检索结果-内蒙古大学图书馆

The estimation of the effective reproductive number from disease outbreak data

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Cintrón-Arias, Ariel Castillo-Chávez, Carlos Bettencourt, Luís M.A. Lloyd, Alun L. Banks, H.T. Statistical and Applied Mathematical Sciences Institute 19 T. W. Alexander Drive P.O. Box 14006 Research Triangle ParkNC27709-4006 United States Department of Mathematics and Statistics Arizona State University P.O. Box 871804 TempeAZ85287 - 1804 Los Alamos National Laboratory Mail Stop B284 Los AlamosNM87545 United States Biomathematics Graduate Program Department of Mathematics North Carolina State University RaleighNC27695 United States Center for Research in Scientific Computation North Carolina State University P.O. Box 8205 RaleighNC27695

We consider a single outbreak susceptible-infected-recovered (SIR) model and corresponding estimation procedures for the effective reproductive number R(t). We discuss the estimation of the underlying SIR parameters with a generalized least squares (GLS) estimation technique. We do this in the context of appropriate statistical models for the measurement process. We use asymptotic statistical theories to derive the mean and variance of the limiting (Gaussian) sampling distribution and to perform post statistical analysis of the inverse problems. We illustrate the ideas and pitfalls (e.g., large condition numbers on the corresponding Fisher information matrix) with both synthetic and influenza incidence data sets. Copyright © 2020, The Authors. All rights reserved.

关键词： Parameter estimation

Comparing demographics of signatories to public letters on diversity in the mathematical sciences

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Topaz, Chad M. Cart, James Eaton, Carrie Diaz Shrout, Anelise Hanson Higdon, Jude A. İnce, Kenan Katz, Brian Lewis, Drew Libertini, Jessica Smith, Christian Michael Department of Mathematics and Statistics Williams College WilliamstownMA United States Institute for the Quantitative Study of Inclusion Diversity and Equity WilliamstownMA United States Office of Institutional Research Williams College WilliamstownMA United States Digital and Computational Studies Program Bates College LewistonME United States Department of Mathematics Westminster College Salt Lake CityUT United States Department of Mathematics and Statistics Smith College NorthamptonMA United States Department of Mathematics and Statistics University of South Alabama MobileAL United States Department of Applied Mathematics Virginia Military Institute LexingtonVA United States Department of Sociology University of Wisconsin-Madison MadisonWI United States

In its December 2019 edition, the Notices of the American Mathematical Society published an essay critical of the use of diversity statements in academic hiring. The publication of this essay prompted many responses, including three public letters circulated within the mathematical sciences community. Each letter was signed by hundreds of people and was published online, also by the American Mathematical Society. We report on a study of the signatories’ demographics, which we infer using a crowdsourcing approach. Letter A highlights diversity and social justice. The pool of signatories contains relatively more individuals inferred to be women and/or members of underrepresented ethnic groups. Moreover, this pool is diverse with respect to the levels of professional security and types of academic institutions represented. Letter B does not comment on diversity, but rather, asks for discussion and debate. This letter was signed by a strong majority of individuals inferred to be white men in professionally secure positions at highly research intensive universities. Letter C speaks out specifically against diversity statements, calling them "a mistake," and claiming that their usage during early stages of faculty hiring "diminishes mathematical achievement." Individuals who signed both Letters B and C, that is, signatories who both privilege debate and oppose diversity statements, are overwhelmingly inferred to be tenured white men at highly research intensive universities. Our empirical results are consistent with theories of power drawn from the social sciences. Copyright © 2019, The Authors. All rights reserved.

关键词： Population statistics

Impact of Prior Bendamustine Exposure on Bispecific Antibody Treatment Outcomes in Patients with Relapsed/Refractory Follicular Lymphoma

学校读者我要写书评

暂无评论

Blood 2024年 144卷 3009-3009页

作者： Gloria Iacoboni Emil Ramsø Kyvsgaard Victor Navarro Garces David A Russler-Germain Alejandro Martín García-Sancho Philip A. Thompson Dylan Therwhanger Itziar Carro David Quintela Pilar Gómez Evelyn Valencia Gala Vega Ana Jiménez Ubieto Mariana Bastos-Oreiro Angel Serna Simon Husby Beatriz De La Cruz Jing Luan Sergi Camarillas Lucia Medina Pau Abrisqueta Costa University Hospital Vall d'Hebron Barcelona Spain Rigshospitalet Department of Hematology Copenhagen Denmark Statistics Unit Vall d'Hebron Institute of Oncology (VHIO) Barcelona Spain Siteman Cancer Center Division of Oncology Washington University School of Medicine Saint Louis MO Hospital Universitario de Salamanca IBSAL CIBERONC Centro de Investigación del Cáncer-IBMCC (USAL-CSIC) Salamanca Spain Department of Clinical Haematology Royal Melbourne Hospital and Peter MacCallum Cancer Centre Melbourne Australia Lymphoma Program Abramson Cancer Center University of Pennsylvania Philadelphia PA Clinical Hematology Department Institut Català d'Oncologia-Hospitalet IDIBELL Barcelona Spain ICO-IJC-Hospital Germans Trias i Pujol Barcelona Spain Hospital La Paz Madrid Spain Department of Hematology Clínica Universidad de Navarra； Hematology and Oncology Program Center for Applied Medical Research (CIMA)； Instituto de Investigaciones Sanitarias de Navarra (IdiSNA) Pamplona Spain University Hospital Fundación Jiménez Díaz Madrid Spain Hematology Department Hospital Universitario 12 de Octubre Madrid Spain Hematology Department Hospital General Universitario Gregorio Marañón Madrid Spain Hematology Department Hospital Universitario La Paz Madrid Spain Department of Medicine Washington University School of Medicine St. Louis MO Hospital Universitario 12 de Octubre Madrid Spain

Introduction: Bispecific antibodies (BsAbs) are approved for patients with relapsed/refractory (R/R) follicular lymphoma (FL) based on single-arm clinical trials. These T-cell engaging agents achieve approximately 60% complete response rate (CRR), but information regarding real-world prognostic factors is scarce. In light of the deleterious impact of pre-treatment bendamustine (benda) exposure on chimeric antigen receptor (CAR) T-cell outcomes, and given the similar mechanism of action, we aimed to assess the impact that this alkylating agent has on BsAbs outcomes in R/R FL patients. Methods: We conducted a multicenter, international study including patients with R/R FL treated at 13 sites with CD20/CD3-targeted BsAbs from November 2017 until March 2024. We analyzed response rates and survival outcomes according to prior benda exposure in the full cohort and according to treatment partner (BsAb monotherapy vs combination). Then, we carried out an inverse probability of treatment weighting (IPTW) analysis in the monotherapy group to compare benda-exposed and naïve patients with matched key baseline characteristics. Finally, we examined the impact of benda dose and washout prior to BsAbs on efficacy outcomes. Results: We included 169 patients with R/R FL who received BsAb therapy, 73 (43%) of whom had been exposed to benda at a median of 42 months (range 1-141) prior to BsAb start; in 31 (42%) patients, it was the last line prior to BsAbs. Most patients (N=114, 67%), received the BsAb in monotherapy. Median follow-up for the full dataset was 21.8 months (95%CI 17.7-31.4). At time of BsAb start, median age was 64 years (IQR 55-72), 89 (53%) patients were male and most had advanced stage of disease (84%), high FLIPI score (54%) and 2 or more prior lines of therapy (77%, median 2 [range 1-9]); 49% were POD24 and 4% had received CAR T-cells. The benda-exposed cohort had more prior lines (>2, 58% vs 29% [p<0.01]) and worse performance status (ECOG >0, 46% vs 29%) in compar

关键词：

Ten quick tips for deep learning in biology

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

Reactive particle-tracking solutions to a benchmark problem on heavy metal cycling in lake sediments

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Schmidt, Michael J. Pankavich, Stephen D. Navarre-Sitchler, Alexis Engdah, Nicholas B. Bolster, Diogo Benson, David A. Department of Civil and Environmental Engineering and Earth Sciences University of Notre Dame Notre DameIN46556 United States Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO80401 United States Hydrologic Science and Engineering Program Department of Geology and Geological Engineering Colorado School of Mines GoldenCO80401 United States Department of Civil and Environmental Engineering Washington State University PullmanWA99164 United States

Geochemical systems are known to exhibit highly variable spatiotemporal behavior. This may be observed both in non-smooth concentration curves in space for a single sampling time and also in variability between samples taken from the same location at different times. However, most models that are designed to simulate these systems provide only single-solution smooth curves and fail to capture the noise and variability seen in the data. We apply a recently developed reactive particle-tracking method to a system that displays highly-complex geochemical behavior. When the method is made to most closely resemble a corresponding Eulerian method, in its unperturbed form, we see near-exact match between solutions of the two models. More importantly, we consider two approaches for perturbing the model and find that the spatially-perturbed condition is able to capture a greater degree of the variability present in the data. This method of perturbation is a task to which particle methods are uniquely suited and Eulerian models are not well-suited. Additionally, because of the nature of the algorithm, noisy spatial gradients can be highly resolved by a large number of mobile particles, and this incurs negligible computational cost, as compared to expensive chemistry calculations. Copyright © 2019, The Authors. All rights reserved.

关键词： Heavy metals

A Stochastic MPEC Approach for Grid Tariff Design with Demand Side Flexibility

学校读者我要写书评

暂无评论

TechRxiv

TechRxiv 2019年

作者： Askeland, Magnus Burandt, Thorsten Gabriel, Steven A. SINTEF Energy Research Department of Electric Power Engineering Norwegian University of Science and Technology Trondheim Norway Technical University of Berlin The Energy Transportation Environment Department German Institute for Economic Research DIW Berlin Berlin Germany Department of Industrial Economics and Technology Management Norwegian University of Science and Technology Trondheim Norway Department of Mechanical Engineering Applied Math & Statistics Scientific Computation Program University of Maryland College Park Maryland United States

As the end-users increasingly can provide flexibility to the power system, it is important to consider how this flexibility can be activated as a resource for the grid. Electricity network tariffs are one option that can be used to activate this flexibility. Therefore, by designing efficient grid tariffs, it might be possible to reduce the total costs in the power system by incentivizing a change in consumption patterns. This paper provides a methodology for optimal grid tariff design under decentralized decision-making and uncertainty in demand, power prices, and renewable generation. A bilevel model is formulated to adequately describe the interaction between the end-users and a distribution system operator. In addition, a centralized decision-making model is provided for benchmarking purposes. The bilevel model is reformulated as a mixed-integer linear problem solvable by branch-and-cut techniques. Results for a deterministic example and a stochastic case study are presented and discussed. © 2019, CC BY-SA.

关键词： Decision making

Classification of datasets with imputed missing values: does imputation quality matter?

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Shadbahr, Tolou Roberts, Michael Stanczuk, Jan Gilbey, Julian Teare, Philip Dittmer, Sören Thorpe, Matthew Torné, Ramon Viñas Sala, Evis Lió, Pietro Patel, Mishal Rudd, James H.F. Mirtti, Tuomas Rannikko, Antti Sakari Aston, John A.D. Tang, Jing Schönlieb, Carola-Bibiane Selby, Ian Breger, Anna Weir-McCall, Jonathan R. Gkrania-Klotsas, Effrossyni Korhonen, Anna Jefferson, Emily Langs, Georg Yang, Guang Prosch, Helmut Preller, Jacobus Stanczuk, Jan Babar, Judith Sánchez, Lorena Escudero Wassin, Marcel Holzer, Markus Walton, Nicholas Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki Finland Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Data Science & Artificial Intelligence AstraZeneca Cambridge United Kingdom Department of Mathematics University of Manchester Manchester United Kingdom Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Department of Radiology University of Cambridge Cambridge United Kingdom Clinical Pharmacology & Safety Sciences AstraZeneca Cambridge United Kingdom Department of Medicine University of Cambridge Cambridge United Kingdom Department of Pathology University of Helsinki Helsinki University Hospital Finland iCAN-Digital Precision Cancer Medicine Flagship Helsinki Finland Department of Urology University of Helsinki Helsinki University Hospital Helsinki Finland Department of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom ZeTeM University of Bremen Bremen Germany Faculty of Mathematics University of Vienna Austria Royal Papworth Hospital Cambridge Royal Papworth Hospital NHS Foundation Trust Cambridge United Kingdom Addenbrooke’s Hospital Cambridge University Hospitals NHS Trust Cambridge United Kingdom Language Technology Laboratory University of Cambridge Cambridge United Kingdom Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab Medical University of Vienna Vienna Austria National Heart and Lung Institute Imperial College London London United Kingdom Contextflow GmbH Vienna Austria Institute of Astronomy University of Cambridge Cambridge United Kingdom

Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete, imputed, samples. The focus of the machine learning researcher is then to optimise the downstream classification performance. In this study, we highlight that it is imperative to consider the quality of the imputation. We demonstrate how the commonly used measures for assessing quality are flawed and propose a new class of discrepancy scores which focus on how well the method recreates the overall distribution of the data. To conclude, we highlight the compromised interpretability of classifier models trained using poorly imputed data. All code and data used in this paper are also released publicly at [inserted upon publication]. © 2022, CC BY.

关键词： Classification (of information)

A Lagrangian Method for Reactive Transport with Solid/Aqueous Chemical Phase Interaction i

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Schmidt, Michael J. Pankavich, Stephen D. Navarre-Sitchler, Alexis Benson, David A. Hydrologic Science and Engineering Program Department of Geology and Geological Engineering Colorado School of Mines GoldenCO80401 United States Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO80401 United States

A significant drawback of Lagrangian (particle-tracking) reactive transport models has been their inability to properly simulate interactions between solid and liquid chemical phases, such as dissolution and precipitation reactions. This work addresses that problem by implementing a mass-transfer algorithm between mobile and immobile sets of particles that allows aqueous species of reactant that are undergoing transport to interact with stationary solid species. This mass-transfer algorithm is demonstrated to solve the diffusion equation for an arbitrarily small level of diffusion and thus does not introduce any spurious mixing. The algorithm can be combined with random walks to simulate the desired total level of diffusion in a reactive transport system. Copyright © 2018, The Authors. All rights reserved.

关键词： Diffusion

Low-rank solution methods for stochastic eigenvalue problems

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Elman, Howard C. Su, Tengfei Department of Computer Science Institute for Advanced Computer Studies University of Maryland College ParkMD20742 United States Applied Mathematics & Statistics Scientific Computation Program University of Maryland College ParkMD20742 United States

We study efficient solution methods for stochastic eigenvalue problems arising from discretization of self-adjoint partial differential equations with random data. With the stochastic Galerkin approach, the solutions are represented as generalized polynomial chaos expansions. A low-rank variant of the inverse subspace iteration algorithm is presented for computing one or several minimal eigenvalues and corresponding eigenvectors of parameter-dependent matrices. In the algorithm, the iterates are approximated by low-rank matrices, which leads to significant cost savings. The algorithm is tested on two benchmark problems, a stochastic diffusion problem with some poorly separated eigenvalues, and an operator derived from a discrete stochastic Stokes problem whose minimal eigenvalue is related to the inf-sup stability constant. Numerical experiments show that the low-rank algorithm produces accurate solutions compared to the Monte Carlo method, and it uses much less computational time than the original algorithm without low-rank approximation. Copyright © 2018, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions