameliorates the motor deficits in SMA mice

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Molecular therapy. Nucleic acids 2023年 32卷 144-160页

作者： Tai-Heng Chen Shih-Hsin Chang Yu-Fu Wu Ya-Ping Yen Fang-Yu Hsu Yen-Chung Chen Yang Ming Ho-Chiang Hsu Yi-Ching Su Sheng-Tang Wong Jui-Hung Hung Shih-Hwa Chiou Yuh-Jyh Jong Jun-An Chen PhD Program in Translational Medicine Kaohsiung Medical University Kaohsiung 80708 and Academia Sinica Taipei 11529 Taiwan. Institute of Molecular Biology Academia Sinica Taipei 11529 Taiwan. Department of Pediatrics Kaohsiung Medical University Kaohsiung 80708 Taiwan. School of Post-Baccalaureate Medicine Kaohsiung Medical University Kaohsiung 80708 Taiwan. Neuroscience Program of Academia Sinica Academia Sinica Taipei 11529 Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu 30093 Taiwan. Institute of Pharmacology School of Medicine National Yang Ming Chiao Tung University Taipei 11221 Taiwan. Innovative Cellular Therapy Center Department of Medical Research Taipei Veterans General Hospital Taipei 11217 Taiwan. Department of Laboratory Medicine Kaohsiung Medical University Kaohsiung 80708 Taiwan. Translational Research Center of Neuromuscular Diseases Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung 80708 Taiwan. Graduate Institute of Clinical Medicine College of Medicine Kaohsiung Medical University Kaohsiung 80708 Taiwan. Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung 80708 Taiwan. Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu 30093 Taiwan.

Spinal muscular atrophy (SMA) is a neurodegenerative disease characterized by the selective loss of spinal motor neurons (MNs) and concomitant muscle weakness. Mutation of is known to cause SMA, and restoring SMN protein levels via antisense oligonucleotide treatment is effective for ameliorating symptoms. However, this approach is hindered by exorbitant costs, invasive procedures, and poor treatment responses of some patients. Here, we seek to circumvent these hurdles by identifying reliable biomarkers that could predict treatment efficacy. We uncovered that MiR34 exhibits consistent downregulation during SMA progression in both human and rodent contexts. Importantly, family-knockout mice display axon swelling and reduced neuromuscular junction (NMJ) endplates, recapitulating SMA pathology. Introducing MiR34a via scAAV9 improved the motor ability of SMNΔ7 mice, possibly by restoring NMJ endplate size. Finally, we observed a consistent decreasing trend in MiR34 family expression in the cerebrospinal fluid (CSF) of type I SMA patients during the loading phase of nusinersen treatment. Baseline CSF MiR34 levels before nusinersen injection proved predictive of patient motor skills 1 year later. Thus, we propose that MiR34 may serve as a biomarker of SMA since it is associated with the pathology and can help evaluate the therapeutic effects of nusinersen.

关键词： ASO MT: Non-coding RNAs MiR34 SMA biomarker microRNA nusinersen spinal muscular atrophy

Mixture-of-experts variational autoencoder for clustering and generating from similarity-based representations

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arXiv 2019年

作者： Kopf, Andreas Fortuin, Vincent Somnath, Vignesh Ram Claassen, Manfred Institute of Molecular Systems Biology Department of Biology ETH Zürich Switzerland Biomedical Informatics Group Department of Computer Science ETH Zürich Switzerland Switzerland Life Science Graduate School Zurich PhD Program Systems Biology Switzerland

Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering gained popularity due to the non-linearity of neural networks, which allows for flexibility in fitting the specific peculiarities of complex data. Here we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering model. The model can learn multi-modal distributions of high-dimensional data and use these to generate realistic data with high efficacy and efficiency. MoE-Sim-VAE is based on a Variational Autoencoder (VAE), where the decoder consists of a Mixture-of-Experts (MoE) architecture. This specific architecture allows for various modes of the data to be automatically learned by means of the experts. Additionally, we encourage the latent representation of our model to follow a Gaussian mixture distribution and to accurately represent the similarities between the data points. We assess the performance of our model on synthetic data, the MNIST benchmark data set, and a challenging real-world task of defining cell subpopulations from mass cytometry (CyTOF) measurements on hundreds of different datasets. MoE-Sim-VAE exhibits superior clustering performance on all these tasks in comparison to the baselines and we show that the MoE architecture in the decoder reduces the computational cost of sampling specific data modes with high fidelity. Copyright © 2019, The Authors. All rights reserved.

关键词： Clustering algorithms

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Correction to: CpG‑creating mutations are costly in many human viruses

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Evolutionary Ecology 2020年第3期34卷 361-362页

作者： Caudill, Victoria R. Qin, Sarina Winstead, Ryan Kaur, Jasmeen Tisthammer, Kaho Pineda, E. Geo Solis, Caroline Cobey, Sarah Bedford, Trevor Carja, Oana Eggo, Rosalind M. Koelle, Katia Lythgoe, Katrina Regoes, Roland Roy, Scott Allen, Nicole Aviles, Milo Baker, Brittany A. Bauer, William Bermudez, Shannel Carlson, Corey Castellanos, Edgar Catalan, Francisca L. Chemel, Angeline Katia Elliot, Jacob Evans, Dwayne Fiutek, Natalie Fryer, Emily Goodfellow, Samuel Melvin Hecht, Mordecai Hopp, Kellen Hopson, E. Deshawn Jaberi, Amirhossein Kim, Jennifer Kinney, Christen Lao, Derek Le, Adrienne Lo, Jacky Lopez, Alejandro G. López, Andrea Lorenzo, Fernando G. Luu, Gordon T. Mahoney, Andrew R. Melton, Rebecca L. Nascimento, Gabriela Do Pradhananga, Anjani Rodrigues, Nicole S. Shieh, Annie Sims, Jasmine Singh, Rima Sulaeman, Hasan Thu, Ricky Tran, Krystal Tran, Livia Winters, Elizabeth J. Wong, Albert Pennings, Pleuni S. Department of Biology San Francisco State University San Francisco USA Department of Biology University of Oregon Eugene USA Quantitative Systems Biology Univeristy of California Merced USA Department of Ecology and Evolution University of Chicago Chicago USA Vaccine and Infectious Disease Division Fred Hutchinson Cancer Research Center Seattle USA Department of Computational Biology School of Computer Science Carnegie Mellon University Pittsburgh USA Department of Infectious Disease Epidemiology London School of Hygiene & Tropical Medicine London UK Department of Biology Emory University Atlanta USA Big Data Institute University of Oxford Oxford UK Department of Environmental Systems Science ETH Zurich Zurich Switzerland Department of Neurological Surgery University of California San Francisco USA Department of Organismic and Evolutionary Biology Harvard University Cambridge USA Department of Plant Biology Carnegie Institution for Science Stanford USA Health Sciences Center University of New Mexico Albuquerque USA UCSD Biomed Sciences PhD Program University of California San Diego USA Biochemistry Molecular Cellular and Developmental Biology Graduate Group University of California Davis USA UCSF Tetrad Graduate Program University of California San Francisco USA

In the original article, the co-author name "Jennifer Kim" has been inadvertently missed during the publication process. The complete author group is given in this correction.

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Some applications of arithmetic groups in cryptography

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arXiv 2018年

作者： Kahrobaei, Delaram Mallahi-Karai, Keivan University of York Department of Computer Science United Kingdom City University of New York CUNY Graduate Center PhD Program in Computer Science New York United States New York University Computer Science and Engineering Department Department of mathematics and logistic Bremen28759 Germany

In this paper we will offer a new symmetric-key cryptographic scheme which is based on the existence of exponentially distorted subgroups in arithmetic groups. Aside from this, we will also provide new examples of distorted subgroups in SLn(Z[x]) which can be utilized for the same purpose. Copyright © 2018, The Authors. All rights reserved.

关键词： Cryptography

Review of the use of mangrove forests in supporting the socio-economic life of fishing communities

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IOP Conference Series: Earth and Environmental science 2020年第1期575卷

作者： Untari R Darma P Betaubun A A Arief PhD. Student on Agricultural System Graduate School Hasanuddin University South Sulawesi Indonesia Department of Agribusiness Faculty of Agriculture Universitas Musamus Papua Indonesia Agribusiness Study Program Faculty of Agriculture Hasanuddin University South Sulawesi Indonesia Department of civil Engineering Faculty of Engineering Universitas Musamus Papua Indonesia Socio Economic of Fisheries Study Program Faculty of Marine Science and Fisheries Hasanuddin University South Sulawesi Indonesia

Mangrove forests have an important function in human life that cannot be replaced by other functions. The existence of mangrove forests has six main functions, namely biological, ecological, physical, social, economic and chemical functions. This article discusses the description of mangrove forest management in meeting the needs of the economic and social functions of the fishing communities on the coast of Merauke Regency, Papua Province, which were reviewed descriptively using relevant literature studies. In terms of economic function, mangrove forests provide economic value for fishing communities because mangrove forests become habitat for crabs, fish and other marine biota that are captured and sold as the main source of family income. In addition, the use of mangrove forests by fishermen on the coast is used as firewood and building materials. The high utilization of mangrove forests by coastal communities that ignores ecological sustainability causes damage to mangrove forests that threatens the sustainability of ecological functions. Damage to mangrove forests in Merauke in the period 2000-2005 was recorded as wide as 2,416 ha while in the 2005-2010 period damage reached 2,233 ha of the total area of the Merauke mangrove forest which reached 216,001.95 Ha or 6.98% of the Merauke forest area. Based on this, the management of mangrove forest utilization needs to get more attention from various parties so that the ecological function of mangrove forests as a condition of sustainable development, especially for fishing communities on the coast, can be realized in Merauke Regency.

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