National Autonomous University of Mexico

About: National Autonomous University of Mexico is a education organization based out in Mexico City, Distrito Federal, Mexico. It is known for research contribution in the topics: Population & Galaxy. The organization has 72868 authors who have published 127797 publications receiving 2285543 citations. The organization is also known as: UNAM & Universidad Nacional Autónoma de México.

Quantum gravity corrections to neutrino propagation

Abstract: Massive spin-1/2 fields are studied in the framework of loop quantum gravity by considering a state approximating, at a length scale L much greater than Planck length lP = 1.2 × 10 33 cm, a spin-1/2 field in flat spacetime. The discrete structure of spacetime at lP yields corrections to the field propagation at scale L. Next, Neutrino Bursts (¯ p � 10 5 GeV) accompaning Gamma Ray Bursts that have travelled cosmological distances, L � 10 10 l.y., are considered. The dominant correction is helicity independent and leads to a time delay w.r.t. the speed of light, c, of order (¯ p lP)L/c � 10 4 s. To next order in ¯ lP the correction has the form of the Gambini and Pullin effect for photons. Its contribution to time delay is comparable to that caused by the mass term. Finally, a dependence L 1 os / ¯ p 2 lP is found for a two-flavour neutrino oscillation length.

Structure and electronic properties of MoS2 nanotubes

Abstract: Structural and electronic properties as well as the stability of MoS2 nanotubes are studied using the density-functional-based tight-binding method. It is found that MoS2 zigzag ( n,0) nanotubes exhibit a narrow direct band gap and MoS2 armchair ( n,n) possess a nonzero moderate direct gap. Interestingly, the ( n,n) tubes show a small indirect gap similar to the direct gap of ( n,0) nanotubes. Simulated electron diffraction patterns confirm the existence of armchair and zigzag disulphide nanotubes. The structure of the MoS2 nanotube tips is explained by introducing topological defects which produce positive and negative curvature.

RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond

Abstract: RegulonDB (http://regulondb.ccg.unam.mx) is one of the most useful and important resources on bacterial gene regulation, as it integrates the scattered scientific knowledge of the best-characterized organism, Escherichia coli K-12, in a database that organizes large amounts of data. Its electronic format enables researchers to compare their results with the legacy of previous knowledge and supports bioinformatics tools and model building. Here, we summarize our progress with RegulonDB since our last Nucleic Acids Research publication describing RegulonDB, in 2013. In addition to maintaining curation up-to-date, we report a collection of 232 interactions with small RNAs affecting 192 genes, and the complete repertoire of 189 Elementary Genetic Sensory-Response units (GENSOR units), integrating the signal, regulatory interactions, and metabolic pathways they govern. These additions represent major progress to a higher level of understanding of regulated processes. We have updated the computationally predicted transcription factors, which total 304 (184 with experimental evidence and 120 from computational predictions); we updated our position-weight matrices and have included tools for clustering them in evolutionary families. We describe our semiautomatic strategy to accelerate curation, including datasets from high-throughput experiments, a novel coexpression distance to search for `neighborhood' genes to known operons and regulons, and computational developments.

The application of artificial neural networks to the analysis of remotely sensed data

Abstract: Artificial neural networks (ANNs) have become a popular tool in the analysis of remotely sensed data. Although significant progress has been made in image classification based upon neural networks, a number of issues remain to be resolved. This paper reviews remotely sensed data analysis with neural networks. First, we present an overview of the main concepts underlying ANNs, including the main architectures and learning algorithms. Then, the main tasks that involve ANNs in remote sensing are described. The limitations and crucial issues relating to the application of the neural network approach are discussed. A brief review of the implementation of ANNs in some of the most popular image processing software packages is presented. Finally, we discuss the application perspectives of neural networks in remote sensing image analysis.