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This study aims to assess the adoption and impact of Artificial Intelligence (A.I.) tools in higher education, focusing on a private university in Latin America. Guided by the question, \"What is the impact, as perceived by university students, of using Artificial Intelligence tools on various dimensions of learning and teaching within the context of higher education?\" the study employs a rigorously validated 30-item instrument to examine five key dimensions: 1) Effectiveness use of A.I. tools, 2) Effectiveness use of ChatGPT, 3) Student's proficiency using A.I. tools, 4) Teacher's proficiency in A.I. and 5) Advanced student skills in A.I. These dimensions form a synthetic index used for comprehensive evaluation. Targeting 4,127 students from the university's schools of Engineering, Business, and Arts, the study garnered 21,449 responses, analyzed using Confirmatory Factor Analysis for validity. Findings indicate a significantly positive impact of A.I. tools on student academic experiences, including enhanced comprehension, creativity, and productivity. Importantly, the study identifies areas with low and high A.I. integration, serving as an institutional diagnostic tool. The data underscores the importance of A.I. proficiency among both educators and students, advocating for its integration as a pedagogical evolution rather than just a technological shift. This research has critical implications for data-driven decision-making in higher education, offering a robust framework for institutions aiming to navigate the complexities of A.I. implementation.

Identifying the relationship between academic stress and mental health of undergraduate university students is crucial for reducing and understanding its negative effects, enhancing students' ability to cope with stressful situations, and thereby reducing the harm it causes on academic performance and overall well-being. This study aims to examine the correlation and predictive value of academic stress on mental health in undergraduate university students. A representative sample of 1,265 undergraduate university students from a private university in Bolivia was assessed using Pearson's correlation analysis to determine the predictive value of academic stress on mental health. To validate the measurements obtained, a stepwise Hierarchical Multiple Linear Regression analysis was applied. A probability model was estimated to identify academic stressors that contribute to the probability of students experiencing Languishing Mental Health. The study revealed that Self-inflicted Stress was the most significant stressor among undergraduate students. This indicates that students' self-demands and self-efficacy perceptions are essential factors in the development of high academic stress levels. There is a clear correlation between high levels of academic stress and the probability of experiencing Languishing Mental Health.

This study investigates the integration of Artificial Intelligence (AI) tools within the School of Arts at a private university in Latin America, focusing on student perceptions and emotional reactions. The research addresses two primary questions: how students perceive the integration of AI tools in their educational experience, and how AI-enhanced classes affect students' emotional reactions compared to traditional lecture-based classes. To explore the first question, we constructed the Synthetic Index of Use of Artificial Intelligence Tools (SIUAIT) to measure the perceived effects of AI usage across five dimensions: effectiveness of AI tools, implementation of ChatGPT, student proficiency, instructor proficiency, and advanced student skills. Confirmatory Factor Analysis (CFA) validated the SIUAIT, revealing an increase from 58.84 in the first semester to 60.60 in the second semester of 2023, indicating growing acceptance and perceived utility of AI tools in arts education. To address the second question, we employed advanced neuromarketing technologies, including eye tracking and facial expression analysis, to assess emotional reactions in AI-enhanced versus traditional lecture-based classes. The findings showed that AI-enhanced classes elicited more positive emotions, such as joy and surprise, compared to traditional methods. Statistical analyses, including Pearson correlation, Student's t-test, and Kruskal-Wallis test, confirmed the significance of these differences. This comprehensive approach provides valuable insights into the benefits and challenges of integrating AI in arts education. The study highlights AI's potential to enhance educational experiences and emotional engagement while emphasizing the need for ongoing training and addressing ethical concerns to ensure the effective and equitable use of AI tools. This study explores how Artificial Intelligence (AI) tools enhance the educational experience in a higher education setting. It focuses on two key aspects: the Synthetic Index of Use of Artificial Intelligence Tools (SIUAIT) and the measurement of students' emotional reactions when taught with AI compared to traditional lecture-based classes. The results show that learning experiences are significantly improved in AI-enhanced classes, with students displaying more positive emotions. Additionally, the SIUAIT provides valuable insights for making informed decisions on training both students and teachers over time. These findings highlight the potential of AI to create more engaging and effective learning environments, emphasizing its importance for future educational strategies and the continuous development of AI proficiency among educators and learners.

This study explored the integration of Artificial Intelligence (AI) tools in finance education, focusing on student perceptions, emotional reactions, and educator experiences. Quantitative data were gathered using the Synthetic Index of Use of AI Tools (SIUAIT) instrument, administered over three semesters. The findings revealed that finance students perceived AI tools as essential for enhancing their learning experience. Notably, Financial Engineering students exhibited higher proficiency and more positive perceptions of AI tools compared to students in other disciplines, such as Engineering and Business. An observational study utilizing eye tracker technology and facial expression analysis highlighted the emotional dynamics between AI-enhanced and traditional lecture-based classes. Positive emotions, such as joy and surprise, were significantly more prevalent in AI-enhanced environments, contributing to a more engaging and emotionally positive learning experience. However, an increase in fear was also observed, which could be considered a negative activating emotion that, ultimately, still fostered learning. Semi-structured interviews with educators revealed both the opportunities and challenges of AI integration. Educators acknowledged AI's benefits in enhancing pedagogy but expressed concerns about over-reliance and ethical implications. Thematic analysis identified key dimensions: knowledge, usage, and ethics in AI. The study concluded that AI tools could significantly transform finance education, offering enhanced learning experiences and better preparing students for future careers. However, a balanced approach, addressing ethical and psychological impacts, was essential to maximize benefits and minimize potential drawbacks. Future research should explore AI's long-term effects and its correlation with academic performance. This study highlights how Artificial Intelligence (AI) tools are shaping finance education by examining their effects on students' learning experiences, emotional responses, and educators' perspectives. AI tools, such as ChatGPT and FinChat, were shown to improve students' engagement and understanding, particularly in finance-related subjects. The study's findings suggest that students, especially in Financial Engineering, not only gain valuable skills but also experience increased positive emotions, like joy and surprise, during AI-enhanced classes. However, an observed rise in fear also indicated the importance of addressing emotional challenges in AI-driven learning. Educators viewed AI tools as beneficial for enhancing teaching but raised concerns about ethical considerations and over-reliance. This research underscores the transformative potential of AI in finance education while advocating for a thoughtful balance between its advantages and challenges. These insights are relevant for educators, students, and policymakers as they consider the future integration of AI in academic environments.

Background The cytochromes P450 (P450s) are a large superfamily of heme-containing monooxygenases involved in the oxidative metabolism of an enormous diversity of substrates. These enzymes require electrons for their activity, and the electrons are supplied by NAD(P)H through a P450 electron donor system, which is generally a cytochrome P450 reductase (CPR). The yeast Xanthophyllomyces dendrorhous has evolved an exclusive P450-CPR system that specializes in the synthesis of astaxanthin, a carotenoid with commercial potential. For this reason, the aim of this work was to identify and characterize other potential P450 genes in the genome of this yeast using a bioinformatic approach. Results Thirteen potential P450-encoding genes were identified, and the analysis of their deduced proteins allowed them to be classified in ten different families: CYP51, CYP61, CYP5139 (with three members), CYP549A, CYP5491, CYP5492 (with two members), CYP5493, CYP53, CYP5494 and CYP5495. Structural analyses of the X. dendrorhous P450 proteins showed that all of them have a predicted transmembrane region at their N-terminus and have the conserved domains characteristic of the P450s, including the heme-binding region (FxxGxRxCxG); the PER domain, with the characteristic signature for fungi (PxRW); the ExxR motif in the K-helix region and the oxygen-binding domain (OBD) (AGxDTT); also, the characteristic secondary structure elements of all the P450 proteins were identified. The possible functions of these P450s include primary, secondary and xenobiotic metabolism reactions such as sterol biosynthesis, carotenoid synthesis and aromatic compound degradation. Conclusions The carotenogenic yeast X. dendrorhous has thirteen P450-encoding genes having potential functions in primary, secondary and xenobiotic metabolism reactions, including some genes of great interest for fatty acid hydroxylation and aromatic compound degradation. These findings established a basis for future studies about the role of P450s in the carotenogenic yeast X. dendrorhous and their potential biotechnological applications.

In Chile, edible herbs are mainly grown by small farmers. This type of horticultural crop typically requires intensive management because it is highly susceptible to insects, some of which transmit viruses that severely affect crop yield and quality. In 2019, in coriander plants tested negative for all previously reported viruses, RNA-Seq analysis of one symptomatic plant revealed a plethora of viruses, including one virus known to infect coriander, five viruses never reported in coriander, and a new cytorhabdovirus with a 14,180 nucleotide RNA genome for which the species name Cytorhabdovirus coriandrum was proposed. Since all the detected viruses were aphid-borne, aphids and weeds commonly growing around the coriander field were screened for viruses. The results showed the occurrence of the same seven viruses and the alfalfa mosaic virus, another aphid-borne virus, in aphids and weeds. Together, our findings document the presence of multiple viruses in coriander and the potential role of weeds as virus reservoirs for aphid acquisition.

The red yeast X. dendrorhous is one of the few natural sources of astaxanthin, a carotenoid used in aquaculture for salmonid fish pigmentation and in the cosmetic and pharmaceutical industries for its antioxidant properties. Genetic control of carotenogenesis is well characterized in this yeast; however, little is known about the regulation of the carotenogenesis process. Several lines of evidence have suggested that carotenogenesis is regulated by catabolic repression, and the aim of this work was to identify and functionally characterize the X. dendrorhous MIG1 gene encoding the catabolic repressor Mig1, which mediates transcriptional glucose-dependent repression in other yeasts and fungi. The identified gene encodes a protein of 863 amino acids that demonstrates the characteristic conserved features of Mig1 proteins, and binds in vitro to DNA fragments containing Mig1 boxes. Gene functionality was demonstrated by heterologous complementation in a S. cerevisiae mig1- strain; several aspects of catabolic repression were restored by the X. dendrorhous MIG1 gene. Additionally, a X. dendrorhous mig1- mutant was constructed and demonstrated a higher carotenoid content than the wild-type strain. Most important, the mig1- mutation alleviated the glucose-mediated repression of carotenogenesis in X. dendrorhous: the addition of glucose to mig1- and wild-type cultures promoted the growth of both strains, but carotenoid synthesis was observed only in the mutant strain. Transcriptomic and RT-qPCR analyses revealed that several genes were differentially expressed between X. dendrorhous mig1- and the wild-type strain when cultured with glucose as the sole carbon source. The results obtained in this study demonstrate that catabolic repression in X. dendrorhous is an active process in which the identified MIG1 gene product plays a central role in the regulation of several biological processes, including carotenogenesis.

Pseudomonas syringae is a Gram-negative bacterium that infects a wide range of plants, causing significant economic losses in agricultural production. The pathogen exhibits a high degree of genetic and phenotypic diversity, which has led to the classification of P. syringae strains into different pathovars based on their host range and disease symptoms. Copper-based products have traditionally been used to manage infections in agriculture, but the emergence of copper-resistant strains has become a significant concern. Biological control is a promising strategy to manage P. syringae, as it offers an environmentally friendly and sustainable approach to disease management. The review includes an overview of the biology and epidemiology of P. syringae, and of the mechanisms of action of various biological control agents, mainly microorganisms (antagonistic bacteria, and fungi) and bacteriophages. Specifically, this review highlights the renewed interest in bacteriophages (bacteria-infecting viruses) due to their advantages over other eco-friendly management methods, thanks to their bactericidal properties and potential to target specific pathogenic bacteria. The potential benefits and limitations of biological control are also examined, along with research directions to optimize the use of this approach for the management of P. syringae.

Se presenta una revisión histórica y un análisis cuantitativo de las relaciones entre el crecimiento económico boliviano y los precios de los recursos naturales predominantes en la estructura de las exportaciones en el período 1970–2013, en particular: el estaño, el zinc, la plata, el oro y el gas natural. Tomando en cuenta la tendencia del precio del estaño en el período 1970–1986 y la del gas natural en el período 2000–2013 encontramos que es posible estimar la tendencia del crecimiento económico, respectivamente, con errores medios de predicción de 1.25 y 0.50 puntos porcentuales, respectivamente. Las tendencias de los precios de estos recursos reproducen el 89 por ciento de la tendencia del crecimiento. La fuerte relación entre las tendencias de estas variables, exentas de fluctuaciones inducidas por factores coyunturales internos o externos a la economía boliviana, demuestra la importancia que tienen los precios de estos recursos como predictores del crecimiento económico. Encontramos, además, que en el período 1987–1999, ninguno de los precios de otros minerales y recursos naturales como el del zinc, la plata y el oro permite predecir la tendencia de crecimiento económico con similar precisión. We present a quantitative analysis of the relationships between economic growth in Bolivia and the price dynamics in 1970–2013 of the most important export natural resources, namely tin, zinc, silver, gold and gas. On the basis of historical analysis, we identify the time periods in which each of these natural resources was closely related to the country’s economic performance. We find that the price trends of tin in 1970–1986 and gas in 2000–2013 allow for reproduction of the economic growth trend, with mean squared errors of 1.25 and 0.5, respectively. Variations in the price trends of these resources account for 89 percent of variability in the economic growth trend. These strong relationships go on to show the power of these price trends as predictors of economic growth. We also find that in 1987–1999, price trends of zinc, silver, and gold are not precise predictors of economic growth.

In this paper we assess the relationship between labor policies and market outcomes in Bolivia, accounting for a large informal sector mostly comprised of self-employed entrepreneurs. We calibrate a job search and matching model to reproduce labor market features in 2013, a period in which important labor policy changes were simultaneously active for the first time. We focus on some effects of three specific policies namely a 14th salary, minimum wage increases and contributions to a 'solidary pension fund' on the sorting of workers between unemployment, formal and informal employment, as well as on the formal wage schedule.