Research Blogging - Computer Science / Engineering - English

Paving the road with nanoclay

2012-05-24T14:18:04Z

Summer time means BBQ season but it’s also the start of road construction. Road construction usually leads to traffic jams and slowdowns, so it makes sense to avoid construction in [...]...

You, Z., Mills-Beale, J., Foley, J., Roy, S., Odegard, G., Dai, Q., & Goh, S. (2011) Nanoclay-modified asphalt materials: Preparation and characterization. Construction and Building Materials, 25(2), 1072-1078. DOI: 10.1016/j.conbuildmat.2010.06.070

Robotics & Mechanical Limbs

2012-05-24T08:55:02Z

As people continue to struggle with problems involving organ donation, a few robotic engineers continue to push the boundaries between humanity and machinery. A recent report in Nature (cited below) showed that two patients were able to overcome some aspects of their paralysis by way of an implant. Reaching and grabbing motions were possible by way [...]...

Hochberg, L., Bacher, D., Jarosiewicz, B., Masse, N., Simeral, J., Vogel, J., Haddadin, S., Liu, J., Cash, S., van der Smagt, P.... (2012) Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature, 485(7398), 372-375. DOI: 10.1038/nature11076

Neurons are like equations

2012-05-22T23:22:00Z

The brain of the clock (I took this picture)A computational model is a surrogate version of something usually made on a computer. An example that most people are familiar with are the computational models used to predict the weather. If you know how low pressure and high pressure fronts interact, and you know where one is and how fast it is moving, you can program software to play the situation out in a simulation, predicting what will happen and how quickly. Computational neuroscience is more or less just like that and it can be used to investigate all levels of neuroscience. Here's a brief intro to three of the basic levels. There are other types of computational models in neuroscience, but these three make up most of them.The Whole BrainIf you know how the thalamus, hippocampus, amygdala, and cortex all work together, you can simulate how inputs into one structure might influence the others. In this case each brain structure would basically be a 'black box' that received input and produced output based on known data. To do this kind of simulation you wouldn't actually simulate the millions of neurons in each structure.The Neural Network(source)On the next level down, you can make a computational model of a neural network inside a single brain structure. If you know the types of neurons in the amygdala and how they interact with each other, you can program those relationships in and test what might happen if one class of neurons fires too much or too little. You can test the effect removing one class of neurons has on the whole network and the output of that brain structure. In this case you are simulating individual neurons, but you are probably not simulating the details of the neurons, such as their dendrites and their specific channel composition. In this kind of computational model, the neurons are the 'black box' which receive input and produce output based on pre-set equations.The Cellular ScaleOne level down from this is a computational model of an individual neuron. In this type of model, the neuron is simulated in detail, with its dendrites, soma, and sometimes the axon. With this kind of model, you can test the effects of different dendrite shapes on the processing of the neuron. Usually the individual channels (such as calcium, potassium and sodium channels) in the neuron are programmed in and the electrical properties of the cells are calculated in detail. In this situation, the specific proteins and channels are the 'black boxes' computing ionic concentrations based on pre-set equations. A detailed tutorial on how to make a biophysically realistic model neuron can be found here.a neuron can be simulated as a series of resistors and capacitorsSidiropoulou et al., (2006) have an excellent review of the neuroscience discoveries that have been made with this cellular level of computational modeling. They start their paper highlighting the most interesting problem in cellular neuroscience."Understanding how the brain works remains one of the most exciting and intricate challenges of modern biology. Despite the wealth of information that has accumulated during the past years about the molecular and biophysical mechanisms that underlie neuronal activity, similar advances have yet to be made in understanding the rules that govern information processing and the relationship between the structure and function of a neuron." (Intro, Sidiropoulou et al., 2006) (red mine)This paper directly argues against the idea that neurons are just 'on-off' switches, and illustrates the complex computational processes that occur in individual locations of the neuron. They cover computational studies analyzing the information processing that occurs in the dendrite, at the synapse, at the soma, and even in the axon. The details are to complicated to get into here, but the paper is free. Finally, they end with a call to action for experimental and computational neuroscientists to work together to solve the really interesting problems in cellular neuroscience. "The following open questions could provide fertile ground for collaborations among molecular biologists, geneticists, physiologists, modellers and behaviourists for further explorations of the mysteries of the brain. Do specific behaviours require certain neuronal computational tasks? Which parts of the neural circuit or the neuron itself are responsible for these tasks? What are the underlying molecular mechanisms for the distinct operating modes of neuronal integration? Such holistic approaches should lend support to the growing idea reinforced by this review: that something smaller than the cell lies at the heart of neural computation." (Discussion, Sidiropoulou et al., 2006)Just as computational models can predict weather patterns with some degree of accuracy, no model is perfect. Similarly computational neuroscience is not going to lead to all the answers, but where it is particularly useful is in making very specific predictions about how certain aspects of a neuron or neural circuit might work. The insight gained from computational models can guide and focus experiments, making them more efficient. This saves time, money, energy, and animal lives. © TheCellularScaleSidiropoulou K, Pissadaki EK, & Poirazi P (2006). Inside the brain of a neuron. EMBO reports, 7 (9), 886-92 PMID: 16953202...

Sidiropoulou K, Pissadaki EK, & Poirazi P. (2006) Inside the brain of a neuron. EMBO reports, 7(9), 886-92. PMID: 16953202

Does Social Status Change Brains?

2012-05-16T12:33:15Z

Photo by The Grappling Source Inc. at Wikimedia CommonsBeing subordinated is stressful. The process of one individual lowering the social rank of another often involves physical aggression, aggressive displays, and exclusion. In addition to the obvious possible costs of being subordinated (like getting beat up), subordinated individuals often undergo physiological changes to their hormonal systems and brains. Sounds pretty scary, doesn’t it? But what if some of those changes are beneficial in some ways?Dominance hierarchies are a fact of life across the animal kingdom. In a social group, everyone can’t be dominant (otherwise, life would always be like an episode of Celebrity Apprentice, and what could possibly be more stressful than that?). Living in a social group is more peaceful and nutritive when a clear dominance hierarchy is established. Establishing that hierarchy often involves a relatively short aggressive phase of jostling for position, followed by a longer more stable phase once everyone knows where they fall in the social group. Established dominance hierarchies are not always stable (they can change over time or from moment to moment) and they are not always linear (for example, Ben can be dominant over Chris, who is dominant over David, who is dominant over Ben). But they do generally help reduce conflict and the risk of physical injury overall.Nonetheless, it can be stressful to be on the subordinate end of a dominance hierarchy and these social interactions are known to cause physiological changes. Researchers Christina Sørensen and Göran Nilsson from the University of Oslo, Cliff Summers from the University of South Dakota and Øyvind Øverli from the Norwegian University of Life Sciences investigated some of these physiological differences among isolated, dominant, and subordinate rainbow trout.A photo of a rainbow trout by Ken Hammond at the USDA. Photo at Wikimedia Commons.Like other salmonid fish, rainbow trout are aggressive, territorial and develop social hierarchies as juveniles. Dominant trout tend to initiate most of the aggressive acts, hog food resources, grow larger, and reproduce the most, whereas subordinate trout display less aggression, feeding, growth, and reproduction. The researchers recorded the behavior, feeding and growth rates in three groups of fish: trout housed alone, trout housed with a more subordinate trout, and trout housed with a more dominant trout. The researchers also measured cortisol (a hormone involved in stress responses), serotonin (a neurotransmitter involved in mood, the perception of food availability, and the perception of social rank, among other things) and the development of new neurons (called neurogenesis) in these same fish.This video of two juvenile rainbow trout was taken by Dr. Erik Höglund. Here is Christina Sørensen’s description of the video: “What you see in the film is two juvenile rainbow trout who have been housed on each side of a dividing wall in a small aquarium. The dividing wall has been removed (for the first time) immediately before filming. You will see that the fish initially show interest for each other, followed by a typical display behaviour, where they circle each other. Finally one of the fish will initiate aggression by biting the other. First the aggression is bidirectional, as they fight for dominance, but after a while, one of the fish withdraws from further aggression and shows only submissive behaviour (escaping from the dominant and in the long run trying to hide... and as is described in the paper, depressed feed intake). The video has been cut to show in quick succession these four stages of development of the dominance hierarchy”. The researchers found that as expected, the dominant trout were aggressive when a pair was first placed together, but the aggression subsided after about 3 days. Also as expected, the dominant and isolated trout were bold feeders with low cortisol levels and high growth rates, whereas the subordinate trout did not feed as well, had high cortisol levels and low growth rates. Additionally, the subordinate trout had higher serotonin activity levels and less neurogenesis than the dominant or isolated trout. These results suggest that the subordination experience causes significant changes to trout brain development (Although we can’t rule out the possibility that fish with more serotonin and less neurogenesis are predisposed to be subordinate). In either case, this sounds like bad news for subordinate brains, right? Maybe it is. Or maybe the decrease in neurogenesis just reflects the decrease in overall growth rates (smaller bodies need smaller brains). Or maybe something about the development of these subordinate brains improves the chances that these individuals will survive and reproduce in their subordination. A crayfish raising its claws. Image by Duloup at Wikimedia.Research on dominance in crayfish by Fadi Issa, Joanne Drummond, and Don Edwards at...

Sørensen, C., Nilsson, G., Summers, C., & Øverli, �. (2012) Social stress reduces forebrain cell proliferation in rainbow trout (Oncorhynchus mykiss). Behavioural Brain Research, 227(2), 311-318. DOI: 10.1016/j.bbr.2011.01.041

Issa, F., Drummond, J., Cattaert, D., & Edwards, D. (2012) Neural Circuit Reconfiguration by Social Status. Journal of Neuroscience, 32(16), 5638-5645. DOI: 10.1523/JNEUROSCI.5668-11.2012

Yeh, S., Fricke, R., & Edwards, D. (1996) The Effect of Social Experience on Serotonergic Modulation of the Escape Circuit of Crayfish. Science, 271(5247), 366-369. DOI: 10.1126/science.271.5247.366

Issa, F., & Edwards, D. (2006) Ritualized Submission and the Reduction of Aggression in an Invertebrate. Current Biology, 16(22), 2217-2221. DOI: 10.1016/j.cub.2006.08.065

Nature’s Hand in Climate Change

2012-05-15T11:00:00Z

The heat wave throughout most of North America in the beginning of April had bought climate change into my mind. Was the heat wave caused by climate change? Likely not, I can’t imagine the effect of climate change happening so abruptly. But it made me think about what really causes climate change on this lovely blue planet of ours?...

J. Wilkinson. (2012) The Sun and Earth’s Climate . New Eyes on the Sun, , 201-217. info:/

Mufti, S., & Shah, G. (2011) Solar-geomagnetic activity influence on Earth's climate. Journal of Atmospheric and Solar-Terrestrial Physics, 73(13), 1607-1615. DOI: 10.1016/j.jastp.2010.12.012

Lockwood, M. (2011) Solar physics: Shining a light on solar impacts. Nature Climate Change, 1(2), 98-99. DOI: 10.1038/nclimate1096

Oreskes, N. (2004) BEYOND THE IVORY TOWER: The Scientific Consensus on Climate Change. Science, 306(5702), 1686-1686. DOI: 10.1126/science.1103618

Rivera, . (2012) Discovery of the Major Mechanism of Global Warming and Climate Change. Journal of Basic and Applied Sciences, 8(1). DOI: 10.6000/1927-5129.2012.08.01.29

'Danger and Evolution in the Twilight Zone': Guest post by Randen Patterson and Gaurav Bhardwaj

2012-05-12T10:27:28Z

Figure 1. PHYRN concept and work flow. 'Danger and Evolution in the twilight zone' I have been communicating with Randen Patterson on and off over the last five years or so about his efforts to try and study the evolution of gene families when the sequence similarity in the gene family is so low that making multiple sequence alignments are very difficult. Recently, Randen moved to UC Davis so I have been talking / emailing with jim more and more about this issue. Of note, Randen has a new paper in PLoS One about this topic: Bhardwaj G, Ko KD, Hong Y, Zhang Z, Ho NL, et al. (2012) PHYRN: A Robust Method for Phylogenetic Analysis of Highly Divergent Sequences. PLoS ONE 7(4): e34261. doi:10.1371/journal.pone.0034261. Figure 8. Model for the Evolution of the DANGER Superfamily. I invited Randen and the first author Gaurav Bhardwaj to do a guest post here providing some of the story behind their paper for my ongoing series on this topic. I note - if you have published an open access paper on some topic related to this blog I would love to have a guest post from you too. I note - I personally love the fact that they used the "DANGER" family as an example to test their method. Here is their guest post: A fundamental problem to phylogenetic inference in the “twilight zone” (<25% pairwise identity), let alone the “midnight zone” (<12% pairwise identity), is the inability to accurately assign evolutionary relationships at these levels of divergence with statistical confidence. This lack of resolution arises from difficulties in separating the phylogenetic signal from the random noise at these levels of divergence. This obviously and ultimately stymies all attempts to truly resolve the Tree of Life. Since most attempts at phylogenetic inferences in twilight/midnight zone have relied on MSA, and with no clear answer on the best phylogenetic methods to resolve protein families in twilight/midnight zone, we have presented rest of this blog post as two questions representative of these problems. Question1: Is MSA required for accurate phylogenetic inference? Our Opinion: MSA is an excellent tool for the inference from conserved data sets, but it has been shown by others and us, that the quality of MSA degrades rapidly in the twilight zone. Further, the quest for an optimal MSA becomes increasingly difficult with increased number of taxa under study. Although, quality of MSA methods has improved in last two decades, we have not made significant improvements towards overcoming these problems. Multiple groups have also designed alignment-free methods (see Hohl and Ragan, Syst. Biol. 2007), but so far none of these methods has been able to provide better phylogenetic accuracy than MSA+ML methods. We recently published a manuscript in PLoS One entitled “PHYRN: A Robust Method for Phylogenetic Analysis of Highly Divergent Sequences” introducing a hybrid profile-based method. Our approach focuses on measuring phylogenetic signal from homologous biological patterns (functional domains, structural folds, etc), and their subsequent amplification and encoding as phylogenetic profile. Further, we adopt a distance estimation algorithm that is alignment-free, and thus bypasses the need for an optimal MSA. Our benchmarking studies with synthetic (from ROSE and Seqgen) and biological datasets show that PHYRN outperforms other traditional methods (distance, parsimony and Maximum Liklihood), and provides significantly accurate phylogenies even in data sets exhibiting ~8% average pairwise identity. While this still needs to be evaluated in other simulations (varying tree shapes, rates, models), we are convinced that these types of methods do work and deserve further exploration. Question 2: How can we as a field critically and fairly evaluate phylogenetic methods? Our Opinion: A similar problem plagued the field of structural biology whereby there were multiple methods for structural predictions, but no clear way of standardizing or evaluating their performance. An additional problem that applies to phylogenetic inference is that, unlike crystal structures of proteins, phylogenies do not have a corresponding “answer” that can be obtained. Synthetic data sets have tried to answer this question to a certain extent by simulating protein evolution and providing true evolutionary histories that can be used for benchmarking. However, these simulations cannot truly replicate biological evolution (e.g. indel distribution, translocations, biologically relevant birth-death models, etc). In our opinion, we need a CASP-like model (solution adopted by our friends in computational structural biology), where same data sets (with true evolutionary history known only to organizers) are inferred by all the research groups, and then submitted for a critical evaluation to the organizers. To convert this thought to reality, we hereby announce CAPE (Critical Assessment of Protein Evolution) for Summer 2012. We are still in pre-production stages, and we welcome any suggestions, comments and inputs about data sets, scoring and evaluating methods. Bhardwaj, G., Ko, K., Hong, Y., Zhang, Z., Ho, N., Chintapalli, S., Kline, L., Gotlin, M., Hartranft, D., Patterson, M., Dave, F., Smith, E., Holmes, E., Patterson, R., & van Rossum, D. (2012). PHYRN: A Robust Method for Phylogenetic Analysis of Highly Divergent Sequences PLoS ONE, 7 (4) DOI: 10.1371/journal.pone.0034261 -------- This is from the "Tree of Life Blog" of Jonathan Eisen, an evolutionary biologist and Open Access advocate at the University of California, Davis. For short updates, follow me on Twitter. --------...

Bhardwaj, G., Ko, K., Hong, Y., Zhang, Z., Ho, N., Chintapalli, S., Kline, L., Gotlin, M., Hartranft, D., Patterson, M.... (2012) PHYRN: A Robust Method for Phylogenetic Analysis of Highly Divergent Sequences. PLoS ONE, 7(4). DOI: 10.1371/journal.pone.0034261

Journal Fire: Bonfire of the Vanity Journals?

2012-05-11T09:58:59Z

When I first heard about Journal Fire, I thought, Great! someone is going to take all the closed-access scientific journals and make a big bonfire of them! At the top of this bonfire is the burning effigy of a wicker man, representing the very worst of the vanity journals....

Deans Andrew R., Yoder Matthew J., & Balhoff James P. (2012) Time to change how we describe biodiversity. Trends in Ecology , 27(2), 84. DOI: 10.1016/j.tree.2011.11.007

Neighbourhood Watch for cloud computing

2012-05-10T13:58:34Z

Hey, you! Get off of that cloud! Cloud computing is on the rise, as we have discussed her on many an occasion. It’s useful for fast and robust web hosting, it’s great for anywhere email access, for remote file storage and backup (DropBox Wuala GoogleDrive etc), for sharing large media files, whether movies, music files, [...]Post from: David Bradley's Sciencetext Tech TalkNeighbourhood Watch for cloud computing...

Sudhir N. Dhage, & B.B. Meshram. (2012) Intrusion detection system in cloud computing environment. International Journal of Cloud Computing, 1(2/3), 261-282. info:/

Science in superheroes

2012-05-07T21:28:31Z

The magic of the movies mean almost anything can happen. You can time travel, control objects with your mind, or even heal yourself no matter how serious your injuries are. But did you know that filmmakers often consult scientists and engineers for their input in movies? Dr. Jim Kakalios, a professor at the University of [...]...

Lynne Robinson. (2012) The Super Materials of the Super Heroes. JOM, 64(1), 13-19. DOI: 10.1007/s11837-012-0256-x

Putting the Squeeze on Microfluidics

2012-05-03T16:30:00Z

Microfluidic devices are able to process small volumes of liquid and are comprised of microscale components, but the devices themselves are not often small themselves. These labs-on-chips are often limited to lives in labs instead of the remote areas that could really benefit from their use. The limitation comes in the form of support equipment used to process or analyze assays that are expensive, bulky, energy consuming and/or require trained professional operators. Syringe pumps are often used in labs to drive liquids used in assays at specific flow rates and to ensure that the right volume is used. The need for complicated, external flow equipment was recently addressed by a group from Peking University. The group’s paper, “Squeeze-chip: a finger-controlled microfluidic flow network device and its application to biochemical assays” was recently featured on the cover of Lab on a Chip....

Li, W., Chen, T., Chen, Z., Fei, P., Yu, Z., Pang, Y., & Huang, Y. (2012) Squeeze-chip: a finger-controlled microfluidic flow network device and its application to biochemical assays. Lab on a Chip, 12(9), 1587. DOI: 10.1039/C2LC40125H