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Item MDSCAN: RMSD-based HDBSCAN clustering of long molecular dynamicsAutores: González-Alemán, Roy; Platero-Rochart, Daniel; Rodríguez-Serradet, Alejandro; Hernández-Rodríguez, Erix W.; Caballero, Julio; Leclerc, Fabrice; Montero-Cabrera, LuisMotivation: The term clustering designates a comprehensive family of unsupervised learning methods allowing to group similar elements into sets called clusters. Geometrical clustering of molecular dynamics (MD) trajectories is a well-established analysis to gain insights into the conformational behavior of simulated systems. However, popular variants collapse when processing relatively long trajectories because of their quadratic memory or time complexity. From the arsenal of clustering algorithms, HDBSCAN stands out as a hierarchical density-based alternative that provides robust differentiation of intimately related elements from noise data. Although a very efficient implementation of this algorithm is available for programming-skilled users (HDBSCAN*), it cannot treat long trajectories under the de facto molecular similarity metric RMSD. Results: Here, we propose MDSCAN, an HDBSCAN-inspired software specifically conceived for non-programmers users to perform memory-efficient RMSD-based clustering of long MD trajectories. Methodological improvements over the original version include the encoding of trajectories as a particular class of vantage-point tree (decreasing time complexity), and a dual-heap approach to construct a quasi-minimum spanning tree (reducing memory complexity). MDSCAN was able to process a trajectory of 1 million frames using the RMSD metric in about 21h with <8 GB of RAM, a task that would have taken a similar time but more than 32 TB of RAM with the accelerated HDBSCAN*implementation generally used.Item Revisión de la normativa de REAS asociada al laboratorio clínicoAutores: Aguilera Acuña, Catalina ElizabethAutor Institucional: Universidad de TalcaProfesor Guía: Araya Ilufiz, Claudia LourdesLos residuos son el material inservible que queda luego de haber realizado un trabajo en cualquier área de desarrollo. En nuestro país se generan cerca de 20 millones de toneladas de residuos por año, aunque la mayoría son no peligrosos, hay un pequeño porcentaje que corresponde a residuos peligrosos, que deben ser manejados correctamente por la institución que los produzca. La importancia de esto recae en que los componentes de estos residuos pueden ser perjudiciales para la salud de las personas, así como el medio ambiente, ya que son uno de los grandes factores contaminantes que existen. Una de las áreas que cuenta con una gran producción de residuos, es la salud, donde a los residuos se les llama REAS (residuos de establecimientos de atención de salud). Aquí se encuentran elementos que van desde las jeringas usadas en tomas de muestra, hasta restos de tejido humano, por esto se deben clasificar según el riesgo que representan, y deben ser manejados de acuerdo con esta clasificación, siguiendo la normativa chilena vigente. Dentro del área de la salud también se encuentran los laboratorios clínicos, que producen variados tipos de residuos, como reactivos, elementos cortopunzantes, muestras de líquidos y fluidos biológicos, entre otros, los que de no ser eliminados correctamente pueden provocar gran daño a la salud de usuarios y funcionarios y al medio ambiente.Item New Generation Sustainable Technologies for Soilless Vegetable ProductionAutores: Fuentes Peñailillo, Fernando; Gutter, Karen; Vega, Ricardo; Silva, Gilda CarrascoThis review article conducts an in-depth analysis of the role of next-generation technologies in soilless vegetable production, highlighting their groundbreaking potential to revolutionize yield, efficiency, and sustainability. These technologies, such as AI-driven monitoring systems and precision farming methods, offer unparalleled accuracy in monitoring critical variables such as nutrient concentrations and pH levels. However, the paper also addresses the multifaceted challenges that hinder the widespread adoption of these technologies. The high initial investment costs pose a significant barrier, particularly for small- and medium-scale farmers, thereby risking the creation of a technological divide in the industry. Additionally, the technical complexity of these systems demands specialized expertise, potentially exacerbating knowledge gaps among farmers. Other considerations are scrutinized, including data privacy concerns and potential job displacement due to automation. Regulatory challenges, such as international trade regulations and policy frameworks, are discussed, as they may need revision to accommodate these new technologies. The paper concludes by emphasizing that while these sustainable technologies offer transformative benefits, their potential for broad adoption is constrained by a complex interplay of financial, technical, regulatory, and social factors.
