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Día Internacional de la Mujer 2011.

Asociación de Madres Solteras y Grupos Vulnerables para el Desarrollo Social \\\"Por un Trato más digno Yo Madre Soltera Aquí Estoy A.C.\\\" ¡Visita la página de Madres Solas Aquí! More »

Entrega de Silla de Ruedas.

Asociación de Madres Solteras y Grupos Vulnerables para el Desarrollo Social \\\"Por un Trato más digno Yo Madre Soltera Aquí Estoy A.C.\\\" Visita la página de Madres Solas Aquí. More »

Compartiendo con nuestras socias y socios de la tercera edad de Molino Abajo, Temoaya, Estado de México.

Asociación de Madres Solteras y Grupos Vulnerables para el Desarrollo Social \\\"Por un Trato más digno Yo Madre Soltera Aquí Estoy A.C.\\\" Visita la página de Madres Solas Aquí. More »

Visita la página de “Código Ayuda A.C.” Aquí

Entrega de Reconocimiento por la AMS a la labor de Gabriela Goldsmith Presidenta de \\\"Código Ayuda A.C.” More »

Día de la Niñez 2011 con nuestras socias y socios de San Lorenzo Tepaltitlán, Toluca, Estado de México.

Asociación de Madres Solteras y Grupos Vulnerables para el Desarrollo Social \\\"Por un Trato más digno Yo Madre Soltera Aquí Estoy A.C.\\\" Visita la página de Madres Solas Aquí. More »

Entrega de Silla de Ruedas.

Asociación de Madres Solteras y Grupos Vulnerables para el Desarrollo Social \\\"Por un Trato más digno Yo Madre Soltera Aquí Estoy A.C.\\\" Visita la página de Madres Solas Aquí. More »

“Yo Me Declaro Defensor” de los Defensores de Derechos Humanos

Participación en la campaña “Yo Me Declaro Defensor” de los Defensores de Derechos Humanos por la Alta Comisionada de los Derechos Humanos de la ONU Navy Pillay. More »

Entrega de Reconocimiento al Lic. Enrique Peña Nieto por su apoyo como gobernador a los grupos vulnerables de nuestra Asociación.

Asociación de Madres Solteras y Grupos Vulnerables para el Desarrollo Social \\\"Por un Trato más digno Yo Madre Soltera Aquí Estoy A.C.\\\" Visita la página de Madres Solas Aquí. More »

Compartiendo con nuestras socias y socios de la tercera edad en Molino Abajo, Temoaya, Estado de México.

Asociación de Madres Solteras y Grupos Vulnerables para el Desarrollo Social \\\"Por un Trato más digno Yo Madre Soltera Aquí Estoy A.C.\\\" ¡Visita la página de Madres Solas Aquí! More »

Compartiendo con nuestras socias y socios de la tercera edad en Molino Abajo, Temoaya, Estado de México.

Asociación de Madres Solteras y Grupos Vulnerables para el Desarrollo Social \\\"Por un Trato más digno Yo Madre Soltera Aquí Estoy A.C.\\\" ¡Visita la página de Madres Solas Aquí! More »

Compartiendo con nuestras socias y socios de la tercera edad en Molino Abajo, Temoaya, Estado de México.

Asociación de Madres Solteras y Grupos Vulnerables para el Desarrollo Social \\\"Por un Trato más digno Yo Madre Soltera Aquí Estoy A.C.\\\" ¡Visita la página de Madres Solas Aquí! More »

Compartiendo con nuestras socias y socios de la tercera edad en Molino Abajo, Temoaya, Estado de México.

Asociación de Madres Solteras y Grupos Vulnerables para el Desarrollo Social \\\"Por un Trato más digno Yo Madre Soltera Aquí Estoy A.C.\\\" ¡Visita la página de Madres Solas Aquí! More »

Thelma Dorantes Autora y Actriz principal de la obra de Teatro \\

Visita de Thelma Dorantes a las oficina de la Asociación de Madres Solteras y Grupos Vulnerables para el Desarrollo Social \\\"Por un Trato más digno Yo Madre Soltera Aquí Estoy A.C.\\\" en Toluca, Estado de México. More »

Thelma Dorantes Autora y Actriz principal de la obra de Teatro \\

Visita de Thelma Dorantes a las oficina de la Asociación de Madres Solteras y Grupos Vulnerables para el Desarrollo Social \\\"Por un Trato más digno Yo Madre Soltera Aquí Estoy A.C.\\\" en Toluca, Estado de México. More »

Thelma Dorantes Autora y Actriz principal de la obra de Teatro \\

Visita de Thelma Dorantes a las oficina de la Asociación de Madres Solteras y Grupos Vulnerables para el Desarrollo Social \\\"Por un Trato más digno Yo Madre Soltera Aquí Estoy A.C.\\\" en Toluca, Estado de México. More »

Premio Nacional del Trabajo 2012.

Entrega a los trabajadores de la Dirección de Organización y Desarrollo Administrativo de la Universidad Autónoma del Estado de México del Premio Nacional del Trabajo 2012 por la Secretaría de Trabajo y Previsión Social del Gobierno de México. More »

 

Democracy, Human Rights, and Labor: U.S.-China Consultation on Foreign Nongovernmental Organization Management

This NEWS was originally shared on Aulanews United States News

Fuente: US Departament of State Democracy Human Rights and Labor

The United States and China held the inaugural U.S.-China Consultation on Foreign Nongovernmental Organization Management on December 14, 2017 in Beijing, China. The two sides discussed the critical role that NGOs play in facilitating people-to-people exchanges between the United States and China. Both sides believe that the strength and success of all countries depends on protecting and supporting civil society. The United States urges China to ensure that the new Foreign NGO Management Law will guarantee the continuous operations of U.S. NGOs in China. The two sides decided to meet again in 2018 and continue working-level consultations to address the concerns of civil society leaders. The Ministry of Public Security and related public security organizations decided to meet with representatives of NGOs and Professional Supervisory Units early next year in order to resolve their questions and concerns about registration.

Google crunches data to help NASA find two new planets

This NEWS was originally shared on Aulanews United States News

Fuente: Reuters Science News

SAN FRANCISCO (Reuters) – Alphabet Inc’s Google and NASA said on Thursday that advanced computer analysis identified two new planets around distant stars, including one that is part of the first star system with as many planets as Earth’s solar system.

FILE PHOTO: The Google logo is shown reflected on an adjacent office building in Irvine, California, U.S. August 7, 2017. REUTERS/Mike Blake/File Photo

The research by Google and the University of Texas at Austin that used data from NASA raised the prospects of new insights into the universe by feeding data into computer programs that can churn through information faster and more in-depth than humanly possibly, a technique known as machine learning.

In this case, software learned differences between planets and other objects by analyzing thousands of data points, achieving 96 percent accuracy, NASA said at a news conference.

The data came from the Kepler telescope which NASA launched into space in 2009 as part of a planet-finding mission that is expected to end next year as the spacecraft runs out of fuel.

The software’s artificial “neural network” combed through data about 670 stars, which led to the discovery of planets Kepler 80g and Kepler 90i. The latter, a scorching, rocky mass 30 percent larger than Earth, is the eighth planet found to be orbiting the same star.

Astronomers had never before observed an eight-planet network beside the solar system that includes Earth, researchers said.

“As the application of neutral networks to Kepler data matures, who knows what might be discovered,” said Jessie Dotson, a NASA project scientist for the Kepler space telescope. “I‘m on the edge of my seat.”

Christopher Shallue, an artificial intelligence researcher at Google, and Andrew Vanderburg, an astronomer at the University of Texas at Austin, said they plan to continue their work by analyzing Kepler data on more than 150,000 other stars.

Advancements in hardware and new techniques for machine learning have made it possible in recent years for automated software to tackle data analysis in science, finance and other industries.

Machine learning had not been applied to data acquired by the Kepler telescope until Shallue came up with the idea, he said.

“In my spare time, I started Googling for ‘finding exoplanets with large data sets’ and found out about the Kepler mission and the huge data set available,” he said. “Machine learning really shines in situations where there is so much data that humans can’t search it for themselves.”

Vanderburg has received funding through a NASA fellowship aimed at distant-planet researchers.

Reporting by Paresh Dave; Editing by Cynthia Osterman

Google crunches data to help NASA find two new planets

SAN FRANCISCO (Reuters) – Alphabet Inc’s Google and NASA said on Thursday that advanced computer analysis identified two new planets around distant stars, including one that is part of the first star system with as many planets as Earth’s solar system.

FILE PHOTO: The Google logo is shown reflected on an adjacent office building in Irvine, California, U.S. August 7, 2017. REUTERS/Mike Blake/File Photo

The research by Google and the University of Texas at Austin that used data from NASA raised the prospects of new insights into the universe by feeding data into computer programs that can churn through information faster and more in-depth than humanly possibly, a technique known as machine learning.

In this case, software learned differences between planets and other objects by analyzing thousands of data points, achieving 96 percent accuracy, NASA said at a news conference.

The data came from the Kepler telescope which NASA launched into space in 2009 as part of a planet-finding mission that is expected to end next year as the spacecraft runs out of fuel.

The software’s artificial “neural network” combed through data about 670 stars, which led to the discovery of planets Kepler 80g and Kepler 90i. The latter, a scorching, rocky mass 30 percent larger than Earth, is the eighth planet found to be orbiting the same star.

Astronomers had never before observed an eight-planet network beside the solar system that includes Earth, researchers said.

“As the application of neutral networks to Kepler data matures, who knows what might be discovered,” said Jessie Dotson, a NASA project scientist for the Kepler space telescope. “I‘m on the edge of my seat.”

Christopher Shallue, an artificial intelligence researcher at Google, and Andrew Vanderburg, an astronomer at the University of Texas at Austin, said they plan to continue their work by analyzing Kepler data on more than 150,000 other stars.

Advancements in hardware and new techniques for machine learning have made it possible in recent years for automated software to tackle data analysis in science, finance and other industries.

Machine learning had not been applied to data acquired by the Kepler telescope until Shallue came up with the idea, he said.

“In my spare time, I started Googling for ‘finding exoplanets with large data sets’ and found out about the Kepler mission and the huge data set available,” he said. “Machine learning really shines in situations where there is so much data that humans can’t search it for themselves.”

Vanderburg has received funding through a NASA fellowship aimed at distant-planet researchers.

Reporting by Paresh Dave; Editing by Cynthia Osterman

Google crunches data to help NASA find two new planets

SAN FRANCISCO (Reuters) – Alphabet Inc’s Google and NASA said on Thursday that advanced computer analysis identified two new planets around distant stars, including one that is part of the first star system with as many planets as Earth’s solar system.

FILE PHOTO: The Google logo is shown reflected on an adjacent office building in Irvine, California, U.S. August 7, 2017. REUTERS/Mike Blake/File Photo

The research by Google and the University of Texas at Austin that used data from NASA raised the prospects of new insights into the universe by feeding data into computer programs that can churn through information faster and more in-depth than humanly possibly, a technique known as machine learning.

In this case, software learned differences between planets and other objects by analyzing thousands of data points, achieving 96 percent accuracy, NASA said at a news conference.

The data came from the Kepler telescope which NASA launched into space in 2009 as part of a planet-finding mission that is expected to end next year as the spacecraft runs out of fuel.

The software’s artificial “neural network” combed through data about 670 stars, which led to the discovery of planets Kepler 80g and Kepler 90i. The latter, a scorching, rocky mass 30 percent larger than Earth, is the eighth planet found to be orbiting the same star.

Astronomers had never before observed an eight-planet network beside the solar system that includes Earth, researchers said.

“As the application of neutral networks to Kepler data matures, who knows what might be discovered,” said Jessie Dotson, a NASA project scientist for the Kepler space telescope. “I‘m on the edge of my seat.”

Christopher Shallue, an artificial intelligence researcher at Google, and Andrew Vanderburg, an astronomer at the University of Texas at Austin, said they plan to continue their work by analyzing Kepler data on more than 150,000 other stars.

Advancements in hardware and new techniques for machine learning have made it possible in recent years for automated software to tackle data analysis in science, finance and other industries.

Machine learning had not been applied to data acquired by the Kepler telescope until Shallue came up with the idea, he said.

“In my spare time, I started Googling for ‘finding exoplanets with large data sets’ and found out about the Kepler mission and the huge data set available,” he said. “Machine learning really shines in situations where there is so much data that humans can’t search it for themselves.”

Vanderburg has received funding through a NASA fellowship aimed at distant-planet researchers.

Reporting by Paresh Dave; Editing by Cynthia Osterman

Google crunches data to help NASA find two new planets

SAN FRANCISCO (Reuters) – Alphabet Inc’s Google and NASA said on Thursday that advanced computer analysis identified two new planets around distant stars, including one that is part of the first star system with as many planets as Earth’s solar system.

FILE PHOTO: The Google logo is shown reflected on an adjacent office building in Irvine, California, U.S. August 7, 2017. REUTERS/Mike Blake/File Photo

The research by Google and the University of Texas at Austin that used data from NASA raised the prospects of new insights into the universe by feeding data into computer programs that can churn through information faster and more in-depth than humanly possibly, a technique known as machine learning.

In this case, software learned differences between planets and other objects by analyzing thousands of data points, achieving 96 percent accuracy, NASA said at a news conference.

The data came from the Kepler telescope which NASA launched into space in 2009 as part of a planet-finding mission that is expected to end next year as the spacecraft runs out of fuel.

The software’s artificial “neural network” combed through data about 670 stars, which led to the discovery of planets Kepler 80g and Kepler 90i. The latter, a scorching, rocky mass 30 percent larger than Earth, is the eighth planet found to be orbiting the same star.

Astronomers had never before observed an eight-planet network beside the solar system that includes Earth, researchers said.

“As the application of neutral networks to Kepler data matures, who knows what might be discovered,” said Jessie Dotson, a NASA project scientist for the Kepler space telescope. “I‘m on the edge of my seat.”

Christopher Shallue, an artificial intelligence researcher at Google, and Andrew Vanderburg, an astronomer at the University of Texas at Austin, said they plan to continue their work by analyzing Kepler data on more than 150,000 other stars.

Advancements in hardware and new techniques for machine learning have made it possible in recent years for automated software to tackle data analysis in science, finance and other industries.

Machine learning had not been applied to data acquired by the Kepler telescope until Shallue came up with the idea, he said.

“In my spare time, I started Googling for ‘finding exoplanets with large data sets’ and found out about the Kepler mission and the huge data set available,” he said. “Machine learning really shines in situations where there is so much data that humans can’t search it for themselves.”

Vanderburg has received funding through a NASA fellowship aimed at distant-planet researchers.

Reporting by Paresh Dave; Editing by Cynthia Osterman

Google crunches data to help NASA find two new planets

SAN FRANCISCO (Reuters) – Alphabet Inc’s Google and NASA said on Thursday that advanced computer analysis identified two new planets around distant stars, including one that is part of the first star system with as many planets as Earth’s solar system.

FILE PHOTO: The Google logo is shown reflected on an adjacent office building in Irvine, California, U.S. August 7, 2017. REUTERS/Mike Blake/File Photo

The research by Google and the University of Texas at Austin that used data from NASA raised the prospects of new insights into the universe by feeding data into computer programs that can churn through information faster and more in-depth than humanly possibly, a technique known as machine learning.

In this case, software learned differences between planets and other objects by analyzing thousands of data points, achieving 96 percent accuracy, NASA said at a news conference.

The data came from the Kepler telescope which NASA launched into space in 2009 as part of a planet-finding mission that is expected to end next year as the spacecraft runs out of fuel.

The software’s artificial “neural network” combed through data about 670 stars, which led to the discovery of planets Kepler 80g and Kepler 90i. The latter, a scorching, rocky mass 30 percent larger than Earth, is the eighth planet found to be orbiting the same star.

Astronomers had never before observed an eight-planet network beside the solar system that includes Earth, researchers said.

“As the application of neutral networks to Kepler data matures, who knows what might be discovered,” said Jessie Dotson, a NASA project scientist for the Kepler space telescope. “I‘m on the edge of my seat.”

Christopher Shallue, an artificial intelligence researcher at Google, and Andrew Vanderburg, an astronomer at the University of Texas at Austin, said they plan to continue their work by analyzing Kepler data on more than 150,000 other stars.

Advancements in hardware and new techniques for machine learning have made it possible in recent years for automated software to tackle data analysis in science, finance and other industries.

Machine learning had not been applied to data acquired by the Kepler telescope until Shallue came up with the idea, he said.

“In my spare time, I started Googling for ‘finding exoplanets with large data sets’ and found out about the Kepler mission and the huge data set available,” he said. “Machine learning really shines in situations where there is so much data that humans can’t search it for themselves.”

Vanderburg has received funding through a NASA fellowship aimed at distant-planet researchers.

Reporting by Paresh Dave; Editing by Cynthia Osterman

Google crunches data to help NASA find two new planets

SAN FRANCISCO (Reuters) – Alphabet Inc’s Google and NASA said on Thursday that advanced computer analysis identified two new planets around distant stars, including one that is part of the first star system with as many planets as Earth’s solar system.

FILE PHOTO: The Google logo is shown reflected on an adjacent office building in Irvine, California, U.S. August 7, 2017. REUTERS/Mike Blake/File Photo

The research by Google and the University of Texas at Austin that used data from NASA raised the prospects of new insights into the universe by feeding data into computer programs that can churn through information faster and more in-depth than humanly possibly, a technique known as machine learning.

In this case, software learned differences between planets and other objects by analyzing thousands of data points, achieving 96 percent accuracy, NASA said at a news conference.

The data came from the Kepler telescope which NASA launched into space in 2009 as part of a planet-finding mission that is expected to end next year as the spacecraft runs out of fuel.

The software’s artificial “neural network” combed through data about 670 stars, which led to the discovery of planets Kepler 80g and Kepler 90i. The latter, a scorching, rocky mass 30 percent larger than Earth, is the eighth planet found to be orbiting the same star.

Astronomers had never before observed an eight-planet network beside the solar system that includes Earth, researchers said.

“As the application of neutral networks to Kepler data matures, who knows what might be discovered,” said Jessie Dotson, a NASA project scientist for the Kepler space telescope. “I‘m on the edge of my seat.”

Christopher Shallue, an artificial intelligence researcher at Google, and Andrew Vanderburg, an astronomer at the University of Texas at Austin, said they plan to continue their work by analyzing Kepler data on more than 150,000 other stars.

Advancements in hardware and new techniques for machine learning have made it possible in recent years for automated software to tackle data analysis in science, finance and other industries.

Machine learning had not been applied to data acquired by the Kepler telescope until Shallue came up with the idea, he said.

“In my spare time, I started Googling for ‘finding exoplanets with large data sets’ and found out about the Kepler mission and the huge data set available,” he said. “Machine learning really shines in situations where there is so much data that humans can’t search it for themselves.”

Vanderburg has received funding through a NASA fellowship aimed at distant-planet researchers.

Reporting by Paresh Dave; Editing by Cynthia Osterman

Google crunches data to help NASA find two new planets

SAN FRANCISCO (Reuters) – Alphabet Inc’s Google and NASA said on Thursday that advanced computer analysis identified two new planets around distant stars, including one that is part of the first star system with as many planets as Earth’s solar system.

FILE PHOTO: The Google logo is shown reflected on an adjacent office building in Irvine, California, U.S. August 7, 2017. REUTERS/Mike Blake/File Photo

The research by Google and the University of Texas at Austin that used data from NASA raised the prospects of new insights into the universe by feeding data into computer programs that can churn through information faster and more in-depth than humanly possibly, a technique known as machine learning.

In this case, software learned differences between planets and other objects by analyzing thousands of data points, achieving 96 percent accuracy, NASA said at a news conference.

The data came from the Kepler telescope which NASA launched into space in 2009 as part of a planet-finding mission that is expected to end next year as the spacecraft runs out of fuel.

The software’s artificial “neural network” combed through data about 670 stars, which led to the discovery of planets Kepler 80g and Kepler 90i. The latter, a scorching, rocky mass 30 percent larger than Earth, is the eighth planet found to be orbiting the same star.

Astronomers had never before observed an eight-planet network beside the solar system that includes Earth, researchers said.

“As the application of neutral networks to Kepler data matures, who knows what might be discovered,” said Jessie Dotson, a NASA project scientist for the Kepler space telescope. “I‘m on the edge of my seat.”

Christopher Shallue, an artificial intelligence researcher at Google, and Andrew Vanderburg, an astronomer at the University of Texas at Austin, said they plan to continue their work by analyzing Kepler data on more than 150,000 other stars.

Advancements in hardware and new techniques for machine learning have made it possible in recent years for automated software to tackle data analysis in science, finance and other industries.

Machine learning had not been applied to data acquired by the Kepler telescope until Shallue came up with the idea, he said.

“In my spare time, I started Googling for ‘finding exoplanets with large data sets’ and found out about the Kepler mission and the huge data set available,” he said. “Machine learning really shines in situations where there is so much data that humans can’t search it for themselves.”

Vanderburg has received funding through a NASA fellowship aimed at distant-planet researchers.

Reporting by Paresh Dave; Editing by Cynthia Osterman

Three-man crew returns from space station – NASA TV

This NEWS was originally shared on Aulanews United States News

Fuente: Reuters Science News

(Reuters) – A capsule carrying U.S., Russian and Italian astronauts from the International Space Station landed in Kazakhstan on Thursday, a NASA TV live broadcast showed.

The spacecraft brought back Randy Bresnik, from the U.S. National Aeronautics and Space Administration; Sergey Ryazanskiy, from Russian space agency Roscosmos; and Italy’s Paolo Nespoli, with the European Space Agency.

Reporting by Olzhas Auyezov; editing by John Stonestreet

Amber discovery shows 'Dracula' sucked blood of feathered dinosaurs

This NEWS was originally shared on Aulanews United States News

Fuente: Reuters Science News

WASHINGTON (Reuters) – Ticks, the notorious disease-spreading parasites, have been making life miserable far longer than human beings have walked the Earth. Even dinosaurs felt their blood-sucking wrath.

Studied tick pieces and extant hard tick for comparison (tick is 5 mm long) as seen in this handout photo received by Reuters December 12, 2017. Enrique Penalver/Handout via REUTERS ATTENTION EDITORS – THIS IMAGE WAS PROVIDED BY A THIRD PARTY. NO RESALES. NO ARCHIVES.

Scientists on Tuesday described a number of ticks, including a previously unknown species that they named after the fictional vampire Dracula, entombed in chunks of amber from Myanmar dating back 99 million years, including one still grasping a feather apparently from a feathered Cretaceous Period dinosaur.

One of the ticks belonging to the species Deinocroton draculi, meaning “Dracula’s terrible tick,” was found so engorged with blood that it increased its size eight-fold. If that sounds familiar, it is the premise of the Jurassic Park books and movies in which DNA extracted from the guts of dinosaur-biting mosquitoes trapped in amber was used to recreate dinosaurs.

Do not expect any dino-cloning from these ticks.

“It seems that modern techniques are unable to extract DNA, or at least sufficiently well-preserved DNA, from amber inclusions (organisms trapped in amber). DNA does not stand the passing of time, of millions of years, when entombed in amber,” said paleontologist Ricardo Pérez-de la Fuente of the Oxford University Museum of Natural History, one of the researchers in the study published in the journal Nature Communications.

Amber, fossilized tree resin, has provided a remarkable window into the past, with numerous types of small animals and plants preserved in superb detail. In this case, the amber offered the first direct evidence of a parasite-host relationship between ticks and feathered dinosaurs. These ticks also are among the oldest examples of these parasites in the fossil record.

The characteristics of the feather being grasped by an immature tick did not allow the researchers to pinpoint a specific type of feathered dinosaur that was the parasite’s blood meal. The researchers suspect it was a running dinosaur or perhaps a primitive bird, the evolutionary offshoots of dinosaurs.

Two of the Dracula ticks provided additional indirect evidence of these parasites feeding on dinosaurs. Hair-like structures from the larvae of so-called skin beetles were found attached to the ticks. Modern skin beetles feed in nests, eating feathers, skin and hair from the nest’s occupants.

“Most people don’t know that Bram Stoker’s Dracula was a fictionalized account of a real person, Vlad the Impaler, both blood-thirsty villains, although the ticks are merely making a living,” said entomologist David Grimaldi of the American Museum of Natural History in New York.

Reporting by Will DunhamEditing by Sandra Maler

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ENLACES

[Lo Nuevo] [Madres Solas] [SUTESUAEM] [Previsión Social] [Ciencias Económico Administrativas]
[
Ciencias Sociales] [Ciencias de la Informaci
ón][México Historia y Cultura]
[Materias de Estudio]
[
SPCU] [Diplomado SPC] [Documentos SPC] [Foro Permanente SPC] [Enlaces SPC][PRONAD-SIIA] [Enlaces]
 

[Joseacontrera's Blog] [Propósito] [Viajeros] [Mis Viajes] [Fotos] [Música] [Deportes] [Correo] [Curriculum] [Godaddy]


NOTICIAS
 [México] [Edomex] [Estados] [Emprendedores Tech] [Prensa Educativa] [Universities] [Empleo] [Trabajo y Sindicatos]
[
Latinoamérica] [Estados Unidos] [Unión Europea] [Asia] [África] [Joseacontreras Diario] [Derechos Humanos Diario]