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Electronic and magnetic properties of the topological semimetal candidate NdSbTe

Physical Review B 2020年第23期101卷 235161-235161页

作者： Krishna Pandey Rabindra Basnet Aaron Wegner Gokul Acharya Md Rafique Un Nabi Jiangwei Liu Jian Wang Y. K. Takahashi Bo Da Jin Hu Materials Science and Engineering Program Institute for Nanoscience and Engineering University of Arkansas Fayetteville Arkansas 72701 USA Department of Physics University of Arkansas Fayetteville Arkansas 72701 USA Research Center for Functional Materials National Institute for Materials Science Tsukuba Ibaraki 305-0047 Japan Institute for Materials Research and Center for Spintronics Research Network Tohoku University Sendai 980–8577 Japan Research Center for Magnetic and Spintronic Materials National Institute for Materials Science Sengen 1-2-1 Tsukuba Ibaraki 305-0047 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba Ibaraki 305-0044 Japan

ZrSiS-type materials represent a large material family with unusual coexistence of topological nonsymmorphic Dirac fermions and nodal-line fermions. As a special group of ZrSiS family, LnSbTe (Ln=lanthaniderare−earth) compounds provide a unique opportunity to explore new quantum phases due to the intrinsic magnetism induced by Ln. Here we report the single-crystal growth and characterization of NdSbTe, a previously unexplored LnSbTe compound. NdSbTe has an antiferromagnetic ground state with field-driven metamagnetic transitions similar to other known LnSbTe, but exhibits distinct enhanced electronic correlations characterized by large a Sommerfeld coefficient of 115mJ/molK2, which is the highest among the known LnSbTe compounds. Furthermore, our transport studies have revealed the coupling with magnetism and signatures of Kondo localization. All these findings establish NdSbTe as a platform for observing phenomena arising from the interplay between magnetism, topology, and electron correlations.

关键词： Magnetic order Magnetotransport Topological phases of matter Magnetic systems Single crystal materials Strongly correlated systems Topological materials Magnetic techniques Specific heat measurements Transport techniques

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Gene set proximity analysis: expanding gene set enrichment analysis through learned geometric embeddings

arXiv

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arXiv 2022年

作者： Cousins, Henry Hall, Taryn Guo, Yinglong Tso, Luke Tzeng, Kathy Tzy-Hwa Cong, Le Altman, Russ Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Medical Scientist Training Program Stanford University School of Medicine StanfordCA United States Optum Labs UnitedHealth Group MinneapolisMN United States Department of Genetics Stanford University School of Medicine StanfordCA United States Department of Pathology Stanford University School of Medicine StanfordCA United States Department of Medicine Stanford University School of Medicine StanfordCA United States Department of Bioengineering Stanford University StanfordCA United States

Gene set analysis methods rely on knowledge-based representations of genetic interactions in the form of both gene set collections and protein-protein interaction (PPI) networks. Explicit representations of genetic interactions often fail to capture complex interdependencies among genes, limiting the analytic power of such methods. Here we propose an extension of gene set enrichment analysis to a latent feature space reflecting PPI network topology, called gene set proximity analysis (GSPA). Compared with existing methods, GSPA provides improved ability to identify disease-associated pathways in disease-matched gene expression datasets, while improving reproducibility of enrichment statistics for similar gene sets. GSPA is statistically straightforward, reducing to classical gene set enrichment through a single user-defined parameter. We apply our method to identify novel drug associations with SARS-CoV-2 viral entry. Finally, we validate our drug association predictions through retrospective clinical analysis of claims data from 8 million patients, supporting a role for gabapentin as a risk factor and metformin as a protective factor for COVID-19 hospitalization. © 2022, CC BY-NC-ND.

关键词： SARS

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Mahakala: a Python-based Modular Ray-tracing and Radiative Transfer Algorithm for Curved Space-times

arXiv

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arXiv 2023年

作者： Sharma, Aniket Medeiros, Lia Chan, Chi-Kwan Halevi, Goni Mullen, Patrick D. Stone, James M. Wong, George N. Indian Institute of Science Education and Research Mohali Sector 81 SAS Nagar PO Manauli Punjab Mohali140306 India School of Natural Sciences Institute for Advanced Study 1 Einstein Drive PrincetonNJ08540 United States Steward Observatory Department of Astronomy University of Arizona 933 N. Cherry Ave. TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Ave. TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 N. Santa Rita TucsonAZ85721 United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States CCS-2 Los Alamos National Laboratory Los AlamosNM United States Center for Theoretical Astrophysics Los Alamos National Laboratory Los AlamosNM United States Princeton Gravity Initiative Princeton University PrincetonNJ08544 United States

We introduce Mahakala, a Python-based, modular, radiative ray-tracing code for curved space-times. We employ Google’s JAX framework for accelerated automatic differentiation, which can efficiently compute Christoffel symbols directly from the metric, allowing the user to easily and quickly simulate photon trajectories through non-Kerr metrics. JAX also enables Mahakala to run in parallel on both CPUs and GPUs and achieve speeds comparable to C-based codes. Mahakala natively uses the Cartesian Kerr-Schild coordinate system, which avoids numerical issues caused by the "pole" of spherical coordinates. We demonstrate Mahakala’s capabilities by simulating the 1.3 mm wavelength images (the wavelength of Event Horizon Telescope observations) of general relativistic magnetohydrodynamic simulations of low-accretion rate supermassive black holes. The modular nature of Mahakala allows us to easily quantify the relative contribution of different regions of the flow to image features. We show that most of the emission seen in 1.3 mm images originates close to the black hole. We also quantify the relative contribution of the disk, forward jet, and counter jet to 1.3 mm images. Copyright © 2023, The Authors. All rights reserved.

关键词： Python

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HRnV-Calc: A software package for heart rate n-variability and heart rate variability analysis

arXiv

引用

arXiv 2021年

作者： Niu, Chenglin Guo, Dagang Hock Ong, Marcus Eng Koh, Zhi Xiong Wah Ho, Andrew Fu Lin, Zhiping Liu, Chengyu Clifford, Gari D. Liu, Nan Duke-NUS Medical School National University of Singapore Singapore Singapore Department of Emergency Medicine Singapore General Hospital Singapore Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Instrument Science and Engineering Southeast University Nanjing China Department of Biomedical Informatics Emory University AtlantaGA United States Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States SingHealth AI Health Program Singapore Health Services Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore

Objective: Heart rate variability (HRV) has been proven to be an important indicator of physiological status for numerous applications. Despite the progress and active developments made in HRV metric research over the last few decades, the representation of the heartbeat sequence upon which HRV is based has received relatively little attention. The recently introduced heart rate n-variability (HRnV) offers an alternative to R-to-R peak interval representations which complements conventional HRV analysis by considering HRV behavior on varying scales. Although HRnV has been shown to improve triage in pilot studies, there is currently no open and standard software to support future research of HRnV and its broader clinical applications. We aimed to develop an open, reliable, and easy to use software package implementing HRnV for further research and improvements of HRnV. This package has been designed to facilitate collaborative investigations between clinicians and researchers to study HRnV in various contexts and applications. Approach: We developed an open-source software, HRnV-Calc, based on the PhysioNet Cardiovascular Signal Toolbox (PCST), which features comprehensive graphical user interfaces (GUIs) for HRnV and HRV analysis. Main results: While preserving the core functionalities and performance of PCST, HRnV-Calc enables step-by-step manual inspection and configuration of HRV and HRnV analysis, so that results can be debugged, easily interpreted, and integrated to downstream applications. Significance: The open-source HRnV-Calc software, an accessible and standardized HRV and HRnV analysis platform, enhances the scope of HRV assessment and is designed to assist in future improvements and applications of HRnV and related research. © 2021, CC BY.

关键词： Heart

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Interpretable bias mitigation for textual data: Reducing gender bias in patient notes while maintaining classification performance

arXiv

引用

arXiv 2021年

作者： Minot, Joshua R. Cheney, Nicholas Maier, Marc Elbers, Danne C. Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab University of Vermont BurlingtonVT05401 United States Vermont Complex Systems Center University of Vermont BurlingtonVT05401 United States Department of Computer Science University of Vermont BurlingtonVT05401 United States Neurobotics Lab University of Vermont BurlingtonVT05401 United States MassMutual Data Science AmherstMA01002 United States VA Cooperative Studies Program VA Boston Healthcare System BostonMA02130 United States Department of Mathematics and Statistics University of Vermont BurlingtonVT05401 United States

Medical systems in general, and patient treatment decisions and outcomes in particular, are affected by bias based on gender and other demographic elements. As language models are increasingly applied to medicine, there is a growing interest in building algorithmic fairness into processes impacting patient care. Much of the work addressing this question has focused on biases encoded in language models—statistical estimates of the relationships between concepts derived from distant reading of corpora. Building on this work, we investigate how word choices made by healthcare practitioners and language models interact with regards to bias. We identify and remove gendered language from two clinical-note datasets and describe a new debiasing procedure using BERT-based gender classifiers. We show minimal degradation in health condition classification tasks for low- to medium-levels of bias removal via data augmentation. Finally, we compare the bias semantically encoded in the language models with the bias empirically observed in health records. This work outlines an interpretable approach for using data augmentation to identify and reduce the potential for bias in natural language processing pipelines. Copyright © 2021, The Authors. All rights reserved.

关键词： Natural language processing systems

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27573 A clinical decision tool to calculate probability of sentinel lymph node metastasis in cutaneous melanoma

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Journal of the American Academy of Dermatology 2021年第3期85卷 AB2-AB2页

作者： Raghav Tripathi Karen Larson Jeremy S. Bordeaux Wesley Y. Yu Case Western Reserve University School of Medicine Insight Data Science Program Boston MA Case Western Reserve University School of Medicine University Hospitals Cleveland Medical Center Oregon Health & Science University

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Structural Characteristics of Compounds With Multitarget Activity

引用

Future Drug Discovery 2021年第2期3卷

作者： Jürgen Bajorath 1 Department of Life Science Informatics & Data Science B-IT LIMES Program Unit Chemical Biology & Medicinal Chemistry Rheinische Friedrich-Wilhelms-Universität Friedrich-Hirzebruch-Allee 6 BonnD-53115 Germany

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54341 Study Design and Recruitment into the North American Post-authorization Safety Study (PASS) to Monitor Pregnancy and Infant Outcomes Following Dupilumab Exposure During Pregnancy

引用

Journal of the American Academy of Dermatology 2024年第3期91卷 AB87-AB87页

作者： Johnson, Diana, MS Xu, Ronghui, PhD Luo, Yunjun, MS Jones, Kenneth Lyons, MD Chambers, Christina, PhD, MPH University of California San Diego Lead Teaching Division of Biostatistics and Bioinformatics Director Research Program of Quantitative Methods in Public Health Herbert Wertheim School of Public Health Department of Mathematics Halicioglu Data Science Institute Department of Pediatric Department of Pediatrics University of California San Diego La Jolla CA USA Department of Pediatrics University of California San Diego La Jolla CA USA

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Topological data Analysis of Black Hole Images

arXiv

引用

arXiv 2022年

作者： Christian, Pierre Chan, Chi-Kwan Hsu, Anthony Özel, Feryal Psaltis, Dimitrios Natarajan, Iniyan Physics Department Fairfield University 1073 N Benson Rd FairfieldCT06824 United States Department of Astronomy University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Ave. TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 N. Santa Rita TucsonAZ85721 United States Department of Computer Science University of Arizona 1040 4th St TucsonAZ85721 United States Wits Centre for Astrophysics University of the Witwatersrand 1 Jan Smuts Avenue Braamfontein Johannesburg2050 South Africa South African Radio Astronomy Observatory Observatory Cape Town7925 South Africa

Features such as photon rings, jets, or hot spots can leave particular topological signatures in a black hole image. As such, topological data analysis can be used to characterize images resulting from high resolution observations (synthetic or real) of black holes in the electromagnetic sector. We demonstrate that persistent homology allows for this characterization to be made automatically by counting the number of connected components and one-dimensional holes. Further, persistent homology also allows for the distance between connected components or diameter of holes to be extracted from the image. In order to apply persistent homology on synthetic black hole images, we also introduce metronization, a new algorithm to prepare black hole images into a form that is suitable for topological analysis. Copyright © 2022, The Authors. All rights reserved.

关键词： Black holes

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An Evaluation of Deep Neural Network Performance on Limited Protein Phosphorylation Site Prediction data

引用

Procedia Computer science 2019年 157卷 25-30页

作者： Favorisen Rosyking Lumbanraja Bharuno Mahesworo Tjeng Wawan Cenggoro Arif Budiarto Bens Pardamean Department of Computer Science Faculty of Mathematics and Natural Science University of Lampung Jalan Prof. Dr. Sumantri Brojonegoro No.17 35145 Bandar Lampung Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

One of the common and important post-translational modification (PTM) types is phosphorylation. Protein phosphorylation is used to regulate various enzyme and receptor activations which include signal pathways. There have been many significant studies conducted to predict phosphorylation sites using various machine learning methods. Recently, several researchers claimed deep learning based methods as the best methods for phosphorylation sited prediction. However, the performance of these methods were backed up with the massive training data used in the researches. In this paper, we study the performance of simple deep neural network on the limited data generally used prior to deep learning employment. The result shows that a deep neural network can still achieve comparable performance in the limited data settings.

关键词： Phosphorylation Site Prediction Protein Phosphorylation Deep Learning Deep Neural Network

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