In addition to plant-derived, fungal pigments have become an alternative in respect to synthetic ones. Besides Monascus sp., several pigment-producing fungi do not have culture conditions well-established yet. In this research, media composition, light wavelength and co-culture were evaluated, results were reported in Absorbance Units per gram of biomass (AU/Bgr). For Fusarium oxysporum a C:N ratio above 7 was advantageous, using both complex and defined media; blue LED light increased the AU/Bgr value from 18013 to 344; co-culture did not enhance pigment production. In Aspergillus chevalieri a high C:N ratio with glucose as carbon source was ideal. When exposing cultures to light, UV and red light gave the highest pigmentation; moreover, differential UV-VIS spectra in all wavelengths suggested production of additional pigments. Particularly a pigment observed when cultured in green light was also found in co-culture with yeast and there was an improvement of AU/Bgr value of 52549%. This is the first report regarding light effect and co-culture for these fungi, as well as C:N ratio for A. chevalieri.

Bayesian multilevel models—also known as hierarchical or mixed models—are used in situations in which the aim is to model the random effect of groups or levels. In this paper, we conduct a simulation study to compare the predictive ability of 1-level Bayesian multilevel logistic regression models with that of 2-level Bayesian multilevel logistic regression models by using the prior Scaled Beta2 and inverse-gamma distributions to model the standard deviation in the 2-level. Then, these models are employed to estimate the correct answers in two questionnaires administered to university students throughout the first academic semester of 2018. The results show that 2-level models have a better predictive ability and provide more precise probability intervals than 1-level models, particularly when the prior Scaled Beta2 distribution is used to model the standard deviation in the second level. Moreover, the probability intervals of 1-level Bayesian multilevel logistic regression models proved to be more precise when Scaled Beta2 distributions, rather than an inverse-gamma distribution, are employed to model the standard deviation or when 1-level Bayesian multilevel logistic regression models, are used.

La movilidad urbana es un aspecto crucial en la configuración de la sostenibilidad y funcionalidad de las ciudades. En este artículo se realiza un análisis de la movilidad urbana en el Corredor Vial Calle 49 de Medellín, Colombia con datos de diversas fuentes, incluidos repositorios de datos abiertos y registros municipales, examinamos aspectos clave como la demanda, la oferta, la infraestructura para proporcionar perspectivas sobre la dinámica del transporte en esta arteria urbana vital, centrándose en el corredor vial de Ayacucho, el cuál sirve como columna vertebral de la red de transporte público de la ciudad, emerge como un punto focal de la investigación, cubriendo diferentes aspectos como la distribución de modos de transporte, el uso de bicicletas y la afluencia al metro. Con la información recolectada se realiza un análisis estadístico, que incluye medidas de tendencia central, gráficos de barra y circulares. Los resultados muestran una alta dependencia en el transporte no motorizado y público, con un uso significativo del tranvía. Las diferencias marcadas en el uso de bicicletas entre géneros y grupos etarios destacan oportunidades para mejorar la infraestructura ciclista y promover una movilidad más inclusiva. Además, los patrones de afluencia al metro, con picos durante horas de la mañana y tarde, y variaciones estacionales, subrayan la necesidad de una planificación integrada y adaptable al entorno. Estos hallazgos proporcionan una base sólida para la toma de decisiones informadas y el desarrollo de estrategias que promuevan una movilidad urbana sostenible, eficiente y equitativa en la ciudad de Medellín.

Student attrition remains a persistent challenge in higher education and is shaped by interacting socioeconomic, academic, institutional, and wellbeing-related mechanisms. Although learning analytics and educational data mining increasingly support early-warning and intervention workflows, dataset reuse is often limited by incomplete documentation and inconsistent variable definitions. This Data Descriptor presents a structured cross-sectional survey dataset on factors influencing student persistence at a Colombian public university campus (La Paz). Data were collected between August and December 2025 through an online questionnaire and subsequently cleaned to remove duplicate entries and personally identifiable information. The released dataset contains 333 student records and 33 variables covering demographics (e.g., age, gender, first-generation status), socioeconomic conditions (e.g., residential stratum, housing, financial aid), academic experience and satisfaction (multiple 1–5 Likert items), perceived dropout intention across personal/socioeconomic/academic domains, thematically coded open-ended items describing challenges and motives, and a self-allocation of 0–100 weights across three dropout-factor domains. We provide a machine-readable codebook, a transparent preprocessing description, and technical validation checks (value ranges, category consistency, and composite-score integrity). The dataset is intended to support reproducible retention research, equity-oriented analyses, and benchmarking of predictive models, while encouraging responsible reuse through privacy-preserving release practices and FAIR-aligned metadata, repository deposition, and versioning.

This study examines the social burden and systemic infrastructure vulnerabilities associated with fatal road traffic incidents in Medellín, Colombia, over the period 2008–2025. Using official records from the Secretaría de Movilidad de Medellín, the analysis quantifies impact through Years of Potential Life Lost (YPLL) and high-resolution spatiotemporal clustering, moving beyond simple fatality counts or economic valuation alone. Phase I applies an age-group proportional allocation method to estimate YPLL for 2762 fatal road traffic incidents, while Phase II employs a spatiotemporal geostatistical framework (ICCE-T) to detect statistically significant concentration clusters. Results indicate that these incidents generated 100,851 years of potential life lost, with individuals aged 15–35 accounting for 64.7% of total YPLL, and the 20–25 age group alone contributing 21.5% of the overall burden. Spatial analysis reveals persistent clustering along key urban corridors, particularly in Comuna 10 (La Candelaria), identifying recurrent nodes of elevated systemic vulnerability. By integrating epidemiological measurement with spatiotemporal analysis, the study provides a decision-oriented analytical framework to support resilient, evidence-based urban mobility interventions and guide strategic public investment under the Safe System approach.