MT1a mRNA Expression in Human Lung Cells (BEAS 2B) After Airborne PM10 and Copper Exposure: A Possible Biomarker for Asthma

Puerto Ricans have the highest prevalence, morbidity and mortality of asthma than any other ethnic group in the U.S. and can be triggered by different stimuli such as airborne Particle pollution (PM). PM transfers transition metals, including cooper, to the human airways generating reactive oxygen species (ROS). Metallothionein protein (MT1A) is a free radical scavenger expected to be induced in lung cells by PM exposure; however, this has never been proven nor demonstrated. The hypothesis involves the amounts of PM10 in 2004, which would be significantly higher in the urban sites during months of March and June, thereby increasing PM metal exposure and inducing MT1A gene at the urban site. PM10 filters for Urban (Guaynabo) and rural (Fajardo) site were provided by the PREQB. Organic extracts were prepared from filters by means of Soxhlet Extraction and heavy metals were determined using Atomic Absorption Spectrometry. BEAS-2B cells were cultured and exposed to PM10 during 4 hrs. RNA was isolated and relative mRNA levels of MT1A were determined by RT-PCR. Urban PM10 concentrations were 35 ug/m3 and 60ug/m3 for the months of March and June, respectively. Concentrations of copper for this urban PM10 were 29.6 ppm and 50.3 ppm, for March and June, respectively. Exposure to Cu leads to an MT1A expression in lung cells demonstrating its response to metal exposure. Cellular response for the mRNA expression of MT1A was greater in relative high inflammation markers than in PM10 samples

Sparse Sampling for Inverse Problems with Tensors

We consider the problem of designing sparse sampling strategies for multidomain signals, which can be represented using tensors that admit a known multilinear decomposition. We leverage the multidomain structure of tensor signals and propose to acquire samples using a Kronecker-structured sensing function, thereby circumventing the curse of dimensionality. For designing such sensing functions, we develop low-complexity greedy algorithms based on submodular optimization methods to compute near-optimal sampling sets. We present several numerical examples, ranging from multi-antenna communications to graph signal processing, to validate the developed theory.Comment: 13 pages, 7 figure

Sampling and Reconstruction of Signals on Product Graphs

In this paper, we consider the problem of subsampling and reconstruction of signals that reside on the vertices of a product graph, such as sensor network time series, genomic signals, or product ratings in a social network. Specifically, we leverage the product structure of the underlying domain and sample nodes from the graph factors. The proposed scheme is particularly useful for processing signals on large-scale product graphs. The sampling sets are designed using a low-complexity greedy algorithm and can be proven to be near-optimal. To illustrate the developed theory, numerical experiments based on real datasets are provided for sampling 3D dynamic point clouds and for active learning in recommender systems.Comment: 5 pages, 3 figure

Cell-like Versus Tissue-like P Systems by Means of Sevilla Carpets

Sevilla Carpets are a handy tool for comparing computations performed by different systems solving the same problem. Such Sevilla Carpets provide on one hand quantitative information through parameters such as Weight, Surface and Average weight, and on the other hand they also provide a fast glimpse on the complexity of the computation thanks to their graphical representation. Up to now, Sevilla Carpets were only used on Cell-like P systems. In this paper we present a first comparison by means of Sevilla Carpets of the computations of three P systems (designed within different models), all of them solving the same instance of the Subset Sum problem. Two of these solutions use Cell-like P systems with active membranes, while the third one uses Tissue-like P systems with cell division.Ministerio de Educación y Ciencia TIN2006-13425Junta de Andalucía P08-TIC-0420

Descriptional Complexity of Tissue-Like P Systems with Cell Division

In this paper we address the problem of describing the complexity of the evolution of a tissue-like P system with cell division. In the computations of such systems the number of (parallel) steps is not sufficient to evaluate the complexity. Following this consideration, Sevilla Carpets were introduced as a tool to describe the space-time complexity of P systems. Sevilla Carpets have already been used to compare two different solutions of the Subset Sum problem (both designed in the framework of P systems with active membranes) running on the same instance. In this paper we extend the comparison to the framework of tissue-like P systems with cell division.Ministerio de Educación y Ciencia TIN2006-13425Junta de Andalucía P08–TIC-0420

TEMA 2-2014: SARCOMA DE EWING

El grupo de tumores conocido como sarcoma de Ewing consiste en neoplasias de células redondas, morfológicamente similares y por la presencia de una translocación cromosómica común. Aunque poco frecuentes, estos tumores constituyen el tercer grupo de tumores primarios de hueso, después del osteosarcoma y el condrosarcoma. Afecta con mayor frecuencia a niños y adolescentes. Algunos casos presentan metástasis, con afección sistémica. El tratamiento se da con múltiples fármacos, así como el control de la enfermedad local con cirugía y/o radioterapia para algunos pacientes

Mercados municipales de Zapopan

Documento final del Proyecto de Aplicación Profesional en el que se presenta una investigación realizada en tres mercados del ayuntamiento de Zapopan: San Isidro, Francisco Sarabia y Tesistán. El objetivo de la investigación fue identificar las problemáticas, hacer propuestas y colaborar en la planeación de estrategias para mejorar las condiciones de los mercados y lograr un impacto positivo que se vea reflejado en las ventas. Para la investigación, se visitaron los mercados y se aplicaron encuestas a los locatarios, consumidores y administradores. La muestra que se usó para sacar el número de encuestas fue infinita con un nivel de confianza del 90 por ciento y un índice de error del 5 por ciento. En este proyecto trabajaron alumnos de las licenciaturas en Mercadotecnia, Administración de Empresas y Administración Financiera.ITESO, A.C