Bench Press

A recent NBER paper (gated) by Benjamin Jones from Northwestern conducts a systematic review of trends in scientific research and made a couple of conclusions that won’t come as a surprise to anyone in science (HT: Inside Higher Ed):

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!

Influenza viruses are negative-stranded, enveloped orthomyxoviruses that contain eight gene segments encoding various viral proteins. During the viral life cycle point mutations drive genetic drift responsible for seasonal influenza epidemics. Novel influenza viruses can also be produced through genetic reassortment between viruses. These viruses can result in devastating pandemics as they expose their host populations to novel antigenicity. These alterations in the influenza viruses’ genomes require that vaccine strains be updated annually to account for changes in virus populations.

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)

I’ve mentioned before that I’m a sucker for the indirect observational techniques that one engages in when doing astronomy/astrophysics. One phenomena that astronomers have yet to be able to observe in any depth is also one of the most fascinating consequences of Einstein’s theory of General Relativity: gravitational waves.

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)

I have always been impressed with the work of astronomers. Unlike biologists and chemists who can, for a wide array of topics, actually touch and feel what they are studying, astronomers have to make conclusions with only careful observations conducted with powerful telescopes and computers informed by understanding the laws of physics (quantum and relativity included) and backed up by complex computational models.

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)