Examinando por Autor "Pestana-Nobles, Roberto"
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Ítem An innovative methodology for segmenting vessel like structures using artificial intelligence and image processing(Springer Nature, 2025) Ayala Mantilla, Cristian Eduardo; Villarreal, Reynaldo; Chamorro-Solano, Sindy; Cantillo, Steffen; Pestana-Nobles, Roberto; Arquez, Sair; Vega-Sampayo, Yolanda; Pacheco-Londoño, Leonardo; Paez, Jheifer; Galan-Freyle, Nataly ; Amar, PaolaInnovation is currently driving enhanced performance and productivity across various fields through process automation. However, identifying intricate details in images can often pose challenges due to morphological variations or specific conditions. Here, artificial intelligence (AI) plays a crucial role by simplifying the segmentation of images.This is achieved by training algorithms to detect specific pixels, thereby recognizing details within images. In this study, an algorithm incorporating modules based on Efficient Sub-Pixel Convolutional Neural Network forimage super-resolution, U-Net based Neural baseline for image segmentation, and image binarization for masking was developed. The combination of these modules aimed to identify capillary structures at pixel level. The method was applied on different datasets containing images of eye fundus, citrus leaves, printed circuit boards to test how well it could segment the capillary structures. Notably, the trained model exhibited versatility in recognizing capillary structures across various image types.When tested with the Set 5 and Set 14 datasets, a PSNR of 37.92 and SSIM of 0.9219 was achieved, surpassing significantly other image superresolution methods.The enhancement module processes the image using three different varaiables in the same way, which imposes a complexity of O(n) and takes 308,734 ms to execute; the segmentation module evaluates each pixel against its neighbors to correctly segment regions of interes, generating an O(n2) quadratic complexity and taking 687,509 ms to execute; the masking module makes several runs through the whole image and in several occasions it calls processes of O(n log n) complexity at 581686 microseconds to execute, which makes it not only the most complex but also the most exhaustive part of the program. This versatility, rooted in its pixel-level operation, enables the algorithm to identify initially unnoticed details, enhancing its applicability across diverse image datasets. This innovation holds significant potential for precisely studying certain structures’ characteristics while enhancing and processing images with high fidelity through AI-driven machine learning algorithms.Ítem Searching hit potential antimicrobials in natural compounds space against biofilm formation(MDPI, 2020) Pestana-Nobles, Roberto; Leyva-Rojas, Jorge A.; Yosa, JuvenalBiofilms are communities of microorganisms that can colonize biotic and abiotic surfaces and thus play a significant role in the persistence of bacterial infection and resistance to antimicrobial. About 65% and 80% of microbial and chronic infections are associated with biofilm formation, respectively. The increase in infections by multi-resistant bacteria instigates the need for the discovery of novel natural-based drugs that act as inhibitory molecules. The inhibition of diguanylate cyclases (DGCs), the enzyme implicated in the synthesis of the second messenger, cyclic diguanylate (c-di-GMP), involved in the biofilm formation, represents a potential approach for preventing the biofilm development. It has been extensively studied using PleD protein as a model of DGC for in silico studies as virtual screening and as a model for in vitro studies in biofilms formation. This study aimed to search for natural products capable of inhibiting the Caulobacter crescentus enzyme PleD. For this purpose, 224,205 molecules from the natural products ZINC15 database, have been evaluated through molecular docking and molecular dynamic simulation. Our results suggest trans-Aconitic acid (TAA) as a possible starting point for hit-to-lead methodologies to obtain new inhibitors of the PleD protein and hence blocking the biofilm formation.