检索结果-内蒙古大学图书馆

Recent advances in T-cell receptor repertoire analysis: Bridging the gap with multimodal single-cell RNA sequencing

学校读者我要写书评

暂无评论

ImmunoInformatics 2022年 5卷

作者： Sebastiaan Valkiers Nicky de Vrij Sofie Gielis Sara Verbandt Benson Ogunjimi Kris Laukens Pieter Meysman Adrem Data Lab Department of Computer Science University of Antwerp Antwerp Belgium Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS) University of Antwerp Antwerp Belgium Clinical Immunology Unit Department of Clinical Sciences Institute of Tropical Medicine Antwerp Belgium Molecular Digestive Oncology Department of Oncology Katholieke Universiteit Leuven Leuven Belgium Centre for Health Economics Research & Modeling Infectious Diseases (CHERMID) Vaccine & Infectious Disease Institute (VAXINFECTIO) University of Antwerp Antwerp Belgium Antwerp Center for Translational Immunology and Virology (ACTIV) Vaccine and Infectious Disease Institute (VAXINFECTIO) University of Antwerp Antwerp Belgium Department of Paediatrics Antwerp University Hospital Antwerp Belgium

T cells exercise a multitude of functions such as cytotoxicity, secretion of immunomodulating cytokines or regulation of tolerance, collectively resulting in an effective control of immune-related disease. Through the unique mechanism of V(D)J recombination, T cells express a highly specific receptor complex known as the T-cell receptor (TCR). Single-cell sequencing technologies have paved the road for interrogating the transcriptome and the paired αβ TCR repertoire of a single T cell in tandem. In contrast, conventional bulk methods are restricted to only one layer of information. This combination of transcriptomic- and repertoire information can provide novel insight into the functional character of T cell immunity. Recently, single-cell technologies have gained in popularity due to improvements in throughput, decrease in cost and the ability for multimodal experiments that integrate different information layers. Consequently, this prompts the need for the development of novel computational tools that integrate transcriptomic profiles and corresponding features of the TCR repertoire. Here we discuss the current progress in the field of single-cell T cell sequencing, with a focus on the multimodality of new approaches that allow the paired profiling of cellular phenotype and clonotype information. In addition, this review provides detailed descriptions of recent computational developments for analyzing single-cell TCR sequencing data in an integrative manner using novel computational approaches. Finally, we present an overview of the available software tools that can be used to perform integrative analysis of gene expression and TCR profiles.

关键词： Single-cell sequencing Single-cell immune profiling Single-cell gene expression T-cell receptor repertoire Multi-omics

Foundational Choices in Enterprise Architecture: The Case of Capability in Defense Frameworks

学校读者我要写书评

暂无评论

Foundational Choices in Enterprise Architecture: The Case of...

International Conference on Enterprise Distributed Object Computing (EDOC)

作者： Gabriel M. Miranda João Paulo A. Almeida Giancarlo Guizzardi Carlos L. B. Azevedo Ontology & Conceptual Modeling Research Group (NEMO) Federal University of Espírito Santo Vitória Brazil Federal University of Espírito Santo Vitoria Brazil Conceptual and Cognitive Modeling Research Group (CORE) Free University of Bozen-Bolzano Italy Data Science and Complex Networks Research Group Federal Institute of Espírito Santo (IFES) Serra Brazil

Enterprise Architecture (EA) description frameworks embody a "worldview" which is used by architects to conceptualize the enterprise and its elements. This "worldview" manifests itself in the choice of language constructs and in modeling rules and guidelines, and, whether explicitly or not, reflects some foundational ontological choices. This paper reflects on the practical consequences of some of these choices. We focus on the capability-related elements in EA defense frameworks (DoDAF, MODAF and NAF). In particular, we analyse the support for modeling capabilities in these frameworks from the prism of a fundamental distinction in formal ontology: two-category vs. four-category ontology. By analyzing the current choices in these frameworks and identifying some adverse consequences, we are able to suggest well-founded recommendations for improvements.

关键词： Ontologies Computer architecture Standards organizations Organizations Computational modeling Guidelines Cats

Correction to "Exploring Mitochondria-Mediated Intrinsic Apoptosis by New Phytochemical Entities: An Explicit Observation of Cytochrome c Dynamics on Lung and Melanoma Cancer Cells"

学校读者我要写书评

暂无评论

Journal of medicinal chemistry 2021年第14期64卷 10512页

作者： Jayadev S Arya Manu M Joseph Daisy R Sherin Jyothi B Nair Thanathu K Manojkumar Kaustabh K Maiti Chemical Sciences and Technology Division (CSTD) CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST) Thiruvananthapuram 695019 Kerala India. Academy of Scientific and Innovative Research (AcSIR) Ghaziabad 201002 India. Centre for Computational Modeling and Data Engineering Indian Institute of Information Technology and Management-Kerala (IIITM-K) Thiruvananthapuram 695581 India.

A systematic review of challenges and proposed solutions in modeling multimodal data

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Farhadizadeh, Maryam Weymann, Maria Blaß, Michael Kraus, Johann Gundler, Christopher Walter, Sebastian Hempen, Noah Binder, Harald Binder, Nadine Institute of General Practice/Family Medicine Faculty of Medicine Medical Center University of Freiburg Elsässer Straße 2M Freiburg79110 Germany Freiburg Center for Data Analysis Modeling and AI University of Freiburg Ernst-Zermelo-Str. 1 Freiburg79104 Germany Institute of Medical Biometry and Statistics Faculty of Medicine Medical Center University of Freiburg Stefan-Meier-Straße 26 Freiburg79104 Germany Institute for Applied Medical Informatics University Medical Center Hamburg-Eppendorf Martinistraße 52 Hamburg20251 Germany Institute of Medical Systems Biology Ulm University Helmholtzstraße 16 Ulm89081 Germany Department of Computer Science Faculty of Engineering University of Freiburg Georges-Köhler-Allee 051 Freiburg79110 Germany

Multimodal data modeling has emerged as a powerful approach in clinical research, enabling the integration of diverse data types such as imaging, genomics, wearable sensors, and electronic health records. Despite its potential to improve diagnostic accuracy and support personalized care, modeling such heterogeneous data presents significant technical challenges. This systematic review synthesizes findings from 69 studies to identify common obstacles, including missing modalities, limited sample sizes, dimensionality imbalance, interpretability issues, and finding the optimal fusion techniques. We highlight recent methodological advances, such as transfer learning, generative models, attention mechanisms, and neural architecture search that offer promising solutions. By mapping current trends and innovations, this review provides a comprehensive overview of the field and offers practical insights to guide future research and development in multimodal modeling for medical applications. © 2025, CC BY.

关键词： Clinical research

Sampled limited memory methods for massive linear inverse problems

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Chung, Julianne Chung, Matthias Tanner Slagel, J. Tenorio, Luis Department of Mathematics Computational Modeling and Data Analytics Division Academy of Integrated Science Virginia Tech BlacksburgVA United States Department of Mathematics Virginia Tech BlacksburgVA United States Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO United States

In many modern imaging applications the desire to reconstruct high resolution images, coupled with the abundance of data from acquisition using ultra-fast detectors, have led to new challenges in image reconstruction. A main challenge is that the resulting linear inverse problems are massive. The size of the forward model matrix exceeds the storage capabilities of computer memory, or the observational dataset is enormous and not available all at once. Row-action methods that iterate over samples of rows can be used to approximate the solution while avoiding memory and data availability constraints. However, their overall convergence can be slow. In this paper, we introduce a sampled limited memory row-action method for linear least squares problems, where an approximation of the global curvature of the underlying least squares problem is used to speed up the initial convergence and to improve the accuracy of iterates. We show that this limited memory method is a generalization of the damped block Kaczmarz method, and we prove linear convergence of the expectation of the iterates and of the error norm up to a convergence horizon. Numerical experiments demonstrate the benefits of these sampled limited memory row-action methods for massive 2D and 3D inverse problems in tomography applications. Copyright © 2019, The Authors. All rights reserved.

关键词： Tomography

Bayesian dose-regimen assessment in early phase oncology incorporating pharmacokinetics and pharmacodynamics

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Gerard, Emma Zohar, Sarah Thai, Hoai-Thu Lorenzato, Christelle Riviere, Marie-Karelle Ursino, Moreno Centre de Recherche des Cordeliers Sorbonne Université Inserm Université de Paris ParisF-75006 France Oncology biostatistics Biostatistics and Programming department Sanofi R&D Vitry-sur-Seine France Statistical Methodology Group Biostatistics and Programming department Sanofi R&D Chilly-Mazarin France Translation Disease Modeling Digital and Data Science Sanofi R&D Chilly-Mazarin France F-CRIN PARTNERS platform AP-HP Université de Paris Paris France

Phase I dose-finding trials in oncology seek to find the maximum tolerated dose (MTD) of a drug under a specific schedule. Evaluating drug-schedules aims at improving treatment safety while maintaining efficacy. However, while we can reasonably assume that toxicity increases with the dose for cytotoxic drugs, the relationship between toxicity and multiple schedules remains elusive. We proposed a Bayesian dose-regimen assessment method (DRtox) using pharmacokinetics/pharmacodynamics (PK/PD) information to estimate the maximum tolerated dose-regimen (MTD-regimen), at the end of the dose-escalation stage of a trial to be recommended for the next phase. We modeled the binary toxicity via a PD endpoint and estimated the dose-regimen toxicity relationship through the integration of a dose-regimen PD model and a PD toxicity model. For the dose-regimen PD model, we considered nonlinear mixed-effects models, and for the PD toxicity model, we proposed the following two Bayesian approaches: a logistic model and a hierarchical model. We evaluated the operating characteristics of the DRtox through simulation studies under various scenarios. The results showed that our method outperforms traditional model-based designs demonstrating a higher percentage of correctly selecting the MTD-regimen. Moreover, the inclusion of PK/PD information in the DRtox helped provide more precise estimates for the entire dose-regimen toxicity curve;therefore the DRtox may recommend alternative untested regimens for expansion cohorts. The DRtox should be applied at the end of the dose-escalation stage of an ongoing trial for patients with relapsed or refractory acute myeloid leukemia (NCT03594955) once all toxicity and PK/PD data are collected. © 2020, CC-BY.

关键词： Toxicity

Flexible krylov methods for pregularization

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Chung, Julianne Gazzola, Silvia Department of Mathematics Computational Modeling Data Analytics Division Academy of Integrated Science Virginia Tech BlacksburgVA United States

In this paper we develop flexible Krylov methods for efficiently computing regular- ized solutions to large-scale linear inverse problems with an 2fit-to-data term and an .p penalization term, for p ≥ 1. First we approximate the p-norm penalization term as a sequence of 2-norm penalization terms using adaptive regularization matrices in an iterative reweighted norm fashion, and then we exploit flexible preconditioning tech- niques to efficiently incorporate the weight updates. To handle general (non-square) .p-regularized least-squares problems, we introduce a flexible Golub-Kahan approach and exploit it within a Krylov-Tikhonov hybrid framework. The key benefits of our approach compared to existing optimization methods for .p regularization are that effi- cient projection methods replace inner-outer schemes and that expensive regularization parameter selection techniques can be avoided. Theoretical insights are provided, and numerical results from image deblurring and tomographic reconstruction illustrate the benefits of this approach, compared to well-established methods. Furthermore, we show that our approach for p = 1 can be used to efficiently compute solutions that are sparse with respect to some transformations. Copyright © 2018, The Authors. All rights reserved.

关键词： Image enhancement

Two-Dimensional Barcodes Usage in Plastic Pipes Blockchain-Based Supply-Chain

学校读者我要写书评

暂无评论

Journal of Physics: Conference Series 2020年第1期1680卷

作者： S Kudryashov Y Madhwal I Maslov A Trepalin Y Yanovich Project Manager Bitfury Russia Bitfury 123112 Moscow Russia PhD student Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology (Skoltech) 121205 MoscowRussia CTO Bitfury Russia Bitfury 123112 Moscow Russia Developer Bitfury Russia Bitfury 123112 Moscow Russia Researcher Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology (Skoltech) 121205 Moscow Russia Researcher Laboratory of Data Mining and Predictive Modeling Institute for Information Transmission Problems (IITP RAS) 127051 Moscow Russia

The use of counterfeit products causes considerable financial damage to the states and companies. Product labeling and tracking in a blockchain-enabled system can overcome this problem. One- and two-dimensional barcodes are accessible and have cheap labeling technologies, but it can be challenging to read the labels on products with curved shapes. This paper presents the plastic pipe labeling problem and its solution with two-dimensional barcodes, integrated with a blockchain-enabled supply chain. The labeling technique choice is based on QR and Aztec barcodes readability empirical study for different barcode parameters, reading software libraries for flat and curved surfaces.

关键词：

CharBot: A simple and effective method for evading DGA classifiers

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Peck, Jonathan Nie, Claire Sivaguru, Raaghavi Grumer, Charles Olumofin, Femi Yu, Bin Nascimento, Anderson De Cock, Martine Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent9000 Belgium Data Mining and Modeling for Biomedicine VIB Inflammation Research Center Ghent9052 Belgium School of Engineering and Technology University of Washington TacomaWA98402 United States Infoblox Santa ClaraCA95054 United States

Domain generation algorithms (DGAs) are commonly leveraged by malware to create lists of domain names which can be used for command and control (C&C) purposes. Approaches based on machine learning have recently been developed to automatically detect generated domain names in real-time. In this work, we present a novel DGA called CharBot which is capable of producing large numbers of unregistered domain names that are not detected by state-of-the-art classifiers for real-time detection of DGAs, including the recently published methods FANCI (a random forest based on human-engineered features) and *** (a deep learning approach). CharBot is very simple, effective and requires no knowledge of the targeted DGA classifiers. We show that retraining the classifiers on CharBot samples is not a viable defense strategy. We believe these findings show that DGA classifiers are inherently vulnerable to adversarial attacks if they rely only on the domain name string to make a decision. Designing a robust DGA classifier may, therefore, necessitate the use of additional information besides the domain name alone. To the best of our knowledge, CharBot is the simplest and most efficient black-box adversarial attack against DGA classifiers proposed to date. Copyright © 2019, The Authors. All rights reserved.

关键词： Classification (of information)