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Airports Are a Pandemic's Best Friend

Airports Are a Pandemic's Best Friend | Amazing Science | Scoop.it

After SARS broke out in China in 2002, it reached 29 countries in seven months. Air travel is a major reason why such infectious diseases spread throughout the globe so quickly. And yet even with such examples to study, scientists have had no way to precisely predict how the next infectious disease might spread through the nexus of world air terminals—until now.

 

In 2010 MIT engineer Ruben Juanes set out to model the movement of a pathogen from a single site of departure to junctions worldwide. If he could predict the flow of disease from a given airport and rank the most contagious ones, government officials could more effectively predict outbreaks and issue lifesaving warnings and vaccines. So Juanes and his team used a computer simulation to seed 40 major U.S. airports with virtual infected travelers. Then they mimicked the individual itineraries of millions of real passengers to model how people move through the system. The travel data included flights, wait times between flights, number of connections to international hubs, flight duration, and length of stay at destinations.

 

JFK International in New York—one of the world’s most heavily trafficked airports—emerged as the biggest culprit in disease spread. Honolulu, despite having just 40 percent of JFK’s traffic, came in third because of its many long-distance flights. The biggest surprise: The number of passengers per day did not directly correlate to contagion risk.

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The Best Scientific Figures of 2012: Sperm Trajectories, Evolving Humans and a Tomato Tapestry

The Best Scientific Figures of 2012: Sperm Trajectories, Evolving Humans and a Tomato Tapestry | Amazing Science | Scoop.it

Figures contained in scientific reports are a neglected area of the design world. Typically intended for display to academic audiences in the cramped confines of a journal, they tend to be utilitarian and esoteric -- yet while looking through hundreds of articles in the course of 2012, certain figures transcended the technical and rose to the level of communication art. They combined visual clarity, information density and insight into some fact of fundamental interest.

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Who are the doctors most trusted by doctors? Big data can tell you

Who are the doctors most trusted by doctors? Big data can tell you | Amazing Science | Scoop.it

ZocDoc, Healthgrades, Vitals, Yelp and other sites can tell you what patients think of their doctors. But finding out in any aggregate way what doctors think of their peers has been much harder, if not near impossible, for patients — up until now. By accessing information in government databases through FOIA (Freedom of Information Act) requests, healthcare innovators are now able to share connections between doctors that are based on millions of physician referrals — a valuable indicator of who doctors hold in esteem.

 

Last month, Fred Trotter, a self-identified “hacktivist,” revealed that he had obtained a dataset of Medicare physician referrals through a FOIA request and was making the initial data available to those who supported a Medstartr crowdfunding campaign meant to build out his “DocGraph” and make it freely available. This week, he announced that he not only blew past his $15,000 funding goal, but was launching a second campaign to integrate his current data with an additional dataset.

 

The new tool, which reflects 25 million doctor referral connections, enables patients to see how many doctors are linked to a particular doctor, as well as their locations. As patients search for new physicians and specialists, being able to see who their current doctors are linked with could help them decide who to visit. It also gives doctors an opportunity to build online networks that reflect their offline networks, Gutman said. In a post about his “DocGraph” project, Trotter said that his data wasn’t strictly a “referral” data set because, in some cases, doctors might be linked through a patient they both happened to see at the same time, not through an active referral. But Gutman emphasized that HealthTap’s DOConnect considered more than Medicare referrals in mapping connections between doctors.


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Data Wall: IBM Think Exhibit

Data Wall: IBM Think Exhibit | Amazing Science | Scoop.it

Located on Jaffe Drive at Lincoln Center in New York, the THINK exhibit combined three unique experiences to engage visitors in a conversation about how we can improve the way we live and work. 

 

The IBM data wall was the introduction to the Think Exhibit at the Lincoln Center. This exhibit celebrated its centennial year, and 100 years of human progress.

The wall aimed to educate the public about five areas of interest to the New York community. These included air quality, water waste, potential solar energy, fraud detection, and traffic sensing.

 

Visit the portfolio link to view detail images of the Data Wall, as well as concept sketches and explanations for the design for the solar and traffic sections.


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Aji Black Stone's comment, November 16, 2012 11:11 AM
wow
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The top 20 scientific data visualisation tools scientists and teachers should know about

The top 20 scientific data visualisation tools scientists and teachers should know about | Amazing Science | Scoop.it

From simple charts to complex maps and infographics, Brian Suda's round-up of the best – and mostly free – tools has everything you need to bring your data to life. A common question is how to get started with data visualisations. Beyond following blogs, you need to practice – and to practice, you need to understand the tools available. In this article, get introduced to 20 different tools for creating visualisations.


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Randy Rebman's curator insight, January 28, 2013 12:33 PM

This looks like it might be a good source for integrating infographics into the classroom.

Caroline Matet's curator insight, April 22, 2013 4:08 PM

Le top 20 des outils pour faire ses propres data visualisations

National Microscope Exchange's comment, February 18, 12:00 AM
Superb Article
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D3, a Data Driven Document Language - Presenting Scientific Data on the Web with Ease

D3, a Data Driven Document Language - Presenting Scientific Data on the Web with Ease | Amazing Science | Scoop.it

D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG and CSS. D3’s emphasis on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework, combining powerful visualization components and a data-driven approach to DOM manipulation.

 

D3 allows you to bind arbitrary data to a Document Object Model (DOM), and then apply data-driven transformations to the document. For example, you can use D3 to generate an HTML table from an array of numbers. Or, use the same data to create an interactive SVG bar chart with smooth transitions and interaction.

 

D3 is not a monolithic framework that seeks to provide every conceivable feature. Instead, D3 solves the crux of the problem: efficient manipulation of documents based on data. This avoids proprietary representation and affords extraordinary flexibility, exposing the full capabilities of web standards such as CSS3, HTML5 and SVG. With minimal overhead, D3 is extremely fast, supporting large datasets and dynamic behaviors for interaction and animation. D3’s functional style allows code reuse through a diverse collection of components and plugins.

 

More on D3: 

http://ofps.oreilly.com/titles/9781449339739/k_00000002.html

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The greatest puzzle: How Do You Assemble a Brain? Randomly...

The greatest puzzle: How Do You Assemble a Brain? Randomly... | Amazing Science | Scoop.it

Swiss researchers show how a chance distribution of six neuronal cell types can connect to form the synapses of a working brain without any overall design control. Is this a clue to complex systems design?

 

How do you assemble and wire an information processing device as complex as the mammalian brain? There are roughly 86 billion neurons in a human brain, forming about a quadrillion synapses. A rat’s brain is just one thousandth that size, but still pretty complex, with 56 million neurons and 500 billion synapses.

 

How does the brain know to put a nest basket cell here, a small basket cell over there, a large basket cell in the middle, a Martinotti cell on the left and a bi-tufted cell on the right, all wired up to pyramidal cells? There has to be a plan, doesn’t there? I mean, the body doesn’t just throw its inventory of brain cells out there like a bunch of pick-up sticks, to fall where they may.


As it turns out, that may be almost exactly what the brain does. Like so much else in capital-L Life, connections in the brain may be emergent: the developing brain lays out its thinking cells in a nearly random mixture, and then wires them up after the fact.


The Blue Brain group (motto: “Reconstructing the brain piece by piece and building a virtual brain on a supercomputer”) at Switzerland’s Ecole Polytechnique Federale de Lausanne (EPFL) has built a computer model of a 298-cell slice of rat cerebral cortex. The model distributed the 6 types of neurons randomly, according to their frequency in natural tissue. They tracked “the incidental overlap of axonal and dendritic arbors,” the tree-like branchings at either end of the nerve cell that reach out and form synapses.

 

The researchers used their software tools (BlueBuilder) to model the nervous system. They then reduced the synapse-identification problem to a very large number of computer graphic “cylinder to cylinder touch” assessments, running on a 16 384-cpu IBM BlueGene/P computer at the Center for Advanced Modeling Science (CADMOS) in Lausanne.


Finally, they compared the results to data from cross-sections of actual rat brains, stained and digitized, and analyzed to show cell types and synapse locations, and found an overall 75% correspondence between the two. Not absolute correspondence…but a good deal better than chance.

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‘Superorganisations’ – Learning from Nature’s Networks

‘Superorganisations’ – Learning from Nature’s Networks | Amazing Science | Scoop.it

Fritjof Capra, in his book ‘The Hidden Connections’ applies aspects of complexity theory, particularly the analysis of networks, to global capitalism and the state of the world; and eloquently argues the case that social systems such as organisations and networks are not just like living systems – they are living systems. The concept and theory of living systems (technically known as autopoiesis) was introduced in 1972 by Chilean biologists Humberto Maturana and Francisco Varela.

 

This is a complete version of a ‘long-blog’ written by Al Kennedy on behalf of ‘The Nature of Business’ blog and BCI: Biomimicry for Creative Innovation www.businessinspired...


Via Peter Vander Auwera, ddrrnt, Spaceweaver, David Hodgson, pdjmoo, Sakis Koukouvis
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A look at how to go organic with business models in a tech age...

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Learning from Nature’s Networks

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YOU ARE INVITED TO FOLLOW MY NEWS AGGREGATES @pdjmoo

 

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First predictive computational model of gene networks that control the development of sea-urchin embryos

First predictive computational model of gene networks that control the development of sea-urchin embryos | Amazing Science | Scoop.it

As an animal develops from an embryo, its cells take diverse paths, eventually forming different body parts—muscles, bones, heart. In order for each cell to know what to do during development, it follows a genetic blueprint, which consists of complex webs of interacting genes called gene regulatory networks.

 

Biologists at the California Institute of Technology (Caltech) have spent the last decade or so detailing how these gene networks control development in sea-urchin embryos. Now, for the first time, they have built a computational model of one of these networks. This model, the scientists say, does a remarkably good job of calculating what these networks do to control the fates of different cells in the early stages of sea-urchin development—confirming that the interactions among a few dozen genes suffice to tell an embryo how to start the development of different body parts in their respective spatial locations. The model is also a powerful tool for understanding gene regulatory networks in a way not previously possible, allowing scientists to better study the genetic bases of both development and evolution.

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Web3.0 - The Evolving Web: Mapping the World's Data

Web3.0 - The Evolving Web: Mapping the World's Data | Amazing Science | Scoop.it

Web 2.0 has been largely about social sharing, collaboration and user-generated content -- contributing to a more detailed web of information. But much of the information presented throughout the Web until recently has tended to be isolated content lacking relevant and dynamic context about how entities -- including people, objects, interests, locations, events or decisions -- are connected.

 

As we move towards Web 3.0 and the Semantic Web -- characterized by related, contextualized and personalized data -- there's a growing push for more robust context and relationship mapping. Several companies, from search engines to social networks, have already begun mapping and graphing the way their customers use, interact with and understand data.

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Google deploys machine learning algorithm on YouTube. Computer self-learns to recognize images of cats

Google deploys machine learning algorithm on YouTube. Computer self-learns to recognize images of cats | Amazing Science | Scoop.it

How Many Computers to Identify a Cat? 16,000 processors!

 

A neural network of computer processors, fed millions of YouTube videos, taught itself to recognize cats, a feat of significance for fields like speech recognition. Inside Google’s secretive X laboratory, known for inventing self-driving cars and augmented reality glasses, a small group of researchers began working several years ago on a simulation of the human brain.

 

There Google scientists created one of the largest neural networks for machine learning by connecting 16,000 computer processors, which they turned loose on the Internet to learn on its own. Presented with 10 million digital images found in YouTube videos, what did Google’s brain do? What millions of humans do with YouTube: looked for cats. The neural network taught itself to recognize cats, which is actually no frivolous activity. This week the researchers will present the results of their work at a conference in Edinburgh, Scotland. The Google scientists and programmers will note that while it is hardly news that the Internet is full of cat videos, the simulation nevertheless surprised them. It performed far better than any previous effort by roughly doubling its accuracy in recognizing objects in a challenging list of 20,000 distinct items.

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Happy or sad? Microsoft has applied for a patent for targeting ads to users based on their emotional state

Happy or sad? Microsoft has applied for a patent for targeting ads to users based on their emotional state | Amazing Science | Scoop.it

Do you look happy? You’ll see ads for vacation packages and consumer electronics, but not weight-loss programs or self-help products. Do you look sad? You won’t see that over-the-top animated ad for children’s birthday parties at the local bowling alley. Feeling frustrated? It’s PC support ads for you.

 

Those are actual examples from the patent application, which incorporates some of the same ideas as the earlier filing for deducing the user’s mood — including scanning messages and social media postings. Also included: audio and video capture devices (to detect facial expressions and tone of voice) in addition to the company’s Kinect sensor, which would be used to analyze body movements as another input for the emotion-detecting algorithm.

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PINdb - A Database of Nuclear Protein Complexes

PINdb - A Database of Nuclear Protein Complexes | Amazing Science | Scoop.it

Proteins Interacting Nucleus database (PINdb) is a database of protein complexes purified from the nucleus of human and yeast cells. It is compiled from the published literature and existing databases. Currently, PINdb contains mostly protein complexes that may be involved in gene transcription. To facilitate comparative analyses and identification of protein complexes, the compositional information is integrated with standardized gene nomenclature, annotation and protein sequences from public databases. The PINdb web interface provides a number of tools for (1) comparison of protein complexes, (2) search for a protein complex by its published name or by a partial list of its components and (3) browsing specific subsets or a functional classification of the complexes.

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Solving puzzles without a picture: New algorithm assembles chromosomes from next generation sequencing data

Solving puzzles without a picture: New algorithm assembles chromosomes from next generation sequencing data | Amazing Science | Scoop.it

One of the most difficult problems in the field of genomics is assembling relatively short "reads" of DNA into complete chromosomes. In a new paper published in Proceedings of the National Academy of Sciences an interdisciplinary group of genome and computer scientists has solved this problem, creating an algorithm that can rapidly create "virtual chromosomes" with no prior information about how the genome is organized.

 

The powerful DNA sequencing methods developed about 15 years ago, known as next generation sequencing (NGS) technologies, create thousands of short fragments. In species whose genetics has already been extensively studied, existing information can be used to organize and order the NGS fragments, rather like using a sketch of the complete picture as a guide to a jigsaw puzzle. But as genome scientists push into less-studied species, it becomes more difficult to finish the puzzle.

 

To solve this problem, a team led by Harris Lewin, distinguished professor of evolution and ecology and vice chancellor for research at the University of California, Davis and Jian Ma, assistant professor at the University of Illinois at Urbana-Champaign created a computer algorithm that uses the known chromosome organization of one or more known species and NGS information from a newly sequenced genome to create virtual chromosomes.

 

"We show for the first time that chromosomes can be assembled from NGS data without the aid of a preexisting genetic or physical map of the genome," Lewin said. The new algorithm will be very useful for large-scale sequencing projects such as G10K, an effort to sequence 10,000 vertebrate genomes of which very few have a map, Lewin said.

 

"As we have shown previously, there is much to learn about phenotypic evolution from understanding how chromosomes are organized in one species relative to other species," he said. The algorithm is called RACA (for reference-assisted chromosome assembly), co-developed by Jaebum Kim, now at Konkuk University, South Korea, and Denis Larkin of Aberystwyth University, Wales. Kim wrote the software tool which was evaluated using simulated data, standardized reference genome datasets as well as a primary NGS assembly of the newly sequenced Tibetan antelope genome generated by BGI (Shenzhen, China) in collaboration with Professor Ri-Li Ge at Qinghai University, China.

 

Larkin led the experimental validation, in collaboration with scientists at BGI, proving that predictions of chromosome organization were highly accurate. Ma said that the new RACA algorithm will perform even better as developing NGS technologies produce longer reads of DNA sequence. "Even with what is expected from the newest generation of sequencers, complete chromosome assemblies will always be a difficult technical issue, especially for complex genomes. RACA predictions address this problem and can be incorporated into current NGS assembly pipelines," Ma said.

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Peta, Exa, Yotta And Beyond: Big Data Reaches Cosmic Proportions

Peta, Exa, Yotta And Beyond: Big Data Reaches Cosmic Proportions | Amazing Science | Scoop.it
Since the advent of big data, it's been a struggle for some to get a real sense of just how big big data really is. You hear strange terms like "peta," exa" and "yotta"… but what does all that really mean? When managing massive amounts of data, the scales were talking about can quickly reach astronomical proportions.

 

Coming from Indiana, where the value of Pi might have once legally been 3, it would be easy to just slap the label "gi-fracking-normous" on the scales we're talking about, but I'm going to push past my native upbringing and focus on recent efforts to quantify big data. A recent infographic from clearCi is one such effort, outlining the scale of data produced on the Internet each day: 2.5 quintillion bytes of data. Storage vendor Seagate figures that a total of 450 exabytes of storage shipped in 2011.

 

How Big Can Data Get? It will get bigger, of course. The next level of data will be the brontobyte, which is 10E27 bytes. The human body contains 7 octillion atoms. If each atom were a byte of information, that's 7 brontobytes of data.

 

No one company, not even Google, will every get to the upper ends of these scales. But with terabyte amounts of data an everyday occurrence and petabytes not so rare anymore, a lot of companies will need to wrap their heads around the notion of what the big in big data really means and bring it down to Earth.

 

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A Curated Selection of Data Visualization Charts and Infographics: The Information Is Beautiful Awards

A Curated Selection of Data Visualization Charts and Infographics: The Information Is Beautiful Awards | Amazing Science | Scoop.it

Robin Good: David McCandlees, the author of the book Information is Beautiful celebrates great data visualization and information design work through the Information is Beautiful Awards.

Together with a jury of experts like Brian Eno, Paola Antonelli, Maria Popova, Simon Rogers and Aziz Kami, he has curated a unique selection of 300 designs and a short list of finalists in the following categories:

 

» Data visualization– A singular visualisation of data or information.» Infographic – Using multiple data visualisations in service to a theme or story

 

» Interactive visualization – Any viz where you can dynamically filter or explore the data.

 

» Data journalism – A combination of text and visualizations in a journalistic format.

 

» Motion infographic – Moving and animated visualizations along a theme or story.

 

» Tool or website – Online tools & apps to aid datavizzing.

 

The selection itself is worth a tour of the site and of this initiative.

 

Check: http://www.informationisbeautifulawards.com/

 

Longlist selection: http://www.informationisbeautifulawards.com/2012/07/our-longlist/

 

Shortlist selection: http://www.informationisbeautifulawards.com/2012/08/awardshortlist/

 

 


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Scientific Data Visualization and Infographics Resources

Scientific Data Visualization and Infographics Resources | Amazing Science | Scoop.it
Data visualizations and infographics can make complex datasets easier to understand and comprehend. By creating a graphical represenatation of data and statistics, complicated concepts and information can make more sense in less time.

 

Many visualizations focus on representing a specific set of data or statistical information. Others focus on less-concrete topics, providing a visual representation of abstract concepts. Generally speaking, the first type appear more like graphs or charts and the latter are often more creative and imaginative.

 

But visualizations and infographics can be used poorly, too. Putting in too much information (or not enough), using improper formats for the information provided, and other failures are common. Many useful resources for infographics and data visualization are compiled. Most are galleries of effective graphics though some also provide how-to information for information designers.

 

Also check out the intelligent continuously updated wall charts based on scoop.it information:

 

http://www.genautica.com/frames/intelligent-frames-infographics.html

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China is building a 100-petaflops supercomputer

China is building a 100-petaflops supercomputer | Amazing Science | Scoop.it

As the U.S. launched what’s expected to be the world’s fastest supercomputer at 20 petaflops (peak performance), China announced it is building a machine intended to be five times faster when it is deployed in 2015. China’s Tianhe-2 supercomputer will run at 100 petaflops (quadrillion floating-point calculations per second) peak performance, designed by China’s National University of Defense Technology, according to the Guangzhou Supercomputing Center, where the machine will be housed. Tianhe-2 could help keep China competitive with the future supercomputers of other countries, as industry experts estimate machines will start reaching 1,000-petaflops (1 exaflop) performance by 2018.

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Visualizing a Full Day of Airplane Paths over the USA

Visualizing a Full Day of Airplane Paths over the USA | Amazing Science | Scoop.it

At any given moment, there can be 30,000 manmade objects in the sky above us: Planes, helicopters, satellites, weather balloons, space debris, and other diverse technologies. They watch, they guide, they protect, they communicate, they transport, they predict, they look out into the stars. In less than 100 years, the deep blue has become a complex web of machinery.

 

Our lives are closely tied to these networks in the sky, but a disjunction has occurred between us and the aerial technologies we use every day. We rarely consider the hulking, physical machines that have now become core to our lifestyle. By not being aware of the hardware we use every day, we may also not be aware of the social, economic, cultural, and political importance of these technologies. By visualizing them, it may lead to a better understanding of the forces that are shaping our future.

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The Internet Map: Revealing the Hidden Structure of the Network - information aesthetics

The Internet Map: Revealing the Hidden Structure of the Network - information aesthetics | Amazing Science | Scoop.it

The Internet Map [ www.internet-map.net ] encompasses over 350,000 websites based in 196 countries, which are clustered according to about 2 million mutual links between them. Developed by a small team of Russian enthusiasts, the interactive Internet map is an "attempt to look into the hidden structure of the network, fathom its colossal scale, and examine that which is impossible to understand from the bare figures of statistics." Every circle on the map stands for a unique website, with its size determined by website web traffic. Its color depends on the country of origin, with red for Russia, yellow for China, purple for Japan, and light-blue for the US.

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Mapping population densities: some interesting models

Mapping population densities: some interesting models | Amazing Science | Scoop.it

New kind of cartograms to map population densities on this planet from an article in the Telegraph. The cartograms use data from the Global Rural-Urban Mapping Project.

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How Airports Quickly Spread Pandemics Like H1N1 And SARS

How Airports Quickly Spread Pandemics Like H1N1 And SARS | Amazing Science | Scoop.it

Public health crises of the past decade — such as the 2003 SARS outbreak, which spread to 37 countries and caused about 1,000 deaths, and the 2009 H1N1 flu pandemic that killed about 300,000 people worldwide — have heightened awareness that new viruses or bacteria could spread quickly across the globe, aided by air travel.

 

While epidemiologists and scientists who study complex network systems — such as contagion patterns and information spread in social networks — are working to create mathematical models that describe the worldwide spread of disease, to date these models have focused on the final stages of epidemics, examining the locations that ultimately develop the highest infection rates.

 

But a new study by researchers in MIT’s Department of Civil and Environmental Engineering (CEE) shifts the focus to the first few days of an epidemic, determining how likely the 40 largest U.S. airports are to influence the spread of a contagious disease originating in their home cities. This new approach could help determine appropriate measures for containing infection in specific geographic areas and aid public health officials in making decisions about the distribution of vaccinations or treatments in the earliest days of contagion.

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Big Data: Graphing the history of philosophy

Big Data: Graphing the history of philosophy | Amazing Science | Scoop.it

Each philosopher is a node in the network and the lines between them (or edges in the terminology of graph theory) represents lines of influence. The node and text are sized according to the number of connections (both in and out). The algorithm that visualises the graph also tends to put the better connected nodes in the centre of the diagram so we see the most influential philosophers, in large text, clustered in the centre. It all seems about right with the major figures in the western philosophical tradition taking the centre stage. Connections are visualized using gephi (http://http://gephi.org/).

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Faster than 50 million laptops -- the race to go exascale

Faster than 50 million laptops -- the race to go exascale | Amazing Science | Scoop.it

A new era in computing that will see machines perform at least 1,000 times faster than today's most powerful supercomputers is almost upon us. By the end of the decade, exaFLOP computers are predicted to go online heralding a new chapter in scientific discovery. The United States, China, Japan, the European Union and Russia are all investing millions of dollars in supercomputer research. In February, the EU announced it was doubling investment in research to €1.2 billion ($1.6 billion).

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UCSC Genomics Text Indexing

UCSC Genomics Text Indexing | Amazing Science | Scoop.it

Text2Genome is using a unique way to map scientific articles to genomic locations: From a full-text scientific article and it's supplementary data files, all words that resemble DNA sequences are extracted and then mapped to public genome sequences. They can then be displayed on genome browser websites and used in data-mining applications.

 

More info: http://text2genome.smith.man.ac.uk/

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