The global agrifood system faces complex challenges related to biodiversity loss, food insecurity, and environmental degradation. Insects, as providers of multiple ecosystem services, offer a largely untapped potential to contribute to sustainability. This article introduces a conceptual framework for understanding the socio-ecological roles (SER) of insects, encompassing not only provisioning, regulating, supporting, and cultural services, but also the risks and challenges associated with their use and management. Through an interdisciplinary synthesis, we analyse four complementary insect management strategies—conservation, pest and vector control, wild insect gathering, and insect farming—and examine the relationship of these strategies to ecosystem services and stakeholder dynamics, as well as the need for policies regulating insect use and conservation. We argue that combining these strategies rather than treating them as isolated approaches enhances the capacity to optimize insect contributions while mitigating trade-offs. This holistic perspective contributes to a systemic, inclusive, evidence-based foundation for decision-making regarding the use of insect biodiversity to achieve resilient, sustainable agrifood systems.

Whole-slide imaging (WSI) in cytopathology poses challenges related to segmentation accuracy, computational efficiency, and image acquisition artifacts. This study aims to evaluate the performance of deep-learning models for instance segmentation in cervical cytology, benchmarking them against state-of-the-art methods on both public and institutional datasets. We tested three architectures—U-Net, vision transformer (ViT), and Detectron2—and evaluated their performance on the ISBI 2014 and CNseg datasets using panoptic quality (PQ), dice similarity coefficient (DSC), and intersection over union (IoU). All models were trained on CNseg and tested on an independent institutional dataset. Data preprocessing involved manual annotation using QuPath, patch extraction guided by GeoJSON files, and exclusion of regions containing less than 60% cytologic material. Our models achieved superior segmentation performance on public datasets, reaching up to 98% PQ. Performance decreased on the institutional dataset, likely due to differences in image acquisition and the presence of blurred nuclei. Nevertheless, the models were able to detect blurred nuclei, highlighting their robustness in suboptimal imaging conditions. In conclusion, the proposed models offer an accurate and efficient solution for instance segmentation in cytology WSI. These results support the development of reliable AI-powered tools for digital cytology, with potential applications in automated screening and diagnostic workflows.

The global COVID-19 crisis has severely affected mass transit in the cities of the global south. Fear of widespread propagation in public spaces and the dramatic decrease in human mobility due to lockdowns have resulted in a significant reduction of public transport options. We analyze the case of TransMilenio in Bogotá, a massive Bus Rapid Transit system that is the main mode of transport for an urban area of roughly 10 million inhabitants. Concerns over social distancing and new health regulations reduced the number of trips to under 20% of its historical values during extended periods of time during the lockdowns. This has sparked a renewed interest in developing innovative data-driven responses to COVID-19 resulting in large corpora of TransMilenio data being made available to the public. In this paper we use a database updated daily with individual passenger card swipe validation microdata including entry time, entry station, and a hash of the card’s ID. The opportunity of having daily detailed minute-to-minute ridership information and the challenge of extracting useful insights from the massive amount of raw data (∼1,000,000 daily records) require the development of tailored data analysis approaches. Our objective is to use the natural representation of urban mobility offered by networks to make pairwise quantitative similarity measurements between daily commuting patterns and then use clustering techniques to reveal behavioral disruptions as well as the most affected geographical areas due to the different pandemic stages. This method proved to be efficient for the analysis of large amount of data and may be used in the future to make temporal analysis of similarly large datasets in urban contexts.

Objetivo Proponer y evaluar un modelo para el ajuste y predicción de la mortalidad en Colombia que permita analizar tendencias por edad, sexo, Departamento y causa.Metodología Los registros de defunciones no fetales fueron utilizados como fuente primaria de análisis. Estos datos se pre-procesaron recodificando las causas y redistribuyendo los códigos basura. El modelo de predicción se formuló como una aproximaciónlineal de un conjunto de variables de interés, en particular la población y el producto interno bruto departamental.Resultados Como caso particular de estudio se tomó la mortalidad de menores de 5 años, se observó una disminución sostenida a partir del año 2000 tanto a nivel nacional como departamental, con excepción de tres departamentos. La evaluación del poderpredictivo de la metodología propuesta se realizó ajustando el modelo con los datos de 2000 a 2011, la predicción para el 2012 fue comparada con la tasa observada, estos resultados muestran que el modelo es suficientemente confiable para la mayor parte de las combinaciones departamento-causa.Conclusiones La metodología y modelo propuesto tienen el potencial de convertirse en un instrumento que permita orientar las prioridades del gasto en salud utilizando algún tipo de evidencia.

We present a ridership model of individual trajectories of users within a public transport network for which there are several different routes between origin and destiny and for which the automatic fare collection data does not include information about the exit station; only card identification and time of entry into the system. This model is implemented in the case of the Troncal component of TransMilenio, the BRT system that is the backbone of public transport in Bogot\'a. The granularity of the data allows to identify the occupation of every bus within the system at any particular day. As a validation, we compare the average bus occupation of two particular days, 17 of May of 2020 and the 1 of December of 2020.