The degradation dynamics of lignin and cellulose were analyzed by means of a solid state biodegradation experiment, using residues from the essential oil extraction of the Palo Santo tree (Bursera graveolens). As such, two native Xylaria spp. and an exotic mushroom Trametes versicolor were incubated on the spent substrate (Residues of B. Graveolens, BGR’s). The relatively high lignin and cellulose contents of the BGRs (9.1% and 19%, respectively) indicated the potential of this resource for the production of methane (biogas) and ethanol. However, the degradation of the lignin and cellulose content could be traced back to the relatively high activity of the enzymes laccase, cellulase, and xylanase, produced by the fungi. The results showed that laccase (30.0 U/L and 26.6 U/L), cellulase (27.3 U/L and 35.8 U/L) and xylanase (189.7U/L and 128.3 U/L) activities of Xylaria feejeensis and Xylaria cf. microceras were generally higher than T. versicolor (9.0 U/L, 29.5 U/L, 99.5 U/L respectively). Furthermore, the total carbon (TC: 47.3%), total nitrogen (TN: 1.5%), total phosphorus (TP: 0.2%) and total potassium (TK: 1.2%) dynamics were analyzed during the experiment and their importance for the degradation process highlighted. The results of this work might serve as guidance for future studies in dry forest areas, while furthering the understanding of the potential use of native fungi as ecologic lignocellulosic decomposers and for industrial proposes.

Due to their catalytic properties, fungal laccases are used in various technological fields, from bioremediation to biofuel production. Here, we revisited the model selection for extracellular laccase activity in Trametes villosa using a central composite design (CCD) with factors including temperature, initial pH, and inoculum volume. To address concerns about overfitting and evaluation leakage in small-N RSM studies, we adopted a strict nested cross-validation (CV) protocol (outer 5-fold for generalization and inner 3-fold for tuning) and compared three shallow learners: quadratic ridge regression, RBF-kernel support-vector regression (SVR), and random forests, against two compact multilayer perceptrons (MLPs). To preserve physical plausibility, we modeled the response as log(y + ε) with ε = 0.1, and reported RMSE and R² from the outer 5-fold CV on the back-transformed scale. Across outer folds, SVR (RBF) achieved the lowest RMSE with the smallest fold-to-fold variance; the random forests were competitive; MLPs were more variable, and the ridge underfitted. SHAP analyses for the best model highlighted temperature as the dominant driver, with pH and inoculum showing secondary, non-monotonic contributions. Our results indicate that shallow nonlinear methods generalize best on small RSM datasets and should be preferred for early-stage process optimization.

Se evaluó el efecto de distintos niveles de cobre (II) sobre la velocidad específica de crecimiento de Trametes villosa en medio PDA 4% y medio PDA 4% con 25% (v/v) de vinaza. La velocidad específica de crecimiento se obtuvo a partir de los datos de área micelial y de la ecuación para el modelo logístico de crecimiento. Las mejores condiciones para el crecimiento micelial fueron PDA 4% con 25% (v/v) de vinaza y niveles de cobre divalente de 0,5 y 1,0 mM. Estos resultados indican que, de acuerdo con las condiciones de cultivo utilizadas, ambos suplementos favorecen el crecimiento micelial y entregan información esencial para el desarrollo de potenciales procesos de reutilización y valorización de la vinaza. The effect of different levels of copper (II) on the specific growth rate of Trametes villosa was evaluated. The strain was grown on 4% PDA and 4% PDA on a 25% (v/v) vinasse dilution. The specific growth rate was obtained from the mycelial area data which was modeled with the logistic equation. The best growth condition was 4% PDA in a 25% (v/v) vinasse dilution with copper (II) concentrations of 0.5 and 1.0 mM. These results indicate that under the utilized culture conditions, both supplements favor mycelial growth and provide essential information for potential processes of vinasse reuse and valorization.

The optimal conditions for laccase production and vinasse biotreatment with a native strain of Trametes villosa were determined by a screening-optimization approach. Eleven factors including nutrient concentration, vinasse dilution (%v/v), inoculum volume, carbon to nitrogen ratio and initial pH, were investigated for their effects on laccase activity applying the Plackett-Burman screening design. The selected factors were optimized using a central composite design, and then evaluated on a vinasse biotreatment experiment. The factors that contributed the most to the enzymatic activity were the concentrations of MgSO4∙7H2O (A), FeSO4∙7H2O (B) and CuSO4∙5H2O (C), alongside initial pH. After 10 days, laccase activity was 544.038 U L-1 for the following concentrations of A, B and C: 0.250 g L-1, 0.020 mg L-1, and 0.100 g L-1, respectively. Vinasse biotreatment under optimized conditions resulted in 82.74%, 78% and 75.97% of phenol, color, and COD removal respectively, while final pH value was 6.90. These results showed that the native strain of T. villosa has a good potential for further research on laccase production and vinasse sustainable management.

La agricultura moderna se apoya en el uso de plaguicidas para incrementar la producción de alimentos y satisfacer las necesidades de una población en constante crecimiento. Sin embargo, el uso inadecuado de plaguicidas puede tener un impacto significativo en los ecosistemas acuáticos, ya que pueden ingresar a las aguas superficiales o filtrarse en las aguas subterráneas. Esto plantea un riesgo para la salud pública, puesto que estos compuestos son persistentes y móviles en las fuentes de agua utilizadas para la producción de agua potable. En este contexto, se realizó una investigación en la península de Azuero, con el objetivo de analizar muestras de agua potable y subterráneas que son utilizadas para consumo. Las muestras se recolectaron durante la temporada de lluvias en septiembre de 2022 y la temporada seca en abril de 2023. Se aplicó el Método EPA 525.2 para detectar 17 plaguicidas utilizando extracción en fase sólida (SPE) y cromatografía de gas acoplada a espectrometría de masas (GC-MS). Los resultados indicaron la presencia de pendimentalina, atrazina y su metabolito atrazina-desetil en concentraciones bajas en muestras de agua potable en los distritos de La Villa de Los Santos y Parita, así como en aguas subterráneas en Santa María. Sin embargo, las concentraciones detectadas estuvieron por debajo de los límites permitidos según la normativa panameña (Reglamento Técnico DGNTI – COPANIT 21-2019) para el agua potable.

Machine learning is increasingly used to predict pollutant removal in adsorption studies, but models are often fitted to small, replicated experimental datasets and evaluated with a single random train/test split. We examined how the validation design affects model selection and surrogate-based optimization for removing phenolic compounds (quantified as tannins) from sugarcane vinasse using citric-acid-modified sugarcane bagasse, based on 315 measurements (105 conditions in triplicate) spanning temperature, contact time, and vinasse dilution. Eight regressors, from linear and quadratic models to kernel methods, tree ensembles, Gaussian processes, and neural networks, were tuned and evaluated by repeated nested cross-validation that kept all replicates of a condition together, and their errors were compared with the replicate (pure-error) standard deviation of 3.56% removal. Under this replicate-aware validation, all non-linear models performed similarly (RMSE 3.89–4.10%, R² 0.83–0.85), whereas the linear model was clearly worse (RMSE 5.06%). A naive random split increased R² by only about 0.01 (mean ΔR² 0.005–0.013), because, with replicate scatter of this size, memorizing replicates gives no advantage over a smooth model. Holding out entire dilution levels, however, separated the models sharply: the quadratic model remained the most accurate (RMSE 4.58%), whereas random forest and gradient boosting failed (R² ≤ 0.13). Permutation and SHAP analyses identified dilution as the dominant variable. Used as the optimization surrogate, the quadratic model placed maximum removal at 10% v/v dilution in a flat near-optimal region spanning about 37–40 °C and 74–130 min (maximum 97.7%); a confirmatory experiment at 40 °C and 100 min, inside this region, gave 97.4 ± 0.4% (n = 5) against 97.5% predicted. For small replicated adsorption datasets, the held-out unit should match the intended use of the model, and predictions away from the sampled levels require models that extrapolate smoothly.