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Espinal-Enriquez J.,National Institute of Genomic Medicine | Espinal-Enriquez J.,Center for Complexity Science | Larralde H.,National Autonomous University of Mexico
PLoS ONE | Year: 2015

Since December 2006, more than a thousand cities in México have suffered the effects of the war between several drug cartels, amongst themselves, as well as with Mexican armed forces. Sources are not in agreement about the number of casualties of this war, with reports varying from 30 to 100 thousand dead; the economic and social ravages are impossible to quantify. In this work we analyze the official report of casualties in terms of the location and the date of occurrence of the homicides. We show how the violence, as reflected by the number of casualties, has increased over time and spread across the country. Next, based on the correlations between cities in the changes of the monthly number of casualties attributed to organized crime, we construct a narco-war network where nodes are the affected cities and links represent correlations between them. We find that close geographical distance between violent cities does not imply a strong correlation amongst them. We observe that the dynamics of the conflict has evolved in short-term periods where a small core of violent cities determines the main theatre of the war at each stage. This kind of analysis may also help to describe the emergence and propagation of gang-related violence waves. © 2015 Espinal-Enríquez, Larralde. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Source


Hernandez-Lemus E.,National Institute of Genomic Medicine
PloS one | Year: 2012

In the past, a great deal of attention has been drawn to thermal driven denaturation processes. In recent years, however, the discovery of stress-induced denaturation, observed at the one-molecule level, has revealed new insights into the complex phenomena involved in the thermo-mechanics of DNA function. Understanding the effect of local pressure variations in DNA stability is thus an appealing topic. Such processes as cellular stress, dehydration, and changes in the ionic strength of the medium could explain local pressure changes that will affect the molecular mechanics of DNA and hence its stability. In this work, a theory that accounts for hysteresis in pressure-driven DNA denaturation is proposed. We here combine an irreversible thermodynamic approach with an equation of state based on the Poisson-Boltzmann cell model. The latter one provides a good description of the osmotic pressure over a wide range of DNA concentrations. The resulting theoretical framework predicts, in general, the process of denaturation and, in particular, hysteresis curves for a DNA sequence in terms of system parameters such as salt concentration, density of DNA molecules and temperature in addition to structural and configurational states of DNA. Furthermore, this formalism can be naturally extended to more complex situations, for example, in cases where the host medium is made up of asymmetric salts or in the description of the (helical-like) charge distribution along the DNA molecule. Moreover, since this study incorporates the effect of pressure through a thermodynamic analysis, much of what is known from temperature-driven experiments will shed light on the pressure-induced melting issue. Source


Contreras A.V.,National Autonomous University of Mexico | Contreras A.V.,National Institute of Genomic Medicine | Torres N.,National Institute of Medical science and Nutrition Salvador Zubiran | Tovar A.R.,National Institute of Medical science and Nutrition Salvador Zubiran
Advances in Nutrition | Year: 2013

Peroxisome proliferator-activated receptors (PPARs) are transcription factors that belong to the superfamily of nuclear hormone receptors and regulate the expression of several genes involved in metabolic processes that are potentially linked to the development of some diseases such as hyperlipidemia, diabetes, and obesity. One type of PPAR, PPAR-α, is a transcription factor that regulates the metabolism of lipids, carbohydrates, and amino acids and is activated by ligands such as polyunsaturated fatty acids and drugs used to treat dyslipidemias. There is evidence that genetic variants within the PPARα gene have been associated with a risk of the development of dyslipidemia and cardiovascular disease by influencing fasting and postprandial lipid concentrations; the gene variants have also been associated with an acceleration of the progression of type 2 diabetes. The interactions between genetic PPARα variants and the response to dietary factors will help to identify individuals or populations who can benefit from specific dietary recommendations. Interestingly, certain nutritional conditions, such as the prolonged consumption of a protein-restricted diet, can produce long-lasting effects on PPARα gene expression through modifications in the methylation of a specific locus surrounding the PPARα gene. Thus, this review underlines our current knowledge about the important role of PPAR-α as a mediator of the metabolic response to nutritional and environmental factors. © 2013 American Society for Nutrition. Source


Rodriguez E.E.,CINVESTAV | Rodriguez E.E.,Autonomous University of Hidalgo | Hernandez-Lemus E.,National Institute of Genomic Medicine | Hernandez-Lemus E.,National Autonomous University of Mexico | And 3 more authors.
PLoS ONE | Year: 2011

The analysis of the interaction and synchronization of relatively large ensembles of neurons is fundamental for the understanding of complex functions of the nervous system. It is known that the temporal synchronization of neural ensembles is involved in the generation of specific motor, sensory or cognitive processes. Also, the intersegmental coherence of spinal spontaneous activity may indicate the existence of synaptic neural pathways between different pairs of lumbar segments. In this study we present a multichannel version of the detrended fluctuation analysis method (mDFA) to analyze the correlation dynamics of spontaneous spinal activity (SSA) from time series analysis. This method together with the classical detrended fluctuation analysis (DFA) were used to find out whether the SSA recorded in one or several segments in the spinal cord of the anesthetized cat occurs either in a random or in an organized manner. Our results are consistent with a non-random organization of the sets of neurons involved in the generation of spontaneous cord dorsum potentials (CDPs) recorded either from one lumbar segment (DFA-α mean = 1.04±0.09) or simultaneously from several lumbar segments (mDFA-α mean = 1.01±0.06), where α>0.5 indicates randomness while α>0.5 indicates long-term correlations. To test the sensitivity of the mDFA method we also examined the effects of small spinal lesions aimed to partially interrupt connectivity between neighboring lumbosacral segments. We found that the synchronization and correlation between the CDPs recorded from the L5 and L6 segments in both sides of the spinal cord were reduced when a lesion comprising the left dorsal quadrant was performed between the segments L5 and L6 (mDFA-α = 0.992 as compared to initial conditions mDFA-α = 1.186). The synchronization and correlation were reduced even further after a similar additional right spinal lesion (mDFA-α = 0.924). In contrast to the classical methods, such as correlation and coherence quantification that define a relation between two sets of data, the mDFA method properly reveals the synchronization of multiple groups of neurons in several segments of the spinal cord. This method is envisaged as a useful tool to characterize the structure of higher order ensembles of cord dorsum spontaneous potentials after spinal cord or peripheral nerve lesions. © 2011 Rodríguez et al. Source


Hernandez-Lemus E.,National Institute of Genomic Medicine | Hernandez-Lemus E.,National Autonomous University of Mexico
Frontiers in Physiology | Year: 2013

Systems biology analyses in cancer are rapidly changing from merely descriptive efforts in the high-throughput experimental works and overtly technical, calculation-centered studies in computational systems biology; toward a more functional, mechanistic paradigm. The ultimate goal of cancer systems biology nowadays is thus, unraveling the mechanisms of action, regulation and control in the complex tangle of biochemical and biophysical interactions behind cancer biology. An outstanding example of this trend is given in the paper by Kessler and coworkers (2013). On this work, the authors combine ideas from gene expression profiling for phenotypic classification (Hedenfalk, 2002; Subramanian et al., 2005), of signaling pathways (Haynes et al., 2013; Leiserson et al., 2013) and of network modularity (Hintze and Adami, 2008; Jiang et al., 2008), in order to show how molecular physiology (i.e., understanding the physiological mechanisms of disease from a molecular standpoint) may have many clues leading to better prognosis and treatment of cancer. © 2013 Hernandez-Lemus. Source

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