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This Is What Happens to the Brain When You Give Up Sugar for Lent

Summary: Decided to cut back on sugary treats? A new article considers why it might not be such a sweet idea for all.

Source: The Conversation.

Anyone who knows me also knows that I have a huge sweet tooth. I always have. My friend and fellow graduate student Andrew is equally afflicted, and living in Hershey, Pennsylvania – the “Chocolate Capital of the World” – doesn’t help either of us.

But Andrew is braver than I am. Last year, he gave up sweets for Lent. I can’t say that I’m following in his footsteps this year, but if you are abstaining from sweets for Lent this year, here’s what you can expect over the next 40 days.

Sugar: natural reward, unnatural fix

In neuroscience, food is something we call a “natural reward.” In order for us to survive as a species, things like eating, having sex and nurturing others must be pleasurable to the brain so that these behaviours are reinforced and repeated.

Evolution has resulted in the mesolimbic pathway, a brain system that deciphers these natural rewards for us. When we do something pleasurable, a bundle of neurons called the ventral tegmental area uses the neurotransmitter dopamine to signal to a part of the brain called the nucleus accumbens. The connection between the nucleus accumbens and our prefrontal cortex dictates our motor movement, such as deciding whether or not to taking another bite of that delicious chocolate cake. The prefrontal cortex also activates hormones that tell our body: “Hey, this cake is really good. And I’m going to remember that for the future.”

Not all foods are equally rewarding, of course. Most of us prefer sweets over sour and bitter foods because, evolutionarily, our mesolimbic pathway reinforces that sweet things provide a healthy source of carbohydrates for our bodies. When our ancestors went scavenging for berries, for example, sour meant “not yet ripe,” while bitter meant “alert – poison!”

Fruit is one thing, but modern diets have taken on a life of their own. A decade ago, it was estimated that the average American consumed 22 teaspoons of added sugar per day, amounting to an extra 350 calories; it may well have risen since then. A few months ago, one expert suggested that the average Briton consumes 238 teaspoons of sugar each week.

Today, with convenience more important than ever in our food selections, it’s almost impossible to come across processed and prepared foods that don’t have added sugars for flavour, preservation, or both.

These added sugars are sneaky – and unbeknown to many of us, we’ve become hooked. In ways that drugs of abuse – such as nicotine, cocaine and heroin – hijack the brain’s reward pathway and make users dependent, increasing neuro-chemical and behavioural evidence suggests that sugar is addictive in the same way, too.

Sugar addiction is real

“The first few days are a little rough,” Andrew told me about his sugar-free adventure last year. “It almost feels like you’re detoxing from drugs. I found myself eating a lot of carbs to compensate for the lack of sugar.”

There are four major components of addiction: bingeing, withdrawal, craving, and cross-sensitisation (the notion that one addictive substance predisposes someone to becoming addicted to another). All of these components have been observed in animal models of addiction – for sugar, as well as drugs of abuse.

A typical experiment goes like this: rats are deprived of food for 12 hours each day, then given 12 hours of access to a sugary solution and regular chow. After a month of following this daily pattern, rats display behaviours similar to those on drugs of abuse. They’ll binge on the sugar solution in a short period of time, much more than their regular food. They also show signs of anxiety and depression during the food deprivation period. Many sugar-treated rats who are later exposed to drugs, such as cocaine and opiates, demonstrate dependent behaviours towards the drugs compared to rats who did not consume sugar beforehand.

Like drugs, sugar spikes dopamine release in the nucleus accumbens. Over the long term, regular sugar consumption actually changes the gene expression and availability of dopamine receptors in both the midbrain and frontal cortex. Specifically, sugar increases the concentration of a type of excitatory receptor called D1, but decreases another receptor type called D2, which is inhibitory. Regular sugar consumption also inhibits the action of the dopamine transporter, a protein which pumps dopamine out of the synapse and back into the neuron after firing.

Image shows candy sprinkles on a woman's lips.

There are four major components of addiction: bingeing, withdrawal, craving, and cross-sensitisation (the notion that one addictive substance predisposes someone to becoming addicted to another). All of these components have been observed in animal models of addiction – for sugar, as well as drugs of abuse. NeuroscienceNews.com image is adapted from the The Conversation article.

In short, this means that repeated access to sugar over time leads to prolonged dopamine signalling, greater excitation of the brain’s reward pathways and a need for even more sugar to activate all of the midbrain dopamine receptors like before. The brain becomes tolerant to sugar – and more is needed to attain the same “sugar high.”

Sugar withdrawal is also real

Although these studies were conducted in rodents, it’s not far-fetched to say that the same primitive processes are occurring in the human brain, too. “The cravings never stopped, [but that was] probably psychological,” Andrew told me. “But it got easier after the first week or so.”

In a 2002 study by Carlo Colantuoni and colleagues of Princeton University, rats who had undergone a typical sugar dependence protocol then underwent “sugar withdrawal.” This was facilitated by either food deprivation or treatment with naloxone, a drug used for treating opiate addiction which binds to receptors in the brain’s reward system. Both withdrawal methods led to physical problems, including teeth chattering, paw tremors, and head shaking. Naloxone treatment also appeared to make the rats more anxious, as they spent less time on an elevated apparatus that lacked walls on either side.

Similar withdrawal experiments by others also report behaviour similar to depression in tasks such as the forced swim test. Rats in sugar withdrawal are more likely to show passive behaviours (like floating) than active behaviours (like trying to escape) when placed in water, suggesting feelings of helplessness.

A new study published by Victor Mangabeira and colleagues in this month’s Physiology & Behavior reports that sugar withdrawal is also linked to impulsive behaviour. Initially, rats were trained to receive water by pushing a lever. After training, the animals returned to their home cages and had access to a sugar solution and water, or just water alone. After 30 days, when rats were again given the opportunity to press a lever for water, those who had become dependent on sugar pressed the lever significantly more times than control animals, suggesting impulsive behaviour.

These are extreme experiments, of course. We humans aren’t depriving ourselves of food for 12 hours and then allowing ourselves to binge on soda and doughnuts at the end of the day. But these rodent studies certainly give us insight into the neuro-chemical underpinnings of sugar dependence, withdrawal, and behaviour.

Through decades of diet programmes and best-selling books, we’ve toyed with the notion of “sugar addiction” for a long time. There are accounts of those in “sugar withdrawal” describing food cravings, which can trigger relapse and impulsive eating. There are also countless articles and books about the boundless energy and new-found happiness in those who have sworn off sugar for good. But despite the ubiquity of sugar in our diets, the notion of sugar addiction is still a rather taboo topic.

Are you still motivated to give up sugar for Lent? You might wonder how long it will take until you’re free of cravings and side-effects, but there’s no answer – everyone is different and no human studies have been done on this. But after 40 days, it’s clear that Andrew had overcome the worst, likely even reversing some of his altered dopamine signalling. “I remember eating my first sweet and thinking it was too sweet,” he said. “I had to rebuild my tolerance.”

And as regulars of a local bakery in Hershey – I can assure you, readers, that he has done just that.

ABOUT THIS NEUROSCIENCE RESEARCH ARTICLE

Source: Jordan Gaines LewisThe Conversation
Image Source: NeuroscienceNews.com image is adapted from the The Conversation article.

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The Conversation “Not So Sweet? This Is What Happens to the Brain When You Give Up Sugar for Lent.” NeuroscienceNews. NeuroscienceNews, 4 March 2017.
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Without keywords, there may be no SEO

Despite what you’ve been listening to, key-word studies isn’t useless. Without keywords, there may be no SEO… right? Let’s study the data.

Ninety-three% of online reports in 2016 started out with a search, and the search begins with words. Keywords will stay applicable as long as human beings use phrases to interact with engines like google. The handiest element that has modified are the additional elements that have prompted how we use keywords for SEO.

The fact is that SERPs are not looking only at keywords, but also price many other elements. Keywords are actually only a minuscule a part of the pie. That isn’t always to say that keyword discovery isn’t important. In reality, it’s miles critical inside the way you move approximately it: we need to head stages deeper – to the proper degree that users are attempting to find, and search engines are indexing for.

This put up will cognizance on the 3 essential focuses of precious keyword research in 2017:

(1) person motive
(2) long-tail keywords
(three) Google voice seek

User Intent

User motive is crucial to keyword research and discovery in 2017. Marketers need to understand how to work together with search engines like google to provide the content customers really want. After all, it’s not about attracting traffic to your website online, however, the proper kind of site visitors.

So what exactly is person rationale? User intent refers to “a consumer’s final goal or intention” in making a search query. Every unmarried seek query has a motive, a goal, possibly a trouble the consumer desires to remedy.

For example, a seek query of “Chicago to New York” offers me flight schedules. Adding “tour” to make it “Chicago to New York journey” does not only provide me with flight outcomes but bus and vehicle data as well.

Adding one phrase to a seek question substantially affects the outcomes of the hunt as Google mechanically acknowledges the alternate in reason. In fact, Google refines its algorithms 500-600 instances a yr to get person motive right. And if Google is that specialize in it, so should you.

You want to make user purpose principal in your keyword studies. It is vital that you understand your person’s dreams once they make seek queries. This expertise or lack of it is able to make or wreck your organic visitors.

The wonderful factor is that consumer reason is pretty smooth to parent out the longer the search query is. Long queries give us a whole lot of statistics on what a person precisely desires so we are able to give it to them. They also permit us to get focused organic visitors for long-tail key phrases. Speaking of which…

Long-Tail Keywords

I’m certain you’ve heard lengthy-tail keywords uttered again and again by means of marketers in 2016 (and even before). It looks as if it’s set to dominate 2017 as properly. I’m positive you’re already familiar with lengthy-tail keywords, being that you’ve been specializing in them, however, allow’s cover the basics first earlier than we delve into how to effectively incorporate it into your search engine optimization approach.

What are long-tail keywords? Long-tail keywords are search terms with four or greater words. In fact, fifty-one% of all search queries in 2016 contained four or greater words (supply).

So where should you start out on locating relevant long-tail keywords to your commercial enterprise? Well, you need to first find actual phrases which might be being used for your unique industry or area.

You don’t want to apply the highly-priced keyword research tools to get a terrific list of long-tail keywords. There is some loose keyword research equipment so as to come up with a good list first of all. Keyword Finder (shows lengthy-tail queries and seek volumes however handiest permits some day by day searches) and Keyword.Io (propose long-tail queries, however, does not provide search volumes) are free (but confined) equipment you can start out with if you’re on a price range. And of direction, there’s usually Google’s very personal Keyword Planner that’s included into Adwords.

By far the nice top class keyword research device is Moz Pro’s Keyword Explorer. You begin by looking your major keyword and it will return a complete list of key phrases that you could rank for relevancy and search extent. From there, you could pass down the list and select out the lengthy tail keyword phrases or seek queries. The amazing element about the Keyword Explorer is that you could click on any long-tail keyword to generate a new listing of queries related to that keyword. The new listing functions greater lengthy-tail queries than the initial listing.

Google’s Voice Search

Perhaps the most exciting improvement in keyword method is Google’s voice search and herbal language capabilities. According to Google, 20% of cell searches at the moment are voice searches! This trend is quick taking and has brought on Google to create solution containers and Knowledge Graph panels. Here you may additionally discover an amusing infographic concerning the problem.

Let’s check how herbal language, both spoken and typed, has changed search. For example, users, such as you, used to look for “keyword studies”, but now ask, “what is keyword studies?” or “what is the handiest keyword research approach?” You can see the alternate from key phrases to long-tail key phrases or queries. Voice search functionality is quickening this improvement in SEO content material marketing.

Google voice queries are rising in popularity and could simplest retain to accomplish that in 2017.
Google voice queries are quickly growing in reputation and could best continue to accomplish that in 2017.
So how are you going to maintain up with this development to plot an effective search engine marketing content material marketing approach?

(1) Look at the Frequently Asked Questions (FAQ) on your industry or logo and healthy this for your existing content material. Do you have pages or posts that address the one’s questions? If so, ensure that the questions are recommended in a very herbal way and are highlighted on the web page.

(2) Create an FAQ web page for questions that you could answer in multiple paragraphs. Answer the questions in clear, concise and herbal language.

(three) Try and provide you with long-shape content for the questions via breaking them up into “What, while, why, how, who and in which?” components. Create subheadings for every in order that search engines can index them and customers can without difficulty find the content material.

Wrapping Up

A properly information and draw close to consumer cause, lengthy-tail key phrases, and Google voice seek will assist you higher refine your keyword, and usual search engine marketing, a method for 2017. Start to discern out what your target audience in reality desires so you can begin communicating with them within the excellent way as quickly as feasible.

Positive reinforcement

2016 became huge for improvements in synthetic intelligence and machine gaining knowledge.

But 2017 may additionally properly supply even more. Here are 5 key matters to stay up for.

Positive reinforcement

AlphaGo’s historical victory towards one of the excellent Go players of all time, Lee Sedol, became a landmark for the sphere of AI, and particularly for the approach called deep reinforcement mastering.

Reinforcement learning takes an idea from the methods that animals learn how positive behaviors have a tendency to bring about advantageous or poor final results. Using this approach, a laptop can say, determine out the way to navigate a maze with the aid of trial and errors and then accomplice the tremendous final results—exiting the maze—with the moves that led as much as it. This we could a machine study without practice or maybe express examples. The concept has been around for many years, but combining it with big (or deep) neural networks offers the strength had to make it paintings on truly complicated troubles (like the game of Go). Through relentless experimentation, as well as evaluation of previous games, AlphaGo found out for itself how to play the sport at a professional degree.

The hope is that reinforcement learning will now show beneficial in many actual-international conditions. And the latest launch of numerous simulated environments must spur progress at the necessary algorithms by means of growing the variety of abilities computer systems can accumulate this manner.

Dueling neural networks

At the banner AI academic accumulating held in Barcelona, the Neural Information Processing Systems conference, plenty of the excitement a new system getting to know technique called generative hostile networks.

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5 Things To Watch In AI And Machine Learning

Five Things To Watch In AI And Machine Learning In 2017

 

Without a doubt, 2016 was an amazing year for Machine Learning (ML) and Artificial Intelligence (AI). During the year, we saw nearly every high tech CEO claim the mantel of becoming an “AI Company”. However, only a few companies were actually able to monetize their significant investments in AI, notably Amazon AMZN +0.71%, Baidu , Facebook FB +1.75%, Google GOOGL +3.77%, IBM IBM -0.02%, Microsoft MSFT +0.29%, Tesla Motors TSLA +1.75% and NVIDIA NVDA -1.29%. But 2016 was nonetheless a year of many firsts. As a posterchild for the potential for ML, Google Deep Mind mastered the subtle and infinitely complex game of GO, soundly beating the reigning world champion. And more than a few cool products were introduced that incorporated Machine Learning, from the first autonomous vehicles to new “intelligent” household assistants such as Google Home and Amazon Echo. But will 2017 finally usher in the long-promised age of Artificial Intelligence?

NVIDIA's Saturn V supercomputer for Machine Learning is the 28th fastest computer in the world, and is the #1 in the Green 500 list of the most power efficient. (Source: NVIDIA)

NVIDIA’s Saturn V supercomputer for Machine Learning is the 28th fastest computer in the world, and is the #1 in the Green 500 list of the most power efficient. (Source: NVIDIA)

Two domains: AI and Machine Learning. These terms are not interchangeable. Machine learning, a completely different way to program a computer by training it with a massive ocean of sample data, is real and is here to stay. General Artificial Intelligence remains a distant goal and is perhaps 5-20 years away depending on the specific domain of the “intelligence” being learned. To be sure, computers trained using Machine Learning hold tremendous promise, as well as the potential for massive disruption in the workplace. But these systems remain a far cry from genuine intelligence. Just ask Apple AAPL -0.14% Siri, and you will see what I mean. The hype around AI, and confusion over what the term actually means, will inevitably lead to some disillusionment as the limitations of this technology become apparent.

With that context in mind, here’s what I expect for the coming year for Machine Learning and AI.

1. Hardware accelerators for Machine Learning will proliferate.

Today, nearly all training of deep neural networks (DNNs) is performed using NVIDIA GPUs. Conversely, DNN inference, or the actual use of a trained network can be done efficiently on CPUs, GPUs, FPGAs, or even specialized ASICs such as the Google TPU, depending on the type of data being analyzed. Both training and inference markets will be hotly contested in 2017, as Advanced Micro Devices GPUs, Intel’s newly acquired Nervana chips, NVIDIA, Xilinx and several startups all launch accelerators specifically targeting this lucrative market. If you would like a deeper dive into the various semiconductor alternatives for AI, please see my companion article on this subject here.

2. Select application domains will leverage Machine Learning to improve efficiency of mission-critical processes.

If you are trying to find the killer AI app, the increasingly pervasive nature of the technology will make it difficult to identify. However, Machine Learning has begun to deliver spectacular results in very specific niches where the pattern recognition capabilities can be exploited, and this trend will continue to expand into new markets in 2017.

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