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Colin Camerer: Neuroscience, game theory, monkeys

When two people are trying to make a deal -- whether they’re competing or cooperating -- what’s really going on inside their brains? Behavioral economist Colin Camerer shows research that reveals just how little we’re able to predict what others are thinking. And he presents an unexpected study that shows chimpanzees might just be better at it than we are. (Filmed at TEDxCalTech.)

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Dynamics of Social Interaction
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Data Driven vs Data Informed: Sydney siege brings Uber criticism (Algorithms going wrong)

Data Driven vs Data Informed: Sydney siege brings Uber criticism (Algorithms going wrong) | Social Foraging | Scoop.it

Cab-ordering firm Uber has been criticised for increasing fares by up to four times normal rates during the hostage crisis in Sydney, Australia.

 

As the police cordoned off a wide area around the Lindt cafe where a gunman was holding staff and customers hostage, Uber's pricing algorithm raised prices as demand spiked.

 

Uber responded to the criticism by offering free journeys out of the city's central business district (CBD).

It also refunded some passengers.

 

But the rapidly expanding cab firm also tweeted that higher rates were still in place "to encourage drivers to get into the CBD".

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Want to influence the world? Map reveals the best languages to speak

Want to influence the world? Map reveals the best languages to speak | Social Foraging | Scoop.it

Speak or write in English, and the world will hear you. Speak or write in Tamil or Portuguese, and you may have a harder time getting your message out. Now, a new method for mapping how information flows around the globe identifies the best languages to spread your ideas far and wide. One hint: If you’re considering a second language, try Spanish instead of Chinese.

 

The study was spurred by a conversation about an untranslated book, says Shahar Ronen, a Microsoft program manager whose Massachusetts Institute of Technology (MIT) master’s thesis formed the basis of the new work. A bilingual Hebrew-English speaker from Israel, he told his MIT adviser, César Hidalgo (himself a Spanish-English speaker), about a book written in Hebrew whose translation into English he wasn’t yet aware of. “I was able to bridge a certain culture gap because I was multilingual,” Ronen says. He began thinking about how to create worldwide maps of how multilingual people transmit information and ideas.

 

Ronen and co-authors from MIT, Harvard University, Northeastern University, and Aix-Marseille University tackled the problem by describing three global language networks based on bilingual tweeters, book translations, and multilingual Wikipedia edits. The book translation network maps how many books are translated into other languages. For example, the Hebrew book, translated from Hebrew into English and German, would be represented in lines pointing from a node of Hebrew to nodes of English and German. That network is based on 2.2 million translations of printed books published in more than 1000 languages. As in all of the networks, the thickness of the lines represents the number of connections between nodes. For tweets, the researchers used 550 million tweets by 17 million users in 73 languages. In that network, if a user tweets in, say, Hindi as well as in English, the two languages are connected. To build the Wikipedia network, the researchers tracked edits in up to five languages done by editors, carefully excluding bots.

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OpenWorm: Building the first digital life form. Open source

OpenWorm is an open source project dedicated to creating a virtual C. elegans nematode in a computer.
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Is Consciousness Computable? Quantifying Integrated Information Using Algorithmic Information Theory

In this article we review Tononi's (2008) theory of consciousness as integrated information. We argue that previous formalizations of integrated information (e.g. Griffith, 2014) depend on information loss. Since lossy integration would necessitate continuous damage to existing memories, we propose it is more natural to frame consciousness as a lossless integrative process and provide a formalization of this idea using algorithmic information theory. We prove that complete lossless integration requires noncomputable functions. This result implies that if unitary consciousness exists, it cannot be modelled computationally.
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Mathematical models for emerging disease

It has been nearly 25 years since the publication of Infectious Disease of Humans, the “vade mecum” of mathematical modeling of infectious disease; the proliferation of epidemiological careers that it initiated is now in its fourth generation. Epidemiological models have proved very powerful in shaping health policy discussions. The complex interactions that lead to pathogen (and pest) outbreaks make it necessary to use models to provide quantitative insights into the counterintuitive outcomes that are the rule of most nonlinear systems. Thus, epidemic models are most interesting when they suggest unexpected outcomes; they are most powerful when they describe the conditions that delineate the worst-case unexpected scenario, and provide a framework in which to compare alternative control strategies. But what are the limits of mathematical models and what kinds provide insight into emerging disease?

 

Mathematical models for emerging disease
Andy Dobson

Science 12 December 2014:
Vol. 346 no. 6215 pp. 1294-1295
http://dx.doi.org/10.1126/science.aaa3441


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Collapse of cooperation in evolving games

Collapse of cooperation in evolving games | Social Foraging | Scoop.it
Game theory provides a quantitative framework for analyzing the behavior of rational agents. The Iterated Prisoner’s Dilemma in particular has become a standard model for studying cooperation and cheating, with cooperation often emerging as a robust outcome in evolving populations. Here we extend evolutionary game theory by allowing players’ payoffs as well as their strategies to evolve in response to selection on heritable mutations. In nature, many organisms engage in mutually beneficial interactions and individuals may seek to change the ratio of risk to reward for cooperation by altering the resources they commit to cooperative interactions. To study this, we construct a general framework for the coevolution of strategies and payoffs in arbitrary iterated games. We show that, when there is a tradeoff between the benefits and costs of cooperation, coevolution often leads to a dramatic loss of cooperation in the Iterated Prisoner’s Dilemma. The collapse of cooperation is so extreme that the average payoff in a population can decline even as the potential reward for mutual cooperation increases. Depending upon the form of tradeoffs, evolution may even move away from the Iterated Prisoner’s Dilemma game altogether. Our work offers a new perspective on the Prisoner’s Dilemma and its predictions for cooperation in natural populations; and it provides a general framework to understand the coevolution of strategies and payoffs in iterated interactions.
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Neuron Networks in Healthy and Diseased Brains

Neuron Networks in Healthy and Diseased Brains | Social Foraging | Scoop.it
The dream of mapping the brain rests on the notion that the trillions of connections between 80 billion neurons form networks that are correlated with mental states. Please see the post, Limits of Current Neuroscience, for a discussion of the many complications in this approach, including the importance of brain waves, glia networks and poor resolution of our imaging devices. Most important is that there is no evidence for a mechanism in the brain that coordinates brain activity to form mental states. There is no center controlling the wide ranging circuits producing the subjective experience of mind. 
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Concurrent Bursty Behavior of Social Sensors in Sporting Events

The advent of social media expands our ability to transmit information and connect with others instantly, which enables us to behave as "social sensors." Here, we studied concurrent bursty behavior of Twitter users during major sporting events to determine their function as social sensors. We show that the degree of concurrent bursts in tweets (posts) and retweets (re-posts) works as a good indicator of winning or losing a game. More specifically, our tweet analysis of Japanese professional baseball games in 2013 revealed that social sensors can immediately show reactions to positive and negative events through bursts of tweets, but that positive events are more likely to induce a subsequent burst of retweets. These findings were also confirmed in an analysis of the 2014 FIFA World Cup final. We further showed active interactions among social sensors by constructing retweet networks during a baseball game. The resulting networks commonly exhibited user clusters depending on the baseball team, with a scale-free connectedness that is indicative of hub sensors. Contrary to the well-tested analogy that "Twitter is a mirror of reality," the results of this study imply more dynamic aspects to social sensors, offering new insights into human behavior in a highly connected world.
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Computers that teach by example: New system enables pattern-recognition systems to convey what they learn to humans.

Computers that teach by example: New system enables pattern-recognition systems to convey what they learn to humans. | Social Foraging | Scoop.it
Computers are good at identifying patterns in huge data sets. Humans, by contrast, are good at inferring patterns from just a few examples.
In a paper appearing at the Neural Information Processing Society’s conference next week, MIT researchers present a new system that bridges these two ways of processing information, so that humans and computers can collaborate to make better decisions.
The system learns to make judgments by crunching data but distills what it learns into simple examples. In experiments, human subjects using the system were more than 20 percent better at classification tasks than those using a similar system based on existing algorithms.
“In this work, we were looking at whether we could augment a machine-learning technique so that it supported people in performing recognition-primed decision-making,” says Julie Shah, an assistant professor of aeronautics and astronautics at MIT and a co-author on the new paper. “That’s the type of decision-making people do when they make tactical decisions — like in fire crews or field operations. When they’re presented with a new scenario, they don’t do search the way machines do. They try to match their current scenario with examples from their previous experience, and then they think, ‘OK, that worked in a previous scenario,’ and they adapt it to the new scenario.”
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The Coming Era of Egocentric Video Analysis

The Coming Era of Egocentric Video Analysis | Social Foraging | Scoop.it
Head-mounted cameras reveal actions without showing the identity of the filmmaker. Now a new type of software can identify these filmmakers.

 

Head-mounted cameras are becoming de rigueur for certain groups—extreme sportsters, cyclists, law enforcement officers, and so on. It’s not hard to find content generated in this way on the Web.

 

So it doesn’t take a crystal ball to predict that egocentric recording is set to become ubiquitous as devices such as Go-Pros and Google Glass become more popular. An obvious corollary to this will be an explosion of software for distilling the huge volumes of data this kind of device generates into interesting and relevant content.

 

Today, Yedid Hoshen and Shmuel Peleg at the Hebrew University of Jerusalem in Israel reveal one of the first applications. Their goal: to identify the filmmaker from biometric signatures in egocentric videos.

One of the interesting features of egocentric videos is that the filmmaker never appears in shot. So whatever these videos show, there is always a question mark over who shot them.

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The Social Bayesian Brain: Does Mentalizing Make a Difference When We Learn?

The Social Bayesian Brain: Does Mentalizing Make a Difference When We Learn? | Social Foraging | Scoop.it
When it comes to interpreting others' behaviour, we almost irrepressibly engage in the attribution of mental states (beliefs, emotions…). Such "mentalizing" can become very sophisticated, eventually endowing us with highly adaptive skills such as convincing, teaching or deceiving. Here, sophistication can be captured in terms of the depth of our recursive beliefs, as in "I think that you think that I think…" In this work, we test whether such sophisticated recursive beliefs subtend learning in the context of social interaction. We asked participants to play repeated games against artificial (Bayesian) mentalizing agents, which differ in their sophistication. Critically, we made people believe either that they were playing against each other, or that they were gambling like in a casino. Although both framings are similarly deceiving, participants win against the artificial (sophisticated) mentalizing agents in the social framing of the task, and lose in the non-social framing. Moreover, we find that participants' choice sequences are best explained by sophisticated mentalizing Bayesian learning models only in the social framing. This study is the first demonstration of the added-value of mentalizing on learning in the context of repeated social interactions. Importantly, our results show that we would not be able to decipher intentional behaviour without a priori attributing mental states to others.
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New Journal: Neuroscience of Consciousness

New Journal: Neuroscience of Consciousness | Social Foraging | Scoop.it

Neuroscience of Consciousness is a new open access journal which will publish papers on the biological basis of consciousness, with an emphasis on empirical neuroscience studies in healthy populations and clinical settings. The journal will also publish empirically and neuroscientifically relevant psychological, methodological, theoretical, and philosophical papers. As well as the primary phenomenon of consciousness itself, relevant topics include interactions between conscious and unconscious processes; selfhood; metacognition and higher-order consciousness; intention, volition, and agency; individual differences in consciousness; altered states of consciousness; disorders of consciousness in psychiatry and neurology; and consciousness in infants and non-human animals.

 

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Meta-list of data set repositories for cool data science projects

Meta-list of data set repositories for cool data science projects | Social Foraging | Scoop.it
Listings featuring free, great, real-life, big or small data sets are published regularly. Our Data Science Apprenticeship webpage lists various data sets that anyone can use, not just our candidates: some are real, some are simulated data sets to study the behavior of algorithms such as Jackknife regression. More are listed below.
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Fish'n'microchips: MIT's soft robotic fish swims 'like the real thing'

Fish'n'microchips: MIT's soft robotic fish swims 'like the real thing' | Social Foraging | Scoop.it
The Massachusetts Institute of Technology (MIT) has unveiled a robot fish that it claims can change direction almost as fast as the real thing.

The fish – or “autonomous soft robot” as it’s described by MIT – can perform escape manoeuvres through rapid convulsions of its body, powered by carbon dioxide released from a canister in its abdomen.

Graduate student Andrew Marchese built the fish using 3D printing technology to create a mould, which was then used to cast the fish’s body from silicone rubber. The fish can execute between 20 or 30 manoeuvres before running out of gas.

“The fish was designed to explore performance capabilities, not long-term operation,” said Marchese in MIT’s announcement of the research. “Next steps for future research are taking that system and building something that’s compromised on performance a little bit but increases longevity.”

That will involve switching carbon dioxide for a pumped-water system that could keep the fish swimming for around 30 minutes of a time. Such a device could eventually be used to swim alongside schools of real fish to study their behaviour in the wild.
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Links that speak: The global language network and its association with global fame

Links that speak: The global language network and its association with global fame | Social Foraging | Scoop.it
Languages vary enormously in global importance because of historical, demographic, political, and technological forces. However, beyond simple measures of population and economic power, there has been no rigorous quantitative way to define the global influence of languages. Here we use the structure of the networks connecting multilingual speakers and translated texts, as expressed in book translations, multiple language editions of Wikipedia, and Twitter, to provide a concept of language importance that goes beyond simple economic or demographic measures. We find that the structure of these three global language networks (GLNs) is centered on English as a global hub and around a handful of intermediate hub languages, which include Spanish, German, French, Russian, Portuguese, and Chinese. We validate the measure of a language’s centrality in the three GLNs by showing that it exhibits a strong correlation with two independent measures of the number of famous people born in the countries associated with that language. These results suggest that the position of a language in the GLN contributes to the visibility of its speakers and the global popularity of the cultural content they produce.
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Worm 'Brain' Uploaded Into Lego Robot

Worm 'Brain' Uploaded Into Lego Robot | Social Foraging | Scoop.it
Can a digitally simulated brain on a computer perform tasks just like the real thing?

For simple commands, the answer, it would seem, is yes it can. Researchers at the OpenWorm project recently hooked a simulated worm brain to a wheeled robot. Without being explicitly programmed to do so, the robot moved back and forth and avoided objects—driven only by the interplay of external stimuli and digital neurons.
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The retail market as a complex system

Aim of this paper is to introduce the complex system perspective into retail market analysis. Currently, to understand the retail market means to search for local patterns at the micro level, involving the segmentation, separation and profiling of diverse groups of consumers. In other contexts, however, markets are modelled as complex systems. Such strategy is able to uncover emerging regularities and patterns that make markets more predictable, e.g. enabling to predict how much a country’s GDP will grow. Rather than isolate actors in homogeneous groups, this strategy requires to consider the system as a whole, as the emerging pattern can be detected only as a result of the interaction between its self-organizing parts. This assumption holds also in the retail market: each customer can be seen as an independent unit maximizing its own utility function. As a consequence, the global behaviour of the retail market naturally emerges, enabling a novel description of its properties, complementary to the local pattern approach. Such task demands for a data-driven empirical framework. In this paper, we analyse a unique transaction database, recording the micro-purchases of a million customers observed for several years in the stores of a national supermarket chain. We show the emergence of the fundamental pattern of this complex system, connecting the products’ volumes of sales with the customers’ volumes of purchases. This pattern has a number of applications. We provide three of them. By enabling us to evaluate the sophistication of needs that a customer has and a product satisfies, this pattern has been applied to the task of uncovering the hierarchy of needs of the customers, providing a hint about what is the next product a customer could be interested in buying and predicting in which shop she is likely to go to buy it.

 

The retail market as a complex system
Pennacchioli D, Coscia M, Rinzivillo S, Giannotti F, Pedreschi D
EPJ Data Science 2014, 3 :33 (11 December 2014)

http://dx.doi.org/10.1140/epjds/s13688-014-0033-x ;


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Eli Levine's curator insight, December 13, 10:43 PM

With this knowledge and insight into the world that's dawning.  One world must be destroyed in order for the new world to come forth; one segment of society must give way to the new in order to facilitate and ease this transition.

 

The top of society is where and what determines the ease or the possibility of this transition.  That is the point that needs to be altered in order to bring about this new dawn.  

 

Unfortunately, it is the point in society that is least willing, although most capable of change.  They'll cling to illusions and delusions of relative power over people and unsustainable material wealth than allow for everyone, including themselves, to realize something that could truly be wonderful for our lives, our well-being, and our health as living organisms.  This is before we talk about the bottom-up resistance that will be experienced as well, especially if the transition is done badly by the people who are at the top of the given social unit.  A shame that something so relatively simple can be so completely complicated and complex to carry out.

 

Silly world.

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Flavour network and the principles of food pairing : Data Science in Gastronomy

Flavour network and the principles of food pairing : Data Science in Gastronomy | Social Foraging | Scoop.it
The cultural diversity of culinary practice, as illustrated by the variety of regional cuisines, raises the question of whether there are any general patterns that determine the ingredient combinations used in food today or principles that transcend individual tastes and recipes. We introduce a flavor network that captures the flavor compounds shared by culinary ingredients. Western cuisines show a tendency to use ingredient pairs that share many flavor compounds, supporting the so-called food pairing hypothesis. By contrast, East Asian cuisines tend to avoid compound sharing ingredients. Given the increasing availability of information on food preparation, our data-driven investigation opens new avenues towards a systematic understanding of culinary practice.
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Brain–machine interface for eye movements

Brain–machine interface for eye movements | Social Foraging | Scoop.it
A number of studies in tetraplegic humans and healthy nonhuman primates (NHPs) have shown that neuronal activity from reach-related cortical areas can be used to predict reach intentions using brain–machine interfaces (BMIs) and therefore assist tetraplegic patients by controlling external devices (e.g., robotic limbs and computer cursors). However, to our knowledge, there have been no studies that have applied BMIs to eye movement areas to decode intended eye movements. In this study, we recorded the activity from populations of neurons from the lateral intraparietal area (LIP), a cortical node in the NHP saccade system. Eye movement plans were predicted in real time using Bayesian inference from small ensembles of LIP neurons without the animal making an eye movement. Learning, defined as an increase in the prediction accuracy, occurred at the level of neuronal ensembles, particularly for difficult predictions. Population learning had two components: an update of the parameters of the BMI based on its history and a change in the responses of individual neurons. These results provide strong evidence that the responses of neuronal ensembles can be shaped with respect to a cost function, here the prediction accuracy of the BMI. Furthermore, eye movement plans could be decoded without the animals emitting any actual eye movements and could be used to control the position of a cursor on a computer screen. These findings show that BMIs for eye movements are promising aids for assisting paralyzed patients.
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Humans 2.0: Seeing Ourselves Anew in ‘Algorithmic Cascades of Data’

Humans 2.0: Seeing Ourselves Anew in ‘Algorithmic Cascades of Data’ | Social Foraging | Scoop.it
Sensors are cheap and abundant. They’re already in our devices, and soon enough, many of us may elect to carry sensors in and on our bodies, and embed them in our homes, offices, and cities. This terrifies people, Jason Silva says in a new video.

Who hasn’t heard of Big Brother or feared the rise of the surveillance state? But Silva says there’s an upside.

As the world is reduced to “algorithmic cascades of data” he thinks we’ll get what Steven Johnson calls the “long view,” like a microscope or telescope for previously invisible information and datasets.

Billions of sensors measuring location, motion, orientation, pressure, temperature, vital signs and more—each of these will be like a pixel. Seen up close, a modestly flashing primary color. But at a distance, individual pixels dissolve. Discrete points will smooth out into a contiguous image no one could have guessed by looking at looking at each pixel alone.

Exactly what image will our sensors reveal?

Silva thinks it will be like looking in a mirror and, seeing ourselves individually and collectively for the first time, will spark a feedback loop—one in which information leads to new behaviors, new behaviors to new information.

“Who knows what we might learn about ourselves?” Silva asks. “How might we be able to take that data and use those insights to feed them back into us?
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Artificial Enzymes from Artificial DNA Challenge Life As We Know It

Artificial Enzymes from Artificial DNA Challenge Life As We Know It | Social Foraging | Scoop.it
In the decade or so since the Human Genome Project was completed, synthetic biology has grown rapidly. Impressive advances include the first bacteria to use a chemically-synthesized genome and creation of a synthetic yeast chromosome.

Recently, scientists from the MRC Laboratory of Molecular Biology in Cambridge, led by Dr. Philip Hollinger, reported creating the first completely artificial enzymes that are functional. The breakthrough was published in the journal Nature and builds on prior success by the group in creating several artificial nucleotides.
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The Internet of Anything: A Social Network for the World's Online Sensors

The Internet of Anything: A Social Network for the World's Online Sensors | Social Foraging | Scoop.it

When her oldest daughter was diagnosed with asthma last March, Yodi Stanton installed air pollution sensors around her London home. She wanted to see if there were links between her daughter’s attacks and the number of dirty particles in the air.

 

Ultimately, she wasn’t able to find a correlation. But maybe some else will find gold in this data. Instead of keeping it to herself, Stanton streamed the data to a public online service she helped create called OpenSensors.io, and from there, it can be accessed and analyzed by public health researchers, journalists, and other concerned citizens—or even feed into online applications that can make use of it.

 

OpenSensors is a service where anyone can publish real-time sensor data. Think of it as Twitter for sensors. You can publish a stream of data from virtually any source to the company’s computer servers—or subscribe to streams of data coming from others, using it for your own research, gadget, or online app.

 

You can publish private feeds of data as well, but the company’s larger goal is to cultivate of a huge repository of open information. “We believe that public data should be shared,” Stanton says, “especially public data that has been paid for by governments and applies to many people. So what we want to do is give the use of data.”

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A deceleration model for bicycle peloton dynamics and group sorting

Extending earlier computer models of bicycle peloton dynamics, we add a deceleration parameter by which deceleration magnitude varies as a function of cyclist strength. This model is validated by applying speed data from a mass-start race composed of 14 cyclists, and running simulation trials using 14 simulated cyclists that generated positional profiles which compare well with the positional profiles observed in the actual mass-start race data. Keeping constant the speed variation profile from the mass-start race as introduced into the simulation, a set of simulation experiments were run, including: varying the number of cyclists; varying the duration of a single near-threshold output event; and varying the course elevation. The results consistently show sorting of pelotons into smaller groups whose mean fitness corresponds with relative group position, i.e. fitter groups are closer to the front. Sorting of pelotons into fitness-related groups provides insight into the mechanics of similar group divisions within biological collectives in which members present heterogeneous physiological fitness capacities.

 

Trenchard, H., Ratamero, E., Richardson, A., Perc, M. A deceleration model for peloton dynamics and group sorting,  App. Math. and Computation, Volume 251, 15 January 2015, Pages 24–34 

http://dx.doi.org/10.1016/j.amc.2014.11.031


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On the Complexity and Behaviour of Cryptocurrencies Compared to Other Markets

We show that the behaviour of Bitcoin has interesting similarities to stock and precious metal markets, such as gold and silver. We report that whilst Litecoin, the second largest cryptocurrency, closely follows Bitcoin's behaviour, it does not show all the reported properties of Bitcoin. Agreements between apparently disparate complexity measures have been found, and it is shown that statistical, information-theoretic, algorithmic and fractal measures have different but interesting capabilities of clustering families of markets by type. The report is particularly interesting because of the range and novel use of some measures of complexity to characterize price behaviour, because of the IRS designation of Bitcoin as an investment property and not a currency, and the announcement of the Canadian government's own electronic currency MintChip.
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Evolution-guided optimization of biosynthetic pathways

Evolution-guided optimization of biosynthetic pathways | Social Foraging | Scoop.it
Engineering biosynthetic pathways for chemical production requires extensive optimization of the host cellular metabolic machinery. Because it is challenging to specify a priori an optimal design, metabolic engineers often need to construct and evaluate a large number of variants of the pathway. We report a general strategy that combines targeted genome-wide mutagenesis to generate pathway variants with evolution to enrich for rare high producers. We convert the intracellular presence of the target chemical into a fitness advantage for the cell by using a sensor domain responsive to the chemical to control a reporter gene necessary for survival under selective conditions. Because artificial selection tends to amplify unproductive cheaters, we devised a negative selection scheme to eliminate cheaters while preserving library diversity. This scheme allows us to perform multiple rounds of evolution (addressing ∼109 cells per round) with minimal carryover of cheaters after each round. Based on candidate genes identified by flux balance analysis, we used targeted genome-wide mutagenesis to vary the expression of pathway genes involved in the production of naringenin and glucaric acid. Through up to four rounds of evolution, we increased production of naringenin and glucaric acid by 36- and 22-fold, respectively. Naringenin production (61 mg/L) from glucose was more than double the previous highest titer reported. Whole-genome sequencing of evolved strains revealed additional untargeted mutations that likely benefit production, suggesting new routes for optimization.
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