Plant species identification represents an extraordinary challenge for machine learning due to visual interspecies similarities and large intraspecies variations. Furthermore, research literature reports that plant species identification usually lacks sufficiently large datasets for training classification models. In this paper, we address this problem with a model that simplifies the VGG-16 architecture, the N-VGG model. The idea behind N-VGG is to reduce experimentally observed overfitting on VGG-16 by using as few trainable parameters as possible. To do this, we substitute the flattening layer on the VGG architecture with a global average pooling layer. This reduces the size of the feature vector. In addition, we eliminate one of the two fully-connected layers and use a new hyper-parameter, N, to indicate the number of nodes on the remaining layer. To show the robustness of the N-VGG model, we conducted extensive experimentation. We trained N-VGG on five datasets for plant species identification. Four of these datasets are publicly available and have been widely used as benchmarks for plant identification models. For all datasets, we compare the accuracy of N-VGG to that of the VGG-16, Inception-v4, and EfficienNet-B3 models. The experimental results show that the N-VGG model achieved the best classification performance for all but one datasets, whereas all the models showed a remarkable performance for the remaining dataset. This evidence supports our initial idea that, for plant species classification, some accuracy might be lost due to overfitting and that having fewer trainable parameters helps in producing a more robust model.

Aquatic environments are recognized as one of the main reservoirs for the emergence and dissemination of high-risk lineages of multidrug-resistant (MDR) bacteria of public health concern. However, the genomic characteristics of antibiotic-resistant Escherichia coli isolates from aquatic origins remain limited. Herein, we examined the antibiotic resistance and virulence genomic profiles of three E. coli recovered from surface water in northwest Mexico. Antimicrobial susceptibility testing, whole-genome sequencing (WGS), and in-depth in silico analysis were performed. Two E. coli exhibited MDR phenotypes. WGS-based typing revealed genetic diversity, and phylogenetic analysis corroborated a notable divergent relationship among the studied E. coli. One E. coli strain, harboring enterotoxigenic and extraintestinal pathogenic-associated virulence genes, was assigned to the ST4 lineage. MDR E. coli, belonging to the international high-risk clones ST410 and ST617, carried genes and mutations conferring resistance to aminoglycosides, β-lactams, quinolones, sulfonamides, tetracyclines, and trimethoprim. This study describes, for the first time, the detection and genomic profiling of high-risk lineages of E. coli ST410 and ST617 from surface water in Mexico. Additionally, our results underscore the role of surface water as a reservoir for critical pathogenic and MDR E. coli clones and the need for the surveillance and monitoring of aquatic environments via WGS from the One Health perspective.

Citrobacter werkmanii is an emerging and opportunistic human pathogen found in developing countries and is a causative agent of wound, urinary tract, and blood infections. The present study conducted comparative genomic analyses of a C. werkmanii strain collection from diverse geographical locations and sources to identify the relevant virulence and antimicrobial resistance genes. Pangenome analyses divided the examined C. werkmanii strains into five distinct clades; the subsequent classification identified genes with functional roles in carbohydrate and general metabolism for the core genome and genes with a role in secretion, adherence, and the mobilome for the shell and cloud genomes. A maximum-likelihood phylogenetic tree with a heatmap, showing the virulence and antimicrobial genes’ presence or absence, demonstrated the presence of genes with functional roles in secretion systems, adherence, enterobactin, and siderophore among the strains belonging to the different clades. C. werkmanii strains in clade V, predominantly from clinical sources, harbored genes implicated in type II and type Vb secretion systems as well as multidrug resistance to aminoglycoside, beta-lactamase, fluoroquinolone, phenicol, trimethoprim, macrolides, sulfonamide, and tetracycline. In summary, these comparative genomic analyses have demonstrated highly pathogenic and multidrug-resistant genetic profiles in C. werkmanii strains, indicating a virulence potential for this commensal and opportunistic human pathogen.

Coronary artery disease (CAD) is one of the leading causes of mortality worldwide, highlighting the importance of developing accurate and efficient diagnostic tools. This study presents a comparative evaluation of three recent YOLO architecture versions (YOLOv8, YOLOv9, and YOLOv11) for the tasks of coronary vessel segmentation and stenosis detection using the ARCADE dataset. Two workflows were explored: one with original angiographic images and another incorporating Contrast Limited Adaptive Histogram Equalization (CLAHE) for image enhancement. Models were trained for 100 epochs using the AdamW optimizer and evaluated with precision, recall, and F1-score under a pixel-based segmentation framework. YOLOv9-E achieved the highest performance in vessel segmentation with an F1-score of 0.4524, while YOLOv11-X was most effective for stenosis detection, achieving an F1-score of 0.7826. Although CLAHE improved local contrast, it did not consistently improve segmentation results and occasionally introduced artifacts that negatively affected model performance. Compared to state-of-the-art methods, the YOLO models demonstrated competitive results, especially for large, well-defined coronary segments, but showed limitations in detecting smaller or more complex pathological structures. These findings support the use of YOLO-based architectures for real-time CAD segmentation tasks and highlight opportunities for future improvement through the integration of attention mechanisms or hybrid deep learning strategies.