Archivio per 11 maggio 2016

11
Mag
16

MODULATION OF DNA CONFORMATION BY HEART-FOCUSED INTENTION

See on Scoop.itBounded Rationality and Beyond

Introduction Cell biologist Dr. Glen Rein had conceived of the idea that DNA would make a good target for testing healers’ ability to affect biological systems, since well established quantitative measures of DNA’s conformational state existed and it potentially offered a more stable and reliable system than cell or bacterial cultures. He had tested this model system with several healers by having them hold test tubes containing DNA while they attempted to create a healing environment, and had obtained some positive indicators that the conformational state of DNA changed when exposed to these environments. In late 1991, Dr. Rein accepted a position at the HeartMath Research Center with the intention of continuing these experiments in addition to a series of cell culture studies. We conducted a number of different experiments with DNA over the next year and a half. The first six months were primarily spent performing a series of control studies to insure the stability of the measurement system and refining the protocols. Doc Childre then added the element of intentionality to the protocols, which proved to be a key factor. Some of the key results of this series of studies were presented at research conferences and published in several conference proceedings.1-4 We have since received so many requests for the results of this research that we are now making a summary of our findings available in this brief report.

See on aipro.info

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11
Mag
16

MODULATION OF DNA CONFORMATION BY HEART-FOCUSED INTENTION

Introduction Cell biologist Dr. Glen Rein had conceived of the idea that DNA would make a good target for testing healers’ ability to affect biological systems, since well established quantitative measures of DNA’s conformational state existed and it potentially offered a more stable and reliable system than cell or bacterial cultures. He had tested this model system with several healers by having them hold test tubes containing DNA while they attempted to create a healing environment, and had obtained some positive indicators that the conformational state of DNA changed when exposed to these environments. In late 1991, Dr. Rein accepted a position at the HeartMath Research Center with the intention of continuing these experiments in addition to a series of cell culture studies. We conducted a number of different experiments with DNA over the next year and a half. The first six months were primarily spent performing a series of control studies to insure the stability of the measurement system and refining the protocols. Doc Childre then added the element of intentionality to the protocols, which proved to be a key factor. Some of the key results of this series of studies were presented at research conferences and published in several conference proceedings.1-4 We have since received so many requests for the results of this research that we are now making a summary of our findings available in this brief report.

Sourced through Scoop.it from: www.aipro.info

See on Scoop.itBounded Rationality and Beyond

11
Mag
16

The Ancient Origins of Consciousness: How the Brain Created Experience (by Todd E. Feinberg & Jon M. Mallatt)

See on Scoop.itCxBooks

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How is consciousness created? When did it first appear on Earth, and how did it evolve? What constitutes consciousness, and which animals can be said to be sentient? In this book, Todd Feinberg and Jon Mallatt draw on recent scientific findings to answer these questions – and to tackle the most fundamental question about the nature of consciousness: how does the material brain create subjective experience?

After assembling a list of the biological and neurobiological features that seem responsible for consciousness, and considering the fossil record of evolution, Feinberg and Mallatt argue that consciousness appeared much earlier in evolutionary history than is commonly assumed. About 520 to 560 million years ago, they explain, the great “Cambrian explosion” of animal diversity produced the first complex brains, which were accompanied by the first appearance of consciousness; simple reflexive behaviors evolved into a unified inner world of subjective experiences. From this they deduce that all vertebrates are and have always been conscious – not just humans and other mammals, but also every fish, reptile, amphibian, and bird. Considering invertebrates, they find that arthropods (including insects and probably crustaceans) and cephalopods (including the octopus) meet many of the criteria for consciousness. The obvious and conventional wisdom–shattering implication is that consciousness evolved simultaneously but independently in the first vertebrates and possibly arthropods more than half a billion years ago. Combining evolutionary, neurobiological, and philosophical approaches allows Feinberg and Mallatt to offer an original solution to the “hard problem” of consciousness.

See on amazon.com

11
Mag
16

Overconfidence in Political Behavior

See on Scoop.itBounded Rationality and Beyond

This paper studies, theoretically and empirically, the role of overconfidence in political behavior. Our model of overconfidence in beliefs predicts that overconfidence leads to ideological extremeness, increased voter turnout, and stronger partisan identification. The model also makes nuanced predictions about the patterns of ideology in society. These predictions are tested using unique data that measure the overconfidence and standard political characteristics of a nationwide sample of over 3,000 adults. Our numerous predictions find strong support in these data. In particular, we document that overconfidence is a substantively and statistically important predictor of ideological extremeness, voter turnout, and partisan identification.

http://www.columbia.edu/~po2205/papers/Ortoleva_Snowberg_Overconfidence.pdf

Alessandro Cerboni’s insight:
Share your insight

See on columbia.edu

11
Mag
16

Overconfidence in Political Behavior

This paper studies, theoretically and empirically, the role of overconfidence in political behavior. Our model of overconfidence in beliefs predicts that overconfidence leads to ideological extremeness, increased voter turnout, and stronger partisan identification. The model also makes nuanced predictions about the patterns of ideology in society. These predictions are tested using unique data that measure the overconfidence and standard political characteristics of a nationwide sample of over 3,000 adults. Our numerous predictions find strong support in these data. In particular, we document that overconfidence is a substantively and statistically important predictor of ideological extremeness, voter turnout, and partisan identification.

http://www.columbia.edu/~po2205/papers/Ortoleva_Snowberg_Overconfidence.pdf

Sourced through Scoop.it from: www.columbia.edu

Share your insight

See on Scoop.itBounded Rationality and Beyond

11
Mag
16

Combining complex networks and data mining: why and how

See on Scoop.itPapers

The increasing power of computer technology does not dispense with the need to extract meaningful information out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now commonly used in the scientific community: data mining and complex network theory. Not only do complex network analysis and data mining share the same general goal, that of extracting information from complex systems to ultimately create a new compact quantifiable representation, but they also often address similar problems too. In the face of that, a surprisingly low number of researchers turn out to resort to both methodologies. One may then be tempted to conclude that these two fields are either largely redundant or totally antithetic. The starting point of this review is that this state of affairs should be put down to contingent rather than conceptual differences, and that these two fields can in fact advantageously be used in a synergistic manner. An overview of both fields is first provided, some fundamental concepts of which are illustrated. A variety of contexts in which complex network theory and data mining have be used in a synergistic manner are then presented. Contexts in which the appropriate integration of complex networks metrics can lead to improved classification rates with respect to classical data mining algorithms and, conversely, contexts in which data mining can be used to tackle important issues in complex network theory applications are illustrated. Finally, ways to achieve a tighter integration between complex networks and data mining, and open lines of research are discussed.

Combining complex networks and data mining: why and how
M. Zanin, D. Papo, P. A. Sousa, E. Menasalvas, A. Nicchi, E. Kubik, S. Boccaletti

http://arxiv.org/abs/1604.08816

See on arxiv.org

11
Mag
16

Combining complex networks and data mining: why and how

The increasing power of computer technology does not dispense with the need to extract meaningful information out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now commonly used in the scientific community: data mining and complex network theory. Not only do complex network analysis and data mining share the same general goal, that of extracting information from complex systems to ultimately create a new compact quantifiable representation, but they also often address similar problems too. In the face of that, a surprisingly low number of researchers turn out to resort to both methodologies. One may then be tempted to conclude that these two fields are either largely redundant or totally antithetic. The starting point of this review is that this state of affairs should be put down to contingent rather than conceptual differences, and that these two fields can in fact advantageously be used in a synergistic manner. An overview of both fields is first provided, some fundamental concepts of which are illustrated. A variety of contexts in which complex network theory and data mining have be used in a synergistic manner are then presented. Contexts in which the appropriate integration of complex networks metrics can lead to improved classification rates with respect to classical data mining algorithms and, conversely, contexts in which data mining can be used to tackle important issues in complex network theory applications are illustrated. Finally, ways to achieve a tighter integration between complex networks and data mining, and open lines of research are discussed.

 

Combining complex networks and data mining: why and how
M. Zanin, D. Papo, P. A. Sousa, E. Menasalvas, A. Nicchi, E. Kubik, S. Boccaletti

http://arxiv.org/abs/1604.08816

Source: arxiv.org




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