Iskanje po repozitoriju

Opis: Brain MRI segmentation plays a crucial role in neuroimaging studies and clinical trials by enabling the precise localization and quantification of brain tissues and structures. The advent of deep learning has transformed the field, offering accurate and fast tools for MRI segmentation. Nevertheless, several challenges limit the widespread applicability of these methods in practice. In this systematic review, we provide a comprehensive analysis of developments in deep learning-based segmentation of brain MRI in adults, segmenting the brain into tissues, structures, and regions of interest. We explore the key model factors influencing segmentation performance, including architectural design, choice of input size and model dimensionality, and generalization strategies. Furthermore, we address validation practices, which are particularly important given the scarcity of manual annotations, and identify the limitations of current methodologies. We present an extensive compilation of existing segmentation works and highlight the emerging trends and key results. Finally, we discuss the challenges and potential future directions in the field.
Ključne besede: magnetic resonance imaging, brain, image segmentation, deep learning
Objavljeno v RUP: 10.10.2025; Ogledov: 615; Prenosov: 6
Celotno besedilo (956,69 KB)
Gradivo ima več datotek! Več...

Opis: This paper describes an open-source computational tool developed for the modeling and simulation of fed-batch bioreactors, particularly for processes employing Chinese Hamster Ovary (CHO) cells, which are integral to biopharmaceutical manufacturing. The software provides a platform for researchers and industry professionals to simulate bioreactor dynamics and investigate the impact of various operational parameters, such as nutrient supply rates, oxygen concentrations, and temperature, prior to physical experimentation. The tool enables users to generate predictions of critical variables including cell density, nutrient consumption, and product concentration profiles over time. These predictions are derived from a mathematical framework based on a system of ordinary differential equations solved using the Runge–Kutta method. A notable capability of the software is the import of experimental data and the application of the Nelder–Mead algorithm for parameter optimization, allowing for the calibration of the model against empirical findings, thereby enhancing its predictive accuracy. The software supports in silico experimentation, which can contribute to reducing the time, cost, and resources associated with optimizing bioreactor configurations and scaling up production processes. By providing a refined and adaptable framework, this instrument assists in improving the understanding of bioreactor dynamics, optimizing biopharmaceutical production methodologies, and correlating theoretical models with practical bioreactor operations. The software is available as an open-source project to promote its adoption and continued development within the scientific community.
Ključne besede: bioreaktor, mathematical modeling, CHO
Objavljeno v RUP: 29.09.2025; Ogledov: 526; Prenosov: 4
Celotno besedilo (939,94 KB)
Gradivo ima več datotek! Več...

Opis: In deep learning-based side-channel analysis (DL-SCA), there may be a proliferation of model parameters as the number of trace power points increases, especially in the case of raw power traces. Determining how to design a lightweight deep learning model that can handle a trace with more power points and has fewer parameters and lower time costs for profiled SCAs appears to be a challenge. In this article, a DL-SCA model is proposed by introducing a non-trained DL technique called random convolutional kernels, which allows us to extract the features of leakage like using a transformer model. The model is then processed by a classifier with an attention mechanism, which finally outputs the probability vector for the candidate keys. Moreover, we analyze the performance and complexity of the random kernels and discuss how they work in theory. On several public AES datasets, the experimental results show that the number of required profiling traces and trainable parameters reduce, respectively, by over 70% and 94% compared with state-of-the-art works, while ensuring that the number of power traces required to recover the real key is acceptable. Importantly, differing from previous SCA models, our architecture eliminates the dependency between the feature length of power traces and the number of trainable parameters, which allows for the architecture to be applied to the case of raw power traces.
Ključne besede: side-channel analysis, deep learning, convolution neural networks, random convolution kernel
Objavljeno v RUP: 26.09.2025; Ogledov: 1553; Prenosov: 7
Celotno besedilo (1,75 MB)
Gradivo ima več datotek! Več...

Opis: Background Aedes koreicus is native to Far East Asia and recorded in Europe since 2008. In Italy, Ae. koreicus is widespread throughout the Northern part of the peninsula, highlighting its invasive potential and spread. However, no clear clues about the dispersal patterns of the species have been collected so far. Methodology/Principal findings Population genetic analyses were performed to assess the genetic structure of populations of Ae. koreicus and to make hypotheses about its dispersal patterns in Northern Italy. Ten microsatellite markers specific for Ae. koreicus were used to genotype 414 individuals from 13 populations in the pre-alpine area of Italy, and neighboring Slovenia. Basic and Bayesian population genetic analyses were performed to evaluate patterns of genetic variation, genetic structure, and demography of selected mosquito populations. While presenting a certain degree of structuring, the Italian and Slovenian populations of Ae. koreicus were poorly differentiated. Moreover, demographic analysis supports the expansion of a single population propagule of Ae. koreicus in Italy and Slovenia and provides evidence of the presence of overwintering populations in the studied area. Conclusions/Significance Our results highlight a common origin, and stable colonization of Northern Italy and Slovenia, as a probable consequence of the expansion of a unique population. This stresses out the importance of continuous monitoring of Ae. koreicus, to finally uncover the geographic origins and entrance pathways of invasive populations and to prevent or limit further introductions.
Ključne besede: Aedes koreicus, Italy, genetics
Objavljeno v RUP: 26.09.2025; Ogledov: 398; Prenosov: 6
Celotno besedilo (1,42 MB)
Gradivo ima več datotek! Več...

Opis: Let ${\mathcal M}$ denote the Bose--Mesner algebra of a commutative $d$-class association scheme ${\mathfrak X}$ (not necessarily symmetric), and $\Gamma$ denote a (strongly) connected (directed) graph with adjacency matrix $A$. Under the assumption that $A$ belongs to ${\mathcal M}$, we describe the combinatorial structure of $\Gamma$. Moreover, we provide an algebraic-combinatorial characterization of $\Gamma$ when $A$ generates ${\mathcal M}$. Among else, we show that, if ${\mathfrak X}$ is a commutative $3$-class association scheme that is not an amorphic symmetric scheme, then we can always find a (directed) graph $\Gamma$ such that the adjacency matrix $A$ of $\Gamma$ generates the Bose--Mesner algebra ${\mathcal M}$ of ${\mathfrak X}$.
Ključne besede: commutative association schemes, association schemes, Bose-Mesner algebra, equitable partition, graphs generating schemes, quotient-polynomial graphs, x-distance-faithful intersection diagram
Objavljeno v RUP: 26.09.2025; Ogledov: 365; Prenosov: 4
Celotno besedilo (483,56 KB)
Gradivo ima več datotek! Več...