One of the hallmarks of diabetes is an increased modification of cellular proteins. The most prominent type of modification stems from the reaction of methylglyoxal with arginine and lysine residues, leading to structural and functional impairments of target proteins. For lysine glycation, several algorithms allow a prediction of occurrence; thus, making it possible to pinpoint likely targets. However, according to our knowledge, no approaches have been published for predicting the likelihood of arginine glycation. There are indications that arginine and not lysine is the most prominent target for the toxic dialdehyde. One of the reasons why there is no arginine glycation predictor is the limited availability of quantitative data. Here, we used a recently published high–quality dataset of arginine modification probabilities to employ an artificial neural network strategy. Despite the limited data availability, our results achieve an accuracy of about 75% of correctly predicting the exact value of the glycation probability of an arginine–containing peptide without setting thresholds upon whether it is decided if a given arginine is modified or not. This contribution suggests a solution for predicting arginine glycation of short peptides.

The monitoring of volcanoes, which show eruptive activity of any type, is very important in order to anticipate an imminent eruption and thus be able to notify the authorities in charge of protecting the potentially exposed population. However, dormant volcanoes should be monitored in the same way, albeit with less equipment. The reasons for this are multiple: 1) to know the activity of the dormant or resting volcano in order to be able to recognize possible changes that may indicate a reactivation 2) the record of "secondary" activity in between phases of activity, such as mass movement processes, such as mud or debris flows (lahars), landslides (rock falls), landslides, among others 3) create awareness in the population and in authorities that the "mountain" is an active volcano that can represent a hazard for the population in its vicinity during a reactivation and also at any time (inter-eruptive processes) 4) to have data (over years) that allow characterizing the volcano. The Seismological and Volcanological Observatory of Veracruz (OSV) is in the process of creating a semi-automated monitoring system for Pico de Orizaba volcano, with the support of CENAPRED, UNAM-Juriquilla, and other more experienced volcano observatories such as the Volcanological Observatory of the University of Colima and the Volcanological Monitoring Center and Seismological (CMVS). El monitoreo de volcanes que muestran actividad eruptiva de cualquier naturaleza es muy importante para anticipar una erupción inminente y en consecuencia, alertar oportunamente a las autoridades encargadas de la protección de la población potencialmente expuesta. Sin embargo, los volcanes en estado de quietud o reposo deben monitorearse de igual forma, con una red básica de instrumentos. Las razones para ello son múltiples: 1) conocer la actividad del volcán dormido o en reposo para reconocer eventuales cambios en el mismo que pueden indicar una reactivación; 2) el registro de actividad “secundaria” en medio de fases de actividad como son los procesos de remoción en masa, flujos de lodo o detrito (lahares), derrumbes (caída de rocas), deslizamientos, entre otros; 3) crear consciencia en la población y en autoridades de que la “montaña” es un volcán activo que puede representar un peligro para la población en su cercanía durante un proceso de reactivación y también en cualquier momento (procesos inter-eruptivos); 4) disponer de datos (de años) que permitan caracterizar al volcán. El Observatorio Sismológico y Vulcanológico (OSV) está en el proceso de crear un sistema de monitoreo semiautomatizado del volcán Pico de Orizaba, con apoyo del CENAPRED, UNAM-Juriquilla, y otros observatorios de volcanes con más experiencia como el Observatorio Vulcanológico de la Universidad de Colima y Centro de Monitoreo Vulcanológico y Sismológico (CMVS).