Upper mesophotic reef fish assemblages at Bahía de Banderas, Mexico.

There is no information on the species associated with the mesophotic reefs of Banderas Bay, located in the central Mexican Pacific. This study analysed the reef fish assemblage from three depths (50, 60 and 70 m) in three sampling sites of the southern submarine canyon of the Bay: Los Arcos, Bajo de Emirio and Majahuitas. Several analyses were performed to test the hypothesis that there are important differences in fish abundance and species composition between sites and depths. Twenty-two species of bony fishes grouped in 14 families were recorded. PERMANOVA results showed that there were no significant differences in fish diversity parameters between sites, indicating a certain uniformity in their distribution. However, nine species were exclusive to one site and depth (five singleton species with only one individual recorded and four unique species recorded only once). On the other hand, there were significant differences between depths, mainly between 50 and 70 m. Diversity decreases with depth and species composition changes. SIMPER, Shade Plot and NMDS analysis show the most representative species at each depth, with at least half of the species (11) recorded only at 50 m and four species at the deeper levels (60 - 70 m). The observed assemblage includes several of the most caught species in the shallow water artisanal fishery, which is the most traditional and common type of fishery in the Bay. In addition, the Pomacanthuszonipectus (Cortés angelfish) is of particular interest, as it has a special protection status in the official Mexican standard (NOM-059-SEMARNAT, 2010) due to its use as an ornamental species in aquaria. We hypothesised that the mesophotic zone may serve as a refuge for these fishes, so we propose that the information obtained is an important basis for new research aimed at the sustainable management of fisheries in the area.

Unraveling the Complex Interactions: Machine Learning Approaches to Predict Bacterial Survival against ZnO and Lanthanum-Doped ZnO Nanoparticles.

The rise in antibiotic-resistant bacteria is a global health challenge. Due to their unique properties, metal oxide nanoparticles show promise in addressing this issue. However, optimizing these properties requires a deep understanding of complex interactions. This study incorporated data-driven machine learning to predict bacterial survival against lanthanum-doped ZnO nanoparticles. The effect of incorporation of lanthanum ions on ZnO was analyzed. Even with high lanthanum concentration, no significant variations in structural, morphological, and optical properties were observed. The antibacterial activity of La-doped ZnO nanoparticles against Gram-positive and Gram-negative bacteria was qualitatively and quantitatively evaluated. Nanoparticles induce 60%, 95%, and 55% bacterial death against Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus, respectively. Algorithms such as Multilayer Perceptron, K-Nearest Neighbors, Gradient Boosting, and Extremely Random Trees were used to predict the bacterial survival percentage. Extremely Random Trees performed the best among these models with 95.08% accuracy. A feature relevance analysis extracted the most significant attributes to predict the bacterial survival percentage. Lanthanum content and particle size were irrelevant, despite what can be assumed. This approach offers a promising avenue for developing effective and tailored strategies to reduce the time and cost of developing antimicrobial nanoparticles.

Modeling bacteria pairwise interactions in human microbiota by Sparse Identification of Nonlinear Dynamics (SINDy).

The gut microbiota is a community of high complexity; its composition changes due to ecological interactions, these are studied to understand the relationship with the human health. External stimuli like the administration of probiotics, prebiotics, or drugs are known to modify these interactions. The high complexity of microbiota composition can be studied by considering pairwise interactions. Pairwise interactions in bacterial communities consider each species' directionality and impact on one another, e.g., commensalism (unidirectional positive interaction) or competition (bidirectional negative interaction). These interactions can either be interspecies or intraspecies. The Lotka-Volterra (LV) model has been implemented to characterize these bacteria interactions, considering the ecological relationship among the different species presented. One of the main challenges is determining the specific interaction parameters in LV structure from experimental data. This study implemented a novel approach based on the sparse identification of nonlinear dynamic method (SINDy). One of the assumptions in SINDy method implies the knowledge of the data derivative structure. To fulfill this requirement, a differential neural network algorithm was implemented. We assessed the performance of this approach considering both a simulated and experimental interspecies scenario. A two-species bacterial LV model was simulated in the initial validation stage, and the resulting kinetic growth data was recorded. This data was utilized for training a differential neural network algorithm, which was used to derive a time-derivative structure for the dataset. After this step, SINDy method was implemented to calculate the interaction parameters. Three conditions were evaluated in intraspecies competition, obtaining an average identification parametric error of less than 2%. For experimental data, parametric analysis results are sensitive to detect the influence of a drug presence over the intraspecies interaction with a reduction of 50% in its typical values.Clinical Relevance- In this study, we devised a strategy to determine how two species of the human gastrointestinal microbiota interact and the impact of drug administration on these interactions.

Multivariate water quality analysis of Lake Cajititlán, Mexico.

Lake Cajititlán is a shallow body of water located in an endorheic basin in western Mexico. This lake receives excess fertilizer runoff from agriculture and approximately 2.3 Hm3 per year of poorly treated wastewater from three municipal treatment plants. Thirteen water quality parameters were monitored at five sampling points within the lake over 9 years. The objective of this work was to characterize the spatial and temporal variations of the water quality and to identify the sources of data variability in order to assess the influence and the impact of different natural and anthropogenic processes. One-way ANOVA tests, principal component analysis (PCA), cluster analysis (CA), and discriminant analysis (DA) were implemented. The one-way ANOVA showed that biochemical oxygen demand and pH present statistically significant spatial variations and that alkalinity, total chloride, conductivity, chemical oxygen demand, total hardness, ammonia, pH, total dissolved solids, and temperature present statistically significant temporal variations. PCA results explained both natural and anthropogenic processes and their relationship with water quality data. The CA results suggested there is no significant spatial variation in the water quality of the lake because of lake mixing caused by wind. The most significant parameters for spatial variations were pH, NO3-, and NO2-, consistent with the configuration of point and nonpoint sources that affect the lake's water quality. The temporal DA results suggested that conductivity, hardness, NO2-, pH, and temperature were the most significant parameters to discriminate between seasons. The temporal behavior of these parameters was associated with the transport pathways of seasonal contaminants.

Discriminant analysis and machine learning approach for evaluating and improving the performance of immunohistochemical algorithms for COO classification of DLBCL.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is classified into germinal center-like (GCB) and non-germinal center-like (non-GCB) cell-of-origin groups, entities driven by different oncogenic pathways with different clinical outcomes. DLBCL classification by immunohistochemistry (IHC)-based decision tree algorithms is a simpler reported technique than gene expression profiling (GEP). There is a significant discrepancy between IHC-decision tree algorithms when they are compared to GEP. METHODS: To address these inconsistencies, we applied the machine learning approach considering the same combinations of antibodies as in IHC-decision tree algorithms. Immunohistochemistry data from a public DLBCL database was used to perform comparisons among IHC-decision tree algorithms, and the machine learning structures based on Bayesian, Bayesian simple, Naïve Bayesian, artificial neural networks, and support vector machine to show the best diagnostic model. We implemented the linear discriminant analysis over the complete database, detecting a higher influence of BCL6 antibody for GCB classification and MUM1 for non-GCB classification. RESULTS: The classifier with the highest metrics was the four antibody-based Perfecto-Villela (PV) algorithm with 0.94 accuracy, 0.93 specificity, and 0.95 sensitivity, with a perfect agreement with GEP (κ = 0.88, P < 0.001). After training, a sample of 49 Mexican-mestizo DLBCL patient data was classified by COO for the first time in a testing trial. CONCLUSIONS: Harnessing all the available immunohistochemical data without reliance on the order of examination or cut-off value, we conclude that our PV machine learning algorithm outperforms Hans and other IHC-decision tree algorithms currently in use and represents an affordable and time-saving alternative for DLBCL cell-of-origin identification.

IL-1α and MMP-9 Tear Levels of Patients with Active Ocular Rosacea before and after Treatment with Systemic Azithromycin or Doxycycline.

AIMS: The purpose of this paper was to determine the lacrimal concentration of IL-1α and MMP-9 in patients with active ocular rosacea before and after systemic treatment with azithromycin or doxycycline. METHODS: After 4 weeks of therapy with azithromycin (500 mg/day, 3 days a week PO) or doxycycline (200 mg/day PO), lacrimal samples were analyzed using an enzyme-linked immunosorbent assay multiplex. RESULTS: There was a significant difference between baseline IL-1α (37.9 pg/mL) and MMP-9 (26.7 ng/mL) in rosacea eyes compared to controls (0.001 pg/mL for IL-1α and 0.2 ng/mL for MMP-9) (p < 0.001). IL-1α decreased from 47.0 pg/mL before azithromycin to 23.5 pg/mL after treatment (p = 0.024), but not after doxycycline therapy. On the contrary, baseline MMP-9 tear levels (10.28 ng/mL) decreased after treatment (8.36 pg/mL) with doxycycline (p = 0.054) but not with azithromycin. There was a strong clinical correlation of higher baseline IL-1α tear levels between patients who responded to doxycycline therapy and those who failed (p = 0.043). Patients unresponsive to azithromycin had significantly higher baseline MMP-9 levels than those with doxycycline (p = 0.040). CONCLUSIONS: While IL-1α levels decreased after azithromycin therapy, MMP-9 did so after doxycycline treatment. Baseline cytokine tear levels tend to be markedly elevated in patients with antibiotic failure, suggesting their potential role as therapeutic biomarkers for the disease.

A PER3 polymorphism is associated with better overall survival in diffuse large B-cell lymphoma in Mexican population.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of malignant lymphoma. Presently, one of the most important clinical predictors of survival in DLBCL patients is the International Prognostic Index (IPI). Circadian rhythms are the approximate 24 hour biological rhythms with more than 10 genes making up the molecular clock. OBJECTIVE: Determine if functional single nucleotide polymorphism in circadian genes may contribute to survival status in patients diagnosed with diffuse large B-cell lymphoma. METHODS: Sixteen high-risk non-synonymous polymorphisms in circadian genes (CLOCK, CRY2, CSNK1E, CSNK2A1, NPAS2, PER1, PER2, PER3, PPP2CA, and TIM) were genotyped by screening PCR. Results were visualized by agarose gel electrophoresis and confirmed by two-direction sequencing. Clinical variables were compared between mutated and non-mutated groups. LogRank survival analysis and Kaplan-Meier method were used to calculate the overall survival. RESULTS: PER3 rs10462020 variant showed significant difference in overall survival between patients containing mutated genotypes and those with non-mutated genotypes (p = 0.047). LDH levels (p = 0.021) and IPI score (p < 0.001) also showed differences in overall survival. No clinical differences were observed in mutated vs. non-mutated patients. CONCLUSIONS: This work suggests a role of PER3 rs10462020 in predicting a prognosis in DLBCL overall survival of patients.