The concept is very cool. Many optometrists today use autorefractors, machines which scans the images formed on the back of a patient’s retina to get a rough, but automated, measure on the quality of your vision. Optometrists will then use phoropters to get to a precise enough measure of your eyes as to be able to prescribe lenses for contacts/glasses.

The problem with this approach is that autorefractors and phoropters are too expensive and too time consuming for widespread use in many places around the world. The NETRA solution that the Camera Culture Group came up with was to build an accessory and an application which force a user to make a pair of lines overlap using controls on the phone. Doing this repeatedly lets the application do a calculation similar to what is done by an autorefractor to calculate the quality of the user’s vision in a process which is much faster (several minutes) and cheaper than a standard eye exam (more details in the video and images below)

The project website shows a very compelling table which compares the relative prices and accuracies of optometry solutions in existence today.

It’ll be interesting to see where this technology can go once displays similar to Apple’s Retina Display become cheaper and more prevalent. This example definitely shows the power of telemedicine approaches and is hopefully a harbinger for more equally compelling and innovative solutions for the needs of scientists and doctors around the world.

(Images and video from NETRA website)

Not all data should be published via a peer-reviewed publication. Not every protocol needs to be. But making the data available via wikis, open data resources is pretty much a no-brainer and not just for the future. You enrich currently available data, and have the ability to leverage an additional layer of resources.

Deepak isn’t the only guy to think this, Derek Lowe from In the Pipeline raised a similar point:

Perhaps there should be a way to dump chemical data directly into some archives, the way X-ray data goes into the Protein Data Bank. That wouldn’t count for much, but it would capture things for future use. Having it not count much would decrease the incentive for anyone to fill it full of fakery, too, since there would be even less point than usual. And before anyone objects to having a big pile of non-peer-reviewed chemical data like this, keep in mind that we already have one: it’s called the patent literature, and it can be quite worthwhile.

(all emphases mine)

I think they both have a very good point. Some form of centralized data repository, even if non-peer reviewed, could help tackle the problem that everyone hears about but nobody ever tries to solve of not having a central place to share negative results and protocols (akin to what this blog proposed previously for bio/pharma companies).

It could also help us re-prioritize publication and peer review efforts away from sheer data collation which, while extremely important, is distinct from experimental/study design, data analyses, and drawing conclusions where peer-review is more valuable (there’s only so much peer-review can do to when looking at a data collection effort in isolation).

With modern internet technologies being as fast and as scalable as they are now, there’s simply no reason to use the traditional journal to chronicle every single discovery or achievement. Better to collect most of it in API-accessible/index-able repositories so that others can share in it and curate it and instead focus publications on building analytical insights.

(Image credit)

As science advances and knowledge accumulates, ensuing generations of innovators spend longer in training and become more narrowly expert, shifting key innovations (i) later in the life cycle and (ii) from solo researchers toward teams

As evidence to this, the average age at which a scientist made a discovery which later qualified for a Nobel prize has increased by 6 years over the course of the 20th century. When looking at publications, the researchers found that the average author list on a publication grew, on average, by 15-20% per decade!

We’ve discussed before the “decline of the Lone Ranger model of science”, but Jones’ paper focuses on looking at the policy implications for such a change. He concludes that the government (and, probably, the academic and private institutions which support researchers) need to adapt policy to reflect this new reality by:

• Tailoring funding and messaging to help keep young researchers interested despite the longer and more difficult training period
• Finding new ways to evaluate the worthiness of proposals as scientist’s expertise becomes more and more specialized
• Altering incentive structures as the team of collaborators replaces the Lone Ranger scientist model of discovery

These policy suggestions are definitely good ones, and are certainly necessary to adapt to a new scientific environment, but one dimension of this which Jones doesn’t discuss as much are the technological (the focus of this blog!) innovations which can help further research in this brave new world.

• Improving science communication with the public. We’ve made multiple mentions of this in the past, but they are no less true here. Active public communications management not only helps secure funding and raise public awareness of the good scientists can do, but it also helps attract the interest of future generations of researchers and policymakers.
• Embracing new collaboration tools. To really kick-start collaboration between scientists across geographies and specialties, we need tools that go beyond just email and fax machines. Tools like Google Wave, wikis, distributed version control, and social media forums like Friendfeed are an early taste of the sort of live collaboration that new web technology can bring about.
• Leveraging cloud computing and heterogeneous compute. One of the reasons discoveries are taking longer and are more expensive is that there is so much more data to collect and to analyze than before. One technological innovation which we’ve talked about at lengths here is the ability of graphics cards/GPUs to make supercomputer-level processing power more readily accessible to research labs. Another is the use of new cloud computing services like Amazon’s to rapidly increase the computational resources that a lab/company has access to. Neither are panaceas for all the data analysis issues which scientists face, but they are definitely ways to make things easier for research groups who have stringent IT budgets.
• Using crowdsourcing to speed innovation. Who says research has to take longer and be more expensive? Perhaps its time to pull on new technological levers which let scientists borrow on the resources and brains of a wider group of people. While new platforms like ChemBioConnect, distributed computing systems like Folding@Home, and volunteer crowdsourcing initiatives like Fold.IT are far from perfect, they hint at a future where researchers can call on resources beyond what their personal computers and brains are capable of.
• Building new research attribution models. When I say new attribution models, I’m referring to two things. The first is embodied by new standards like ORCID which make it easier to understand which person is the author/researcher in question (something which will become more and more important as more people with the same initials/names enter the sciences). The second, and more substantive, is finding new ways to understand who contributed what to a particular study. In today’s digital age, I find it laughable that we still rely on simple author list order to determine the relative roles and positions of the researchers listed on a publication. Employing metadata and other graphical cues can help scientists achieve the recognition they deserve, as well as provide appropriate incentives for teams of researchers to contribute.
• Contributing negative and after-publication results to open repository. While I can understand the hesitation for most research groups to pursue a pure open access strategy, those concerns should not hold with negative or post-publication experimental data. While opening up access to data from failed/negative experiments does little to hurt a lab’s ability to publish first, it can be a dramatic boon for other research groups (especially new labs or labs with interdisciplinary focuses) who can not only use the data for their own analyses and experimental designs, but avoid committing resources to experiments which have already been conducted. If it can work for biotechs and pharma companies, then there’s no reason it should be any different for non-commercial groups.

These suggestions only scratch the surface of what new technologies and policies can do to help scientists in a world where scientific training takes longer and where scientific discoveries need to be more collaborative. If anyone else has any other suggestions, feel free to leave them in the comments!

Currently two types of vaccines are utilized to combat seasonal flu, each with their own limitations:

1. Chemically inactivated virus delivered via injection – mainly acts by inducing antibody response rather than cellular immunity and has suboptimal efficacy in elderly patients.
2. Live attenuated influenza virus vaccine of cold-adapted virus delivered via nasal spray – induces both humoral and cellular immunity, but is restricted to healthy children, adolescents, and adults, performing better in immunologically naive young children than adults.

So despite the relative effectiveness of the current offerings there is definitely room for improvement. That’s where research being done by Dr. Steffen Mueller and colleagues at Stony Brook University comes in. Building on prior research on producing synthetically attenuated polio viruses, Dr. Mueller’s group utilized a technique dubbed Synthetic Attenuated Virus Engineering (SAVE) to generate synthetic influenza virus vaccine candidates. They detail their work in a paper in Nature Biotechnology titled Live attenuated influenza virus vaccines by computer-aided rational design.

What’s truly unique about SAVE is that it only uses silent mutations within the viral genome to produce live attenuated virus. As described in the paper:

The central idea of SAVE is to recode and synthesize a viral genome in a way that perfectly preserves the WT amino acid sequence, while rearranging existing synonymous codons to create a suboptimal arrangement of pairs of codons. For reasons that are not understood, some pairs of codons occur more frequently, and others less frequently, than expected. … Although the mechanism of attenuation is unclear, preliminary evidence suggests that translation is affected. Attenuation can be ‘titrated’ by adjusting the extent of codon-pair deoptimization. Because codon-pair deoptimization results from miniscule effects at each of hundreds or thousands of nucleotide mutations (without changing amino acid sequences), reversion to virulence is extremely unlikely. Aided by computer algorithms, codon pair–deoptimized viral genomes can be rapidly designed and synthesized, and live virus can be generated by reverse genetics.

Figure 1

Utilizing SAVE Dr. Meuller’s group generated influenza strains with the following proteins synthetically deoptimized, polymerase subunit B1 (PB1), nucleoprotein (NP), and hemagglutinin (HA). They tested strains with a single deoptimized gene as well as one strain with all three altered genes. First Dr. Meuller’s group analyzed each of their strains in vitro to assess their growth characteristics. They found that all the mutant viruses produced plaques similar to wild type and produced reasonable titers although slightly lower (about tenfold lower). What was most interesting about their in vitro characterization however was their western blot analysis of the synthetically designed proteins. In each mutant it is clear that the gene that underwent SAVE produces significantly less protein in comparison to wild type (PR8) (Figure 1). The effect is specific and lends support to the idea that codon-pair deoptimization results in an effect on translation.

After checking their in vitro characteristics, Dr. Meuller’s group tested for attenuation by infected mice. They found that despite the reasonably robust viral growth each mutant strain demonstrated an attenuation effect. A strain combining all three modified genes (PR8^3F) resulted in an increase in median LD₅₀ of about 13,000 fold. Further tests on PR8^3F showed effective attenuation of symptoms and viral load in comparison to wild type (Figure 2).

Figure 2

In addition to assessing attenuation of the virus, Dr. Meuller’s group assessed the immune response and protective immunity in detail by immunizing mice and then challenging them 28 days after inoculation. As seen in Figure 3 below (a, b) strain PR8^3F had a much larger range of safe doses in comparison to wild type. Viral load is effectively limited (c) in PR8^3F inoculated animals and antibody response is robust (d) even in very low doses in comparison to LD₅₀ of PR8^3F.

Figure 3

Ultimately, this paper illustrates a really interesting new technology that seems capable of revolutionizing vaccine development. It provides a novel vaccine design strategy that appears to produce robust immune protection. It remains to be seen if SAVE vaccine candidates can address the limitations of current flu vaccines as well as other issues, but this paper is a strong first step for this technology.

(Source – Nature Biotechnology)

A rigorous understanding of gravitational waves is far beyond the scope of this post (and also far far far far far far beyond my limited comprehension), but the basic concept comes from Einstein’s idea that gravity as we know it is actually a “bending” in spacetime that happens wherever there is mass. This curvature is what causes most of the effects of gravity that we can observe (i.e. you feel a pull towards the center of the Earth because the Earth’s mass bends the neighboring spacetime that you are in). As the Earth moves around the Sun (and as the Sun moves around the Milky Way galaxy and as the Milky Way moves…), the curvature in spacetime also moves with it. In certain types of motion (i.e. when an object is part of a binary orbiting system), this movement in spacetime curvature actually results in “waves” of spacetime curvature emanating outwards, kind of like ripples in a pond. These “waves” are called gravitational waves and, because they carry energy, are also called gravitational radiation. Because of the nature of these waves, they have three unique properties which make them interesting tools in the study of astronomy:

• they move at the speed of light
• unlike light, these waves don’t get significantly scattered/blocked
• they don’t require the existence of matter (so they can be used to study black holes)

The problem? They are extremely difficult to detect, because their effects are remarkably small. In fact, the first indirect observation of gravitational waves, found in the change in orbits of the Hulse-Taylor binary system (pictured above-right) won the researchers the 1993 Nobel Prize in Physics.

So, what to do? While there have been many attempts to do this, they are plagued by the difficulty of detecting such weak waves in the presence of as much noise on Earth. Potential solution? The use of a multinational space-borne laser interferometry setup called LISA (Laser Interferometer Space Antenna). With the use of laser light and interferometry (which allows you to measure small changes in distance by observing interference between a beam of light and a reflection), three identical solar-powered spacecraft will be set up in an equilateral triangle orbiting the sun at an angle relative to where the plane of the orbit of the other planets in the solar system (see below).

What’s especially remarkable is the precautions NASA is taking with the LISA spacecraft to correct for error and insure greater accuracy and precision in their measurements:

• Use of microthrusters to maintain drag-free flight by constantly monitoring the position of the test-weights the LISA spacecraft is flying around (and maintaining position of the instruments relative to the test-weights of ~10nm)
• Use of a transponder (which calculates the phase of an incoming beam of laser light and electronically setting the phase of the outgoing beams) instead of a mirror for interferometry to avoid diffraction (light scattering) from a traditional reflection approach
• Use of time-delay interferometry and continuous frequency monitoring/stabilization to correct for the effects of frequency noise

Given the technology and the theory involved, LISA’s potential to change astronomy could potentially rival the Hubble telescope’s, opening up new ways to study distant astronomical phenomena and potentially some of the more exotic topics in physics like string theory and strong-field gravity. There’s a lot more information on the potential topics of scientific inquiry which LISA could be used to study on NASA’s LISA science page.

Let’s cross our fingers that it will stay on schedule for launch in the 2018-2020 range and deliver not only concrete observations of gravitational waves but a whole wealth of information on the universe we live in.

(Image credit: NASA) (Image credit: NASA)

One phenomena which astronomers can use to better explore deep space is an effect predicted by Einstein’s theory of general relativity called gravitational lensing. General relativity predicts that the path of light can be bent by gravitational fields; the most dramatic example of this would be a black hole, where gravity is so strong that light “falls” back into it. The same effect, on a less dramatic scale, could result in the path of light being bent on its way to Earth by the gravity of another object. The term “gravitational lens” refers to the fact that this bending is similar to the bending of light by a telescope lens.

Now, for the layperson, the fact that light can bend is probably just a cool effect which has no practical importance. But, to a well-trained astronomer, the knowledge of how gravity works lets them use the phenomena of gravitational lensing to understand both the objects that are emitting light (because the lensing effect allows us to see objects which are so far away that they are blocked by another object) and the “lens” itself (understanding the mass, structure, and position of what is bending the light).

Take a look at the pictures (HT: Wikipedia) below of Einstein rings. These occur when the line of sight to a bright, faraway object is being blocked by another object. However, because of the gravitational lens effect, the light from that faraway object can bend around the closer object, resulting in a ring which gives scientists a chance to study not only the faraway object but also understand the structure of the intervening space.

There are countless other examples of the application of gravitational lensing in the study of astronomy, but one of the most clever that I heard about recently was the study of dark matter. The theory in a nutshell: the universe is believed to be mostly dark matter – matter which does not reflect or emit any light whatsoever. Because it doesn’t seem to emit or reflect electromagnetic radiation, there has been no direct observational way to study it. However, dark matter does have mass. This means it has gravity and can thus bend light as a gravitational lens!

Researchers were able to took astronomical survey data from around the world and, using sophisticated computer algorithms and programs, compile a picture of gravitational lensing due to dark matter. From that, they were then able to digitally put together a picture of the structure of the dark matter in (at least part of) the universe and get a sense for how it’s evolved over time (the further from Earth you look, the further back in time):

And with this they made a striking conclusion – we all have dark matter to thank for the existence of the stars and the galaxies:

Our results are consistent with predictions of gravitationally induced structure formation, in which the initial, smooth distribution of dark matter collapses into filaments then into clusters, forming a gravitational scaffold into which gas can accumulate, and stars can be built.

Awesome.

(Image credit) (Image credit)

And, as my Tweets that day pointed out, I was blown away:

There are some today who think high-def/3D is usually a gimmick by movie studios and digital display sellers, but that was definitely not true for either of these films. The 3D really enhanced the impact of the visuals. It let the audience, many of whom are unlikely to ever conduct spacewalks or scuba-dive where the Under the Sea 3D crew went to really feel what it was like to see undersea life. And, in the case of some of the deep space Hubble 3D shots, it gave the audience a very cool new look at objects so far away that its almost inconceivable that human beings will ever actually get to visit them.

Couple that with strong performances on interesting material by Leonardo DiCaprio in Hubble 3D and Jim Carrey in Under the Sea 3D and you get a strong combination which, if I’m any judge, not only gives the audience a juicy taste of why science is cool, but why its important to continue to study it.

I have definitely been sold on these, and I not only plan to check out more of these as they come out (I’ve got my eye on Sea Rex 3D), but would recommend this to anyone who has an hour to spend or would like to check out a visually stunning way to learn something new.

That’s why I was particularly impressed by a paper in Nature by Ryan Calsbeek and Robert M. Cox, who wanted to explore the importance of selective pressures on anole lizards in the Caribbean. Field experiments to measure the effect of selective pressures are rare for a variety of reasons. A major one being the difficulty of finding animals and environments which can be manipulated in a controlled manner. Drs. Calsbeek and Cox didn’t let this stop them as they utilized a group of small islands in the Caribbean, each small enough to throw a ball end to end, as their test beds. There they removed the resident brown anole lizards and replaced them with experimental animals which had been carefully measured, tested for stamina, and tagged to identify at the end of the experiment.

Now that they had their experimental populations they needed to set up islands that tested the hypothesis that competition played a larger selective role than predation in island anole lizard populations. They established islands that had low and high density populations, and for each density type they setup islands inhabited by lizard-eating birds alone, lizard-eating birds and snakes, as well as islands free of predators (accomplished with a generous covering of netting as seen below). An unmodified control island was also monitored as a natural reference population.

They distributed the lizards in May and four months later at the end of the breeding season, September, they came back to capture the survivors and census the population. While this was difficult work they were able to collect a large amount of data which confirmed the hypothesis that competition is a more powerful selective force in these populations. They saw no real phenotypic differences in the lizards on islands experiencing differing predation, but saw that lizards surviving on crowded islands were significantly bigger and had greater stamina than those on less crowded islands (seen below). This indicated competition between lizards pushed the population while predation did not.  

While their clever experiment does a great job explaining the relative importance of selective pressures on this particular species of lizards on islands in the Caribbean it may not say anything about natural selection in other species. Despite that this paper remains awesome because as much as I like to see technology change the way we do science, I still appreciate a well constructed experiment to answer tough questions.

(Nature: Experimentally assessing the relative importance of predation and competition as agents of selection)

The magic of new, cheap and powerful technologies and information sources like Google Earth is that they can also be used to push additional experiments and discoveries. Widely available mathematical analysis tools like Mathematica let a professor figure out what instruments made up the mysterious “twang” at the beginning of the Beatles classic “It’s Been a Hard Day’s Night.” Advances in gaming and graphics technology make it possible for gaming consoles and personal computers to do sophisticated number-crunching. And, Google Earth helped make it possible for a team of students to take pictures from near-space for only $150.

More recently, Google Earth helped a team of researchers led by the University of the Witwatersrand’s Professor Lee Berger to find and unearth new fossil remains, including those of the newly discovered Australopithecus sediba (pictured right) in the Cradle of Humankind World Heritage Site in South Africa.

Berger and his associates used Google Earth to learn how to identify cave sites from satellite imagery and to help supplement on-foot exploration to map out ~500 previously unknown caves and, subsequently, 25 new fossil sites!

This discovery has been especially exciting as some have argued it could potentially be “the point from which the genus Homo [the genus we humans are a part of] arises” and “a good candidate for being the transitional species between the southern African ape-man Australopithecus africanus (like the Taung Child and Mrs. Ples) and either Homo habilis or even a director ancestor of Homo erectus (like Turkana Boy, Java man, or Peking man).”

Check out the University’s web coverage of the discovery as well as the Google blog’s coverage of the event as well as the video celebrating Professor Berger’s find (below):

(Image credit – University of the Witwatersrand website)

Science papers by Berger’s group: (1) and (2)

The game was designed by two self-admitted “mega-geek” physicians, Dr. Arun Mathews and Dr. Francis Kong, who met in medical school and created the company Nerdcore Learning to promote The Healing Blade and other medicine-related “edutainment” paraphernalia. As to why they created the concept, Dr. Mathews notes:

I was struck upon the complexity and yet innate nature of gaming within the choice I would make for putting some of my sick patients on particular antibiotics … Essentially, in a similar way, when you are playing a complex multi-tiered video game, we are making similar choices by obtaining data from our cultures [and] making risk-management decisions.

Truer words were never spoken.

Amazingly, while Mathews and Kong had only intended to bring 30 copies of the game to launch at the American Medical Students Association annual meeting, a printing error turned that into over 100 copies, 90% of which sold! Mathews describes the sight:

We had this gaggle of students just sitting down, spreading out on a bunch of tables, all playing the game. That is one memory that will take a while to fade, because it was such a neat thing to see students getting super excited about infectious disease and therapies.

As an unabashed former-Magic-and-Yu-gi-oh player, I can definitely see the appeal. There is something very compelling about the mix of chance and strategy in trading card gameplay. Sadly, at the time of the writing of this blog post, The Healing Blade’s online purchase form shows that the game is sold out. So, in the meantime, I will have to leave you with some pictures of some very nice-looking game card art:

(Images and video from Healing Blade website)