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The coronavirus disease 2019 (COVID-19) pandemic has been spreading quickly, threatening the public health system. Consequently, positive COVID-19 cases must be rapidly detected and treated. Automatic detection systems are essential for controlling the COVID-19 pandemic. Molecular techniques and medical imaging scans are among the most effective approaches for detecting COVID-19. Although these approaches are crucial for controlling the COVID-19 pandemic, they have certain limitations. This study proposes an effective hybrid approach based on genomic image processing (GIP) techniques to rapidly detect COVID-19 while avoiding the limitations of traditional detection techniques, using whole and partial genome sequences of human coronavirus (HCoV) diseases. In this work, the GIP techniques convert the genome sequences of HCoVs into genomic grayscale images using a genomic image mapping technique known as the frequency chaos game representation. Then, the pre-trained convolution neural network, AlexNet, is used to extract deep features from these images using the last convolution (conv5) and second fully-connected (fc7) layers. The most significant features were obtained by removing the redundant ones using the ReliefF and least absolute shrinkage and selection operator (LASSO) algorithms. These features are then passed to two classifiers: decision trees and k-nearest neighbors (KNN). Results showed that extracting deep features from the fc7 layer, selecting the most significant features using the LASSO algorithm, and executing the classification process using the KNN classifier is the best hybrid approach. The proposed hybrid deep learning approach detected COVID-19, among other HCoV diseases, with 99.71% accuracy, 99.78% specificity, and 99.62% sensitivity.

This study aimed to evaluate the impact of UVC (Ultraviolet C Radiation), detergent foam, and alcohol (70%) sterilization methods on the surface morphology of acrylonitrile–butadiene–styrene (ABS) specimens using a novel SEM (Scanning Electron Microscope) image processing approach. Twelve 3D-printed specimens were prepared, and five concentric circular regions of interest (ROIs) per specimen were analyzed. Three quantitative descriptors—defect area fraction, anisotropy ratio, and RMS (Root Mean Square) roughness—were extracted to assess surface alterations. To validate the image-based findings, EDX (Energy-Dispersive X-ray Spectroscopy) elemental analysis for carbon (C), nitrogen (N), and oxygen (O) was employed as a complementary and traditional benchmark technique. Statistical comparisons and p-value heat maps revealed strong convergence between SEM and EDX results. UVC sterilization consistently preserved surface morphology and elemental stability, showing the lowest defect fraction (p = 0.2684), balanced anisotropy (p = 0.02481), and minimal oxygen incorporation (O = 7.6). Foam sterilization produced intermediate effects, with significant anisotropy changes (p = 0.007456) and reduced nitrogen (19.6). Alcohol sterilization induced the most severe damage, characterized by high defect density, increased roughness, and elemental imbalance (N = 17.3, O = 13.9), confirming oxidative degradation. The convergence of SEM and EDX outcomes demonstrates that SEM image processing is a reliable novel method validated by traditional elemental analysis. Together, these approaches provide a robust framework for ranking sterilization efficacy, with UVC identified as the most favorable method, detergent foam as an acceptable alternative, and alcohol as the least effective due to its destabilizing effects.

The application of artificial intelligence techniques in computer aided detection and diagnosis problems has been among the most promising areas with interest from the scientific community and healthcare industry. Recently, deep learning has become the prime tool for such application with many studies focusing on developing variants that optimize diagnostic performance. Despite the widely accepted success of this class of techniques in this application by the scientific community, it is not prudent to consider it as the only tool available for such purpose. In particular, statistical machine learning offers a variety of techniques that can also be applied at a much lower computational cost. Unfortunately, the results from both strategies cannot be directly compared due to the differences in experimental setups and datasets used in available research studies. Therefore, we focus in this study on this direct comparison using the same dataset and similar data preprocessing as the input to both. We compare statistical machine learning to deep learning in the context of computer-aided detection of breast cancer from mammographic images. The results are compared using diagnostic performance metrics and suggest that simpler statistical machine learning techniques may provide better performance with simpler architectures that allow explanation of results.

High-throughput microarrays contain a huge number of genes. Determining the relationships between all these genes is a time-consuming computation. In this paper, the authors provide a parallel algorithm for finding the Pearson’s correlation coefficient between genes measured in the Affymetrix microarrays. The main idea in the proposed algorithm, ForkJoinPcc, mimics the well-known parallel programming model: the fork–join model. The parallel MATLAB APIs have been employed and evaluated on shared or distributed multiprocessing systems. Two performance metrics—the processing and communication times—have been used to assess the performance of the ForkJoinPcc. The experimental results reveal that the ForkJoinPcc algorithm achieves a substantial speedup on the cluster platform of 62× compared with a 3.8× speedup on the multicore platform.

Plus de cent algorithmes ont été développés pour déduire des réseaux de régulation de gènes (GRN) décrivant les relations entre gènes. La construction de GRN est un domaine d’intérêt pour les chercheurs depuis le début du siècle actuel. De nombreux concours ont été organisés pour encourager le développement d'algorithmes d'inférence GRN. Ces concours offrent des données synthétiques pour permettre la validation des algorithmes proposés. Un GRN est construit à partir d'une matrice d'adjacence qui contient les relations entre les gènes. Les développeurs de nombreux algorithmes d'inférence GRN ont défini un seuil pour la matrice d'adjacence afin de construire un GRN basé sur des poids de relation gène-gène élevés. Cette stratégie de seuil a été suivie dans des études précédentes pour augmenter la précision de tout algorithme, sans pour autant s'appuyer sur aucune règle bien connue. Une autre perspective consiste à comparer différents algorithmes d'inférence GRN sans définir de seuil. La comparaison dans ce travail est faite entre différents algorithmes d'inférence GRN en implémentant tous les algorithmes sans seuil sur les valeurs des matrices d'adjacence: Méthodes d'équation différentielle (TSNI), causalité de Granger, GP4GRN, GENIE3, NIMEFI (SVR) et PLSNET. Une autre comparaison entre différentes équations métriques de distance pour créer une matrice d'adjacence est également étudiée dans le but de construire un GRN. GP4GRN et GENIE3 contribuent à produire les meilleurs résultats pour dream4 InSilico_Size10, tandis que GENIE3 fournit les meilleurs résultats pour tous les réseaux de dream4 InSilico_Size100. More than hundred algorithms were developed to infer Gene Regulatory Networks (GRN) describing relations between genes. GRN construction has been a field of interest to researchers since the beginning of the current century. Many competitions were held to encourage the development of GRN inference algorithms, such competitions offer synthetic data to enable the validation of proposed algorithms. AGRN is constructed from an adjacency matrix which contains relations between genes. The developers of many of the GRN inference algorithms set a threshold on the adjacency matrix to construct GRN based on high gene-gene relation weights. This threshold strategy was followed in previous studies to increase the accuracy of any algorithm but yet based on no well-known rule. Adifferent perspective here is to compare different GRN inference algorithms without setting any threshold. Comparison in this work is among different GRN inference algorithms by implementing all algorithms with no threshold on values of adjacency matrices: Differential Equation methods (TSNI), Granger Causality, GP4GRN, GENIE3, NIMEFI (SVR), and PLSNET. Another comparison between different distance metric equations to create adjacency matrix is also studied in an attempt to construct GRN. GP4GRN and GENIE3 participate in producing best results for dream4 InSilico_Size10 while GENIE3 produce best results for all networks of dream4 InSilico_Size100.