Video: Why Don’t We Have Baby Formula That’s As Good As Breast Milk?

Sam chats with chemist Dr. Steven Townsend, who’s trying to figure out which sugar molecules in breast milk make it so unique and difficult to recreate in the lab.

Watch video at You Tube (7:26 minutes) . . . . .

How mRNA Went from a Scientific Backwater to a Pandemic Crusher

David Cox wrote . . . . . . . . .

In 1995, Katalin Karikó was at her lowest ebb. A biochemist at the University of Pennsylvania (UPenn), Karikó had dedicated much of the previous two decades to finding a way to turn one of the most fundamental building blocks of life, mRNA, into a whole new category of therapeutics.

More often than not, Karikó found herself hitting dead ends. Numerous grant applications were rejected, and an attempt to raise funding from venture capitalists in New York to form a spin-off company had proved to be a fruitless endeavour. ”They initially promised to give us money, but then they never returned my phone calls,” she says.

By the mid 1990s, Karikó’s bosses at UPenn had run out of patience. Frustrated with the lack of funding she was generating for her research, they offered the scientist a bleak choice: leave or be demoted. It was a demeaning prospect for someone who had once been on the path to a full professorship. For Karikó’s dreams of using mRNA to create new vaccines and drugs for many chronic illnesses, it seemed to be the end of the road.

Thirty four years earlier, the discovery of mRNA had been announced amidst a clamour of scientific excitement in the summer of 1961. For more than a decade, researchers in the US and Europe had been attempting to unravel exactly how DNA is involved in the creation of proteins – the long strings of amino acids that are vital to the growth and functioning of all life forms.

It transpired that mRNA was the answer. These molecules act like digital tape recorders, repeatedly copying instructions from DNA in the cell nucleus, and carrying them to protein-making structures called ribosomes. Without this key role, DNA would be nothing but a useless string of chemicals, and so some have dubbed mRNA the ‘software of life.’

At the time the nine scientists credited with discovering mRNA were purely interested in solving a basic biological mystery, but by the 1970s the scientific world had begun to wonder if it could exploit this cellular messaging system to turn our bodies into medicine-making factories.

Artificial mRNA, designed and created in a petri dish and then delivered to the cells of sick patients through tiny packages called nanoparticles, offered a way of instructing the body to heal itself. Research groups around the world began looking into whether mRNA could be used to create the vaccines of the future by delivering messages to cells, teaching them to create specific antibodies to fight off a viral infection. Others started investigating whether mRNA could help the immune system recognise and destroy cancerous tissue.

Karikó was first exposed to these ideas as an undergraduate student in 1976, during a lecture at the University of Szeged in her native Hungary. Intrigued, she began a PhD, studying how mRNA might be used to target viruses. While the concept of gene therapy was also beginning to take off at the same time, capturing the imagination of many scientists, she felt mRNA had the potential to help many more people.

“I always thought that the majority of patients don’t actually need new genes, they need something temporary like a drug, to cure their aches and pains,” she said. “So mRNA was always more interesting to me.”

At the time, the technology required to make such grand ambitions a reality did not yet exist. While scientists knew how to isolate mRNA from cells, creating artificial forms was not possible. But in 1984, the American biochemist Kary Mullis invented polymerase chain reaction (PCR), a method of amplifying very small amounts of DNA so it can be studied in detail. By 1989, other researchers had found a way to utilise PCR to generate mRNA from scratch, by amplifying DNA strands and using an enzyme called RNA polymerase to create mRNA molecules from these strands. “For scientists working on mRNA, this was very empowering,” said Karikó. “Suddenly we felt like we could do anything.”

With an mRNA boom taking place on the other side of the Atlantic, Karikó decided it was time to leave Hungary and head for the US. So in 1985, she accepted a job at Temple University and moved to Philadelphia along with her husband, two year old daughter, and a teddy bear with £900 sewn into it – the proceeds from the sale of their car on the black market.

It did not take long for the American dream to sour. After four years, she was forced to leave Temple University for neighbouring UPenn after a dispute with her boss, who then attempted to have her deported. There she began working on mRNA therapies which could be used to improve blood vessel transplants, by producing proteins to keep the newly transplanted vessels alive.

However, by the early to mid 1990s, some of the early excitement surrounding mRNA was beginning to fade. While scientists had cracked the problem of how to create their own mRNA, a new hurdle had emerged. When they injected it into animals it induced such a severe inflammatory response from the immune system that they died immediately. Any thoughts of human trials were impossible.

This was a serious problem, but one Karikó was determined to solve. She recalls spending one Christmas and New Year’s Eve conducting experiments and writing grant applications. But many other scientists were turning away from the field, and her bosses at UPenn felt mRNA had shown itself to be impractical and she was wasting her time. They issued an ultimatum, if she wanted to continue working with mRNA she would lose her prestigious faculty position, and face a substantial pay cut.

”It was particularly horrible as that same week, I had just been diagnosed with cancer,” said Karikó. “I was facing two operations, and my husband, who had gone back to Hungary to pick up his green card, had got stranded there because of some visa issue, meaning he couldn’t come back for six months. I was really struggling, and then they told me this.”

While undergoing surgery, Karikó assessed her options. She decided to stay, accept the humiliation of being demoted, and continue to doggedly pursue the problem. This led to a chance meeting which would both change the course of her career, and that of science.

In 1997, Drew Weissman, a respected immunologist, moved to UPenn. This was long before the days where scientific publications were available online, and so the only way for scientists to peruse the latest research was to photocopy it from journals. “I found myself fighting over a photocopy machine in the department with this scientist called Katalin Karikó,” he remembered. ”So we started talking, and comparing what each other did.”

While Karikó’s academic status at UPenn remained lowly, Weissman had the funding to finance her experiments, and the two began a partnership. “This gave me optimism, and kept me going,” she said. “My salary was lower than the technician who worked next to me, but Drew was supportive and that’s what I concentrated on, not the roadblocks I’d had to face.”

Karikó and Weissman realised that the key to creating a form of mRNA which could be administered safely, was to identify which of the underlying nucleosides – the letters of RNA’s genetic code – were provoking the immune system and replace them with something else. In the early 2000s, Karikó happened across a study which showed that one of these letters, Uridine, could trigger certain immune receptors. It was the crucial piece of information she had been searching for.

In 2005, Karikó and Weissman published a study announcing a specifically modified form of mRNA, which replaced Uridine with an analog – a molecule which looked the same, but did not induce an immune response. It was a clever biological trick, and one which worked. When mice were injected with this modified mRNA, they lived. “I just remember Drew saying, ’Oh my god, it’s not immunogenic,’” said Karikó. “We realised at that moment that this would be very important, and it could be used in vaccines and therapies. So we published a paper, filed a patent, established a company, and then found there was no interest. Nobody invited us anywhere to talk about it, nothing.”

Unbeknown to them, however, some scientists were paying attention. Derrick Rossi, then a postdoctoral researcher at Stanford University, read Karikó and Weissman’s paper and was immediately intrigued. In 2010, Rossi co-founded a biotech company called Moderna, with a group of Harvard and MIT professors, with the specific aim of using modified mRNA to create vaccines and therapeutics. A decade on, Moderna is now one of the leaders in the Covid-19 vaccine race and valued at approximately $35 billion (£26b), after reporting that its mRNA based vaccine showed 94 per cent efficacy in a Phase III clinical trial.

But it was not novel infectious disease vaccines which got the world interested in mRNA again. Around the same time, Rossi was establishing Moderna, Karikó and Weissman were also finally managing to commercialise their finding, licensing their technology to a small German company called BioNTech, after five years of trying and failing.

Both Moderna and BioNTech – which had been founded by a Turkish born entrepreneur called Ugur Sahin – had their eye on the lucrative fields of cancer immunotherapy, cardiovascular and metabolic diseases. Now that Karikó and Weissman’s discovery made it possible to safely administer mRNA to patients, some of the original goals for mRNA back in the 1970s, had become viable possibilities again.

Vaccines were also on the horizon. In 2017, Moderna began developing a potential Zika virus vaccine, while in 2018 BioNTech entered into a partnership with Pfizer to develop mRNA vaccines for influenza, although the large scale funding which drives vaccine projects was still nowhere to be seen.

That has all changed in 2020. With the Covid-19 pandemic requiring vaccine development on an unprecedented scale, mRNA vaccine approaches held a clear advantage over the more traditional but time consuming method of using a dead or inactivated form of the virus to create an immune response. In April, Moderna received $483 million (£360m) from the US Biomedical Advanced Research and Development Authority to fasttrack its Covid-19 vaccine program.

Karikó has been at the helm of BioNTech’s Covid-19 vaccine development. In 2013, she accepted an offer to become Senior Vice President at BioNTech after UPenn refused to reinstate her to the faculty position she had been demoted from in 1995. “They told me that they’d had a meeting and concluded that I was not of faculty quality,” she said. ”When I told them I was leaving, they laughed at me and said, ‘BioNTech doesn’t even have a website.’”

Now, BioNTech is a household name, following reports last month that the mRNA Covid-19 vaccine it has co-developed with Pfizer works with more than 95 per cent efficacy. Along with Moderna, it is set to supply billions of doses around the globe by the end of 2021.

For Karikó, seeing the results of BioNTech’s Phase III trial, simply brought a sense of quiet satisfaction. “I didn’t jump or scream,” she said. “I expected that it would be very effective.”

But after so many years of adversity, and struggling to convince people that her research was worthwhile, she is still trying to comprehend the fact that her breakthrough in mRNA technology could now change the lives of billions around the world, and help end the global pandemic.

“I always wanted to help people, to try and get something into the clinic,” she said. “That was the motivation for me, and I was always optimistic. But to help that many people, I never imagined that. It makes me very happy to know that I’ve played a part in this success story.”

Source : WIRED

Video: Will It Kombucha?

Kombucha is a bubbly, fermented tea that has gained popularity in the health and wellness scene over the last decade –– but what is it exactly?

This video breaks down kombucha’s chemistry and investigates which ordinary beverages they can turn into kombucha.

Watch video at You Tube (8:32 minutes) . . . . .

DeepMind’s AI Makes Gigantic Leap in Solving Protein Structures

Ewen Callaway wrote . . . . . . . . .

An artificial intelligence (AI) network developed by Google AI offshoot DeepMind has made a gargantuan leap in solving one of biology’s grandest challenges — determining a protein’s 3D shape from its amino-acid sequence.

DeepMind’s program, called AlphaFold, outperformed around 100 other teams in a biennial protein-structure prediction challenge called CASP, short for Critical Assessment of Structure Prediction. The results were announced on 30 November, at the start of the conference — held virtually this year — that takes stock of the exercise.

“This is a big deal,” says John Moult, a computational biologist at the University of Maryland in College Park, who co-founded CASP in 1994 to improve computational methods for accurately predicting protein structures. “In some sense the problem is solved.”

AI protein-folding algorithms solve structures faster than ever

The ability to accurately predict protein structures from their amino-acid sequence would be a huge boon to life sciences and medicine. It would vastly accelerate efforts to understand the building blocks of cells and enable quicker and more advanced drug discovery.

AlphaFold came top of the table at the last CASP — in 2018, the first year that London-based DeepMind participated. But, this year, the outfit’s deep-learning network was head-and-shoulders above other teams and, say scientists, performed so mind-bogglingly well that it could herald a revolution in biology.

“It’s a game changer,” says Andrei Lupas, an evolutionary biologist at the Max Planck Institute for Developmental Biology in Tübingen, Germany, who assessed the performance of different teams in CASP. AlphaFold has already helped him find the structure of a protein that has vexed his lab for a decade, and he expects it will alter how he works and the questions he tackles. “This will change medicine. It will change research. It will change bioengineering. It will change everything,” Lupas adds.

In some cases, AlphaFold’s structure predictions were indistinguishable from those determined using ‘gold standard’ experimental methods such as X-ray crystallography and, in recent years, cryo-electron microscopy (cryo-EM). AlphaFold might not obviate the need for these laborious and expensive methods — yet — say scientists, but the AI will make it possible to study living things in new ways.

The structure problem

Proteins are the building blocks of life, responsible for most of what happens inside cells. How a protein works and what it does is determined by its 3D shape — ‘structure is function’ is an axiom of molecular biology. Proteins tend to adopt their shape without help, guided only by the laws of physics.

For decades, laboratory experiments have been the main way to get good protein structures. The first complete structures of proteins were determined, starting in the 1950s, using a technique in which X-ray beams are fired at crystallized proteins and the diffracted light translated into a protein’s atomic coordinates. X-ray crystallography has produced the lion’s share of protein structures. But, over the past decade, cryo-EM has become the favoured tool of many structural-biology labs.

Scientists have long wondered how a protein’s constituent parts — a string of different amino acids — map out the many twists and folds of its eventual shape. Early attempts to use computers to predict protein structures in the 1980s and 1990s performed poorly, say researchers. Lofty claims for methods in published papers tended to disintegrate when other scientists applied them to other proteins.

Moult started CASP to bring more rigour to these efforts. The event challenges teams to predict the structures of proteins that have been solved using experimental methods, but for which the structures have not been made public. Moult credits the experiment — he doesn’t call it a competition — with vastly improving the field, by calling time on overhyped claims. “You’re really finding out what looks promising, what works, and what you should walk away from,” he says.

DeepMind’s 2018 performance at CASP13 startled many scientists in the field, which has long been the bastion of small academic groups. But its approach was broadly similar to those of other teams that were applying AI, says Jinbo Xu, a computational biologist at the University of Chicago, Illinois.

The first iteration of AlphaFold applied the AI method known as deep learning to structural and genetic data to predict the distance between pairs of amino acids in a protein. In a second step that does not invoke AI, AlphaFold uses this information to come up with a ‘consensus’ model of what the protein should look like, says John Jumper at DeepMind, who is leading the project.

The team tried to build on that approach but eventually hit the wall. So it changed tack, says Jumper, and developed an AI network that incorporated additional information about the physical and geometric constraints that determine how a protein folds. They also set it a more difficult, task: instead of predicting relationships between amino acids, the network predicts the final structure of a target protein sequence. “It’s a more complex system by quite a bit,” Jumper says.

Startling accuracy

CASP takes place over several months. Target proteins or portions of proteins called domains — about 100 in total — are released on a regular basis and teams have several weeks to submit their structure predictions. A team of independent scientists then assesses the predictions using metrics that gauge how similar a predicted protein is to the experimentally determined structure. The assessors don’t know who is making a prediction.

AlphaFold’s predictions arrived under the name ‘group 427’, but the startling accuracy of many of its entries made them stand out, says Lupas. “I had guessed it was AlphaFold. Most people had,” he says.

Some predictions were better than others, but nearly two-thirds were comparable in quality to experimental structures. In some cases, says Moult, it was not clear whether the discrepancy between AlphaFold’s predictions and the experimental result was a prediction error or an artefact of the experiment.

AlphaFold’s predictions were poor matches to experimental structures determined by a technique called nuclear magnetic resonance spectroscopy, but this could be down to how the raw data is converted into a model, says Moult. The network also struggles to model individual structures in protein complexes, or groups, whereby interactions with other proteins distort their shapes.

Overall, teams predicted structures more accurately this year, compared with the last CASP, but much of the progress can be attributed to AlphaFold, says Moult. On protein targets considered to be moderately difficult, the best performances of other teams typically scored 75 on a 100-point scale of prediction accuracy, whereas AlphaFold scored around 90 on the same targets, says Moult.

About half of the teams mentioned ‘deep learning’ in the abstract summarizing their approach, Moult says, suggesting that AI is making a broad impact on the field. Most of these were from academic teams, but Microsoft and the Chinese technology company Tencent also entered CASP14.

Mohammed AlQuraishi, a computational biologist at Columbia University in New York City and a CASP participant, is eager to dig into the details of AlphaFold’s performance at the contest, and learn more about how the system works when the DeepMind team presents its approach on 1 December. It’s possible — but unlikely, he says — that an easier-than-usual crop of protein targets contributed to the performance. AlQuraishi’s strong hunch is that AlphaFold will be transformational.

“I think it’s fair to say this will be very disruptive to the protein-structure-prediction field. I suspect many will leave the field as the core problem has arguably been solved,” he says. “It’s a breakthrough of the first order, certainly one of the most significant scientific results of my lifetime.”

Demis Hassabis, DeepMind’s chief executive, says that the company is learning what biologists want from AlphaFold.Credit: OLI SCARFF/AFP/Getty

Faster structures

An AlphaFold prediction helped to determine the structure of a bacterial protein that Lupas’s lab has been trying to crack for years. Lupas’s team had previously collected raw X-ray diffraction data, but transforming these Rorschach-like patterns into a structure requires some information about the shape of the protein. Tricks for getting this information, as well as other prediction tools, had failed. “The model from group 427 gave us our structure in half an hour, after we had spent a decade trying everything,” Lupas says.

Demis Hassabis, DeepMind’s co-founder and chief executive, says that the company plans to make AlphaFold useful so other scientists can employ it. (It previously published enough details about the first version of AlphaFold for other scientists to replicate the approach.) It can take AlphaFold days to come up with a predicted structure, which includes estimates on the reliability of different regions of the protein. “We’re just starting to understand what biologists would want,” adds Hassabis, who sees drug discovery and protein design as potential applications.

In early 2020, the company released predictions of the structures of a handful of SARS-CoV-2 proteins that hadn’t yet been determined experimentally. DeepMind’s predictions for a protein called Orf3a ended up being very similar to one later determined through cryo-EM, says Stephen Brohawn, a molecular neurobiologist at the University of California, Berkeley, whose team released the structure in June. “What they have been able to do is very impressive,” he adds.

Real-world impact

AlphaFold is unlikely to shutter labs, such as Brohawn’s, that use experimental methods to solve protein structures. But it could mean that lower-quality and easier-to-collect experimental data would be all that’s needed to get a good structure. Some applications, such as the evolutionary analysis of proteins, are set to flourish because the tsunami of available genomic data might now be reliably translated into structures. “This is going to empower a new generation of molecular biologists to ask more advanced questions,” says Lupas. “It’s going to require more thinking and less pipetting.”

“This is a problem that I was beginning to think would not get solved in my lifetime,” says Janet Thornton, a structural biologist at the European Molecular Biology Laboratory-European Bioinformatics Institute in Hinxton, UK, and a past CASP assessor. She hopes the approach could help to illuminate the function of the thousands of unsolved proteins in the human genome, and make sense of disease-causing gene variations that differ between people.

AlphaFold’s performance also marks a turning point for DeepMind. The company is best known for wielding AI to master games such Go, but its long-term goal is to develop programs capable of achieving broad, human-like intelligence. Tackling grand scientific challenges, such as protein-structure prediction, is one of the most important applications its AI can make, Hassabis says. “I do think it’s the most significant thing we’ve done, in terms of real-world impact.”

Source : Nature

The Science behind ‘Wok Hei’ an Essential Ingredient in the Perfect Bowl of Fried Rice

Maggie Hiufu Wong wrote . . . . . . . . .

Chef Kwok Keung Tung tosses the wok with one hand, using the other to stir with a metal spatula.

Both hands occupied, he uses his knee to nudge the gas stove’s lever up and down to control the fire fan, sporadically engulfing a third of the wok in flames.

It takes only three minutes for the lump of white rice to transform into the bowl of golden fried rice he places on the serving counter.

“This is what you’re looking for — wok hei (the breath of wok),” Danny Yip, co-founder of Hong Kong restaurant The Chairman, tells CNN Travel.

“Wok is the essence of Chinese cooking in South China. And Cantonese chefs are the master of fire and wok.”

If anyone’s an authority on the subject of wok hei, it’s Yip.

The Chairman is the highest-ranking Chinese restaurant on the World’s 50 Best Restaurant 2019 list (there was no 2020 list due to the pandemic) and was named the no.2 restaurant in Asia in 2020.

For those who grew up in a Cantonese family, it’s almost impossible to go to a Chinese restaurant without hearing someone — usually older — comment “gau wok hei” (enough wok hei) or “ng gau wok hei” (not enough wok hei) when establishing a benchmark of how authentic a Chinese restaurant actually is.

Hei (also Romanized as “hay”) is the Cantonese word for “chi,” meaning energy flow. It was once a hard-to-explain and largely ethereal concept mostly popular in the South China region. In other parts of China or Asia, even though they used woks, they didn’t focus on wok hei.

It wasn’t until the legendary American Chinese food writer Grace Young coined it poetically as “the breath of a wok” in her book “The Wisdom of the Chinese Kitchen: Classic Family Recipes for Celebration and Healing” in the 1990s that the concept of wok hei was introduced officially to international audiences.

“Wok hei is not simply hot food; it’s that elusive seared taste that only lasts for a minute or two,” Young wrote.

In other words, it’s a combination of that steaming aroma you breathe in and the almost-burning sensation on your tongue that somehow enhances the flavors of the dish.

How a wok works

In recent years, an increasing number of food writers and scientists have been modernizing Chinese cooking while looking deeper into its origins, including wok hei.

After realizing how little scientific research has been done on Chinese cuisine, Hung-tang Ko, doctoral student at the Georgia Institute of Technology, co-published a research paper titled “The physics of tossing fried rice” with David Hu — a scientist most famous for his studies on fire ants and an Ig Nobel Prize-winning investigation into why wombats have cube-shaped poop.

“Wok hei and the Maillard reaction require high heat. The commercial Chinese stoves have a mind-blowing amount of heat coming out of them,” explains Ko, who spent months studying how and why chefs toss fried rice with a wok, while also simulating rice trajectories.

The Maillard reaction is a chemical interaction that occurs between amino acids and reducing sugars in food placed under high heat. It causes foods to brown and releases aroma and flavors.

But why does it have to be cooked in high heat and in such a hurry?

“That’s how to extract the maximum wok hei in the shortest amount of time. So the aroma you unlocked from the Maillard reaction won’t escape,” explains The Chairman’s Yip.

Hence, an important component of wok hei — apart from the fire and the actual wok — is the chef’s tossing skill.

The right way to toss a wok

Tossing a wok is a skill that takes time to develop.

A young chef at The Chairman spends more than a year practicing on the wok by cooking staff meals before he or she is allowed to stir fry a dish for customers.

“Why don’t other chefs use a wok? It’s heavy and the fire can be intimidating and hard to control — now you know why none of the Chinese chefs have any arm hair left,” says Yip, only half-jokingly.

Why won’t stirring suffice? In the case of fried rice, every time it leaves the hot wok surface it cools down and avoids getting burnt, as demonstrated in the above video.

“Tossing the wok allows better mixing, which is essential when you have super high heat. Stirring under high heat will likely lead to burning,” says Ko.

uring Ko’s research, he discovered that chefs often pivot their woks using the edge of the stove — instead of lifting the entire wok away from the stove — to save energy and increase speed.

Two motions happen simultaneously with each toss: “Back and forth pushing and pulling”, and “tilting and rotating the wok back and forth” in a see-saw motion.

So what makes the round-bottomed and highly conductive wok such a unique piece of cooking equipment?

“Potentially, other utensils would work, too. But you just need to mix at amazing speeds to make sure that the heat is going into your ingredients uniformly,” explains Ko.

On average, the chefs in the study tossed their wok at a speed of 2.7 times per second.

This is also why many Chinese chefs suffer from muscle injuries.

One of the goals of Ko’s study was to see if it’s possible to create a robot that could help chefs toss their wok to reduce the physical strain on their limbs. Ko thinks his published research can potentially be applied in other parts of life.

“Can you imagine a laundry drying machine that uses the wok tossing mechanics to toss clothes? My gut feeling is that it will be more efficient — and funnier,” says Ko.

How to make perfect fried rice

Fried rice was brought into the spotlight in July, thanks to a viral YouTube video titled “Uncle Roger DISGUSTED by this Egg Fried Rice Video.”

In the clip, “Uncle Roger,” a character created by UK-based Malaysian stand-up comedian Nigel Ng, reacts to a BBC video on how to cook egg fried rice.

He points out everything done wrong in the original egg fried rice video, a response that has gathered more than 17 million views so far. Among the major offenses in the original video? Watery rice.

It’s an issue that sits close to the hearts of Hong Kong’s chefs.

“Fried rice and fried beef noodles are the two dishes often used to judge the wok hei of a restaurant,” says Yip. “It is difficult to get each piece of rice or noodle slightly toasted and mixed evenly with the rest of the ingredients without burning it.”

Ko agrees.

“Fried rice is a very symbolic cuisine,” he says. “It is surprisingly hard to make perfect fried rice although it looks really simple. The general principle is to keep it hot — by avoiding putting in watery content that cools the materials down — and mix a lot to prevent sticking and burning.”

Ko suggests using rice that’s been cooked the night before.

“It goes back to the high heat argument. When you put (dried leftover rice) in the wok, the moisture will be minimal … that prevents cooling the wok down or the rice from sticking together,” explains the scientist.

The Chairman, however, does things a bit differently.

“We know most people use leftover rice as it’s drier. We don’t as we want to keep the inside of the rice moist and retain the most aroma. The trick is to use eggs,” says Yip.

Kwok, the chef, demonstrates.

He first quickly fries the finely chopped ingredients in the wok, drying them before setting them aside. Then he pours in the oil, egg mixture and rice separately.

“Egg dries faster than rice. The chef has to act fast and mix all the ingredients. See, you don’t even see the egg anymore,” says Yip, hurrying this writer to take a bite before the aroma escapes.

It’s true. The slightly toasted and steaming rice is dry on the surface and each grain is perfectly coated in golden yellow — you don’t see the egg anymore. Each bite of the fried rice is still steaming and packed with flavors.

“Taste that?” asks Yip. “This is wok hei.”

Source: CNN