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The freeze-thaw cycle effect on blood serum autofluorescence, Raman spectroscopy and SERS: implications for sample classification and disease diagnostics.
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January 2025
Faculty of Physics, M.V. Lomonosov Moscow State University, 1-2 Leninskie Gory, Moscow, 119991, Russia.
The issue of variability introduced into blood plasma and serum analysis by preanalytical procedures is the major obstacle to obtaining accurate and reproducible results. While the question of how to overcome this issue has been discussed in biochemical detection of analytes and omics technologies, its relevance to the field of optical spectroscopy remains mostly unexplored. In this work, we evaluated the freeze-thaw cycle (FTC)-induced alternations in blood serum optical properties by means of autofluorescence and Raman spectroscopy, including surface-enhanced Raman spectroscopy (SERS).
View Article and Find Full Text PDFElucidating the relationships between a class I peptide antigen, a CD8 T cell receptor (TCR) specific to that antigen, and the T cell phenotype that emerges following antigen stimulation, remains a mostly unsolved problem, largely due to the lack of large data sets that can be mined to resolve such relationships. Here, we describe Antigen-TCR Pairing and Multiomic Analysis of T-cells (APMAT), an integrated experimental-computational framework designed for the high-throughput capture and analysis of CD8 T cells, with paired antigen, TCR sequence, and single-cell transcriptome. Starting with 951 putative antigens representing a comprehensive survey of the SARS-CoV-2 viral proteome, we utilize APMAT for the capture and single cell analysis of CD8 T cells from 62 HLA A*02:01 COVID-19 participants.
View Article and Find Full Text PDFEmpirical networks are sparse: Enhancing multiedge models with zero-inflation.
PNAS Nexus
January 2025
Chair of Systems Design, ETH Zurich, Weinbergstrasse 56/58, Zurich 8092, Switzerland.
Real-world networks are sparse. As we show in this article, even when a large number of interactions is observed, most node pairs remain disconnected. We demonstrate that classical multiedge network models, such as the , configuration models, and stochastic block models, fail to accurately capture this phenomenon.
View Article and Find Full Text PDFIn-memory encryption using the advanced encryption standard.
Philos Trans A Math Phys Eng Sci
January 2025
IBM Research-Europe, 8803 Rüschlikon, Zurich, Switzerland.
Encryption and decryption of data with very low latency and high energy efficiency is desirable in almost every application that deals with sensitive data. The advanced encryption standard (AES) is a widely adopted algorithm in symmetric key cryptography with numerous efficient implementations. Nonetheless, in scenarios involving extensive data processing, the primary limitations on performance and efficiency arise from data movement between memory and the processor, rather than data processing itself.
View Article and Find Full Text PDFNon-Stationarity in Time-Series Analysis: Modeling Stochastic and Deterministic Trends.
Multivariate Behav Res
January 2025
Psychological Methods Group, University of Amsterdam, Amsterdam, Netherlands.
Time series analysis is increasingly popular across scientific domains. A key concept in time series analysis is stationarity, the stability of statistical properties of a time series. Understanding stationarity is crucial to addressing frequent issues in time series analysis such as the consequences of failing to model non-stationarity, how to determine the mechanisms generating non-stationarity, and consequently how to model those mechanisms (i.
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