Turmeric could help ward off Parkinson’s disease

By Holly Rogers

Curcumin, found in turmeric, has been shown to prevent protein clumping in the brain. This clumping has been recognised as an early stage of Parkinson’s disease.

Scientists at Michigan State University have used lasers to watch proteins being rescued by curcumin, building on research released earlier this year into the mechanism of clumping.

“Our research shows that curcumin can rescue proteins from aggregation, the first steps to many debilitating diseases,” said Lisa Lapidus, co-author of the study.

KurkuminaProteins are needed to carry out most of the work done by cells, and are built through a process known as folding. If the protein does not fold fast enough, it begins to clump and bind to other proteins around it. Curcumin not only stops this binding from happening, but speeds up the folding process, lowering the chances of it starting again. However, there is still more research to be done before curcumin becomes a routine treatment.

“Curcumin’s usefulness as an actual drug may be pretty limited since it doesn’t go into the brain easily,” said Professor Lapidus. “But this kind of study showcases the technique of measuring reconfiguration and opens the door for developing drug treatments.”

Curcumin is being currently investigated for possible benefits in various clinical conditions such as Alzheimer’s disease, and some types of cancer.


B. Ahmad and L. J. Lapidus, Curcumin Prevents Aggregation in α-synuclein by Increasing the Reconfiguration Rate, Journal of Biological Chemistry, 2012.

You can find the original article here: http://www.jbc.org/content/287/12/9193.abstract


Anxiety enhances sense of smell

By Maria Panagiotidi

Anxious people have a heightened sense of smell, when it comes to sniffing out a threat, according to a new study by Elizabeth Krusemark and Wen Li from the University of Wisconsin-Madison in the US. The results of their study will be published online in the journal Chemosensory Perception.

The sense of smell is an essential tool for survival in animals. It allows them to detect, locate and identify predators in the surrounding environment. In fact, the olfactory-mediated defence system is so important in animals, that the mere presence of predator odours can evoke potent fear and anxiety responses.

Smells also evoke powerful emotional responses in humans. Krusemark and Li hypothesized that in humans, detection of a particular bad smell may signal danger of a noxious airborne substance, or a decaying object that carries disease. Also, they speculated that the level of response to the above could underlie phobias or anxiety related disorders.

The researchers tested their hypotheses by combining assessment of state-level anxiety, psychophysical testing, and functional magnetic resonance imaging (fMRI) techniques.  They recruited 14 young adult participants who were exposed to three types of odours: neutral pure odor, neutral odor mixture, and negative odor mixture. The participants were asked to detect the presence or absence of an odour in an MRI scanner. During scanning, the researchers also measured skin conductance response (a measure of arousal level), and monitored the subjects’ breathing patterns. After completing the odour detection task, the participants were asked to rate their current level of anxiety using a standardised clinical test.

The authors found that as anxiety levels rose, so did the subjects’ ability to discriminate negative odours accurately – suggesting a ‘remarkable’ olfactory acuity to threat in anxious subjects. The same pattern was found in the skin conductance results which showed that anxiety also heightened emotional arousal to smell-induced threats.

Krusemark and Li uncovered amplified communication between the sensory and emotional areas of the brain in response to negative odours, particularly in anxiety. This increased connectivity could be responsible for the heightened arousal to threats.

These findings could help researchers elucidate the aetiology of the unfortunate and debilitating symptoms that perpetuate anxiety disorders.



Krusemark EA & Li W (2012). Enhanced olfactory sensory perception of threat in anxiety: an event-related fMRI study. Chemosensory Perception. DOI 10.1007/s12078-011-9111-7

You can find the article here: http://www.springerlink.com/content/a268t518p1x59v68/

Hydrogen-powered Robojelly preparing for maiden voyage

By Holly Rogers

A robotic jellyfish made of smart materials could be used in search and rescue operations, say researchers from Virginia Tech.

The tentacled creation, known as Robojelly, is made from a collection of materials that change shape or size to match their environment, held in place with carbon nanotubes. As well as its intelligent build, it could theoretically run forever – the clever cnidaria is powered entirely by hydrogen.

“To our knowledge, this is the first successful powering of an underwater robot using external hydrogen as a fuel source”, said Yonas Tadesse, the lead author of the study.

Robojelly is made from “shape memory alloys”, which are smart materials that remember their original shape. These materials are wrapped in carbon nanotubes and coated in platinum powder, which is the key to the fuel source. The platinum powder reacts with oxygen and hydrogen from the surround water and produces heat, which powers the robot’s movements.

Its swimming technique mimics that of a jellyfish – the “bell” chamber  fills with water, which then collapses, forcing the water out and driving the body forwards. In jellyfish, this is done with muscle contractions, but Robojelly makes use of heat produced by the fuel cell to transform its smart material body. However, although Robojelly has been successfully tested in a water tank, it’s not quite ready for service yet. Developers need to add individual controls to each segment of the robot, which will allow it to be steered in different directions. Until then, it can be seen in testing phase below:



Y. Tadesse, A. Villanueva, C. Haines, D. Novitski, R. Baughman and S. Priya, Hydrogen-fuel-powered bell segments of biomimetic jellyfish, Smart Materials and Structures, 21, 2012.

The paper can be found at: http://iopscience.iop.org/0964-1726/21/4/045013

End of the Line for Superluminal Neutrinos?

By Stephen Sadler

Physicists were shocked last September when a paper published by the OPERA collaboration suggested that neutrinos in their detector in the Gran Sasso underground lab in Italy had been caught travelling faster than the speed of light. If correct, the result calls into question Einstein’s Theory of Relativity, and open the door to all sorts of weird and wonderful effects such as the reversal of causality and time travel. In fact, so perturbed were the researchers at the implications of their results, that they delayed publishing their findings for 5 months while they meticulously checked the experiment for errors, before finally concluding that they could do no more without the help of the wider particle physics community.

Unsurprisingly, interest has been huge, and as of today (February 26th 2012) a search of the keywords ‘superluminal neutrino’ on arXiv.org yields 163 papers on the subject. However, despite the focused attention of some of the world’s top minds, until recently no mistakes in the experimental method or data analysis had come to light. Indeed, the OPERA collaboration repeated their experiment with a neutrino beam configuration that allows for more precise timing, and found the same effect. Many scientists still doubted the result though, and last December Ramanath Cowsik, professor of physics in Arts & Sciences and director of the McDonnell Center for the Space Sciences at Washington University in St. Louis, and his team of collaborators pointed out a glaring problem which had been overlooked.

Neutrino beams for particle physics experiments are produced in a three-step process that begins by accelerating protons to 99.9999991% the speed of light, in an accelerator such as the Large Hadron Collider at CERN in Switzerland. These ultra-relativistic protons are then smashed into a graphite target producing, amongst other debris, secondary particles called pions. Finally, these short-lived pions are focussed into a tight beam by magnetic horns before they quickly decay into a beam of neutrinos and charged sister particles of the electron called muons, each of which carries off some fraction of the total pion momentum. Finally a ‘beam dump’ at the end of the decay pipe stops all particles other than the neutrinos, leaving a pure neutrino beam.

The trouble is that in order to produce the high energy neutrinos observed at OPERA, the fraction of momentum carried off by the neutrinos needs to be less than about 0.05. This, in turn, implies that the decaying pions must have an extremely high momentum, and Einstein’s theory of relativity tells us that this very high momentum would extend the pions’ lifetime so much that they would not have time to decay in the beam pipe at CERN before smashing into the concrete beam dump.

“We’ve shown in this paper that if the neutrino that comes out of a pion decay were going faster than the speed of light, the pion lifetime would get longer, and the neutrino would carry a smaller fraction of the energy shared by the neutrino and the muon,” Cowsik says. “So we are saying that in the present framework of physics, superluminal neutrinos would be difficult to produce.”

Now, it seems as though Cowsik was right to be skeptical, as an email from CERN Director General Rolf Heuer to CERN staff last week announced that the OPERA collaboration had identified two possible sources of error in their neutrino velocity measurement. The first has to do with an oscillator used in the timing system of the experiment, and could only increase the size of the faster-than-light effect. The second, though, concerns a potentially faulty optical fibre connection that sends an external GPS signal to the OPERA master clock, and could serve to bring the velocity of the neutrinos back down to the sub-light-speeds physicists are used to.

The OPERA collaboration have fixed the problems and are now in the process of determining the effect they may have had on the results. New data taken with the repaired detector is expected in May, but for now scientists around the world are applauding the OPERA team for the open and transparent way in which they have reported their surprising result. In an interview for BBC News Sergio Bertolucci, director of research at CERN, said “One has to realise that the collaboration has never stopped to try to ‘kill’ the measurement (proving that it was erroneous)”. Even if the result turns out to be a false alarm due to loose wiring, the story has been a textbook example of good scientific practice.

The paper announcing the superluminal measurement can be found at:  http://arxiv.org/abs/1109.4897, whilst a preprint of Cowsik’s work detailing the problems raised by pion decay kinematics appears here: http://arxiv.org/abs/1110.0241v2.

The dangers of self-report

A common methodology in behavioural science is to use self-report questionnaires to gather data. Data from these questionnaire can be used to identify relationships between scores on the variable(s) that the questionnaire is assumed to measure and either performance on behavioural tasks, physiological measures taken during an experiment, or even scores obtained from other questionnaires (some studies just report on the correlations between batches of self-report measures!). Self-report measures are popular for a number of reasons. Firstly they represent a ‘cheap’ way (in terms of both time and cost) of obtaining data. Secondly they can be easily implemented to large samples, especially with the advent of on-line questionnaire distribution sites such as Survey Monkey. Finally they can be used to measure constructs that would be difficult to obtain with behavioural or physiological measures (for example facets of personality such as introversion). This issue of self-report methodology is important because studies that use this method are regularly reported in the media (see http://www.bbc.co.uk/news/health-17209448 for a recent example) and therefore have a significant impact on how the general public perceive scientific research. I therefore think it is important to discuss potential problems with self-report measures.

Most (but certainly not all) questionnaires that are used in behavioural research undergo  testing for reliability, to check that they produce consistent results when applied to the same population over time. More importantly they are normally also tested for validity, to check that the questionnaire measures what it claims to measure. Such tests are done following the logic that the questionnaire should be able to discriminate participants in a similar way to relevant non-self report measures. For example scores on a questionnaire measuring depression should be able to discriminate between depressed patients and controls, while scores on a questionnaire measuring diet should be able to predict the ‘Body Fat Percentage’ of respondents with reasonable accuracy. While such tests can act to increase confidence that a questionnaire is measuring what it claims to measure they are not foolproof. For example just because a depression questionnaire can discriminate between patients and controls does not mean that it measures depression well, as the two groups will likely vary in several different ways. Likewise a questionnaire that distinguishes between patients and controls may not be able to identify the (presumably) more subtle differences between depressed and non-depressed healthy individuals, or the range of depressive tendencies within the healthy population. In fact that are a large number of reasons why questionnaire may not be entirely valid, including the following:

Honesty/Image management – researchers who use self-report questionnaires are relying on the honesty of their participants. The degree to which this is a problem will undoubtedly vary with the topic of the questionnaire, for example participants are less likely to be honest about measures relating to sexual behaviour, or drug use, than they are about caffeine consumption, although it is unwise to assume, even when you are measuring something relatively benign, that participants will always be truthful. Worse, the level at which participants will want to manage how they appear will no doubt vary depending on personality, which means that the level of dishonesty may vary significantly between different groups that a study is trying to compare.

Introspective ability – Even if a participant is trying to be honest, they may lack the introspective ability to provide an accurate response to a question. We are probably all aware of people who appear to view themselves in a completely different light to how others see them. Undoubtedly we are all to some extent unable to introspectively assess ourselves completely accurately. Therefore any self-report information we provide may be incorrect despite our best efforts to be honest and accurate.

Understanding – Participants may also varying regarding their understanding or interpretation of particular questions. This is less a problem with questionnaires measuring concrete things like alcohol consumption, but is a very big problem when measuring more abstract concepts such as personality. From personal experience I have participated in an experiment where I was asked at regular intervals to report how ‘dominant’ I felt. As I can honestly say I don’t monitor my feelings of ‘dominance’ and how they change over time, I know that my responses to the question were pretty random. Even if I could conjure an understanding of what the question was getting at, it would be impossible to ensure that everyone who completed the questionnaire interpreted that question in the same way that I did.

Rating scales – Many questionnaires use rating scales to allow respondents to provide more nuanced responses than just yes/no. While yes/no questions do often appear restrictive in terms of how you can respond, using rating scales can bring their own problems. People interpret and use scales differently, what I might rate as ‘8’ on a 10 point scale, someone with the same opinion might only rate as a ‘6’ because they interpret the meanings of the scale points differently. There is research which suggests that people have different ways of filling out ratings scales (1). Some people are ‘extreme responders’ who like to use the edges of the scales, whereas other like to hug around the midpoints and rarely use the most outer points. This naturally produces differences in scores between participants that reflects something other than what the questionnaire was designed to measure. A related problem is that of producing nonsense distinctions. For example studies sometimes appear where participants are given a huge rating scale to choose from, for example a scale of 1-100 to rate the confidence of a decision as to whether two lines are the same length (2).  Is anyone really capable of segmenting their certainty over such a decision into 100 different units? Is there really any meaningful difference, even within the same individual, between a certainty of 86 and a certainty of 72 in such a paradigm? Any differences found in such experiments therefore run the risk of being spurious.

Response bias – This refers to individual’s tendency to respond a certain way, regardless of the actual evidence they are assessing. For example on a yes/no questionnaire asking about personal experiences, some participants might be biased towards responding yes (i.e. they may only require minimal evidence to decide on a yes response, so if an experience has happened only once they may still respond ‘yes’ to a question relating to whether they have had that experience). Alternatively other participants may have a conservative response bias and only respond positively to such questions if the experience being inquired about has happened regularly. This is a particular problem when the relationship between different questionnaires is assessed, as a correlation between two different questionnaires may simply reflect the response bias of the participants being consistent across questionnaires, rather than any genuine relationship between the variables the questionnaire is measuring.

Ordinal Measures – Almost all self-report measures produce ordinal data. Ordinal data is that which only tells you the order that units can be ranked in, not the distances between them. It is contrasted with interval data which tells you the exact distances between different units. This distinction is easiest to define by thinking of a race. The position in which each runner finishes in is an ordinal measure. It tells you who is fastest and slowest, but not the relative differences between the different runners. In contrast the finishing time is an interval measure, as it provides information relating to the relative differences between the runners. Even when the questionnaire measures something that could be measured in SI units, and is therefore theoretically an interval scale (i.e. alcohol consumption) it is doubtful whether the responses can really be treated as interval because of the problems relating to response accuracy raised above. More pertinently most self-report measures in behavioural science relate to constructs, such a personality measures, that can’t be measured in interval units and are therefore always ordinal. The problem with ordinal data is not the data itself, but the common practice of using parametric statistical techniques with such data, because these tests make assumptions about the distribution of the data that cannot be met when said data is ordinal. Deviations from such assumptions can lead to incorrect inferences being made (3) bringing the conclusions of such studies into question.

Control of sample – this has become more of an issue with the advent of online questionnaire distribution sites like Survey Monkey. Previously a researcher had to be present when a participant completed a questionnaire, now with these tools the researcher need never meet any of their participants. While this allows much bigger samples to be collected much more quickly, it does cause several concerns over the sample make up. For example there are few controls to stop the same person filling in the same questionnaire multiple times. There is also little disincentive for participants to respond with spurious responses, and there is little control over how much attention the participant pays to various parts of the questionnaire. Conversely, from personal experience, I know that sometimes it is hard to complete these questionnaires because there is no way of asking the researcher for clarification as to the meaning of various questions. Finally as the researcher has lost control over the make up of their sample, they may end up with a sample which is vastly skewed towards a certain type of person, as only certain types of people are likely to fill in such questionnaires. These issues existed even before the advent of online data collection (e.g. (4)), but collecting data ‘in absentia’ exacerbates the size of such problems.

Although there are many problems with using self-report questionnaires they will continue to be a popular methodology in behavioural science because of their utility. While it might be preferable for every variable a researcher wants to investigate to be manipulated systematically using behavioural techniques, this is in practice impossible as it would severely restrict what each individual research design could achieve, and would make certain topics effectively impossible to research. Self-report measures are therefore a necessary tool for behavioural research. Furthermore some of the problems listed above can be countered through the careful design and application of self-report measures. For example response bias can be removed by ‘reversing’ half the questions on a questionnaire so that the variable is scored by positive responses on half the questions and negative responses on the other half, thus cancelling out any response bias. Likewise statistical techniques are being devised to attempt to pick out dishonest reporting, a problem that can also be attenuated by ensuring anonymity and confidentiality of responses (e.g. the researcher leaving the room when the participant is completing the questionnaire). Given this it would be wrong to dismiss any findings that are reliant on self-report measures. However whenever you read about research where self-report measures have been used to draw conclusions about human behaviour, it is always worth bearing in mind the multitude of problems associated with such measures, and how they might impact on the validity of the conclusions that have been drawn.

(1) Austin, E. J., Gibson, G. J., Deary, I. J., McGregor, M. J., & Dent, J. B. (1998). Individual response spread in self-report scales: personality correlations and consequences. Personality and Individual Differences, 24, 421–438. http://www.sciencedirect.com/science/article/pii/S019188699700175X

(2) Balakrishnan, J. D. (1999). Decision processes in discrimination: Fundamental misrepresentations of signal detection theory. Journal of Experimental Psychology: Human Perception & Performance, 25, 1189-1206. http://psycnet.apa.org/psycinfo/1999-11444-002

(3) Wilcox, R. R. (2005). Introduction to robust estimation and hypothesis testing. Academic Press. ISBN: 0127515429

(4) Fan, X., Miller, B. C., Park, K., Winward, B. W., Christensen, M., Grotevant, H. D., et al. (2006). An exploratory study about inaccuracy and invalidity in adolescent self-report surveys. Field Methods,18, 223–244. http://fmx.sagepub.com/content/18/3/223.short