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Monday, February 13th, 2017
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| 12:00a |
Scientists make huge dataset of nearby stars available to public The search for planets beyond our solar system is about to gain some new recruits.
Today, a team that includes MIT and is led by the Carnegie Institution for Science has released the largest collection of observations made with a technique called radial velocity, to be used for hunting exoplanets. The huge dataset, taken over two decades by the W.M. Keck Observatory in Hawaii, is now available to the public, along with an open-source software package to process the data and an online tutorial.
By making the data public and user-friendly, the scientists hope to draw fresh eyes to the observations, which encompass almost 61,000 measurements of more than 1,600 nearby stars.
“This is an amazing catalog, and we realized there just aren’t enough of us on the team to be doing as much science as could come out of this dataset,” says Jennifer Burt, a Torres Postdoctoral Fellow in MIT’s Kavli Institute for Astrophysics and Space Research. “We’re trying to shift toward a more community-oriented idea of how we should do science, so that others can access the data and see something interesting.”
Burt and her colleagues have outlined some details of the newly available dataset in a paper to appear in The Astronomical Journal. After taking a look through the data themselves, the researchers have detected over 100 potential exoplanets, including one orbiting GJ 411, the fourth-closest star to our solar system.
“There seems to be no shortage of exoplanets,” Burt says. “There are a ton of them out there, and there is ton of science to be done.”
Splitting starlight
The newly available observations were taken by the High Resolution Echelle Spectrometer (HIRES), an instrument mounted on the Keck Observatory’s 10-meter telescope at Mauna Kea in Hawaii. HIRES is designed to split a star’s incoming light into a rainbow of color components. Scientists can then measure the precise intensity of thousands of color channels, or wavelengths, to determine characteristics of the starlight.
Early on, scientists found they could use HIRES’ output to estimate a star’s radial velocity — the very tiny movements a star makes either as a result of its own internal processes or in response to some other, external force. In particular, scientists have found that when a star moves toward and away from Earth in a regular pattern, it can signal the presence of an exoplanet orbiting the star. The planet’s gravity tugs on the star, changing the star’s velocity as the planet moves through its orbit.
“[HIRES] wasn’t specifically optimized to look for exoplanets,” Burt says. “It was designed to look at faint galaxies and quasars. However, even before HIRES was installed, our team worked out a technique for making HIRES an effective exoplanet hunter.”
For two decades, these scientists have pointed HIRES at more than 1,600 “neighborhood” stars, all within a relatively close 100 parsecs, or 325 light years, from Earth. The instrument has recorded almost 61,000 observations, each lasting anywhere from 30 seconds to 20 minutes, depending on how precise the measurements needed to be. With all these data compiled, any given star in the dataset can have several days’, years’, ore even more than a decade’s worth of observations.
“We recently discovered a six-planet system orbiting a star, which is a big number,” Burt says. “We don’t often detect systems with more than three to four planets, but we could successfully map out all six in this system because we had over 18 years of data on the host star.”
More eyes on the skies
Within the newly available dataset, the team has highlighted over 100 stars that are likely to host exoplanets but require closer inspection, either with additional measurements or further analysis of the existing data.
The researchers have, however, confirmed the presence of an exoplanet around GJ 411, which is the fourth-closest star to our solar system and has a mass that is roughly 40 percent that of our sun. The planet has an extremely tight orbit, circling the star in less than 10 days. Burt says that there is a good chance that others, looking through the dataset and combining it with their own observations, may find similarly intriguing candidates.
“We’ve gone from the early days of thinking maybe there are five or 10 other planets out there, to realizing almost every star next to us might have a planet,” Burt says.
HIRES will continue to record observations of nearby stars in the coming years, and the team plans to periodically update the public dataset with those observations.
“This dataset will slowly grow, and you’ll be able to go on and search for whatever star you’re interested in and download all the data we’ve ever taken on it. The dataset includes the date, the velocity we measured, the error on that velocity, and measurements of the star’s activity during that observation,” Burt says. “Nowadays, with access to public analysis software like Systemic, it’s easy to load the data in and start playing with it.”
Then, Burt says, the hunt for exoplanets can really take off.
“I think this opens up possibilities for anyone who wants to do this kind of work, whether you’re an academic or someone in the general public who’s excited about exoplanets,” Burt says. “Because really, who doesn’t want to discover a planet?”
This research was supported, in part, by the National Science Foundation. | | 11:00a |
Making single-cell RNA sequencing widely available Sequencing messenger RNA molecules from individual cells offers a glimpse into the lives of those cells, revealing what they’re doing at a particular time. However, the equipment required to do this kind of analysis is cumbersome and not widely available.
MIT researchers have now developed a portable technology that can rapidly prepare the RNA of many cells for sequencing simultaneously, which they believe will enable more widespread use of this approach. The new technology, known as Seq-Well, could allow scientists to more easily identify different cell types found in tissue samples, helping them to study how immune cells fight infection and how cancer cells respond to treatment, among other applications.
“Rather than trying to pick one marker that defines a cell type, using single-cell RNA sequencing we can go in and look at everything a cell is expressing at a given moment. By finding common patterns across cells, we can figure out who those cells are,” says Alex K. Shalek, the Hermann L.F. von Helmholtz Career Development Assistant Professor of Health Sciences and Technology, an assistant professor of chemistry, and a member of MIT’s Institute for Medical Engineering and Science.
Shalek and his colleagues have spent the past several years developing single-cell RNA sequencing strategies. In the new study, he teamed up with J. Christopher Love, an associate professor of chemical engineering at MIT’s Koch Institute for Integrative Cancer Research, to create a new version of the technology that can rapidly analyze large numbers of cells, with very simple equipment.
“We’ve combined his technologies with some of ours in a way that makes it really accessible for researchers who want to do this type of sequencing on a range of different clinical samples and settings,” Love says. “It overcomes some of the barriers that are facing the adoption of these techniques more broadly.”
Love and Shalek are the senior authors of a paper describing the new technique in the Feb. 13 issue of Nature Methods. The paper’s lead authors are Research Associate Todd Gierahn and graduate students Marc H. Wadsworth II and Travis K. Hughes.
Speedy analysis
Most cells in the human body express only a fraction of the genes found in their genome. Those genes are copied into molecules of messenger RNA, also known as RNA transcripts, which direct the cells to build specific proteins. Each cell’s gene expression profile varies depending on its function.
Sequencing the RNA from many individual cells of a blood or tissue sample offers a way to distinguish the cells based on patterns of gene expression. This gives scientists the opportunity to determine cell functions, including their roles in disease or response to treatment.
Key to sequencing large populations of cells is keeping track of which RNA transcripts came from which cell. The earliest techniques for this required sorting the cells into individual tubes or compartments of multiwell plates, and then separately transforming each into a sequencing library.
That process works well but can’t handle large samples containing thousands of cells, such as blood samples or tissue biopsies, and costs between $25 and $35 per cell. Shalek and others have recently developed microfluidic techniques to help automate and parallelize the process considerably, but the amount of equipment required makes it impossible to be easily transported.
Shalek and Love, who have worked on other projects together, realized that technology Love had previously developed to analyze protein secretions from single cells could be adapted to do single-cell RNA sequencing rapidly and inexpensively using a portable device.
Over the past several years, Love’s lab has developed a microscale system that can isolate individual cells and measure the antibodies and other proteins that each cell secretes. The device resembles a tiny ice cube tray, with individual compartments for each cell. Love also developed a process known as microengraving that uses these trays, which can hold tens of thousands of cells, to measure each cell’s protein secretions.
To use this approach for sequencing RNA, the researchers created arrays of nanowells that each capture a single cell plus a barcoded bead to capture the RNA fragments. The nanowells are sealed with a semipermeable membrane that allows the passage of chemicals needed to break the cells apart, while the RNA stays contained. After the RNA binds to the beads, it is removed and sequenced. Using this process, the cost per cell is less than $1.
Uncovering unknowns
Similar to previous single-cell RNA sequencing techniques, the Seq-Well process captures and analyzes about 10 to 15 percent of the total number of RNA transcripts per cell.
“That is still a very rich set of information that maps to several thousand genes,” Love says. “If you look at sets of these genes, you can start to understand the identity of those cells based on the sets of genes that are expressed in common.”
In this paper, the researchers used Seq-Well to analyze immune cells called macrophages, which were infected with tuberculosis, allowing them to identify different pre-existing populations and responses to infection. Shalek and members of his lab also brought the technology to South Africa and analyzed tissue samples from TB- and HIV-infected patients there.
“Having a simple system that can go everywhere I think is going to be incredibly empowering,” Shalek says.
Jonathan Weissman, a professor of cellular and molecular pharmacology at the University of California at San Francisco, says he expects this technology could significantly boost access to rapid RNA sequencing.
“It’s a really nice technical innovation that will help democratize the technology by lowering the price of doing single-cell RNA sequencing in the field, allowing it to be done in places where it couldn’t be done previously,” says Weissman, who was not involved in the research.
Love’s lab is now using this approach to analyze immune cells from people with food allergies, which could help researchers determine why some people are more likely to respond well to therapies designed to treat their allergies. “There are still a lot of unknowns in chronic diseases, and these types of tools help you uncover new insights,” Love says.
The research team has also joined forces with clinical investigators at Dana-Farber/Harvard Cancer Center to apply this technology toward the discovery of new combination immunotherapies to treat cancer as part of the Bridge Project partnership.
The research was funded by the Searle Scholars Program, the Beckman Young Investigator Program, an NIH New Innovator Award, the Bill and Melinda Gates Foundation, the Ragon Institute, the Burroughs Wellcome Foundation, the W.M. Keck Foundation, the U.S. Army Research Office through MIT’s Institute for Soldier Nanotechnologies, and the Koch Institute Support Grant from the National Cancer Institute. | | 11:00a |
Chemical engineers boost bacteria’s productivity MIT chemical engineers have designed a novel genetic switch that allows them to dramatically boost bacteria’s production of useful chemicals by shutting down competing metabolic pathways in the cells.
In a paper appearing in the Feb. 13 issue of Nature Biotechnology, the researchers showed that they could significantly enhance the yield of glucaric acid, a chemical that is a precursor to products such as nylons and detergents. This genetic switch could also be easily swapped into bacteria that generate other products, the researchers say.
“We can engineer microbial cells to produce many different chemicals from simple sugars, but the cells would rather use those sugars to grow and reproduce. The challenge is to engineer a system where we get enough growth to have a productive microbial ‘chemical factory’ but not so much that we can’t channel enough of the sugars into a pathway to make large quantities of our target molecules,” says Kristala Prather, an associate professor of chemical engineering at MIT and the senior author of the study.
The paper’s lead author is Apoorv Gupta, an MIT graduate student. Other authors are Irene Brockman Reizman, a former MIT graduate student who is now an assistant professor at Rose-Hulman Institute of Technology; and Christopher Reisch, a former MIT postdoc who is now an assistant professor at the University of Florida.
A dynamic switch
For decades, scientists have been manipulating the genes of microbes to get them to produce large quantities of products such as insulin or human growth hormone. Often this can be achieved by simply adding the gene for the desired product or ramping up expression of an existing gene.
More recently, researchers have been trying to engineer microbes to generate more complex products, including pharmaceuticals and biofuels. This usually requires adding several genes encoding the enzymes that catalyze each step of the overall synthesis.
In many cases, this approach also requires shutting down competing pathways that already exist in the cell. However, the timing of this shutdown is important because if the competing pathway is necessary for cell growth, turning it off limits the population size, and the bacteria won’t produce enough of the desired compound.
Prather’s lab has previously engineered E. coli to produce glucaric acid by adding three genes — one each from yeast, mice, and a strain of bacteria called Pseudomonas syringae. Using these three genes, bacteria can transform a compound called glucose-6-phosphate into glucaric acid. However, glucose-6-phosphate is also an intermediate in a critical metabolic pathway that breaks down glucose and converts it into the energy cells need to grow and reproduce.
To generate large quantities of glucaric acid, the researchers had to come up with a way to shut down the glucose-breakdown pathway, allowing glucose-6-phosphate to be diverted into their alternative metabolic pathway. However, they had to carefully time the shutdown so that the cell population would be large enough to produce a substantial amount of glucaric acid. More importantly, they wanted to do so without adding any new chemicals or changing the process conditions in any way.
“The idea is to autonomously stop the cells from growing, midway through the production run, so that they can really focus all the available glucose sugars into glucaric acid production,” Gupta says.
To achieve this, the researchers took advantage of a phenomenon known as quorum sensing, which is used by many species of bacteria to coordinate gene regulation in response to their population density.
In addition to adding the genes for glucaric acid production, the researchers engineered each cell to produce a protein that synthesizes a small molecule called AHL. The cells secrete this molecule into their environment, and when the concentration surrounding the cells gets to a certain point, it activates a switch that makes all of the cells stop producing an enzyme called phosphofructokinase (Pfk), which is part of the glucose breakdown pathway. With this enzyme turned
off, glucose-6-phosphate accumulates and gets diverted into the alternative pathway that produces glucaric acid. By constructing a library of cells that produce AHL at different rates, the researchers could identify the best time to trigger shutdown of Pfk.
Using this switch, the researchers were able to generate about 0.8 grams of glucaric acid per liter of the bacterial mixture, while cells that were engineered to produce glucaric acid but did not have the metabolic switch produced hardly any.
Alternative pathways
This type of switch should also be applicable to other engineered metabolic pathways because the genetic circuit can be targeted to shut off other genes.
To demonstrate this versatility, the researchers tested their approach with a metabolic pathway that produces a molecule called shikimate, which is a precursor to several different amino acids and is also an ingredient in some drugs including the influenza drug Tamiflu. They used the AHL quorum-sensing molecule to shut off an enzyme that moves shikimate further along in the amino acid synthesis pathway, allowing shikimate to build up in the cells. Without the switch, the cells could not accumulate any shikimate.
“This paper shows a great potential of dynamic regulation of pathway fluxes, especially the quorum sensing system developed here, which allows precise regulation of a foreign pathway under different conditions including lab and industrial ones. It is therefore very important to invest further to see more value-added products be produced under dynamic metabolic engineering conditions,” says Guo-Qiang Chen, a professor of microbiology and biomaterials at Tsinghua University, who was not involved in the study.
The MIT team is now working on strategies to set up multiple layers of autonomous control, allowing them to shut off one pathway while also turning another one on.
The research was funded by the National Science Foundation, the National Institutes of Health, and the U.S. Department of Agriculture. | | 12:00p |
Voice control everywhere The butt of jokes as little as 10 years ago, automatic speech recognition is now on the verge of becoming people’s chief means of interacting with their principal computing devices.
In anticipation of the age of voice-controlled electronics, MIT researchers have built a low-power chip specialized for automatic speech recognition. Whereas a cellphone running speech-recognition software might require about 1 watt of power, the new chip requires between 0.2 and 10 milliwatts, depending on the number of words it has to recognize.
In a real-world application, that probably translates to a power savings of 90 to 99 percent, which could make voice control practical for relatively simple electronic devices. That includes power-constrained devices that have to harvest energy from their environments or go months between battery charges. Such devices form the technological backbone of what’s called the “internet of things,” or IoT, which refers to the idea that vehicles, appliances, civil-engineering structures, manufacturing equipment, and even livestock will soon have sensors that report information directly to networked servers, aiding with maintenance and the coordination of tasks.
"Speech input will become a natural interface for many wearable applications and intelligent devices,” says Anantha Chandrakasan, the Vannevar Bush Professor of Electrical Engineering and Computer Science at MIT, whose group developed the new chip. “The miniaturization of these devices will require a different interface than touch or keyboard. It will be critical to embed the speech functionality locally to save system energy consumption compared to performing this operation in the cloud."
“I don’t think that we really developed this technology for a particular application,” adds Michael Price, who led the design of the chip as an MIT graduate student in electrical engineering and computer science and now works for chipmaker Analog Devices. “We have tried to put the infrastructure in place to provide better trade-offs to a system designer than they would have had with previous technology, whether it was software or hardware acceleration.”
Price, Chandrakasan, and Jim Glass, a senior research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory, described the new chip in a paper Price presented last week at the International Solid-State Circuits Conference.
The sleeper wakes
Today, the best-performing speech recognizers are, like many other state-of-the-art artificial-intelligence systems, based on neural networks, virtual networks of simple information processors roughly modeled on the human brain. Much of the new chip’s circuitry is concerned with implementing speech-recognition networks as efficiently as possible.
But even the most power-efficient speech recognition system would quickly drain a device’s battery if it ran without interruption. So the chip also includes a simpler “voice activity detection” circuit that monitors ambient noise to determine whether it might be speech. If the answer is yes, the chip fires up the larger, more complex speech-recognition circuit.
In fact, for experimental purposes, the researchers’ chip had three different voice-activity-detection circuits, with different degrees of complexity and, consequently, different power demands. Which circuit is most power efficient depends on context, but in tests simulating a wide range of conditions, the most complex of the three circuits led to the greatest power savings for the system as a whole. Even though it consumed almost three times as much power as the simplest circuit, it generated far fewer false positives; the simpler circuits often chewed through their energy savings by spuriously activating the rest of the chip.
A typical neural network consists of thousands of processing “nodes” capable of only simple computations but densely connected to each other. In the type of network commonly used for voice recognition, the nodes are arranged into layers. Voice data are fed into the bottom layer of the network, whose nodes process and pass them to the nodes of the next layer, whose nodes process and pass them to the next layer, and so on. The output of the top layer indicates the probability that the voice data represents a particular speech sound.
A voice-recognition network is too big to fit in a chip’s onboard memory, which is a problem because going off-chip for data is much more energy intensive than retrieving it from local stores. So the MIT researchers’ design concentrates on minimizing the amount of data that the chip has to retrieve from off-chip memory.
Bandwidth management
A node in the middle of a neural network might receive data from a dozen other nodes and transmit data to another dozen. Each of those two dozen connections has an associated “weight,” a number that indicates how prominently data sent across it should factor into the receiving node’s computations. The first step in minimizing the new chip’s memory bandwidth is to compress the weights associated with each node. The data are decompressed only after they’re brought on-chip.
The chip also exploits the fact that, with speech recognition, wave upon wave of data must pass through the network. The incoming audio signal is split up into 10-millisecond increments, each of which must be evaluated separately. The MIT researchers’ chip brings in a single node of the neural network at a time, but it passes the data from 32 consecutive 10-millisecond increments through it.
If a node has a dozen outputs, then the 32 passes result in 384 output values, which the chip stores locally. Each of those must be coupled with 11 other values when fed to the next layer of nodes, and so on. So the chip ends up requiring a sizable onboard memory circuit for its intermediate computations. But it fetches only one compressed node from off-chip memory at a time, keeping its power requirements low.
“For the next generation of mobile and wearable devices, it is crucial to enable speech recognition at ultralow power consumption,” says Marian Verhelst, a professor of microelectronics at the Catholic University of Leuven in Belgium. “This is because there is a clear trend toward smaller-form-factor devices, such as watches, earbuds, or glasses, requiring a user interface which can no longer rely on touch screen. Speech offers a very natural way to interface with such devices.”
The research was funded through the Qmulus Project, a joint venture between MIT and Quanta Computer, and the chip was prototyped through the Taiwan Semiconductor Manufacturing Company’s University Shuttle Program. | | 1:15p |
Featured video: Tackling science and technology together
Addressing the 21st century’s great challenges requires the world’s best minds to come together and collaborate on solutions. That is why, reflecting the borderless nature of science and technology, MIT President L. Rafael Reif charges each graduating class with a mission to spread far and wide the lessons they’ve learned while in Cambridge. “Hack the world,” he says, “until you make the world a little more like MIT.”
The Institute comprises a diverse, international community of nearly 23,500 students, postdocs, faculty, and staff. MIT faculty alone hail from 73 countries — including the seven affected by the presidential executive order of Jan. 27.
“All my colleagues are from different countries, different cultures, and I think that’s what makes this whole place an amazing place,” says Nikta Fakhri, an expert on the mechanics of living systems and an assistant professor in the Department of Physics, who hails from Iran.
Dina Katabi, a Syrian-born pioneer in wireless technologies and a professor of electrical engineering and computer science, says: “We’re talking about dedicated, smart people who can contribute so much to our nation, but also to making the world a better place.”
Video by: MIT Video Productions | 2 min, 9 sec | | 3:50p |
Coming together for immuno-oncology Takeda Pharmaceutical Company Limited and MIT’s Koch Institute for Integrative Cancer Research recently announced that Takeda will support groundbreaking science in immuno-oncology at the Koch Institute (KI).
The $1 million gift will help, over the next two years, to both build upon research currently being conducted at the KI on the role of the immune response in cancer, and to develop potential novel treatments. The gift will also allow investigators to test their most innovative new ideas and collaborations, conducting early-stage seed projects that can have a major impact but are difficult to fund.
“The Koch Institute was created to promote the best in science and engineering to develop new approaches in the fight against cancer,” said Tyler Jacks, director of the Koch Institute. “Immuno-oncology is a major focus of our efforts, and we are grateful to Takeda for their support in this important area of research.”
Investigators at the Koch Institute are exploring the relationship between the immune system and cancer in animal models and human patients to improve immune responses to cancer.
The KI also works on developing new methods for analyzing cellular immune responses; tools for drug delivery, therapeutic strategies that engage both the innate and the adaptive immune response, including therapeutic and preventative vaccines; and therapeutic antibodies created using state-of-the-art protein engineering methods.
“Takeda embraces innovative science both inside and outside of our organization, and as part of our commitment to patients with cancer, we look to support academic institutions that are leading research in immuno-oncology,” said Christopher Arendt, head of immunology discovery at Takeda. “We are encouraged by the groundbreaking work underway at the Koch Institute in immuno-oncology, which has been a priority area of focus for Takeda and arguably one of the most significant breakthroughs in cancer research over the last few years. The Koch Institute’s dedication to the convergence of life sciences and engineering offers unique opportunities to advance this exciting field.” |
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