RESUMEN
Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks1. Accurate and timely warnings are critical for mitigating flood risks2, but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that artificial intelligence-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a five-day lead time that is similar to or better than the reliability of nowcasts (zero-day lead time) from a current state-of-the-art global modelling system (the Copernicus Emergency Management Service Global Flood Awareness System). In addition, we achieve accuracies over five-year return period events that are similar to or better than current accuracies over one-year return period events. This means that artificial intelligence can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed here was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings.
Asunto(s)
Inteligencia Artificial , Simulación por Computador , Inundaciones , Predicción , Predicción/métodos , Reproducibilidad de los Resultados , Ríos , Hidrología , Calibración , Factores de Tiempo , Planificación en Desastres/métodosRESUMEN
Circular RNAs (circRNAs) are abundant and evolutionarily conserved RNAs of largely unknown function. Here, we show that a subset of circRNAs is translated in vivo. By performing ribosome footprinting from fly heads, we demonstrate that a group of circRNAs is associated with translating ribosomes. Many of these ribo-circRNAs use the start codon of the hosting mRNA, are bound by membrane-associated ribosomes, and have evolutionarily conserved termination codons. In addition, we found that a circRNA generated from the muscleblind locus encodes a protein, which we detected in fly head extracts by mass spectrometry. Next, by performing in vivo and in vitro translation assays, we show that UTRs of ribo-circRNAs (cUTRs) allow cap-independent translation. Moreover, we found that starvation and FOXO likely regulate the translation of a circMbl isoform. Altogether, our study provides strong evidence for translation of circRNAs, revealing the existence of an unexplored layer of gene activity.
Asunto(s)
Proteínas de Drosophila/biosíntesis , Drosophila melanogaster/metabolismo , Proteínas Nucleares/biosíntesis , Biosíntesis de Proteínas , ARN/metabolismo , Ribosomas/metabolismo , Animales , Línea Celular , Codón Iniciador , Codón de Terminación , Proteínas de Drosophila/genética , Proteínas de Drosophila/metabolismo , Drosophila melanogaster/genética , Factores de Transcripción Forkhead/metabolismo , Genotipo , Cabeza , Espectrometría de Masas , Ratones , Mutación , Proteínas Nucleares/genética , Conformación de Ácido Nucleico , Estado Nutricional , Fenotipo , ARN/química , ARN/genética , Caperuzas de ARN/química , Caperuzas de ARN/genética , ARN Circular , Ratas , Ribosomas/química , Ribosomas/genética , Inanición/genética , Inanición/metabolismo , Relación Estructura-Actividad , TransfecciónRESUMEN
Here we describe a new integrative approach for accurate annotation and quantification of circRNAs named Short Read circRNA Pipeline (SRCP). Our strategy involves two steps: annotation of validated circRNAs followed by a quantification step. We show that SRCP is more sensitive than other individual pipelines and allows for more comprehensive quantification of a larger number of differentially expressed circRNAs. To facilitate the use of SRCP, we generate a comprehensive collection of validated circRNAs in five different organisms, including humans. We then utilize our approach and identify a subset of circRNAs bound to the miRNA-effector protein AGO2 in human brain samples.