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  • May 31, 2012
  • 06:36 AM

Want to build the perfect smartphone? Take a lesson from your cells

by John Ankers in Too Many Live Wires

Today's smartphones could do better. Yes, they send texts, make video calls, talk to satellites, take, edit (and share) your pictures, play games and music... one even makes a whipping noise if you waggle it a bit. Some of them can make phone calls too. But surely there's so much more that could be crammed in?

The human cell has functionality that would put any smartphone to shame. The secret, as new research investigates, was learning how to multitask.... Read more »

  • May 29, 2012
  • 01:17 PM

First Ever Chemical Circuit Created

by Jason Carr in Wired Cosmos

Klas Tybrandt, doctoral student in organic electronics at Linkoping University, Sweden, has developed an integrated chemical chip. The results have just been published in the prestigious journal Nature Communications (cited below). The Organic Electronics research group at Linköping University previously developed ion transistors for transport of both positive and negative ions, as well as biomolecules. [...]... Read more »

Tybrandt, K., Forchheimer, R., & Berggren, M. (2012) Logic gates based on ion transistors. Nature Communications, 871. DOI: 10.1038/ncomms1869  

  • May 25, 2012
  • 11:54 AM

Integrative Copy Number Analysis of Cancer Exomes

by Daniel Koboldt in Massgenomics

Cancer genomes often harbor numerous types of genetic alterations - mutations, structural variation, gene conversion events, etc. No single approach can survey everything at once, but exome sequencing is advantageous because mutations, copy number changes, and zygosity changes can be characterized simultaneously.... Read more »

Koboldt DC, Zhang Q, Larson DE, Shen D, McLellan MD, Lin L, Miller CA, Mardis ER, Ding L, & Wilson RK. (2012) VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome research. PMID: 22300766  

  • May 24, 2012
  • 02:18 PM

Paving the road with nanoclay

by Cath in Basal Science (BS) Clarified

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 [...]... Read more »

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  

  • May 24, 2012
  • 08:55 AM

Robotics & Mechanical Limbs

by Jason Carr in Wired Cosmos

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 [...]... Read more »

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  

  • May 22, 2012
  • 11:22 PM

Neurons are like equations

by TheCellularScale in The Cellular Scale

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... Read more »

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

  • May 16, 2012
  • 12:33 PM

Does Social Status Change Brains?

by Miss Behavior in The Scorpion and the Frog

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 ... Read more »

  • May 15, 2012
  • 11:00 AM

Nature’s Hand in Climate Change

by Char in Basal Science (BS) Clarified

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?... Read more »

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  

  • May 12, 2012
  • 10:27 AM

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

by Jonathan Eisen in The Tree of Life

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.


... Read more »

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  

  • May 11, 2012
  • 09:58 AM

Journal Fire: Bonfire of the Vanity Journals?

by Duncan Hull in O'Really?

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.... Read more »

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  

  • May 10, 2012
  • 01:58 PM

Neighbourhood Watch for cloud computing

by David Bradley in Sciencetext

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
... Read more »

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:/

  • May 7, 2012
  • 09:28 PM

Science in superheroes

by Cath in Basal Science (BS) Clarified

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 [...]... Read more »

  • May 3, 2012
  • 04:30 PM

Putting the Squeeze on Microfluidics

by Hector Munoz in Microfluidic Future

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.... Read more »

  • May 3, 2012
  • 10:00 AM

Need to re-invent the Web (badly)? There’s an App for that!

by Duncan Hull in O'Really?

I love the convenience of mobile applications but hate the way they re-invent the wheel and are killing the Web. What can be done about it?... Read more »

Zittrain J. (2007) Saving the internet. Harvard business review, 85(6), 49. PMID: 17580647  

  • May 3, 2012
  • 05:00 AM

User-generated content Informality

by Alejandro Mosquera in amsqr

The relevance of informality analysis in social media texts... Read more »

Alejandro Mosquera, & Paloma Moreda. (2011) Enhancing the discovery of informality levels in Web 2.0 texts. Proceedings of the 3rd Language Technology Conference (LTC 2011), Poland. info:/

  • May 1, 2012
  • 12:50 PM

Virtual Reality for Worms

by TheCellularScale in The Cellular Scale

How do you build a virtual environment for a worm? The Nematode C. Elegans with glowing neurons (source)Using a little optogenetic trickery, you can directly activate specific worm neurons with light.  If you know your worm neurons, you can stimulate ones that make it think it has suddenly touched something with its nose or that the environment is suddenly very salty.  Before we dive into worm VR, let's back up and discuss this specific worm.The Magnificent C. ElegansC. Elegans is a surprisingly popular subject of study in neuroscience. It has a simple and well defined nervous system that contains only 302 neurons (in the hermaphrodite, the rare males have a few extra neurons).  All the neurons and even all the connections between the neurons have been pretty well characterized.  They are small (hundreds can fit on a standard sized petri dish) and they reproduce quickly.  And it that wasn't enough to make C. elegans a desirable subject for study, they can be genetically altered with relative ease, and exhibit rudimentary learning skills.  A recent technological development has made clever use of genetic tools that allow calcium influx (an indicator of neural activity) to be visualized in neurons and allow neurons to be activated by light. Faumont et al., (2011) have created a worm tracking system that uses the fluorescence from a genetically altered neuron to locate the worm and recenter the microscope on the worm in real time. This allows for completely non-invasive visualization of neuronal calcium/activity in the awake behaving animal.  The recent paper in PLoS One, describes exactly how they got the microscope to track the worm in real time without blurring of the signal or messing up the calcium imaging. The paper is open access, so you can go read the details for free.To see this larger and more clearly, you can download this video and their 4 other supplementary videos here.  In this video, you can see the animal moving around in the top left, the path it follows in the top right, the calcium fluorescence signal in the bottom left (notice the calcium neuron is always in the field of view), and the activity of this particular neuron when the worm is traveling either forward (blue) or backward (red).  The "Dedicated Circuit" HypothesisThe neuron imaged in this video is called AVB, and it is a 'command neuron'.  Faumont et al. show that it increases in activity when the worm is moving forward and decreases when the worm moves backwards.  A similar command neuron, AVA, does just the opposite, increasing when the worm moves backward and decreasing when it moves forward.  These data support what is called the "dedicated circuit hypothesis" which says that the worm uses one set of neurons to go forward and a completely different set of neurons to move backwards.While Faumont et al. shows that the dedicated circuit hypothesis is supported for command neurons, they find that the activity of the actual motor neurons (the neurons on the body wall that control contraction of the muscles) does not support this hypothesis.  If the dedicated circuit hypothesis was true, the A-type motor neurons should only be active and oscillating during backward movement, and the B-type motor neurons should only be active during forward movement.  They found that this wasn't true, that both were active and oscillating during both forward and backward motion.  Virtual Reality for WormsNow back to virtual reality.  This Faumont et al. paper is a showcase of new tools that can be used to study C. Elegans in a simultaneously macroscopic and microscopic way.  One of the new techniques the introduce is the optogenetic stimulation of specific neurons in specific places to create and 'environment' for the worm.  Faumont et al., 2011 Figure 2When they genetically express channel rhodopsin, the channel which activates neurons when exposed to blue light in the ASH neuron (a neuron sensitive to osmolarity, or saltiness, changes), they can activate that neuron whenever they want by turning on the blue light.  They create a virtual environment by tracking the worm as it travels in a field, and activating the blue light when it reaches a certain xy coordinate.  In the figure above they activate the neuron when the worm's nose is within the outer ring (traces turn blue).  This makes the worm 'think' that the ring is full of saltier liquid than the rest of the area.  This virtual environment takes away all the technical difficulties of actually creating a ring of salty water in a pool of less salty water, and the VR environment can be quickly and easily changed into any shape or size, when desired.  This new tracking method, in combination with calcium imaging and optogenetics, represents a leap forward in cellular scale neuroscience, to non-invasively visualize neuronal activity, activate neurons, and record the coinciding behavior is a combination mammalian neuroscientists can only dream about.Note: there are ways to image calcium in the neurons of moving mice, but even this requires installing a 'window' into the skull and mounting a mini-microscope on the mouse's head. In addition, the neurons visualized are limited to the ones closest to the surface of the brain.© TheCellularScale... Read more »

  • May 1, 2012
  • 08:24 AM

Cybernetics – Left Ventricular Assist Device

by Jason Carr in Wired Cosmos

Left ventricular assist device technology isn’t necessarily new, but it is one of the biggest harbingers of cybernetic technology. People with weak hearts that are waiting for a donor can use these sorts of heart pumps to bridge patients over until they can receive a full transplant. However, such LVAD machines are usually located in [...]... Read more »

Rizzieri, A., Verheijde, J., Rady, M., & McGregor, J. (2008) Ethical challenges with the left ventricular assist device as a destination therapy. Philosophy, Ethics, and Humanities in Medicine, 3(1), 20. DOI: 10.1186/1747-5341-3-20  

  • April 30, 2012
  • 03:03 PM

Math Shows Today's Writers Are Less Influenced by the Past

by Elizabeth Preston in Inkfish

When Charles Dickens wrote It was the of, it was the of, the immortal first words in A Tale of Two Cities, he can't have imagined that 21st-century computer scientists would parse his prepositions and pronouns as part of vast literary data sets. But today's researchers are studying the unimportant words in books to find important literary trends. With the meaty words taken out, language becomes a numbers game.

To see how literary styles evolve over time--a science dubbed "stylometry"--researchers led by James Hughes at Dartmouth College turned to Project Gutenberg. The site contains the full text of more than 38,000 out-of-copyright books. Researchers began their mining expedition by digging out every author who wrote after 1550, had a known date of birth and (when relevant) death, and had at least 5 English-language books digitized.

These criteria gave the researchers a set of 537 authors with 7,733 published works. But they weren't interested in every word of those books. Nouns and adjectives were out: No Kareninas or Lolitas, nothing nice or bad or beautiful, no roads or homes or people. Most verbs were out, except for forms of the utilitarian to be. No one could speak or walk or Fly, good Fleance!

It may seem that the researchers were stripping all the information-containing words out of the sentences, and in fact that was their goal: "Content-free" words were all they wanted. The 307-word vocabulary that remained from the books was mostly prepositions, conjunctions, and articles.

This linguistic filler, the little stitches that hold together the good stuff, is known to contain a kind of authorial fingerprint. We may not think much about these words when we're writing or speaking, but scientists can use them to define our style.

Hughes and his team used computer analysis to score each author's similarity to every other author. They found that before the late 18th century, authors's stylistic similarity didn't depend on how close to each other they lived. (Each author was represented by a single year, the midpoint between his or her birth and death.) During this time period, authors who lived in the same generation didn't influence each other's styles much more than authors who lived hundreds of years away.

But from the late 18th century to today, it was a different story. Stylistically, authors were more similar to their contemporaries than to other writers. By the late 19th century, writers closely matched the style of other writers who lived at the same time (at least according to the computers tallying up their non-content words). This influence dropped off outside of 30 years. In other words, authors who lived more than three decades away each other may as well have lived centuries away, for all the similarity between their writing.

Looking at more recent books, that window of influence seems to become even tighter. Among authors from the first half of the 20th century, the similarity of style drops off beyond just 23 years.

Over time, authors have become more and more influenced by the other authors writing at the same time. The researchers say this may simply be due to the number of books published. In the early part of their dataset, there were few enough books around that a studious person could read, well, most of them. But as more and more books were published, contemporary books made up a larger share of what was available to read. Authors have filled more and more shelves in their libraries with books by their peers--and this has made them more likely to echo each other's styles.

Because Project Gutenberg relies on public-domain material, there weren't very many authors after the mid-20th century included in this study. Looking forward, "You would expect a continued diminishing of influence," says Daniel Rockmore, the paper's senior author. Contemporary books take up an ever greater portion of what's available to read. In addition to the huge number of books published each year (more than 288,000 in the United States in 2009), there are now e-books and e-readers and Japanese Twitter novels.

A century from now, we may be able to look back and see that today's authors had an ever-condensing frame of influence. Of course, by then literary styles might only last a week. Most books will be forgotten, but every author will be a revolutionary.

James M. Hughes, Nicholas J. Foti, David C. Krakauer, & Daniel N. Rockmore (2012). Quantitative patterns of stylistic influence in the evolution of literature PNAS : 10.1073/pnas.1115407109

Image: Library of Congress from ep_jhu/Flickr

... Read more »

James M. Hughes, Nicholas J. Foti, David C. Krakauer, & Daniel N. Rockmore. (2012) Quantitative patterns of stylistic influence in the evolution of literature. PNAS. info:/10.1073/pnas.1115407109

  • April 26, 2012
  • 10:43 PM

Phthalates–why you should care about these plastic additives

by Cath in Basal Science (BS) Clarified

Aside from cost, aesthetics, and functionality, materials selection is now a topic priority for many consumers when they make a purchase. Consumers are becoming more aware of their choices for sustainable and reusable materials—even the potential health risks/toxicity associated with materials. This is especially true for products containing plastics, particularly the additives used to make [...]... Read more »

  • April 25, 2012
  • 11:54 AM

Can a Horde of Idiots be a Genius?

by Miss Behavior in The Scorpion and the Frog

Let’s face it: The typical individual is not that bright. Just check out these human specimens: Yet somehow, if you get enough numbskulls together, the group can make some pretty intelligent decisions. We’ve seen this in a wide variety of organisms facing a number of different challenges.In a brilliant series of studies, Jean-Louis Deneubourg, a professor at the Free University of Brussels, and his colleagues tested the abilities of Argentine ants (a common dark-brown ant species) to collectively solve foraging problems. In one of these studies, the ants were provided with a bridge that connected the nest to a food source. This bridge split and fused in two places (like eyeglass frames), but at each split one branch was shorter than the other, resulting in a single shortest-path and multiple longer paths. After a few minutes, explorers crossed the bridge (by a meandering path) and discovered the food. This recruited foragers, each of which chose randomly between the short and the long branch at each split. Then suddenly, the foragers all started to prefer the shortest route. How did they do that?This figure from the Goss et al 1989 paper in Naturwissemschaften shows (a) the design of a single module, (b) ants scattered on the bridge after 4 minutes (I promise they’re there), and (c) ants mostly on the shortest path after 8 minutesYou can think of it this way: a single individual often tries to make decisions based on the uncertain information available to it. But if you have a group of individuals, they will likely each have information that differs somewhat from the information of others in the group. If they each make a decision based on their own information alone, they will likely result in a number of poor decisions and a few good ones. But if they can each base their decisions on the accumulation of all of the information of the group, they stand a much better chance of making a good decision. The more information accumulated, the more likely they are to make the best possible decision. In the case of the Argentine ant, the accumulated information takes the form of pheromone trails. Argentine ants lay pheromone trails both when leaving the nest and when returning to the nest. Ants that are lucky enough to take a shorter foraging route return to the nest sooner, increasing the pheromone concentration of the route each way. In this way, shorter routes develop more concentrated pheromone trails faster, which attract more ants, which further increase pheromone concentration of the shortest routes. In this way, an ant colony can make an intelligent decision (take the shortest foraging route) without any individual doing anything more intelligent than following a simple rule (follow the strongest pheromone signal). Home is where the heart is. Photo of a bee swarm by Tom SeeleyHoneybee colonies also solve complicated tasks with the use of communication. Tom Seeley at Cornell University and his colleagues have investigated the honeybee group decision-making process of finding a new home. When a colony outgrows their hive, hundreds of scouts will go in search of a suitable new home, preferably one that is high off the ground with a south-facing entrance and room to grow. If a scout finds such a place, she returns to the colony and performs a waggle dance, a dance in which her body position and movements encode the directions to her site and her dancing vigor relates to how awesome she thinks the site is.  Some scouts that see her dance may be persuaded to follow her directions and check out the site for themselves, and if impressed, may return to the hive and perform waggle dances too. Or they may follow another scout’s directions to a different site or even strike out on their own. Eventually, the majority of the scouts are all dancing the same vigorous dance. But interestingly, few scouts ever visit more than one site. Better sites simply receive more vigorous “dance-votes” and then attract more scouts to do the same. Like ants in search of a foraging path, the intensity of the collective signal drives the group towards the best decision. Once a quorum is reached, the honeybees fly off together to their new home.But groups can develop better solutions than individuals even without communication. Gaia Dell’Ariccia at the University of Zurich in Switzerland and her colleagues explored homing pigeon navigation by placing GPS trackers on the backs of pigeons and releasing them from a familiar location either alone or in a group of six. Because they were all trained to fly home from this site, they all found their way home regardless of whether they were alone or in a group. But as a flock, the pigeons left sooner, rested less, flew faster, and took a more direct route than did the same birds when making the trip alone. By averaging the directional tendencies of everyone in the group, they were able to mutually correct the errors of each individual and follow the straightest path. In each of these examples, each individual has limited and uncertain information, but each individual has information that may be slightly different than their neighbors’. By combining this diverse information and making a collective decision, hordes of idiots can make genius decisions.Want to know more? Check these out:1. Couzin, I. (2009). Collective cognition in animal groups Trends in Cognitive Sciences, 13 (1), 36-43 DOI: 10.1016/j.tics.2008.10.002 2. ... Read more »

Couzin, I. (2009) Collective cognition in animal groups. Trends in Cognitive Sciences, 13(1), 36-43. DOI: 10.1016/j.tics.2008.10.002  

Goss, S., Aron, S., Deneubourg, J., & Pasteels, J. (1989) Self-organized shortcuts in the Argentine ant. Naturwissenschaften, 76(12), 579-581. DOI: 10.1007/BF00462870  

Dussutour, A., Nicolis, S., Deneubourg, J., & Fourcassié, V. (2006) Collective decisions in ants when foraging under crowded conditions. Behavioral Ecology and Sociobiology, 61(1), 17-30. DOI: 10.1007/s00265-006-0233-x  

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