ABSTRACT
Transcriptional regulation, involving the complex interplay between regulatory sequences and proteins, directs all biological processes. Computational models of transcription lack generalizability to accurately extrapolate in unseen cell types and conditions. Here, we introduce GET, an interpretable foundation model designed to uncover regulatory grammars across 213 human fetal and adult cell types. Relying exclusively on chromatin accessibility data and sequence information, GET achieves experimental-level accuracy in predicting gene expression even in previously unseen cell types. GET showcases remarkable adaptability across new sequencing platforms and assays, enabling regulatory inference across a broad range of cell types and conditions, and uncovering universal and cell type specific transcription factor interaction networks. We evaluated its performance on prediction of regulatory activity, inference of regulatory elements and regulators, and identification of physical interactions between transcription factors. Specifically, we show GET outperforms current models in predicting lentivirus-based massive parallel reporter assay readout with reduced input data. In fetal erythroblasts, we identify distal (>1Mbp) regulatory regions that were missed by previous models. In B cells, we identified a lymphocyte-specific transcription factor-transcription factor interaction that explains the functional significance of a leukemia-risk predisposing germline mutation. In sum, we provide a generalizable and accurate model for transcription together with catalogs of gene regulation and transcription factor interactions, all with cell type specificity.
ABSTRACT
Lithium, a critical natural resource integral to modern technology, has influenced diverse industries since its discovery in the 1950s. Of particular interest is lithium-7, the most prevalent lithium isotope on Earth, playing a vital role in applications such as batteries, metal alloys, medicine, and nuclear research. However, its extraction presents significant environmental and logistical challenges. This article explores the potential for lithium exploration on the Moon, driven by its value as a resource and the prospect of cost reduction due to the Moon's lower gravity, which holds promise for future space exploration endeavors. Additionally, the presence of lithium in the solar wind and its implications for material transport across celestial bodies are subjects of intrigue. Drawing from a limited dataset collected during the Apollo missions (Apollo 12, 15, 16, and 17) and leveraging artificial intelligence techniques and sample expansion through bootstrapping, this study develops predictive models for lithium-7 concentration based on spectral patterns. The study areas encompass the Aitken crater, Hadley Rima, and the Taurus-Littrow Valley, where higher lithium concentrations are observed in basaltic lunar regions. This research bridges lunar geology and the formation of the solar system, providing valuable insights into celestial resources and enhancing our understanding of space. The data used in this study were obtained from the imaging sensors (infrared, visible, and ultraviolet) of the Clementine satellite, which significantly contributed to the success of our research. Furthermore, the study addresses various aspects related to statistical analysis, sample quality validation, resampling, and bootstrapping. Supervised machine learning model training and validation, as well as data import and export, were explored. The analysis of data generated by the Clementine probe in the near-infrared (NIR) and ultraviolet-visible (UVVIS) spectra revealed evidence of the presence of lithium-7 (Li-7) on the lunar surface. The distribution of Li-7 on the lunar surface is non-uniform, with varying concentrations in different regions of the Moon identified, supporting the initial hypothesis associating surface Li-7 concentration with exposure to solar wind. While a direct numerical relationship between lunar topography and Li-7 concentration has not been established due to morphological diversity and methodological limitations, preliminary results suggest significant economic and technological potential in lunar lithium exploration and extraction.
ABSTRACT
Los sanitarios son sometidos a estresores, relacionados con trastornos del sueño. Este estudio evalúa las características del sueño en sanitarios de la zona sur de Madrid. Durante diciembre de 2021 y enero de 2022, se realizó, en los hospitales de Móstoles, Getafe, Fuenlabrada y Alcorcón, una encuesta anonimizada con cuestionarios autoadministrados que incluyó datos demoFiguras, escala de ansiedad GAD-7, índice de Pittsburgh (PSQI) e Índice de higiene del sueño (SHI). Obtuvimos una muestra de 329 sujetos. El 83,3% de sanitarios padece ansiedad, siendo las variables auxiliar de enfermería y trabajo a turnos las más afectadas. El 85% presentaron mala calidad del sueño, siendo las variables auxiliar de enfermería, experiencia >15 años y familiares a cargo las más significativas. La higiene del sueño se distribuye entre buena (48%) y mala (52%). Debemos considerar medidas concretas de protección y prevención para sanitarios. (AU)
Health workers are subjected to stressors, related to the development of sleep disorders. This study evaluates the characteristics of sleep in health workers in the southern area of Madrid. During December 2021 and January 2022, In the hospitals of Móstoles, Getafe, Fuenlabrada, and Alcorcón an anonymous survey with self-administered questionnaires that included demographic data, GAD-7 anxiety scale, Pittsburgh Index (PSQI) and Index sleep hygiene (SHI). We obtained a sample of 329 subjects. 83.3% of health workers suffer from anxiety, with nursing assistant and shift work variables being the most affected. 85% presented poor sleep quality, being the nursing assistant variables, >15 years of experience and dependent relatives the most significant. Sleep hygiene is distributed between good hygiene (48%) and poor hygiene (52%). We must consider specific protection and prevention measurements for health workers. (AU)
