Sunday September 22, 2019

This is How Your Brain Senses an itch

The research team was then able to show that the NPY neurotransmitter controls the level of Y1 neuron excitability; in other words, NPY signalling acts as a kind of thermostat to control our sensitivity to light touch

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Haryana, School, Brain

Researchers have discovered how neurons in the spinal cord help transmit itch signals to the brain.

Their findings, published in the Journal of the Mechanical Behaviour of Biomedical Materials, will help contribute to a better understanding of itch and could lead to the invention of new drugs to treat chronic itch, which occurs in such conditions as eczema, diabetes and even some cancers.

“The takeaway is that this mechanical itch sensation is distinct from other forms of touch and it has this specialised pathway within the spinal cord,” said Martyn Goulding, Professor at the Salk Institute in California.

The researchers had previously discovered a set of inhibitory neurons in the spinal cord that act like cellular brakes, keeping the mechanical itch pathway in the spinal cord turned off most of the time.

Without these neurons, which produce the neurotransmitter neuropeptide Y (NPY), the mechanical itch pathway is constantly on, causing chronic itch.

When the NPY inhibitory neurons are missing, neurons in the spinal cord that normally transmit light touch begin to act like an accelerator stuck in the “on” position.

alzheimer
FILE – Dr. William Burke goes over a PET brain scan, Aug. 14, 2018, at Banner Alzheimers Institute in Phoenix, Arizona. (Representational image). VOA

The research team then identified a candidate for these “light touch neurons,” a population of excitatory neurons in the spinal cord that express the receptor for NPY, the so-called Y1 spinal neurons.

To test whether these neurons were indeed acting as an accelerator, they undertook an experiment that involved selectively getting rid of both the NPY “brake” and Y1 “accelerator” neurons.

Without Y1 neurons, the mice didn’t scratch, even in response to light-touch stimuli that normally make them scratch.

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Moreover, when the researchers gave the animals drugs that activated the Y1 neurons, the mice scratched spontaneously even in the absence of any touch stimuli.

The research team was then able to show that the NPY neurotransmitter controls the level of Y1 neuron excitability; in other words, NPY signalling acts as a kind of thermostat to control our sensitivity to light touch.

While Y1 neurons transmit the itch signal in the spinal cord, other neurons are thought to be responsible for mediating the final response in the brain but more research is needed to continue mapping out the full pathway, according to the researchers. (IANS)

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Researchers Develop New Algorithm to Identify Cyber-bullies on Twitter

“In a nutshell, the algorithms ‘learn’ how to tell the difference between bullies and typical users by weighing certain features as they are shown more examples,” said Blackburn

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FILE - A man reads tweets on his phone in front of a displayed Twitter logo. VOA

Researchers have developed machine learning algorithms which can identify bullies and aggressors on Twitter with 90 per cent accuracy.

For the study published in the journal Transactions on the Web, the research team analysed the behavioural patterns exhibited by abusive Twitter users and their differences from other users.

“We built crawlers — programs that collect data from Twitter via variety of mechanisms,” said study researcher Jeremy Blackburn from Binghamton University in the US.

“We gathered tweets of Twitter users, their profiles, as well as (social) network-related things, like who they follow and who follows them,” Blackburn said.

The researchers then performed natural language processing and sentiment analysis on the tweets themselves, as well as a variety of social network analyses on the connections between users.

twitter, white swan, suicide, awareness
Twitter is a social media app that encourages short tweets and brief conversations. Pixabay

They developed algorithms to automatically classify two specific types of offensive online behaviour, i.e. cyber-bullying and cyber-aggression.

The algorithms were able to identify abusive users — who engage in harassing behaviour like those who send death threats or make racist remarks — on Twitter with 90 per cent accuracy.

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“In a nutshell, the algorithms ‘learn’ how to tell the difference between bullies and typical users by weighing certain features as they are shown more examples,” said Blackburn.

“Our research indicates that machine learning can be used to automatically detect users that are cyber-bullies, and thus could help Twitter and other social media platforms remove problematic users,” Blackburn added. (IANS)