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Biotechnological potential of Oscillatoria sp. for acid cheese whey remediation: insights into mixotrophic metabolism and nutrient removal

Wed, 01 Jan 2025 00:00:00 GMT

Título: Biotechnological potential of Oscillatoria sp. for acid cheese whey remediation: insights into mixotrophic metabolism and nutrient removal Autor: Carralero Bon, Iván; Lione, Danisa; Fideleff, Sofía; Chenevier, Delfina; Bergara, Claudia Daniela; Lario, Luciana Daniela; Pérez, Leonardo Martín Resumen: Background: The unsafe disposal of milk processing effluents has a negative impact on the environment due to their high content of nutrients and organic matter. Green alternatives can be applied to effectively manage and valorize these effluents, reducing their environmental footprint. Methods: The ability of the free-living cyanobacterium Oscillatoria sp. to grow in real cheese whey was evaluated as a potential strategy for integrating dairy wastewater treatment with biomass valorization. Autotrophic and mixotrophic cultures were maintained under controlled laboratory conditions and monitored over 28 days for growth, cell viability, biomass, pigment content, and physicochemical parameters, including pH, protein, carbohydrate, and chemical oxygen demand (COD). Results: Oscillatoria sp. successfully adapted to the initial acidic conditions of the effluent (pH 2.8–2.9), increasing the pH of the treated whey to levels suitable for industrial wastewater disposal (pH 6.0–9.0). A 5-fold increase in dehydrogenase activity was observed after a 28-day culture, with no signs of oxidative damage. Cyanobacterial biomass cultivated under mixotrophic conditions displayed a significant reduction (∼55%) in photosynthetic pigments, including chlorophyll a and total carotenoids, compared to autotrophic cultures. Notably, Oscillatoria sp. biomass increased by 2.3-fold under mixotrophy, compared to the autotrophic control. The higher biomass production was accompanied by a significant reduction in the whey COD from 35,250 mg/L to 8500 mg/L, along with a 65% and 80% decrease in protein and carbohydrate content, respectively. Conclusions: These findings provide new insights into the metabolic behavior of Oscillatoria sp. during cheese whey bioremediation, highlighting the potential of mixotrophic cyanobacteria for managing dairy wastewater management.

Low-Cost, High-Accuracy Reactivity Modeling: integrating Genetic Algorithms and Machine Learning with Multilevel DFT Calculations

Wed, 01 Jan 2025 00:00:00 GMT

Título: Low-Cost, High-Accuracy Reactivity Modeling: integrating Genetic Algorithms and Machine Learning with Multilevel DFT Calculations Autor: Pérez, José A.; Zanardi, María Marta; Sarotti, Ariel Marcelo Resumen: Accurate prediction of Gibbs activation energies (ΔG‡) for Diels–Alder (DA) reactions remains a critical challenge in computational chemistry, as conventional density functional theory (DFT) methods often fail to consistently achieve chemical accuracy (<1 kcal mol–1). In this work, we demonstrate that no single method reliably meets this threshold through a systematic evaluation of 720 DFT approaches across 24 DA reactions. Here, we introduce a proof-of-concept framework that integrates a genetic algorithm and machine learning (GA-ML) to intelligently select cost-effective, multilevel DFT combinations. Our optimized GA1 model identified four low-cost DFT combinations that yield ΔG‡ predictions with a mean absolute error (MAE) of 0.4 kcal mol–1 in both training and external validation sets, matching the accuracy of high-level CCSD(T) calculations at a fraction of the computational cost. To further enhance adaptability, we introduce Dynamic Generalization-Driven Transfer Learning (DGDTL), a novel method that adaptively optimizes linear coefficients, ensuring robust predictions for both known and unseen reactions. The integration of GA-ML and DGDTL establishes a scalable, high-fidelity paradigm for reactivity prediction, with potential applications in catalysis, drug design, and materials science.

A stabilized local integral method using RBFs for Helmholtz problems arising from electrodynamics

Thu, 01 Jan 2026 00:00:00 GMT

Título: A stabilized local integral method using RBFs for Helmholtz problems arising from electrodynamics Autor: Ponzellini Marinelli, L.; Raviola, L. Resumen: In this paper we present the Stabilized Local Boundary Domain Integral Method (SLBDIM), which is a local integral boundary element technique with stable computation based on Radial Basis Function (RBF) approximations, applied to Helmholtz problems. We present numerical results for small shape parameters of the RBF that stabilize the errors. We also discuss accuracy, conditioning and comparisons with other methods for various case studies. The virtues of the method are demonstrated through its application to problems arising in wave chaos, acoustics, and dielectric microresonators. The SLBDIM is computationally efficient and well suited to geometries with arbitrarily shaped domains, including those with corners.

Solvent Matters: Bridging Theory and Experiment in Quantum-Mechanical NMR Structural Elucidation

Thu, 01 Jan 2026 00:00:00 GMT

Título: Solvent Matters: Bridging Theory and Experiment in Quantum-Mechanical NMR Structural Elucidation Autor: Cortés, Iván; Cuadrado, Cristina; Gavín, José A.; Zanardi, María Marta; Hernández Daranas, Antonio; Sarotti, Ariel M. Resumen: Quantum-mechanical NMR (QM-NMR) is widely used in structure elucidation. A long-sought holey grail in this field is solving structures from a simple 1H NMR spectrum with AI- driven workflows. Yet, solvent effects on chemical shifts, though long recognized, remain overlooked. We show in a theory−experiment study that implicit solvation models miss solvent- induced variations and introduce a Python tool to quantify solvent sensitivity, aiding more reliable QM-NMR structural assignments.