Fried Chicken Pizza – Pizza with Fried Chicken as Base

KFC’s Chizza

The pizza is available in KFC Restaurants in Hong Kong, Philipines and Singapore.


Watch video on how Chizza was made at You Tube (0:30 minutes) . . . . .

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What Being Overweight or Obese Means

Overweight and obese are labels for weight ranges. According to the Centers for Disease Control and Prevention, weights in these ranges are higher than what is generally considered healthy for a given height. Having a weight in one of these categories may increase your risk for certain diseases and health problems. The definitions of overweight and obese are different for adults than children.

Definitions for Adults

Weight ranges for adults are defined using body mass index — a number, usually between 15 and 40, calculated from a person’s height and weight. The easiest way to determine your BMI is to use the Academy online BMI calculator. A calculator will give you both your BMI and the weight category your BMI falls within.


Weight Ranges for Adults

BMI

 

 

Weight Category

Below 18.5

 

Underweight

18.5 to 24.9

 

Normal or healthy weight

25.0 to 29.9

 

Overweight

30.0 and above

 

Obese


While most people associate BMI with body fat, it is not a measurement of body fat. This means some people can have a BMI in the overweight range even though they do not have excess body fat, which is especially true for athletes.

Definitions for Children and Teens

For people ages 2 to 19, BMI is referred to as BMI-for-age and is determined using height, weight, age and gender. Body fat varies at different ages; boys and girls tend to have different amounts of body fat.

BMI-for-age is given as a percentile that shows where a child’s or teen’s BMI falls in comparison to others of the same age and gender. (See CDC’s BMI Calculator for Child and Teen.)


Weight Ranges for Children and Teens

BMI

 

 

Weight Category

Less than 5th percentile

 

Underweight

5th to 85th percentile

 

Normal or healthy weight

85th to less than 95th percentile

 

Overweight

Equal to or greater than 95th percentile

 

Obese


As with adults, BMI-for-age may be used as a screening tool, not as a diagnostic test. A health care provider needs more information to determine if excess fat is a health problem. In addition to calculating BMI-for-age, a health care provider may ask about family health history, eating habits and the amount of physical activity your child gets. Additional assessments may include skin fold thickness measurements and lab tests for cholesterol and blood sugar levels.

Overweight and Obese as Stereotypes

While the terms overweight and obese have precise definitions as noted above, these labels take on other meanings in our weight-obsessed society. Often, overweight and obese people are stereotyped, even enduring unfair treatment because of their weight. Larger children often are the target of weight-related bullying by other children and adults.

Overweight and obese are terms that refer only to a general estimate of an individual’s body weight. They do not in any way reflect on a person’s competence, self-discipline, drive or ability to lead a healthy lifestyle.

Source: Academy of Nutrition and Dietetics

The Internet and Your Brain are More Alike than You Think

Although we spend a lot of our time online nowadays–streaming music and video, checking email and social media, or obsessively reading the news–few of us know about the mathematical algorithms that manage how our content is delivered. But deciding how to route information fairly and efficiently through a distributed system with no central authority was a priority for the Internet’s founders. Now, a Salk Institute discovery shows that an algorithm used for the Internet is also at work in the human brain, an insight that improves our understanding of engineered and neural networks and potentially even learning disabilities.

“The founders of the Internet spent a lot of time considering how to make information flow efficiently,” says Salk Assistant Professor Saket Navlakha, coauthor of the new study that appears online in Neural Computation on February 9, 2017. “Finding that an engineered system and an evolved biological one arise at a similar solution to a problem is really interesting.”

In the engineered system, the solution involves controlling information flow such that routes are neither clogged nor underutilized by checking how congested the Internet is. To accomplish this, the Internet employs an algorithm called “additive increase, multiplicative decrease” (AIMD) in which your computer sends a packet of data and then listens for an acknowledgement from the receiver: If the packet is promptly acknowledged, the network is not overloaded and your data can be transmitted through the network at a higher rate. With each successive successful packet, your computer knows it’s safe to increase its speed by one unit, which is the additive increase part. But if an acknowledgement is delayed or lost your computer knows that there is congestion and slows down by a large amount, such as by half, which is the multiplicative decrease part. In this way, users gradually find their “sweet spot,” and congestion is avoided because users take their foot off the gas, so to speak, as soon as they notice a slowdown. As computers throughout the network utilize this strategy, the whole system can continuously adjust to changing conditions, maximizing overall efficiency.

Navlakha, who develops algorithms to understand complex biological networks, wondered if the brain, with its billions of distributed neurons, was managing information similarly. So, he and coauthor Jonathan Suen, a postdoctoral scholar at Duke University, set out to mathematically model neural activity.

Because AIMD is one of a number of flow-control algorithms, the duo decided to model six others as well. In addition, they analyzed which model best matched physiological data on neural activity from 20 experimental studies. In their models, AIMD turned out to be the most efficient at keeping the flow of information moving smoothly, adjusting traffic rates whenever paths got too congested. More interestingly, AIMD also turned out to best explain what was happening to neurons experimentally.

It turns out the neuronal equivalent of additive increase is called long-term potentiation. It occurs when one neuron fires closely after another, which strengthens their synaptic connection and makes it slightly more likely the first will trigger the second in the future. The neuronal equivalent of multiplicative decrease occurs when the firing of two neurons is reversed (second before first), which weakens their connection, making the first much less likely to trigger the second in the future. This is called long-term depression. As synapses throughout the network weaken or strengthen according to this rule, the whole system adapts and learns.

“While the brain and the Internet clearly operate using very different mechanisms, both use simple local rules that give rise to global stability,” says Suen. “I was initially surprised that biological neural networks utilized the same algorithms as their engineered counterparts, but, as we learned, the requirements for efficiency, robustness, and simplicity are common to both living organisms and the networks we have built.”

Understanding how the system works under normal conditions could help neuroscientists better understand what happens when these results are disrupted, for example, in learning disabilities. “Variations of the AIMD algorithm are used in basically every large-scale distributed communication network,” says Navlakha. “Discovering that the brain uses a similar algorithm may not be just a coincidence.”

Source: Salk Institute

French-style Baked Eggs with Kale

Ingredients

1 cup finely shredded stemmed kale (about 6 leaves)
1/2 cup diced Paris ham or cooked pancetta (2 ounces)
2 tbsp heavy cream
2 large eggs
truffle salt or sea salt
fresh pepper
grating of nutmeg
1/2 cup grated Gruyère cheese (2 ounces)
1 baguette

Method

  1. Preheat the oven to 425°F.
  2. Divide the shredded kale between two 6-ounce shallow ramekins. Sprinkle the diced ham on top. Pour the cream over the ham and crack 2 eggs into each ramekin. Season with the salt or truffle salt and pepper and a light grating of nutmeg. Finally sprinkle the shredded Gruyère over the eggs.
  3. Bake in the oven for 12 to 15 minutes, until the yolks are just set and the eggs jiggle slightly when the ramekins are shaken. Serve with the baguette.

Makes 2 servings.

Source: Chef Eric Ripert

In Pictures: Home-cooked Breakfasts


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