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Planar median graphs and cubesquare-graphs

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arXiv 2021年

作者： Seemann, Carsten R. Moulton, Vincent Stadler, Peter F. Hellmuth, Marc Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany School of Computing Sciences University of East Anglia NorwichNR4 7TJ United Kingdom Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Germany Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine Leipzig University Härtelstraße 16-18 LeipzigD-04107 Germany Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Facultad de Ciencias Universidad Nacional de Colombia Sede Bogotá Colombia The Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States Department of Mathematics Faculty of Science Stockholm University StockholmSE - 106 91 Sweden

Median graphs are connected graphs in which for all three vertices there is a unique vertex that belongs to shortest paths between each pair of these three vertices. In this paper we provide several novel characterizations of planar median graphs. More specifically, we characterize when a planar graph G is a median graph in terms of forbidden subgraphs and the structure of isometric cycles in G, and also in terms of subgraphs of G that are contained inside and outside of 4-cycles with respect to an arbitrary planar embedding of G. These results lead us to a new characterization of planar median graphs in terms of cubesquare-graphs that is, graphs that can be obtained by starting with cubes and square graphs, and iteratively replacing 4-cycle boundaries (relative to some embedding) by cubes or square-graphs. As a corollary we also show that a graph is planar median if and only if it can be obtained from cubes and square-graphs by a sequence of "square-boundary" amalgamations. These considerations also lead to an O(n log n)-time recognition algorithm to compute a decomposition of a planar median graph with n vertices into cubes and square-graphs. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos

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arXiv 2024年

作者： Turishcheva, Polina Fahey, Paul G. Vystrčilová, Michaela Hansel, Laura Froebe, Rachel Ponder, Kayla Qiu, Yongrong Willeke, Konstantin F. Bashiri, Mohammad Baikulov, Ruslan Zhu, Yu Ma, Lei Yu, Shan Huang, Tiejun Li, Bryan M. De Wulf, Wolf Kudryashova, Nina Hennig, Matthias H. Rochefort, Nathalie L. Onken, Arno Wang, Eric Ding, Zhiwei Tolias, Andreas S. Sinz, Fabian H. Ecker, Alexander S. Institute of Computer Science University of Göttingen Campus Institute Data Science Germany Department of Neuroscience Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine StanfordCA United States Stanford Bio-X Stanford University StanfordCA United States Wu Tsai Neurosciences Institute Stanford University StanfordCA United States International Max Planck Research School for Intelligent Systems Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany lRomul Russia Institute of Automation Chinese Academy of Sciences China Beijing Academy of Artificial Intelligence China The Alan Turing Institute United Kingdom School of Informatics University of Edinburgh United Kingdom Centre for Discovery Brain Sciences University of Edinburgh United Kingdom Simons Initiative for the Developing Brain University of Edinburgh United Kingdom Department of Electrical Engineering Stanford University StanfordCA United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Machine learning has benefited tremendously from benchmarks that compare different model on the same task under standardized conditions. However, there was no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we established the SENSORIUM 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice. This dataset includes responses from 78,853 neurons to 2 hours of dynamic stimuli per neuron, together with the behavioral measurements such as running speed, pupil dilation, and eye movements. The competition ranked models in two tracks based on predictive performance for neuronal responses on a held-out test set: one focusing on predicting in-domain natural stimuli and another on out-of-distribution (OOD) stimuli to assess model generalization. As part of the NeurIPS 2023 competition track, we received more than 160 model submissions from 22 teams. Several new architectures for predictive models were proposed, and the winning teams improved the previous state-of-the-art model by 50%. Access to the dataset as well as the benchmarking infrastructure will remain online at ***. © 2024, CC BY-NC-ND.

关键词： Neurons

#1. Acid ceramidase (ASAH1) is a key mediator of drug resistance in relapsed/refractory multiple myeloma

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Journal of Bone Oncology 2024年 45卷 100539-100539页

作者： Ryan T. Bishop Tao Li Raghunandan R. Alugubelli Mostafa Nasr Karl J. Nyman Praneeth Sudalagunta Mark Meads Jeremy Frieling Niveditha Nerlakanti Marilena Tauro Oliver Hampton Bin Fang Steven Grant John Koomen Ariosto Siqueira Silva Kenneth H. Shain Conor C. Lynch Tumor Biology Dept. H. Lee Moffitt Cancer Center and Research Institute Tampa FL USA Collaborative Data Services Core H. Lee Moffitt Cancer Center & Research Institute Tampa FL 33612 USA Cancer Biology Ph.D. Program University of South Florida Tampa FL USA Department of Metabolism and Physiology H. Lee Moffitt Cancer Center and Research Institute Tampa Florida Bioinformatics and Biostatistics Department M2GEN Tampa FL USA Chemical Biology & Molecular Medicine H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA Virginia Commonwealth University (VCU) Massey Cancer Center Richmond VA 23298 USA Department of Molecular Oncology H. Lee Moffitt Cancer Center & Research Institute Tampa FL 33612 USA Department of Malignant Hematology H. Lee Moffitt Cancer Center & Research Institute Tampa FL 33612 USA

Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies

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arXiv 2023年

作者： Huang, Yu-Ning Love, Michael I. Ronkowski, Cynthia Flaire Deshpande, Dhrithi Schriml, Lynn M. Wong-Beringer, Annie Mons, Barend Corbett-Detig, Russell Hunter, Christopher I. Moore, Jason H. Garmire, Lana X. Reddy, T.B.K. Hide, Winston A. Butte, Atul J. Robinson, Mark D. Mangul, Serghei Department of Clinical Pharmacy Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences University of Southern California Los AngelesCA90089 United States Department of Genetics University of North Carolina at Chapel Hill Chapel HillNC27514 United States Department of Biostatistics University of North Carolina at Chapel Hill Chapel HillNC27514 United States Department of Epidemiology and Public Health Institute for Genome Sciences University of Maryland School of Medicine BaltimoreMD21201 United States Leiden University Medical Center Leiden Academic Centre for Drug Research GO FAIR foundation Leiden Netherlands Department of Biomolecular Engineering University of California Santa Cruz Santa CruzCA95064 United States Genomics Institute University of California Santa Cruz Santa CruzCA95064 United States GigaDB Director GigaScience Press Hong Kong Department of Computational Biomedicine Cedars-Sinai Medical Center Los AngelesCA90069 United States Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann ArborMI48105 United States DOE Joint Genome Institute Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Pathology Beth Israel Deaconess Medical Center Harvard Medical School 330 Brookline Avenue BostonMA02215-5491 United States Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA94143 United States Center for Data-Driven Insights and Innovation University of California Office of the President OaklandCA94607 United States SIB Swiss Institute of Bioinformatics Department of Molecular Life Sciences University of Zurich Zurich8057 Switzerland Department of Clinical Pharmacy Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences University of Southern California Los AngelesCA90033 United States Department of Quantitative and Computational Biology USC Dornsife College of Letters Arts and Sciences Univer

Metadata, often termed "data about data," is crucial for organizing, understanding, and managing vast omics datasets. It aids in efficient data discovery, integration, and interpretation, enabling users to access, comprehend, and utilize data effectively. Its significance spans the domains of scientific research, facilitating data reproducibility, reusability, and secondary analysis. However, numerous perceptual and technical barriers hinder the sharing of metadata among researchers. These barriers compromise the reliability of research results and hinder integrative meta-analyses of omics studies. This study highlights the key barriers to metadata sharing, including the lack of uniform standards, privacy and legal concerns, limitations in study design, limited incentives, inadequate infrastructure, and the dearth of well-trained personnel for metadata management and reuse. Proposed solutions include emphasizing the promotion of standardization, educational efforts, the role of journals and funding agencies, incentives and rewards, and the improvement of infrastructure. More accurate, reliable, and impactful research outcomes are achievable if the scientific community addresses these barriers, facilitating more accurate, reliable, and impactful research outcomes. © 2023, CC BY-NC-SA.

关键词： Metadata

STATISTICAL DESIGN AND RATIONALE OF THE BIOMARKERS FOR EVALUATING SPINE TREATMENTS (BEST) TRIAL

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arXiv 2025年

作者： Sperger, John Kidwell, Kelley M. Mauck, Matthew C. Zhao, Beibo Anstrom, Kevin J. Batorsky, Anna Carey, Timothy S. Clauw, Daniel J. Freeman, Nikki L.B. Greco, Carol M. Ivanova, Anastasia Berkeley, Sara Jones McLean, Samuel A. Psioda, Matthew A. Rowland, Bryce Sowa, Gwendolyn A. Wasan, Ajay D. Zitovsky, Joshua P. Kosorok, Michael R. Department of Biostatistics Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Biostatistics School of Public Health University of Michigan United States Department of Anesthesiology University of North Carolina Chapel Hill United States Collaborative Studies Coordinating Center Department of Biostatistics Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Medicine University of North Carolina Chapel Hill United States Department of Anesthesiology Chronic Pain & Fatigue Research Center University of Michigan Ann Arbor United States Department of Biostatistics and Bioinformatics Duke University United States Department of Psychiatry University of Pittsburgh School of Medicine United States Department of Epidemiology Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Psychiatry University of North Carolina Chapel Hill United States Statistics and Data Science Innovation Hub Biostatistics GSK Department of Physical Medicine and Rehabilitation University of Pittsburgh School of Medicine United States Departments of Anesthesiology and Perioperative Medicine and Psychiatry University of Pittsburgh School of Medicine United States Amazon Postdoctoral Scientist United States Ferguson Laboratory for Orthopaedic and Spine Research Bethel Musculoskeletal Research Center Department of Orthopaedic Surgery University of Pittsburgh School of Medicine United States Department of Bioengineering University of Pittsburgh Swanson School of Engineering United States Clinical and Translational Science Institute University of Pittsburgh United States Carolina Pragmatic Trials Laboratory North Carolina Translational and Clinical Sciences Institute UNC School of Medicine United States Duke Clinical Research Institute United States

Background/Aims: Chronic low back pain (cLBP) is a widely prevalent condition with potentially severe impacts on day-to-day functioning and quality of life. A variety of evidence-based treatment modalities exist, but all approaches have modest average treatment effects—potentially due to individual variation in treatment response and the diverse etiologies of cLBP. The Biomarkers for Evaluating Spine Treatments (BEST) trial was designed to investigate the potential for using patient features including biomarkers and phenotypic measurements to inform cLBP treatment. Methods: We provide an overview of the BEST trial and detail the statistical rationale for key study design decisions. We discuss the salient features of the scientific and clinical context of cLBP treatment that motivated the study design and planned analysis, describe modifications to existing methods that were needed because of the setting, and present simulation studies that were used to inform the study design. Results: The BEST trial was designed as a multi-site sequential, multiple-assignment randomized trial (SMART) of four cLBP treatment modalities. The primary objective was to inform a precision medicine approach to the treatment of cLBP by estimating the optimal treatment or combination of treatments based on patient features and individual patient’s response to the initial treatment. The primary analysis includes estimating an interpretable dynamic treatment regime for recommending treatments based on patient features and investigating the subgroups that respond best to each one of the treatment modalities. The trial aimed to enroll 630 protocol completers. Broad eligibility criteria were used to improve the generalizability of study results. Notably, participants could be excluded from one of the four interventions and still be eligible for the study. We modified the minimization method for reducing covariate imbalance to allow for these exclusions. Conclusion: The BEST trial may pave the way

关键词： Patient treatment

A Phase III, Randomized Study of Daratumumab, Cyclophosphamide, Bortezomib and Dexamethasone (DARA-VCD) Induction Followed By Autologous Stem Cell Transplant or Dara-VCD Consolidation and Daratumumab Maintenance in Patients with Newly Diagnosed AL Amyloidosis

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Blood 2024年 144卷 3309.1-3309.1页

作者： Patrick Hagen Surbhi Sidana Terri L. Parker Brian A. Walker Vaishali Sanchorawala Jeffrey A. Zonder Taxiarchis Kourelis Anita D'Souza Heather J. Landau Adam Rosenthal Antje Hoering Sikander Ailawadhi Robert Z. Orlowski Division of Hematology and Oncology Loyola University Health System Maywood IL Division of Blood and Marrow Transplantation and Cellular Therapy Stanford University School of Medicine Stanford CA Department of Internal Medicine Section of Hematology Yale University School of Medicine and Yale Cancer Center New Haven CT Center for Computational Biology and Bioinformatics Indiana University School of Medicine Indianapolis IN Amyloidosis Center Boston University Chobanian and Avedisian School of Medicine and Boston Medical Center Boston MA Karmanos Cancer Institute Detroit MI Division of Hematology Mayo Clinic Rochester MN CIBMTR (Center for International Blood and Marrow Transplant Research) Department of Medicine Medical College of Wisconsin Milwaukee WI Center for International Blood and Marrow Transplant Research Department of Medicine Medical College of Wisconsin Milwaukee WI Adult Bone Marrow Transplant Service Department of Medicine Memorial Sloan Kettering Cancer Center New York NY SWOG Statistical and Data Management Center Seattle WA Cancer Research And Biostatistics Seattle WA Division of Hematology-Oncology Mayo Clinic-Florida Jacksonville FL Department of Lymphoma & Myeloma The University of Texas MD Anderson Cancer Center Houston TX

Background and significance: Light chain (AL) amyloidosis is a plasma cell malignancy characterized by the production of amyloidgenic clonal light chains that deposit in various tissues. The addition of daratumumab to bortezomib, cyclophosphamide and dexamethasone (Dara-VCD) has improved the depth of hematologic and organ responses and is an approved induction therapy in newly diagnosed AL amyloid patients (DOI: 10.1056/NEJMoa2028631). However, this approach has not yet demonstrated an overall survival (OS) benefit and follow up remains short. With strict adherence to eligibility criteria, in particular severity of cardiac involvement, autologous stem cell transplantation (ASCT) as consolidative therapy is safe with transplant related mortality on par with multiple myeloma. This approach generates deep and sustained responses, but it is unclear if it offers an additional survival benefit following Dara-VCD induction. The only randomized trial evaluating the role of ASCT in AL amyloid is outdated (DOI: 10.1056/NEJMoa070484) and no randomized data exist to inform the optimal use of ASCT in the era of Dara-VCD induction. Furthermore, multi-organ involvement in AL amyloid drives morbidity and mortality thus the impact of any consolidative therapy on cardiac and renal involvement in addition to overall survival but must be defined. Finally, the approval of Dara-VCD therapy was based on complete hematologic response rates but there is increasing data demonstrating that achieving minimal residual disease (MRD) negativity by either bone marrow next generation flow cytometry (DOI: 10.1002/ajh.26562) or peripheral blood mass spectrometry (DOI: 10.1038/s41408-020-0291-8) generates improved organ response, hematologic progression free survival, and possibly OS. Methods: This randomized phase 3 SWOG led intergroup trial (S2213; NCT06022939) is being conducted within the United States and will accrue a total of 143 participants per arm. The primary endpoint will compare MOD-PFS (

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Quantile regression on inactivity time

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arXiv 2020年

作者： Balmert, Lauren C. Li, Ruosha Peng, Limin Jeong, Jong-Hyeon Feinberg School of Medicine Northwestern University Chicago United States Department of Biostatistics and Data Science University of Texas Health Science Center Huston United States Department of Biostatistics and Bioinformatics Rollins School of Public Health Emory University Atlanta United States Department of Biostatistics Graduate School of Public Health University of Pittsburgh Pittsburgh United States

The inactivity time, or lost lifespan specifically for mortality data, concerns time from occurrence of an event of interest to the current time point and has recently emerged as a new summary measure for cumulative information inherent in time-to-event data. This summary measure provides several benefits over the traditional methods, including more straightforward interpretation yet less sensitivity to heavy censoring. However, there exists no systematic modeling approach to inferring the quantile inactivity time in the literature. In this paper, we propose a regression method for the quantiles of the inactivity time distribution under right censoring. The consistency and asymptotic normality of the regression parameters are established. To avoid estimation of the probability density function of the inactivity time distribution under censoring, we propose a computationally efficient method for estimating the variance-covariance matrix of the regression coefficient estimates. Simulation results are presented to validate the finite sample properties of the proposed estimators and test statistics. The proposed method is illustrated with a real dataset from a clinical trial on breast cancer. Copyright © 2020, The Authors. All rights reserved.

关键词： Statistical tests

Maximum pairwise-rank-likelihood-based inference for the semiparametric transformation model

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arXiv 2021年

作者： Yu, Tao Li, Pengfei Chen, Baojiang Yuan, Ao Qin, Jing Department of Statistics & Applied Probability National University of Singapore Singapore117546 Singapore Department of Statistics and Actuarial Science University of Waterloo WaterlooONN2L 3G1 Canada Department of Biostatistics and Data Science University of Texas Health Science Center at Houston School of Public Health in Austin AustinTX78701 United States Department of Biostatistics Bioinformatics and Biomathematics Georgetown University WashingtonDC20057 United States National Institute of Allergy and Infectious Diseases National Institutes of Health MD20892 United States

MSC Codes 62J02, 62J05, 62J86In this paper, we study the linear transformation model in the most general setup. This model includes many important and popular models in statistics and econometrics as special cases. Although it has been studied for many years, the methods in the literature are based on kernel-smoothing techniques or make use of only the ranks of the responses in the estimation of the parametric components. The former approach needs a tuning parameter, which is not easily optimally specified in practice;and the latter is computationally expensive and may not make full use of the information in the data. In this paper, we propose two methods: a pairwise rank likelihood method and a score-function-based method based on this pairwise rank likelihood. We also explore the theoretical properties of the proposed estimators. Via extensive numerical studies, we demonstrate that our methods are appealing in that the estimators are not only robust to the distribution of the random errors but also lead to mean square errors that are in many cases comparable to or smaller than those of existing methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Linear transformations

Direct mapping of Peptide-to-Spectra-Matches to genome information facilitates qualifying proteomics information

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Research Square

Research Square 2021年

作者： Anders, John Petruschke, Hannes Jehmlich, Nico Haange, Sven-Bastiaan von Bergen, Martin Stadler, Peter F. Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16–18 LeipzigD-04107 Germany Institute of Biochemistry Faculty of Life Sciences University of Leipzig Talstraße 33 LeipzigD-04103 Germany Department of Molecular Systems Biology Helmholtz Centre for Environmental Research - UFZ Permoserstrasse 15 LeipzigD-04318 Germany Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases University Leipzig LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Department of Theoretical Chemistry University of Vienna Währinger Straße 17 ViennaA-1090 Austria Faculdad de Ciencias Universidad Nacional de Colombia Sede Bogotá Ciudad Universitaria D.C. BogotáCOL-111321 Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

Background: Small Proteins have received increasing attention in recent years. They have in particular been implicated as signals contributing to the coordination of bacterial communities. In genome annotations they are often missing or hidden among large numbers of hypothetical proteins because genome annotation pipelines often exclude short open reading frames or over-predict hypothetical proteins based on simple models. The validation of novel proteins, and in particular of small proteins (sProteins), therefore requires additional evidence. Proteogenomics is considered the gold standard for this purpose. It extends beyond established annotations and includes all possible open reading frames (ORFs) as potential sources of peptides, thus allowing the discovery of novel, unannotated proteins. Typically this results in large numbers of putative novel small proteins fraught with large fractions of false-positive predictions. Results: We observe that number and quality of the Peptide-to-Spectra-Matches (PSMs) that map to a candidate ORF can be highly informative for the purpose of distinguishing proteins from spurious ORF annotations. We report here on a workflow that aggregates PSM quality information and local context into simple descriptors and reliably separates likely proteins from the large pool of false-positive, i.e., most likely untranslated ORFs. We investigated the artificial gut microbiome model SIHUMIx, comprising eight different species, for which we validate 5114 proteins that previously have been annotated only as hypothetical ORFs. In addition, we identified 37 non-annotated protein candidates for which we found evidence in proteomic and transcriptomic level. Half (19) of these candidates have close functional homologs in other species. Another 12 candidates have homologs designated as hypothetical proteins in other species. The remaining six candidates are short ( © 2021, CC BY.

关键词： Peptides