Universidade Federal do ABC

Climate change, fossil fuel depletion, and rising global energy demand underscore the need for strategic alternatives in energy conversion. In this context, the citrus processing industry, particularly orange juice production, represents a promising pathway for bioenergy recovery. Global orange processing (17.14 million metric tons), led by Brazil, Mexico, and the United States, generates large volumes of wastes such as peel, pulp, and seeds. This study characterized the chemical and thermophysical properties of these wastes and evaluated their energy availability potential. The results revealed attractive fuel characteristics, with a heating value ( HHV db = 17.64 MJ kg −1 ) and ultimate and proximate compositions comparable to those of sugarcane bagasse, a biomass extensively used in Brazilian sugar–energy facilities. Despite the high theoretical energy potential (∼429 thousand toe), the high as-received moisture content (>80%) remains a major barrier to efficient combustion. To address this constraint, scenarios combining mechanical pressing (reducing moisture to 65%) and subsequent thermal rotary drying were evaluated in terms of net energy availability. Under these conditions, the net energy available (∼202 thousand toe) not only met the thermal demand of the juice concentration stage but also generated surplus energy for processes such as juice pasteurization or sale through cogeneration. Although the use of orange wastes as solid biofuels may not yield the highest financial returns, it presents a technically viable strategy to strengthen energy self-sufficiency, reduce dependence on external fossil fuels, and mitigate environmental liabilities in the citrus industry. • Fuel properties comparable to sugarcane bagasse used in cogeneration. • Orange waste can be applied as a solid biofuel for use in industrial boilers. • Orange residues high moisture requires pre-drying before combustion. • Mechanical pressing plus drying in rotary dryer greatly increases net energy yield.

An integrated upstream process was developed for the high-level production of a low-immunogenic L-asparaginase mutant (EcaII P40S/S206C) in Escherichia coli, combining high-cell-density fed-batch cultivation with controlled induction and post-induction nutrient feeding. Biomass accumulation was achieved by exponential substrate feeding, followed by pH-stat-based nutrient supply during the expression phase. Induction protocols were optimized using biomass-specific IPTG dosing (µmol IPTG/gcell), applied either as a pulse or via exponential feeding. This combined strategy resulted in a maximum volumetric enzyme activity of 134,516 U/L and a specific productivity of 3387 U/gcell, corresponding to 91-fold and 24-fold increases, respectively, compared with standard batch cultivation. Overall, these results indicate that the coordinated control of feeding strategy and induction conditions substantially improves both volumetric and cell-specific productivity of recombinant L-asparaginase and provides a robust upstream framework for the efficient manufacture of improved enzyme variants.

In this note, I discuss the temporal dimension of informal settlement planning and development through a theoretical lens of expectations and decision-making. Prevailing approaches tend to generate a conceptual impasse. While orthodox economics is organized around rational expectations, present-value accounting, and the alignment between planning and realized outcomes, political economy emphasizes fictitious capital as a speculative claim on an inherently unknowable future. I develop the concept of fictional expectations—that is, expectations that have the potential to create a world of their own—as a way out of this impasse and contribute to revisable mid-level theories on the relational production of contemporary informal settlements and their entanglements with urban space. Using the trajectory of favela upgrading in Brazil, I illustrate the potential of this concept for “worlding” informal settlements in the urban Global South.

Abstract 4-Quinolones are a class of heterocycles traditionally known for their biological properties. More recently, those derivatives have gained attention as versatile ligands for the construction of metal-centered molecular materials, such as coordination polymers (CPs), opening new possibilities for environmental applications. In this study, a novel dicarboxylated organic compound, 1-ethyl-4-oxo-1,4-dihydroquinoline-3,6-dicarboxylic acid ( H 2 L ), was synthesized and employed to construct zinc-based materials. The synthetic parameters (temperature, time, modulator type, and modulator/ligand precursor ratio) were systematically evaluated, notably highlighting the replacement of the conventional solvothermal solvent (DMF) with a greener alternative (H 2 O) to synthesize the Zn-based materials. Under optimized conditions, two new materials were obtained, namely Material 1 (synthesized in DMF) and Material 2 (synthesized in H 2 O). Additionally, Material 2 - [Zn(L)(H 2 O)] n - was obtained as single crystals and structurally characterized via synchrotron X-ray diffraction. As for the application, Material 2 demonstrated an adsorption capacity of 24.6 mg g -1 for the removal of the toxic dye methylene blue (MB). Different adsorption essays revealed that the Langmuir isotherm and pseudo-second-order kinetic model best described the behavior of Material 2 . This material achieved 100% selectivity toward the cationic dye (MB) and demonstrated excellent reusability, with only approximately 10% loss in removal efficiency over three adsorption/desorption cycles. Post-adsorption characterization provided valuable insights into the underlying interaction mechanisms. Together, these results highlight the potential of the developed quinolone-based coordination polymer as an efficient material for environmental remediation applications and underscore the relevance of further exploring this emerging class of compounds. Graphical Abstract

Abstract Human learning in sequential environments often involves uncovering latent statistical structures that govern event regularities. In this study, we investigate how individuals adapt their internal predictive models while playing the Goalkeeper Game, a stochastic sequence prediction task driven by a probabilistic context tree. We introduce a real-time context-tree inference framework that reconstructs the evolving internal models underlying participants’ trial-by-trial choices. By tracking the entropy of the inferred context trees, we reveal that learning unfolds through two intertwined processes: frequent refinements, corresponding to gradual adjustments of transition probabilities within a stable structure, and rare transitions, corresponding to structural reorganizations of the predictive model. Entropy reductions parallel improvements in success rate, demonstrating that participants progressively internalize the underlying generative process. The waiting-time distribution of transitions follows a sub-exponential Weibull law, indicating history-dependent reorganization dynamics consistent with bursty, non-memoryless adaptation. These findings suggest that human statistical learning proceeds through a balance between exploitation and exploration. Our framework provides a quantitative and interpretable tool for modeling the continuous–discrete dynamics of adaptive learning in probabilistic environments.

The preparative expression, purification, and refoldingof a novelbifunctional chimeric enzyme, 63N-hC_hARG1,engineered for dual amino acid depletion therapy, are described. Aguinea pig–human l-asparaginase hybrid (63N-hC) was fused to human arginase 1 through a rigid helicallinker, and the codon-optimized gene was expressed in Escherichia coli BL21­(DE3). Nonclassical inclusionbodies (IBs) were obtained, exhibiting activities of 2.16 ± 0.04U mL–1 for 63N-hC and 13.42± 0.09 U L–1 for hARG1, with a preparativeyield of 25.8 ± 0.6 mg mL–1 from the lysate.After solubilization in 8 M urea, size-exclusion chromatography andreverse-dilution refolding were performed, restoring activities to0.22 ± 0.05 U mL–1 and 4.66 ± 0.9 U L–1, respectively. Structural compatibility and potentialdual functionality were supported by in-silico modeling and moleculardocking. A scalable workflow for expression optimization and functionalrecovery of multimeric chimeric proteins from IBs is thus demonstrated,highlighting key parameters governing the refolding of complex fusionproteins and the value of nonclassical IBs as reservoirs for therapeuticenzyme production.

Background/Objectives: The study investigated the anti-inflammatory potential of neolignan derivatives of dehydrodieugenol (CP1–CP5), focusing on the inhibition of secretory phospholipase A2 (sPLA2), a key enzyme in inflammation. Methods: Comprehensive quantitative docking analysis using four independent algorithms (PLP, ASP, ChemScore, GoldScore) revealed exceptional multitarget binding profiles for CP1 and CP2, with scores consistently above activity thresholds for acetylcholinesterase (AChE), cyclooxygenase-2 (COX-2), and sPLA2 from Crotalus durissus terrificus in both monomeric (Mcdt) and quaternary (Tcdt) forms. Results: Among the compounds, CP1 demonstrated the highest predicted affinity (AChE: 78.5, COX-2: 83.8, sPLA2: 82.7–83.4) and most potent experimental activity, reducing sPLA2 catalytic velocity through mixed-type inhibition involving the active site (His47, Asp48) and Ca2+ binding loop. In vivo assays in sPLA2-induced paw edema demonstrated that CP1 and CP2 achieved remarkable anti-inflammatory effects (up to 68.3% reduction), significantly exceeding their protective potential by direct enzyme inhibition, confirming the multitarget mechanism. The strong correlation between predicted docking scores and paw edema reduction (R2 = 0.89, p < 0.01) creates a firm foundation for establishing structure–activity relationship explanations. Conclusions: These findings highlight an integrated mechanism involving: (1) partial sPLA2 modulation, (2) neuroimmune regulation via AChE inhibition, and (3) prostaglandin synthesis blockade through COX-2 inhibition. This multitarget approach, combined with the natural origin of the compounds, positions dehydrodieugenol derivatives as promising candidates for developing therapies against complex inflammatory diseases, offering significant advantages over single-target strategies.