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Social Foraging
Dynamics of Social Interaction
Curated by Ashish Umre
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Bayesian Inference of Spatial Organizations of Chromosomes

Bayesian Inference of Spatial Organizations of Chromosomes | Social Foraging | Scoop.it

Knowledge of spatial chromosomal organizations is critical for the study of transcriptional regulation and other nuclear processes in the cell. Recently, chromosome conformation capture (3C) based technologies, such as Hi-C and TCC, have been developed to provide a genome-wide, three-dimensional (3D) view of chromatin organization. Appropriate methods for analyzing these data and fully characterizing the 3D chromosomal structure and its structural variations are still under development. Here we describe a novel Bayesian probabilistic approach, denoted as “Bayesian 3D constructor for Hi-C data” (BACH), to infer the consensus 3D chromosomal structure. In addition, we describe a variant algorithm BACH-MIX to study the structural variations of chromatin in a cell population. Applying BACH and BACH-MIX to a high resolution Hi-C dataset generated from mouse embryonic stem cells, we found that most local genomic regions exhibit homogeneous 3D chromosomal structures. We further constructed a model for the spatial arrangement of chromatin, which reveals structural properties associated with euchromatic and heterochromatic regions in the genome. We observed strong associations between structural properties and several genomic and epigenetic features of the chromosome. Using BACH-MIX, we further found that the structural variations of chromatin are correlated with these genomic and epigenetic features. Our results demonstrate that BACH and BACH-MIX have the potential to provide new insights into the chromosomal architecture of mammalian cells.

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Percolation Centrality: Quantifying Graph-Theoretic Impact of Nodes during Percolation in Networks

Percolation Centrality: Quantifying Graph-Theoretic Impact of Nodes during Percolation in Networks | Social Foraging | Scoop.it

A number of centrality measures are available to determine the relative importance of a node in a complex network, and betweenness is prominent among them. However, the existing centrality measures are not adequate in network percolation scenarios (such as during infection transmission in a social network of individuals, spreading of computer viruses on computer networks, or transmission of disease over a network of towns) because they do not account for the changing percolation states of individual nodes. We propose a new measure, percolation centrality, that quantifies relative impact of nodes based on their topological connectivity, as well as their percolation states. The measure can be extended to include random walk based definitions, and its computational complexity is shown to be of the same order as that of betweenness centrality. We demonstrate the usage of percolation centrality by applying it to a canonical network as well as simulated and real world scale-free and random networks.

 

Piraveenan M, Prokopenko M, Hossain L (2013) Percolation Centrality: Quantifying Graph-Theoretic Impact of Nodes during Percolation in Networks. PLoS ONE 8(1): e53095. doi:10.1371/journal.pone.0053095

 


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Engineers solve a biological mystery and boost artificial intelligence

Engineers solve a biological mystery and boost artificial intelligence | Social Foraging | Scoop.it
By simulating 25,000 generations of evolution within computers, Cornell University engineering and robotics researchers have discovered why biological networks tend to be organized as modules -- a finding that will lead to a deeper understanding of the evolution of complexity.

 

The new insight also will help evolve artificial intelligence, so robot brains can acquire the grace and cunning of animals.

 

From brains to gene regulatory networks, many biological entities are organized into modules -- dense clusters of interconnected parts within a complex network. For decades biologists have wanted to know why humans, bacteria and other organisms evolved in a modular fashion. Like engineers, nature builds things modularly by building and combining distinct parts, but that does not explain how such modularity evolved in the first place. Renowned biologists Richard Dawkins, Günter P. Wagner, and the late Stephen Jay

Gould identified the question of modularity as central to the debate over "the evolution of complexity."

 

For years, the prevailing assumption was simply that modules evolved because entities that were modular could respond to change more quickly, and therefore had an adaptive advantage over their non-modular competitors. But that may not be enough to explain the origin of the phenomena.

 

The team discovered that evolution produces modules not because they produce more adaptable designs, but because modular designs have fewer and shorter network connections, which are costly to build and maintain. As it turned out, it was enough to include a "cost of wiring" to make evolution favor modular architectures.

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Jose Santos's curator insight, May 26, 2013 11:54 AM

another role for modularity in network design

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Scientists build the One Million Dollar man

Scientists build the One Million Dollar man | Social Foraging | Scoop.it

Scientists have built a man from artificial limbs, and while he might not be a bionic superhero, he cost a lot less to create than The Six Million Dollar Man.

 

One million dollar Rex – short for robotic exoskeletons – was built using the most advanced artificial limbs and organs from across the world.

 

And he shows that from bionic arms and legs to artificial organs, science is beginning to catch up with science fiction in the race to replace body parts with man-made alternatives.

 

In the 70s TV series The Six Million Dollar Man astronaut Steve Austin, played by Lee Majors, was left horribly injured after his craft crashed and was given a bionic arm and legs and an artificial zoom-lens eye.

 

6ft Rex also raises ethical dilemmas, as research on advanced prosthetic arms and legs, as well as artificial eyes, hearts, lungs - and even hybrids between computer chips and living brains - means that scientists can not only replace body parts but may even be able to improve on human abilities.

 

This has led scientists to warn against creating a modern Frankenstein.

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PABLO SILVA's curator insight, November 27, 2013 12:28 AM

LOS SERES HUMANOS TRATAMOS DE CONSTRUIR ROBOTS SEMEJANTES A NOSOTROS PORQUE NO PODEMOS CONVIVIR CON ESPECIES QUE NO SEAN IGUALES, SE CREARIA UNA INCOMODIDAD CONSTANTE 

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Europeans to Fund Billion-euro Human Brain Project

Nature Magazine reports that the European Commission has awarded up to €1 billion in research funding for the Human Brain Project led by neuroscientist Henry Markram. And while the official announcement is not expected until January 28, part of the project will be dedicated to the development of a exaflop supercomputer that will be used for the simulation of the brain model.

 

Brain researchers are generating 60,000 papers per year,” said Markram as he explained the concept in Bern. “They’re all beautiful, fantastic studies — but all focused on their one little corner: this molecule, this brain region, this function, this map.” The HBP would integrate these discoveries, he said, and create models to explore how neural circuits are organized, and how they give rise to behaviour and cognition — among the deepest mysteries in neuroscience. Ultimately, said Markram, the HBP would even help researchers to grapple with disorders such as Alzheimer’s disease. “If we don’t have an integrated view, we won’t understand these diseases,” he declared.

 

The Human Brain Project is one of the two winner projects of the Future and Emerging Technologies Flagship competition launched by the European Commission, the other one being the “Graphene” project led by Swedish theoretical physicist Jari Kinaret.
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Dataset of 13 billion clicks available | Center for Complex Networks and Systems Research

Dataset of 13 billion clicks available | Center for Complex Networks and Systems Research | Social Foraging | Scoop.it

To foster the study of the structure and dynamics of Web traffic networks, we are making available to the research community a large Click Dataset of about 13 billion HTTP requests collected at Indiana University. During about seven months of collection in 2006-2007, our system generated data at a rate of about 60 million requests per day, or about 30 GB/day of raw data. We hope that this data will help develop a better understanding of user behavior online and create more realistic models of Web traffic. The potential applications of this data include improved designs for networks, sites, and server software; more accurate forecasting of traffic trends; classification of sites based on the patterns of activity they inspire; and improved ranking algorithms for search results.


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Fil Menczer's comment, January 31, 2013 9:24 AM
Actually it turns out the dataset has 53+ billion records and it spans until 2010.
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Storing Digital Data in DNA

Storing Digital Data in DNA | Social Foraging | Scoop.it

Scientists have stored audio and text on fragments of DNA and then retrieved them with near-perfect fidelity—a technique that eventually may provide a way to handle the overwhelming data of the digital age.

 

The scientists encoded in DNA—the recipe of life—an audio clip of Martin Luther King Jr.'s "I Have a Dream" speech, a photograph, a copy of Francis Crick and James Watson's famous "double helix" scientific paper on DNA from 1953 and Shakespeare's 154 sonnets. They later were able to retrieve them with 99.99% accuracy.

 

The experiment was reported Wednesday in the journal Nature.

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Dynamic Finite Size Effects in Spiking Neural Networks

We investigate the dynamics of a deterministic finite-sized network of synaptically coupled spiking neurons and present a formalism for computing the network statistics in a perturbative expansion. The small parameter for the expansion is the inverse number of neurons in the network. The network dynamics are fully characterized by a neuron population density that obeys a conservation law analogous to the Klimontovich equation in the kinetic theory of plasmas. The Klimontovich equation does not possess well-behaved solutions but can be recast in terms of a coupled system of well-behaved moment equations, known as a moment hierarchy. The moment hierarchy is impossible to solve but in the mean field limit of an infinite number of neurons, it reduces to a single well-behaved conservation law for the mean neuron density. For a large but finite system, the moment hierarchy can be truncated perturbatively with the inverse system size as a small parameter but the resulting set of reduced moment equations that are still very difficult to solve. However, the entire moment hierarchy can also be re-expressed in terms of a functional probability distribution of the neuron density. The moments can then be computed perturbatively using methods from statistical field theory. Here we derive the complete mean field theory and the lowest order second moment corrections for physiologically relevant quantities. Although we focus on finite-size corrections, our method can be used to compute perturbative expansions in any parameter.

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How Sensitive Is the Human Visual System to the Local Statistics of Natural Images?

How Sensitive Is the Human Visual System to the Local Statistics of Natural Images? | Social Foraging | Scoop.it

A key hypothesis in sensory system neuroscience is that sensory representations are adapted to the statistical regularities in sensory signals and thereby incorporate knowledge about the outside world. Supporting this hypothesis, several probabilistic models of local natural image regularities have been proposed that reproduce neural response properties. Although many such physiological links have been made, these models have not been linked directly to visual sensitivity. Previous psychophysical studies of sensitivity to natural image regularities focus on global perception of large images, but much less is known about sensitivity to local natural image regularities. We present a new paradigm for controlled psychophysical studies of local natural image regularities and compare how well such models capture perceptually relevant image content. To produce stimuli with precise statistics, we start with a set of patches cut from natural images and alter their content to generate a matched set whose joint statistics are equally likely under a probabilistic natural image model. The task is forced choice to discriminate natural patches from model patches. The results show that human observers can learn to discriminate the higher-order regularities in natural images from those of model samples after very few exposures and that no current model is perfect for patches as small as 5 by 5 pixels or larger. Discrimination performance was accurately predicted by model likelihood, an information theoretic measure of model efficacy, indicating that the visual system possesses a surprisingly detailed knowledge of natural image higher-order correlations, much more so than current image models. We also perform three cue identification experiments to interpret how model features correspond to perceptually relevant image features.

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Embracing Emergence: How Collective Impact Addresses Complexity (SSIR)

Embracing Emergence: How Collective Impact Addresses Complexity (SSIR) | Social Foraging | Scoop.it

Organizations around the world have begun to see collective impact as a new and more effective process for social change. They have grasped the difference our past articles emphasized between the isolated impact of working for change through a single organization versus a highly structured cross-sector coalition. Yet, even as practitioners work toward the five conditions of collective impact we described earlier, many participants are becoming frustrated in their efforts to move the needle on their chosen issues. (See “The Five Conditions of Collective Impact,” below.)

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SPF-GMKL: generalized multiple kernel learning with a million kernel

Multiple Kernel Learning (MKL) aims to learn the kernel in an SVM from training data. Many MKL formulations have been proposed and some have proved effective in certain applications. Nevertheless, as MKL is a nascent field, many more formulations need to be developed to generalize across domains and meet the challenges of real world applications. However, each MKL formulation typically necessitates the development of a specialized optimization algorithm.The lack of an efficient, general purpose optimizer capable of handling a wide range of formulations presents a significant challenge to those looking to take MKL out of the lab and into the real world.

 

This problem was somewhat alleviated by the development of the Generalized Multiple Kernel Learning (GMKL) formulation which admits fairly general kernel parameterizations and regularizers subject to mild constraints. However, the projected gradient descent GMKL optimizer is inefficient as the computation of the step size and a reasonably accurate objective function value or gradient direction are all expensive. We overcome these limitations by developing a Spectral Projected Gradient (SPG) descent optimizer which: a) takes into account second order information in selecting step sizes; b) employs a non-monotone step size selection criterion requiring fewer function evaluations; c) is robust to gradient noise, and d) can take quick steps when far away from the optimum.

 

We show that our proposed SPG-GMKL optimizer can be an order of magnitude faster than projected gradient descent on even small and medium sized datasets. In some cases, SPG-GMKL can even outperform state-of-the-art specialized optimization algorithms developed for a single MKL formulation. Furthermore, we demonstrate that SPG-GMKL can scale well beyond gradient descent to large problems involving a million kernels or half a million data points. Our code and implementation are available publically.

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Big Data Apps: The Ultimate in Conversion Optimization

Big Data Apps: The Ultimate in Conversion Optimization | Social Foraging | Scoop.it

Big data is all the rage right now. Companies like Amazon and Facebook are finding innovative ways to turn massive amounts of data into strategic marketing decisions. Based on the amount of news coverage big data gets these days and the investments organizations have been making (see below), it is clear that big data is not simply a buzzword that will be dead next year.

 

With all the hype, it is important to start thinking about what this means for the non-Amazon’s of the world. As a search marketer, I work with clients that are still facing challenges in dealing with “small” data. And now they have to tackle big data?

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New approach to 'spell checking' gene sequences

New approach to 'spell checking' gene sequences | Social Foraging | Scoop.it

A PhD student from CSIRO and the University of Queensland has found a better way to 'spell check' gene sequences and help biologists better understand the natural world.

 

The student, Lauren Bragg, has contributed to the May issue of the journal Nature Methods highlighting her new approach and its software implementation called Acacia.

 

Acacia analyses the output of next-generation gene sequencing instruments which read the four-letter alphabet of As, Cs, Ts and Gs -- the 'bases' that code for DNA and spell out the genes of different living organisms. Acacia specifically applies to important parts of microbe genes called amplicons.

Just as a computer spell checker finds typing errors in words, so Acacia finds errors in the DNA code of amplicon sequences produced during gene sequencing.

 

Acacia shows clear improvements over the two error-correction tools currently used by biologists for amplicon sequences and it's easier for biologists to use.

Ms Bragg's development of Acacia is part of the field of bioinformatics, a blend of computer science, statistics and biology. Despite her surname, however, she is modest about her achievements.

 

"It's exciting to be published in a journal like Nature Methods but I get more satisfaction from hearing how my software is helping biologists fix sequencing errors." she said.

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UQ_Alumni_CR's curator insight, April 18, 2013 9:08 PM

Lauren Bragg, when does she graduate?

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Maximizing the Information Content of Experiments in Systems Biology

Maximizing the Information Content of Experiments in Systems Biology | Social Foraging | Scoop.it

Our understanding of most biological systems is in its infancy. Learning their structure and intricacies is fraught with challenges, and often side-stepped in favour of studying the function of different gene products in isolation from their physiological context. Constructing and inferring global mathematical models from experimental data is, however, central to systems biology. Different experimental setups provide different insights into such systems. Here we show how we can combine concepts from Bayesian inference and information theory in order to identify experiments that maximize the information content of the resulting data. This approach allows us to incorporate preliminary information; it is global and not constrained to some local neighbourhood in parameter space and it readily yields information on parameter robustness and confidence. Here we develop the theoretical framework and apply it to a range of exemplary problems that highlight how we can improve experimental investigations into the structure and dynamics of biological systems and their behavior.

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The evolutionary origins of modularity

A central biological question is how natural organisms are so evolvable (capable of quickly adapting to new environments). A key driver of evolvability is the widespread modularity of biological networks—their organization as functional, sparsely connected subunits—but there is no consensus regarding why modularity itself evolved. Although most hypotheses assume indirect selection for evolvability, here we demonstrate that the ubiquitous, direct selection pressure to reduce the cost of connections between network nodes causes the emergence of modular networks. Computational evolution experiments with selection pressures to maximize network performance and minimize connection costs yield networks that are significantly more modular and more evolvable than control experiments that only select for performance. These results will catalyse research in numerous disciplines, such as neuroscience and genetics, and enhance our ability to harness evolution for engineering purposes.

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Swarm Creativity:Competitive Advantage through Collaborative Innovation Networks

Swarm Creativity:Competitive Advantage through Collaborative Innovation Networks | Social Foraging | Scoop.it

Swarm Creativity introduces a powerful new concept-Collaborative Innovation Networks, or COINs. Its aim is to make the concept of COINs as ubiquitous among business managers as any methodology to enhance quality and competitive advantage. The difference though is that COINs are nothing like other methodologies. A COIN is a cyberteam of self-motivated people with a collective vision, enabled by technology to collaborate in achieving a common goal--n innovation-by sharing ideas, information, and work. It is no exaggeration to state that COINs are the most productive engines of innovation ever. COINs have been around for hundreds of years. Many of us have already been a part of one without knowing it. What makes COINs so relevant today, though is that the concept has reached its tipping point-thanks to the Internet and the World Wide Web. This book explores why COINS are so important to business success in the new century. It explains the traits that characterize COIN members and COIN behavior. It makes the case for why businesses ought to be rushing to uncover their COINs and nurture them, and provides tools for building organizations that are more creative, productive and efficient by applying principles of creative collaboration, knowledge sharing and social networking. Through real-life examples in several business sectors, the book shows how to leverage COINs to develop successful products in R & D, grow better customer relationships, establish better project management, and build higher-performing teams. In short, this book answers four key questions: Why are COINs better at innovation? What are the key elements of COINs? Who are the people that participate in COINs and how do they become members? And how does an organization transform itself into a Collaborative Innovation Network?

 

http://www.tlu.ee/~kpata/uusmeedia/Swarm%20Creativity_Competitive%20Advantage%20through%20Collaborative%20Innovation%20Networks.pdf

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luiy's curator insight, January 30, 2013 11:34 AM

CONTENTS


Introduction: At the Tipping Point 3


1 COINs and Their Benefits 9
2 Collaborative Innovation through Swarm Creativity 19
3 The DNA of COINS: Creativity, Collaboration,
and Communication 49
4 Ethical Codes in Small Worlds 71
5 Real-Life Examples: Lessons Learned from COINs 91
6 COINs and Communications Technology 115
appendixes 125
A Collaborative Knowledge Networks (CKNs) 127
B Temporal Communication Flow Analysis (TeCFlow) 141
C Knowledge Flow Optimization (KFO) 173
Notes 193
References 199
Additional Resources 203
Index 209

 

http://www.tlu.ee/~kpata/uusmeedia/Swarm%20Creativity_Competitive%20Advantage%20through%20Collaborative%20Innovation%20Networks.pdf

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Highly Efficient Light-Trapping Structure Design Inspired By Natural Evolution

Highly Efficient Light-Trapping Structure Design Inspired By Natural Evolution | Social Foraging | Scoop.it

Recent advances in nanophotonic light trapping open up the new gateway to enhance the absorption of solar energy beyond the so called Yablonovitch Limit. It addresses the urgent needs in developing low cost thin-film solar photovoltaic technologies. However, current design strategy mainly relies on the parametric approach that is subject to the predefined topological design concepts based on physical intuition. Incapable of dealing with the topological variation severely constrains the design of optimal light trapping structure. Inspired by natural evolution process, here we report a design framework driven by topology optimization based on genetic algorithms to achieve a highly efficient light trapping structure. It has been demonstrated that the optimal light trapping structures obtained in this study exhibit more than 3-fold increase over the Yablonovitch Limit with the broadband absorption efficiency of 48.1%, beyond the reach of intuitive designs.

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Diet, parental behavior and preschool can boost children's IQ

Diet, parental behavior and preschool can boost children's IQ | Social Foraging | Scoop.it

Supplementing children's diets with fish oil, enrolling them in quality preschool, and engaging them in interactive reading all turn out to be effective ways to raise a young child's intelligence, according to a new report published in Perspectives on Psychological Science, a journal of the Association for Psychological Science.

 

Using a technique called meta-analysis, a team led by John Protzko, a doctoral student at the NYU Steinhardt School of Culture, Education, and Human Development, combined the findings from existing studies to evaluate the overall effectiveness of each type of intervention. In collaboration with NYU Steinhardt professors Joshua Aronson and Clancy Blair, leaders in the field of intelligence, Protzko analyzed the best available studies involving samples of children from birth and kindergarten from their newlyassembled "Database of Raising Intelligence."

 

"Our aim in creating this database is to learn what works and what doesn't work to raise people's intelligence," said Protzko. "For too long, findings have been disconnected and scattered throughout a wide variety of journals. The broad consensus about what works is founded on only two or three very high-profile studies."

 

All of the studies in this database rely on a normal population (participants without clinical diagnoses of intellectual disabilities), focus on interventions that are sustained over time, use widely accepted measures of intelligence, and, most importantly, are randomly controlled trials (participants selected at random to receive one of the interventions).

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Social norms guide internet behaviour

Social norms guide internet behaviour | Social Foraging | Scoop.it

"Most people think if you are not an engineer you have nothing to say about technology", regrets social scientist Suvi Silfverberg, "I think it's quite the opposite: as a technology user you have a lot to contribute."

 

In her PhD thesis Silfverberg discusses how Facebook, Foursqare, LinkedIn and Last.fm user profiles relate to self and identity. She is collecting her data from interviews as well as from real-life and online focus groups.

 

"People keep their online profiles as products; information is manipulated in order to sell a self-chosen concept of themselves," states Silfverberg. "But this is not it yet," she continues. "Quite like cultural norms influence the way we speak, our internet behaviour is determined by norms, too. Some of these norms support what we do on the net, some restrict our behaviour."

 

A female Facebook user might, for instance, avoid publishing an attractive photo of herself in a bikini because she expects criticism. A Last.fm user might feel forced to listen to a variety of music considered good taste just to mask his liking for a cheesy song. "As a consequence," concludes Silfverberg, "we all start behaving similarly, which continuously re-enforces these norms."

 

A set of unwritten social rules have developed for each social media platform. "Finns for example do not appreciate the sharing of too much content, or obviously tuning your profile to put yourself into a favourable light. This 'pursuit of authenticity' also applies to Last.fm where the user is expected to stay true to their taste and to really listen to the music they play on their computer", summarizes Silfverberg.

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Harry Madigan's curator insight, October 4, 2014 2:56 AM

A very very interesting article, not directly related to my topic/ criteria, however it explores compelling theories and ideas such as ""People keep their online profiles as products; information is manipulated in order to sell a self-chosen concept of themselves,"


what i gathered from this article was that individuals are hesitating to share, post and write certain things online due to the fear of being placed in a negative light. 

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Darwin’s extra sense: How mathematics is revolutionizing biology

Darwin’s extra sense: How mathematics is revolutionizing biology | Social Foraging | Scoop.it

“Darwin’s Extra Sense,” a new video produced by SFI External Professor Dan Rockmore and collaborators, explores the ways applied mathematics is opening doors to astonishing insights in the life sciences – from evolutionary biology to protein folding and brain science.

 

Watch “Darwin’s Extra Sense” here (45 minutes)

 

“The field of biology had taken awhile for quantitative efforts to enter it, and now it has been truly transformed,” says Rockmore, a professor of mathematics and computer science at Dartmouth College. He produced the film in collaboration with filmmakers Wendy Conquest and Bob Drake, with financial support from the National Science Foundation and SFI.

 

The film tells the story of how the mathematical articulation of heredity by Gregor Mendel and others saved Charles Darwin’s initially flawed theory of evolution theory.

 

Darwin is quoted on his inability to apply math rigorously to his research: “I deeply regretted that I did not proceed far enough at least to understand something of the great leading principles of mathematics, for men thus endowed seem to have an extra sense.”

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The Human Functional Brain Network Demonstrates Structural and Dynamical Resilience to Targeted Attack

The Human Functional Brain Network Demonstrates Structural and Dynamical Resilience to Targeted Attack | Social Foraging | Scoop.it

In recent years, the field of network science has enabled researchers to represent the highly complex interactions in the brain in an approachable yet quantitative manner. One exciting finding since the advent of brain network research was that the brain network can withstand extensive damage, even to highly connected regions. However, these highly connected nodes may not be the most critical regions of the brain network, and it is unclear how the network dynamics are impacted by removal of these key nodes. This work seeks to further investigate the resilience of the human functional brain network. Network attack experiments were conducted on voxel-wise functional brain networks and region-of-interest (ROI) networks of 5 healthy volunteers. Networks were attacked at key nodes using several criteria for assessing node importance, and the impact on network structure and dynamics was evaluated. The findings presented here echo previous findings that the functional human brain network is highly resilient to targeted attacks, both in terms of network structure and dynamics.

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Cooperative societies in three- dimensional space: On the origins of aggregations, flocks, and schools, with special reference to dolphins and fish

Cooperative societies in three- dimensional space: On the origins of aggregations, flocks, and schools, with special reference to dolphins and fish | Social Foraging | Scoop.it

 

In three-dimensional open space habits, and to a lesser degree open terrestrial habitats, cooperative groupings of animals have repeatedly evolved. These cooperative systems have been observed in a wide variety of animal taxa, ranging from sea urchins to cetaceans. Various attempts have been made to relate the origins of such patterns to kin or altruism theory. An evolutionary stable strategy appears to be involved.

 

We propose a graded series of group structures of increasing complexity by means of which three-dimensional groupings could have evolved without recourse to either group selection or even necessarily kin selection or reciprocal altruism. These structures are asocial and social aggregations, and polarized schools. Social aggregations and polarized schools allow cooperative feeding and avoidance of predation. They confer three predation-related advantages over living alone for animals in open environments: (1) the dilution effect of large prey numbers relative to those of predators, (2) the encounter effect, which provides some protection from searching predators, and (3) the confusion effect by means of which visual tracking by a predator is confounded. We suggest that the gaze stabilization system of the visual system is involved in the most advanced version of the confusion effect.

 

In polarized schools members sense and react to each other, forming a sensory integration system (SIS). This system allows detection and transmission of information across a school, flock, or herd in three dimensions.

Because members watch beyond their immediate neighbors the transmission of such group reactions can greatly exceed the reaction speed of individual members, or any predator. Because the confusion effect and the SIS depend upon uniformity of behavior the polarized school is uncommonly difficult and perhaps impossible to cheat against. We perceive this as a key factor in the establishment of the evolutionarily stable strategy of schooling.

 

Polarized schools and aggregations are considered as the extremes of a behavioral continuum. Because in daytime the polarized school is a safer place to be and because the aggregation allows more freedom of movement for such activities as food finding, groups in open space oscillate between the these extremes during varying levels of predation.

 

The social complexity of fish schools seems modest whereas dolphin schools show the complexities of fairly typical mammalian organization. Occupancy of open space by both oceanic dolphins and schooling fish seems to have fostered promiscuous mating. In both open water fish and mammals elements of a cooperative disposition occur, which involves both cooperation and suppression of some aspects of individuality. Such dispositional elements allow the automatic support of a cooperative society. Dolphin schools, which during daytime rest or danger react like fish schools, express typical mammalian organization at other times. Dolphin echolocation has probably allowed the expression of mammalian behavior patterns at sea because it confers a major advantage over shark predators. The expression of mammalian social complexity may have required both kin and reciprocal altruistic patterns in different species.

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Cells 'flock' to heal wounds: Researchers analyze physics of epithelial cell cooperation

Cells 'flock' to heal wounds: Researchers analyze physics of epithelial cell cooperation | Social Foraging | Scoop.it

Like flocks of birds, cells coordinate their motions as they race to cover and ultimately heal wounds to the skin. How that happens is a little less of a mystery today.

 

Researchers once thought only the cells at the edge of a growing patch of wounded skin were actively moving while dividing cells passively filled in the middle. But that's only part of the picture. Rice University physicist Herbert Levine and his colleagues have discovered that the process works much more efficiently if highly activated cells in every part of the patch exert force as they pull their neighbors along.

 

There's a need to understand how cells cooperate to protect the site of a wound in the hours and days after injury, said Levine, who has introduced the first iteration of a computer model to analyze the two-dimensional physics of epithelial sheets. He hopes it will give new insight into a process with long-term implications not only for healing but also for understanding cancer, a prime motivator in his research since joining Rice under a grant from the Cancer Research and Prevention Institute of Texas.

 

A paper on the research by Levine, based at Rice University's BioScience Research Collaborative, and colleagues at the University of California at San Diego and in Germany and France appears January 23 in the Proceedings of the National Academy of Science.

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Big Data Apps: The Ultimate in Conversion Optimization

Big Data Apps: The Ultimate in Conversion Optimization | Social Foraging | Scoop.it

Big data is all the rage right now. Companies like Amazon and Facebook are finding innovative ways to turn massive amounts of data into strategic marketing decisions. Based on the amount of news coverage big data gets these days and the investments organizations have been making (see below), it is clear that big data is not simply a buzzword that will be dead next year.

 

With all the hype, it is important to start thinking about what this means for the non-Amazon’s of the world. As a search marketer, I work with clients that are still facing challenges in dealing with “small” data. And now they have to tackle big data?

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Computer, electrical engineers working to help biologists cope with big data

Computer, electrical engineers working to help biologists cope with big data | Social Foraging | Scoop.it

Liang Dong held up a clear plastic cube, an inch or so across, just big enough to hold 10 to 20 tiny seeds.

 

Using sophisticated sensors and software, researchers can precisely control the light, temperature, humidity and carbon dioxide inside that cube.

 

Dong -- an Iowa State University assistant professor of electrical and computer engineering, of chemical and biological engineering and an associate of the U.S. Department of Energy's Ames Laboratory -- calls it a "microsystem instrument." Put hundreds of those cubes together and researchers can simultaneously grow thousands of seeds and seedlings in different conditions and see what happens. How, for example, do the plants react when it is hot and dry? Or carbon dioxide levels change? Or light intensity is adjusted very slightly?

 

The instrument designed and built by Dong's research group will keep track of all that by using a robotic arm to run a camera over the cubes and take thousands of images of the growing seeds and seedlings.

 

Plant scientists will use the images to analyze the plants' observable characteristics -- the leaf color, the root development, the shoot size. All those observations are considered a plant's phenotype. And while plant scientists understand plant genetics very well, Dong said they don't have a lot of data about how genetics and environment combine to influence phenotype.

 

Dong's instrument will provide researchers with lots of data -- too much for scientists to easily sort and analyze. That's a problem known as big data. And it's increasingly common in the biological sciences.

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Steven Jackson, MBA's curator insight, January 26, 2013 9:38 PM

Too much data to easily sort and analyze?  You've could have Big Data.