http://www.sciencemag.org/content/334/6062/1503.full

Ideas are like fire, observed Thomas Jefferson in 1813—information can be passed on without relinquishing it (1). Indeed, the ease and benefit of sharing information select for individuals to aggregate into groups, driving the buildup of complexity in the biological world (2, 3). Once the members of some collective—whether cells of a fruit fly or citizens of a democratic society—have accumulated information, they must integrate that information and make decisions based upon it. When these members share a common interest, as do the stomata on the surface of a plant leaf (4), integrating distributed information may be a computational challenge. But when individuals do not have entirely coincident interests, strategic problems arise. Members of animal herds, for example, face a tension between aggregating information for the benefit of the herd as a whole, and avoiding manipulation by self-interested individuals in the herd. Which collective decision procedures are robust to manipulation by selfish players (5)? On page 1578 of this issue, Couzin et al. (6) show how the presence of uninformed agents can promote democratic outcomes in collective decision problems.

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Distributed information processing.

(A) Research in this domain comprises four areas: multisensor integration (12), social choice theory (13), cooperative distributed computation (14), and tactical distributed computation (5). The Couzin et al. study lies in the lower right quadrant, where the challenges of both social choice and distributed computation must be solved. (B) In this schematic of tactical distributed computation, an uncertain world is observed imperfectly by agents with different preferences. By means of local interactions, they aggregate the information and preferences to arrive at a collective decision.

Research in distributed information processing broadly falls into four domains (see the figure, panel A), which differ depending on whether there is local or central control over the collective decision and on whether the agents share common interests (7). The scenario addressed by Couzin et al. lies in the particularly challenging lower right quadrant. In this domain, one must simultaneously consider both the local nature of information exchange in distributed systems and the strategic issues that arise in social choice theory.

We can think of tactical distributed control as having two stages (see the figure, panel B). In an initial social choice stage, each agent imperfectly observes the world and selects a preferred outcome. In a subsequent distributed computation stage, individual preferences are aggregated through local interactions among agents to select a consensus decision. In such a situation, agents can pursue selfish aims not only through strategic choice of preferred outcome (8)—much as a far-left voter might back a moderate democrat with a chance of winning instead of a fringe candidate with a more liberal platform— but also through tactical behavior during the process of local information exchange.

Couzin et al. consider cases in which the group must decide between two options. Allowing only two options simplifies the problem considerably: There is no incentive for strategic voting, but incentives remain for manipulating the process of information integration. Furthermore, when groups must select among more than two options, they face a host of voting paradoxes. Thomas Jefferson’s acquaintance and correspondent Marquis de Condorcet noted the basic reason for this more than two centuries ago (9): If a group has intransitive preferences—its members collectively prefer A to B and B to C in pairwise comparisons, yet they also prefer C to A—there is no straightforward way to select a single best course of action. This poses a serious social choice problem, because even when no single individual has intransitive preferences, the aggregate preferences of the group can be intransitive.

With two options, the task of determining the majority opinion is analogous to the classic density classifier problem in the study of distributed computation (100). But whereas the vast majority of work on the density classifier looks at cooperative distributed computation, Couzin et al. look at—and even implement in a vertebrate system, namely schooling fish—an extension to the noncooperative case.

The authors develop three different models of the information integration process. In each of these, agents are probabilistically influenced to adopt the opinions of their neighbors, and can promote their own opinions by being reluctant to change them. In this way, an intransigent minority can convert the entire group over to their minority opinion. Such behavior can impose costs on the group, including a reduced responsiveness to the state of the environment, an increased time to make a collective decision, and an increased risk of group fragmentation.

One might expect groups with uninformed members to be particularly susceptible to tactical behavior by minority subpopulations. If that tactical behavior involved some sort of active proselytizing to accelerate conversion to the minority opinion, one would be right. But Couzin et al. show that when the tactical behavior involves intransigence, uninformed individuals have the opposite effect. Their presence allows the majority to wrest control back from a manipulative minority. In each of their models, this occurs because the uninformed individuals tend to adopt the opinions of those around them, amplifying the majority opinion and preventing erosion by an intransigent minority. In this way, adding uninformed individuals to a group can facilitate fair representation during the process of information integration. Jefferson’s passionate arguments on the importance of education for democratic society notwithstanding (11), Couzin et al. have identified circumstances in which ignorance can promote democracy.

References and Notes

1. ↵
   1. T. Jefferson

   , Letter to Isaac McPherson, 13 August 1813; in The Writings of Thomas Jefferson (Thomas Jefferson Memorial Association of the United States, Washington, DC, 1903), vol. 13, pp. 333–334. “He who receives an idea from me, receives instruction himself without lessening mine; as he who lights his taper at mine, receives light without darkening me…[Ideas are] like fire, expansible over all space, without lessening their density in any point.”

2. ↵
   1. J. Maynard Smith,
   2. E. Szathmary

   , Major Transitions in Evolution (Oxford Univ. Press, New York, 1998).

3. ↵
   1. M. Lachmann,
   2. G. Sella,
   3. E. Jablonka

   , Proc. Biol. Sci. 267, 1287 (2000).
   Abstract/FREE Full Text

4. ↵
   1. D. Peak,
   2. J. D. West,
   3. S. M. Messinger,
   4. K. A. Mott

   , Proc. Natl. Acad. Sci. U.S.A. 101, 918 (2004).
   Abstract/FREE Full Text

5. ↵
   1. M. Ben-Or,
   2. N. Linial

   , in Proceedings of the 26th Annual Symposium on Foundations of Computer Science (FOCS, Portland, OR, 1985), pp. 408–416.

6. ↵
   1. I. D. Couzin
   2. et al

   ., Science 334, 1578 (2011).
   Abstract/FREE Full Text

7. ↵
   1. L. Conradt,
   2. T. J. Roper

   , Trends Ecol. Evol. 20, 449 (2005).
   CrossRefMedline

8. ↵
   1. A. Gibbard

   , Econometrica 41, 587 (1973).
   CrossRefWeb of Science

9. ↵
   1. H. P. Young

   , Am. Polit. Sci. Rev. 82, 1231 (1988).
   CrossRefWeb of Science

10. ↵
    1. N. H. Packard

    , Dynamic Patterns in Complex Systems (World Scientific, River Edge, NJ, 1988).

11. ↵
    1. T. Jefferson

    , Letter to Charles Yancey, 6 January 1816; in The Writings of Thomas Jefferson (Thomas Jefferson Memorial Association of the United States, Washington, DC, 1903), vol. 10, p. 4. “If a nation expects to be ignorant and free, in a state of civilization, it expects what never was and never will be. The people cannot be safe without information.”

12. ↵
    1. R. C. Luo,
    2. M. G. Kay

    , IEEE Trans. 19, 901 (1989).

13. ↵
    1. A. D. Taylor

    , Social Choice and the Mathematics of Manipulation (Cambridge Univ. Press, Cambridge, 2005).

14. ↵
    1. J. P. Crutchfeld,
    2. M. Mitchell

    , Proc. Natl. Acad. U.S.A 92, 10742 (1995).
    Abstract/FREE Full Text

15. We thank T. Bergstrom, E. Chastain, M. Rosvall, and D. Peak for helpful discussions. This work was supported in part by NSF grant ATB-1038590 to C.T.B.

Is there a strong correlation between the number of hours you are physically present in a lab and the pace and success of your project?

The furore over Nature’s 24/7 lab feature, published a few weeks ago, is still sending out the occasional ripple. In case you missed it, the 31 August issue of the journal featured three pieces: a beautifully written account of Alfredo Quiñones-Hinojosa’s high-powered, workaholic lab at Johns Hopkins in Baltimore by journalist Heidi Ledford; the opposing viewpoint for the importance of work/life balance, presented in the first person by Julie Overbaugh, a successful group leader at the Fred Hutchison in Seattle; and an editorial, which seemed, on balance, to come down largely in favor of the turbo-gunner approach. Indeed, it finishes with the rather ominous observation: “As research funding declines in many countries, science will intensify. Anyone lacking the inner intellectual drive and a capacity for relentless focus to get to the heart of the way the world works should stay away.”

All of you have probably known about labs like Quiñones-Hinojosa’s – especially if you’ve spent time in any prestigious research hub in America. And some of you may have lived the 24/7 lab lifestyle yourself at some point in your career.

I, personally, have been there. But rather ironically, my 24/7 epoch – which stretched over the five and half years I was a PhD student in Seattle – happened in the lab of the aforementioned Julie Overbaugh herself. Hers was a relatively new lab at the time, and I ended up being the first PhD student to graduate from her group. My work ethic wasn’t precisely 24/7, but I would routinely work 12-14 hour days on the weekdays, and 8-10 each weekend day: never fewer than 80 hours a week, and sometimes approaching 90 or 100. This was the epoch in which I single-handedly cloned and sequenced more than a megabase of DNA, the old-fashioned manual way, with radioactive sulfur and hand-poured gels, and typed the results in manually at the computer each morning until the G, C, A, and T keys were visibly worn.

You could find me in the lab at six in the morning, or at midnight, or putting in a few hours on Christmas day. I once set off the intruder alert alarm in the Regional Primate Center after coming in from clubbing to check on some cell cultures at 3 AM – the goth clothes I was wearing at the time didn’t seem to reassure the security guard that I was actually an authorized PhD student. I’d ride those long hours on an adrenalin-fuelled buzz that only a 20-something year old kid could carry off for long periods of time, buffered through the failures by sporadic bright sparks of promising data and a constant stream of incredibly loud grunge or indie music.

It is important to stress that all of this behavior was entirely self-imposed. Julie worked normal hours and repeatedly stressed that we – the Overbabes, as we called ourselves back then – could all do as we liked. Some in her lab worked normal hours, and some worked longer. I think we were all a little scared of Julie: someone who applies no pressure is a pressure of its own. But I think the fact that it wasn’t imposed or expected is very important. Nobody has a right to treat a worker like a slave – something I think that some lab heads forget. “That’s how we did it in my day” is no justification.

If you look at my track record, you’ll see that my 24/7 PhD stint bought me four first-author papers, two first-author reviews and two co-authorships. Impressive, perhaps, until you factor in that I didn’t get my first paper out until my fourth year. In many projects, you have to labor for years to get a system up and running; the 24/7 thing isn’t necessarily going to strike gold during your typical post-doctoral short-term contract span. And with age, inevitably, comes the weakness of the flesh. Nowadays, I get tired, I get hungry; I can’t force myself to work the long hours I used to do with such ease. As I approach middle age, and life starts to feel finite, I find that spending time with my loved ones is more important than cranking out so many papers that I never get to see them.

And I think Julie is right about the creativity angle: I get scientific ideas when I’m running in the woods, or swimming laps, or lying sprawled on a sunny blanket in the weekend garden staring at the clouds. Sometimes in the lab or at my computer, I feel a block that won’t ease until I step away from the problem. I abandoned the 24/7 ethic completely from my second post-doc onwards (I’m on only about 50-60 hours now, if you include the time I spend writing papers and reading the literature at home), but my publication record remains as strong as it was at the outset, and some of the work I’m most proud of happened in a 9-to-5 industry culture.

Personally, although I’m glad Nature gave someone like Julie a platform to voice an opinion that many of us feel is obvious, I’m a little bit disappointed that its editorial team decided to side with the sweatshop mentality. Judging by the comment threads on the three pieces, most readers were equally disappointed. Quantity is seldom quality in science, and there are many different styles and diverse approaches in the quest for knowledge. The sleep-in-the-lab scientific stereotype is getting a little stale. As a community, shouldn’t we be moving beyond all that?

Source: http://electroncafe.wordpress.com/2011/05/04/scientific-process-rage/

Saturday, September 17, 2011

Research provides new insight into why some individuals may be more aggressive than others

Fluctuations of serotonin levels in the brain, which often occur when someone hasn’t eaten or is stressed, affects brain regions that enable people to regulate anger, new research from the University of Cambridge has shown.

Although reduced serotonin levels have previously been implicated in aggression, this is the first study which has shown how this chemical helps regulate behaviour in the brain as well as why some individuals may be more prone to aggression. The research findings were published today, 15 September, in the journal Biological Psychiatry.

For the study, healthy volunteers’ serotonin levels were altered by manipulating their diet. On the serotonin depletion day, they were given a mixture of amino acids that lacked tryptophan, the building block for serotonin. On the placebo day, they were given the same mixture but with a normal amount of tryptophan.

The researchers then scanned the volunteers’ brains using functional magnetic resonance imaging (fMRI) as they viewed faces with angry, sad, and neutral expressions. Using the fMRI, they were able to measure how different brain regions reacted and communicated with one another when the volunteers viewed angry faces, as opposed to sad or neutral faces.

The research revealed that low brain serotonin made communications between specific brain regions of the emotional limbic system of the brain (a structure called the amygdala) and the frontal lobes weaker compared to those present under normal levels of serotonin. The findings suggest that when serotonin levels are low, it may be more difficult for the prefrontal cortex to control emotional responses to anger that are generated within the amygdala.

Using a personality questionnaire, they also determined which individuals have a natural tendency to behave aggressively. In these individuals, the communications between the amygdala and the prefrontal cortex was even weaker following serotonin depletion. ‘Weak’ communications means that it is more difficult for the prefrontal cortex to control the feelings of anger that are generated within the amygdala when the levels of serotonin are low. As a result, those individuals who might be predisposed to aggression were the most sensitive to changes in serotonin depletion.

Dr Molly Crockett, co-first author who worked on the research while a PhD student at Cambridge’s Behavioural and Clinical Neuroscience Institute (and currently based at the University of Zurich) said: "We’ve known for decades that serotonin plays a key role in aggression, but it’s only very recently that we’ve had the technology to look into the brain and examine just how serotonin helps us regulate our emotional impulses. By combining a long tradition in behavioral research with new technology, we were finally able to uncover a mechanism for how serotonin might influence aggression."

Dr Luca Passamonti, co-first author who worked on the research while a visiting scientist at the Cognition and Brain Sciences Unit of the Medical Research Council in Cambridge (and currently based at the Consiglio Nazionale delle Ricerche (CNR), Unità di Ricerca Neuroimmagini, Catanzaro), said: "Although these results came from healthy volunteers, they are also relevant for a broad range of psychiatric disorders in which violence is a common problem. For example, these results may help to explain the brain mechanisms of a psychiatric disorder known as intermittent explosive disorder (IED). Individuals with IED typically show intense, extreme and uncontrollable outbursts of violence which may be triggered by cues of provocation such as a facial expression of anger.

"We are hopeful that our research will lead to improved diagnostics as well as better treatments for this and other conditions."

###

For additional information please contact:
Genevieve Maul, Office of Communications, University of Cambridge
Tel: direct, +44 (0) 1223 765542, +44 (0) 1223 332300
Mob: +44 (0) 7774 017464
Email: Genevieve.maul

Notes to editors:

1. The paper ‘Effects of Acute Tryptophan Depletion on Prefrontal-Amygdala Connectivity While Viewing Facial Signals of Aggression’ was published online on 14 September in Biological Psychiatry.

2. Funders: This study was supported by the James S. McDonnell Foundation

21st Century Collaborative Award/Bridging Brain, Mind and Behavior (Award No. 22002015501) to E. A. Phelps and T. W. Robbins and completed within the Behavioural and Clinical Neuroscience Institute, which is cofunded by the Medical Research Council and the Wellcome Trust. Dr. Passamonti was funded by the Betty Behrens Research Fellowship at the Clare Hall College of the University of Cambridge. The J. S. McDonnell Foundation Network grant supported Dr. Apergis-Schoute. Dr. Crockett was supported by the National Science Foundation.

Dr. Rowe was supported by the Wellcome Trust (Grant No. WT 088324); Dr. Calder by the Medical Research Council (Project Code MC_US_A060_0017). Development of the MacBrain Face Stimulus Set (NimStim) was overseen byNimTottenham and supported by the John D. and Catherine T. MacArthur Foundation Research Network on Early Experience and Brain Development.

The Council of Graduate Schools (CGS) and the Educational Testing Service (ETS) today announced a 14-member Commission to Study Pathways through Graduate School and into Careers. The name derives from an April 2010 report issued by the two group, called The Path Forward, that spelled out needed changes to U.S. graduate education, including a shorter time to degree, fewer dropouts, and attracting more minorities into science. It also criticized graduate schools for providing poor-quality career counseling.

The new commission will focus on that last issue, says its chair, Patrick Osmer, graduate school dean at Ohio State University in Columbus. "We want to understand what current graduate students know about their career opportunities, and what graduate programs are doing to prepare them. We also want to find out more about the needs of the workplace. There are no good data on what students and faculty think about these issues, and it’s also vital that graduate programs understand the needs of industry." The information will come from reanalyzing existing data and new surveys to be conducted by CGS and ETS.

The 2010 report called for a 5-year, $10-billion boost in graduate education to train an additional 25,000 students annually in a variety of interdisciplinary programs leading to masters’ as well as doctoral degrees. Osmer says he hopes the commission’s findings, to be presented next spring at the council’s annual meeting, will be part of a "continuing national conversation" on ways to improve graduate education.

http://news.sciencemag.org/scienceinsider/2011/09/new-us-panel-examines-career-paths.html?ref=em

Friday, August 12, 2011

New measurement technologies and techniques provide researchers more complete look at neurological activity

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In 1991, Carl Lewis was both the fastest man on earth and a profound long jumper, perhaps the greatest track-and-field star of all time in the prime of his career. On June 14th of that year, however, Carl Lewis was human. Leroy Burrell blazed through the 100-meters, besting him by a razor-thin margin of three-hundredths of a second. In the time it takes the shutter to capture a single frame of video, Lewis’s three-year-old world record was gone.

In a paper just published in the journal Neuron, a team at the Stanford School of Engineering, led by electrical engineers Krishna Shenoy and Maneesh Sahani, explored the neurological explanations for why Lewis may have lost that day. The team, which included graduate students Afsheen Afshar, Gopal Santhanam, Byron Yu, and post-doctoral researcher Stephen Ryu, studied how the brain plans for and executes movements in reaction to a “go” signal.

The advent of new measurement technologies that permit researchers to monitor up to hundreds of individual neurons simultaneously, combined with new analytical mathematics, are providing a revealing look inside the brain and a better understanding of the neurological processes behind the planning and execution of motion.

“This research holds great promise in many areas of neuroscience, in particular human prostheses that can be controlled by the brain,” said Shenoy.

Imprecision

The ability of humans to time the onset of planned movements is imprecise, often frustratingly so. In Carl Lewis’s case, that imprecision cost him the race and the record. In fact, experts later pointed out that Burrell was not really the faster man that day; he was merely the faster off the blocks, beating Lewis at the gun by about five one-hundredths of a second, a difference that provided the margin of victory.

“Lewis may well have lost because he wasn’t able to optimize his own motor plan and thus his reaction time was slow,” said Shenoy.

“Thanks to new tools, for the first time we are able to understand what the neurons are telling us,” said Sahani. “We can hypothesize about how the activity of a group of neurons gives rise to movement.”

Testing the hypothesis

Graduate students trained two rhesus monkeys to perform the task of touching a target on cue. The researchers then neurosurgically implanted on the surface of the monkeys’ brains a
four-millimeter-square electronic chip arrayed with 100 tiny electrodes.

The researchers concentrated on one particular area of the brain known as the dorsal pre-motor cortical area, which shows high levels of activity during the delay when arm movement planning takes place. Activity in this region varies depending upon the direction, distance and speed of a pending movement.

Where most historical data had been limited to single neurons, the new technology allows researchers to monitor in real-time the activity of hundreds of individual neurons down to the millisecond. They can now account for reaction times in single motor events, something previously impossible.

New directions

What Shenoy, Sahani and colleagues have found is a departure from the way many scientists had theorized the process worked. The existing hypothesis, known as “rise-to-threshold,” held that in anticipation of a “go” cue, our brains begin to plan the motions necessary to satisfactorily complete the movement by simply increasing the activity of neurons.

Neurons begin to fire, but not enough to cause the movement to take place. Upon the “go” signal, the brain accelerates this neural firing until it crosses a “threshold” initiating the motion. According to the theory, the longer a preparatory period one has, the greater the neural activity will be and, thus, the faster the reaction time.

The Stanford team was able to document a process based less on the amount of activity and more on the trajectory of the neural activity through the brain. In graphs of neural activity prior to display of the target, the monkeys’ neural activity appears somewhat scattered. The moment a target is displayed, however, the neural activity concentrates in an activity region that the researchers dubbed the “optimal sub-space.”

“We can watch as the pattern of neural activity gets focused in a specific region at the moment the target appears,” explained Shenoy, “and then when the ‘go’ cue is given, the activity moves again, ending with the successful touching of the target.”

The key to reaction time, the researchers found, is the relationship between where the neural activity is and its speed along the ideal trajectory just prior to the go cue. If the neural activity is closer to the final destination, then the reaction time will be shorter; if farther away, then longer.

“We get our brains into a sort of ideal zone – an ‘optimal space’ – of neural activity,” said Shenoy. “The planned movement is possible from anywhere within this space, but some points – those closer to the intended target along the ideal neural pathway – are more advantageous than others in terms of the reaction time.”

From this new understanding, the researchers were able to shape a deeper understanding of the neural patterns and craft a model to predict reaction time.

“Our model allows us to predict with four times greater accuracy what the reaction time of any single arm motion is going to be based on the neural activity observed prior to movement,” said Sahani.

Practical Applications

Returning to the practical applications, Shenoy and Sahani pointed immediately to improving “neural prostheses” – artificial limbs and computer cursors that can be manipulated by the brain to help amputees and paralytics.

“A fundamental understanding of planning and movement is a central question in building electronic interfaces that convert neural activity into signals that can control computer cursors and prosthetic arms. These are also major areas of our research,” said Shenoy.

This project was supported by the Collaborative Research in
Computational Neuroscience (CRCNS) program – a joint initiative of the National Institutes of Health (NIH) and the National Science Foundation to support partnerships between experimental and
computational neuroscientists. Afshar was supported by the NIH Medical Scientist Training Program, and Shenoy is funded by an NIH Director’s Pioneer Award.

“This was a unique collaboration; Shenoy’s team with its expertise in physiology and engineering and Sahani’s expertise in computational modeling enabled them to take an innovative approach to understanding how the brain initiates movement. This research may ultimately have a significant impact on the development of neural prosthetics” said Yuan Liu, Ph.D., from NIH’s National Institute of Neurological Disorders and Stroke, who was involved in the early development of the CRCNS program.

“For most of us, reaction times usually don’t matter. Not many of us have to perform at the level of a Carl Lewis, after all,” said Sahani, “but if you are an amputee hoping for a state-of-the-art prosthetic hand that you can control with your own brain, then understanding how the brain plans and executes motion is very important.”

###

This article was written by Andrew Myers, associate director of communications at the Stanford School of Engineering.

India Gets BSL-4 Lab. The National Institute of Virology in India has announced that a Biosafety Level 4, or BSL-4, laboratory located at its Pashan campus will be operational by November of this year. The laboratory will be the first one of its kind in Southeast Asia.

Source:AAAS

An exciting new experiment on the motor cortex of monkeys, by Shenoy and colleagues, begins to elucidate how the neuronal ensemble travels in a systematic fashion through state space. This trajectory through state space may help to explain how the motor cortex sets up and then triggers arm movements.

Source:Cell Press

Summary

The process by which neural circuitry in the brain plans and executes movements is not well understood. Until recently, most available data were limited either to single-neuron electrophysiological recordings or to measures of aggregate field or metabolism. Neither approach reveals how individual neurons’ activities are coordinated within the population, and thus inferences about how the neural circuit forms a motor plan for an upcoming movement have been indirect. Here we build on recent advances in the measurement and description of population activity to frame and test an “initial condition hypothesis” of arm movement preparation and initiation. This hypothesis leads to a model in which the timing of movements may be predicted on each trial using neurons’ moment-by-moment firing rates and rates of change of those rates. Using simultaneous microelectrode array recordings from premotor cortex of monkeys performing delayed-reach movements, we compare such single-trial predictions to those of other theories. We show that our model can explain approximately 4-fold more arm-movement reaction-time variance than the best alternative method. Thus, the initial condition hypothesis elucidates a view of the relationship between single-trial preparatory neural population dynamics and single-trial behavior.

CREDIT: REDLINK/CORBIS

There’s a lot to think about when you try to plan an urban expansion. Is there enough water, and infrastructure to pump it around? What about electricity? What traffic patterns work best? What will the balance be between housing and commercial real estate? Amid all of these considerations, one critical factor may fall by the wayside—how loud will it be? Noise pollution is a rather complicated function of the surroundings, and as a result it can be hard to model and mitigate. Of course, that’s little consolation to the residents. In an effort to better forecast the noise implications of urban growth, Xie et al. adapt a land-use regression framework previously applied to modeling air pollution. Their geographical focus is Dalian Municipality in Northwest China, which currently hosts two million people. The model fits measured sound pressure levels as a function of different land-use allocations (such as industrial or residential) within successive buffer perimeters (from 20 m radius outward). Given promising overall results, the authors note that buffer sizes could potentially be optimized further and that future models could hone the time scale over which the forecasting is valid.

Environ. Sci. Technol. 45, 10.1021/es200785x (2011).

Source:AAAS