Spiders Ingest Nanotubes, Then Weave Silk Reinforced with Carbon

uditha wickramanayaka/Flickr

Spiders sprayed with water containing carbon nanotubes and graphene flakes have produced the toughest fibers ever measured, say materials scientists.

Spider silk is one of the more extraordinary materials known to science. The protein fiber, spun by spiders to make webs, is stronger than almost anything that humans can make.

The dragline silk spiders use to make a web’s outer rim and spokes is amazing stuff. It matches high-grade alloy steel for tensile strength but is about a sixth as dense. It is also highly ductile, sometimes capable of stretching to five times its length.

This combination of strength and ductility makes spider silk extremely tough, matching the toughness of state-of-the-art carbon fibers such as Kevlar.

So it goes without saying that the ability to make spider silk even stronger and tougher would be a significant scientific coup. Which is why the work of Nicola Pugno at the University of Trento in Italy and a few pals is something of a jaw-dropper.

These guys have found a way to incorporate carbon nanotubes and graphene into spider silk and increase its strength and toughness beyond anything that has been possible before. The resulting material has properties such as 

• fracture strength, 
• Young’s modulus, and 
• toughness modulus 

higher than anything ever measured.

The team’s approach is relatively straightforward. They started with 15 Pholcidae spiders, collected from the Italian countryside, which they kept in controlled conditions in their lab. They collected samples of dragline silk produced by these spiders as a reference.

The team then used a neat trick to introduce carbon nanotubes and graphene flakes into the spider silk. They simply sprayed the spiders with water containing the nanotubes or flakes and then measured the mechanical properties of the silk that the spiders produced.

For each strand of silk, they fixed the fiber between two C-shaped cardboard holders and placed it in a device that can measure the load on a fiber with a resolution of 15 nano-newtons and any fiber displacement with a resolution of 0.1 nanometers.

The results make for impressive reading. “We measure a fracture strength up to 5.4 GPa, a Young’s modulus up to 47.8 GPa and a toughness modulus up to 2.1 GPa,” say Pugno and co. “This is the highest toughness modulus for a fibre, surpassing synthetic polymeric high performance fibres (e.g. Kelvar49) and even the current toughest knotted fibers,” they say.

In other words, giving spiders water that is infused with carbon nanotubes makes them weave silk stronger than any known fiber.

The work raises some interesting questions. For a start, exactly how the spiders incorporate carbon nanotubes and graphene flakes into their silk is not clear. The team use spectroscopic methods to show that the carbon-based materials are present in the fiber but are unable to show exactly how.

One possibility is that the silk becomes coated with these carbon-based materials after it is spun. Pugno and co cannot rule this out but say it is unlikely because the resulting structure would not have the strength they measured. “Such external coating on the fibre surface is not expected to significantly contribute to the observed mechanical strengthening,” they say.

Instead, the team say it is more likely that the spiders ingest the water along with the carbon-based materials and these are then incorporated into the fiber as it is spun. So the nanotubes and graphene end up in the central part of each fibere where they can have the biggest impact on its strength.

The team have even simulated the resulting molecular structure and say that the mechanical properties are in good agreement with the experimental results.

There are challenges ahead, of course. Nobody has discovered an efficient way to harvest spider silk, although not for lack of trying. So an important future step will be the development of such a technique that can work on an industrial scale. That would open the way to widespread applications in everything from tissue repair to garment design.

This isn’t the first time that researchers have attempted to modify spider silk. Various groups have added metallic elements by placing the silk in the appropriate vapor. In this way they have significantly increased the strength and toughness of the silk, although never to the extent that Pugno and co have managed.

Which is why their work is impressive. The extraordinary properties of spider silk are the result of 400 million years of evolution. So such a significant improvement is clearly something special.

And the technique’s simplicity suggests that a similar approach could be used on other organisms. “This new reinforcing procedure could also be applied to other animals and plants, leading to a new class of bionic materials,” they say.

Ref: arxiv.org/abs/1504.06751 : Silk Reinforced With Graphene Or Carbon Nanotubes Spun By Spiders

ORIGINAL: Technology Review

May 6, 2015

Scientists Crack the Code to Protein Self-Assembly

If news stories pertaining to pharmaceutical drugs have got you down, here's something to perk you up: Duke University scientists have successfully hacked the genetic code controlling how and when proteins self-assemble and disassemble. It's a huge step forward for designer proteins, synthetic biology, and a whole host of other future-minded pieces of medical research. Perhaps most importantly, these findings may result in new and effective ways to deliver drugs to vital areas within the body.

It's now possible for medical researchers to emulate computer programmers in construction, manipulation, and execution of code — except in this case, we're talking genetic coding rather than digital.

The researchers' study, which appears in this month's Nature, details the many possible environmental stimuli that result in protein assembly and disassembly, and then demonstrates that the researchers have learned to replicate them in a lab. For example, the researchers have pinned down the complex relationship between protein structures and heat.

If you wrap medicine in a protein shell and heat it just right, you can control when and where in the body that shell will give way and the medicine will be delivered. The shell become more than just a shell; it becomes a "bioactive component" of the drug, according to Ashutosh Chilkoti, chair of the Department of Biomedical Engineering at Duke.

In what other ways can we expect human health research to take big steps forward? Dr. Francois Nader on what the amazing things the future brings:

It's now possible for medical researchers to emulate computer programmers in construction, manipulation, and execution of code — except in this case, we're talking genetic coding rather than digital. Input the correct specs, flip a switch, and the proteins assemble or disassemble themselves. This degree of control is unprecedented and should lead to a host of new discoveries and innovations in the years to come.
--

Read more at Science Daily.


ORIGINAL: Big Think
by ROBERT MONTENEGRO

Robert Montenegro is a writer, playwright, and dramaturg who lives in Washington DC. His beats include the following: tech, history, sports, geography, culture, and whatever Elon Musk has said on Twitter over the past couple days. He is a graduate of Loyola Marymount University in Los Angeles.You can follow him on Twitter at @Monteneggroll and visit his po'dunk website at robertmontenegro.com.

IBM's SystemML machine learning system becomes Apache Incubator project

There's a race between tech giants to open source machine learning systems and become a dominant platform. Apache SystemML has clear enterprise spin.

IBM on Monday said its machine learning system, dubbed SystemML, has been accepted as an open source project by the Apache Incubator.

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The Apache Incubator is an entry to becoming a project of The Apache Software Foundation. The general idea behind the incubator is to ensure code donations adhere to Apache's legal guidelines and communities follow guiding principles.

IBM said it would donate SystemML as an open source project in June.

What's notable about IBM's SystemML milestone is that open sourcing machine learning systems is becoming a trend. To wit:

• Google recently open sourced its TensorFlow machine learning tool under an Apache 2.0 license.
• Facebook has also contributed its machine learning and artificial intelligence tools to the Torch open source project.

For enterprises, the upshot is that there will be a bevy of open source machine learning code bases to consider. Google TensorFlow and Facebook Torch are tools to train neural networks. SystemML is aimed a broadening the ecosystem to business use.

Why are tech giants going open source with their machine learning tools? 

The machine learning platform that gets the most data will learn faster and then become more powerful. That cycle will just result in more data to ingest. IBM is looking to work the enterprise angle on machine learning. Microsoft may be another entry on the enterprise side, but may not go the Apache route.

In addition, there are precedents to how open sourcing big analytics ideas can pay off. MapReduce and Hadoop started as open source projects and would be a cousin of whatever Apache machine learning system wins out.

IBM's SystemML, which is now Apache SystemML, is used to create industry specific machine learning algorithms for enterprise data analysis. IBM created SystemML so it could write one codebase that could apply to multiple industries and platforms. If SystemML can scale, IBM's Apache move could provide a gateway to its other analytics wares.

The Apache SystemML project has included more than 320 patches for everything from APIs, data ingestion and documentation, more than 90 contributions to Apache Spark and 15 additional organizations adding to the SystemML engine.

Here's the full definition of the Apache SystemML project:

SystemML provides declarative large-scale machine learning (ML) that aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, in-memory computations, to distributed computations on Apache Hadoop and Apache Spark. ML algorithms are expressed in a R or Python syntax, that includes linear algebra primitives, statistical functions, and ML-specific constructs. This high-level language significantly increases the productivity of data scientists as it provides (1) full flexibility in expressing custom analytics, and (2) data independence from the underlying input formats and physical data representations. Automatic optimization according to data characteristics such as distribution on the disk file system, and sparsity as well as processing characteristics in the distributed environment like number of nodes, CPU, memory per node, ensures both efficiency and scalability.

ORIGINAL: ZDNet

By Larry Dignan for Between the Lines

November 23, 2015

Allen Institute researchers decode patterns that make our brains human

Each of our human brains is special, carrying distinctive memories and giving rise to our unique thoughts and actions. Most research on the brain focuses on what makes one brain different from another. But recently, Allen Institute researchers turned the question around.

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“So much research focuses on the variations between individuals, but we turned that question on its head to ask, what makes us similar?” says Ed Lein, Ph.D., Investigator at the Allen Institute for Brain Science. “What is the conserved element among all of us that must give rise to our unique cognitive abilities and human traits?”

Their work, published this month in Nature Neuroscience, looked at gene expression across the entire human brain and identified a surprisingly small set of molecular patterns that dominate gene expression in the human brain and appear to be common to all individuals.

“Looking at the data from this unique vantage point enables us to study gene patterning that we all share,” says Mike Hawrylycz, Ph.D., Investigator at the Allen Institute for Brain Science. “We used the Allen Human Brain Atlas data to quantify how consistent the patterns of expression for various genes are across human brains, and to determine the importance of the most consistent and reproducible genes for brain function.”

Despite the anatomical complexity of the brain and the complexity of the human genome, most of the patterns of gene usage across all 20,000 genes could be characterized by just 32 expression patterns. The most highly stable genes—the genes that were most consistent across all brains—include those that are associated with diseases and disorders like autism and Alzheimer’s and include many existing drug targets. These patterns provide insights into what makes the human brain distinct and raise new opportunities to target therapeutics for treating disease.

Allen Institute researchers decode patterns that make our brains human

Conserved gene patterning across human brains provide insights into health and disease

The human brain may be the most complex piece of organized matter in the known universe, but Allen Institute researchers have begun to unravel the genetic code underlying its function. Research published this month in Nature Neuroscience identified a surprisingly small set of molecular patterns that dominate gene expression in the human brain and appear to be common to all individuals, providing key insights into the core of the genetic code that makes our brains distinctly human.

“So much research focuses on the variations between individuals, but we turned that question on its head to ask, what makes us similar?” says Ed Lein, Ph.D., Investigator at the Allen Institute for Brain Science. “What is the conserved element among all of us that must give rise to our unique cognitive abilities and human traits?”

Researchers used data from the publicly available Allen Human Brain Atlas to investigate how gene expression varies across hundreds of functionally distinct brain regions in six human brains. They began by ranking genes by the consistency of their expression patterns across individuals, and then analyzed the relationship of these genes to one another and to brain function and association with disease.

“Looking at the data from this unique vantage point enables us to study gene patterning that we all share,” says Mike Hawrylycz, Ph.D., Investigator at the Allen Institute for Brain Science. “We used the Allen Human Brain Atlas data to quantify how consistent the patterns of expression for various genes are across human brains, and to determine the importance of the most consistent and reproducible genes for brain function.”

Despite the anatomical complexity of the brain and the complexity of the human genome, most of the patterns of gene usage across all 20,000 genes could be characterized by just 32 expression patterns. While many of these patterns were similar in human and mouse, the dominant genetic model organism for biomedical research, many genes showed different patterns in human. Surprisingly, genes associated with neurons were most conserved across species, while those for the supporting glial cells showed larger differences.

The most highly stable genes—the genes that were most consistent across all brains—include those that are associated with diseases and disorders like autism and Alzheimer’s and include many existing drug targets. These patterns provide insights into what makes the human brain distinct and raise new opportunities to target therapeutics for treating disease.

The researchers also found that the pattern of gene expression in cerebral cortex is correlated with “functional connectivity” as revealed by neuroimaging data from the Human Connectome Project. “It is exciting to find a correlation between brain circuitry and gene expression by combining high quality data from these two large-scale projects,” says David Van Essen, Ph.D., professor at Washington University in St. Louis and a leader of the Human Connectome Project.

“The human brain is phenomenally complex, so it is quite surprising that a small number of patterns can explain most of the gene variability across the brain,” says Christof Koch, Ph.D., President and Chief Scientific Officer at the Allen Institute for Brain Science. “There could easily have been thousands of patterns, or none at all. This gives us an exciting way to look further at the functional activity that underlies the uniquely human brain.”

This research was conducted in collaboration with the Cincinnati Children’s Hospital and Medical Center and Washington University in St. Louis.

The project described was supported by award numbers 1R21DA027644 and 5R33DA027644 from the National Institute on Drug Abuse. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health and the National Institute on Drug Abuse.

About the Allen Institute for Brain Science

The Allen Institute for Brain Science is an independent, 501(c)(3) nonprofit medical research organization dedicated to accelerating the understanding of how the human brain works in health and disease. Using a big science approach, the Allen Institute generates useful public resources used by researchers and organizations around the globe, drives technological and analytical advances, and discovers fundamental brain properties through integration of experiments, modeling and theory. Launched in 2003 with a seed contribution from founder and philanthropist Paul G. Allen, the Allen Institute is supported by a diversity of government, foundation and private funds to enable its projects. Given the Institute’s achievements, Mr. Allen committed an additional $300 million in 2012 for the first four years of a ten-year plan to further propel and expand the Institute’s scientific programs, bringing his total commitment to date to $500 million. The Allen Institute’s data and tools are publicly available online at brain-map.org.

ORIGINAL: Allen Institute

November 16, 2015

How swarm intelligence could save us from the dangers of AI

Image Credit: diez artwork/Shutterstock

We’ve heard a lot of talk recently about the dangers of artificial intelligence. From Stephen Hawking and Bill Gates, to Elon Musk, and Steve Wozniak, luminaries around the globe have been sounding the alarm, warning that we could lose control over this powerful technology — after all, AI is about creating systems that have minds of their own. A true AI could one day adopt goals and aspirations that harm us.

But what if we could enjoy the benefits of AI while ensuring that human values and sensibilities remain an integral part of the system?

This is where something called Artificial Swarm Intelligence comes in – a method for building intelligent systems that keeps humans in the loop, merging the power of computational algorithms with the wisdom, creativity, and intuition of real people. A number of companies around the world are already exploring swarms.

• There’s Enswarm, a UK startup that is using swarm technologies to assist with recruitment and employment decisions. 
• There’s Swarm.fund, a startup using swarming and crypto-currencies like Bitcoin as a new model for fundraising. 
• And the human swarming company I founded, Unanimous A.I., creates a unified intellect from any group of networked users.

This swarm intelligence technology may sound like science fiction, but it has its roots in nature.

It all goes back to the birds and the bees – fish and ants too. Across countless species, social groups have developed methods of amplifying their intelligence by working together in closed-loop systems. Known commonly as flocks, schools, colonies, and swarms, these natural systems enable groups to combine their insights and thereby outperform individual members when solving problems and making decisions. Scientists call this “Swarm Intelligence” and it supports the old adage that many minds are better than one.

But what about us humans?
Clearly, we lack the natural ability to form closed-loop swarms, but like many other skills we can’t do naturally, emerging technologies are filling a void. Leveraging our vast networking infrastructure, new software techniques are allowing online groups to form artificial swarms that can work in synchrony to answer questions, reach decisions, and make predictions, all while exhibiting the same types of intelligence amplifications as seen in nature. The approach is sometimes called “blended intelligence” because it combines the hardware and software technologies used by AI systems with populations of real people, creating human-machine systems that have the potential of outsmarting both humans and pure-software AIs alike.

It should be noted that “swarming” is different from traditional “crowdsourcing,” which generally uses votes, polls, or surveys to aggregate opinions. While such methods are valuable for characterizing populations, they don’t employ the real-time feedback loops used by artificial swarms to enable a unique intelligent system to emerge. It’s the difference between measuring what the average member of a group thinks versus allowing that group to think together and draw conclusions based upon their combined knowledge and intuition.

Outside of the companies I mentioned above, where else can such collective technologies be applied? One area that’s currently being explored is medical diagnosis, a process that requires deep factual knowledge along with the experiential wisdom of the practitioner. Can we merge the knowledge and wisdom of many doctors into a single emergent diagnosis that outperforms the diagnosis of a single practitioner? The answer appears to be yes. In a recent study conducted by Humboldt-University of Berlin and RAND Corporation, a computational collective of radiologists outperformed single practitioners when viewing mammograms, reducing false positives and false negatives. In a separate study conducted by John Carroll University and the Cleveland Clinic, a collective of 12 radiologists diagnosed skeletal abnormalities. As a computational collective, the radiologists produced a significantly higher rate of correct diagnosis than any single practitioner in the group. Of course, the potential of artificially merging many minds into a single unified intelligence extends beyond medical diagnosis to any field where we aim to exceed natural human abilities when making decisions, generating predictions, and solving problems.

Now, back to the original question of why Artificial Swarm Intelligence is a safer form of AI.
Although heavily reliant on hardware and software, swarming keeps human sensibilities and moralities as an integral part of the processes. As a result, this “human-in-the-loop” approach to AI combines the benefits of computational infrastructure and software efficiencies with the unique values that each person brings to the table:

• creativity, 
• empathy, 
• morality, and 
• justice. 

And because swarm-based intelligence is rooted in human input, the resulting intelligence is far more likely to be aligned with humanity – not just with our values and morals, but also with our goals and objectives.

How smart can an Artificial Swarm Intelligence get?
That’s still an open question, but with the potential to engage millions, even billions of people around the globe, each brimming with unique ideas and insights, swarm intelligence may be society’s best hope for staying one step ahead of the pure machine intelligences that emerge from busy AI labs around the world.

Louis Rosenberg is CEO of swarm intelligence company Unanimous A.I. He did his doctoral work at Stanford University in robotics, virtual reality, and human-computer interaction. He previously developed the first immersive augmented reality system as a researcher for the U.S. Air Force in the early 1990s and founded the VR company Immersion Corp and the 3D digitizer company Microscribe.

ORIGINAL: VentureBeat

LOUIS ROSENBERG, UNANIMOUS A.I. 

NOVEMBER 22, 2015

Ancient Seeds Yield Once Extinct Squash

Most foods, with the exception of honey and Twinkies, have expiration dates. Natural foods, like fruits and vegetables, slowly rot away over time, but what about their seeds? Surely those have an expiration date, right? 

Students from Winnipeg, Canada recently discovered a stash of 800-year-old seeds while on an archaeological dig. The mysterious seeds, once planted, grew into a rare species of squash that has been extinct for hundreds of years. While we don't know if the seeds themselves were safe to eat, the squash that they harvested was absolutely delicious. Check out the images below to see the rare gourd for yourself and learn more about this discovery.

Students discovered the seeds buried in a clay pot on Wisconsin's Menemonee Reservation.

When they opened it up, they were met with a pile of strange seeds that they determined were over 800 years old. Is it just me, or do these seeds look like tiny, oval-shaped pancakes?

While not all of the seeds produced fruit, some grew this once-extinct squash.

Imagine seeing these at your local farmer's market. The students named the squash "gete-okosomin," or "really cool old squash."

There won't be any shortage of seeds moving forward.

Each squash appears to be roughly the size of a Chipotle burrito.

These students put in a lot of hard work, and it's finally time for them to enjoy the fruits of their labor. After all, it isn't often that archaeologists get to eat their discoveries.

ORIGINAL: Wimp

JAKE BRANNON

NOV 21, 2015 

How will the children of the future learn about science?

Image by :  opensource.com

From the advent of the printing press to the emergence of the Internet, knowledge has become increasingly abundant and accessible. But as we move into the future, I ponder

• how accessible, interactive, and understandable knowledge will be as more and more individuals are connected to the Internet and as computing technologies advance. 
• How will the techniques by which we share knowledge change? 
• How will the children of the future learn about science? 
• How will the scientists of the future expand our thinking? 

I think the answer is Jupyter Notebooks.

Jupyter Notebooks are interactive web applications that allow users to create and share documents that contain live code, equations, visualizations, and explanatory text. Project Jupyter is an open source project that develops the Notebook and other components that relate to it. But what excites me most about the project is not 

• the technology, or 
• the fantastic development team, or 
• the extremely open nature of the community, 

rather 

• its potential to change the way that millions of people understand science. 

Originally a Python-only project called IPython, Project Jupyter is an effort to expand the project to allow interactive notebooks to be written in other languages as well.

A Jupyter Notebook rendered as a webpage. CC BY-SA 4.0.

As our understanding of the world expands, it is important to ensure that that knowledge is equally accessible by all members of our society. This is vital to the progress of humanity. This philosophy, which is shared by the open source software movement, is not new; it has been around since the 1600s when the first academic journals were published for public reading. The Jupyter Notebook hints at what the academic journals of tomorrow will look like and paints a promising picture. They will be 

• interactive, 
• visualization-focused, 
• user-friendly, and 
• include code and data as first-class citizens. 

I believe that these unique characteristics will go a long way toward bridging the gap of understanding between the scientific community and the general public through both narrative and code—a gap that, when bridged, will have a significant impact on our society.

But the work that has been completed thus far is only the beginning for the project. In a grant proposal that was recently submitted to several nonprofit organizations, Fernando Perez and Brian Granger outline several bold visions for the project that will allow the development of software that is as collaborative and rigorous as the scientific community itself. I believe that with our dedicated and passionate core team, our willingness to grow as an open source project, and our ambitious visions, the project is set to accomplish these goals and many more.

If you'd like to experiment with a Jupyter Notebook, please visit our demo page. If you are interested in contributing to the project (and to the future of open science), please visit our GitHub organization to explore the projects that we maintain. If you are interested in joining the discussion around Jupyter, feel free to join our mailing list.

Project Jupyter is financially sponsored by the Helmsley Charitable Trust, the Alfred P. Sloan Foundation, the Gordon and Betty Moore Foundation, and various corporate sponsors. The project is part of the 501(c)3 NumFOCUS Foundation, a nonprofit that supports the development of open source computing tools for scientific computing and data science. The development of the project is supported by several full-time and volunteer developers, and we are always looking for individuals and organizations with which to collaborate.

ORIGINAL: OpenSource

Safia Abdalla

18 Nov 2015

'Chemical Laptop' Could Search for Signs of Life Outside Earth

Researchers took the Chemical Laptop to JPL's Mars Yard, where they placed the device on a test rover. This image shows the size comparison between the Chemical Laptop and a regular laptop.

Credits: NASA/JPL-Caltech

If you were looking for the signatures of life on another world, you would want to take something small and portable with you. That's the philosophy behind the "Chemical Laptop" being developed at NASA's Jet Propulsion Laboratory in Pasadena, California: a miniaturized laboratory that analyzes samples for materials associated with life.

"If this instrument were to be sent to space, it would be the most sensitive device of its kind to leave Earth, and the first to be able to look for both amino acids and fatty acids," said Jessica Creamer, a NASA postdoctoral fellow based at JPL.

Like a tricorder from "Star Trek," the Chemical Laptop is a miniaturized on-the-go laboratory, which researchers hope to send one day to another planetary body such as Mars or Europa. It is roughly the size of a regular computing laptop, but much thicker to make room for chemical analysis components inside. But unlike a tricorder, it has to ingest a sample to analyze it. 

"Our device is a chemical analyzer that can be reprogrammed like a laptop to perform different functions," said Fernanda Mora, a JPL technologist who is developing the instrument with JPL's Peter Willis, the project's principal investigator. "As on a regular laptop, we have different apps for different analyses like amino acids and fatty acids."

Amino acids are building blocks of proteins, while fatty acids are key components of cell membranes. Both are essential to life, but can also be found in non-life sources. The Chemical Laptop may be able to tell the difference.

JPL researchers Jessica Creamer, Fernanda Mora and Peter Willis (left to right) pose with the Chemical Laptop, a device designed to detect amino acids and fatty acids. At left is a near-identical copy of the Curiosity rover, which has been on Mars since 2012. Credits: NASA/JPL-Caltech

What it's looking for

Amino acids come in two types: Left-handed and right-handed. Like the left and right hands of a person, these amino acids are mirror images of each other but contain the same components. Some scientists hypothesize that life on Earth evolved to use just left-handed amino acids because that standard was adopted early in life's history, sort of like the way VHS became the standard for video instead of Betamax in the 1980s. It's possible that life on other worlds might use the right-handed kind. 

"If a test found a 50-50 mixture of left-handed and right-handed amino acids, we could conclude that the sample was probably not of biological origin," Creamer said. "But if we were to find an excess of either left or right, that would be the golden ticket. That would be the best evidence so far that life exists on other planets."

The analysis of amino acids is particularly challenging because the left- and right-handed versions are equal in size and electric charge. Even more challenging is developing a method that can look for all the amino acids in a single analysis.

When the laptop is set to look for fatty acids, scientists are most interested in the length of the acids' carbon chain. This is an indication of what organisms are or were present.

How it works

The battery-powered Chemical Laptop needs a liquid sample to analyze, which is more difficult to obtain on a planetary body such as Mars. The group collaborated with JPL's Luther Beegle to incorporate an "espresso machine" technology, in which the sample is put into a tube with liquid water and heated to above 212 degrees Fahrenheit (100 degrees Celsius). The water then comes out carrying the organic molecules with it. The Sample Analysis at Mars (SAM) instrument suite on NASA's Mars Curiosity rover utilizes a similar principle, but it uses heat without water.

Once the water sample is fed into the Chemical Laptop, the device prepares the sample by mixing it with a fluorescent dye, which attaches the dye to the amino acids or fatty acids. The sample then flows into a microchip inside the device, where the amino acids or fatty acids can be separated from one another. At the end of the separation channel is a detection laser. The dye allows researchers see a signal corresponding to the amino acids or fatty acids when they pass the laser.

Inside a "separation channel" of the microchip, there are already chemical additives that mix with the sample. Some of these species will only interact with right-handed amino acids, and some will only interact with the left-handed variety. These additives will change the relative amount of time the left and right-handed amino acids are in the separation channel, allowing scientists to determine the "handedness" of amino acids in the sample.

The Chemical Laptop, developed at JPL, analyzes liquid samples and detects amino acids and fatty acids. These are both chemicals that are essential to life.

Credits: NASA/JPL-Caltech

Testing for future uses

Last year the researchers did a field test at JPL's Mars Yard, where they placed the Chemical Laptop on a test rover.

"This was the first time we showed the instrument works outside of the laboratory setting. This is the first step toward demonstrating a totally portable and automated instrument that can operate in the field," said Mora.

For this test, the laptop analyzed a sample of "green rust," a mineral that absorbs organic molecules in its layers and may be significant in the origin of life, said JPL's Michael Russell, who helped provide the sample.

"One ultimate goal is to put a detector like this on a spacecraft such as a Mars rover, so for our first test outside the lab we literally did that," said Willis.

Since then, Mora has been working to improve the sensitivity of the Chemical Laptop so it can detect even smaller amounts of amino acids or fatty acids. Currently, the instrument can detect concentrations as low as parts per trillion. Mora is currently testing a new laser and detector technology.

Coming up is a test in the Atacama Desert in Chile, with collaboration from NASA's Ames Research Center, Moffett Field, California, through a grant from NASA's Planetary Science & Technology Through Analog Research (PSTAR) program.

"This could also be an especially useful tool for icy-worlds targets such as Enceladus and Europa. All you would need to do is melt a little bit of the ice, and you could sample it and analyze it directly," Creamer said.

The Chemical Laptop technology has applications for Earth, too. It could be used for environmental monitoring -- analyzing samples directly in the field, rather than taking them back to a laboratory. Uses for medicine could include testing whether the contents of drugs are legitimate or counterfeit. 

Creamer recently won an award for her work in this area at JPL's Postdoc Research Day Poster Session.

NASA's PICASSO program, part of the agency's Science Mission Directorate in Washington, supported this research. The California Institute of Technology in Pasadena manages JPL for NASA.

ORIGINAL: NASA

By Elizabeth Landau. NASA's Jet Propulsion Laboratory, Pasadena, Calif.

Nov. 16, 2015

Last Updated: Nov. 16, 2015

818-354-6425

Elizabeth.Landau@jpl.nasa.gov

Editor: Martin Perez

Watch: This 3-minute animation will change your perception of time

Business Insider

We all know that Earth is old, but it’s hard to put into perspective just how old it is. After all, what does 4.5 billion years *really* mean? How do you even comprehend that amount of time with our short-lived human brains?

Well, Business Insider has done a pretty incredible job of it in this 3-minute animation, by displaying the timeline of Earth if time was the distance from Los Angeles to New York. And, oh boy, our world-view will never be the same.

We start our journey in Los Angeles, back when Earth first formed 4.54 billion years ago. But we don’t get very far on our road trip through time and space before the Moon shows up after Earth is hit by a planetary body.

About halfway across the top of Arizona, the world’s largest rock forms 3.95 billion years ago, and then a few miles down the road – 3.8 billion years ago – the first evidence of life shows up, in the form of replicating molecules. 

But it’s not until Kansas, 2.7 billion years ago, when oxygen-producing cyanobacteria first emerge, and then 200 million years later that significant amounts of oxygen build up in Earth’s atmosphere. And then, believe it or not, it’s not until Pennsylvania – halfway across the country – that multicellular organisms evolve, just 600 million years ago.

A lot happens in Pennsylvania, like plants colonising the land and amphibians evolving. And by the time dinosaurs become extinct we’re already in New York State.

So where do humans fit in? Well we’ll let you watch the video above to find that out, and let’s just say that it will blow your freaking mind. Especially when you see how much we’ve done in just 5.6 feet (1.7 metres) of time.

ORIGINAL: Science Alert

FIONA MACDONALD

20 NOV 2015

Animal magnetic sense explained by tiny protein 'compasses'

Donjiy/Shutterstock.com

This is so cool.

When it was first proposed, the notion that animals can detect the Earth’s magnetic field was ridiculed, but we now know that everything from birds and whales to butterflies, worms, and wolves know instinctively where north is thanks to an innate magnetic sense. How else are dogs supposed to find their preferred north-south pooping axis?

What scientists couldn’t figure out was how they do it. Now, for the first time, a team in China has identified tiny clumps of protein that appear to align themselves with Earth’s geomagnetic field lines like a compass, and it’s thought that they can influence the nervous system to help animals navigate their surroundings. 

Until now, the two most popular hypotheses used to explain animal magnetic sense were cryptochromes - proteins that respond to magnetic fields and are thought to play a role in maintaining the circadian rhythms of plants and animals - and iron-binding magnetite crystals, which are found in various organs and become magnetised when exposed to a magnetic field. 

While promising, neither option was convincing enough on its own to fully explain the phenomenon, and scientists couldn’t figure out how the two could possibly be linked. So a team from Peking University decided to investigate cryptochromes in the fruit fly genome to see if there was something they were missing. 

Sure enough, they identified an iron-binding protein called MagR, which forms rod-like clumps with cryptochromes. It’s thought that this MagR-cryptochrome cluster functions like a compass, responding to changes in the magnetic field and signalling nearby cells, which in turn prompt the nervous system to give the body cues about its whereabouts.

"The nanoscale biocompass has the tendency to align itself along geomagnetic field lines and to obtain navigation cues from a geomagnetic field," lead researcher Can Xie told Ian Sample at The Guardian. "We propose that any disturbance in this alignment may be captured by connected cellular machinery, which would channel information to the downstream neural system, forming the animal’s magnetic sense."

These protein clusters aren’t just found in fruit flies - the team found them in pigeons and monarch butterflies too, and when isolated, they behaved really, really strangely. "In the lab, the proteins snapped into alignment in response to a magnetic field. They were so strongly magnetic that they flew up and stuck to the researchers’ tools, which contained iron. So the team had to use custom tools made of plastic," New Scientist reports.

Interestingly, further research found that these protein structures can also form in mole rat, minke whale, and even human cells, which offers up the possibility that we could have the same innate magnetic sense of other animals, but perhaps we’re just not as attuned to it as they are. 

"Human sense of direction is complicated," Xie told The Guardian. "However, I believe that magnetic sense plays a key role in explaining why some people have a good sense of direction."

It's still just a hypothesis - Xie and his team are yet to figure out exactly how the clusters communicate with other parts of the body to influence an animal's behaviour, but it's the best hypothesis we've got so far. The next step will be to observe how animals behave when the gene that codes for MagR is removed, which will prove if these clusters are indeed involved in the mechanism. If they are, and we can observe them working in humans, it's going to be mind-blowing.

The study has been published in Nature Materials.

ORIGINAL: Science Alert

BEC CREW

17 NOV 2015

A Visual History of Human Knowledge | Manuel Lima | TED Talks

How does knowledge grow? 

Source: EPFL Blue Brain Project. Blue Brain Circuit

Sometimes it begins with one insight and grows into many branches. Infographics expert Manuel Lima explores the thousand-year history of mapping data — from languages to dynasties — using trees of information. It's a fascinating history of visualizations, and a look into humanity's urge to map what we know.

ORIGINAL: TED

Sep 10, 2015

Scientists have created the first ever porous liquid

Queen’s University Belfast

It could filter carbon emissions out of the air.

You're probably familiar with porous rocks – rocks that can hold and filter liquids – and now scientists from Queen's University in Belfast have created a synthetic liquid with similar properties. The newly developed substance has a huge range of potential uses, including being able to capture harmful carbon emissions to prevent them from entering the Earth's atmosphere.

The porous liquid collects and absorbs gas through its pores, and researchers think it could open up new ways to collect and filter chemicals without relying on solid materials for the job: that obviously gives manufacturers and scientists much more flexibility.

The substance is still under development but the academics from Queen's University, together with colleagues from across the world, are confident in the results they've seen so far.

"Materials which contain permanent holes, or pores, are technologically important," explained Stuart James, one of the lead researchers. "They are used for manufacturing a range of products from plastic bottles to petrol. However, until recently, these porous materials have been solids."

"A few more years' research will be needed, but if we can find applications for these porous liquids they could result in new or improved chemical processes," he adds. "At the very least, we have managed to demonstrate a very new principle – that by creating holes in liquids we can dramatically increase the amount of gas they can dissolve. These remarkable properties suggest interesting applications in the long term."

The team of scientists designed their liquid from the bottom up, shaping the molecules themselves (actually called "cage molecules") so they would be unable to fill all the space available to them.

Once the holes had been pre-programmed into the liquid, researchers found it was able to dissolve unusually large amounts of gas. Any process which requires dissolution of gases could potentially make use of the new substance.

Carbon capture is the example use quoted by the team of scientists: imagine a power plant where running liquid is used to absorb the gas emissions from the power production process and carry them safely away. It has taken the group of researchers three years to get to this point and they're hopeful that more improvements can be made.

The full potential of this porous liquid won't be clear for some time, but the breakthrough is a significant one that could aid all kinds of environmentally sensitive projects. The findings of the research team have been published in the journal Nature.

ORIGINAL: Science Alert

DAVID NIELD

13 NOV 2015

BioPartsBuilder: a synthetic biology tool for combinatorial assembly of biological parts

BioPartsBuilder: a synthetic biology tool for combinatorial assembly of biological parts

Kun Yang 1∗ , Giovanni Stracquadanio 1∗ , Jingchuan Luo 2 , Jef D. Boeke 2 and Joel S. Bader 1 †

1Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles Street,

Baltimore, MD 21218

2Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology,

NYU Langone Medical Center, New York, NY 10016

Abstract

Summary: Combinatorial assembly of DNA elements is an efficient method for building large-scale synthetic pathways from standardized, reusable components. These methods are particularly useful because they enable assembly of multiple DNA fragments in one reaction, at the cost of requiring that each fragment satisfy design constraints. We developed BIOPARTSBUILDER as a biologist-friendly web tool to design biological parts that are compatible with DNA combinatorial assembly methods, such as Golden Gate and related methods. 

It 

• retrieves biological sequences, 
• enforces compliance with assembly design standards, and 
• provides a fabrication plan for each fragment.

Availability: BIOPARTSBUILDER is accessible at http://public.biopartsbuilder.org and an Amazon Web Services image is available from the AWS Market Place (AMI ID: ami-508acf38). Source code is released under the MIT license, and available for download at https://github.com/baderzone/biopartsbuilder.

Full PDF

Contact: joel.bader@jhu.edu

ABSTRACT

Summary: Combinatorial assembly of DNA elements is an efficient method for building large-scale synthetic pathways from standardized, reusable components. These methods are particularly useful because they enable assembly of multiple DNA fragments in one reaction, at the cost of requiring that each fragment satisfy design constraints. We developed BIO PARTS BUILDER as a biologist-friendly web tool to design biological parts that are compatible with DNA combinatorial assembly methods, such as Golden Gate and related methods. It retrieves biological sequences, enforces compliance with assembly design standards, and provides a fabrication plan for each fragment.

Availability: BIO PARTS BUILDER is accessible at http://public.biopartsbuilder.org and an Amazon Web Services image is available from the AWS Market Place (AMI ID: ami-508acf38).

Source code is released under the MIT license, and available for download at https://github.com/baderzone/biopartsbuilder.

Contact: joel.bader@jhu.edu

1 INTRODUCTION

DNA synthesis technologies are improving faster than Moore’s law, allowing the synthesis of genes, pathways (Ro et al. (2.06)), bacterial genomes (Gibson et al. (2.10)), eukaryotic chromosomes (Dymond et al. (2.11); Annaluru et al. (2.14)), and eventually entire eukaryotic genomes. Many projects have individual ‘parts’ as synthetic targets, such as promoters, coding domains, and transcriptional terminators. While individual parts can be characterized, predicting how parts will operate together remains a challenge. Rather than building a single construct, therefore, it can be more efficient to specify multiple alternatives for each part, then use massively parallel synthesis and assembly to generate a combinatorial library that can be screened for the desired function. In particular, Golden Gate assembly is an efficient and effective strategy to assemble combinatorial libraries (Engler et al. (2.09)). However, Golden Gate assembly requires a computationally challenging design step to create ‘standardized’ parts that have compatible overhangs, lack pre-defined restriction sites, and comply with other constraints.

To streamline the process of designing standardized biological parts for Golden Gate assembly, we developed BIO PARTS BUILDER , which retrieves sequence data from different sources and ensures compliance with design standards that are compatible with combinatorial assembly. Though there are tools for automated parts retrieval (Scher et al. (2.14)) and subsequent primer design for DNA assembly (Bode et al. (2.09); Rouillard et al. (2.04)), the choices for Golden Gate assembly are limited. Compared to existing Golden Gate designers (Hillson et al. (2.12.), BIO PARTS BUILDER is distributed open source software and freely modified by both academic and commercial users. BIO PARTS BUILDER also provides a repository system that stores the designed parts and shares data within members associated with the same laboratory. BIO PARTS BUILDER therefore provides useful, new, integrated and extendable functionality for the synthetic biology community.

2.SOFTWARE MODULES

BIO PARTS BUILDER provides an easy interface to retrieve, design and order parts (Fig. 1) that are compatible with Golden Gate (Engler et al. (2.09)), BglBrick (Anderson et al. (2.10)), or user-defined assembly standards.

2.1 Part Retrieval

BIOPARTSBUILDER implements a sophisticated sequence retrieval system to gather data from different sources. Users can submit a list of RefSeq protein/nucleotide accession numbers to retrieve sequences and annotations from NCBI, or for parts without RefSeq accessions or with customized sequences and annotations, users can upload a file in F ASTA or CSV format. As retrieving a large number of arbitrary parts from a genome and upload to the system is tedious, BIO PARTS BUILDER implements an advanced search engine for retrieving parts from annotated genomes, similar to GENOME CARVER software (Scher et al. (2.14)). It parses annotations, generates and stores a search index, and provides access to structured search terms (Table S1-S2. through the Apache SOLR query language. 

2.2.Part Design

Parts imported into BIO PARTS BUILDER can be re-designed according to pre-defined or additional user-defined design standards. Users can customize the design workflow to perform one or more of the following steps. 

• Codon optimization: Users can specify host organism for codon optimization. This option can be left blank for parts that lack protein-coding regions, such as promoters or terminators. This step can be amended also to accomodate vendors’ specific recoding strategies. 
• Restriction enzyme constraints: Combinatorial assembly techniques largely rely on the use of specific restriction enzymes to create a unique assembly. For this reason, BIO PARTS BUILDER provides: 
  1. RESTRICTION ENZYME REMOVER that changes the nucleotide sequence to avoid restriction sites corresponding to a user-specified list of restriction enzymes; and 
  2. RESTRICTION ENZYME LOCATOR that detects the presence of user-specified restriction enzymes without recoding the sequence. BIO PARTS BUILDER organizes data and users by laboratories. People in the same laboratory can share parts and designs that remain private to other laboratories and the public. BIO PARTS BUILDER provides an administration panel specifically for laboratory administrators to manage members and design standards. The initial creator of a new laboratory in BIO PARTS BUILDER automatically becomes the laboratory administrator. 
• Prefix and suffix insertion: Users can specify sequences to be added to the beginning and the end of each part.
• Fabrication: Parts can be larger that the synthesis capability of commercial providers. In this case, BIO PARTS BUILDER splits the sequence in fragments of user-defined length using unique overlaps, which allow unambiguous assembly (see Supplementary Text). 

BIO PARTS BUILDER assigns a unique Job ID to each design task. Users can check the status and error report of design tasks online. When the design task is finished, BIO PARTS BUILDER sends an email to notify the user.

2.3 Order 

Design results are accessible online. And users can also use the ORDER module to collect specific designs and prepare files for ordering parts from companies. The ORDER module creates statistical summaries for user-selected designs and provides tables of parts, constructs and design standards. It generates spreadsheets, sequence files, and summary report files for users to download.

2.4 AutoBuild

To streamline the entire design process, BIO PARTS BUILDER has a fully automated design module, AUTO BUILD , which allows users to retrieve, design and create orders for a batch of parts with one click. This module serves as a convenient ‘wizard’ for users whose needs are met by the most common design standards, which are already defined in the software.

3 DESIGN WORKFLOW EXAMPLE 

Using Autobuild it is possible to quickly design both coding and non-coding parts for Golden Gate using these two workflows. 

• Coding region . In the “Search Genomes” tab, input query “systematic name:YBR019C”; select “Golden Gate - CDS” design standard and assign a name to the Order. Click create parts, then select the part and confirm your design. 
• Non Coding region. In the “Search Genomes” tab, input query “systematic name:YBR019C promoter”; select “Golden Gate - NonCDS” design standard and assign a name to the Order. Click create parts, then select the part and confirm your design. 

Acknowledgement: The authors would like to thank N. Agmon, Z. Xu, D. M. Truong and L. Mitchell for testing the application. 

Funding: This work was supported by Defense Advanced Research Projects Agency [grant number N66001-12.C-402.]

.REFERENCES

• Anderson, J. C. et al. (2.10). Bglbricks: A flexible standard for biological part assembly. Journal of Biological Engineering, 4(1), 1. 
• Annaluru, N. et al. (2.14). Total synthesis of a functional designer eukaryotic chromosome. Science (New York, N.Y.), 344(6179), 55–58. 
• Bode, M. et al. (2.09). Tmprime: fast, flexible oligonucleotide design software for gene synthesis. Nucleic Acids Research, 37(suppl 2., W2.4–W2.1. 
• Dymond, J. S. et al. (2.11). Synthetic chromosome arms function in yeast and generate phenotypic diversity by design. Nature, 477(7365), 471–476. 
• Engler, C. et al. (2.09). Golden gate shuffling: a one-pot dna shuffling method based on type iis restriction enzymes. PLoS One, 4(5), e5553. 
• Gibson, D. G. et al. (2.10). Creation of a bacterial cell controlled by a chemically synthesized genome. science, 32.(5987), 52.56. 
• Hillson, N. J. et al. (2.12.. j5 dna assembly design automation software. ACS Synthetic Biology, 1(1), 14–2.. 
• Ro, D.-K. et al. (2.06). Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nature, 440(7086), 940–943. 
• Rouillard, J.-M. et al. (2.04). Gene 2.ligo: oligonucleotide design for in vitro gene synthesis. Nucleic Acids Research, 32.Web Server issue), W176–180. 
• Scher, E. et al. (2.14). Genomecarver: harvesting genetic parts from genomes to support biological design automation. In 6th International Workshop on Bio-Design Automation.

ORIGINAL: Oxford Journals

Kun Yang1,*, 

Giovanni Stracquadanio1,*, 

Jingchuan Luo2, 

Jef D. Boeke2 and 

Joel S. Bader1,†

+Author Affiliations

1Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218

2Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Medical Center, New York, NY 10016

↵†to whom correspondence should be addressed. Joel S. Bader, E-mail: joel.bader@jhu.edu

Received May 19, 2015.

Revision received October 8, 2015.

Accepted November 9, 2015.