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arXiv 2022年

作者： Cotrufo, Michele Cordaro, Andrea Sounas, Dimitrios L. Polman, Albert Alù, Andrea Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Van der Waals-Zeeman Institute Institute of Physics University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Center for Nanophotonics AMOLF Science Park 104 Amsterdam1098 XG Netherlands Department of Electrical and Computer Engineering Wayne State University DetroitMI48202 United States Physics Program Graduate Center of the City University of New York New YorkNY10016 United States

Nonreciprocal devices – in which light is transmitted with different efficiencies along opposite directions – are key technologies for modern photonic applications, yet their compact and miniaturized implementation remains an open challenge. Among different avenues, nonlinearity-induced nonreciprocity has attracted significant attention due to the absence of external bias and integrability within conventional material platforms. So far, nonlinearity-induced nonreciprocity has been demonstrated only in guided platforms using high-Q resonators. Here, we demonstrate ultrathin optical metasurfaces with large nonreciprocal response for free-space radiation based on silicon third-order nonlinearities. Our metasurfaces combine an out-of-plane asymmetry – necessary to obtain nonreciprocity – with in-plane broken symmetry, which finely tunes the radiative linewidth of quasi-bound states in the continuum (q-BICs). Third-order nonlinearities naturally occurring in silicon, engaged by q-BICs, are shown to enable over 10 dB of nonreciprocal transmission while maintaining less than 3 dB in insertion loss. The demonstrated devices merge the field of nonreciprocity with ultrathin metasurface technologies, offering an exciting functionality for signal processing and routing, communications, and protection of high-power laser cavities. © 2022, CC BY.

关键词： High power lasers

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Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology

Research Square

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Research Square 2021年

作者： Cai, Tian Xie, Li Chen, Muge Liu, Yang He, Di Zhang, Shuo Mura, Cameron Bourne, Philip E. Xie, Lei Ph.D. Program in Computer Science The Graduate Center The City University of New York New York10016 United States Department of Computer Science Hunter College The City University of New York New York10065 United States Master Program in Computer Science Courant Institute of Mathematical Sciences New York University United States School of Data Science Department of Biomedical Engineering University of Virginia VA22903 United States Helen and Robert Appel Alzheimer’s Disease Research Institute Feil Family Brain & Mind Research Institute Weill Cornell Medicine Cornell University New York10021 United States

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods. Compared with AlphaFold2-based protein-ligand docking, Portal Learning significantly improved the performance by 79% in PR-AUC and 27% in ROC-AUC, respectively. The superior performance of Portal Learning allowed us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific inquiry. © 2021, CC BY.

关键词： Coronavirus

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Exploration of dark chemical genomics space via portal learning: Applied to targeting the undruggable genome and COVID-19 anti-infective polypharmacology

arXiv

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arXiv 2021年

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods, thereby allowing us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific *** Codes 68T07 © 2021, CC BY.

关键词： Genes

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Loss tangent fluctuations due to two-level systems in superconducting microwave resonators

arXiv

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arXiv 2024年

作者： Vallières, A. Russell, M.E. You, X. Garcia-Wetten, D.A. Goronzy, D.P. Walker, M.J. Bedzyk, M.J. Hersam, M.C. Romanenko, A. Lu, Y. Grassellino, A. Koch, J. McRae, C.R.H. Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Chemistry Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Center for Applied Physics and Superconducting Technologies Northwestern University EvanstonIL60208 United States Electrical Computing and Energy Engineering Department University of Colorado BoulderCO80309 United States Department of Physics University of Colorado BoulderCO80309 United States National Institute of Standards and Technology BoulderCO80305 United States

Superconducting microwave resonators are critical to quantum computing and sensing technologies. Additionally, they are common proxies for superconducting qubits when determining the effects of performance-limiting loss mechanisms such as from two-level systems (TLS). The extraction of these loss mechanisms is often performed by measuring the internal quality factor Qi as a function of power or temperature. In this work, we investigate large temporal fluctuations of Qi at low powers over periods of 12 to 16 hours (relative standard deviation σQi/Qi = 13%). These fluctuations are ubiquitous across multiple resonators, chips and cooldowns. We are able to attribute these fluctuations to variations in the TLS loss tangent due to two main indicators. First, measured fluctuations decrease as power and temperature increase. Second, for interleaved measurements, we observe correlations between low- and medium-power Qi fluctuations and an absence of correlations with high-power fluctuations. Agreement with the TLS loss tangent mean is obtained by performing measurements over a time span of a few hours. We hypothesize that, in addition to decoherence due to coupling to individual near-resonant TLS, superconducting qubits are affected by these observed TLS loss tangent fluctuations. © 2024, CC BY.

关键词： Qubits

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Combined electric and photocontrol of selective light reflection at an oblique helicoidal cholesteric liquid crystal doped with azoxybenzene derivative

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Physical Review E 2021年第4期104卷 044702-044702页

作者： Kamal Thapa Olena S. Iadlovska Hari Krishna Bisoyi Daniel A. Paterson John M. D. Storey Corrie T. Imrie Quan Li Sergij V. Shiyanovskii Oleg D. Lavrentovich Advanced Materials and Liquid Crystal Institute Kent State University Kent Ohio 44242 USA Department of Physics Kent State University Kent Ohio 44242 USA Department of Chemistry School of Natural and Computing Sciences University of Aberdeen AB24 3UE Scotland United Kingdom Materials Science Graduate Program Kent State University Kent Ohio 44242 USA

An oblique helicoidal cholesteric liquid crystal ChOH represents a unique optical material with a single-harmonic periodic modulation of the refractive index and a pitch that can be tuned by an electric or magnetic field in a broad range from submicrometers to micrometers. In this work, we demonstrate that the oblique helicoidal cholesteric doped with azoxybenzene molecules can be tuned by both the electric field and light irradiation. The tuning mechanism is explained by the kinetics of trans-cis photoisomerization of the azoxybenzene molecules. At a fixed voltage, UV irradiation causes a redshift of the reflection peak by more than 200 nm. The effect is caused by an increase of the bend elastic constant of ChOH under irradiation. The demonstrated principle has the potential for applications such as smart windows, sensors, tunable lasers, and filters.

关键词： Chirality

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Enriching and Characterizing T-Cell Repertoires from 3' Barcoded Single-Cell Whole Transcriptome Amplification Products

arXiv

引用

arXiv 2022年

作者： Jivanjee, Tasneem Ibrahim, Samira Nyquist, Sarah K. James Gatter, G. Bromley, Joshua D. Jaiswal, Swati Berger, Bonnie Behar, Samuel M. Christopher Love, J. Shalek, Alex K. Institute for Medical Engineering & Science Department of Chemistry Massachusetts Institute of Technology CambridgeMA United States Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology CambridgeMA United States Ragon Institute of MGH MIT and Harvard CambridgeMA United States Broad Institute of MIT and Harvard CambridgeMA United States Massachusetts Institute of Technology CambridgeMA United States Program in Computational and Systems Biology Massachusetts Institute of Technology CambridgeMA United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States Microbiology Graduate Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Microbiology and Physiological Systems University of Massachusetts Medical School WorcesterMA United States Department of Mathematics Massachusetts Institute of Technology CambridgeMA United States Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA United States

Antigen-specific T cells play an essential role in immunoregulation and many diseases such as cancer. Characterizing the T cell receptor (TCR) sequences that encode T cell specificity is critical for elucidating the antigenic determinants of immunological diseases and designing therapeutic remedies. However, methods of obtaining single-cell TCR sequencing data are labor and cost intensive, typically requiring both cell sorting and full length single-cell RNA-sequencing (scRNA-seq). New high-throughput 3' cell-barcoding scRNA-seq methods can simplify and scale this process;however, they do not routinely capture TCR sequences during library preparation and sequencing. While 5' cell-barcoding scRNA-seq methods can be used to examine TCR repertoire at single-cell resolution, doing so requires specialized reagents which cannot be applied to samples previously processed using 3' cell-barcoding methods. Here, we outline a method for sequencing TCRα and TCRβ transcripts from samples already processed using 3' cell-barcoding scRNA-seq platforms, ensuring TCR recovery at a single-cell resolution. In short, a fraction of the 3' barcoded whole transcriptome amplification (WTA) product typically used to generate a massively parallel 3' scRNA-seq library is enriched for TCR transcripts using biotinylated probes, and further amplified using the same universal primer sequence from WTA. Primer extension using TCR V-region primers and targeted PCR amplification using a second universal primer results in a 3' barcoded single-cell CDR3-enriched library that can be sequenced with custom sequencing primers. Coupled with 3' scRNA-seq of the same WTA, this method enables simultaneous analysis of single-cell transcriptomes and TCR sequences which can help interpret inherent heterogeneity among antigen-specific T cells and salient disease biology. The method presented here can also be adapted readily to enrich and sequence other transcripts of interest from both 3' and 5' barcoded scRNA-seq

关键词： RNA

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DTCMS: Dynamic traffic congestion management in Social Internet of Vehicles (SIoV)

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Internet of Things (Netherlands) 2021年 16卷

作者： Roopa, M.S. Ayesha Siddiq, S. Buyya, Rajkumar Venugopal, K.R. Iyengar, S.S. Patnaik, L.M. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru 560-001 India Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems University of Melbourne Melbourne 3053 VIC Australia Bangalore University Bengaluru 560-001 India Department of Computer Science and Engineering Florida International University Miami 33199 FL United States Consciousness Studies Program National Institute of Advanced Studies Indian Institute of Science Bengaluru 560-012 India

With the augmentation of traffic exponentially, we observe that traffic congestion does not guarantee road safety or enhance the driving experience. In the recent past, Social Internet of Vehicles (SIoV), a social network paradigm permits social relationships among every vehicle in the network or with any road infrastructure to render a radically useful environment. SIoV is beneficial for the drivers, in improving road safety, avoiding mishaps, and providing a friendly-driving experience. In this paper, we propose a traffic scheduling algorithm to gain the maximum throughput for the flow of vehicles at a road intersection with the formation of social relationships among the vehicles and with the Road Side Units (RSUs). The algorithm estimates the flow rate of vehicles for lanes at the intersections exploiting the volume of traffic moving through the given road. A condition matrix is designed for the consistent movement of traffic considering different routes on the road segments. Social relationships are devised on various aspects of travel needs for a safe, agile, and better driving experience. Simulation results illustrate the efficacy of the proposed scheme with high traffic throughput, service rate and reduce the total travelling time, delay time, and average waiting time in comparison with Dynamic Throughput Maximization Framework and Adaptive Traffic Control Algorithm. © 2020 Elsevier B.V.

关键词： Dynamic traffic control Social Internet of Vehicles (SIoV) Social relationships Traffic congestion management

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Characterization of State-Preparation Uncertainty in Quantum Key Distribution

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Physical Review Applied 2023年第1期19卷 014048-014048页

作者： Anqi Huang Akihiro Mizutani Hoi-Kwong Lo Vadim Makarov Kiyoshi Tamaki Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 People’s Republic of China Graduate School of Engineering Science Osaka University Toyonaka Osaka 560-8531 Japan Department of Electrical & Computer Engineering and Department of Physics Centre for Quantum Information and Quantum Control (CQIQC) University of Toronto Toronto Ontario M5S 3G4 Canada Department of Physics University of Hong Kong Pokfulam Hong Kong Quantum Bridge Technologies Inc. 100 College Street Toronto Ontario M5G 1L5 Canada Russian Quantum Center Skolkovo Moscow 121205 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai 201315 People’s Republic of China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow 119049 Russia Faculty of Engineering University of Toyama Gofuku 3190 Toyama 930-8555 Japan

To achieve secure quantum key distribution, all imperfections in the source unit must be incorporated in a security proof and measured in the lab. Here we perform a proof-of-principle demonstration of the experimental techniques for characterizing the source phase and intensity fluctuation in commercial quantum key distribution systems. When we apply the measured phase-fluctuation intervals to the security proof that takes into account fluctuations in the state preparation, it predicts a key distribution distance of over 100km of fiber. The measured intensity fluctuation intervals are, however, so large that the proof predicts zero key, indicating a source improvement may be needed. Our characterization methods pave the way for a future certification standard.

关键词： Fluctuations & noise Optical quantum information processing Quantum cryptography Quantum engineering Quantum state engineering

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Ig SF11 regulates osteoclast differentiation through association with the scaffold protein PSD-95

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Bone Research 2020年第1期8卷 97-106页

作者： Hyunsoo Kim Noriko Takegahara Matthew CWalsh Sarah AMiddleton Jiyeon Yu Jumpei Shirakawa Jun Ueda Yoshitaka Fujihara Masahito Ikawa Masaru Ishii Junhyong Kim Yongwon Choi Department of Pathology and Laboratory Medicine University of Pennsylvania Perelman School of MedicinePhiladelphiaPA 19104USA Department of Biology Department of Computer and Information ScienceSchool of Arts and SciencesProgram in Single Cell BiologyUniversity of PennsylvaniaPhiladelphiaPA 19104USA Research Institute for Microbial Diseases Osaka UniversitySuitaOsaka 565-0871Japan Department of Immunology and Cell Biology Graduate School of Medicine and Frontier BiosciencesOsaka UniversitySuitaOsaka 565-0871Japan

Osteoclasts are multinucleated, giant cells derived from myeloid progenitors. While receptor activator of NF-κB ligand(RANKL)stimulation is the primary driver of osteoclast differentiation, additional signaling further contributes to osteoclast ***, we demonstrate that immunoglobulin superfamily member 11(Ig SF11), whose expression increases during osteoclast differentiation, regulates osteoclast differentiation through interaction with postsynaptic density protein 95(PSD-95), a scaffold protein with multiple protein interaction domains. Ig SF11 deficiency in vivo results in impaired osteoclast differentiation and bone resorption but no observed defect in bone formation. Consequently, Ig SF11-deficient mice exhibit increased bone *** in vitro osteoclast culture systems, we show that Ig SF11 functions through homophilic interactions. Additionally, we demonstrate that impaired osteoclast differentiation in Ig SF11-deficient cells is rescued by full-length Ig SF11 and that the Ig SF11-PSD-95 interaction requires the 75 C-terminal amino acids of Ig SF11. Our findings reveal a critical role for Ig SF11 during osteoclast differentiation and suggest a role for Ig SF11 in a receptor-and signal transduction molecule-containing protein complex.

关键词： SF11 osteoclast impaired

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A simple student appraisal evaluation model based on multi-stage rule fuzzy logic

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AIP Conference Proceedings 2023年第1期2896卷

作者： Ditdit Nugeraha Utama Siti Sumiyati Irmawan Rahyadi Norhaslinda Zainal Abidin Norazura Ahmad Aida Mauziah Benjamin Wan Laailatul Hanim Binti Mat Desa Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Communication Department BINUS Graduate Program - Master of Strategic Marketing Communication Bina Nusantara University Jakarta Indonesia 11480 Institute of Strategic Industrial Decision Modelling School of Quantitative Sciences Universiti Utara Malaysia 06010 Sintok Kedah Malaysia Decision Science Department School of Quantitative Sciences Universiti Utara Malaysia 06010 Sintok Kedah Malaysia

An evaluating process that involves the multi parameters and rules is complex and complicated. Such complexity shows that the problem is a semi-unstructured problem. Thus, the problem faced is possibly solved by proposing a new intelligentdecision model to get the quantitative decision. Here, the experimental research was conducted by methodically operating the conception of multi-stage rule-based fuzzy logic. The experimental research is a Python programming language experiment and it is one implementation of the multi-stage rule-based fuzzy logic in solving the problem of a simple case for evaluating student performance. The evaluation model was designed thru an object-oriented approach and was conclusively able to show the result of100 dummy students’ performance values.

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