Moving… Thinking of moving… Standing still!

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Brain-computer interfaces (BCI) work on the principle that measurable changes in electrical brain activity occur just by thinking about performing a task. Signals can be read, evaluated, and then converted into control signals via a machine learning system, which can then be used to operate a computer or a prosthesis. In a recently published study, researchers from the Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, the Public University of Navarre, and TU Berlin demonstrated that after just one hour’s training with a BCI significant changes could be detected in test subjects’ brains, meaning that training with the BCI also has direct repercussions on the neuronal structure and function of the brain. (1)

Move.

And you will change.

Think of moving.

And you will change.

We are so much consumed by our faith in reality that we cannot see the obvious.

We are too consumed looking up to reality and trying to comprehend it…

That we cannot see that reality is looking upon us to determine where it will go next…

Move.

And the cosmos will start moving.

Don’t you see?

Achilles will never reach the turtle.

It is the turtle which wants to be reached…

Hunger…

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Researchers discovers how hunger might make food tastier: Using optogenetic and chemogenetic techniques, researchers have identified brain circuits underlying hunger-induced changes in the preferences for sweet and aversive tastes in mice. These circuits involved Agouti-related peptide-expressing neurons, which projected to glutamate neurons in the lateral hypothalamus. From there, glutamate neurons projecting to the lateral septum increased sweetness preferences, and glutamate neurons projecting to the lateral habenula decreased sensitivity to aversive tastes. (1)

Humans are always hungry for life…

We are always hungry for knowledge.

But be careful.

Question whatever you wish.

For one day you might get it.

And you will never question whether there was anything to get in the first place…

Statistical significance. Not so… significant!

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In science, the success of an experiment is often determined by a measure called “statistical significance.” A result is considered to be “significant” if the difference observed in the experiment between groups (of people, plants, etc) would be very unlikely if no difference actually exists. The common cutoff for “very unlikely” is that you’d see a difference as big or bigger only 5 percent of the time if it wasn’t really there — a cutoff that might seem, at first sight, very strict.

Statistical significance has been used to draw a bright line between experimental success and failure. Achieving an experimental result with statistical significance often determines if a scientist’s paper gets published or if further research gets funded. That makes the measure far too important in deciding research priorities, statisticians say, and so it’s time to throw it in the trash.

More than 800 statisticians and scientists are calling for an end to judging studies by statistical significance in a comment published in Nature. An accompanying special issue of the American Statistician makes the manifesto crystal clear in its introduction: “‘statistically significant’ — don’t say it and don’t use it.” Science and statistics have never been so simple as to cater to convenient cutoffs. A P value, no matter how small, is just a probability. It doesn’t mean an experiment worked. And it doesn’t tell you if the difference in results between experimental groups is big or small. In fact, it doesn’t even say whether the difference is meaningful.

There is good reason to want to scrap statistical significance. But with so much research now built around the concept, it’s unclear how — or with what other measures — the scientific community could replace it. (1)

All science is based on the probability that something will happen.

But it is the improbable things which shape the cosmos.

The birth of the universe.

The death of a God.

The genesis of a human being…

People living for the wrong things.

People dying for the right ones.

Yes, it is a paradoxical world. Not a sane one.

Stop looking at the probable things.

Look at the clouds. Feel the sun behind them.

There is nothing more dull than the things which happen.

Predicting everything. Knowing it all.

The cornerstone of our civilization.

The cause of our demise.

Can you ever predict anything without knowing everything?

Can you ever know anything without already experiencing everything?

Start looking at the things which will never happen.

And right when you won’t be looking…

You will know they already did!

Knowledge. Destruction.

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Powerful DNA-sequencing techniques have spurred an avalanche of discoveries about ancient humans, but each one comes at a price: the partial destruction of the specimens from which the DNA was taken. Anthropologists Keolu Fox and John Hawks call for researchers to think harder about safeguarding. “Unless some ground rules are established, future scientists, armed with better, potentially less-invasive methods for extracting DNA from ancient samples could well look back on this era as a time of heedless destruction, fuelled by the relentless pressure to publish,” says Fox and Hawks. (1)

We should not be alarmed or surprised though.

Knowledge IS destruction.

Every time we understand something, we dissolve it into pieces.

Every time we get to know something, we forget something else.

The cosmos was once at our fingertips.

Until we tried to touch it.

And it became real…

Seeing better. And better. And better. Until we see nothing at all…

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A few years ago, a team of scientists at EPFL’s Laboratory of Nanoscale Biology, headed by Aleksandra Radenovic in the School of Engineering, developed an algorithm that can estimate a microscope’s resolution in just a few seconds based on a single image. The algorithm’s result indicates how closely a microscope is operating to its full potential. This could be particularly useful for the automated microscopes that have started appearing in research labs. The team’s findings have just been published in Nature Methods.

The scientists used Fourier’s transform as the basis for their algorithm, but they modified it so as to extract as much information as possible from a single image.

The results indicates how closely a microscope is operating to its full potential. The algorithm performs the calculation in just a few seconds and generates a single number. “Researchers can compare this number with the microscope’s maximum possible resolution to see whether the instrument can work even better or modify the experimental conditions and observe how the resolution evolves” says Adrien Descloux, the study’s lead author. (1)

We want to see better. We want to see everything.

So we magnify.

Until we see all the details.

And more.

And more.

And more!

Pushing it to the limit! To see everything!

Until we can distinguish nothing anymore!

Isn’t it funny? The more we analyze the cosmos the more we reach absolute zero. At the end, the point is a circle with zero radius. (source) At the end, in the midst of our greatest triumph, we will see nothing.

Ghosts casting shadows…

In a cosmos without any light…

Except the light we bring on our own…

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