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Colombia’s continuous contamination of water resources and the low alternatives to produce biofuels have affected the fulfillment of the objectives of sustainable development, deteriorating the environment and affecting the economic productivity of this country. Due to this reality, projects on environmental and economic sustainability, phytoremediation, and the production of biofuels such as ethanol and hydrogen were combined. The objective of this article was to design and develop a sustainable system for wastewater treatment and the generation of biofuels based on the biomass of the aquatic plant Eichhornia crassipes . A system that simulates an artificial wetland with live E. crassipes plants was designed and developed, removing organic matter contaminants; subsequently, and continuing the sustainability project, bioreactors were designed, adapted, and started up to produce bioethanol and biohydrogen with the hydrolyzed biomass used in the phytoremediation process, generating around 12 g/L of bioethanol and around 81 ml H 2 /g. The proposed research strategy suggests combining two sustainable methods, bioremediation and biofuel production, to preserve the natural beauty of water systems and their surroundings.

Observational studies have consistently described poor clinical outcomes and increased ICU mortality in patients with severe coronavirus disease 2019 (COVID-19) who require mechanical ventilation (MV). Our study describes the clinical characteristics and outcomes of patients with severe COVID-19 admitted to ICU in the largest health care system in the state of Florida, United States. Retrospective cohort study of patients admitted to ICU due to severe COVID-19 in AdventHealth health system in Orlando, Florida from March 11th until May 18th, 2020. Patients were characterized based on demographics, baseline comorbidities, severity of illness, medical management including experimental therapies, laboratory markers and ventilator parameters. Major clinical outcomes analyzed at the end of the study period were: hospital and ICU length of stay, MV-related mortality and overall hospital mortality of ICU patients. Out of total of 1283 patients with COVID-19, 131 (10.2%) met criteria for ICU admission (median age: 61 years [interquartile range (IQR), 49.5-71.5]; 35.1% female). Common comorbidities were hypertension (84; 64.1%), and diabetes (54; 41.2%). Of the 131 ICU patients, 109 (83.2%) required MV and 9 (6.9%) received ECMO. Lower positive end expiratory pressure (PEEP) were observed in survivors [9.2 (7.7-10.4)] vs non-survivors [10 (9.1-12.9] p = 0.004]. Compared to non-survivors, survivors had a longer MV length of stay (LOS) [14 (IQR 8-22) vs 8.5 (IQR 5-10.8) p< 0.001], Hospital LOS [21 (IQR 13-31) vs 10 (7-1) p< 0.001] and ICU LOS [14 (IQR 7-24) vs 9.5 (IQR 6-11), p < 0.001]. The overall hospital mortality and MV-related mortality were 19.8% and 23.8% respectively. After exclusion of hospitalized patients, the hospital and MV-related mortality rates were 21.6% and 26.5% respectively. Our study demonstrates an important improvement in mortality of patients with severe COVID-19 who required ICU admission and MV in comparison to previous observational reports and emphasizes the importance of standard of care measures in the management of COVID-19.

Von Economo neurons (VENs) are a specialized type of large, highly elongated projection neurons located in specific cortical regions. Despite their implication in higher-order cognitive functions and psychiatric disorders in humans, consistent and objective identification criteria for VENs remain lacking. We analyzed 761 digitally reconstructed neurons from the NeuroMorpho.Org database. We applied six supervised machine learning algorithms and a convolutional neural network with Grad-CAM visualization to classify the reconstructions into VENs and pyramidal neurons. Variable importance was evaluated using information-driven and expert-based selection. We compared the classifications made by machine learning algorithms to the reconstructions’ original labels. Reconstructions misclassified by the classifier models were further examined by a neuroanatomy expert. Machine learning models generally achieved high classification accuracy. Morphometric features such as dendritic length and number of stems emerged as some of the key discriminators. Expert ratings only partially aligned with machine findings, and there was low agreement between experts. Most misclassifications made by the classifier models were attributable to reconstruction artifacts or ambiguous morphology rather than model limitations. Our findings demonstrate the utility of combining machine learning with expert insight for distinguishing VENs from pyramidal neurons. While soma shape remains important for the characterization of VENs, classifier models revealed that dendritic architecture may be equally as specific and could help distinguish between borderline cases. This framework offers a replicable, data-driven method for studying VENs and can be utilized for future research on their distribution and function.

The field of emotion recognition from physiological signals is a growing area of research with significant implications for both mental health monitoring and human–computer interaction. This study introduces a novel approach to detecting emotional states based on fractal analysis of electrodermal activity (EDA) signals. We employed detrended fluctuation analysis (DFA), Hurst exponent estimation, and wavelet entropy calculation to extract fractal features from EDA signals obtained from the CASE dataset, which contains physiological recordings and continuous emotion annotations from 30 participants. The analysis revealed significant differences in fractal features across five emotional states (neutral, amused, bored, relaxed, and scared), particularly those derived from wavelet entropy. A cross-correlation analysis showed robust correlations between fractal features and both the arousal and valence dimensions of emotion, challenging the conventional view of EDA as a predominantly arousal-indicating measure. The application of machine learning for emotion classification using fractal features achieved a leave-one-subject-out accuracy of 84.3% and an F1 score of 0.802, surpassing the performance of previous methods on the same dataset. This study demonstrates the potential of fractal analysis in capturing the intricate, multi-scale dynamics of EDA signals for emotion recognition, opening new avenues for advancing emotion-aware systems and affective computing applications.

Peptide synthesis is an area with a wide field of application, from biomedicine to nanotechnology, that offers the option of simultaneously synthesizing a large number of sequences for the purpose of preliminary screening, which is a powerful tool. Nevertheless, standard protocols generate large volumes of solvent waste. Here, we present a protocol for the multiple Fmoc solid-phase peptide synthesis in tea bags, where reagent recycling steps are included. Fifty-two peptides with wide amino acid composition and seven to twenty amino acid residues in length were synthesized in less than three weeks. A clustering analysis was performed, grouping the peptides by physicochemical features. Although a relationship between the overall yield and the physicochemical features of the sequences was not established, the process showed good performance despite sequence diversity. The recycling system allowed to reduce N, N-dimethylformamide usage by 25–30% and reduce the deprotection reagent usage by 50%. This protocol has been optimized for the simultaneous synthesis of a large number of peptide sequences. Additionally, a reagent recycling system was included in the procedure, which turns the process into a framework of circular economy, without affecting the quality of the products obtained.

Piscirickettsiosis is the most severe, persistent, and damaging disease that has affected the Chilean salmon industry since its origins in the 1980s. As a preventive strategy for this disease, different vaccines have been developed and used over the last 30 years. However, vaccinated salmon and trout frequently die in the sea cages and the use of antibiotics is still high demonstrating the low efficiency of the available vaccines. The reasons why the vaccines fail so often are still debated, but it could involve different extrinsic and intrinsic factors. Among the extrinsic factors, mainly associated with chronic stress, we can distinguish: 1) biotic including coinfection with sea lice, sealions attacks or harmful algal blooms; 2) abiotic including low oxygen or high temperature; and 3) farm-management factors including overcrowding or chemical delousing treatments. Among the intrinsic factors, we can distinguish: 1) fish-related factors including host’s genetic variability (species, population and individual), sex or age; 2) pathogen-related factors including their variability and ability to evade host immune responses; and 3) vaccine-related factors including low immunogenicity and poor matches with the circulating pathogen strain. Based on the available evidence, in order to improve the development and the efficacy of vaccines against P. salmonis we recommend: a) Do not perform efficacy evaluations by intraperitoneal injection of pathogens because they generate an artificial protective immune response, instead cohabitation or immersion challenges must be used; b) Evaluate the diversity of pathogen strains in the field and ensure a good antigenic match with the vaccines; c) Investigate whether host genetic diversity could be improved, e.g. through selection, in favor of better and longer responses to vaccination; d) To reduce the stressful effects at the cage level, controlling the co-infection of pathogens and avoiding fish overcrowding. To date, we do not know the immunological mechanisms by which the vaccines against P. salmonis may or may not generate protection. More studies are required to identify what type of response, cellular or molecular, is required to develop effective vaccines.

This paper examines the relationship between economic growth and rural unemployment in Brazil and Mexico, incorporating the effects of climate variability and spatial interactions. Okun’s Law serves as the theoretical framework, and a dynamic spatial panel model is applied to estimate short-term causal effects. The analysis uses data from Brazil’s IBGE, Mexico’s INEGI, and the U.S. NCEI. The results indicate that Okun’s Law is only partially validated in Mexico, where lagged income growth reduces rural unemployment, while in Brazil, the relationship is not statistically confirmed. Climate variables also play a critical role: higher local temperatures reduce unemployment in Brazil and, with a lag, in Mexico, although temperature increases in neighboring regions raise unemployment in Mexico. Rainfall has a consistent positive effect on rural unemployment in both countries, highlighting the disruptive impact of extreme weather events. From a spatial perspective, no contemporaneous effects are observed. However, lagged spatial effects are negative in Brazil and positive in Mexico, suggesting different adjustment dynamics across territories. Overall, the findings reveal that climate variability influences the growth-unemployment nexus differently depending on the national context and temporal dimension. These results underscore the importance of designing public policies that integrate territorial coordination, address the differentiated impacts of climate variability, and strengthen the adaptive capacity and resilience of rural areas in Latin America.

Fish Red-Blood Cells (RBCs) are nucleated cells that can modulate the expression of different sets of genes in response to stimuli, playing an active role in the homeostasis of the fish immune system. Nowadays, vaccination is one of the main ways to control and prevent viral diseases in aquaculture and the development of novel vaccination approaches is a focal point in fish vaccinology. One of the strategies that has recently emerged is the use of nanostructured recombinant proteins. Nanostructured cytokines have already been shown to immunostimulate and protect fish against bacterial infections. To explore the role of RBCs in the immune response to two nanostructured recombinant proteins, TNF and a G-VHSV protein fragment, we performed different and studies. We show for the first time that rainbow trout RBCs are able to endocytose nanostructured TNF and G-VHSV protein fragment , despite not being phagocytic cells, and in response to nanostructured TNF and G-VHSV fragment, the expression of different immune genes could be modulated.