A cellular automaton is a discrete model studied in computability theory and mathematics. It consists of an infinite, regular grid of cells, each in one of a finite number of states. The grid can be in any finite number of dimensions. Time is also discrete, and the state of a cell at time t is a function of the state of a finite number of cells called the neighborhood at time t-1. These neighbors are a selection of cells relative to some specified, and does not change (Though the cell itself may be in its neighborhood, it is not usually considered a neighbor). Every cell has the same rule for updating, based on the values in this neighbourhood. Each time the rules are applied to the whole grid a new generation is produced.The simplest nontrivial CA would be one-dimensional, with two possible states per cell, and a cell's neighbors defined as the cell on either side of it. A cell and its two neighbors forms a neighborhood of 3 cells, so there are 23=8 possible patterns for a neighborhood. So, there are 28=256 possible rules. These 256 CAs are generally referred to using a standard naming convention invented by Wolfram. The name of a CA is a number which, in binary, gives the rule table. Examples:Rule 90Rule 30http://amsqr.github.com/chromanin.js/ruletool.htmlReferences:Stephen Wolfram (2002). History of Cellular Automata A New Kind of Science... Read more »
Stephen Wolfram. (2002) History of Cellular Automata. A New Kind of Science. info:/
Lego blocks are awesome—they snap together easily and perfectly. These blocks are made using injection molding and wouldn’t fit so flawless together without the precise features of the metal molds [...]... Read more »
Cells are quite valuable, especially when used for regenerative research, diagnostics or research. But harvested cells do not come presorted and need to be separated from a heterogeneous mixture of cells. There are already numerous methods to sort cells according to biophysical properties such as size, density, morphology, and dielectric or magnetic susceptibility. Cell sorting based on labels can have a higher specificity, but introduces extra steps to add and remove labels, which can affect the phenotype of the cell. Rohit Karnik of MIT has developed a cell sorting method based on cell rolling. The continuous, label-free process is described in “Cell sorting by deterministic rolling” in Lab on a Chip.... Read more »
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 »
Wong JV, Li B, & You L. (2012) Tension and robustness in multitasking cellular networks. PLoS computational biology, 8(4). PMID: 22577355
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 »
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
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
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
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 »
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 »
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
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
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
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
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
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 »
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:/
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 »
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 »
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
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 »
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:/
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 »
Faumont S, Rondeau G, Thiele TR, Lawton KJ, McCormick KE, Sottile M, Griesbeck O, Heckscher ES, Roberts WM, Doe CQ.... (2011) An image-free opto-mechanical system for creating virtual environments and imaging neuronal activity in freely moving Caenorhabditis elegans. PloS one, 6(9). PMID: 21969859
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
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