Thursday April 26, 2018

Coffee can predict Parkinson’s disease

The team involved 108 people who had Parkinson's disease for an average of about six years and 31 people of the same age who did not have the disease and consumed about two cups of coffee per day

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Parkinson’s disease is named after Dr James Parkinson (1755-1824), the doctor that first identified the condition. Wikimedia commons
Parkinson’s disease is named after Dr James Parkinson (1755-1824), the doctor that first identified the condition. Wikimedia commons
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A neurodegenerative disorder which leads to progressive deterioration of motor function due to loss of dopamine-producing brain cells. Yes, that’s Parkinson’s disease. Quite horrifying, isn’t it?

However, there maybe a chance of predicting it.

The way your body metabolises your cup of coffee each morning may determine your chances of developing Parkinson’s disease.

The reason that Parkinson’s disease develops is not known. Wikimedia commons
The reason that Parkinson’s disease develops is not known. Wikimedia commons

Findings

  • People with Parkinson’s disease had significantly lower levels of caffeine in their blood than people without the disease, even if they consumed the same amount of caffeine.
  • Thus, testing the level of caffeine in the blood may provide a simple way to aid the diagnosis of Parkinson’s disease, the researchers said.

“Previous studies have shown a link between caffeine and a lower risk of developing Parkinson’s disease, but we haven’t known much about how caffeine metabolises within the people with the disease,” said Shinji Saiki, MD at the Juntendo University School of Medicine in Tokyo.

“If these results can be confirmed, they would point to an easy test for early diagnosis of Parkinson’s, possibly even before symptoms are appearing,” added David G. Munoz, MD, at the University of Toronto.

“This is important because Parkinson’s disease is difficult to diagnose, especially at the early stages,” Munoz noted.

The main symptoms of Parkinson’s disease are tremor, slowness of movement (bradykinesia) and muscle stiffness or rigidity. Wikimedia commons
The main symptoms of Parkinson’s disease are tremor, slowness of movement (bradykinesia) and muscle stiffness or rigidity. Wikimedia commons

Methodology

  • The team involved 108 people who had Parkinson’s disease for an average of about six years and 31 people of the same age who did not have the disease and consumed about two cups of coffee per day.
  • Their blood was tested for caffeine and for 11 byproducts the body makes as it metabolises caffeine. They were also tested for mutations in genes that can affect caffeine metabolism.
  • The caffeine level was an average of 79 picomoles per 10 microliters for people without Parkinson’s disease, compared to 24 picomoles per 10 microliters for people with the disease.
  • However, there were no differences found in the caffeine-related genes between the two groups.

The study was published in journal Neurology. (IANS)

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Google AI can focus on individual speakers in a crowd

The visual signal not only improves the speech separation quality significantly in cases of mixed speech, but, importantly, it also associates the separated, clean speech tracks

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Google india launches 'Tz' to help people pay their utility bills. Wikimedia Commons
Google AI to identify speakers from crowd. Wikimedia Commons

Just as most smartphone cameras now allow users to focus on a single object among many, it may soon be possible to pick out individual voices in a crowd by suppressing all other sounds, thanks to a new Artificial Intelligence (AI) system developed by Google researchers.

This is an important development as computers as not as good as humans at focusing their attention on a particular person in a noisy environment. Known as the cocktail party effect, the capability to mentally “mute” all other voices and sounds comes natural to us humans.

Google has collaborated with getty images. Wikimedia Commons
Google AI will identify individual speakers now. Wikimedia Commons

However, automatic speech separation — separating an audio signal into its individual speech sources — remains a significant challenge for computers, Inbar Mosseri and Oran Lang, software engineers at Google Research, wrote in a blog post this week. In a new paper, the researchers presented a deep learning audio-visual model for isolating a single speech signal from a mixture of sounds such as other voices and background noise.

“In this work, we are able to computationally produce videos in which speech of specific people is enhanced while all other sounds are suppressed,” Mosseri and Lang said. The method works on ordinary videos with a single audio track, and all that is required from the user is to select the face of the person in the video they want to hear, or to have such a person be selected algorithmically based on context.

Also Read: Want To Know What Facebook, Google Know About You?

The researchers believe this capability can have a wide range of applications, from speech enhancement and recognition in videos, through video conferencing, to improved hearing aids, especially in situations where there are multiple people speaking. “A unique aspect of our technique is in combining both the auditory and visual signals of an input video to separate the speech,” the researchers said.

google
This will also help in speech enhancement . VOA

“Intuitively, movements of a person’s mouth, for example, should correlate with the sounds produced as that person is speaking, which in turn can help identify which parts of the audio correspond to that person,” they explained.

The visual signal not only improves the speech separation quality significantly in cases of mixed speech, but, importantly, it also associates the separated, clean speech tracks with the visible speakers in the video, the researchers said. IANS