# ‘tis But A Scratch | Python

Solar System: 5 Things To Know This Week

Our solar system is huge, so let us break it down for you. Here are 5 things to know this week: 

1. Make a Wish

The annual Leonids meteor shower is not known for a high number of “shooting stars” (expect as many as 15 an hour), but they’re usually bright and colorful. They’re fast, too: Leonids travel at speeds of 71 km (44 miles) per second, which makes them some of the fastest. This year the Leonids shower will peak around midnight on Nov. 17-18. The crescent moon will set before midnight, leaving dark skies for watching. Get more viewing tips HERE.

2. Back to the Beginning

Our Dawn mission to the dwarf planet Ceres is really a journey to the beginning of the solar system, since Ceres acts as a kind of time capsule from the formation of the asteroid belt. If you’ll be in the Washington DC area on Nov. 19, you can catch a presentation by Lucy McFadden, a co-investigator on the Dawn mission, who will discuss what we’ve discovered so far at this tiny but captivating world. Find out how to attend HERE. 

3. Keep Your Eye on This Spot

The Juno spacecraft is on target for a July 2016 arrival at the giant planet Jupiter. But right now, your help is needed. Members of the Juno team are calling all amateur astronomers to upload their telescopic images and data of Jupiter. This will help the team plan their observations. Join in HERE.

4. The Ice Volcanoes of Pluto

The more data from July’s Pluto flyby that comes down from the New Horizons spacecraft, the more interesting Pluto becomes. The latest finding? Possible ice volcanoes. Using images of Pluto’s surface to make 3-D topographic maps, scientists discovered that some mountains on Pluto, such as the informally named Piccard Mons and Wright Mons, had structures that suggested they could be cryovolcanoes that may have been active in the recent geological past.

5. Hidden Storm

Cameras aboard the Cassini spacecraft have been tracking an impressive cloud hovering over the south pole of Saturn’s moon Titan. But that cloud has turned out to be just the tip of the iceberg. A much more massive ice cloud system has been found lower in the stratosphere, peaking at an altitude of about 124 miles (200 kilometers).

Make sure to follow us on Tumblr for your regular dose of space: http://nasa.tumblr.com

Miss El Salvador

(Image caption: diagram of the research findings (Taken from article’s Table of Contents Image) bFGF is produced in the injured zone of the cerebral cortex. Ror2 expression is induced in some population of the astrocytes that receive the bFGF signal, restarting their proliferation by accelerating the progression of their cell cycle)

How brain tissue recovers after injury: the role of astrocytes

A research team led by Associate Professor Mitsuharu ENDO and Professor Yasuhiro MINAMI (both from the Department of Physiology and Cell Biology, Graduate School of Medicine, Kobe University) has pinpointed the mechanism underlying astrocyte-mediated restoration of brain tissue after an injury. This could lead to new treatments that encourage regeneration by limiting damage to neurons incurred by reduced blood supply or trauma. The findings were published on October 11 in the online version of GLIA.

When the brain is damaged by trauma or ischemia (restriction in blood supply), immune cells such as macrophages and lymphocytes dispose of the damaged neurons with an inflammatory response. However, an excessive inflammatory response can also harm healthy neurons.

Astrocytes are a type of glial cell*, and the most numerous cell within the human cerebral cortex. In addition to their supportive role in providing nutrients to neurons, studies have shown that they have various other functions, including the direct or active regulation of neuronal activities.

It has recently become clear that astrocytes also have an important function in the restoration of injured brain tissue. While astrocytes do not normally proliferate in healthy brains, they start to proliferate and increase their numbers around injured areas and minimize inflammation by surrounding the damaged neurons, other astrocytes, and inflammatory cells that have entered the damaged zone. Until now the mechanism that prompts astrocytes to proliferate in response to injury was unclear.

The research team focused on the fact that the astrocytes which proliferate around injured areas acquire characteristics similar to neural stem cells. The receptor tyrosine kinase Ror2, a cell surface protein, is highly expressed in neural stem cells in the developing brain. Normally the Ror2 gene is “switched off” within adult brains, but these findings showed that when the brain was injured, Ror2 was expressed in a certain population of the astrocytes around the injured area.

Ror2 is an important cell-surface protein that regulates the proliferation of neural stem cells, so the researchers proposed that Ror2 was regulating the proliferation of astrocytes around the injured areas. They tested this using model mice for which the Ror2 gene did not express in astrocytes. In these mice, the number of proliferating astrocytes after injury showed a remarkable decrease, and the density of astrocytes around the injury site was reduced. Using cultured astrocytes, the team analyzed the mechanism for activating the Ror2 gene, and ascertained that basic fibroblast growth factor (bFGF) can “switch on” Ror2 in some astrocytes.

This research showed that in injured brains, the astrocytes that show (high) expression of Ror2 induced by bFGF signal are primarily responsible for starting proliferation. bFGF is produced by different cell types, including neurons and astrocytes in the injury zone that have escaped damage. Among the astrocytes that received these bFGF signals around the injury zone, some express Ror2 and some do not. The fact that proliferating astrocytes after brain injury are reduced during aging raises the possibility that the population of astrocytes that can express Ror2 might decrease during aging, which could cause an increase in senile dementia. Researchers are aiming to clarify the mechanism that creates these different cell populations of astrocytes.

By artificially controlling the proliferation of astrocytes, in the future we can potentially minimize damage caused to neurons by brain injuries and establish a new treatment that encourages regeneration of damaged brain areas.

*Glial cell: a catch-all term for non-neuronal cells that belong to the nervous system. They support neurons in various roles.

Dynamic Network Visualization - Wars on Earth over time (1816-2001)

This dynamic network visualization shows a dynamic picture of the global war conflicts between 1816 and 2001. The network relationships indicate which country was in conflict with another country. In the first part of the video the network data was overlayed over a geographic world map to show global reach. The second part shows the pure network layout in 3D. The dynamic network analysis and animations were generated with the software Commetrix (www.commetrix.de) by M.Schulz and R.Hillmann of IKMResearch at TU Berlin.

The Paradoxical Commandments

The Paradoxical Commandments were written in 1968 by Dr. Kent M. Keith. Mother Theresa reffered to them often. People are illogical, unreasonable, and self-centered. Love them anyway.   If you do good, people will accuse you of selfish ulterior motives. Do good anyway.   If you are successful, you will win false friends and true enemies. Succeed anyway.   The good you do today will be forgotten tomorrow. Do good anyway.   Honesty and frankness make you vulnerable. Be honest and frank anyway.   The biggest men and women with the biggest ideas can be shot down by the smallest men and women with the smallest minds. Think big anyway.   People favor underdogs but follow only top dogs. Fight for a few underdogs anyway.   What you spend years building may be destroyed overnight. Build anyway.   People really need help but may attack you if you do help them. Help people anyway.   Give the world the best you have and you'll get kicked in the teeth. Give the world the best you have anyway. © Copyright Kent M. Keith 1968, renewed 2001

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Let’s learn Python!

print(“Hello World”)

Solar System: 5 Things To Know This Week

Our solar system is huge, so let us break it down for you. Here are 5 things to know this week: 

1. Make a Wish

The annual Leonids meteor shower is not known for a high number of “shooting stars” (expect as many as 15 an hour), but they’re usually bright and colorful. They’re fast, too: Leonids travel at speeds of 71 km (44 miles) per second, which makes them some of the fastest. This year the Leonids shower will peak around midnight on Nov. 17-18. The crescent moon will set before midnight, leaving dark skies for watching. Get more viewing tips HERE.

2. Back to the Beginning

Our Dawn mission to the dwarf planet Ceres is really a journey to the beginning of the solar system, since Ceres acts as a kind of time capsule from the formation of the asteroid belt. If you’ll be in the Washington DC area on Nov. 19, you can catch a presentation by Lucy McFadden, a co-investigator on the Dawn mission, who will discuss what we’ve discovered so far at this tiny but captivating world. Find out how to attend HERE. 

3. Keep Your Eye on This Spot

The Juno spacecraft is on target for a July 2016 arrival at the giant planet Jupiter. But right now, your help is needed. Members of the Juno team are calling all amateur astronomers to upload their telescopic images and data of Jupiter. This will help the team plan their observations. Join in HERE.

4. The Ice Volcanoes of Pluto

The more data from July’s Pluto flyby that comes down from the New Horizons spacecraft, the more interesting Pluto becomes. The latest finding? Possible ice volcanoes. Using images of Pluto’s surface to make 3-D topographic maps, scientists discovered that some mountains on Pluto, such as the informally named Piccard Mons and Wright Mons, had structures that suggested they could be cryovolcanoes that may have been active in the recent geological past.

5. Hidden Storm

Cameras aboard the Cassini spacecraft have been tracking an impressive cloud hovering over the south pole of Saturn’s moon Titan. But that cloud has turned out to be just the tip of the iceberg. A much more massive ice cloud system has been found lower in the stratosphere, peaking at an altitude of about 124 miles (200 kilometers).

Make sure to follow us on Tumblr for your regular dose of space: http://nasa.tumblr.com

Model sheds light on purpose of inhibitory neurons

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory have developed a new computational model of a neural circuit in the brain, which could shed light on the biological role of inhibitory neurons — neurons that keep other neurons from firing.

The model describes a neural circuit consisting of an array of input neurons and an equivalent number of output neurons. The circuit performs what neuroscientists call a “winner-take-all” operation, in which signals from multiple input neurons induce a signal in just one output neuron.

Using the tools of theoretical computer science, the researchers prove that, within the context of their model, a certain configuration of inhibitory neurons provides the most efficient means of enacting a winner-take-all operation. Because the model makes empirical predictions about the behavior of inhibitory neurons in the brain, it offers a good example of the way in which computational analysis could aid neuroscience.

The researchers presented their results at the conference on Innovations in Theoretical Computer Science. Nancy Lynch, the NEC Professor of Software Science and Engineering at MIT, is the senior author on the paper. She’s joined by Merav Parter, a postdoc in her group, and Cameron Musco, an MIT graduate student in electrical engineering and computer science.

For years, Lynch’s group has studied communication and resource allocation in ad hoc networks — networks whose members are continually leaving and rejoining. But recently, the team has begun using the tools of network analysis to investigate biological phenomena.

“There’s a close correspondence between the behavior of networks of computers or other devices like mobile phones and that of biological systems,” Lynch says. “We’re trying to find problems that can benefit from this distributed-computing perspective, focusing on algorithms for which we can prove mathematical properties.”

Artificial neurology

In recent years, artificial neural networks — computer models roughly based on the structure of the brain — have been responsible for some of the most rapid improvement in artificial-intelligence systems, from speech transcription to face recognition software.

An artificial neural network consists of “nodes” that, like individual neurons, have limited information-processing power but are densely interconnected. Data are fed into the first layer of nodes. If the data received by a given node meet some threshold criterion — for instance, if it exceeds a particular value — the node “fires,” or sends signals along all of its outgoing connections.

Each of those outgoing connections, however, has an associated “weight,” which can augment or diminish a signal. Each node in the next layer of the network receives weighted signals from multiple nodes in the first layer; it adds them together, and again, if their sum exceeds some threshold, it fires. Its outgoing signals pass to the next layer, and so on.

In artificial-intelligence applications, a neural network is “trained” on sample data, constantly adjusting its weights and firing thresholds until the output of its final layer consistently represents the solution to some computational problem.

Biological plausibility

Lynch, Parter, and Musco made several modifications to this design to make it more biologically plausible. The first was the addition of inhibitory “neurons.” In a standard artificial neural network, the values of the weights on the connections are usually positive or capable of being either positive or negative. But in the brain, some neurons appear to play a purely inhibitory role, preventing other neurons from firing. The MIT researchers modeled those neurons as nodes whose connections have only negative weights.

Many artificial-intelligence applications also use “feed-forward” networks, in which signals pass through the network in only one direction, from the first layer, which receives input data, to the last layer, which provides the result of a computation. But connections in the brain are much more complex. Lynch, Parter, and Musco’s circuit thus includes feedback: Signals from the output neurons pass to the inhibitory neurons, whose output in turn passes back to the output neurons. The signaling of the output neurons also feeds back on itself, which proves essential to enacting the winner-take-all strategy.

Finally, the MIT researchers’ network is probabilistic. In a typical artificial neural net, if a node’s input values exceed some threshold, the node fires. But in the brain, increasing the strength of the signal traveling over an input neuron only increases the chances that an output neuron will fire. The same is true of the nodes in the researchers’ model. Again, this modification is crucial to enacting the winner-take-all strategy.

In the researchers’ model, the number of input and output neurons is fixed, and the execution of the winner-take-all computation is purely the work of a bank of auxiliary neurons. “We are trying to see the trade-off between the computational time to solve a given problem and the number of auxiliary neurons,” Parter explains. “We consider neurons to be a resource; we don’t want too spend much of it.”

Inhibition’s virtues

Parter and her colleagues were able to show that with only one inhibitory neuron, it’s impossible, in the context of their model, to enact the winner-take-all strategy. But two inhibitory neurons are sufficient. The trick is that one of the inhibitory neurons — which the researchers call a convergence neuron — sends a strong inhibitory signal if more than one output neuron is firing. The other inhibitory neuron — the stability neuron — sends a much weaker signal as long as any output neurons are firing.

The convergence neuron drives the circuit to select a single output neuron, at which point it stops firing; the stability neuron prevents a second output neuron from becoming active once the convergence neuron has been turned off. The self-feedback circuits from the output neurons enhance this effect. The longer an output neuron has been turned off, the more likely it is to remain off; the longer it’s been on, the more likely it is to remain on. Once a single output neuron has been selected, its self-feedback circuit ensures that it can overcome the inhibition of the stability neuron.

Without randomness, however, the circuit won’t converge to a single output neuron: Any setting of the inhibitory neurons’ weights will affect all the output neurons equally. “You need randomness to break the symmetry,” Parter explains.

The researchers were able to determine the minimum number of auxiliary neurons required to guarantee a particular convergence speed and the maximum convergence speed possible given a particular number of auxiliary neurons.

Adding more convergence neurons increases the convergence speed, but only up to a point. For instance, with 100 input neurons, two or three convergence neurons are all you need; adding a fourth doesn’t improve efficiency. And just one stability neuron is already optimal.

But perhaps more intriguingly, the researchers showed that including excitatory neurons — neurons that stimulate, rather than inhibit, other neurons’ firing — as well as inhibitory neurons among the auxiliary neurons cannot improve the efficiency of the circuit. Similarly, any arrangement of inhibitory neurons that doesn’t observe the distinction between convergence and stability neurons will be less efficient than one that does.

Assuming, then, that evolution tends to find efficient solutions to engineering problems, the model suggests both an answer to the question of why inhibitory neurons are found in the brain and a tantalizing question for empirical research: Do real inhibitory neurons exhibit the same division between convergence neurons and stability neurons?

“This computation of winner-take-all is quite a broad and useful motif that we see throughout the brain,” says Saket Navlakha, an assistant professor in the Integrative Biology Laboratory at the Salk Institute for Biological Studies. “In many sensory systems — for example, the olfactory system — it’s used to generate sparse codes.”

“There are many classes of inhibitory neurons that we’ve discovered, and a natural next step would be to see if some of these classes map on to the ones predicted in this study,” he adds.

“There’s a lot of work in neuroscience on computational models that take into account much more detail about not just inhibitory neurons but what proteins drive these neurons and so on,” says Ziv Bar-Joseph, a professor of computer science at Carnegie Mellon University. “Nancy is taking a global view of the network rather than looking at the specific details. In return she gets the ability to look at some larger-picture aspects. How many inhibitory neurons do you really need? Why do we have so few compared to the excitatory neurons? The unique aspect here is that this global-scale modeling gives you a much higher-level type of prediction.”

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